Method and device for simulating contamination diffusion by combining dry and wet deposition with radioactive decay

Abstract

The application provides a pollution diffusion simulation method and device fusing dry and wet deposition and radioactive decay, and relates to the technical field of pollution diffusion simulation. The application provides a pollution diffusion simulation method and device fusing dry and wet deposition and radioactive decay, and relates to the technical field of pollution diffusion simulation. The application provides a pollution diffusion simulation method and device fusing dry and wet deposition and radioactive decay, and relates to the technical field of pollution diffusion simulation. The application provides a pollution diffusion simulation method and device fusing dry and wet deposition and radioactive decay, and relates to the technical field of pollution diffusion simulation. The application provides a pollution diffusion simulation method and device fusing dry and wet deposition and radioactive decay, and relates to the technical field of pollution diffusion simulation. The application provides a pollution diffusion simulation method and device fusing dry and wet deposition and radioactive decay, and relates to the technical field of pollution diffusion simulation.

Description

Technical Field

[0001] This application relates to the field of pollution diffusion simulation technology, and in particular to a pollution diffusion simulation method and apparatus that integrates dry and wet deposition with radioactive decay. Background Technology

[0002] Radioactive contamination diffusion simulation is a core technological support for nuclear safety management, environmental risk early warning, and pollution emergency response. Its simulation accuracy directly impacts the scientific validity of regional ecological protection, public health safeguards, and emergency decision-making. With the expanding application of nuclear technology, the environmental risks posed by radioactive pollutant leaks are becoming increasingly prominent, placing higher demands on the accuracy, comprehensiveness, and timeliness of contamination diffusion simulation.

[0003] However, most existing pollution diffusion simulation systems use a single removal coefficient for estimation, failing to distinguish between the different physical processes of dry and wet deposition. When simulating radioactive decay, they often use fixed decay parameters and uniform decay durations, resulting in deviations in the calculation of residual activity concentrations of nuclides. When calculating pollution concentrations, the simulation systems do not consider the combined effects of deposition and decay, making it impossible to accurately represent the pollution intensity at different times and in different regions. This leads to insufficient accuracy in the simulation results of radioactive pollution diffusion, resulting in the failure of nuclear safety control, delayed or misjudged environmental risk warnings, and consequently affecting the effectiveness of public health protection. Summary of the Invention

[0004] To address the aforementioned problems, in a first aspect, the present invention provides a pollution diffusion simulation method that integrates dry and wet deposition with radioactive decay, comprising: The three-dimensional meteorological field data, medium characteristic data, radioactive pollutant source term data and topographic data of the target area are acquired through a comprehensive data acquisition device. The removal coefficients are calculated by classifying the three-dimensional meteorological field data and media characteristic data to obtain the removal coefficients for dry and wet sediments. A radioactive decay and time-series correlation model was constructed. Radioactive pollutant source term data were input into the radioactive decay and time-series correlation model to calculate the activity concentration and obtain the residual activity concentration of the nuclide. A pollution diffusion model is constructed, and the model is geomorphologically corrected based on topographic data to obtain a pollution concentration field distribution model. The dry deposition removal coefficient, wet deposition removal coefficient, and residual radionuclide activity concentration were input into the pollution concentration field distribution model to simulate the diffusion and obtain the pollution diffusion simulation results.

[0005] Optional: The three-dimensional meteorological field data includes wind speed, wind direction, atmospheric stability, precipitation, precipitation type, and boundary layer height; Media characteristic data include land cover type, vegetation canopy thickness, soil porosity, and water physicochemical properties; Radioactive contaminant source term data includes nuclide type, initial activity concentration of the nuclide, and release rate; Topographic data includes topographic elevation, surface morphology, distribution of obstructions, and surface roughness.

[0006] Optionally, the calculation process for the dry deposition removal coefficient includes: The atmospheric diffusion coefficient of radioactive pollutants is calculated based on wind speed, wind direction and boundary layer height. The soil porosity correction factor corresponding to soil porosity is obtained. The aerodynamic drag corresponding to atmospheric stability is obtained. The vegetation canopy drag corresponding to vegetation canopy thickness is obtained. The surface drag corresponding to surface cover type is obtained. The dry sediment removal coefficient is calculated based on the atmospheric diffusion coefficient of radioactive pollutants, soil porosity correction factor, aerodynamic drag, vegetation canopy drag, and surface drag.

[0007] Optionally, the calculation process for the wet deposition removal coefficient includes: Obtain the scour coefficient corresponding to the precipitation type, obtain the precipitation intensity correction index corresponding to the precipitation amount, obtain the water body pH and organic matter content corresponding to the water body physicochemical properties, and calculate the water body physicochemical property correction factor based on the water body pH and organic matter content. The wet sediment removal coefficient is calculated based on the scour coefficient, precipitation, precipitation intensity correction index, and water body physicochemical property correction factor.

