Pollutant diffusion simulation system and simulation method based on time effect

Abstract

The invention belongs to the technical field of pollutant diffusion simulation, and particularly relates to a pollutant diffusion simulation system and method based on a time effect. The system comprises a meteorological data acquisition module, a pollutant diffusion module, a time effect conversion module and a simulation analysis module, the meteorological data acquisition module is used for acquiring meteorological data and preprocessing the acquired data; the pollutant diffusion module is used for acquiring a three-dimensional Gaussian diffusion model changing along with time based on the Gaussian diffusion model of the continuous point source; the time effect conversion module is used for establishing a coordinate system by taking the current downwind direction as an x axis when the wind direction and the wind speed in the meteorological data are changed, acquiring a conversion relation between a reference coordinate system and a coordinate system at the current calculation moment, and performing conversion of a three-dimensional Gaussian diffusion model by utilizing the conversion relation; and the simulation analysis module is used for calculating the concentration of the pollutants according to the converted three-dimensional Gaussian diffusion model so as to realize a pollutant diffusion simulation system.

Description

Technical Field

[0001] The present invention belongs to the technical field of pollutant diffusion simulation, and in particular relates to a pollutant diffusion simulation system and a simulation method based on time effect. Background Art

[0002] The Gaussian diffusion model is a classic mathematical model widely used in simulating the diffusion of atmospheric pollutants. The model is based on the Gaussian probability distribution theory and is used to predict the concentration distribution of pollutants released from point sources in the atmosphere. Its basic assumptions include that atmospheric turbulence is isotropic, pollutants follow a normal distribution in the horizontal and vertical directions, and meteorological conditions (such as wind speed, wind direction, etc.) are stable and unchanged.

[0003] However, the existing technology is mainly applicable to situations where meteorological conditions are stable and uniform, which makes it difficult to deal with complex meteorological changes. For example, when environmental factors such as wind speed and wind direction change, it does not consider the impact of these factors, so the simulation results have certain limitations. Summary of the invention

[0004] In view of this, the present invention provides a pollutant diffusion simulation system and simulation method based on time effect, which can take the time effect factor into consideration so that the constructed model can better simulate the actual pollutant diffusion situation.

[0005] The technical solution for implementing the present invention is as follows:

[0006] In a first aspect, an embodiment of the present application provides a pollutant diffusion simulation system based on time effect, comprising: a meteorological data acquisition module, a pollutant diffusion module, a time effect conversion module, and a simulation analysis module;

[0007] A meteorological data acquisition module is used to acquire meteorological data and pre-process the acquired data;

[0008] Pollutant diffusion module, based on the Gaussian diffusion model of continuous point sources, obtains a three-dimensional Gaussian diffusion model that changes with time;

[0009] The time effect conversion module is used to establish a coordinate system with the downwind direction as the x-axis when the wind direction and wind speed in the meteorological data change, obtain the conversion relationship between the reference coordinate system and the coordinate system at the current calculation time, and use the conversion relationship to convert the three-dimensional Gaussian diffusion model;

[0010] The simulation analysis module is used to calculate the concentration of pollutants according to the converted three-dimensional Gaussian diffusion model to realize a pollutant diffusion simulation system.

[0011] Furthermore, in the pollutant diffusion module of the present invention, the three-dimensional Gaussian diffusion model that changes with time is obtained as follows:

[0012] Assuming that the pollutant leakage diffusion time is t, n instantaneous leakage sources are divided by the time interval Δt, and the three-dimensional Gaussian diffusion model of the concentration distribution of the smoke cloud generated by the i-th leakage source diffuses along the wind direction to the time t is:

[0013]

[0014] The concentration of pollutants generated by each instantaneous leakage source is superimposed to obtain a three-dimensional Gaussian diffusion model of the pollutant concentration distribution generated by the continuous emission pollution source:

[0015]

[0016] Among them, σ x , σ y , σ z are the diffusion parameters in the x, y, and z directions; u is the average wind speed; x is the distance from the spatial point to the source along the downwind direction; y is the distance from the spatial point to the source along the direction perpendicular to the wind direction; z is the height of the spatial point; H is the pollutant release height, and q is the source strength.

