Calculation method and device of atmospheric dispersion factor, and site selection method of nuclear power plant

Abstract

The invention discloses a calculation method and device of an atmospheric dispersion factor and a site selection method of a nuclear power plant. The method comprises the following steps: acquiring reanalysis meteorological data; determining the central position of the target area, wherein the central position comprises latitude and longitude coordinates; and according to a Gaussian plume model, and / or a Gaussian puff model, and the reanalysis meteorological data, calculating an atmospheric dispersion factor at a preset distance from the center position. According to the method, the problem that effective evaluation of the atmospheric dispersion factor cannot be carried out for a target area in which meteorological station construction and meteorological element observation are not carried out temporarily in the prior art can be solved.

Description

Technical Field

[0001] The invention belongs to the technical field of radiation environmental impact assessment, and specifically relates to a method and device for calculating an atmospheric dispersion factor, and a method for selecting a site for a nuclear power plant. Background Art

[0002] The accident (short-term) atmospheric diffusion factor used in accident consequence assessment reflects the diffusion capacity of radioactive pollutants in the environment under an accident. In the site selection and design stages, the short-term atmospheric diffusion factor after an accident is used to assess the consequences of hypothetical site accidents and design basis accidents, and is one of the direct bases for determining the scope of non-residential areas. The consequences of radioactive doses received by the public outside the plant are jointly determined by the accident source term and the atmospheric diffusion factor. When a nuclear power unit adopts a standard design, the activity of radionuclides released to the environment by hypothetical site accidents and various design basis accidents is clear. At this time, the atmospheric diffusion factor must be less than a certain envelope value in order to make the public exposure dose at the non-residential area boundary less than the prescribed dose control value. If the atmospheric diffusion factor of the proposed site is greater than the envelope value, it is necessary to modify and optimize the standard design of the nuclear power unit, or change the boundary of the non-residential area of ​​the site and the boundary of the planned restricted area.

[0003] However, during the site selection stage, the proposed site area usually has not yet carried out the construction of meteorological stations and meteorological element observations, so it is impossible to effectively evaluate the atmospheric diffusion factor in the proposed site area. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for calculating the atmospheric diffusion factor and a method for selecting a site for a nuclear power plant in view of the above-mentioned deficiencies in the prior art, which can calculate the atmospheric diffusion factor of any target area and provide a reference for the site selection of a nuclear power plant.

[0005] In a first aspect, the present invention provides a method for calculating an atmospheric diffusion factor, comprising: acquiring reanalysis meteorological data; determining a center position of a target area, wherein the center position comprises longitude and latitude coordinates; and calculating an atmospheric diffusion factor at a preset distance from the center position based on a Gaussian plume model and / or a Gaussian puff model and the reanalysis meteorological data.

[0006] Preferably, the atmospheric diffusion factor at a preset distance from the center position is calculated based on the Gaussian plume model and the reanalysis meteorological data, specifically including: screening out a target grid point of the reanalysis meteorological data based on the center position, wherein the number of target grid points is one; obtaining a three-dimensional joint frequency of the target grid point, wherein variables of the three-dimensional joint frequency include wind direction, wind speed, and stability; and calculating the atmospheric diffusion factor at a preset distance from the center position based on the three-dimensional joint frequency and the Gaussian plume model.

[0007] Preferably, filtering out the target grid point of the reanalysis meteorological data according to the center position specifically includes: filtering out the grid point closest to the center position in the reanalysis meteorological data grid; judging whether the grid point is located on land; if the grid point is located on land, determining the grid point as the target grid point; if the grid point is located in the ocean, filtering out the target grid point that is second closest to the center position, and so on, until the target grid point is located on land.

[0008] Preferably, obtaining the three-dimensional joint frequency of the target grid point specifically includes: extracting the annual radiation of the target grid point, the U wind speed in the east-west direction at a preset height, the V wind speed in the north-south direction at a preset height, and the temperature from the reanalysis meteorological data; calculating the stability according to the radiation, the U wind speed in the east-west direction at a preset height, the V wind speed in the north-south direction at a preset height, the temperature, and an improved Pasquier algorithm; calculating the three-dimensional joint frequency of the target grid point according to the U wind speed in the east-west direction at a preset height, the V wind speed in the north-south direction at a preset height, and the stability.

