Nuclear accident environment gamma radiation field reconstruction method based on same variable assimilation

Abstract

The invention relates to a nuclear accident environment gamma radiation field reconstruction method based on same variable assimilation, and the method comprises the steps: constructing an initial gamma dose rate field numerical calculation model, carrying out the grid scale division of a three-dimensional space region, and determining a gamma dose rate obtained through calculation at a grid point as a background state variable; acquiring real-time environment gamma dose rate data of a plurality of observation station positions, and constructing an observation vector; based on the background state variable and the observation vector, constructing a mapping relation between a model space and an observation space, and determining an observation operator; a three-dimensional variational assimilation method is adopted, target function minimization is used as an optimization target, assimilation correction is carried out on the basis of background state variables, and assimilation state variable solutions are obtained. By constructing the dose rate field model, the gamma dose rate is directly used as an assimilation variable, subsequent parameter conversion is not needed, and the purposes of assimilating the gamma dose rate, directly correcting the gamma dose rate, obtaining a theoretical optimal solution of the gamma dose rate and improving the accuracy of gamma radiation field construction are achieved.

Description

Technical Field

[0001] This invention relates to the field of nuclear accident consequence assessment and emergency decision-making technology, and in particular to a method for reconstructing the γ-radiation field of a nuclear accident environment based on isovariate assimilation. Background Technology

[0002] In the early stages of a nuclear accident, it is crucial to rapidly obtain information such as the concentration of radioactive materials in the air and the gamma external radiation dose rate. With the continuous expansion of nuclear energy utilization, nuclear emergency response plays an increasingly important role in ensuring public safety. After a nuclear accident, accurately and quickly determining the spatial distribution of radioactive materials in the environment is key to emergency response and decision support. On the one hand, early-stage environmental radiation monitoring methods rely on on-site monitoring equipment, obtaining radiation field information through spatial interpolation of dose rate data from monitoring points. However, this method is limited by the number and distribution of monitoring points and is insufficient to describe complex three-dimensional spatial variations. In recent years, numerical atmospheric diffusion models have been introduced into the radiation field reconstruction process. These models simulate the transport process of pollutants using atmospheric diffusion mechanisms, and then combine this with actual monitoring data for inversion analysis, gradually becoming the mainstream method. Data assimilation technology, as an important means of fusing models and observational data, has achieved significant results in weather forecasting and environmental science, and is also gradually being applied to dose rate field estimation in nuclear emergencies.

[0003] In nuclear accident consequence assessment systems, dose rate is a direct reflection of actual observation data. However, many related technologies use other state variables related to dose rate as assimilation parameters to convert them into dose rate. However, errors may occur during the conversion process, leading to reduced accuracy of the results.

[0004] The above problems urgently need to be addressed. Summary of the Invention

[0005] This invention discloses a method for reconstructing the gamma radiation field in a nuclear accident environment based on isovariate assimilation, aiming to solve the technical problems existing in the prior art.

[0006] This invention employs the following technical solution, comprising: constructing an initial gamma dose rate field numerical calculation model; dividing the three-dimensional spatial region into grid scales; determining the gamma dose rate calculated at each grid point as a background state variable, wherein the background state variable is used to indicate the initial gamma dose rate spatial distribution field obtained under different computational grids; and the initial gamma dose rate field numerical calculation model is a three-dimensional spatial model; acquiring real-time environmental gamma dose rate data at multiple observation station locations and constructing observation vectors; based on the background state variable and the observation vectors, constructing a mapping relationship between the model space and the observation space, and determining the observation operator, wherein the observation operator... The observation operator is used to project the background state variables in the model space onto the locations of the observation stations. The model space indicates the spatial distribution of the initial gamma dose rate field model after being divided based on different grid scales. The observation space indicates the spatial distribution of the locations of the multiple observation stations. Based on the observation operator, a three-dimensional variational assimilation method is used, with the minimum objective function as the optimization objective. Assimilation correction is performed on the background state variables to obtain the assimilated state variable solution. The minimum value of the objective function is solved iteratively using the gradient descent method. The assimilated state variable solution indicates the final optimized spatial distribution field of gamma dose rate obtained under different computational grids.

