Ozone pollution dominant meteorological factor identification method, device, equipment, medium and product

Abstract

The invention discloses an ozone pollution dominant meteorological factor identification method and device, equipment, a medium and a product, and relates to the field of ozone pollution analysis. Obtaining an ozone daily average value and a meteorological factor matched with the pollutant concentration data, and performing time sequence decomposition by using KZ filtering to obtain a baseline component of ozone and a baseline component of the meteorological factor; according to the baseline component of the ozone and the baseline component of the meteorological factor, utilizing a first machine learning model and KZ filtering decomposition to obtain a long-term component related to ozone emission; determining ozone weather-related long-term components according to the ozone emission-related long-term components; and determining a dominant meteorological factor by using a second machine learning model and an SHAP algorithm according to the ozone meteorological related long-term component and the long-term component of the meteorological factor. The method can reduce the calculation cost, and improves the recognition efficiency.

Description

Technical Field

[0001] The present application relates to the field of ozone pollution analysis, and particularly to a method, device, equipment, medium and product for identifying the dominant meteorological factors of ozone pollution. Background Art

[0002] As a secondary pollutant, ozone (O3) has attracted wide attention for its impact on the environment, public health and climate change. The generation of O3 mainly depends on complex photochemical reactions, which involve the interaction of precursors such as volatile organic compounds (VOCs), nitrogen oxides (NO x ) and solar radiation. In this reaction, when the reaction of NO with O3 (NO + O3 → NO2 + O2) is interfered by peroxyalkyl radicals (RO2) and perhydroxyl radicals (HO2) generated by the oxidation of VOCs and carbon monoxide (CO), NO is oxidized to NO2, resulting in the accumulation of O3. This chemical process promotes the net increase of O3 concentration, thus exacerbating photochemical pollution. In addition, the occurrence of O3 pollution is closely related to meteorological conditions. Weather factors such as high temperature, strong solar radiation and gentle breeze are favorable conditions for photochemical reactions, and they regulate the O3 concentration by accelerating the rate of photochemical reactions and affecting processes such as atmospheric transport and wet and dry deposition.

[0003] Analyzing the driving factors of O3 pollution and clarifying the relative contributions of meteorological conditions and emission factors are the keys to deeply understanding the pollution mechanism. By differentiating the different effects of anthropogenic factors and meteorological factors on the change of O3 concentration, it can provide a scientific basis for formulating more efficient pollution control strategies, thereby protecting the ecological environment and improving public health. Summary of the Invention

[0004] The purpose of the present application is to provide a method, device, equipment, medium and product for identifying the dominant meteorological factors of ozone pollution, which can reduce the calculation cost and improve the identification efficiency.

[0005] To achieve the above purpose, the present application provides the following solutions: In the first aspect, the present application provides a method for identifying the dominant meteorological factors of ozone pollution, including: Obtaining ozone pollutant concentration data and meteorological factors within a set time in a set area; Preprocessing the ozone pollutant concentration data and meteorological factors to obtain the daily average value of ozone and the meteorological factors matching the pollutant concentration data; Performing time series decomposition on the daily average value of ozone and the meteorological factors matching the pollutant concentration data respectively by using KZ filtering to obtain the baseline component of ozone and the baseline component of meteorological factors; Using the first machine learning model and the KZ filter decomposition based on the baseline component of the ozone and the baseline component of the meteorological factors, a long-term component related to ozone emissions is obtained; Determine the long-term component related to ozone meteorology based on the long-term component related to ozone emissions; Using the second machine learning model and the SHAP algorithm, determine the dominant meteorological factors based on the long-term component related to ozone meteorology and the long-term component of the meteorological factors; wherein, the long-term component of the meteorological factors is determined based on the baseline component of the meteorological factors.

[0006] In one embodiment, preprocess the ozone pollutant concentration data and the meteorological factors to obtain the daily average value of ozone and the meteorological data meteorological factors matched with the pollutant concentration data, specifically including: Preprocess the ozone pollutant concentration data and calculate the daily average value of ozone; the daily average value of ozone is the maximum 8-hour average value of ozone per day; Use the bilinear interpolation method to spatially match the meteorological factors with the ozone pollutant concentration data to obtain the meteorological factors matched with the pollutant concentration data.