[0017] Furthermore, the wind direction conversion relationship of the present invention is:

[0018] Assume that the reference coordinate system is xoy. When the wind direction and wind speed change, take the current downwind direction as the x-axis to establish the instantaneous coordinate system x′oy′. The conversion relationship between the two coordinate systems is:

[0019] y′=[yy m (n)]cosβ(n)-[xx m (n)]sinβ(n)

[0020]

[0021] Where: (x, y) is the coordinate of the calculation point in the xoy coordinate system; (x′, y′) is the coordinate of the calculation point in the x′oy′ coordinate system; x m (n), y m (n) are the horizontal and vertical coordinates of the initial position of the nth group of smoke clouds, and can be set to (0, 0) if the origin remains unchanged; β(n) is the angle between the wind direction in the nth time step and the x-axis of the reference coordinate system.

[0022] Furthermore, the simulation analysis module of the present invention is used to determine the simulation area, set the grid resolution for grid division and initialize the grid data; set the time step, perform concentration calculation according to the converted three-dimensional Gaussian diffusion model, organize the calculated pollutant concentration data at each time step into a unified format, and display the spatial distribution of pollutant concentration in the form of a contour map.

[0023] Furthermore, the meteorological data acquisition module of the present invention acquires meteorological data through the WRF model or ERA5 data set, and preprocessing includes format conversion, time alignment, and spatial interpolation to ensure that the meteorological data can match the geographic and time coordinates in the model.

[0024] In the second aspect, the embodiment of the present application provides a pollutant diffusion simulation method based on time effect, and the specific process is as follows:

[0025] Step 1: Acquire meteorological data and pre-process the acquired data;

[0026] Step 2, based on the Gaussian diffusion model of continuous point sources, obtaining a three-dimensional Gaussian diffusion model that changes with time;

[0027] Step 3: When the wind direction and wind speed in the meteorological data change, a coordinate system is established with the downwind direction as the x-axis, and a conversion relationship between the reference coordinate system and the coordinate system at the current calculation time is obtained, and the three-dimensional Gaussian diffusion model is converted using the conversion relationship;

[0028] Step 4: Calculate the concentration of pollutants according to the converted three-dimensional Gaussian diffusion model to realize the pollutant diffusion simulation system.

[0029] Furthermore, the three-dimensional Gaussian diffusion model that changes with time is obtained as follows:

[0030] Assuming that the pollutant leakage diffusion time is t, n instantaneous leakage sources are divided by the time interval Δt, and the three-dimensional Gaussian diffusion model of the concentration distribution of the smoke cloud generated by the i-th leakage source diffuses along the wind direction to the time t is:

[0031]

[0032] The concentration of pollutants generated by each instantaneous leakage source is superimposed to obtain a three-dimensional Gaussian diffusion model of the pollutant concentration distribution generated by the continuous emission pollution source:

[0033]

[0034] Among them, σ x , σ y , σ z are the diffusion parameters in the x, y, and z directions; u is the average wind speed; x is the distance from the spatial point to the source along the downwind direction; y is the distance from the spatial point to the source along the direction perpendicular to the wind direction; z is the height of the spatial point; H is the pollutant release height, and q is the source strength.

[0035] Furthermore, the wind direction conversion relationship of the present invention is:

[0036] Assume that the reference coordinate system is xoy. When the wind direction and wind speed change, take the current downwind direction as the x-axis to establish the instantaneous coordinate system x′oy′. The conversion relationship between the two coordinate systems is:

[0037] y′=[yy m (n)]cosβ(n)-[xx m (n)]sinβ(n)

[0038]

[0039] Where: (x, y) is the coordinate of the calculation point in the xoy coordinate system; (x′, y′) is the coordinate of the calculation point in the x′oy′ coordinate system; x m (n), y m (n) are the horizontal and vertical coordinates of the initial position of the nth group of smoke clouds, and can be set to (0, 0) if the origin remains unchanged; β(n) is the angle between the wind direction in the nth time step and the x-axis of the reference coordinate system.