[0007] Optionally, the step of constructing a mapping relationship between the model space and the observation space based on the background state variables and the observation vector, and determining the observation operator, includes: when the grid division in the model space coincides with the location of the observation station, directly constructing a mapping relationship between the model space and the observation space; when the grid division in the model space does not coincide with the location of the observation station, using spatial interpolation to calculate an approximate dose rate value at the location of the observation station for the background state variables, correcting the background state variables based on the approximate dose rate value to obtain new background state variables, and then performing the mapping between the model space and the observation space.

[0008] Optionally, when the mesh division in the model space does not coincide with the location of the observation station, spatial interpolation is used for the background state variables to calculate the approximate dose rate value at the location of the observation station, including: determining the coordinates of any observation station location; and, based on the mesh division and with the coordinates of the observation station location as the center, determining the location coordinates of the observation station location in the lower left corner. Bottom right Top left and the upper right The offset corner coordinates are used to calculate the relative position of the observation station within the grid. The grid is the grid where the position coordinates of the observation station are located after grid division, and the relative position is used to indicate the movable position of the observation station within the grid. The dose rate at the offset corner coordinates is weighted and summed based on the relative position as a weighting value to obtain the approximate dose rate value.

[0009] Optionally, calculating the relative position of the observation station within the grid based on the offset corner coordinates includes: calculating the x-coordinate value of the relative position as follows: in, This represents the x-coordinate value in the relative position. This refers to the x-coordinate value in the coordinate system of the observation station. This represents the x-coordinate value on the left side of the grid edge. This represents the x-coordinate value within the right-hand grid edge. The y-coordinate value in the relative position is calculated as follows: in, This represents the y-coordinate value in the relative position. This represents the y-coordinate value in the coordinate system of the observation station. Here are the y-coordinates of the edge of the grid being measured. This represents the y-coordinate value within the lower grid edge.

[0010] Optionally, the step of weighting the dose rates at the offset corner coordinates based on the relative position to obtain the approximate dose rate value includes: the approximate dose rate value is calculated as follows: in, This is an approximate dose rate value. The dose rate value is located at the bottom left corner. The dose rate value is located in the lower right corner. The dose rate value is located at the top left corner. This is the dose rate value located in the upper right corner.

[0011] Optionally, the step of using a three-dimensional variational assimilation method based on the observation operator, with the objective function minimization as the optimization objective, and performing assimilation correction on the background state variables to obtain the assimilated state variable solution, includes: using a three-dimensional variational assimilation method, setting an assimilated state variable with the same spatial dimension as the background state variable, using the observation vector and the background state variable as constraints, and using the assimilated state variable as the variable to determine the objective function; using the gradient descent method to determine the gradient formula corresponding to the objective function; and performing gradient iteration on the assimilated state variable based on the gradient formula to obtain the assimilated state variable solution.

[0012] Optionally, the three-dimensional variational assimilation method is adopted, using the observation vector and background state variables as constraints, and the assimilated state variables as variables, to determine the objective function, including: the objective function is as follows: in, Let be the objective function. Assimilate state variables, Let be the background state variable values, B be the background error covariance matrix, o be the observation vector, and R be the observation error covariance matrix. For observation operators.

[0013] Optionally, determining the gradient formula corresponding to the objective function using gradient descent includes: in, This is used to decrease the gradient of the objective function with solutions containing state variables.

[0014] Optionally, the step of performing gradient iteration on the assimilated state variables based on the gradient formula to obtain the solution of the assimilated state variables includes: the iteration formula is as follows: Where k is the number of iterations. The solution for the assimilated state variables in the k-th iteration. Let α be the assimilated state variable solution for the (k+1)th iteration, and α be the iteration step size.

[0015] The technical solution adopted in this invention can achieve at least one of the following beneficial effects: In this embodiment of the invention, by constructing an initial gamma dose rate field model, the gamma dose rate is used as a background state variable and directly as an assimilation parameter without subsequent parameter transformation. This achieves gamma dose rate assimilation, directly corrects the gamma dose rate to be close to the actual observation data, and improves the accuracy of gamma radiation field construction. This achieves the technical effect of reducing the error between parameter conversion and dose rate and improving the accuracy of gamma radiation field construction. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below, forming part of the present invention. The illustrative embodiments of the present invention and their descriptions explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings: Figure 1 This is a flowchart of a method for reconstructing a nuclear accident environment gamma radiation field based on isovariate assimilation in Embodiment 1 of the present invention; Figure 2 This is a dose rate correction result diagram in a nuclear accident environment gamma radiation field reconstruction method based on isovariate assimilation in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of a nuclear accident environment gamma radiation field reconstruction system based on isovariate assimilation in Embodiment 2 of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. In the description of this invention, it should be noted that the term "or" is generally used to include the meaning of "and / or," unless otherwise expressly indicated.