[0009] Preferably, the atmospheric diffusion factor at a preset distance from the center position is calculated based on the Gaussian puff model and the reanalysis meteorological data, specifically comprising: screening out target grid points of the reanalysis meteorological data based on the center position, wherein the number of target grid points is multiple and the target grid points are closest to the center position; extracting meteorological element data of the target grid points, wherein the meteorological elements include cloud cover, radiation, air pressure at different altitudes, temperature, wind speed, and relative humidity; converting the meteorological element data into ground files and high-altitude files corresponding to the Gaussian puff model; and calculating the atmospheric diffusion factor at a preset distance from the center position within a year based on the ground files, the high-altitude files, and the Gaussian puff model.

[0010] In a second aspect, the present invention also provides a method for site selection of a nuclear power plant, comprising: obtaining atmospheric dispersion factors corresponding to a plurality of candidate sites according to the atmospheric dispersion factor calculation method described in the first aspect; and selecting the candidate site corresponding to the smallest atmospheric dispersion factor as the target site.

[0011] In a third aspect, the present invention further provides a device for calculating an atmospheric diffusion factor, comprising: an acquisition module for acquiring reanalysis meteorological data; a determination module for determining a central position of a target area, wherein the central position includes longitude and latitude coordinates; and a calculation module connected to the acquisition module and the determination module, for calculating an atmospheric diffusion factor at a preset distance from the central position based on a Gaussian plume model and / or a Gaussian puff model and the reanalysis meteorological data.

[0012] Preferably, the calculation module comprises a first calculation unit.

[0013] The first calculation unit is used to filter out target grid points of the reanalysis meteorological data according to the central position, the number of target grid points is one, and is also used to obtain the three-dimensional joint frequency of the target grid points, the variables of the three-dimensional joint frequency include wind direction, wind speed, stability, and to calculate the atmospheric diffusion factor at a preset distance from the central position according to the three-dimensional joint frequency and the Gaussian plume model.

[0014] Preferably, the calculation module comprises a second calculation unit.

[0015] The second calculation unit is used to filter out target grid points of the reanalysis meteorological data according to the central position, the number of target grid points is multiple, and the target grid points are closest to the central position, and extract meteorological element data of the target grid points, the meteorological elements include cloud cover, radiation, air pressure at different altitudes, temperature, wind speed, and relative humidity. It is also used to convert the meteorological element data into ground files and high-altitude files corresponding to the Gaussian puff model, and to calculate the atmospheric diffusion factor at a preset distance from the central position within the year based on the ground file, the high-altitude file, and the Gaussian puff model.

[0016] In a fourth aspect, the present invention further provides a device for calculating an atmospheric diffusion factor, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for calculating the atmospheric diffusion factor described in the first aspect by executing the computer instructions.

[0017] The method and device for calculating the atmospheric diffusion factor and the method for selecting a site for a nuclear power plant provided by the present invention perform reanalysis of meteorological data on the center position of a target area based on a Gaussian plume model and / or a Gaussian puff model, thereby calculating the atmospheric diffusion factor corresponding to the target area. Since the reanalysis meteorological data has the advantages of wide spatial coverage, good observation time continuity, rich observation means (such as satellites, meteorological stations, etc.), comprehensive observation of meteorological elements, and high data accuracy, the atmospheric diffusion factor calculated based on the reanalysis meteorological data has high accuracy and is not affected by whether meteorological observation is carried out in the proposed site selection area. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a method for calculating an atmospheric dispersion factor according to Embodiment 1 of the present invention;

[0019] Figure 2 A schematic diagram of a method for calculating an atmospheric dispersion factor according to Embodiment 2 of the present invention;

[0020] Figure 3 is a schematic diagram of a three-dimensional joint frequency result of Example 2 of the present invention;

[0021] Figure 4 This is a schematic diagram of the structure of an atmospheric diffusion factor calculation device according to Example 4 of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the technical solution of the present invention, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0023] It should be understood that the specific embodiments and drawings described herein are only used to explain the present invention rather than to limit the present invention.

[0024] It can be understood that, in the absence of conflict, the various embodiments of the present invention and the various features in the embodiments can be combined with each other.