[0007] In one embodiment, the meteorological factors specifically include boundary layer height, downward surface solar radiation, 2m temperature, 2m dew point temperature, mean sea level pressure, east-west wind speed at a set height, north-south wind speed at a set height, total cloud cover, and total precipitation.

[0008] In one embodiment, using the first machine learning model and the KZ filter decomposition based on the baseline component of the ozone and the baseline component of the meteorological factors, a long-term component related to ozone emissions is obtained, specifically including: Construct the first machine learning model based on the baseline component of the ozone and the baseline component of the meteorological factors; Use the KZ filter decomposition on the first residual in the first machine learning model to obtain the long-term component related to ozone emissions.

[0009] In one embodiment, using the second machine learning model and the SHAP algorithm, determine the dominant meteorological factors based on the long-term component related to ozone meteorology and the long-term component of the meteorological factors, specifically including: Construct the second machine learning model based on the long-term component related to ozone meteorology and the long-term component of the meteorological factors; Use the SHAP algorithm to summarize the second machine learning model to determine the dominant meteorological factors.

[0010] In one embodiment, the first machine learning model is a multiple linear regression model, a random forest model, LightGBM, or XGBoost; the second machine learning model is a multiple linear regression model, a random forest model, LightGBM, or XGBoost.

[0011] In a second aspect, the present application provides an ozone pollution-dominated meteorological factor identification device, including: An acquisition module, configured to acquire ozone pollutant concentration data and meteorological factors within a set time in a set area; A preprocessing module, configured to preprocess the ozone pollutant concentration data and meteorological factors to obtain the daily average value of ozone and meteorological factors matching the pollutant concentration data; A time series decomposition module, configured to perform time series decomposition on the daily average value of ozone and meteorological factors matching the pollutant concentration data respectively by using KZ filtering to obtain the baseline component of ozone and the baseline component of meteorological factors; A long-term component decomposition module related to ozone emissions, configured to use a first machine learning model and KZ filtering decomposition based on the baseline component of ozone and the baseline component of meteorological factors to obtain a long-term component related to ozone emissions; A long-term component determination module related to ozone meteorology, configured to determine a long-term component related to ozone meteorology based on the long-term component related to ozone emissions; A dominant meteorological factor determination module, configured to determine a dominant meteorological factor by using a second machine learning model and the SHAP algorithm based on the long-term component related to ozone meteorology and the long-term component of meteorological factors; wherein, the long-term component of meteorological factors is determined based on the baseline component of meteorological factors.

[0012] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the ozone pollution-dominated meteorological factor identification method.

[0013] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the ozone pollution-dominated meteorological factor identification method is implemented.

[0014] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the ozone pollution-dominated meteorological factor identification method is implemented.

[0015] According to the specific embodiments provided by the present application, the following technical effects are disclosed by the present application: The present application provides a method, apparatus, device, medium and product for identifying meteorological factors dominant in ozone pollution. The ozone pollutant concentration data and meteorological factors are preprocessed to obtain the daily average value of ozone and the meteorological factors matched with the pollutant concentration data. The time series decomposition is respectively performed on the daily average value of ozone and the meteorological factors matched with the pollutant concentration data by using the KZ filter to obtain the baseline component of ozone and the baseline component of the meteorological factors. The long-term component related to ozone emissions is obtained by using the first machine learning model and the KZ filter decomposition according to the baseline component of ozone and the baseline component of the meteorological factors. The long-term component related to ozone meteorology is determined according to the long-term component related to ozone emissions. The dominant meteorological factors are determined by using the second machine learning model and the SHAP algorithm according to the long-term component related to ozone meteorology and the long-term component of the meteorological factors. Among them, the long-term component of the meteorological factors is determined according to the baseline component of the meteorological factors. By performing time series decomposition on the daily average value of ozone and the meteorological factors matched with the pollutant concentration data through the KZ filter and finally using the first machine learning model and the second machine learning model for processing, the calculation cost can be reduced and the calculation efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is an application environment diagram of a method for identifying meteorological factors dominant in ozone pollution in an embodiment of the present application.