[0018] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or a magnetic connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. Furthermore, in the description of this application, the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. In the description of this invention, "a plurality of" means at least two, such as two, three, or more, unless otherwise explicitly specified.

[0019] Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0020] To address the problems existing in related technologies, this application provides a method for reconstructing the gamma radiation field of a nuclear accident environment based on isovariate assimilation.

[0021] Example 1 This embodiment provides a method for reconstructing the gamma radiation field of a nuclear accident environment based on isovariate assimilation, such as Figure 1 As shown, Figure 1 This is a flowchart of a method for reconstructing a nuclear accident environment gamma radiation field based on isovariate assimilation, according to Embodiment 1 of the present invention. The method includes: Step S102: Construct an initial gamma dose rate field numerical calculation model, divide the three-dimensional spatial region into grid scales, and determine the gamma dose rate calculated at the grid points as a background state variable. The background state variable is used to indicate the initial gamma dose rate spatial distribution field obtained under different calculation grids. The initial gamma dose rate field numerical calculation model is a three-dimensional spatial model. Optionally, an atmospheric diffusion model (such as Gaussian plume, Gaussian cloud, Lagrange particle model, or Eulerian model) can be used to simulate the spatiotemporal diffusion of the nuclear accident release source term, obtaining an initial gamma dose rate field model. The initial γ dose rate spatial distribution field, referred to as the background field, is obtained under a specified region and grid scale. (That is, the background state variable): in, This represents the gamma dose rate estimate of the atmospheric diffusion model at the i-th three-dimensional spatial grid, where n represents the dimension of the state variables (i.e., the total number of spatial grid points). The background field reflects the model's predicted output without observational corrections and forms the basis for assimilation corrections.

[0022] Step S104: Obtain real-time environmental gamma dose rate data at multiple observation station locations and construct observation vectors; Optionally, a gamma radiation monitoring network deployed within the accident's impact area can be used to acquire real-time environmental gamma dose rate data from multiple observation stations, which can then be used to construct an observation vector o: in, This represents the measured dose rate value at the j-th observation station, where m is the total number of observation points. Before processing, all observation data must undergo standardized unit conversion, time synchronization, and quality control (such as outlier removal and instrument error calibration) to ensure data consistency and reliability.

[0023] Step S106: Based on the background state variables and observation vectors, construct the mapping relationship between the model space and the observation space, and determine the observation operator. The observation operator is used to project the background state variables in the model space onto the location of the observation station. The model space is used to indicate the spatial distribution of the initial gamma dose rate field model after being divided according to different grid scales. The observation space is used to indicate the spatial distribution of multiple observation station locations. In some preferred embodiments, a mapping relationship between the model space and the observation space is constructed based on the background state variables and the observation vector, and the observation operator is determined. This includes: when the grid division in the model space coincides with the location of the observation station, the mapping relationship between the model space and the observation space is directly constructed; when the grid division in the model space does not coincide with the location of the observation station, spatial interpolation is used on the background state variables to calculate the approximate dose rate value at the location of the observation station, the background state variables are corrected based on the approximate dose rate value to obtain new background state variables, and then the mapping between the model space and the observation space is performed.

[0024] Optionally, to establish a mapping relationship between the model space and the observation space, an observation operator H is introduced. This observation operator is responsible for projecting the state variables in the model grid space to the observation point locations, obtaining the observation prediction vector corresponding to the initial gamma dose rate field model. When the model grid and the observation point location do not coincide, spatial interpolation is required to approximate the dose rate value at the observation point. In this case, the observation operator H is a sparse matrix, and its structure depends on the positional distribution of the observation point in the model grid.

[0025] In some preferred embodiments, when the grid division in the model space does not coincide with the location of the observation station, spatial interpolation is used for the background state variables to calculate the approximate dose rate value at the location of the observation station. This includes: determining the coordinates of any observation station location; and, based on the grid division and with the observation station location coordinates as the center, determining the location of the observation station location coordinates in the lower left corner. Bottom right Top left and the upper right The offset corner coordinates are calculated; based on the offset corner coordinates, the relative position of the observation station within the grid is calculated, where the grid is the grid where the position coordinates of the observation station are located after grid division, and the relative position is used to indicate the movable position of the observation station within the grid; based on the relative position as a weighting value, the dose rate at the offset corner coordinates is weighted and summed to obtain an approximate dose rate value.

