Accurate urban near-surface fine wind field and pollutant concentration simulation system

Abstract

The invention discloses a precise urban near-surface fine wind field and pollutant concentration simulation system. The system comprises a data acquisition module, a wind field simulation module, a pollutant diffusion model module and a visualization module. The data acquisition module acquires comprehensive and accurate meteorological and geographical information through various sensors, GIS integration and data transmission processing. The wind field simulation module is based on a fluid mechanics theory, adopts a numerical model and an algorithm, considers influences such as an urban heat island effect, and realizes high-precision simulation of a near-surface wind field. The pollutant diffusion model simulates pollutant diffusion by applying a mathematical model and a proper calculation method in combination with wind field and meteorological data and the like. The visualization module converts simulation results and generates various visual graphs such as map views and time series diagrams, and is further provided with a user interaction interface and an analysis and prediction tool. The system can accurately simulate urban near-surface wind field and pollutant concentration, and provides scientific basis and powerful support for urban planning, environment management and decision making.

Description

Technical Field

[0001] The present invention belongs to the technical field related to environmental science, and specifically relates to a precise urban near-ground fine wind field and pollutant concentration simulation system. Background Art

[0002] Accurate simulation of near-surface wind fields and pollutant concentrations is crucial in urban environmental management. However, traditional simulation methods have some significant shortcomings.

[0003] Traditional wind field simulations are often based on simple theories and models with low accuracy, and it is difficult to accurately reflect the impact of factors such as complex urban terrain and building distribution on the wind field.

[0004] In terms of pollutant concentration simulation, the model is not perfect and does not fully consider the diffusion and sedimentation processes of pollutants, resulting in a large deviation between the predicted results and the actual situation.

[0005] The data collection methods are single and limited, and it is impossible to obtain comprehensive and accurate information such as meteorology, geography and pollutant concentrations, resulting in low quality of simulation input data.

[0006] The lack of real-time visualization function makes it difficult to intuitively display the distribution of wind fields and pollutant concentrations, which is not conducive to rapid understanding and analysis by relevant personnel.

[0007] Traditional simulation systems do not take into account the urban heat island effect, changes in meteorological conditions, etc., and have poor adaptability.

[0008] Moreover, previous simulation systems performed poorly in decision support and could not provide effective scientific basis and early warning information for urban planning and environmental management.

[0009] In summary, in order to more effectively manage the urban environment and improve air quality, there is an urgent need for a near-surface wind field and pollutant concentration simulation system with high accuracy, comprehensive data, strong visualization, and powerful decision support. Summary of the invention

[0010] The purpose of the present invention is to provide a precise urban near-ground fine wind field and pollutant concentration simulation system to solve the problems raised in the above-mentioned background technology.

[0011] To achieve the above objectives, the present invention provides the following technical solutions:.

[0012] A precise urban near-surface wind field and pollutant concentration simulation system, including:

[0013] Data collection module: collects the city’s meteorological and geographic information through sensors, weather stations and satellite data;

[0014] Wind field simulation module: Based on fluid mechanics theory, it can realize high-precision simulation of near-ground wind fields;

[0015] Pollutant diffusion model: Use mathematical models to predict the diffusion and concentration changes of pollutants under different meteorological conditions;

[0016] Visualization module: displays wind field and pollutant concentration distribution in real time, and provides a user-friendly graphical interface.

[0017] Preferably, the data acquisition module includes:

[0018] Sensor Networks

[0019] Meteorological sensors: used to measure meteorological parameters such as temperature, humidity, wind speed, wind direction, and atmospheric pressure. The sensors should have high accuracy and low power consumption to adapt to the changing conditions in urban environments.

[0020] Pollutant monitors: monitor the pollutant concentrations of PM2.5, PM10, NOx, SO2, CO, and O3. These monitors should meet national standards and have high sensitivity and selectivity;

[0021] Geographic Information System (GIS) Integration Module: collects and integrates spatial data such as topography, land use, building height, etc., and provides background information for model building;

[0022] Data transmission system

[0023] Wireless communication technology: The collected data is transmitted to the central processing unit in real time through wireless communication technologies such as Wi-Fi, LoRa, and NB-IoT;

[0024] Data storage and backup: Set up cloud storage or local storage to ensure long-term retention and backup of data for historical analysis and model training;

[0025] Data processing unit

[0026] Preprocessing module: cleans, denoises and corrects the collected data to ensure data accuracy;

[0027] Data fusion algorithm: Fuse data from different sensors to generate more accurate meteorological and pollutant concentration information.