[0008] Optionally, the step of constructing a radioactive decay and time-series correlation model, which involves inputting radioactive contaminant source term data into the model for activity concentration calculation to obtain the residual activity concentration of the nuclide, includes: A radioactive decay and time-series correlation model was constructed, which includes a nuclide decay parameter library unit and a decay duration control unit. The decay constant matching the nuclide type is called through the nuclide decay parameter library unit; The decay duration is set to match the release rate via the decay duration control unit; The remaining activity concentration of a nuclide is calculated using the decay formula based on its initial activity concentration, decay constant, and decay duration.

[0009] Optionally, the step of performing geomorphological correction on the pollution diffusion model based on topographic data to obtain a pollution concentration field distribution model includes: Based on the terrain elevation, the elevation correction coefficient is calculated using a terrain adaptation algorithm. Obtain the landform weight coefficient library, and call the corresponding shading correction factor in the landform weight coefficient library according to the landform type and the distribution of shading objects; The roughness attenuation coefficient is calculated based on surface roughness, wind speed, and boundary layer height. The pollution diffusion model is iteratively corrected by using elevation correction coefficient, shading correction factor and roughness attenuation coefficient to obtain pollution concentration field distribution model.

[0010] Optionally, the step of inputting the dry deposition removal coefficient, wet deposition removal coefficient, and residual radionuclide activity concentration into the pollution concentration field distribution model for diffusion simulation to obtain pollution diffusion simulation results includes: The atmospheric environment sensitivity level of the target area is obtained. Based on the atmospheric environment sensitivity level, weighting coefficients are configured for dry sediment removal coefficient, wet sediment removal coefficient and residual radionuclide activity concentration, respectively, to obtain the optimized dry sediment removal coefficient, optimized wet sediment removal coefficient and optimized residual radionuclide activity concentration. Spatial gridding is performed on the pollution concentration field distribution model to obtain an optimized pollution concentration field distribution model. The optimized dry deposition removal coefficient, optimized wet deposition removal coefficient, and optimized residual radionuclide activity concentration are input into the optimized pollution concentration field distribution model to construct the pollution diffusion equation. The pollution diffusion equation is solved using the finite volume algorithm to obtain the spatiotemporal distribution data of pollutant concentration, which is then used as the result of the pollution diffusion simulation.

[0011] Secondly, the present invention provides a pollution diffusion simulation device that integrates dry and wet deposition with radioactive decay, for implementing the pollution diffusion simulation method that integrates dry and wet deposition with radioactive decay, the device comprising: The data acquisition module is used to acquire three-dimensional meteorological field data, medium characteristic data, radioactive pollutant source term data and topographic data of the target area through the integrated acquisition device; The sediment removal coefficient acquisition module is used to classify and calculate the removal coefficient based on three-dimensional meteorological field data and medium characteristic data, and obtain the dry sediment removal coefficient and wet sediment removal coefficient. The module for obtaining the residual activity concentration of nuclides is used to construct a radioactive decay and time-series correlation model. Radioactive pollutant source term data is input into the radioactive decay and time-series correlation model to calculate the activity concentration and obtain the residual activity concentration of nuclides. The pollution concentration field distribution model construction module is used to build a pollution diffusion model. It performs geomorphological correction on the pollution diffusion model based on topographic data to obtain the pollution concentration field distribution model. The pollution diffusion simulation result acquisition module is used to input the dry deposition removal coefficient, wet deposition removal coefficient and residual activity concentration of nuclides into the pollution concentration field distribution model to perform diffusion simulation and obtain pollution diffusion simulation results.

[0012] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the pollution diffusion simulation method that integrates dry and wet deposition and radioactive decay.

[0013] Fourthly, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the aforementioned method for simulating pollution diffusion by integrating dry and wet deposition and radioactive decay.

[0014] The present invention has the following beneficial effects: 1. The removal coefficient is classified into dry and wet deposition and calculated separately, breaking through the limitations of traditional single-coefficient estimation and accurately reflecting the deposition patterns of pollutants under different environmental conditions. 2. By using a radioactive decay and time-series correlation model, the decay constant of nuclides is accurately matched to the decay duration of the release rate, avoiding errors in nuclide concentration estimation caused by ignoring time-series effects and improving the calculation accuracy of residual nuclide activity concentration. 3. The pollution diffusion model is modified using multi-dimensional geomorphological data. Through weighted iteration of elevation coefficient, shading factor, and roughness attenuation coefficient, the pollution diffusion model is adapted to different landforms such as plains, mountains, and valleys, solving the problem of one-size-fits-all terrain adaptation in traditional models. 4. The dry deposition removal coefficient, wet deposition removal coefficient, and residual nuclide activity concentration are input into the pollution concentration field distribution model for diffusion simulation, obtaining spatiotemporal distribution data of pollutant concentration. This accurately presents the pollution intensity at different times and in different regions, improving the accuracy of pollution diffusion simulation.