[0026] Optionally, bilinear interpolation will be used. Taking the two-dimensional case (considering only x, y as an example), assuming the observation point coordinates are (x, y), the interpolation requires the use of the four corner points around the observation point (lower left, middle right, and lower right). , ), bottom right ( , ), top left ( , ), top right ( , )).

[0027] First, calculate the relative position (scale coordinates) of the observation point within this unit: in, .

[0028] The interpolation D(x,y) at the observation point can be expressed as the dose rate weighted sum of the four corner point values: In some preferred embodiments, the relative position of the observation station within the grid is calculated based on the offset corner coordinates, including the calculation of the x-coordinate value in the relative position as follows: in, This represents the x-coordinate value in the relative position. This refers to the x-coordinate value in the coordinate system of the observation station. This represents the x-coordinate value on the left side of the grid edge. This represents the x-coordinate value within the right-hand grid edge. The y-coordinate value in the relative position is calculated as follows: in, This represents the y-coordinate value in the relative position. This represents the y-coordinate value in the coordinate system of the observation station. Here are the y-coordinates of the edge of the grid being measured. This represents the y-coordinate value within the lower grid edge.

[0029] In some preferred embodiments, the dose rates at the offset corner coordinates are weighted and summed based on relative position to obtain an approximate dose rate value, including: the approximate dose rate value is calculated as follows: in, This is an approximate dose rate value. The dose rate value is located at the bottom left corner. The dose rate value is located in the lower right corner. The dose rate value is located at the top left corner. This is the dose rate value located in the upper right corner.

[0030] Step S108: Based on the observation operator, a three-dimensional variational assimilation method is adopted, with the minimization of the objective function as the optimization objective. Assimilation corrections are performed on the background state variables to obtain the assimilated state variable solution. The minimum value of the objective function is solved iteratively using the gradient descent method. The assimilated state variable solution is used to indicate the final optimized spatial distribution field of gamma dose rate obtained under different computational grids. For example... Figure 2 As shown, Figure 2This is a dose rate correction result diagram in a nuclear accident environment γ radiation field reconstruction method based on isovariate assimilation in Embodiment 1 of the present invention.

[0031] In some preferred embodiments, based on the observation operator, a three-dimensional variational assimilation method is employed, with the goal of minimizing the objective function. Assimilation corrections are performed on the background state variables to obtain the assimilated state variable solution. This includes: using the three-dimensional variational assimilation method, setting assimilated state variables with the same spatial dimension as the background state variables; using the observation vector and the background state variables as constraints, and the assimilated state variables as variables, to determine the objective function; using the gradient descent method to determine the gradient formula corresponding to the objective function; and based on the gradient formula, performing gradient iteration on the assimilated state variables to obtain the assimilated state variable solution.

[0032] Optionally, to accurately reflect the uncertainty of the model's background field and its spatial correlation, a background error covariance matrix B is introduced and approximated using a Gaussian spatially decreasing correlation model: in, Let L represent the variance of the background field error, and L be the spatial correlation length scale. , Let be the three-dimensional spatial coordinates of the i-th and j-th grid points. This model assumes that the error correlation between grid points that are farther apart is weaker, reflecting the natural attenuation of the spatial structure, which helps to control the smoothness and physical consistency of the assimilated field in space.

[0033] In practical calculations, since B is a huge size (an n×n matrix), the computational complexity is often reduced by controlling the effective correlation range, localization methods, or principal component dimensionality reduction techniques (such as EOF expansion).

[0034] Optionally, considering the differences in sampling methods, equipment performance, and environmental conditions at different observation stations, the observation error covariance matrix R is set to a diagonal matrix, indicating that each observation error is independent of the others. in, This represents the measurement error variance of the j-th observation point. The error variance can be obtained through analysis of historical observation data, equipment calibration records, or empirical settings.

[0035] In some preferred embodiments, a three-dimensional variational assimilation method is employed, using the observation vector and background state variables as constraints, and the assimilated state variables as variables, to determine the objective function, which includes the following: in, Let be the objective function. Assimilate state variables, Let B be the background state variable, O be the observation vector, and R be the observation error covariance matrix. For the observation operator, a nonlinear observation operator is selected, where the nonlinear observation operator is the nonlinear matrix of the observation operator.