[0025] It can be understood that, for the convenience of description, the drawings of the present invention only show the parts related to the present invention, while the parts irrelevant to the present invention are not shown in the drawings.

[0026] It can be understood that each unit and module involved in the embodiments of the present invention may correspond to only one physical structure, or may be composed of multiple physical structures, or multiple units and modules may be integrated into one physical structure.

[0027] It can be understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of the present invention may occur in an order different from that marked in the drawings.

[0028] It is understood that the flowcharts and block diagrams of the present invention illustrate the possible architectures, functions, and operations of the systems, devices, equipment, and methods according to the various embodiments of the present invention. Each box in the flowchart or block diagram may represent a unit, module, program segment, or code, which contains executable instructions for implementing the specified functions. Moreover, each box or combination of boxes in the block diagram and flowchart may be implemented by a hardware-based system that implements the specified functions, or may be implemented by a combination of hardware and computer instructions.

[0029] It can be understood that the units and modules involved in the embodiments of the present invention can be implemented by software or hardware. For example, the units and modules can be located in a processor.

[0030] Embodiment 1:

[0031] like Figure 1 As shown, this embodiment provides a method for calculating an atmospheric diffusion factor, including:

[0032] Step 101, obtaining reanalysis meteorological data.

[0033] In this embodiment, the reanalysis meteorological data is generated by reprocessing and reanalyzing the existing meteorological observation data to generate global or regional meteorological data. Reanalysis meteorological data usually comes from data sources of different observation methods such as meteorological satellites, ground-based meteorological observation sites, ocean buoys, and aircraft soundings. After the data undergoes steps such as quality control, correction, interpolation, and model reprocessing, reanalysis meteorological data suitable for various meteorological studies and applications are generated. Compared with the observation data of national stations, reanalysis meteorological data has the advantages of wide spatial coverage, good observation time continuity, rich observation methods (such as satellites, meteorological stations, etc.), comprehensive observation of meteorological elements, and high data accuracy.

[0034] Step 102, determining the center position of the target area, where the center position includes longitude and latitude coordinates.

[0035] In this embodiment, the target area refers to the area to be evaluated or the area to be selected, and the target area can be any area in the world or nationwide. In this embodiment, the center position is represented by longitude and latitude coordinates. For example, the center position is (19.45N, 108.89E).

[0036] Step 103, calculating the atmospheric diffusion factor at a preset distance from the center position based on the Gaussian plume model and / or the Gaussian puff model and reanalyzing the meteorological data.

[0037] In this embodiment, the atmospheric diffusion factor is calculated based on the Gaussian plume model, which is a fast calculation method and can quickly obtain the calculation results. The atmospheric diffusion factor is calculated based on the Gaussian plume model, which is a fine calculation method and can obtain accurate (or consistent with reality) calculation results. The preset distances include 500 meters, 600 meters, and 800 meters. In this embodiment, the example calculates the atmospheric diffusion factor at the probability level of 95% of the target area and 99.5% of each direction.

[0038] Specifically, based on the Gaussian plume model and reanalysis meteorological data, the atmospheric dispersion factor at a preset distance from the center position is calculated, including:

[0039] Step 103A1, select target grid points of the reanalyzed meteorological data according to the center position, and the number of target grid points is one.

[0040] Specifically, screening out target grid points of the reanalysis meteorological data according to the center position includes: screening out the grid points closest to the center position in the reanalysis meteorological data grid; judging whether the grid points are located on land; if the grid points are located on land, determining the grid points as target grid points; if the grid points are located in the ocean, screening out the target grid points that are second closest to the center position, and so on, until the target grid points are located on land.

[0041] In this embodiment, since the coordinates of the center position of the target area are often not completely aligned with the points of the reanalysis data grid, it is necessary to screen out the grid points closest to the center of the target area in the reanalysis data grid. The screening process involves calculating the distance between the center of the target area and each grid point to find the most suitable grid point. However, due to the large differences in meteorological elements between land and sea, such as temperature, humidity, wind speed, etc., which often have significantly different characteristics, choosing the wrong grid point will lead to inaccurate calculation results, affecting the authenticity and accuracy of the research results. If the screened grid point is located on the ocean, it is necessary to re-screen the grid point with the second closest distance until the screened grid point is located on land.