[0040] Furthermore, the specific process of step four of the present invention is: determine the simulation area, set the grid resolution to perform grid division and initialize the grid data; set the time step, perform concentration calculation according to the converted three-dimensional Gaussian diffusion model, organize the calculated pollutant concentration data at each time step into a unified format, and use contour maps to display the spatial distribution of pollutant concentrations.

[0041] Furthermore, the specific process of step one of the present invention is as follows: the meteorological data is obtained through the WRF model or the ERA5 dataset, and the preprocessing includes format conversion, time alignment, and spatial interpolation to ensure that the meteorological data can match the geographic and time coordinates in the model.

[0042] Beneficial effects:

[0043] The present invention first processes the meteorological data, then performs multi-step calculations by improving the Gaussian diffusion model, and then visualizes the diffusion simulation result data, and finally obtains the atmospheric pollutant diffusion distribution map over time under the time effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 It is the pollutant diffusion simulation algorithm flow based on real-time meteorological data in the present invention;

[0046] Figure 2 is the three-dimensional coordinate system of the Gaussian diffusion model;

[0047] Figure 3 It is a calculation flow chart of Gaussian diffusion model in the present invention;

[0048] Figure 4 It is a schematic diagram of iterative superposition calculation of Gaussian diffusion model;

[0049] Figure 5 It is a flow chart of algorithm design for improving Gaussian diffusion model in the present invention;

[0050] Figure 6 It is a schematic diagram of the results of the development of pollutant concentration over time in the present invention. DETAILED DESCRIPTION

[0051] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0052] It should be noted that the following embodiments and features in the embodiments may be combined with each other in the absence of conflict; and, based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making any creative work are within the scope of protection of the present disclosure.

[0053] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0054] The embodiment of the present application provides a pollutant diffusion simulation system based on time effect, including: a meteorological data acquisition module, a pollutant diffusion module, a time effect conversion module and a simulation analysis module;

[0055] A meteorological data acquisition module is used to acquire meteorological data and pre-process the acquired data;

[0056] Pollutant diffusion module, based on the Gaussian diffusion model of continuous point sources, obtains a three-dimensional Gaussian diffusion model that changes with time;

[0057] The time effect conversion module is used to establish a coordinate system with the downwind direction as the x-axis when the wind direction and wind speed in the meteorological data change, obtain the conversion relationship between the reference coordinate system and the coordinate system at the current calculation time, and use the conversion relationship to convert the three-dimensional Gaussian diffusion model;

[0058] The simulation analysis module is used to calculate the concentration of pollutants according to the converted three-dimensional Gaussian diffusion model to realize a pollutant diffusion simulation system.

[0059] The present application embodiment provides a pollutant diffusion and simulation method based on time effect, such as Figure 1 As shown, it includes the following steps:

[0060] Step 1: Obtain meteorological data and pre-process the acquired data;

[0061] Acquire meteorological model data and preprocess it. In this embodiment, meteorological data can be acquired through WRF model or ERA5 data set.

[0062] (1) Obtaining meteorological data using the WRF model

[0063] The WRF (Weather Research and Forecasting) model is used to simulate and provide atmospheric environmental parameters. The WRF model can simulate and predict a variety of physical and chemical processes in the atmosphere, including temperature, humidity, wind speed, wind direction, air pressure, and precipitation. Its unique cross-scale simulation capability can accurately capture weather phenomena from the mesoscale to the global scale.