[0036] Optionally, a three-dimensional variational data assimilation (3D-Var) method can be used, with the core idea of ​​minimizing the objective function: Here, the first term represents the weighted penalty for the deviation between the analysis field and the background field, and the second term represents the weighted penalty for the deviation between the model simulation values ​​and the observed values. By optimizing this objective function, a set of optimal state variable solutions balancing the model's prior information and the observed data is obtained. (That is, the optimal assimilation state variable). Since H is a nonlinear function matrix calculated through spatial interpolation (that is, a nonlinear observation operator), the minimum point of the objective function J(a) is usually solved iteratively by the gradient descent method.

[0037] The goal of three-dimensional variational assimilation (3D-Var) is to minimize the objective function J(a) in the background field. Under the constraint of the observed data o, the optimal assimilation state variable solution is obtained. .

[0038] In some preferred embodiments, the gradient formula corresponding to the objective function is determined using the gradient descent method, including: in, This is used to decrease the gradient of the objective function with solutions containing state variables.

[0039] In some preferred embodiments, the assimilated state variables are solved iteratively based on the gradient formula to obtain the solution of the assimilated state variables, including the following iterative formula: Where k is the number of iterations. The solution for the assimilated state variables in the k-th iteration. Let α be the assimilated state variable solution for the (k+1)th iteration, and α be the iteration step size.

[0040] Optionally, a cost function can be set, where the cost function is a subset of the objective function. The change in cost function J( )right Calculating the gradient, we get: This formula is the core gradient formula for the three-dimensional variational assimilation problem, consisting of two parts: a background term and an observation term.

[0041] The optimal analytical field is solved iteratively using the gradient descent method.

[0042] Iteration formula: Substituting the gradient, we get: Where: α is the iteration step size. Let be the analysis field for the k-th iteration.

[0043] When the gradient is zero, that is With J(x)=0, the theoretical optimal solution can be obtained directly: therefore: Simplifying the above formula yields the standard analytical solution for three-dimensional variational assimilation, as follows: in, For the corrected assimilated state variable solution, K is the Kalman gain matrix, and o is the observation vector.

[0044] Optionally, the Kalman gain is a core weighting coefficient in the Kalman filter algorithm. The corrected assimilation state variable solution is the dose rate of the radiation field itself, which is selected as the assimilation state variable above. Therefore, the assimilation state variable solution is the dose rate field that approximates the background state variable to the actual measured value, and is defined as the analysis field.

[0045] Specifically, the Kalman gain matrix K represents the assimilation weights, and its calculation formula is as follows: The solution has the optimal estimation characteristic in the sense of minimum mean square error, and can adaptively adjust the relative contribution of the observation and background fields under different spatiotemporal observation sparsity and model error levels.

[0046] Optionally, the final analysis field will be... The data is converted into dose rate values ​​in three-dimensional physical space, generating a complete three-dimensional gamma radiation distribution map. This radiation field can be further used for downstream analysis, such as population dose assessment, refuge zone delineation, and emergency response path optimization. The assimilation process can be run periodically, such as hourly, to integrate the latest monitoring data in real time, dynamically update the gamma radiation field status, and achieve continuous tracking and refined assessment of the nuclear accident evolution process.

[0047] Through the above steps S102 to S108, dose rate assimilation is achieved, directly correcting the dose rate to be closer to the actual observation data, thereby improving the accuracy of gamma radiation field construction. This achieves the technical effect of reducing the error between parameter conversion and dose rate conversion, and improving the accuracy of gamma radiation field construction.

[0048] By directly using the γ dose rate field as the assimilation variable and introducing a three-dimensional variational data assimilation method to correct its spatial distribution, the accuracy of γ radiation field reconstruction in the nuclear accident consequence assessment system is rapidly improved. This method has low dependence on model structure, high computational efficiency, and can be applied to rapid response and assessment tasks in various nuclear emergency scenarios.

