Atmosphere data analysis method and system based on high-molecular nanometer material

Abstract

The invention discloses an atmosphere data analysis method and a pollution abatement system based on a high-molecular nanometer material, and belongs to the field of environmental pollution control, and the method specifically comprises the following steps: preparing the high-molecular nanometer material as a sensing layer, and constructing a target pollutant response sensor; collecting atmospheric pollution data in real time through the sensor, and preprocessing the atmospheric pollution data; establishing an atmospheric data analysis and prediction model based on the preprocessed data, and identifying an atmospheric pollution mode; in combination with meteorological data and geographic information, analyzing a transmission path and an influence range of atmospheric pollution, and evaluating potential health risks by using a health influence evaluation model; and formulating and implementing an atmospheric pollution control strategy according to an evaluation result, and adjusting and optimizing the strategy after evaluation so as to realize effective monitoring and control of atmospheric pollution.

Description

Technical Field

[0001] The invention belongs to the field of environmental pollution control, and specifically relates to an atmospheric data analysis method and system based on polymer nanomaterials. Background Art

[0002] With the rapid development of industrialization and urbanization, the problem of air pollution has become increasingly serious, posing a huge threat to the environment and human health. Although traditional atmospheric analysis methods, such as gas chromatography-mass spectrometry and infrared spectroscopy, have high analytical accuracy, they have shortcomings such as complex operation, expensive equipment, and long analysis cycle. Therefore, it is particularly important to develop a simple, fast, and low-cost atmospheric data analysis method.

[0003] For example, a Chinese patent with the authorization announcement number CN111825241B discloses a method and device for treating pollutants based on micro-nano motor materials, including: adding micro-nano motor materials to the sewage to be treated, and then adding hydrogen peroxide to the sewage to start the treatment process, wherein the micro-nano motor material undergoes a catalytic reaction in the water to be treated, continuously generates micro-nano bubbles, drives the pollutants in the water to float to the water surface and enriches in the bubble foam phase, and then separates the foam phase from the sewage to achieve the purpose of treating microplastic pollutants; wherein the outer diameter of the micro-nano bubbles is 20nm to 5000μm. By adopting this technical solution, the self-driven motion, self-stirring and high-speed microbubble generation capabilities of the micro-nano motor are utilized to realize the adsorption bubble separation process of pollutants suspended in the water, and to achieve the treatment of microplastic pollutants. This technical solution is simple, easy to operate, low cost, less environmental pollution, and has certain potential for industrial application.

[0004] For example, a Chinese patent with the authorization announcement number CN101386430B discloses a method for efficiently and conveniently treating non-point source pollution using iron oxide nanomaterials, including: using non-toxic, biodegradable and cheap high molecular polymers and common iron salts as raw materials, and preparing nano iron materials through simple synthesis technology. This material can be infiltrated and dispersed in the sub-surface layer of the soil to form a soil wall containing iron oxide nanomaterials, which absorbs phosphorus in phosphorus-containing water flowing through the soil wall, thereby treating phosphorus non-point source pollution caused by excessive use of pesticides and fertilizers, human and animal feces and urine, and domestic sewage discharge, etc., with the characteristics of high efficiency, convenience and wide application range.

[0005] The above existing technologies all have the following problems: in CN111825241B, the scope of application is relatively limited and is only applicable to specific sewage treatment scenarios, and the treatment objects are different. At the same time, it relies on the catalytic reaction of micro-nano motor materials and bubble separation technology to treat pollutants, and lacks the ability to predict future pollution trends; in CN101386430B, it mainly relies on the adsorption capacity of iron oxide nanomaterials to treat non-point source pollution, and lacks the ability to deeply analyze, predict and assess risks of environmental data; at the same time, neither of them involves the assessment of potential health risks. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention proposes an atmospheric data analysis method and system based on polymer nanomaterials, prepares polymer nanomaterials as a sensing layer, and constructs a target pollutant response sensor; collects atmospheric pollution data in real time through the sensor and performs preprocessing; establishes an atmospheric data analysis and prediction model based on the preprocessed data to identify atmospheric pollution patterns; combines meteorological data and geographic information to analyze the transmission path and impact range of atmospheric pollution, and uses a health impact assessment model to assess potential health risks; formulates and implements atmospheric pollution control strategies based on the assessment results, and adjusts and optimizes the strategies after assessment to achieve effective monitoring and control of atmospheric pollution.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] Atmospheric data analysis methods based on polymer nanomaterials include:

[0009] Step S1: preparing a polymer nanomaterial, and using the prepared polymer nanomaterial as a sensing layer to obtain a target pollutant responsive sensor;

[0010] Step S2: collecting air pollution data in real time according to the deployed target pollutant response sensors, and preprocessing the collected air pollution data;

[0011] Step S3: Based on the pre-processed air pollution data, an air data analysis and prediction model is established, the air pollution data is predicted using the established air data analysis and prediction model, and the air pollution pattern is identified according to the prediction result;

[0012] Step S4: according to the identification results of the air pollution pattern, the transmission path and impact range of the air pollution are analyzed using meteorological data and geographic information methods, and based on the analysis results of the air pollution transmission path and impact range, the potential health risks are evaluated in combination with the health impact assessment model;

[0013] Step S5: Formulate an air pollution control strategy based on the health risk assessment results, implement and evaluate the air pollution control strategy, and adjust and optimize the air pollution control strategy based on the assessment results.