[0042] Step 103A2, obtaining the three-dimensional joint frequency of the target grid point, the variables of the three-dimensional joint frequency include wind direction, wind speed, and stability.

[0043] Specifically, obtaining the three-dimensional joint frequency of the target grid point includes: extracting the annual radiation of the target grid point, the U wind speed in the east-west direction at a preset height, the V wind speed in the north-south direction at a preset height, and the temperature from the reanalysis meteorological data; calculating the stability according to the radiation, the U wind speed in the east-west direction at a preset height, the V wind speed in the north-south direction at a preset height, the temperature, and an improved Pasquier algorithm; calculating the three-dimensional joint frequency of the target grid point according to the U wind speed in the east-west direction at a preset height, the V wind speed in the north-south direction at a preset height, and the stability.

[0044] In this embodiment, the preset height is 10 meters or set according to user needs. Calculating the three-dimensional joint frequency of the target grid point includes: counting the number of times the three variables of wind direction, wind speed, and stability appear at the target grid point, and then calculating the probability of the three variables appearing at the same time.

[0045] Step 103A3, calculating the atmospheric diffusion factor at a preset distance from the center position based on the three-dimensional joint frequency and Gaussian plume model.

[0046] Optionally, the atmospheric diffusion factor at a preset distance from the center position is calculated based on the Gaussian puff model and reanalyzed meteorological data, specifically including:

[0047] Step 103B1, select target grid points of the reanalyzed meteorological data according to the center position, the number of target grid points is multiple, and the target grid points are closest to the center position.

[0048] In this embodiment, the number of target grid points is 9 or 4. The target grid points are selected according to the distance between the center position and the grid of the reanalysis data.

[0049] Step 103B2, extracting meteorological element data of the target grid point, the meteorological elements include cloud cover, radiation, air pressure at different altitudes, temperature, wind speed, and relative humidity.

[0050] Step 103B3, converting the meteorological element data into ground files and high-altitude files corresponding to the Gaussian smoke puff model.

[0051] In this embodiment, the meteorological element data of the target grid points are converted into ground files (surf.dat for example) and high-altitude files (up1.dat, up2.dat, ..., upn.dat for example, where n is the number of target grid points) required by the Gaussian puff model.

[0052] Step 103B4, calculating the atmospheric diffusion factor at a preset distance from the center position within the year based on the ground file, the high-altitude file, and the Gaussian smoke puff model.

[0053] In this embodiment, the reanalysis meteorological data is combined with the Gaussian smoke puff model to calculate the atmospheric diffusion factor, which can improve the prediction accuracy and fully consider the influence of the temporal and spatial changes of meteorological elements, especially in complex terrain or (and) low wind conditions, to obtain the calculation results of the atmospheric diffusion factor with high accuracy.

[0054] The calculation method of the atmospheric diffusion factor provided in this embodiment is based on the Gaussian plume model and / or the Gaussian puff model to analyze the reanalysis meteorological data at the center position of the target area, so as to calculate the atmospheric diffusion factor corresponding to the target area. Since the reanalysis meteorological data has the advantages of wide spatial coverage, good observation time continuity, rich observation means (such as satellites, meteorological stations, etc.), comprehensive observation of meteorological elements, and high data accuracy, the atmospheric diffusion factor calculated based on the reanalysis meteorological data is highly accurate and is not affected by whether the proposed site selection area conducts meteorological observations. The atmospheric diffusion factor of any target area can be calculated to provide a reference for the site selection of nuclear power plants. In addition, combining the reanalysis meteorological data with the Gaussian puff model to calculate the atmospheric diffusion factor can improve the prediction accuracy and fully consider the influence of the temporal and spatial changes of meteorological elements, especially under complex terrain or (and) small calm wind conditions, to obtain a highly accurate calculation result of the atmospheric diffusion factor. Finally, based on the Gaussian plume model and the Gaussian puff model, the meteorological data of the center of the target area were reanalyzed to calculate the atmospheric diffusion factor corresponding to the target area. Not only fast calculation results but also high-accuracy calculation results were obtained. Through fast calculation, the scope and probability of the surrounding residential areas being affected can be estimated so that people can be evacuated in time. At the same time, the transmission and diffusion of radioactive release in the atmosphere can be simulated through precise calculations to prepare risk assessment and control measures in advance. The combination of these two calculation methods to deal with radioactive release accidents can better protect the safety of the surrounding environment and personnel.