[0015] 2. By combining specific grid elevations with regional average elevations using a terrain adaptation algorithm, elevation correction coefficients are calculated. This avoids the limitations of traditional planar simulations and accurately reflects diffusion differences in areas with varying elevations, such as mountains and valleys, improving simulation accuracy in complex terrains. Utilizing a geomorphic weighting coefficient library, the model precisely matches combined scenarios of surface morphology and obstruction distribution, transforming obstruction effects into calculable obstruction correction factors, thus solving the problem of difficulty in quantifying the diffusion blocking effect of obstructions. The roughness attenuation coefficient integrates three key parameters: surface roughness, wind speed, and boundary layer height. This ensures that diffusion attenuation conforms to both surface physical characteristics and meteorological conditions, avoiding calculation errors caused by single parameters. Through weighted fusion of the three coefficients and multiple iterations, the model's fit with actual terrain is continuously optimized, effectively reducing systematic errors. The final output pollution concentration field distribution model more closely resembles real diffusion scenarios.

[0016] 3. Weights are allocated differently according to the atmospheric environmental sensitivity level, ensuring that key parameters are optimized to align with regional pollution prevention and control priorities. Spatial gridding is used to associate each grid with specific topographic, meteorological, and weight parameters, refining the pollution concentration field distribution model from a regional overall level to a grid-level precision, capturing local pollution differences within complex regions. Optimized dry and wet deposition removal coefficients and residual radionuclide activity concentrations are input into the optimized pollution concentration field distribution model to construct a pollution diffusion equation. This equation integrates three core influencing factors: deposition effects, radioactive decay, and regional sensitivity characteristics. Compared to single-factor models, the pollution diffusion equation has a more complete physical meaning and can truly reflect the pollution diffusion patterns under multi-factor coupling. A finite volume algorithm is used to solve the pollution diffusion equation, ensuring numerical stability and accurately outputting pollution diffusion simulation results with a high degree of time-grid-concentration matching, providing directly applicable quantitative evidence for pollution early warning and regional control. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a method according to an embodiment of the present invention; Figure 2 This is a structural diagram of the device according to an embodiment of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0020] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0021] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

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

[0023] Reference Figure 1 This invention provides a pollution diffusion simulation method that integrates dry and wet deposition with radioactive decay, comprising: The S100 acquires three-dimensional meteorological field data, medium characteristic data, radioactive pollutant source term data, and topographic data of the target area through a comprehensive acquisition device.

[0024] In some embodiments, the three-dimensional meteorological field data includes wind speed, wind direction, atmospheric stability, precipitation, precipitation type, and boundary layer height; Media characteristic data include land cover type, vegetation canopy thickness, soil porosity, and water physicochemical properties; Radioactive contaminant source term data includes nuclide type, initial activity concentration of the nuclide, and release rate; Topographic data includes topographic elevation, surface morphology, distribution of obstructions, and surface roughness.

[0025] In some embodiments, wind speed and direction are continuously collected at 10-minute intervals by automatic weather stations deployed in the target area; atmospheric stability is classified into six stability levels (A-F6) using the Pasquale-Trnal classification method, combined with surface temperature, cloud cover, and solar radiation intensity data; precipitation is collected using a tipping bucket rain gauge with a resolution of 0.1 mm, and the start and end times of precipitation are recorded simultaneously; precipitation type is observed based on the intensity of weather radar echoes to distinguish between rain, snow, hail, and other types; boundary layer height is determined by obtaining temperature and humidity profiles at different altitudes using a radiosonde or microwave radiometer, and the boundary layer height is determined by abrupt changes in the potential temperature gradient.

[0026] Land cover types were classified into eight categories, including cultivated land, forest land, and water bodies, based on high-resolution remote sensing imagery and supervised classification. Vegetation canopy thickness was obtained through inversion of lidar point cloud data. Soil porosity was calculated by collecting 0-20cm topsoil samples using the ring cutter method and calculating volumetric porosity using the drying method, with 15-20 sampling points set up in each area. The physicochemical properties of water bodies were determined by collecting surface water or groundwater samples on-site and testing pH value and organic matter content in the laboratory.

