Medium-and-small-scale nuclear accident source intensity estimation method for coupling numerical forecasting product

Abstract

The invention discloses a medium and small-scale nuclear accident source intensity estimation method for a coupling numerical forecasting product. The method comprises the following steps: acquiring radiation monitoring data and monitoring point position information in a simulation area; identifying the type of an available numerical weather forecast product, and decoding the weather data corresponding to the simulation area to obtain a decoding file; obtaining a wind field data file with encrypted temporal-spatial resolution, and generating a wind field file consistent with the radiation monitoring frequency by diagnosing a wind field mode; calling a particle diffusion mode according to the wind field file to calculate each nuclide concentration field, and outputting a concentration field file consistent with the radiation monitoring frequency; according to the position information of the monitoring points, diffusion factor time series data corresponding to the monitoring points are extracted, and an inversion matrix in which radiation monitoring data and diffusion factors are in one-to-one correspondence is constructed; and inputting the inversion matrix into a data assimilation algorithm, and carrying out iterative inversion by adopting unscented Kalman filtering to realize source intensity estimation.

Description

Technical Field

[0001] This invention relates to the field of nuclear safety, specifically to a method for estimating the source strength of small- and medium-scale nuclear accidents coupled with numerical weather prediction products. Background Technology

[0002] Existing real-time online source term inversion methods for nuclear accidents rely heavily on a single meteorological data source, making the models highly dependent on meteorological observation stations in the computational area. This dependence manifests in two ways: First, model startup requires the presence of both tower-level and surface meteorological observation stations in the simulation area to ensure the representativeness of meteorological data on real-time meteorological conditions in the simulated area; inaccurate data directly affects the accuracy of source term inversion. Second, due to a lack of coupling capabilities with multiple numerical weather prediction products, existing methods struggle to effectively integrate meteorological data from different sources. When data transmission channels fail, the model loses its inversion calculation capabilities due to a lack of startup data. This limitation makes it difficult for the model to provide reliable source term estimates in the face of sudden nuclear accidents, thus impacting the effectiveness of emergency response and decision support. Summary of the Invention

[0003] To achieve the above and other related objectives, this invention discloses a method for estimating the source intensity of small-scale nuclear accidents coupled with numerical weather prediction products, comprising: Obtain radiation monitoring data and monitoring point location information within the simulated area, perform validity checks on the radiation monitoring data, remove invalid data, and complete unit conversion; Identify the types of available numerical weather forecast products and decode the meteorological data corresponding to the simulated area to obtain a decoded file; Based on the decoded file, a spatiotemporally encrypted wind field data file is obtained through a numerical weather prediction model, and a wind field file consistent with the radiation monitoring frequency is generated through a diagnostic wind field model by combining the simulated regional topographic elevation file. Based on the wind field file, the particle diffusion model or commercial atmospheric diffusion numerical simulation software is called to calculate the concentration field of each nuclide and output a concentration field file consistent with the radiation monitoring frequency. Based on the location information of the monitoring points, the time series data of the diffusion factors corresponding to the monitoring points are extracted from the concentration field file, and an inversion matrix in which the radiation monitoring data and the diffusion factors correspond one-to-one is constructed. The inversion matrix is ​​input into the data assimilation algorithm, and iterative inversion is performed using unscented Kalman filtering. The time series of the release rate at the accident release point is output, and the calculation proceeds to the next time step based on the fact that the adjacent step size error within the evaluation time meets the preset convergence condition, so as to realize the time-continuous source strength estimation.

[0004] Preferably, the validity check of the radiation monitoring data includes: checking the data file to identify source term information and radiation monitoring data information; marking the corresponding monitoring point data as invalid data when the monitoring instrument does not return data or the monitoring value is background data; checking the monitoring point location data and removing monitoring point data that exceeds the calculation area, while outputting a reminder file.

[0005] Preferably, the unit conversion includes converting the gamma-ray air absorbed dose rate data into radionuclide activity concentration data, satisfying the following relationship: in, This represents the activity concentration of a certain nuclide in the air. The air absorbed dose rate of gamma rays; This is the conversion factor from dose rate to activity concentration.

[0006] Preferably, after identifying the type of numerical weather forecast product, the preprocessing decoding module PreData is called to decode the range of the simulated area to obtain a decoded file for subsequent processing of numerical weather forecast models and diagnostic wind field models.

[0007] Preferably, the numerical weather prediction mode is the WRF mode, which uses the decoded file to obtain a spatiotemporally resolved encrypted wind field data file.

[0008] Preferably, the diagnostic wind field mode is the CALMET mode, which calculates a simulated regional wind field file with the same frequency as the radiation monitoring data based on the simulated regional topographic elevation file and the radiation monitoring frequency.