[0055] Embodiment 2:

[0056] like Figure 2As shown, this embodiment provides a method for calculating the atmospheric diffusion factor, which is a method for calculating the atmospheric diffusion factor of any target area based on reanalyzed meteorological data. Without obtaining actual observation data of the plant site and data from meteorological stations in surrounding countries, a preliminary assessment of the diffusion situation of the plant site area is performed. The calculation method includes:

[0057] S1, select the site where the evaluation is to be carried out, i.e. the target area, and give the longitude and latitude coordinates (lat, lon) of its center.

[0058] In this embodiment, taking a certain location in Hainan as an example, the center of the selected target area is (19.45N, 108.89E).

[0059] S2, select the evaluation method, including fast calculation (conservative) and precise calculation (realistic).

[0060] S3, if fast calculation is selected, the grid closest to the plant site is selected, that is, according to the central coordinates of the target area, the reanalysis meteorological data grid closest to the center is selected. Considering the large differences in meteorological elements between land and sea, if the selected grid is located at sea, it is necessary to reselect the next closest grid until the requirements are met.

[0061] In this embodiment, the reanalysis meteorological data grid point (19.5N, 109.0E) closest to the center is selected, wherein the resolution of the reanalysis meteorological data is 0.25°.

[0062] S4, extract the meteorological elements such as radiation, 10-meter U wind speed, 10-meter V wind speed at the grid point throughout the year, calculate the stability using the improved Pasquier method, and count the three-dimensional joint frequency of wind direction-wind speed-stability.

[0063] In this embodiment, the radiation, 10-meter U wind speed, and 10-meter V wind speed are extracted, and the three-dimensional joint frequency is calculated as follows: Figure 3 shown.

[0064] S5, select the calculation distance (usually 500 meters, 600 meters or 800 meters), use the Gaussian plume model (as shown in the figure) to calculate the atmospheric diffusion factor at this distance, and give the atmospheric diffusion factor at the 99.5% probability level for all directions of the plant site and the 95% probability level for the entire plant site.

[0065] In this embodiment, the calculation distance is selected as 500 meters, and the atmospheric dispersion factors (conservative) at the 99.5% probability level in each direction and the 95% probability level for the entire plant site are calculated using the Gaussian plume model as shown in Table 1.

[0066] Table 1 Atmospheric dispersion factors calculated based on the Gaussian plume model

[0067]

[0068]

[0069] S6, if you choose fine calculation, select the nine grid points closest to the plant site, that is, according to the central coordinates of the target area, select the nine grid points closest to the surrounding area (the number of grid points can be adjusted as needed), and extract meteorological elements such as cloud cover, air pressure at different heights, temperature, wind speed, relative humidity, etc. at each grid point.

[0070] In this embodiment, the nine nearest grid points (19.25N, 108.75E), (19.25N, 109.0E), (19.25N, 109.25E), (19.5N, 108.75E), (19.5N, 109.0E), (19.5N, 109.25E), (19.75N, 108.75E), (19.75N, 109.0E), and (19.75N, 109.25E) are selected to extract the radiation of each grid point, the temperature at different altitudes, the air pressure, the wind speed and other meteorological elements.

[0071] S7, organize the extracted meteorological elements into a Gaussian smoke model (as shown in the figure) to calculate the required ground file (surf.dat) and high-altitude files (up1.dat, up2.dat, ... upn.dat (n is the number of grid points)).

[0072] S8, using the Gaussian puff model to simulate calculations for a whole year, statistically analyze the results, calculate the atmospheric diffusion factors at different distances, and give the diffusion factors at the 99.5% probability level for each direction of the plant site and the 95% probability level for the entire plant site.

[0073] In this embodiment, the Gaussian puff model is used to perform simulation calculations for the entire year, a distance of 500 meters is selected, and a compiled post-processing statistical program is used to calculate the atmospheric diffusion factors (actual) at 99.5% probability levels in all directions and 95% probability levels for the entire plant site, as shown in Table 2.