[0014] Specifically, the specific steps of step S1 include:

[0015] S1.1: dispersing the reduced graphene in an ethylene glycol solution by ultrasonic treatment to obtain a graphene ethylene glycol solution;

[0016] S1.2: adding an ethylene glycol solution containing 1-pyrenemethylamine to the graphene ethylene glycol solution, allowing the 1-pyrenemethylamine to self-assemble on the graphene surface by stirring, and while stirring, adding a chloroplatinic acid ethylene glycol solution to allow platinum ions to react with functional groups on the graphene surface, thereby loading platinum nanoparticles on the graphene surface, and obtaining a graphene ethylene glycol mixed solution loaded with platinum nanoparticles;

[0017] S1.3: adjusting the pH value of the mixed solution with sodium hydroxide, and placing the adjusted mixed solution at a temperature of 160° C. for a reaction time period of Δt;

[0018] S1.4: After the reaction is completed, unreacted platinum ions and impurities are removed by centrifugation to obtain graphene loaded with platinum nanoparticles;

[0019] S1.5: Compounding the graphene loaded with platinum nanoparticles with a polymer matrix by solution blending, and processing the composite material to obtain a polymer nanocomposite material;

[0020] S1.6: Coat the prepared polymer nanocomposite material on the sensor substrate to form a uniform sensing layer. According to the sensing principle, construct the target pollutant responsive sensor structure, and perform performance tests on the target pollutant responsive sensor.

[0021] Specifically, the specific steps of step S3 include:

[0022] S3.1: Obtain the pre-processed air pollution data, and perform feature extraction on the pre-processed air pollution data to obtain air pollution feature data X = {x1,...,x n}, where x n represents the nth atmospheric pollution characteristic data, and n represents the number of atmospheric pollution characteristic data;

[0023] S3.2: Set the prediction target, load the pre-built Gaussian model, and transform X = {x1,...,x n} as input and the prediction target as output, the Gaussian model is trained to obtain a trained atmospheric data analysis and prediction model, wherein the prediction target refers to the pollutant concentration and air quality index at time t;

[0024] S3.3: Acquire real-time atmospheric pollution characteristic data, and input the real-time atmospheric pollution data into a trained atmospheric data analysis and prediction model for prediction, and identify atmospheric pollution patterns based on the prediction results, wherein the atmospheric pollution patterns include the changing trend of pollutant concentrations and the changes in air quality levels.

[0025] Specifically, the specific steps of step S4 include:

[0026] S4.1: Obtaining atmospheric pollution pattern recognition results p , and at the same time, obtain real-time collected air pollution data and geographic information data Y = {y1,...,y q},in, represents the mth air pollution data, m represents the number of air pollution data, y q represents the qth geographic information data, where q represents the number of geographic information data;

[0027] S4.2: Use real-time collected air pollution data and geographic information data to build an air pollution transmission model and p and Input into the atmospheric pollution transmission model to simulate the diffusion and transmission process of pollutants in the atmosphere. According to the simulation results, the concentration distribution of pollutants and the transmission path of atmospheric pollution are determined. According to the simulation results, the transmission path diagram is drawn. The formula is:

[0028]

[0029] Among them, C(x′,y′,z′,t) represents the pollutant concentration in the direction of (x′,y′,z′) at time t, x′, y′, z′ represent the spatial coordinates, and Q(t) represents the intensity of the pollution source that changes with time. represents the function describing the influence of terrain and meteorological conditions on pollutant diffusion, represents the lateral diffusion parameter that varies with time and meteorological conditions, represents the longitudinal diffusion parameter that varies with time and meteorological conditions, Wind(t) represents the wind speed that varies with time, H(x′,y′) represents the height of the pollution source that varies with geographical location, g(u) represents the impact function of spatial heterogeneity on pollutant concentration, and exp(·) represents the exponential function.

[0030] Specifically, the specific steps of step S4 also include:

[0031] S4.3: Use geographic information system software to compare the simulation results with Y = {y1, ..., y q} to conduct overlay analysis, and identify the key areas and potential risk points affected by air pollution based on the overlay analysis results, and then calculate the potential risk points based on Y = {y1,...,yq}Correct the identification results to determine the impact range of air pollution;

[0032] S4.4: Set a buffer distance based on the revised air pollution impact range, and use geographic information system software to generate a buffer polygon based on the set buffer distance;

[0033] S4.5: In the geographic information system software, draw a map of the impact range of air pollution based on the results of the buffer zone analysis, and use different colors to represent the degree of pollution impact in different areas to form a visual result;

[0034] S4.6: Collect health impact data, build a health impact assessment model, input the results of the air pollution transmission path and impact range analysis into the health impact assessment model, assess potential health risks, and output a health risk assessment report.