[0009] Preferably, the particle diffusion model calculates and simulates the diffusion process of each nuclide in the region, outputs a concentration field file with the same frequency as the radiation monitoring data, and extracts the diffusion factor time series data of the corresponding monitoring point based on the grid position of the radiation monitoring instrument in the concentration field data.

[0010] Preferably, the unscented Kalman filtering data assimilation and inversion includes establishing state equations and observation equations, and performing inversion calculations based on the monitoring data array to estimate the intensity of the accident release source. The state equations and observation equations satisfy: in, For a specific moment; Let be the state variable at time k+1. Let k be the state variable at time k; The observation at time k; It is a state equation function; The observed equation function; This is process noise; To observe noise.

[0011] Preferably, the convergence condition includes: calculating the adjacent step size error within the evaluation time. The calculation proceeds to the next time step when the following condition is met; otherwise, it continues iterating until the convergence condition is met: The threshold satisfies: in, To evaluate the adjacent step size error within a given time period; Convergence threshold Secondly, this invention discloses a system for estimating the source intensity of small-scale nuclear accidents coupled with numerical weather prediction products, comprising: The monitoring data preprocessing module is used to acquire radiation monitoring data and monitoring point location information within the simulated area, and to perform validity checks, remove invalid data, and convert units on the radiation monitoring data. The meteorological product identification and decoding module is used to identify the type of numerical weather forecast product and call PreData to decode the corresponding meteorological data of the simulated area to obtain the decoded file; The wind field generation module is used to call the WRF mode to generate spatiotemporal resolution encrypted wind field data files, and to call the CALMET mode to combine the simulated area topographic elevation file to generate a wind field file consistent with the radiation monitoring frequency. The diffusion calculation module is used to call particle diffusion models or commercial atmospheric diffusion numerical simulation software to generate a concentration field file consistent with the radiation monitoring frequency. The factor extraction and matrix construction module is used to extract the diffusion factor time series from the concentration field file based on the monitoring point location information and construct the inversion matrix; The assimilation inversion and convergence determination module is used to iteratively invert the inversion matrix using unscented Kalman filtering, output the release rate time series of the accident release point, and proceed to the next time step calculation when the adjacent step size error meets the preset threshold.

[0012] By employing the above technical solution, the radiation monitoring data undergoes validity checks and invalid data removal / marking, and a unified dimensional conversion from dose rate to activity concentration is completed. Combined with the automatic identification and decoding of logarithmic meteorological forecast products, a spatiotemporally encrypted wind field is obtained using WRF, and a diagnostic wind field consistent with the monitoring frequency is generated via CALMET. This ensures precise temporal and spatial alignment between diffusion calculations and monitoring data. Based on this, a concentration field is obtained through particle diffusion calculations, and diffusion factors at monitoring points are extracted to construct an inversion matrix. Unscented Kalman filtering is used for data assimilation iteration, with the adjacent step size error threshold serving as the convergence criterion, achieving continuous temporal inversion of the source strength at the accident release point. Therefore, this invention can improve the stability and robustness of source strength estimation under conditions of complex terrain and rapidly changing weather at small and medium scales, reduce inversion bias caused by missing monitoring data, noise, or insufficient wind field resolution, enhance the temporal continuity and interpretability of estimation results, and facilitate the rapid formation of source term parameters that can be used for diffusion assessment and decision support in emergency response. Attached Figure Description

[0013] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 This is a flowchart of an embodiment of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Reference Figure 1 This invention provides a method for estimating the source intensity of small-scale nuclear accidents coupled with numerical weather prediction products, including: Obtain radiation monitoring data and monitoring point location information within the simulated area, perform validity checks on the radiation monitoring data, remove invalid data, and complete unit conversion; Identify the types of available numerical weather forecast products and decode the meteorological data corresponding to the simulated area to obtain a decoded file; Based on the decoded file, a spatiotemporally encrypted wind field data file is obtained through a numerical weather prediction model, and a wind field file consistent with the radiation monitoring frequency is generated through a diagnostic wind field model by combining the simulated regional topographic elevation file. Based on the wind field file, the particle diffusion model or commercial atmospheric diffusion numerical simulation software is called to calculate the concentration field of each nuclide and output a concentration field file consistent with the radiation monitoring frequency. Based on the location information of the monitoring points, the time series data of the diffusion factors corresponding to the monitoring points are extracted from the concentration field file, and an inversion matrix in which the radiation monitoring data and the diffusion factors correspond one-to-one is constructed. The inversion matrix is ​​input into the data assimilation algorithm, and iterative inversion is performed using unscented Kalman filtering. The time series of the release rate at the accident release point is output, and the calculation proceeds to the next time step based on the fact that the adjacent step size error within the evaluation time meets the preset convergence condition, so as to realize the time-continuous source strength estimation.