[0049] Example 2 This embodiment also provides a nuclear accident environment gamma radiation field reconstruction system based on isovariate assimilation. This system is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the terms "module" and "system" can refer to a combination of software and / or hardware that performs a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0050] According to embodiments of the present invention, a system embodiment for implementing the above-described method for reconstructing the gamma radiation field in a nuclear accident environment based on isovariate assimilation is also provided. Figure 3 This is a schematic diagram of a nuclear accident environment gamma radiation field reconstruction system based on isovariate assimilation in Embodiment 2 of the present invention, as shown below. Figure 3 As shown, the above system includes: a model construction module 201, an observation data acquisition module 202, a mapping module 203, and a correction module 204, wherein: Model module 201 is constructed to build a numerical calculation model of the initial gamma dose rate field. The three-dimensional spatial region is divided into grid scales, and the gamma dose rate calculated at the grid point is determined to be the background state variable. The background state variable is used to indicate the spatial distribution field of the initial gamma dose rate under different calculation grids. The numerical calculation model of the initial gamma dose rate field is a three-dimensional spatial model. The observation data acquisition module 202 is connected to the model building module 201 to acquire real-time environmental gamma dose rate data at multiple observation station locations and construct observation vectors. The mapping module 203 is connected to the observation data acquisition module 202. Based on the background state variables and the observation vector, it constructs the mapping relationship between the model space and the observation space and determines the observation operator. The observation operator is used to project the background state variables in the model space onto the location of the observation station. The model space is used to indicate the spatial distribution of the initial gamma dose rate field model after being divided according to different grid scales. The observation space is used to indicate the spatial distribution of multiple observation station locations. The correction module 204, connected to the mapping module 203, is based on the observation operator and adopts a three-dimensional variational assimilation method. With the minimum objective function as the optimization objective, it performs assimilation correction on the background state variables to obtain the assimilated state variable solution. The minimum value of the objective function is solved iteratively by the gradient descent method. The assimilated state variable solution is used to indicate the final optimized gamma dose rate spatial distribution field obtained under different computational grids.

[0051] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0052] It should be noted that the aforementioned model construction module 201, observation data acquisition module 202, mapping module 203, and correction module 204 correspond to steps S102 to S108 in the embodiments. The instances and application scenarios implemented by these modules and their corresponding steps are the same, but they are not limited to the content disclosed in the above embodiments. It should also be noted that these modules, as part of the system, can run on a computer terminal.

[0053] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0054] The aforementioned nuclear accident environment gamma radiation field reconstruction system based on isovariate assimilation may further include a processor and a memory. The aforementioned model construction module 201, observation data acquisition module 202, mapping module 203, correction module 204, etc., are all stored in the memory as program modules, and the processor executes the aforementioned program modules stored in the memory to realize the corresponding functions.

[0055] The processor contains a core that retrieves the corresponding program modules from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0056] According to an embodiment of this application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program runs, it controls the device where the non-volatile storage medium is located to execute any of the aforementioned nuclear accident environment gamma radiation field reconstruction methods based on isovariate assimilation.

[0057] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals, and the non-volatile storage medium includes stored programs.

[0058] Optionally, during program execution, the device containing the non-volatile storage medium may be controlled to perform the following functions: constructing a numerical calculation model of the initial gamma dose rate field; dividing the three-dimensional spatial region into grids; determining the gamma dose rate calculated at each grid point as a background state variable, where the background state variable indicates the spatial distribution field of the initial gamma dose rate obtained under different computational grids; the numerical calculation model of the initial gamma dose rate field is a three-dimensional spatial model; acquiring real-time environmental gamma dose rate data at multiple observation station locations and constructing observation vectors; based on the background state variable and the observation vectors, constructing a mapping relationship between the model space and the observation space, and determining... An observation operator is used to project background state variables in the model space onto the locations of observation stations. The model space indicates the spatial distribution of the initial gamma dose rate field model after being divided into different grid scales, and the observation space indicates the spatial distribution of multiple observation station locations. Based on the observation operator, a three-dimensional variational assimilation method is adopted, with the goal of minimizing the objective function. Assimilation correction is performed on the background state variables to obtain the assimilated state variable solution. The minimum value of the objective function is solved iteratively using the gradient descent method. The assimilated state variable solution indicates the final optimized spatial distribution field of gamma dose rate obtained under different computational grids.

[0059] According to an embodiment of this application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any of the above-described methods for reconstructing the gamma radiation field of a nuclear accident environment based on isovariate assimilation.

[0060] According to an embodiment of this application, an embodiment of a computer program product is also provided. Optionally, in this embodiment, the computer program product includes a computer program that, when executed by a processor, implements the steps of any of the above-described methods for reconstructing a nuclear accident environment gamma radiation field based on isovariate assimilation.