[0018] Figure 2 It is a schematic flow chart of a method for identifying meteorological factors dominant in ozone pollution provided in an embodiment of the present application.

[0019] Figure 3 It is a schematic diagram of a method for identifying meteorological factors dominant in ozone pollution provided in an embodiment of the present application.

[0020] Figure 4 It is a schematic diagram of the functional modules of a device for identifying meteorological factors dominant in ozone pollution provided in another embodiment of the present application.

[0021] Figure 5 It is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0023] Traditional analysis of ozone pollution mainly focuses on single factors or simple meteorological models, lacking in-depth analysis of the interannual changes in meteorological conditions and the comprehensive influence of multiple meteorological factors. To accurately evaluate air quality changes, it is necessary to comprehensively consider the twin effects of meteorological factors and anthropogenic factors. Therefore, conducting multi-factor and comprehensive research and exploring the relationship between meteorological factors and O3 concentration by combining machine learning technology have become the key directions of current research.

[0024] In summary, the present application intends to adopt the KZ filtering method, multiple linear regression model, and machine learning interpretation algorithm to systematically evaluate the influence degree of different meteorological factors on O3 concentration and provide scientific support for regional air pollution control policies. This research not only fills the existing academic gap but also provides important references for other regions with similar geographical and climatic characteristics.

[0025] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0026] The method for identifying the dominant meteorological factors of ozone pollution provided by the embodiments of the present application can be applied, for example, to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set separately, integrated on the server 104, placed on the cloud or other servers. The terminal 102 can send the ozone pollutant concentration data to be processed and meteorological factors to the server 104. After receiving the ozone pollutant concentration data to be processed and meteorological factors, for the ozone pollutant concentration data to be processed and meteorological factors, the server 104 preprocesses the ozone pollutant concentration data and meteorological factors to obtain the daily average value of ozone and the meteorological factors matching the pollutant concentration data; respectively perform time series decomposition on the daily average value of ozone and the meteorological factors matching the pollutant concentration data using the KZ filter to obtain the baseline component of ozone and the baseline component of meteorological factors; use the first machine learning model and KZ filter decomposition based on the baseline component of ozone and the baseline component of meteorological factors to obtain the long-term component related to ozone emissions; determine the long-term component related to ozone meteorology based on the long-term component related to ozone emissions; use the second machine learning model and SHAP algorithm to determine the dominant meteorological factors based on the long-term component related to ozone meteorology and the long-term component of meteorological factors. The server 104 can feedback the obtained dominant meteorological factors to the terminal 102. In addition, in some embodiments, the method for identifying the dominant meteorological factors of ozone pollution can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly identify the dominant meteorological factors of ozone pollution for the ozone pollutant concentration data to be processed and meteorological factors, or the server 104 can obtain the ozone pollutant concentration data to be processed and meteorological factors from the data storage system and identify the dominant meteorological factors of ozone pollution for the ozone pollutant concentration data to be processed and meteorological factors.

[0027] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0028] In an exemplary embodiment, as Figure 2 and Figure 3 shown, a method for identifying the dominant meteorological factors of ozone pollution is provided. This method is executed by a computer device, and can specifically be executed alone by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiments of the present application, taking this method as applied to Figure 1Taking server 104 in [the context] as an example, it includes the following steps 201 to 206.

[0029] Step 201: Obtain ozone pollutant concentration data and meteorological factors within a set time in a set area.

[0030] Step 202: Preprocess the ozone pollutant concentration data and meteorological factors to obtain the daily average ozone value and meteorological factors matching the pollutant concentration data.

[0031] Step 203: Respectively perform time series decomposition on the basis of the daily average ozone value and meteorological factors matching the pollutant concentration data using KZ filtering to obtain the baseline component of ozone and the baseline component of meteorological factors.

[0032] Step 204: Based on the baseline component of ozone and the baseline component of meteorological factors, use the first machine learning model and KZ filtering decomposition to obtain the long-term component related to ozone emissions.