[0028] Preferably, the wind field simulation module includes:

[0029] Numerical model

[0030] Fluid dynamics model: Use the basic fluid dynamics equations of the Navier-Stokes equations to simulate the wind field through numerical calculation methods such as finite difference, finite element or spectral methods;

[0031] Equation parameterization: Select appropriate turbulence model according to actual conditions to adapt to the complex flow characteristics of cities;

[0032] Input data processing unit

[0033] Meteorological data input: Receive real-time meteorological data from the data acquisition module, including temperature, humidity, air pressure, wind speed and wind direction;

[0034] Terrain database: Integrate urban terrain information, including terrain elevation, building distribution, vegetation type, etc., as basic data for wind field simulation;

[0035] Simulation Algorithm

[0036] Grid generation and division: Generate computational grids with appropriate resolution according to terrain characteristics to ensure the precision of simulation results;

[0037] Time advancement algorithm: Use appropriate time advancement strategy to achieve dynamic simulation of unsteady wind field; cooling and heating model

[0038] Considering the impact of urban heat island effect and changes in surface characteristics on wind field, design appropriate parameterization schemes to simulate thermal effects at different times and locations;

[0039] Output analysis and visualization

[0040] The simulation results will be processed and visualized so that users and decision makers can intuitively understand the wind field characteristics of the city;

[0041] Preferably, the pollutant diffusion model includes

[0042] Mathematical model

[0043] Diffusion equation: Use the modified continuity equation and Navier-Stokes equation to describe the diffusion and transport of pollutants in the airflow;

[0044] Pollutant sedimentation model: Consider the concentration change of particulate matter due to gravity sedimentation and create sedimentation terms.

[0045] Input data processing

[0046] Wind field data: Get real-time wind speed and direction data from the wind field simulation module;

[0047] Meteorological data: Obtain meteorological data such as temperature, humidity, and air pressure to calculate the chemical reactions and physical properties of pollutants;

[0048] Pollution source data: obtain information such as the location, emission intensity, and emission type of pollution sources;

[0049] Calculation method

[0050] Numerical solution method: Choose the appropriate numerical method according to your needs. Finite difference method, finite element method or spectral method to solve the equations, taking into account the spatial and temporal resolution of the simulation;

[0051] Particulate matter and gaseous pollutants: Different diffusion models are developed for different types of pollutants, gaseous pollutants and particulate matter, taking into account their different movement and sedimentation characteristics;

[0052] Result output and visualization

[0053] After the model calculation, the concentration distribution of pollutants at different time and spatial locations is output, and visual results are generated for subsequent analysis and decision-making.

[0054] Preferably, the visualization module includes

[0055] Data receiving and processing unit

[0056] Data interface: connected with the wind field simulation module and pollutant diffusion model to receive simulation results and related meteorological data in real time;

[0057] Data format conversion: converting the collected raw data into the format required for visualization, including standardized processing of wind field data, concentration data, and meteorological data;

[0058] Visual graph generator

[0059] Map view: Based on GIS technology, the pollutant concentration and wind field distribution are displayed on the map, providing air quality information for various areas of the city;

[0060] Time series graph: Generate a graph showing the change of pollutant concentration over time, which is convenient for observing the impact of pollution source intensity and meteorological changes on air quality;

[0061] Wind vector diagram: Displays wind speed and direction in vector form, allowing users to intuitively understand wind field characteristics and flow patterns;

[0062] Heat map: Apply heat map technology to show the pollution level in different areas, and the color gradient clearly conveys the air quality information of each area;

[0063] User Interface

[0064] Dashboard design: Build user-friendly dashboards that allow users to customize data views by selecting different time periods, locations, or specific pollutant types;

[0065] Layer control: Provides layer switching function, users can choose to display different types of data, including wind field, PM2.5 concentration, and NOx concentration, for detailed analysis;

[0066] Information prompts: The visualization interface should include data descriptions, real-time monitoring and forecast information, to improve users’ understanding of the data;

[0067] Analysis and forecasting tools

[0068] Prediction model integration: Integrated prediction tools enable users to view short-term and long-term pollutant concentration forecasts and gain insight into future air quality trends;

[0069] Scenario Analysis: Provides air quality simulation results under different pollution source scenarios, and can perform scenario analysis under user-set conditions to support the decision-making process.