[0027] The nuclide type was determined by using a portable gamma spectrometer to detect and match the characteristic peaks of nuclide decay, identifying specific nuclides such as U-238 and Cs-137. The initial activity concentration of the nuclide was obtained by analyzing aerosol or soil samples collected around the pollution source using a high-purity germanium detector. The release rate was calculated by continuously recording the pollutant release flow and real-time activity using a source term monitoring system, with the release rate calculated as an hourly average.

[0028] The terrain elevation was obtained using digital elevation model data with a resolution of ≤30m, and the elevation values ​​of the target area were extracted. The landform type was divided into 6 categories, including plains, hills, mountains, and valleys, based on the DEM and remote sensing imagery. A slope of ≥25° was classified as a mountain. The distribution of obstructions was obtained through target identification in remote sensing imagery, and the location, height, and coverage of obstructions were recorded. The surface roughness was selected based on empirical values ​​according to the surface cover type, such as 0.03m for cultivated land and 0.5m for forest land.

[0029] S200 performs classification calculations of the removal coefficient based on three-dimensional meteorological field data and medium characteristic data to obtain the removal coefficients for dry and wet sediments.

[0030] In some embodiments, the calculation process of the dry deposition removal coefficient includes: The atmospheric diffusion coefficient of radioactive pollutants is calculated based on wind speed, wind direction and boundary layer height. The soil porosity correction factor corresponding to soil porosity is obtained. The aerodynamic drag corresponding to atmospheric stability is obtained. The vegetation canopy drag corresponding to vegetation canopy thickness is obtained. The surface drag corresponding to surface cover type is obtained. The dry sediment removal coefficient is calculated based on the atmospheric diffusion coefficient of radioactive pollutants, soil porosity correction factor, aerodynamic drag, vegetation canopy drag, and surface drag.

[0031] In some embodiments, a Gaussian diffusion model is used, combined with wind speed, wind direction, and boundary layer height, to calculate the atmospheric diffusion coefficient K of radioactive pollutants. The calculation formula is as follows:

[0032] in, For wind speed, Where is the standard deviation of horizontal diffusion from wind direction, and H is the boundary layer height. It is the angle between the wind direction and the main axis of pollution source diffusion.

[0033] According to soil porosity Matching soil porosity correction factor , hour , hour , hour .

[0034] Aerodynamic drag Based on empirical values ​​of the Pasquale-Trnal stability grades corresponding to atmospheric stability, the stability grades are A (extremely unstable), B (unstable), C (weakly unstable), D (neutral), E (weakly stable), and F (stable). Aerodynamic drag... (s / m) corresponds to values ​​of 5~10, 10~20, 20~35, 35~50, 50~80, and 80~120.

[0035] Vegetation canopy resistance According to vegetation canopy thickness Divide into, hour s / m, hour s / m, hour s / m.

[0036] Surface resistance Empirical values ​​are taken based on land cover type, which includes cultivated land, forest land, water bodies, built-up land, grassland, and bare land. Surface resistance is... The values ​​are 80~150, 200~400, 10~30, 150~250, 100~200, and 50~100 respectively.

[0037] Dry sediment removal coefficient The calculation formula is: The unit is .

[0038] In some embodiments, the calculation process of the wet deposition removal coefficient includes: Obtain the scour coefficient corresponding to the precipitation type, obtain the precipitation intensity correction index corresponding to the precipitation amount, obtain the water body pH and organic matter content corresponding to the water body physicochemical properties, and calculate the water body physicochemical property correction factor based on the water body pH and organic matter content. The wet sediment removal coefficient is calculated based on the scour coefficient, precipitation, precipitation intensity correction index, and water body physicochemical property correction factor.

[0039] In some embodiments, the scouring coefficient Empirical values ​​are matched according to precipitation type to reflect the pollutant flushing efficiency of different precipitation forms. Precipitation types include rain, snow, and hail; scour coefficient The corresponding values ​​are 0.008~0.015, 0.003~0.007, and 0.012~0.020.

[0040] Rainfall P was collected using a tipping bucket rain gauge, unit It is divided into light rain ( ), moderate rain (10≤ Heavy rain (25≤) ),rainstorm( The precipitation intensity correction index n corresponds to 0.6~0.8, 0.8~1.0, 1.0~1.2, and 1.2~1.4.

[0041] Based on the corresponding pH value and organic matter content (OM) of the water body's physicochemical properties, a weighted formula was used to calculate the correction factor for the water body's physicochemical properties. ,in, For pH correction, take 1.0 for pH values ​​between 6.5 and 8.5, 0.7-0.9 for pH values ​​< 6.5, and 0.8-1.0 for pH values ​​> 8.5. For the organic matter correction item, take 1.0-1.2 when OM < 2%, take 0.9-1.0 when 2% ≤ OM ≤ 5%, and take 0.7-0.9 when OM > 5%.