[0033] Step 205: Determine the long-term component related to ozone meteorology based on the long-term component related to ozone emissions.

[0034] Step 206: Use the second machine learning model and SHAP algorithm to determine the dominant meteorological factors based on the long-term component related to ozone meteorology and the long-term component of meteorological factors; among them, the long-term component of meteorological factors is determined based on the baseline component of meteorological factors.

[0035] Implementing the above steps 201 to 206 can reduce the calculation cost and improve the recognition efficiency.

[0036] In an exemplary embodiment, preprocessing the ozone pollutant concentration data and meteorological factors to obtain the daily average ozone value and meteorological factors matching the pollutant concentration data specifically includes: preprocessing the ozone pollutant concentration data and calculating the daily average ozone value; the daily average ozone value is the maximum daily 8-hour average of ozone; using bilinear interpolation to spatially match the meteorological factors with the ozone pollutant concentration data to obtain meteorological factors matching the pollutant concentration data.

[0037] Specifically, collect ozone pollutant concentration data and meteorological factors in a study area within a certain time. After collection, preprocess the data, calculate the maximum daily 8-hour average (MDA8) of ozone, and match the meteorological factors with adjacent pollutant observation stations.

[0038] The meteorological factors used include: Boundary layer height (BLH, m), downward surface solar radiation (SSR, Jm -1)、2m temperature (T2M, K), 2m dew point temperature (D2M, K), mean sea level pressure (MSP, m), u10 wind (m / s -1 )、v10 wind (m / s -1 ), total cloud cover (TCC, unitless), total precipitation (TP, mm).

[0039] Among them, according to the formula: , relative humidity (RH, unitless) is calculated.

[0040] The data is obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 reanalysis dataset. https: / / cds.climate.copernicus.eu / datasets / reanalysis-era5-single-levels?tab=overview. After obtaining the data, bilinear interpolation is used to match the meteorological factors with the pollutant concentration data.

[0041] When establishing a multiple linear regression model later, the meteorological factors used include: T2M, RH, BLH, u10, v10, MSP, TCC, TP, and SSR.

[0042] In an exemplary embodiment, the meteorological factors specifically include boundary layer height, downward surface solar radiation, 2m temperature, 2m dew point temperature, mean sea level pressure, east-west wind speed at a set height, north-south wind speed at a set height, total cloud cover, and total precipitation. In practical applications, the set height is 10 meters, the east-west wind speed at 10 meters is represented by u10, and the north-south wind speed at 10 meters is represented by v10.

[0043] Using the KZ filter for time series decomposition respectively according to the daily average value of ozone and the meteorological factors matched with the pollutant concentration data, the baseline component of ozone and the baseline component of meteorological factors are obtained. Specifically, the KZ filter (Kolmogorov-Zurbenko filter) is used for time series scale decomposition of MDA8 O3. The daily concentration time series of atmospheric pollutants can be mainly divided into long-term components, seasonal components, and short-term components. In this application, the short-term components are not considered. Among them, the sum of the seasonal and long-term components is the baseline component: ; ; In the formula, X(t) is the MDA8 time series during the research period; X LT (t) is the long-term component; X SN (t) is the seasonal component; X ST (t) is the short-term component; X BL(t) is the baseline component, where "X" indicates ozone-related quantities and "MET" indicates meteorological factor-related quantities. (m，p) , select the time window m = 15 days, the number of iterations p = 5 times, apply it to X(t), and pass KZ (15,5) After filtering out the daily fluctuations and random noise with a fluctuation period of less than 33 days, the obtained X BL (t) Long-term trends and seasonal variations will be preserved: ; The above three equations can be used to calculate the short-term component X ST (t): ; Further X BL (t) is decomposed into the recurring climate seasonal cycle during the study period and the sum of the first residual ε(t), KZ (365,3) The filter is applied to ε(t) to remove fluctuations with a time period shorter than 1.7 years to obtain the long-term component X LT (t): ; ; Then the seasonal component X SN (t) can be expressed as: .