[0074] Table 2 Atmospheric dispersion factors calculated based on the Gaussian puff model

[0075]

[0076]

[0077] Considering the site selection stage of nuclear power projects, meteorological observations are generally not carried out at the plant sites, and the National Meteorological Station has also cancelled relevant observations of cloud cover, and the data from the National Meteorological Station is inconvenient to obtain. The calculation method of the atmospheric dispersion factor provided in this embodiment uses the existing full-year reanalysis data of the Chinese region to easily calculate the atmospheric dispersion conditions of the target area (or the area to be evaluated), providing a reference for the selection of the plant site.

[0078] Embodiment 3:

[0079] This embodiment provides a method for selecting a site for a nuclear power plant, including:

[0080] Obtaining atmospheric dispersion factors corresponding to a plurality of candidate plant sites according to the atmospheric dispersion factor calculation method described in Example 1 or Example 2;

[0081] The candidate site corresponding to the smallest atmospheric dispersion factor is selected as the target site.

[0082] Embodiment 4:

[0083] like Figure 4 As shown, this embodiment provides a device for calculating an atmospheric diffusion factor, including:

[0084] The acquisition module 41 is used to acquire reanalysis meteorological data.

[0085] The determination module 42 is used to determine the center position of the target area, wherein the center position includes longitude and latitude coordinates.

[0086] The calculation module 43 is connected to the acquisition module 41 and the determination module 42, and is used to calculate the atmospheric diffusion factor at a preset distance from the center position based on the Gaussian plume model and / or the Gaussian puff model and reanalyze meteorological data.

[0087] Optionally, the calculation module includes a first calculation unit.

[0088] The first calculation unit is used to screen out target grid points of the reanalysis meteorological data according to the central position, the number of the target grid points is one, and is also used to obtain the three-dimensional joint frequency of the target grid points, the variables of the three-dimensional joint frequency include wind direction, wind speed, stability, and to calculate the atmospheric diffusion factor at a preset distance from the central position according to the three-dimensional joint frequency and the Gaussian plume model.

[0089] Optionally, the first calculation unit is used to screen out the grid point closest to the center position in the reanalysis meteorological data grid, and determine whether the grid point is located on land. If the grid point is located on land, the grid point is determined as the target grid point; if the grid point is located in the ocean, the target grid point with the second closest distance to the center position is screened out, and so on, until the target grid point is located on land.

[0090] Optionally, the first calculation unit is used to extract the annual radiation of the target grid point, the east-west wind speed U at a preset height, the north-south wind speed V at a preset height, and the temperature from the reanalysis meteorological data, and calculate the stability based on the radiation, the east-west wind speed U at a preset height, the north-south wind speed V at a preset height, the temperature, and the improved Pasquier algorithm, and calculate the three-dimensional joint frequency of the target grid point based on the east-west wind speed U at a preset height, the north-south wind speed V at a preset height, and the stability.

[0091] Optionally, the computing module includes a second computing unit.

[0092] The second calculation unit is used to filter out target grid points of the reanalysis meteorological data according to the central position, the number of target grid points is multiple, and the target grid points are closest to the central position, and extract meteorological element data of the target grid points, the meteorological elements include cloud cover, radiation, air pressure at different altitudes, temperature, wind speed, and relative humidity. It is also used to convert the meteorological element data into ground files and high-altitude files corresponding to the Gaussian puff model, and to calculate the atmospheric diffusion factor at a preset distance from the central position within the year based on the ground file, the high-altitude file, and the Gaussian puff model.

[0093] Embodiment 5:

[0094] This embodiment provides a device for calculating an atmospheric diffusion factor, including: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for calculating the atmospheric diffusion factor described in Example 1 or Example 2 by executing the computer instructions.

[0095] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present invention, but the present invention is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for calculating atmospheric dispersion factor, characterized in that: include: Obtain reanalysis meteorological data; Determine the center position of the target area, wherein the center position includes longitude and latitude coordinates; The atmospheric diffusion factor at a preset distance from the center position is calculated based on the Gaussian plume model and / or the Gaussian puff model and the reanalyzed meteorological data.