[0016] Preferably, the validity check of the radiation monitoring data includes: checking the data file to identify source term information and radiation monitoring data information; when the monitoring instrument does not return data or the monitoring value is background data, marking the corresponding monitoring point data as -999 and determining it as invalid data; checking the monitoring point location data and removing monitoring point data that exceeds the calculation area, while outputting a reminder file to remind the user to check or modify.

[0017] Preferably, the unit conversion includes converting the gamma-ray air absorbed dose rate data into radionuclide activity concentration data, satisfying the following relationship: in, This represents the activity concentration of a certain nuclide in the air. The air absorbed dose rate of gamma rays; This is the conversion factor from dose rate to activity concentration.

[0018] Preferably, after identifying the type of numerical weather forecast product, the preprocessing decoding module PreData is invoked to decode the range of the simulated area. The preprocessing decoding module PreData is numerical weather product decoding software used to obtain the decoding file for subsequent processing of numerical weather forecast models and diagnostic wind field models.

[0019] Preferably, the numerical weather prediction mode is the WRF mode. The WRF mode uses the decoded file to obtain a spatiotemporally resolved encrypted wind field data file. The WRF mode is wind field calculation software, and the wind field data includes latitude and longitude, wind direction, wind speed, temperature, humidity, terrain altitude, and rainfall data.

[0020] Preferably, the diagnostic wind field mode is the CALMET mode. The CALMET mode calculates a simulated area wind field file with the same frequency as the radiation monitoring data based on the simulated area topographic elevation file and the radiation monitoring frequency. The CALMET mode is a wind field processing software, and the simulated area topographic elevation file is a publicly available topographic file downloaded from the network, which is converted into a grid file according to actual requirements.

[0021] Preferably, the particle diffusion mode calculates the diffusion process of each nuclide in the simulation area and outputs a concentration field file with the same frequency as the radiation monitoring data; and extracts the diffusion factor time series data corresponding to the monitoring point position according to the grid point position of the radiation monitoring instrument in the concentration field data. In the embodiment of the present invention, the particle diffusion mode performs diffusion calculation at the same time interval as the monitoring frequency and extracts the dose data of the grid point corresponding to the monitoring point position in the calculated concentration field file.

[0022] Preferably, the unscented Kalman filtering data assimilation and inversion includes establishing state equations and observation equations, and performing inversion calculations based on the monitoring data array to estimate the intensity of the accident release source. The state equations and observation equations satisfy: (1) (2) Equation (1) is the time update equation or state equation, and equation (2) is the observation equation.

[0023] k represents time point; X represents the state variable, which in the inversion is an n-dimensional covariance matrix of monitoring data, with a mean of _____. , with variance P; W(k) is Gaussian white noise with a covariance matrix; V(k) is Gaussian white noise with a covariance matrix; f is the state equation function; h is the observation equation function; k-time: The state update equation at time k is... Time update equation at time k+1: Time update covariance: Predicted observations: i=1,2,…,2n+1.

[0024] System prediction mean and covariance: Kalman gain matrix: System state update and covariance update: Preferably, the convergence condition includes: calculating the adjacent step size error within the evaluation time. The calculation proceeds to the next time step when the following condition is met; otherwise, it continues iterating until the convergence condition is met: The threshold satisfies: in, To evaluate the adjacent step size error within a given time period; Convergence threshold Secondly, this invention discloses a system for estimating the source intensity of small-scale nuclear accidents coupled with numerical weather prediction products, comprising: The monitoring data preprocessing module is used to acquire radiation monitoring data and monitoring point location information within the simulated area, and to perform validity checks, remove invalid data, and convert units on the radiation monitoring data. The meteorological product identification and decoding module is used to identify the type of numerical weather forecast product and call PreData to decode the corresponding meteorological data of the simulated area to obtain the decoded file; The wind field generation module is used to call the WRF mode to generate spatiotemporal resolution encrypted wind field data files, and to call the CALMET mode to combine the simulated area topographic elevation file to generate a wind field file consistent with the radiation monitoring frequency. The diffusion calculation module is used to call particle diffusion models or commercial atmospheric diffusion numerical simulation software to generate a concentration field file consistent with the radiation monitoring frequency. The factor extraction and matrix construction module is used to extract the diffusion factor time series from the concentration field based on the grid position of the monitoring points and construct the inversion matrix; The assimilation inversion and convergence determination module is used to iteratively invert the inversion matrix using unscented Kalman filtering, output the release rate time series of the accident release point, and proceed to the next time step calculation when the adjacent step size error meets the preset threshold.