[0070] Compared with the prior art, the present invention provides a precise urban near-ground fine wind field and pollutant concentration simulation system, which has the following beneficial effects:

[0071] Accurate simulation: Based on advanced fluid mechanics theory and mathematical models, it can simulate near-ground wind fields and pollutant diffusion with high precision, providing an accurate basis for urban planning and environmental management;

[0072] Comprehensive data collection: Through multiple sensors and GIS integration, comprehensive meteorological and geographic information, as well as pollutant concentration data, are obtained to provide rich and accurate input for the model;

[0073] Real-time visualization: Provides an intuitive, user-friendly graphical interface to display wind field and pollutant concentration distribution in real time, making it easier for relevant personnel to quickly understand and analyze urban environmental conditions;

[0074] Optimize decision support: provide scientific basis for city managers to formulate pollution control measures and plan urban development, and improve the scientificity and effectiveness of decision-making;

[0075] Early warning: It can predict the trend of pollutant concentration changes and issue early warnings in time, which will help take preventive measures to protect the health of residents and the safety of the urban environment;

[0076] Refined management: Detailed simulation and analysis of different regions and pollutant types will help achieve refined management and targeted governance of the urban environment;

[0077] Strong adaptability: Considering multiple factors such as urban heat island effect and topography, it adapts to complex urban environmental conditions and improves the authenticity and reliability of simulation;

[0078] Risk reduction: Help assess air quality in different scenarios and reduce health risks and economic losses caused by environmental pollution;

[0079] Improve efficiency: Automated data collection, processing and simulation processes save manpower and time costs and improve work efficiency;

[0080] Promote sustainable development: Provide strong technical support for the sustainable development of cities and promote the realization of a green, healthy and livable urban environment. DETAILED DESCRIPTION

[0081] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] The present invention provides a precise urban near-ground fine wind field and pollutant concentration simulation system, comprising:

[0083] Data collection module: collects the city’s meteorological and geographic information through sensors, weather stations and satellite data;

[0084] Wind field simulation module: Based on fluid mechanics theory, it can realize high-precision simulation of near-ground wind fields;

[0085] Pollutant diffusion model: Use mathematical models to predict the diffusion and concentration changes of pollutants under different meteorological conditions;

[0086] Visualization module: displays wind field and pollutant concentration distribution in real time, and provides a user-friendly graphical interface.

[0087] The data acquisition module includes:

[0088] Sensor Networks

[0089] Meteorological sensors: used to measure meteorological parameters such as temperature, humidity, wind speed, wind direction, and atmospheric pressure. The sensors should have high accuracy and low power consumption to adapt to the changing conditions in urban environments.

[0090] Pollutant monitors: monitor the pollutant concentrations of PM2.5, PM10, NOx, SO2, CO, and O3. These monitors should meet national standards and have high sensitivity and selectivity;

[0091] Geographic Information System (GIS) Integration Module: collects and integrates spatial data such as topography, land use, building height, etc., and provides background information for model building;

[0092] Data transmission system

[0093] Wireless communication technology: The collected data is transmitted to the central processing unit in real time through wireless communication technologies such as Wi-Fi, LoRa, and NB-IoT;

[0094] Data storage and backup: Set up cloud storage or local storage to ensure long-term retention and backup of data for historical analysis and model training;

[0095] Data processing unit

[0096] Preprocessing module: cleans, denoises and corrects the collected data to ensure data accuracy;

[0097] Data fusion algorithm: Fuse data from different sensors to generate more accurate meteorological and pollutant concentration information.

[0098] Data collection process

[0099] Sensor deployment: Sensors are placed at different locations in the city (such as parks, residential areas, industrial areas, traffic arteries, etc.) to ensure coverage of major meteorological and pollution hotspots.

[0100] Data collection:

[0101] The sensor acquires real-time data at regular intervals (such as every minute) and uploads the data to the data processing unit through the communication module.

[0102] During extreme weather conditions or sudden pollution events, sensors can increase sampling frequency to obtain more detailed information.

[0103] Data transmission:

[0104] The uploaded data is transmitted to the central server or cloud platform for further storage and processing, ensuring the stability and low latency of data transmission.

[0105] Data processing:

[0106] After the data arrives, it is first preprocessed to eliminate outliers and erroneous data.