[0044] In an exemplary embodiment, a first machine learning model and KZ filter decomposition are performed based on the baseline component of ozone and the baseline component of meteorological factors to obtain a long-term component related to ozone emissions, specifically comprising: constructing a first machine learning model based on the baseline component of ozone and the baseline component of meteorological factors; and decomposing the first residual in the first machine learning model using KZ filter decomposition to obtain a long-term component related to ozone emissions.

[0045] Specifically, the long-term changes in air pollutants may be affected by changes in emissions and meteorological conditions. LT (t) is the long-term component related to emissions [ Long-term components related to meteorology[ ], then X BL (t) can also be expressed as: ; To separate and , establishing MLR model is an effective method, using the baseline component MET of meteorological factors BL (t) and the concentration of ozone pollutants XBL (t) Construct the following MLR model: ; where i is different meteorological factors; a0 is the intercept; a i is the regression coefficient of different meteorological factors; is the second residual. It not only includes the variability of O3 related to the long-term change of air pollutant emissions, but also includes the small seasonal changes of O3 caused by meteorological impacts that cannot be explained in the MLR model. By using KZ (365,3) Remove the small seasonal changes of, the following can be isolated , as follows: ; At the same time, the following can also be obtained : .

[0046] In an exemplary embodiment, the dominant meteorological factors are determined according to the long-term components related to ozone meteorology and the long-term components of meteorological factors by using a second machine learning model and the SHAP algorithm, specifically including: constructing a second machine learning model according to the long-term components related to ozone meteorology and the long-term components of meteorological factors; using the SHAP algorithm to summarize the second machine learning model to determine the dominant meteorological factors.

[0047] A multiple linear regression model is established using the long-term components related to ozone meteorology and the long-term components of meteorological factors, and the SHAP (Shapley Additive ExPlanation) algorithm is used to explain the model results to determine the dominant meteorological factors. The summary result of each meteorological factor is obtained by calculating the average value of the absolute value of the SHAP value (mean |SHAP|). By analyzing the summarized SHAP values, the meteorological factors that have the greatest impact on the long-term trend of air pollutants can be identified, that is, the variables with the largest mean|SHAP| value. The SHAP value calculation formula is: ; This formula is an explanation of the principle of the SHAP method. In the formula, represents the SHAP value of the feature contribution value of feature i; N is the set of all features; S is a subset that does not include feature i; f(S) represents the model prediction value when only using the feature subset.

[0048] Taking the long-term components related to meteorology as the dependent variable and the long-term components of meteorological factors as the independent variable, a multiple linear regression model is established, that is , where is the intercept; is the regression coefficient; is the long-term component of meteorological factors, and is the model residual. Subsequently, the SHAP algorithm is used to interpret the model. The summary of SHAP values provides an explanation of the overall impact of each meteorological factor on the dependent variable of the model. Moreover, using the average of the absolute values of SHAP values (mean|SHAP|) as an indicator, the dominant meteorological factors can be determined.

[0049] Evaluating the impacts of anthropogenic emissions and meteorological changes on air quality is an important issue in environmental change. The change of air pollution is generally determined by the emissions of air pollutants and meteorological conditions jointly. However, meteorological conditions have a strong impact on the change of air quality, and its impact may obscure the impact of the change of emission levels over time. Therefore, in order to ensure an accurate assessment of the change of air quality and further determine the key directions and specific treatment measures for air pollution prevention and control, it is extremely important to study the impact of meteorological factors on air quality. Since this application chooses to establish a multiple linear regression model, it has the advantages of simple implementation method, low calculation cost, high efficiency, etc. In addition, other methods can be selected when establishing the model, such as machine learning models like random forest, LightGBM, XGBoost, etc.

[0050] In an exemplary embodiment, the first machine learning model is a multiple linear regression model, a random forest model, LightGBM or XGBoost; the second machine learning model is a multiple linear regression model, a random forest model, LightGBM or XGBoost.