[0064] (2) Obtaining meteorological data from the ERA5 dataset

[0065] ERA5 is the fifth generation of ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric reanalysis datasets of global climate from 1940 to the present. ERA5 provides hourly estimates of a large number of atmospheric, land and ocean climate variables. ERA5 combines model data with observational data from around the world, using data assimilation techniques to form a globally complete and consistent dataset.

[0066] Preprocessing includes format conversion, time alignment, and spatial interpolation to ensure that the meteorological data can match the geographic and temporal coordinates in the model.

[0067] Step 2: Based on the Gaussian diffusion model of continuous point sources, a three-dimensional Gaussian diffusion model that changes with time is obtained;

[0068] The traditional Gaussian model is applicable to uniform atmospheric conditions and is based on the following assumptions: the wind speed inside the simulation space is uniform and stable; the release of pollutant sources is continuous and uniform; the mass of pollutants remains conserved during the overall diffusion process (without considering material transformations such as nuclide decay); in addition, the theory assumes that the concentration of pollutants during continuous point source diffusion conforms to the law of normal distribution.

[0069] To simulate the diffusion of pollutants under point source release using the above Gaussian model, we first need to establish a three-dimensional coordinate system, taking the vertical projection point of the pollutant release point to the ground as the coordinate origin, taking the z-axis along this point perpendicular to the ground, taking the main wind direction as the x-axis, and obtaining the corresponding direction of the y-axis through the right-hand rule. Establish a three-dimensional coordinate system in Gaussian space, as shown in the attached figure. Figure 2 shown.

[0070] Based on the above assumptions, the pollutant concentration distribution function at any point in the simulation area can be obtained as:

[0071]

[0072] Where: C is the concentration of pollutants at any point (x, y, z) in the target area, kg / m 3 ; A(x) is the function to be determined; σ y , σ z is the diffusion parameter in the y and z directions, m.

[0073] Considering the continuous emission of point sources and the law of mass conservation, on any cross section perpendicular to the x-axis, there exists:

[0074]

[0075] Where: q is the source strength, that is, the rate at which point sources emit pollutants; u is the average ambient wind speed, m / s.

[0076] Further considering the stability and uniformity of the wind field, the undetermined function can be obtained:

[0077] A(x)=q / 2πuσ y σ z (3)

[0078] In summary, the core formula of the Gaussian diffusion model of continuous point sources can be derived:

[0079]

[0080] The specific calculation steps of pollutant concentration are as follows: Figure 3 As shown:

[0081] The Gaussian diffusion model of continuous point sources in the above formula (4) is a traditional Gaussian model that only considers wind speed and wind direction. It is suitable for continuous leakage diffusion and uniform meteorological fields, and can solve the steady-state distribution characteristics of pollutant diffusion in this specific case. However, for the actual diffusion of pollutants in the atmosphere, the background field of meteorological elements will change over time, that is, the time effect should be considered in the Gaussian diffusion model to obtain the nuclear pollutant diffusion results that change over time.

[0082] Generally speaking, the smoke released by a point source starts from the origin o and migrates continuously with the wind direction, and diffuses in the y and z directions at the same time. According to the material balance, the following three-dimensional Gaussian diffusion formula that changes with time can be further derived:

[0083]

[0084] Where C is the concentration of pollutants at any point in space (x, y, z), kg / m 3 ; σ x , σ y , σ z are the diffusion parameters in the x, y, and z directions; u is the average wind speed; x is the distance from the spatial point to the source along the downwind direction; y is the distance from the spatial point to the source along the direction perpendicular to the wind direction; z is the height of the spatial point; and H is the pollutant release height.