[0035] Specifically, the atmospheric pollution data in step S2 includes pollutant concentration data, spatial distribution data of atmospheric pollutants and meteorological parameters; the pollutant concentration data includes PM2.5, PM10, sulfur dioxide and nitrogen oxides; the meteorological parameters include air pressure, temperature, humidity, wind speed, wind direction and precipitation.

[0036] Specifically, the air pollution control strategy in step S5 includes source control, process control, end-of-pipe control and monitoring and evaluation.

[0037] The air pollution control system based on polymer nanomaterials includes: sensor design module, data acquisition module, prediction module, evaluation module, and control strategy formulation module;

[0038] The sensor design module is responsible for the preparation of polymer nanomaterials and the design of sensors responsive to target pollutants;

[0039] The data acquisition module is used to collect air pollution data in real time by deploying target pollutant response sensors and preprocess the collected air pollution data;

[0040] The prediction module is used to establish a data analysis and prediction model based on the pre-processed air pollution data, predict the air pollution data, and identify the air pollution pattern;

[0041] The assessment module is used to analyze the transmission path and impact range of air pollution based on the identification results of air pollution patterns, and to assess potential health risks in combination with a health impact assessment model;

[0042] The control strategy formulation module is used to formulate air pollution control strategies based on health risk assessment results, implement and evaluate the effectiveness of air pollution control strategies, and adjust and optimize air pollution control strategies based on the evaluation results.

[0043] Specifically, the governance strategy formulation module includes: a governance strategy formulation unit, a strategy implementation unit, and an evaluation and optimization unit;

[0044] The control strategy formulation unit is used to formulate air pollution control strategies based on the health risk assessment results;

[0045] The strategy implementation unit is used to implement the formulated air pollution control strategy;

[0046] The evaluation and optimization unit is used to continuously monitor air pollution data, evaluate the effectiveness of air pollution control strategies, and adjust and optimize air pollution control strategies based on the evaluation results.

[0047] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of an atmospheric data analysis method based on polymer nanomaterials when executing the computer program.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. The present invention proposes an air pollution control system based on polymer nanomaterials, and optimizes and improves the architecture, operation steps and processes. The system has the advantages of simple process, low investment and operation costs, and low production work costs.

[0050] 2. The present invention proposes an atmospheric data analysis method based on polymer nanomaterials. By preparing a target pollutant response sensor using polymer nanomaterials as the sensing layer, it is capable of collecting atmospheric pollution data in real time and accurately, and establishing an atmospheric data analysis and prediction model after preprocessing. This not only improves the efficiency and accuracy of atmospheric pollution monitoring, but also can identify atmospheric pollution patterns based on the prediction results.

[0051] 3. The present invention proposes an atmospheric data analysis method based on polymer nanomaterials, combines meteorological data and geographic information methods to analyze the transmission path and impact range of atmospheric pollution, and uses a health impact assessment model to assess potential health risks, providing strong support for the formulation of targeted atmospheric pollution control strategies; by implementing and evaluating control strategies, and adjusting and optimizing them according to the evaluation results, the system and method can more effectively reduce the impact of atmospheric pollution on human health and the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1This is a schematic diagram of the atmospheric data analysis method based on polymer nanomaterials of the present invention;

[0053] Figure 2 This is a principle flow chart of the atmospheric data analysis method based on polymer nanomaterials of the present invention;

[0054] Figure 3 This is a diagram of the architecture of the air pollution control system based on polymer nanomaterials of the present invention. DETAILED DESCRIPTION

[0055] Example 1

[0056] See also Figure 1 and Figure 2 , an embodiment provided by the present invention: an atmospheric data analysis method based on polymer nanomaterials, comprising the following steps:

[0057] Step S1: preparing a polymer nanomaterial, and using the prepared polymer nanomaterial as a sensing layer to obtain a target pollutant responsive sensor;

[0058] Step S2: collecting air pollution data in real time according to the deployed target pollutant response sensors, and preprocessing the collected air pollution data;

[0059] The atmospheric pollution data in step S2 includes pollutant concentration data, spatial distribution data of atmospheric pollutants and meteorological parameters; the pollutant concentration data includes PM2.5, PM10, sulfur dioxide and nitrogen oxides; the meteorological parameters include air pressure, temperature, humidity, wind speed, wind direction and precipitation.