[0051] This application also provides an application scenario that applies the above-mentioned method for identifying the dominant meteorological factors of ozone pollution. Specifically: in the research, by collecting the O3 concentration data and meteorological factors in the main urban area of a certain city from 2015 to 2023, preprocessing the data to calculate MDA8 O3, using the KZ filtering method to decompose the original time series of MDA8 O3, decomposing it into short-term, seasonal and long-term components, and performing the same time series decomposition on the meteorological factors. Then, using the baseline component (the sum of the short-term component and the seasonal component) of MDA8 O3 as the dependent variable and the baseline component of the meteorological factors as the independent variable, a multiple linear regression equation is established. Through the results of the equation, the long-term components related to meteorology and emissions are further obtained. Then, using the long-term component related to meteorology as the dependent variable and the long-term component of the meteorological factors as the independent variable, a multiple linear regression equation is established again, and the SHAP algorithm is used to interpret it. The impact of meteorological factors on O3 pollution in the main urban area of Chongqing under the long-term trend can be obtained, and the dominant meteorological factors can be identified, clarifying the meteorological contribution and emission contribution of O3 pollution. And based on the research results, the key directions and specific treatment measures for O3 pollution prevention and control in a certain city are proposed, providing a feasible emission reduction plan and policy recommendations for areas with similar terrain and climate characteristics.

[0052] Based on the same inventive concept, an embodiment of the present application further provides an ozone pollution-dominated meteorological factor identification device for implementing the ozone pollution-dominated meteorological factor identification method involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the ozone pollution-dominated meteorological factor identification device provided below can refer to the limitations on the ozone pollution-dominated meteorological factor identification method in the above text, and will not be repeated here.

[0053] As Figure 4 shown, in an exemplary embodiment, an ozone pollution-dominated meteorological factor identification device is provided, which includes the following modules.

[0054] An acquisition module 401, configured to acquire ozone pollutant concentration data and meteorological factors within a set time in a set area.

[0055] A preprocessing module 402, configured to preprocess the ozone pollutant concentration data and meteorological factors to obtain the daily average value of ozone and meteorological factors matching the pollutant concentration data.

[0056] A time series decomposition module 403, configured to respectively perform time series decomposition on the daily average value of ozone and meteorological factors matching the pollutant concentration data by using the KZ filter to obtain the baseline component of ozone and the baseline component of meteorological factors.

[0057] A long-term component decomposition module 404 related to ozone emissions, configured to perform decomposition by using a first machine learning model and the KZ filter according to the baseline component of ozone and the baseline component of meteorological factors to obtain a long-term component related to ozone emissions.

[0058] A long-term component determination module 405 related to ozone meteorology, configured to determine a long-term component related to ozone meteorology according to the long-term component related to ozone emissions.

[0059] A dominant meteorological factor determination module 406, configured to determine the dominant meteorological factor by using a second machine learning model and the SHAP algorithm according to the long-term component related to ozone meteorology and the long-term component of meteorological factors; wherein, the long-term component of meteorological factors is determined according to the baseline component of meteorological factors.

[0060] In an exemplary embodiment, a computer device is provided. This computer device can be a server or a terminal, and its internal structure diagram can be as Figure 5As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store ozone pollution dominant meteorological factor identification data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for identifying ozone pollution dominant meteorological factors.

[0061] Those skilled in the art can understand that Figure 5 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the above method embodiments are implemented.

[0062] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the above method embodiments are implemented.

[0063] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the above method embodiments are implemented.

[0064] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0065] In this application, all actions of obtaining signals, information, or data are carried out on the premise of complying with the corresponding data protection regulations and policies of the country where it is located and obtaining authorization from the owner of the corresponding device.