[0042] wet sediment removal coefficient The calculation formula is: The unit is m / s.

[0043] S300 constructs a radioactive decay and time-series correlation model, inputs radioactive contaminant source term data into the radioactive decay and time-series correlation model to calculate activity concentration, and obtains the residual activity concentration of nuclides.

[0044] In some embodiments, the construction of the radioactive decay and time-series correlation model, which involves inputting radioactive contaminant source term data into the radioactive decay and time-series correlation model to calculate activity concentration and obtain the residual activity concentration of the nuclide, includes: A radioactive decay and time-series correlation model was constructed, which includes a nuclide decay parameter library unit and a decay duration control unit. The decay constant matching the nuclide type is called through the nuclide decay parameter library unit; The decay duration is set to match the release rate via the decay duration control unit; The remaining activity concentration of a nuclide is calculated using the decay formula based on its initial activity concentration, decay constant, and decay duration.

[0045] In some embodiments, the radioactive decay and time-series correlation model is a dual-unit modular structure. The core logic involves providing basic decay parameters through a parameter library unit, adapting the source term release characteristics through a duration control unit, and outputting the remaining activity concentration using the decay formula. The nuclide decay parameter library unit is constructed using a multilayer perceptron. The input layer takes the nuclide feature peak energy and auxiliary features as input, the hidden layer is a two-layer fully connected structure, and the output layer outputs the decay constant of the corresponding nuclide. Training is performed using the IAEA nuclide database, with L2 regularization added to prevent overfitting. The decay duration control unit is constructed using a temporal convolutional network (TCN). The input layer takes the release rate time-series data as input, the convolutional layer is a two-layer causal convolutional structure, and the output layer outputs a segmented decay duration array.

[0046] The nuclide decay parameter library unit stores standard decay constants for common radionuclides such as U-238, Cs-137, and Co-60. The data is sourced from the IAEA nuclide database and is accurate to six decimal places. A portable gamma-ray spectrometer is used to detect the characteristic peaks of the target nuclide and extract its type. For example, for the Cs-137 characteristic peak at 661.7 keV, the system automatically performs a fuzzy match with the corresponding nuclide in the parameter library and outputs a unique decay constant. .

[0047] The decay duration T is set by the decay duration control unit to match the release rate. The release rate is calculated based on hourly averages, so the decay duration is uniformly expressed in hours, with any fraction of an hour counted as a full hour. The starting point is the time of the first release from the pollution source. The simulation time is the termination point. decay time .

[0048] Residual activity concentration of nuclides The calculation formula is: ,in This represents the initial activity concentration of the nuclide.

[0049] The S400 constructs a pollution diffusion model, and then performs geomorphological correction on the pollution diffusion model based on topographic data to obtain a pollution concentration field distribution model.

[0050] In some embodiments, the step of performing geomorphological correction on the pollution diffusion model based on topographic data to obtain a pollution concentration field distribution model includes: Based on the terrain elevation, the elevation correction coefficient is calculated using a terrain adaptation algorithm. Obtain the landform weight coefficient library, and call the corresponding shading correction factor in the landform weight coefficient library according to the landform type and the distribution of shading objects; The roughness attenuation coefficient is calculated based on surface roughness, wind speed, and boundary layer height. The pollution diffusion model is iteratively corrected by using elevation correction coefficient, shading correction factor and roughness attenuation coefficient to obtain pollution concentration field distribution model.

[0051] In some embodiments, elevation correction factor The calculation formula is:

[0052] Where i is the grid number, Let i be the terrain elevation of grid point i; The average elevation of the target area. The range of values ​​is .

[0053] The geomorphic weight coefficient library stores the combined weights of landform type and shading distribution. The data comes from remote sensing image classification and target recognition results. The landform type and shading coverage of the target area are determined through remote sensing images, and a unique shading correction factor is accurately matched in the coefficient library. Landform types include unshaded plains, sparsely shaded plains, moderately shaded hills, densely shaded mountains, and unshaded river valleys, with corresponding shading correction factors. The values ​​are 0.95-1.0, 0.85-0.95, 0.75-0.85, 0.6-0.75, and 0.9-1.0, respectively.

[0054] Roughness attenuation coefficient The calculation formula is:

[0055] in, For surface roughness, For wind speed, The boundary layer height, The range of values ​​is The greater the roughness, the lower the wind speed. The smaller.