[0107] By fusing data from different sensors, more accurate values ​​of wind speed, wind direction, temperature and pollutant concentration can be calculated.

[0108] Data Visualization:

[0109] The processed data will be sent to the visualization module to generate a real-time monitoring panel, allowing relevant personnel to obtain the current meteorological and pollution conditions at a glance.

[0110] The wind farm simulation module includes:

[0111] Numerical model

[0112] Fluid dynamics model: Use the basic fluid dynamics equations of the Navier-Stokes equations to simulate the wind field through numerical calculation methods such as finite difference, finite element or spectral methods;

[0113] Equation parameterization: Select appropriate turbulence model according to actual conditions to adapt to the complex flow characteristics of cities;

[0114] Input data processing unit

[0115] Meteorological data input: Receive real-time meteorological data from the data acquisition module, including temperature, humidity, air pressure, wind speed and wind direction;

[0116] Terrain database: Integrate urban terrain information, including terrain elevation, building distribution, vegetation type, etc., as basic data for wind field simulation;

[0117] Simulation Algorithm

[0118] Grid generation and division: Generate computational grids with appropriate resolution according to terrain characteristics to ensure the precision of simulation results;

[0119] Time advancement algorithm: Use appropriate time advancement strategy to achieve dynamic simulation of unsteady wind field; cooling and heating model

[0120] Considering the impact of urban heat island effect and changes in surface characteristics on wind field, design appropriate parameterization schemes to simulate thermal effects at different times and locations;

[0121] Output analysis and visualization

[0122] The simulation results will be processed and visualized so that users and decision makers can intuitively understand the wind field characteristics of the city;

[0123] Wind farm simulation process

[0124] Data Acquisition

[0125] The real-time meteorological data and terrain information are obtained from the data acquisition module. These data will be used as the initial conditions and boundary conditions of the simulation.

[0126] Mesh Generation

[0127] Generate appropriate computational meshes based on the city’s topographical features and required resolution, and ensure that the mesh is refined to key areas (such as town centers, near factories, etc.).

[0128] Simulation run

[0129] Based on the initialization grid, the fluid dynamics model is used for numerical simulation to calculate the changes in wind speed and wind direction fields at different time steps.

[0130] Model verification

[0131] The simulation results are verified and calibrated using measured wind speed and direction data to ensure the real-world adaptability and accuracy of the model.

[0132] Result Output

[0133] Output wind field simulation results, including information such as wind speed, wind direction and flow pattern, and pass them to subsequent pollutant diffusion models.

[0134] Pollutant dispersion models include

[0135] Mathematical model

[0136] Diffusion equation: Use the modified continuity equation and Navier-Stokes equation to describe the diffusion and transport of pollutants in the airflow;

[0137] Pollutant sedimentation model: Consider the concentration change of particulate matter due to gravity sedimentation and create sedimentation terms.

[0138] Input data processing

[0139] Wind field data: Get real-time wind speed and direction data from the wind field simulation module;

[0140] Meteorological data: Obtain meteorological data such as temperature, humidity, and air pressure to calculate the chemical reactions and physical properties of pollutants;

[0141] Pollution source data: obtain information such as the location, emission intensity, and emission type of pollution sources;

[0142] Calculation method

[0143] Numerical solution method: Choose the appropriate numerical method according to your needs. Finite difference method, finite element method or spectral method to solve the equations, taking into account the spatial and temporal resolution of the simulation;

[0144] Particulate matter and gaseous pollutants: Different diffusion models are developed for different types of pollutants, gaseous pollutants and particulate matter, taking into account their different movement and sedimentation characteristics;

[0145] Result output and visualization

[0146] After the model calculation, the concentration distribution of pollutants at different time and spatial locations is output, and visual results are generated for subsequent analysis and decision-making.

[0147] Pollutant dispersion simulation process

[0148] Data Acquisition

[0149] Acquire real-time meteorological data and wind field data from the data acquisition module and wind field simulation module to determine initial conditions and boundary conditions.

[0150] Model initialization

[0151] Initialize the computational domain and incorporate pollution sources and environmental characteristics into the model, including information such as the city's geography, population density, and traffic flow.

[0152] Simulation run

[0153] Apply the diffusion equation and use numerical methods to advance time and calculate the distribution changes of pollutants. According to different time steps, simulate how pollutants diffuse with the development of wind field.