[0060] Step S3: Based on the pre-processed air pollution data, an air data analysis and prediction model is established, the air pollution data is predicted using the established air data analysis and prediction model, and the air pollution pattern is identified according to the prediction result;

[0061] Step S4: according to the identification results of the air pollution pattern, the transmission path and impact range of the air pollution are analyzed using meteorological data and geographic information methods, and based on the analysis results of the air pollution transmission path and impact range, the potential health risks are evaluated in combination with the health impact assessment model;

[0062] Step S5: Formulate an air pollution control strategy based on the health risk assessment results, implement and evaluate the air pollution control strategy, and adjust and optimize the air pollution control strategy based on the assessment results.

[0063] The air pollution control strategy in step S5 includes source control, process control, end-of-pipe control, and monitoring and evaluation.

[0064] Furthermore, the specific steps of step S5 include:

[0065] (1) Obtain air pollution data and, through survey data, understand the impact of air pollution on human health, including morbidity and mortality data for respiratory diseases, cardiovascular diseases, allergic diseases, etc.;

[0066] (2) According to the collected data types and research objectives, a qualitative risk assessment method is selected to calculate the potential risk of air pollution to human health, including multiple steps such as exposure assessment, toxicity assessment, and risk characterization. Among them, the qualitative risk assessment method is the prior art content in this field and is not an inventive solution of this application, and is not described in detail here;

[0067] (3) Interpret the risk assessment results to identify which pollutants, areas or groups of people face higher health risks;

[0068] (4) Based on the results of the health risk assessment, determine the priorities and key areas for air pollution control, and follow the principles of source control, comprehensive prevention and control, and regional coordination to formulate air pollution control strategies and emergency plans to deal with possible heavy pollution weather;

[0069] (5) Formulate a detailed implementation plan, clarify the specific implementation time, responsible departments and individuals for each measure, and allocate necessary financial, human, material and other resources to ensure the smooth implementation of the governance measures;

[0070] (6) Determine evaluation indicators, such as the reduction in pollutant concentrations and the proportion of days with good air quality, and collect monitoring data after the implementation of the control strategy, analyze and compare them, and evaluate the control effect;

[0071] (7) Write an evaluation report summarizing the implementation, effectiveness, and existing problems of the governance strategy;

[0072] (8) Based on the evaluation results, identify problems and deficiencies in the governance strategy, adjust and optimize the governance strategy in response to the existing problems and deficiencies, and propose improvement measures and suggestions.

[0073] The specific steps of step S1 include:

[0074] S1.1: dispersing the reduced graphene in an ethylene glycol solution by ultrasonic treatment to obtain a graphene ethylene glycol solution;

[0075] S1.2: adding an ethylene glycol solution containing 1-pyrenemethylamine to the graphene ethylene glycol solution, allowing the 1-pyrenemethylamine to self-assemble on the graphene surface by stirring, and while stirring, adding a chloroplatinic acid ethylene glycol solution to allow platinum ions to react with functional groups on the graphene surface, thereby loading platinum nanoparticles on the graphene surface, and obtaining a graphene ethylene glycol mixed solution loaded with platinum nanoparticles;

[0076] S1.3: adjusting the pH value of the mixed solution with sodium hydroxide, and placing the adjusted mixed solution at a temperature of 160° C. for a reaction time period of Δt;

[0077] S1.4: After the reaction is completed, unreacted platinum ions and impurities are removed by centrifugation to obtain graphene loaded with platinum nanoparticles, wherein the centrifugation operation is a prior art in the art and is not an inventive solution of the present application, and is not described in detail herein;

[0078] S1.5: Compounding the graphene loaded with platinum nanoparticles with a polymer matrix by solution blending, and processing the composite material to obtain a polymer nanocomposite material;

[0079] S1.6: Coat the prepared polymer nanocomposite material on the sensor substrate to form a uniform sensing layer. According to the sensing principle, construct the target pollutant responsive sensor structure, and perform performance tests on the target pollutant responsive sensor.

[0080] The specific steps of step S3 include:

[0081] S3.1: Obtain the pre-processed air pollution data, and perform feature extraction on the pre-processed air pollution data to obtain air pollution feature data X = {x1,...,x n}, where x n represents the nth atmospheric pollution characteristic data, and n represents the number of atmospheric pollution characteristic data;

[0082] S3.2: Set the prediction target, load the pre-built Gaussian model, and transform X = {x1,...,x n} as input and the prediction target as output, the Gaussian model is trained to obtain a trained atmospheric data analysis prediction model, wherein the prediction target refers to the pollutant concentration and the air quality index at time t, wherein the Gaussian model is the prior art content in this field and is not the inventive solution of the present application, and will not be described in detail here;

[0083] S3.3: Acquire real-time atmospheric pollution characteristic data, and input the real-time atmospheric pollution data into a trained atmospheric data analysis and prediction model for prediction, and identify atmospheric pollution patterns based on the prediction results, wherein the atmospheric pollution patterns include the changing trend of pollutant concentrations and the changes in air quality levels.