[0066] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include Read-Only Memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0067] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0068] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0069] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for identifying meteorological factors dominated by ozone pollution, characterized in that, The method for identifying the dominant meteorological factors of ozone pollution includes: Obtaining ozone pollutant concentration data and meteorological factors within a set time in a set area; Preprocessing the ozone pollutant concentration data and meteorological factors to obtain the daily average value of ozone and meteorological factors matching the pollutant concentration data; Performing time series decomposition on the daily average value of ozone and the meteorological factors matching the pollutant concentration data respectively using the KZ filter to obtain the baseline component of ozone and the baseline component of meteorological factors; Using the first machine learning model and KZ filter decomposition based on the baseline component of ozone and the baseline component of meteorological factors to obtain the long-term component related to ozone emissions; Determining the long-term component related to ozone meteorology based on the long-term component related to ozone emissions; Determining the dominant meteorological factors using the second machine learning model and SHAP algorithm based on the long-term component related to ozone meteorology and the long-term component of meteorological factors; wherein, the long-term component of meteorological factors is determined based on the baseline component of meteorological factors.

2. The ozone pollution-dominated meteorological factor identification method according to claim 1, wherein Preprocessing the ozone pollutant concentration data and meteorological factors to obtain the daily average value of ozone and the meteorological data meteorological factors matching the pollutant concentration data, specifically including: Preprocessing the ozone pollutant concentration data and calculating the daily average value of ozone; the daily average value of ozone is the maximum 8-hour average value of ozone per day; Using the bilinear interpolation method to spatially match the meteorological factors with the ozone pollutant concentration data to obtain the meteorological factors matching the pollutant concentration data.

3. The ozone pollution-dominated meteorological factor identification method according to claim 1, characterized in that The meteorological factors specifically include boundary layer height, downward surface solar radiation, 2m temperature, 2m dew point temperature, mean sea level pressure, east-west wind speed at a set height, north-south wind speed at a set height, total cloud cover, and total precipitation.

4. The ozone pollution-dominated meteorological factor identification method according to claim 1, wherein, Using the first machine learning model and KZ filter decomposition based on the baseline component of ozone and the baseline component of meteorological factors to obtain the long-term component related to ozone emissions, specifically including: Constructing the first machine learning model based on the baseline component of ozone and the baseline component of meteorological factors; Decomposing the first residual in the first machine learning model using the KZ filter to obtain the long-term component related to ozone emissions.

5. The ozone pollution-dominated meteorological factor identification method according to claim 1, wherein Determining the dominant meteorological factors using the second machine learning model and SHAP algorithm based on the long-term component related to ozone meteorology and the long-term component of meteorological factors, specifically including: Constructing the second machine learning model based on the long-term component related to ozone meteorology and the long-term component of meteorological factors; Summarizing the second machine learning model using the SHAP algorithm to determine the dominant meteorological factors.

6. The method for identifying meteorological factors dominant in ozone pollution according to claim 1, wherein, The first machine learning model is a multiple linear regression model, random forest model, LightGBM, or XGBoost; the second machine learning model is a multiple linear regression model, random forest model, LightGBM, or XGBoost.

7. An ozone pollution-dominated meteorological factor identification device, characterized in that The device for identifying the dominant meteorological factors of ozone pollution includes: An acquisition module for acquiring ozone pollutant concentration data and meteorological factors within a set time in a set area; A preprocessing module for preprocessing the ozone pollutant concentration data and meteorological factors to obtain the daily average value of ozone and the meteorological factors matching the pollutant concentration data; A time series decomposition module, which is used to perform time series decomposition on the daily average value of ozone and meteorological factors matched with pollutant concentration data respectively by using the KZ filter to obtain the baseline component of ozone and the baseline component of meteorological factors; A long-term component decomposition module related to ozone emissions, which is used to obtain the long-term component related to ozone emissions by using the first machine learning model and KZ filter decomposition based on the baseline component of ozone and the baseline component of meteorological factors; A long-term component determination module related to ozone meteorology, which is used to determine the long-term component related to ozone meteorology according to the long-term component related to ozone emissions; A dominant meteorological factor determination module, which is used to determine the dominant meteorological factor by using the second machine learning model and SHAP algorithm according to the long-term component related to ozone meteorology and the long-term component of meteorological factors; wherein, the long-term component of meteorological factors is determined according to the baseline component of meteorological factors.

8. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the ozone pollution dominant meteorological factor identification method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the ozone pollution dominant meteorological factor identification method according to any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the ozone pollution dominant meteorological factor identification method according to any one of claims 1-6.

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