[0056] The three coefficients are weighted and incorporated into the pollution diffusion model. Through multiple iterations, the deviation between the model and the actual terrain is reduced. The steps are as follows: Using the basic Gaussian diffusion model as the initial pollution diffusion model, , , The overall correction coefficient is obtained by weighting the coefficients by 0.3, 0.4, and 0.3. The concentration distribution of the initial model is scaled and corrected.

[0057] The correction process is repeated, with the coefficient weights adjusted in each iteration. Feedback is provided based on the deviation from the previous correction result; for example, if the occlusion effect exceeds expectations, the weights are increased. Set the weight to 0.5 and iterate 3-5 times.

[0058] When the difference in pollution concentration field distribution between two iterations is ≤1%, the iteration is stopped, and the final pollution concentration field distribution model is output to ensure that the diffusion simulation fits the actual landform characteristics.

[0059] The S500 inputs the dry deposition removal coefficient, wet deposition removal coefficient, and residual radionuclide activity concentration into the pollution concentration field distribution model to perform diffusion simulation and obtain pollution diffusion simulation results.

[0060] In some embodiments, the step of inputting the dry deposition removal coefficient, the wet deposition removal coefficient, and the residual activity concentration of the nuclide into the pollution concentration field distribution model for diffusion simulation to obtain pollution diffusion simulation results includes: The atmospheric environment sensitivity level of the target area is obtained. Based on the atmospheric environment sensitivity level, weighting coefficients are configured for dry sediment removal coefficient, wet sediment removal coefficient and residual radionuclide activity concentration, respectively, to obtain the optimized dry sediment removal coefficient, optimized wet sediment removal coefficient and optimized residual radionuclide activity concentration. Spatial gridding is performed on the pollution concentration field distribution model to obtain an optimized pollution concentration field distribution model. The optimized dry deposition removal coefficient, optimized wet deposition removal coefficient, and optimized residual radionuclide activity concentration are input into the optimized pollution concentration field distribution model to construct the pollution diffusion equation. The pollution diffusion equation is solved using the finite volume algorithm to obtain the spatiotemporal distribution data of pollutant concentration, which is then used as the result of the pollution diffusion simulation.

[0061] In some embodiments, the assessment of atmospheric environmental sensitivity levels is shown in Table 1 below.

[0062] Table 1 Classification of Atmospheric Environmental Sensitivity Levels

[0063] The weighting coefficients are configured as shown in Table 2 below.

[0064] Table 2 Weighting Coefficient Configuration Table

[0065] Optimize dry sediment removal coefficient Optimize wet sediment removal coefficient Optimize the residual activity concentration of nuclides , 、 、 The initial dry sediment removal coefficient, wet sediment removal coefficient, and residual nuclide activity concentration are given.

[0066] GIS tools are used to divide the target area into regular grids, generating grid vector files. Topographic elevation, surface morphology, distribution of obstructions, and meteorological data are then spatially overlaid on the grids, ensuring each grid is uniquely associated with topographic and meteorological parameters. The optimized data is then... , , By matching and allocating data according to grids, a grid-multi-parameter associated dataset is formed, which is the optimized pollution concentration field distribution model.

[0067] The finite volume algorithm is used to solve for pollutant concentrations. The core logic of the algorithm is based on the law of conservation of mass, treating each grid cell as a control volume, discretizing the pollution diffusion equation, and solving for the average concentration of each control volume. This algorithm is adaptable to complex terrain and parameter distributions. The specific steps are as follows: The expression for the pollution diffusion equation is:

[0068] Where C represents the spatiotemporal distribution data of the pollutant concentration to be solved; t represents time. , , These are the wind speed components in the x, y, and z directions; This is the roughness attenuation coefficient; D is the elevation correction factor; D is the pollutant turbulent diffusion coefficient. The decay constant; S represents the initial activity concentration of the radionuclide; S represents the intensity of the pollution source. This is the divergence operator.

[0069] Using a 30m×30m grid as the basic control volume, with four adjacent grids forming the solution element, the boundary of the control volume is clearly defined; the pollution diffusion equation is then applied. The equations are transformed into a system of algebraic equations. The SOR iterative method is used to solve the system of algebraic equations. The convergence condition is that the concentration error between two adjacent iterations is ≤0.5% and the number of iterations is ≤50. The concentration data is output at 1-hour intervals in the time dimension and the concentration value of each grid is output in the spatial dimension, thus forming the spatiotemporal distribution data of pollutant concentration in time-grid-concentration.