[0084] Furthermore, the specific steps of S3.3 include:

[0085] (1) Load the trained atmospheric data analysis and prediction model;

[0086] (2) Inputting the preprocessed real-time air pollution data into the trained air data analysis and prediction model;

[0087] (3) The trained atmospheric data analysis and prediction model outputs prediction results based on the input real-time atmospheric pollution data, including the changing trend of pollutant concentration and the change of air quality level;

[0088] (4) Analyze and interpret the forecast results to identify air pollution patterns, including determining rising and falling trends in pollutant concentrations and changes in air quality levels;

[0089] (5) Provide decision-making support to environmental protection departments based on the identified air pollution patterns, such as formulating targeted control measures and issuing air quality warnings.

[0090] The specific steps of step S4 include:

[0091] S4.1: Obtaining atmospheric pollution pattern recognition results p , and at the same time, obtain real-time collected air pollution data and geographic information data Y = {y1,...,y q},in, represents the mth air pollution data, m represents the number of air pollution data, y q represents the qth geographic information data, where q represents the number of geographic information data;

[0092] S4.2: Use real-time collected air pollution data and geographic information to build an air pollution transmission model and p and Input into the atmospheric pollution transmission model to simulate the diffusion and transmission process of pollutants in the atmosphere. According to the simulation results, the concentration distribution of pollutants and the transmission path of atmospheric pollution are determined. According to the simulation results, the transmission path diagram is drawn. The formula is:

[0093]

[0094] Among them, C(x′,y′,z′,t) represents the pollutant concentration in the direction of (x′,y′,z′) at time t, x′, y′, z′ represent the spatial coordinates, and Q(t) represents the intensity of the pollution source that changes with time. represents the function describing the influence of terrain and meteorological conditions on pollutant diffusion, represents the lateral diffusion parameter that varies with time and meteorological conditions, represents the longitudinal diffusion parameter that varies with time and meteorological conditions, Wind(t) represents the wind speed that varies with time, H(x′,y′) represents the height of the pollution source that varies with geographical location, g(u) represents the effect function of spatial heterogeneity on pollutant concentration, and exp(·) represents the exponential function;

[0095] It should be noted that in the present invention, by introducing the time variable t and the terrain factor The simulation results can more accurately reflect the temporal and spatial changes and terrain effects in actual atmospheric movement. This dynamic and regionalized processing method improves the accuracy and practicality of the simulation. and It represents the diffusion standard deviation of meteorological factors in the y′ and z′ axes, which vary with time t and meteorological conditions. This design enables the simulation to better capture the diffusion and evolution laws of meteorological factors. At the same time, by adjusting these parameters, it can flexibly adapt to different meteorological conditions and terrain features, thereby improving the adaptability and flexibility of the simulation.

[0096] S4.3: Use geographic information system software to compare the simulation results with Y = {y1, ..., y q} to conduct overlay analysis, and identify the key areas and potential risk points affected by air pollution based on the overlay analysis results, and then calculate the potential risk points based on Y = {y1,...,y q} Correct the identification results to determine the impact range of air pollution, wherein the prior art content in the field of geographic information system software is not the inventive solution of this application and will not be described in detail here;

[0097] Furthermore, the specific steps of S4.3 include:

[0098] (1) Prepare air pollution simulation results and geographic information data and perform preprocessing;

[0099] (2) In the geographic information system software, the overlay analysis module in ArcGIS is used to overlay the simulation result data with the geographic information data, and based on the overlay analysis results, the key areas and potential risk points affected by air pollution are identified;

[0100] (3) Correct the identification results based on geographic information data, such as topography, meteorological conditions, and population density. For example, in mountainous areas, pollutants may be blocked by the terrain and difficult to diffuse, so it is necessary to adjust the boundaries of the affected area. At the same time, other factors that may affect the diffusion of pollutants, such as buildings, roads, and green spaces, are considered to further correct the results.

[0101] (4) Comprehensively analyze the superposition analysis results and the corrected data to determine the impact range of air pollution, including delineating the boundaries of key areas and potential risk points, and providing pollutant concentration thresholds.

[0102] S4.4: Set a buffer distance based on the revised air pollution impact range, and use geographic information system software to generate a buffer polygon based on the set buffer distance;

[0103] Furthermore, the process of generating a buffer polygon according to the set buffer distance includes:

[0104] (1) Open the geographic information system software and load the geographic information data and air pollution impact range data;

[0105] (2) Select the "Buffer Zone Analysis" tool in the software, input the air pollution impact range data, and set the buffer zone distance;

[0106] (3) Perform buffer zone analysis. The software will automatically generate a buffer zone polygon that represents the area within a certain distance outside the range of atmospheric pollution influence.

[0107] S4.5: In the geographic information system software, draw a map of the impact range of air pollution based on the results of the buffer zone analysis, and use different colors to represent the degree of pollution impact in different areas to form a visual result;

[0108] S4.6: Collect health impact data, build a health impact assessment model, input the results of the air pollution transmission path and impact range analysis into the health impact assessment model, assess potential health risks, and output a health risk assessment report.