[0025] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0026] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0027] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0028] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating the source intensity of small-scale nuclear accidents coupled with numerical weather prediction products, characterized in that, include: Obtain radiation monitoring data and monitoring point location information within the simulated area, perform validity checks on the radiation monitoring data, remove invalid data, and complete unit conversion; Identify the types of available numerical weather forecast products and decode the meteorological data corresponding to the simulated area to obtain a decoded file; Based on the decoded file, a spatiotemporally encrypted wind field data file is obtained through a numerical weather prediction model, and a wind field file consistent with the radiation monitoring frequency is generated through a diagnostic wind field model by combining the simulated regional topographic elevation file. Based on the wind field file, the particle diffusion model or commercial atmospheric diffusion numerical simulation software is called to calculate the concentration field of each nuclide and output a concentration field file consistent with the radiation monitoring frequency. Based on the location information of the monitoring points, the time series data of the diffusion factors corresponding to the monitoring points are extracted from the concentration field file, and an inversion matrix in which the radiation monitoring data and the diffusion factors correspond one-to-one is constructed. The inversion matrix is ​​input into the data assimilation algorithm, and iterative inversion is performed using unscented Kalman filtering. The time series of the release rate at the accident release point is output, and the calculation proceeds to the next time step based on the fact that the adjacent step size error within the evaluation time meets the preset convergence condition, so as to realize the time-continuous source strength estimation.

2. The method according to claim 1, characterized in that, The validity check of radiation monitoring data includes: checking the data file to identify source term information and radiation monitoring data information; marking the corresponding monitoring point data as invalid data when the monitoring instrument does not return data or the monitoring value is background data; checking the monitoring point location data and removing monitoring point data that are outside the calculation area, while outputting a reminder file.

3. The method according to claim 1, characterized in that, The unit conversion includes converting gamma-ray air absorbed dose rate data into radionuclide activity concentration data, satisfying the following relationship: in, This represents the activity concentration of a certain nuclide in air. The air absorbed dose rate of gamma rays; This is the conversion factor from dose rate to activity concentration.

4. The method according to claim 1, characterized in that, After identifying the type of numerical weather forecast product, the PreData preprocessing decoding module is called to decode the range of the simulated area to obtain a decoded file for subsequent processing of numerical weather forecast models and diagnostic wind field models.

5. The method according to claim 1, characterized in that, The numerical weather prediction model is the WRF model, which uses the decoded file to obtain a spatiotemporally resolved encrypted wind field data file.

6. The method according to claim 1, characterized in that, The diagnostic wind field mode is the CALMET mode, which calculates a simulated regional wind field file with the same frequency as the radiation monitoring data based on the simulated regional topographic elevation file and the radiation monitoring frequency.

7. The method according to claim 1, characterized in that, The particle diffusion model calculates and simulates the diffusion process of each nuclide in the simulated region, outputs a concentration field file with the same frequency as the radiation monitoring data, and extracts the diffusion factor time series data of the corresponding monitoring point based on the grid position of the radiation monitoring instrument in the concentration field data.

8. The method according to claim 1, characterized in that, The unscented Kalman filter data assimilation and inversion includes establishing state equations and observation equations, and performing inversion calculations based on the monitoring data array to estimate the intensity of the accident release source. The state equations and observation equations satisfy the following: in, For a specific moment; Let be the state variable at time k+1. Let k be the state variable at time k; The observation at time k; It is a state equation function; The observed equation function; This is process noise; To observe noise.

9. The method according to claim 1, characterized in that, The convergence condition includes: calculating the adjacent step size error within the evaluation time. The calculation proceeds to the next time step when the following condition is met; otherwise, it continues iterating until the convergence condition is met: The threshold satisfies: in, To evaluate the adjacent step size error within a given time period; This is the convergence threshold.

10. A system for estimating the source intensity of small-scale nuclear accidents coupled with numerical weather prediction products, characterized in that, include: The monitoring data preprocessing module is used to acquire radiation monitoring data and monitoring point location information within the simulated area, and to perform validity checks, remove invalid data, and convert units on the radiation monitoring data. The meteorological product identification and decoding module is used to identify the type of numerical weather forecast product and call PreData to decode the corresponding meteorological data of the simulated area to obtain the decoded file; The wind field generation module is used to call the WRF mode to generate spatiotemporal resolution encrypted wind field data files, and to call the CALMET mode to combine the simulated area topographic elevation file to generate a wind field file consistent with the radiation monitoring frequency. The diffusion calculation module is used to call particle diffusion models or commercial atmospheric diffusion numerical simulation software to generate a concentration field file consistent with the radiation monitoring frequency. The factor extraction and matrix construction module is used to extract the diffusion factor time series from the concentration field file based on the monitoring point location information and construct the inversion matrix; The assimilation inversion and convergence determination module is used to iteratively invert the inversion matrix using unscented Kalman filtering, output the release rate time series of the accident release point, and proceed to the next time step calculation when the adjacent step size error meets the preset threshold.

Images