[0070] Reference Figure 2 This invention provides a pollution diffusion simulation device 20 that integrates dry and wet deposition with radioactive decay, used to realize a pollution diffusion simulation method that integrates dry and wet deposition with radioactive decay. The device includes: Data acquisition module 21 is used to acquire three-dimensional meteorological field data, medium characteristic data, radioactive pollutant source term data and topographic data of the target area through the integrated acquisition device; The sediment removal coefficient acquisition module 22 is used to classify and calculate the removal coefficient based on three-dimensional meteorological field data and medium characteristic data to obtain the dry sediment removal coefficient and the wet sediment removal coefficient. The nuclide residual activity concentration acquisition module 23 is used to construct a radioactive decay and time-series correlation model. Radioactive pollutant source term data is input into the radioactive decay and time-series correlation model to calculate the activity concentration and obtain the nuclide residual activity concentration. The pollution concentration field distribution model construction module 24 is used to construct a pollution diffusion model. Based on topographic data, the pollution diffusion model is geomorphologically corrected to obtain the pollution concentration field distribution model. The pollution diffusion simulation result acquisition module 25 is used to input the dry deposition removal coefficient, wet deposition removal coefficient and residual activity concentration of nuclides into the pollution concentration field distribution model to perform diffusion simulation and obtain pollution diffusion simulation results.

[0071] This application provides an electronic device, including a processor and a memory; the memory stores a computer program, wherein the computer program, when executed by the processor, implements a pollution diffusion simulation method that integrates dry and wet deposition and radioactive decay, as described above.

[0072] Specifically, the processor may include, for example, a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor may also include onboard memory for caching purposes. The processor may be a single processing unit or multiple processing units for performing different actions of the method flow according to embodiments of this application.

[0073] Memory can be any medium capable of containing, storing, transmitting, propagating, or transmitting instructions. For example, memory can include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, instruments, or propagation media. Specific examples of memory include: magnetic storage devices such as magnetic tape or hard disk drives (HDDs); optical storage devices such as optical discs (CD-ROMs); and also random access memory (RAM) or flash memory; and / or wired / wireless communication links.

[0074] This application also provides a computer-readable medium storing a computer program thereon, which, when executed by a processor, implements a pollution diffusion simulation method that integrates dry and wet deposition with radioactive decay, as described above. This computer-readable medium may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into that device / apparatus / system. The aforementioned computer-readable medium carries one or more programs, which, when executed, implement the method as described in the embodiments of this application.

[0075] According to embodiments of this application, a computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wired, optical fiber, radio frequency signals, etc., or any suitable combination thereof.

[0076] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application. Therefore, the scope of this application should not be limited to the above embodiments, but should be defined not only by the appended claims, but also by their equivalents. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for simulating pollution diffusion by integrating dry and wet deposition with radioactive decay, characterized in that, include: The three-dimensional meteorological field data, medium characteristic data, radioactive pollutant source term data and topographic data of the target area are acquired through a comprehensive data acquisition device. The removal coefficients are calculated by classifying the three-dimensional meteorological field data and media characteristic data to obtain the removal coefficients for dry and wet sediments. A radioactive decay and time-series correlation model was constructed. Radioactive pollutant source term data were input into the radioactive decay and time-series correlation model to calculate the activity concentration and obtain the residual activity concentration of the nuclide. A pollution diffusion model is constructed, and the model is geomorphologically corrected based on topographic data to obtain a pollution concentration field distribution model. The dry deposition removal coefficient, wet deposition removal coefficient, and residual radionuclide activity concentration were input into the pollution concentration field distribution model to simulate the diffusion and obtain the pollution diffusion simulation results.

2. The pollution diffusion simulation method integrating dry and wet deposition and radioactive decay according to claim 1, characterized in that: The three-dimensional meteorological field data includes wind speed, wind direction, atmospheric stability, precipitation, precipitation type, and boundary layer height; Media characteristic data include land cover type, vegetation canopy thickness, soil porosity, and water physicochemical properties; Radioactive contaminant source term data includes nuclide type, initial activity concentration of the nuclide, and release rate; Topographic data includes topographic elevation, surface morphology, distribution of obstructions, and surface roughness.

3. The pollution diffusion simulation method integrating dry and wet deposition and radioactive decay according to claim 2, characterized in that, The calculation process for the dry sediment removal coefficient includes: The atmospheric diffusion coefficient of radioactive pollutants is calculated based on wind speed, wind direction and boundary layer height. The soil porosity correction factor corresponding to soil porosity is obtained. The aerodynamic drag corresponding to atmospheric stability is obtained. The vegetation canopy drag corresponding to vegetation canopy thickness is obtained. The surface drag corresponding to surface cover type is obtained. The dry sediment removal coefficient is calculated based on the atmospheric diffusion coefficient of radioactive pollutants, soil porosity correction factor, aerodynamic drag, vegetation canopy drag, and surface drag.