[0061] Optionally, when the aforementioned computer program product is executed on a data processing device, it is suitable to execute an initialization program with the following method steps: constructing an initial gamma dose rate field numerical calculation model, dividing the three-dimensional spatial region into grid scales, determining the gamma dose rate calculated at each grid point as a background state variable, wherein the background state variable is used to indicate the initial gamma dose rate spatial distribution field obtained under different computational grids, and the initial gamma dose rate field numerical calculation model is a three-dimensional spatial model; acquiring real-time environmental gamma dose rate data at multiple observation station locations and constructing observation vectors; based on the background state variable and the observation vectors, constructing a model space and observation space... The mapping relationship is established to determine the observation operator, which projects the background state variables in the model space onto the observation site locations. The model space indicates the spatial distribution of the initial gamma dose rate field model after being divided based on different grid scales, and the observation space indicates the spatial distribution of multiple observation site locations. Based on the observation operator, a three-dimensional variational assimilation method is adopted, with the minimization of the objective function as the optimization objective. Assimilation correction is performed on the background state variables to obtain the assimilated state variable solution. The minimum value of the objective function is solved iteratively using the gradient descent method. The assimilated state variable solution indicates the final optimized spatial distribution field of gamma dose rate obtained under different computational grids.

[0062] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: constructing an initial gamma dose rate field numerical calculation model; dividing the three-dimensional spatial region into grid scales; determining the gamma dose rate calculated at each grid point as a background state variable, wherein the background state variable indicates the spatial distribution field of the initial gamma dose rate obtained under different computational grids; and the initial gamma dose rate field numerical calculation model is a three-dimensional spatial model; acquiring real-time environmental gamma dose rate data at multiple observation station locations and constructing an observation vector; and constructing a model based on the background state variable and the observation vector. The mapping relationship between the model space and the observation space is established to determine the observation operator. The observation operator is used to project the background state variables in the model space onto the locations of the observation stations. The model space is used to indicate the spatial distribution of the initial gamma dose rate field model after being divided according to different grid scales. The observation space is used to indicate the spatial distribution of multiple observation station locations. Based on the observation operator, a three-dimensional variational assimilation method is adopted, with the minimization of the objective function as the optimization objective. Assimilation correction is performed on the background state variables to obtain the assimilated state variable solution. The minimum value of the objective function is solved iteratively using the gradient descent method. The assimilated state variable solution is used to indicate the final optimized spatial distribution field of gamma dose rate obtained under different computational grids.

[0063] The order of the above embodiments of the present invention is merely for description and does not represent the superiority or inferiority of the embodiments.

[0064] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0065] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The system embodiments described above are merely illustrative; for example, the division of modules described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between modules, and may be electrical or other forms.

[0066] The modules described above as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0067] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0068] If the aforementioned integrated modules are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0069] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for reconstructing the gamma radiation field of a nuclear accident environment based on isovariate assimilation, characterized in that, include: A numerical calculation model for the initial gamma dose rate field is constructed. The three-dimensional spatial region is divided into grid scales, and the gamma dose rate calculated at the grid point is determined as the background state variable. The background state variable is used to indicate the spatial distribution field of the initial gamma dose rate under different calculation grids. The numerical calculation model for the initial gamma dose rate field is a three-dimensional model. Acquire real-time environmental gamma dose rate data at multiple observation site locations and construct observation vectors; Based on the background state variables and the observation vector, a mapping relationship between the model space and the observation space is constructed, and an observation operator is determined. The observation operator is used to project the background state variables in the model space onto the location of the observation station. The model space is used to indicate the spatial distribution of the initial gamma dose rate field model after being divided based on different grid scales. The observation space is used to indicate the spatial distribution of the multiple observation station locations. Based on the observation operator, a three-dimensional variational assimilation method is adopted, with the objective function minimization as the optimization objective. Assimilation correction is performed on the background state variables to obtain the assimilated state variable solution. The minimum value of the objective function is solved iteratively using the gradient descent method. The assimilated state variable solution is used to indicate the final optimized gamma dose rate spatial distribution field obtained under different computational grids.