[0085] The above diffusion model is suitable for leakage in a short period of time. If it is to be used for continuous point source release diffusion, the model needs to be further improved. Specifically, the leakage process can be divided in time and considered as multiple short-term leakage sources according to the time interval. The pollutant concentration distribution in the area generated by continuous emission is the algebraic superposition of the concentration distribution generated by multiple instantaneous sources, as shown in the attached figure. Figure 4 As shown. That is, assuming that the diffusion time of pollutant leakage is t, and n instantaneous leakage sources are divided with a time interval Δt (consistent with the time resolution of the meteorological element background field), the concentration distribution of the smoke cloud generated by the i-th leakage source diffused along the wind direction to the time t can be calculated by the following formula:

[0086]

[0087] By superimposing the concentration of pollutants generated by each instantaneous leakage source, the concentration distribution of pollutants generated by the continuous emission pollution source can be obtained. The three-dimensional Gaussian diffusion model that changes with time can be expressed as follows:

[0088]

[0089] Step 3: When the wind direction and wind speed in the meteorological data change, a coordinate system is established with the downwind direction as the x-axis, and a conversion relationship between the reference coordinate system and the coordinate system at the current calculation time is obtained, and the three-dimensional Gaussian diffusion model is converted using the conversion relationship;

[0090] The above Gaussian model assumes that wind direction and wind speed are constant parameters, but in reality, wind speed and wind direction change randomly, that is, environmental factors change. Therefore, coordinate correction is required during the superposition calculation iteration process. Take the reference coordinate system xoy as a reference. When the wind direction and wind speed change, the instantaneous coordinate system x′oy′ is established with the new downwind direction as the x-axis. The spatial position between the two coordinate systems needs to be rotated and translated. The coordinate transformation expression is:

[0091] y′=[yy m (n)]cosβ(n)-[xx m (n)]sinβ(n) (8)

[0092]

[0093] Where: (x, y) is the coordinate of the calculation point in the xoy coordinate system; (x′, y′) is the coordinate of the calculation point in the x′oy′ coordinate system; x m (n), y m (n) are the horizontal and vertical coordinates of the initial position of the nth group of smoke clouds, and can be set to (0, 0) if the origin remains unchanged; β(n) is the angle between the wind direction in the nth time step and the x-axis of the reference coordinate system.

[0094] Step 4: Calculate the concentration of pollutants according to the converted three-dimensional Gaussian diffusion model to implement a pollutant diffusion simulation system;

[0095] Attached Figure 5 To improve the algorithm design flow chart of Gaussian diffusion model, the specific steps are as follows:

[0096] 1. Pollution source setting

[0097] Set the basic information of the pollutant emission source, such as location, release height, and emission speed. These information will be used as input parameters of the model.

[0098] 2. Meteorological element settings

[0099] ① Data source selection: Determine whether to use the WRF model or the ERA5 dataset as the source of meteorological data.

[0100] ②Data acquisition: Obtain the required meteorological data through WRF model simulation or download from the ERA5 official website.

[0101] ③Data preprocessing: The acquired data needs to be preprocessed, including format conversion, time alignment, spatial interpolation, etc., to ensure that the meteorological data can match the geographic and time coordinates in the model. During data processing, the appropriate time and spatial resolution can be selected according to research needs.

[0102] 3. Simulate regional settings

[0103] ① Determine the simulation area: clarify the geographical area of ​​the simulation, which is usually determined based on the research objectives, the location of pollution sources, and sensitive areas of concern. The simulation area should be large enough to include all important areas where pollutants may spread, while also taking into account the limitations of computing resources.

[0104] ② Set the grid resolution: The grid resolution refers to the size of each cell in the grid, which determines the spatial accuracy of the algorithm model. The choice of resolution should be based on research needs, data availability, and computing resources.

[0105] ③ Grid division: According to the determined simulation area and grid resolution, the entire area is divided into a series of regular grid cells.

[0106] ④ Initialize grid data: After the grid division is completed, each grid unit needs to be initialized, including setting the initial concentration value (usually 0, indicating no pollutants), recording the grid coordinate information, etc.

[0107] 4. Diffusion formula calculation

[0108] ① Time step setting: Set an appropriate time step (such as every hour) based on research requirements and the temporal resolution of the data.