4. The pollution diffusion simulation method integrating dry and wet deposition and radioactive decay according to claim 2, characterized in that, The calculation process for the wet sediment removal coefficient includes: Obtain the scour coefficient corresponding to the precipitation type, obtain the precipitation intensity correction index corresponding to the precipitation amount, obtain the water body pH and organic matter content corresponding to the water body physicochemical properties, and calculate the water body physicochemical property correction factor based on the water body pH and organic matter content. The wet sediment removal coefficient is calculated based on the scour coefficient, precipitation, precipitation intensity correction index, and water body physicochemical property correction factor.

5. The pollution diffusion simulation method integrating dry and wet deposition and radioactive decay according to claim 2, characterized in that, The construction of the radioactive decay and time-series correlation model involves inputting radioactive contaminant source term data into the model to calculate activity concentration and obtain the residual activity concentration of the nuclide, including: A radioactive decay and time-series correlation model was constructed, which includes a nuclide decay parameter library unit and a decay duration control unit. The decay constant matching the nuclide type is called through the nuclide decay parameter library unit; The decay duration is set to match the release rate via the decay duration control unit; The remaining activity concentration of a nuclide is calculated using the decay formula based on its initial activity concentration, decay constant, and decay duration.

6. The pollution diffusion simulation method integrating dry and wet deposition and radioactive decay according to claim 2, characterized in that, The step of performing geomorphological correction on the pollution diffusion model based on topographic data to obtain a pollution concentration field distribution model includes: Based on the terrain elevation, the elevation correction coefficient is calculated using a terrain adaptation algorithm. Obtain the landform weight coefficient library, and call the corresponding shading correction factor in the landform weight coefficient library according to the landform type and the distribution of shading objects; The roughness attenuation coefficient is calculated based on surface roughness, wind speed, and boundary layer height. The pollution diffusion model is iteratively corrected by using elevation correction coefficient, shading correction factor and roughness attenuation coefficient to obtain pollution concentration field distribution model.

7. The pollution diffusion simulation method integrating dry and wet deposition and radioactive decay according to claim 2, characterized in that, The dry deposition removal coefficient, wet deposition removal coefficient, and residual radionuclide activity concentration are input into the pollution concentration field distribution model for diffusion simulation to obtain pollution diffusion simulation results, including: The atmospheric environment sensitivity level of the target area is obtained. Based on the atmospheric environment sensitivity level, weighting coefficients are configured for dry sediment removal coefficient, wet sediment removal coefficient and residual radionuclide activity concentration, respectively, to obtain the optimized dry sediment removal coefficient, optimized wet sediment removal coefficient and optimized residual radionuclide activity concentration. Spatial gridding is performed on the pollution concentration field distribution model to obtain an optimized pollution concentration field distribution model. The optimized dry deposition removal coefficient, optimized wet deposition removal coefficient, and optimized residual radionuclide activity concentration are input into the optimized pollution concentration field distribution model to construct the pollution diffusion equation. The pollution diffusion equation is solved using the finite volume algorithm to obtain the spatiotemporal distribution data of pollutant concentration, which is then used as the result of the pollution diffusion simulation.

8. A pollution diffusion simulation apparatus integrating dry and wet deposition with radioactive decay, used to implement the pollution diffusion simulation method integrating dry and wet deposition with radioactive decay as described in any one of claims 1 to 7, characterized in that, The device includes: The data acquisition module is used to acquire three-dimensional meteorological field data, medium characteristic data, radioactive pollutant source term data and topographic data of the target area through the integrated acquisition device; The sediment removal coefficient acquisition module is used to classify and calculate the removal coefficient based on three-dimensional meteorological field data and medium characteristic data, and obtain the dry sediment removal coefficient and wet sediment removal coefficient. The module for obtaining the residual activity concentration of nuclides is used to construct a radioactive decay and time-series correlation model. Radioactive pollutant source term data is input into the radioactive decay and time-series correlation model to calculate the activity concentration and obtain the residual activity concentration of nuclides. The pollution concentration field distribution model construction module is used to build a pollution diffusion model. It performs geomorphological correction on the pollution diffusion model based on topographic data to obtain the pollution concentration field distribution model. The pollution diffusion simulation result acquisition module is used to input the dry deposition removal coefficient, wet deposition removal coefficient and residual activity concentration of nuclides into the pollution concentration field distribution model to perform diffusion simulation and obtain pollution diffusion simulation results.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the pollution diffusion simulation method that integrates dry and wet deposition and radioactive decay as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the pollution diffusion simulation method that integrates dry and wet deposition and radioactive decay as described in any one of claims 1 to 7.

Images