2. The method for reconstructing the γ-radiation field of a nuclear accident environment based on isovariate assimilation according to claim 1, characterized in that, The step of constructing a mapping relationship between the model space and the observation space based on the background state variables and the observation vector, and determining the observation operator, includes: When the grid division in the model space coincides with the location of the observation station, the mapping relationship between the model space and the observation space is directly constructed; When the grid division in the model space does not coincide with the location of the observation station, spatial interpolation is used to calculate the approximate dose rate value at the location of the observation station for the background state variable. Based on the approximate dose rate value, the background state variable is corrected to obtain a new background state variable, and then the mapping between the model space and the observation space is performed.

3. The method for reconstructing the γ-radiation field of a nuclear accident environment based on isovariate assimilation according to claim 2, characterized in that, When the grid division in the model space does not coincide with the location of the observation station, spatial interpolation is used for the background state variables to calculate the approximate dose rate value at the location of the observation station, including: Determine the coordinates of any observation station location; Centered on the coordinates of the observation station, and based on grid division, the coordinates of the observation station are determined to be in the lower left corner. Bottom right Top left and the upper right The coordinates of the offset corner point; Based on the offset corner coordinates, the relative position of the observation station within the grid is calculated, wherein the grid is the grid where the position coordinates of the observation station are located after grid division, and the relative position is used to indicate the movable position of the observation station within the grid; Based on the relative position as a weighting value, the dose rate at the offset corner coordinates is weighted and summed to obtain the approximate dose rate value.

4. The method for reconstructing the γ-radiation field of a nuclear accident environment based on isovariate assimilation according to claim 3, characterized in that, The calculation of the relative position of the observation station within the grid based on the offset corner coordinates includes: The x-coordinate value in the relative position is calculated as follows: in, This represents the x-coordinate value in the relative position. This refers to the x-coordinate value in the coordinate system of the observation station. This represents the x-coordinate value on the left side of the grid edge. This represents the x-coordinate value within the right-hand grid edge. The y-coordinate value in the relative position is calculated as follows: in, This represents the y-coordinate value in the relative position. This represents the y-coordinate value in the coordinate system of the observation station. Here are the y-coordinates of the edge of the grid being measured. This represents the y-coordinate value within the lower grid edge.

5. The method for reconstructing the γ-radiation field of a nuclear accident environment based on isovariate assimilation according to claim 4, characterized in that, The step of using the relative position as a weighting value to perform a weighted sum of the dose rates at the offset corner coordinates to obtain the approximate dose rate value includes: The approximate dose rate value is calculated as follows: in, This is an approximate dose rate value. The dose rate value is located at the bottom left corner. The dose rate value is located in the lower right corner. The dose rate value is located at the top left corner. This is the dose rate value located in the upper right corner.

6. The method for reconstructing the γ-radiation field of a nuclear accident environment based on isovariate assimilation according to claim 1, characterized in that, Based on the observation operator, a three-dimensional variational assimilation method is employed, with the objective function minimization as the optimization goal. Assimilation corrections are performed on the background state variables to obtain the assimilated state variable solution, including: A three-dimensional variational assimilation method is adopted, and an assimilation state variable with the same spatial dimension as the background state variable is set. The objective function is determined by using the observation vector and the background state variable as constraints and the assimilation state variable as variables. The gradient formula corresponding to the objective function is determined using the gradient descent method. Based on the gradient formula, the assimilated state variables are solved by gradient iteration to obtain the solution of the assimilated state variables.

7. The method for reconstructing the γ-radiation field of a nuclear accident environment based on isovariate assimilation according to claim 6, characterized in that, The method employing three-dimensional variational assimilation, using the observation vector and background state variables as constraints, and the assimilated state variables as variables, determines the objective function, including: The objective function is as follows: in, Let be the objective function. Assimilate state variables, Let B be the background state variable, O be the observation vector, and R be the observation error covariance matrix. For observation operators.

8. The method for reconstructing the γ-radiation field of a nuclear accident environment based on isovariate assimilation according to claim 7, characterized in that, The step of determining the gradient formula corresponding to the objective function using gradient descent includes: in, This is used to decrease the gradient of the objective function with solutions containing state variables.

9. The method for reconstructing the γ-radiation field of a nuclear accident environment based on isovariate assimilation according to claim 8, characterized in that, The step of performing gradient iteration on the assimilated state variables based on the gradient formula to obtain the solution of the assimilated state variables includes: The iterative formula is as follows: Where k is the number of iterations. The solution for the assimilated state variables in the k-th iteration. Let α be the assimilated state variable solution for the (k+1)th iteration, and α be the iteration step size.

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