[0109] ② Concentration calculation: According to Gaussian diffusion theory, the pollutant concentration at any point downwind can be calculated using the improved Gaussian diffusion model formula, which involves the standard deviation in each direction (i.e., diffusion coefficient), and these parameters are related to the location of the grid point. In each time step, the current meteorological data and pollution source data are used to calculate the concentration distribution of pollutants using the Gaussian diffusion formula.

[0110] ③ Iterative calculation: Since meteorological conditions such as wind speed and wind direction will change over time, it is necessary to continuously update meteorological data during the calculation process and adjust the parameters in the Gaussian diffusion formula accordingly. That is, perform a separate calculation for each time step (such as every hour), then advance the time one step forward, repeat the above process until the simulation ends, and superimpose the results.

[0111] 5. Result visualization steps

[0112] ①Solution of simulation results: The pollutant concentration data at each time step calculated by the Gaussian diffusion model are sorted into a unified format, including the spatial coordinates (x, y, z) and the corresponding concentration value C.

[0113] ② Draw a concentration distribution map: Use contour maps to show the spatial distribution of pollutant concentrations. The depth of color or the size of the symbol can indicate the concentration, and the specific value is shown in the legend color scale.

[0114] Combined with the visualization results such as Figure 6 As shown, the temporal and spatial variation characteristics of pollutant diffusion are analyzed and discussed, the possible influencing factors and potential risks are analyzed, and data reference is provided for relevant decision-making.

[0115] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A pollutant diffusion simulation system based on time effect, characterized in that: include: Meteorological data acquisition module, pollutant diffusion module, time effect conversion module and simulation analysis module; A meteorological data acquisition module is used to acquire meteorological data and pre-process the acquired data; Pollutant diffusion module, based on the Gaussian diffusion model of continuous point sources, obtains a three-dimensional Gaussian diffusion model that changes with time; The time effect conversion module is used to establish a coordinate system with the downwind direction as the x-axis when the wind direction and wind speed in the meteorological data change, obtain the conversion relationship between the reference coordinate system and the coordinate system at the current calculation time, and use the conversion relationship to convert the three-dimensional Gaussian diffusion model; The simulation analysis module is used to calculate the concentration of pollutants according to the converted three-dimensional Gaussian diffusion model to realize a pollutant diffusion simulation system.

2. The pollutant diffusion simulation system based on time effect according to claim 1 is characterized in that: In the pollutant diffusion module, the three-dimensional Gaussian diffusion model that varies with time is obtained as: Assuming that the pollutant leakage diffusion time is t, n instantaneous leakage sources are divided by the time interval Δt, and the three-dimensional Gaussian diffusion model of the concentration distribution of the smoke cloud generated by the i-th leakage source diffuses along the wind direction to the time t is: The concentration of pollutants generated by each instantaneous leakage source is superimposed to obtain a three-dimensional Gaussian diffusion model of the pollutant concentration distribution generated by the continuous emission pollution source: Among them, σ x , σ y , σ z are the diffusion parameters in the x, y, and z directions; u is the average wind speed; x is the distance from the spatial point to the source along the downwind direction; y is the distance from the spatial point to the source along the direction perpendicular to the wind direction; z is the height of the spatial point; H is the pollutant release height, and q is the source strength.

3. The pollutant diffusion simulation system based on time effect according to claim 2 is characterized in that: The wind direction conversion relationship is: Assume that the reference coordinate system is xoy. When the wind direction and wind speed change, take the current downwind direction as the x-axis to establish the instantaneous coordinate system x′oy′. The conversion relationship between the two coordinate systems is: y′=[y-y m (n)]cosβ(n)-[x-x m (n)]sinβ(n) Where: (x, y) is the coordinate of the calculation point in the xoy coordinate system; (x′, y′) is the coordinate of the calculation point in the x′oy′ coordinate system; x m (n), y m (n) are the horizontal and vertical coordinates of the initial position of the nth group of smoke clouds, and can be set to (0, 0) if the origin remains unchanged; β(n) is the angle between the wind direction in the nth time step and the x-axis of the reference coordinate system.