[0109] Furthermore, the specific steps of S4.6 include:

[0110] (1) Collect health impact data through questionnaires, medical institutions, wearable devices, and literature, and perform pre-processing such as cleaning and standardization of the collected health impact data;

[0111] (2) According to the research purpose and data characteristics, a pre-built logistic regression model is loaded, and the pre-built logistic regression model is trained using the pre-processed health impact data to obtain a trained health impact assessment model, wherein the logistic regression model is a prior art content in this field and is not an inventive solution of the present application, and is not described in detail here;

[0112] (3) sorting out the transmission path and impact range analysis results obtained from the atmospheric pollution simulation, extracting the concentration distribution, transmission path, and impact range information of the pollutants, and inputting the extracted concentration distribution, transmission path, and impact range information of the pollutants into the health impact assessment model as input variables of the health impact assessment model, and outputting potential health risks, wherein the feature extraction process is the prior art content in this field, is not the inventive solution of the present application, and is not elaborated here;

[0113] (4) Analyze the output of the health impact assessment model to identify high-risk areas and high-risk populations, as well as the types and extent of health impacts that may be experienced;

[0114] (5) Prepare a health risk assessment report based on the assessment results of the health impact assessment model. The report should include the assessment objectives, methods, results, conclusions and recommendations.

[0115] The air pollution control strategy in step S5 includes source control, process control, end-of-pipe control, and monitoring and evaluation.

[0116] Among them, source control is the primary strategy for air pollution control, which aims to reduce pollutant emissions from the source, including: 1) Strengthen environmental protection laws and regulations: formulate and implement strict environmental protection laws and regulations, punish enterprises and individuals that emit pollutants, and promote them to adopt clean production technologies; 2) Promote clean energy: encourage the use of clean energy such as solar energy and wind energy, reduce the consumption of traditional energy such as coal and oil, and thus reduce air pollutant emissions; 3) Optimize energy structure: improve energy utilization efficiency, reduce energy consumption, and reduce pollutant emission intensity; 4) Industrial emission reduction: strengthen industrial emission management, improve process technology level, promote clean production, and reduce industrial waste gas emissions;

[0117] Process control aims to intervene in the process of pollutant emission to reduce the generation and emission of pollutants, including: 1) Strengthening the treatment of motor vehicle exhaust: implementing automobile exhaust emission standards, promoting new energy vehicles, and reducing motor vehicle exhaust emissions; 2) Controlling dust pollution: strengthening dust management in construction, road cleaning and other links to reduce dust pollution; 3) Optimizing urban planning: rationally planning urban layout, reducing traffic congestion and the impact of industrial areas on residential areas;

[0118] End-of-pipe treatment is the treatment of pollutants after they are discharged to reduce their impact on the environment, including: 1) Construction of pollution control facilities: Construction of pollution control equipment such as combustion, filtration and adsorption to deeply treat the discharged pollutants; 2) Implementation of regional joint prevention and control: Establishment of a regional air pollution prevention and control cooperation mechanism to jointly deal with cross-regional air pollution problems;

[0119] Monitoring and evaluation are important means to ensure the effectiveness of air pollution control, including: 1) Strengthening air quality monitoring: Establishing a complete air quality monitoring network to grasp the air quality status in real time; 2) Conducting health risk assessment: Evaluating the impact of air pollution on public health and providing a basis for formulating control strategies; 3) Evaluating control effects: Regularly evaluating the effectiveness of air pollution control, and adjusting and optimizing control strategies based on the evaluation results.

[0120] Example 2

[0121] See also Figure 3 Another embodiment provided by the present invention is an air pollution control system based on polymer nanomaterials, comprising:

[0122] Sensor design module, data acquisition module, prediction module, evaluation module, and governance strategy formulation module;

[0123] The sensor design module is responsible for the preparation of polymer nanomaterials and the design of sensors that respond to target pollutants;

[0124] The data acquisition module is used to collect air pollution data in real time by deploying target pollutant response sensors and pre-process the collected air pollution data to ensure the quality and availability of the data;

[0125] The prediction module is used to establish a data analysis and prediction model based on the pre-processed air pollution data, predict the air pollution data, and identify air pollution patterns, such as the changing patterns of pollutant concentrations and the temporal and spatial distribution characteristics;

[0126] An assessment module is used to analyze the transmission path and impact range of air pollution based on the identification results of air pollution patterns, and to evaluate potential health risks in combination with a health impact assessment model;

[0127] The control strategy formulation module is used to formulate air pollution control strategies based on the results of health risk assessment, implement and evaluate the effectiveness of air pollution control strategies, and adjust and optimize air pollution control strategies based on the evaluation results.

[0128] The sensor design module includes: material preparation unit and sensor design unit;

[0129] The material preparation unit is responsible for selecting and preparing polymer nanomaterials for air pollution detection. The present invention selects platinum and graphene;

[0130] The sensor design unit is used to design and manufacture sensors that respond to specific pollutants using prepared polymer nanomaterials.