[0154] Model Validation

[0155] Use actual monitoring data (such as data from air quality monitoring stations) to validate the model, evaluate the accuracy of the predicted results compared with the measured values, and adjust the model parameters to improve accuracy.

[0156] Result output and analysis

[0157] Generate spatial distribution maps, time series maps and meteorological factors impact maps of pollutant concentrations, analyze air quality risks in different areas of the city, and form reports for use by relevant departments.

[0158] Early warning and decision support

[0159] Based on the simulation results, predictions of pollutant concentrations are provided to help city managers issue pollution warnings, assess air quality under different scenarios, and formulate corresponding governance measures.

[0160] The visualization module includes

[0161] Data receiving and processing unit

[0162] Data interface: connected with the wind field simulation module and pollutant diffusion model to receive simulation results and related meteorological data in real time;

[0163] Data format conversion: converting the collected raw data into the format required for visualization, including standardized processing of wind field data, concentration data, and meteorological data;

[0164] Visual graph generator

[0165] Map view: Based on GIS technology, the pollutant concentration and wind field distribution are displayed on the map, providing air quality information for various areas of the city;

[0166] Time series graph: Generate a graph showing the change of pollutant concentration over time, which is convenient for observing the impact of pollution source intensity and meteorological changes on air quality;

[0167] Wind vector diagram: Displays wind speed and direction in vector form, allowing users to intuitively understand wind field characteristics and flow patterns;

[0168] Heat map: Apply heat map technology to show the pollution level in different areas, and the color gradient clearly conveys the air quality information of each area;

[0169] User Interface

[0170] Dashboard design: Build user-friendly dashboards that allow users to customize data views by selecting different time periods, locations, or specific pollutant types;

[0171] Layer control: Provides layer switching function, users can choose to display different types of data, including wind field, PM2.5 concentration, and NOx concentration, for detailed analysis;

[0172] Information prompts: The visualization interface should include data descriptions, real-time monitoring and forecast information, to improve users’ understanding of the data;

[0173] Analysis and forecasting tools

[0174] Prediction model integration: Integrated prediction tools enable users to view short-term and long-term pollutant concentration forecasts and gain insight into future air quality trends;

[0175] Scenario Analysis: Provides air quality simulation results under different pollution source scenarios, and can perform scenario analysis under user-set conditions to support the decision-making process.

[0176] Visualize the process

[0177] Data Integration:

[0178] Receive data from wind field simulation and pollutant dispersion models in real time, and perform data processing and format conversion in the background.

[0179] Graphics generation:

[0180] Generate dynamic maps, graphs, vector graphs, and heat maps based on real-time data.

[0181] Provide necessary data annotation and timestamp information to improve data readability.

[0182] User Interaction:

[0183] Users select the area and time period of interest through the interface, and the interface dynamically updates and displays various information under the selected conditions.

[0184] Provides data download and sharing functions to facilitate users to export visualization results for further analysis.

[0185] Real-time monitoring and alerts:

[0186] Set up a real-time monitoring function on the visual interface, and automatically issue an alarm when the pollutant concentration exceeds the preset threshold.

[0187] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A precise urban near-surface wind field and pollutant concentration simulation system, characterized in that: include: Data collection module: collects the city’s meteorological and geographic information through sensors, weather stations and satellite data; Wind field simulation module: Based on fluid mechanics theory, it can realize high-precision simulation of near-ground wind fields; Pollutant diffusion model: Use mathematical models to predict the diffusion and concentration changes of pollutants under different meteorological conditions; Visualization module: displays wind field and pollutant concentration distribution in real time, and provides a user-friendly graphical interface.

2. According to claim 1, a precise urban near-surface fine wind field and pollutant concentration simulation system is characterized by: The data acquisition module includes: Sensor Networks Meteorological sensors: used to measure meteorological parameters such as temperature, humidity, wind speed, wind direction, and atmospheric pressure. The sensors should have high accuracy and low power consumption to adapt to the changing conditions in urban environments. Pollutant monitors: monitor the pollutant concentrations of PM2.5, PM10, NOx, SO2, CO, and O3. These monitors should meet national standards and have high sensitivity and selectivity; Geographic Information System (GIS) Integration Module: collects and integrates spatial data such as topography, land use, building height, etc., and provides background information for model building; Data transmission system Wireless communication technology: The collected data is transmitted to the central processing unit in real time through wireless communication technologies such as Wi-Fi, LoRa, and NB-IoT; Data storage and backup: Set up cloud storage or local storage to ensure long-term retention and backup of data for historical analysis and model training; Data processing unit Preprocessing module: cleans, denoises and corrects the collected data to ensure data accuracy; Data fusion algorithm: Fuse data from different sensors to generate more accurate meteorological and pollutant concentration information.