4. The pollutant diffusion simulation system based on time effect according to claim 1 is characterized in that: The simulation analysis module is used to determine the simulation area, set the grid resolution for grid division and initialize the grid data; set the time step, perform concentration calculation according to the converted three-dimensional Gaussian diffusion model, organize the calculated pollutant concentration data at each time step into a unified format, and use contour maps to display the spatial distribution of pollutant concentrations.

5. The pollutant diffusion simulation system based on time effect according to claim 1 is characterized in that: The meteorological data acquisition module acquires meteorological data through the WRF model or ERA5 data set, and preprocessing includes format conversion, time alignment, and spatial interpolation to ensure that the meteorological data can match the geographic and time coordinates in the model.

6. A pollutant diffusion simulation method based on time effect, characterized in that: The specific process is: Step 1: Acquire meteorological data and pre-process the acquired data; Step 2, based on the Gaussian diffusion model of continuous point sources, obtaining a three-dimensional Gaussian diffusion model that changes with time; Step 3: When the wind direction and wind speed in the meteorological data change, a coordinate system is established with the downwind direction as the x-axis, and a conversion relationship between the reference coordinate system and the coordinate system at the current calculation time is obtained, and the three-dimensional Gaussian diffusion model is converted using the conversion relationship; Step 4: Calculate the concentration of pollutants according to the converted three-dimensional Gaussian diffusion model to realize the pollutant diffusion simulation system.

7. The pollutant diffusion simulation method based on time effect according to claim 6 is characterized in that: The three-dimensional Gaussian diffusion model that changes with time is obtained as follows: Assuming that the pollutant leakage diffusion time is t, n instantaneous leakage sources are divided by the time interval Δt, and the three-dimensional Gaussian diffusion model of the concentration distribution of the smoke cloud generated by the i-th leakage source diffuses along the wind direction to the time t is: The concentration of pollutants generated by each instantaneous leakage source is superimposed to obtain a three-dimensional Gaussian diffusion model of the pollutant concentration distribution generated by the continuous emission pollution source: Among them, σ x , σ y , σ z are the diffusion parameters in the x, y, and z directions; u is the average wind speed; x is the distance from the spatial point to the source along the downwind direction; y is the distance from the spatial point to the source along the direction perpendicular to the wind direction; z is the height of the spatial point; H is the pollutant release height, and q is the source strength.

8. The pollutant diffusion simulation method based on time effect according to claim 7 is characterized in that: The wind direction conversion relationship is: Assume that the reference coordinate system is xoy. When the wind direction and wind speed change, take the current downwind direction as the x-axis to establish the instantaneous coordinate system x′oy′. The conversion relationship between the two coordinate systems is: y′=[y-y m (n)]cosβ(n)-[x-x m (n)]sinβ(n) Where: (x, y) is the coordinate of the calculation point in the xoy coordinate system; (x′, y′) is the coordinate of the calculation point in the x′oy′ coordinate system; x m (n), y m (n) are the horizontal and vertical coordinates of the initial position of the nth group of smoke clouds, and can be set to (0, 0) if the origin remains unchanged; β(n) is the angle between the wind direction in the nth time step and the x-axis of the reference coordinate system.

9. The pollutant diffusion simulation method based on time effect according to claim 6 is characterized in that: The specific process of step four is: determine the simulation area, set the grid resolution for grid division and initialize the grid data; set the time step, perform concentration calculation according to the converted three-dimensional Gaussian diffusion model, organize the calculated pollutant concentration data at each time step into a unified format, and use contour maps to display the spatial distribution of pollutant concentrations.

10. The pollutant diffusion simulation method based on time effect according to claim 6, characterized in that: The specific process of step 1 is as follows: the meteorological data is obtained through the WRF model or ERA5 dataset, and the preprocessing includes format conversion, time alignment, and spatial interpolation to ensure that the meteorological data can match the geographic and time coordinates in the model.

Images