[0131] The data acquisition module includes: a data acquisition unit and a preprocessing unit;

[0132] A data collection unit is used to deploy sensors in target areas to collect air pollution data in real time;

[0133] The preprocessing unit is used to clean, denoise, and process missing values ​​of the collected data to improve the quality of the data.

[0134] The prediction module includes: a model building unit and a pattern recognition unit;

[0135] A model building unit, used to build an atmospheric data analysis and prediction model using machine learning or deep learning techniques;

[0136] The pattern recognition unit is used to predict the air pollution data using the established model and identify the air pollution pattern based on the prediction results.

[0137] The governance strategy formulation module includes: governance strategy formulation unit, strategy implementation unit, and evaluation and optimization unit;

[0138] The control strategy formulation unit is used to formulate air pollution control strategies based on the health risk assessment results;

[0139] The strategy implementation unit is used to implement the formulated air pollution control strategy;

[0140] The evaluation and optimization unit is used to continuously monitor air pollution data, evaluate the effectiveness of air pollution control strategies, and adjust and optimize air pollution control strategies based on the evaluation results.

[0141] In summary, the sensor design module, data acquisition module, prediction module, evaluation module, governance strategy formulation module and their corresponding units all have their specific roles and functions. They work together to form a complete air pollution control system, which aims to effectively monitor, analyze and control air pollution and protect human health and the ecological environment.

[0142] Example 3

[0143] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of an atmospheric data analysis method based on polymer nanomaterials are implemented. For details, please refer to the above method embodiment, which will not be repeated here.

[0144] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0145] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0146] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention, and all of these are within the protection of the present invention.

Claims

1. An atmospheric data analysis method based on polymer nanomaterials, characterized in that: include: Step S1: preparing a polymer nanomaterial, and using the prepared polymer nanomaterial as a sensing layer to obtain a target pollutant responsive sensor; Step S2: collecting air pollution data in real time according to the deployed target pollutant response sensors, and preprocessing the collected air pollution data; Step S3: Based on the pre-processed air pollution data, an air data analysis and prediction model is established, the air pollution data is predicted using the established air data analysis and prediction model, and the air pollution pattern is identified according to the prediction result; Step S4: according to the identification results of the air pollution pattern, the transmission path and impact range of the air pollution are analyzed using meteorological data and geographic information methods, and based on the analysis results of the air pollution transmission path and impact range, the potential health risks are evaluated in combination with the health impact assessment model; Step S5: Formulate an air pollution control strategy based on the health risk assessment results, implement and evaluate the air pollution control strategy, and adjust and optimize the air pollution control strategy based on the assessment results.

2. The atmospheric data analysis method based on polymer nanomaterials according to claim 1, characterized in that: The specific steps of step S1 include: S1.1: dispersing the reduced graphene in an ethylene glycol solution by ultrasonic treatment to obtain a graphene ethylene glycol solution; S1.2: adding an ethylene glycol solution containing 1-pyrenemethylamine to the graphene ethylene glycol solution, allowing the 1-pyrenemethylamine to self-assemble on the graphene surface by stirring, and while stirring, adding a chloroplatinic acid ethylene glycol solution to allow platinum ions to react with functional groups on the graphene surface, thereby loading platinum nanoparticles on the graphene surface, and obtaining a graphene ethylene glycol mixed solution loaded with platinum nanoparticles; S1.3: adjusting the pH value of the mixed solution using sodium hydroxide, and placing the adjusted mixed solution at a temperature of 160° C. for a reaction time period of Δt; S1.4: After the reaction is completed, unreacted platinum ions and impurities are removed by centrifugation to obtain graphene loaded with platinum nanoparticles; S1.5: Compounding the graphene loaded with platinum nanoparticles with a polymer matrix by solution blending, and processing the composite material to obtain a polymer nanocomposite material; S1.6: Coat the prepared polymer nanocomposite material on the sensor substrate to form a uniform sensing layer. According to the sensing principle, construct the target pollutant responsive sensor structure, and perform performance tests on the target pollutant responsive sensor.

3. The atmospheric data analysis method based on polymer nanomaterials according to claim 2, characterized in that: The specific steps of step S3 include: S3.1: Obtain the pre-processed air pollution data, and perform feature extraction on the pre-processed air pollution data to obtain air pollution feature data X = {x1,...,x n }, where x n represents the nth atmospheric pollution characteristic data, and n represents the number of atmospheric pollution characteristic data; S3.2: Set the prediction target, load the pre-built Gaussian model, and transform X = {x1,...,x n } as input and the prediction target as output, the Gaussian model is trained to obtain a trained atmospheric data analysis and prediction model, wherein the prediction target refers to the pollutant concentration and air quality index at time t; S3.3: Acquire real-time atmospheric pollution characteristic data, and input the real-time atmospheric pollution data into a trained atmospheric data analysis and prediction model for prediction, and identify atmospheric pollution patterns based on the prediction results, wherein the atmospheric pollution patterns include the changing trend of pollutant concentrations and the changes in air quality levels.