2. The method according to claim 1, characterized in that The step of calculating the atmospheric dispersion factor at a preset distance from the center position according to the Gaussian plume model and the reanalyzed meteorological data specifically includes: Filtering out target grid points of the reanalyzed meteorological data according to the central position, the number of the target grid points being one; Acquire the three-dimensional joint frequency of the target grid point, where variables of the three-dimensional joint frequency include wind direction, wind speed, and stability; The atmospheric diffusion factor at a preset distance from the center position is calculated based on the three-dimensional joint frequency and the Gaussian plume model.

3. The method according to claim 2, characterized in that The step of selecting the target grid point of the reanalyzed meteorological data according to the central position specifically includes: In the reanalysis meteorological data grid, a grid point closest to the center position is selected; Determining whether the grid point is located on land; If the grid point is located on land, determining the grid point as a target grid point; If the grid point is located in the ocean, the target grid point that is the second closest to the center position is selected, and so on until the target grid point is located on land.

4. The method according to claim 2, characterized in that: The obtaining of the three-dimensional joint frequency of the target grid point specifically includes: Extracting the annual radiation of the target grid point, the east-west wind speed U at a preset height, the north-south wind speed V at a preset height, and the temperature from the reanalyzed meteorological data; The stability is calculated based on the radiation, the east-west wind speed U at a preset height, the north-south wind speed V at a preset height, the temperature, and the improved Pasquier algorithm; The three-dimensional joint frequency of the target grid point is calculated based on the east-west wind speed U at the preset height, the north-south wind speed V at the preset height, and the stability.

5. The method according to claim 1, characterized in that The step of calculating the atmospheric diffusion factor at a preset distance from the center position according to the Gaussian puff model and the reanalyzed meteorological data specifically includes: Filtering target grid points of the reanalyzed meteorological data according to the central position, the number of target grid points being multiple, and the target grid points being closest to the central position; Extracting meteorological element data of the target grid point, the meteorological elements including cloud cover, radiation, air pressure at different altitudes, temperature, wind speed, and relative humidity; Converting the meteorological element data into ground files and high-altitude files corresponding to the Gaussian puff model; The atmospheric diffusion factor at a preset distance from the center position within a year is calculated based on the ground file, the high-altitude file, and the Gaussian smoke puff model.

6. A method for selecting a site for a nuclear power plant, characterized in that: include: Obtaining atmospheric dispersion factors corresponding to a plurality of candidate plant sites according to the atmospheric dispersion factor calculation method according to any one of claims 1 to 5; The candidate site corresponding to the smallest atmospheric dispersion factor is selected as the target site.

7. A device for calculating atmospheric diffusion factor, characterized in that: include: Acquisition module, used to obtain reanalysis meteorological data, A determination module is used to determine the center position of the target area, wherein the center position includes longitude and latitude coordinates. The calculation module is connected to the acquisition module and the determination module, and is used to calculate the atmospheric diffusion factor at a preset distance from the center position based on the Gaussian plume model and / or the Gaussian puff model and the reanalyzed meteorological data.

8. The computing device according to claim 7, characterized in that: The calculation module comprises a first calculation unit, The first calculation unit is used to filter out the target grid points of the reanalyzed meteorological data according to the central position, and the number of the target grid points is one. It is also used to obtain the three-dimensional joint frequency of the target grid point, the variables of the three-dimensional joint frequency include wind direction, wind speed, and stability, And, it is used to calculate the atmospheric diffusion factor at a preset distance from the center position based on the three-dimensional joint frequency and the Gaussian plume model.

9. The computing device according to claim 7, characterized in that: The computing module includes a second computing unit, The second calculation unit is used to filter out target grid points of the reanalyzed meteorological data according to the central position, the number of target grid points is multiple, and the target grid points are closest to the central position, and extract meteorological element data of the target grid points, the meteorological elements including cloud cover, radiation, air pressure at different heights, temperature, wind speed, and relative humidity. It is also used to convert the meteorological element data into ground files and high-altitude files corresponding to the Gaussian puff model, And, it is used to calculate the atmospheric diffusion factor at a preset distance from the center position within the year based on the ground file, the high-altitude file, and the Gaussian smoke puff model.

10. A device for calculating atmospheric diffusion factor, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for calculating the atmospheric diffusion factor according to any one of claims 1 to 5 by executing the computer instructions.

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