3. According to claim 1, a precise urban near-surface fine wind field and pollutant concentration simulation system is characterized by: The wind farm simulation module includes: Numerical model Fluid dynamics model: Use the basic fluid dynamics equations of the Navier-Stokes equations to simulate the wind field through numerical calculation methods such as finite difference, finite element or spectral methods; Equation parameterization: Select appropriate turbulence model according to actual conditions to adapt to the complex flow characteristics of cities; Input data processing unit Meteorological data input: Receive real-time meteorological data from the data acquisition module, including temperature, humidity, air pressure, wind speed and wind direction; Terrain database: Integrate urban terrain information, including terrain elevation, building distribution, vegetation type, etc., as basic data for wind field simulation; Simulation Algorithm Grid generation and division: Generate computational grids with appropriate resolution according to terrain characteristics to ensure the precision of simulation results; Time advancement algorithm: adopt appropriate time advancement strategy to realize dynamic simulation of non-steady-state wind field; Cooling and heating models Considering the impact of urban heat island effect and changes in surface characteristics on wind field, design appropriate parameterization schemes to simulate thermal effects at different times and locations; Output analysis and visualization The simulation results will be processed and visualized so that users and decision makers can intuitively understand the wind field characteristics of the city.

4. According to claim 1, a precise urban near-surface fine wind field and pollutant concentration simulation system is characterized by: Pollutant dispersion models include Mathematical model Diffusion equation: Use the modified continuity equation and Navier-Stokes equation to describe the diffusion and transport of pollutants in the airflow; Pollutant sedimentation model: Consider the concentration change of particulate matter due to gravity sedimentation and create sedimentation terms. Input data processing Wind field data: Get real-time wind speed and direction data from the wind field simulation module; Meteorological data: Obtain meteorological data such as temperature, humidity, and air pressure to calculate the chemical reactions and physical properties of pollutants; Pollution source data: obtain information such as the location, emission intensity, and emission type of pollution sources; Calculation method Numerical solution method: Select the appropriate numerical method according to your needs. Finite difference, finite element or spectral methods to solve the equations, taking into account the spatial and temporal resolution of the simulation; Particulate matter and gaseous pollutants: Different diffusion models are developed for different types of pollutants, gaseous pollutants and particulate matter, taking into account their different movement and sedimentation characteristics; Result output and visualization After the model calculation, the concentration distribution of pollutants at different time and spatial locations is output, and visual results are generated for subsequent analysis and decision-making.

5. According to claim 1, a precise urban near-surface fine wind field and pollutant concentration simulation system is characterized by: The visualization module includes Data receiving and processing unit Data interface: connected with the wind field simulation module and pollutant diffusion model to receive simulation results and related meteorological data in real time; Data format conversion: converting the collected raw data into the format required for visualization, including standardized processing of wind field data, concentration data, and meteorological data; Visual graph generator Map view: Based on GIS technology, the pollutant concentration and wind field distribution are displayed on the map, providing air quality information for various areas of the city; Time series graph: Generate a graph showing the change of pollutant concentration over time, which is convenient for observing the impact of pollution source intensity and meteorological changes on air quality; Wind vector diagram: Displays wind speed and direction in vector form, allowing users to intuitively understand wind field characteristics and flow patterns; Heat map: Apply heat map technology to show the pollution level in different areas, and the color gradient clearly conveys the air quality information of each area; User Interface Dashboard design: Build user-friendly dashboards that allow users to customize data views by selecting different time periods, locations, or specific pollutant types; Layer control: Provides layer switching function, users can choose to display different types of data, including wind field, PM2.5 concentration, and NOx concentration, for detailed analysis; Information prompts: The visualization interface should include data descriptions, real-time monitoring and forecast information, to improve users’ understanding of the data; Analysis and forecasting tools Prediction model integration: Integrated prediction tools enable users to view short-term and long-term pollutant concentration forecasts and gain insight into future air quality trends; Scenario Analysis: Provides air quality simulation results under different pollution source scenarios, and can perform scenario analysis under user-set conditions to support the decision-making process.