4. The atmospheric data analysis method based on polymer nanomaterials according to claim 3, characterized in that: The specific steps of step S4 include: S4.1: Obtaining atmospheric pollution pattern recognition results p , and at the same time, obtain real-time collected air pollution data and geographic information data Y = {y1,...,y q },in, represents the mth air pollution data, m represents the number of air pollution data, y q represents the qth geographic information data, where q represents the number of geographic information data; S4.2: Use real-time collected air pollution data and geographic information data to build an air pollution transmission model and p and Input into the atmospheric pollution transmission model to simulate the diffusion and transmission process of pollutants in the atmosphere. According to the simulation results, the concentration distribution of pollutants and the transmission path of atmospheric pollution are determined. According to the simulation results, the transmission path diagram is drawn. The formula is: Among them, C(x′,y′,z′,t) represents the pollutant concentration in the direction of (x′,y′,z′) at time t, x′, y′, z′ represent the spatial coordinates, and Q(t) represents the intensity of the pollution source that changes with time. represents the function describing the influence of terrain and meteorological conditions on pollutant diffusion, represents the lateral diffusion parameter that varies with time and meteorological conditions, represents the longitudinal diffusion parameter that varies with time and meteorological conditions, Wind(t) represents the wind speed that varies with time, H(x′,y′) represents the height of the pollution source that varies with geographical location, g(u) represents the impact function of spatial heterogeneity on pollutant concentration, and exp(·) represents the exponential function.

5. The atmospheric data analysis method based on polymer nanomaterials according to claim 4, characterized in that: The specific steps of step S4 also include: S4.3: Use geographic information system software to compare the simulation results with Y = {y1, ..., y q } to conduct overlay analysis, and identify the key areas and potential risk points affected by air pollution based on the overlay analysis results, and then calculate the potential risk points based on Y={y1,...,y q }Correct the identification results to determine the impact range of air pollution; S4.4: Set a buffer distance based on the revised air pollution impact range, and use geographic information system software to generate a buffer polygon based on the set buffer distance; S4.5: In the geographic information system software, draw a map of the impact range of air pollution based on the results of the buffer zone analysis, and use different colors to represent the degree of pollution impact in different areas to form a visual result; S4.6: Collect health impact data, build a health impact assessment model, input the results of the air pollution transmission path and impact range analysis into the health impact assessment model, assess potential health risks, and output a health risk assessment report.

6. The atmospheric data analysis method based on polymer nanomaterials according to claim 5, characterized in that: The atmospheric pollution data in step S2 includes pollutant concentration data, spatial distribution data of atmospheric pollutants and meteorological parameters; the pollutant concentration data includes PM2.5, PM10, sulfur dioxide and nitrogen oxides; the meteorological parameters include air pressure, temperature, humidity, wind speed, wind direction and precipitation.

7. The atmospheric data analysis method based on polymer nanomaterials according to claim 6, characterized in that: The air pollution control strategy in step S5 includes source control, process control, end-of-pipe control and monitoring and evaluation.

8. An air pollution control system based on polymer nanomaterials, which is used to implement the air data analysis method based on polymer nanomaterials according to any one of claims 1 to 7, characterized in that: include: Sensor design module, data acquisition module, prediction module, evaluation module, and governance strategy formulation module; The sensor design module is responsible for the preparation of polymer nanomaterials and the design of sensors responsive to target pollutants; The data acquisition module is used to collect air pollution data in real time by deploying target pollutant response sensors and preprocess the collected air pollution data; The prediction module is used to establish a data analysis and prediction model based on the pre-processed air pollution data, predict the air pollution data, and identify the air pollution pattern; The assessment module is used to analyze the transmission path and impact range of air pollution based on the identification results of air pollution patterns, and to assess potential health risks in combination with a health impact assessment model; The control strategy formulation module is used to formulate air pollution control strategies based on health risk assessment results, implement and evaluate the effectiveness of air pollution control strategies, and adjust and optimize air pollution control strategies based on the evaluation results.

9. The air pollution control system based on polymer nanomaterials according to claim 8, characterized in that: The governance strategy formulation module includes: a governance strategy formulation unit, a strategy implementation unit, and an evaluation and optimization unit; The control strategy formulation unit is used to formulate air pollution control strategies based on the health risk assessment results; The strategy implementation unit is used to implement the formulated air pollution control strategy; The evaluation and optimization unit is used to continuously monitor air pollution data, evaluate the effectiveness of air pollution control strategies, and adjust and optimize air pollution control strategies based on the evaluation results.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the atmospheric data analysis method based on polymer nanomaterials according to any one of claims 1 to 7 are implemented.

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