Gamma radiation field reconstruction method based on Gaussian plume model parameter assimilation
By using a gamma radiation field reconstruction method based on Gaussian plume model parameter assimilation, the problem of inaccurate simulation results of Gaussian plume models in nuclear accident emergency management is solved, and accurate reconstruction and rapid assessment of gamma radiation fields are achieved.
Patent Information
- Application Number
- CN202511654712.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-12
- Publication Date
- 2026-03-20
AI Technical Summary
Existing Gaussian plume models are highly sensitive to key parameters in nuclear accident emergency management, leading to inaccurate γ-radiation field simulation results, especially in early emergency scenarios where there is a lack of effective three-dimensional environmental γ-radiation field reconstruction methods.
A gamma radiation field reconstruction method based on Gaussian plume model parameter assimilation is adopted. By constructing a Gaussian plume model, the source term and wind field parameters are determined. The objective function is solved iteratively using the gradient descent method with a three-dimensional variational assimilation method. The assimilation vector value is corrected, the Gaussian plume model is dynamically corrected, and a three-dimensional spatial reconstruction is performed to determine the gamma radiation field.
It improves the accuracy of gamma radiation field simulation, achieves data assimilation of key parameters, ensures the accuracy and real-time nature of simulation results, and supports rapid assessment in the early stages of nuclear emergencies.
Smart Images
Figure CN121706529A_ABST
Abstract
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 a γ-radiation field based on Gaussian plume model parameter assimilation. Background Technology
[0002] In nuclear accident emergency management, accurately assessing the diffusion and distribution of radioactive materials in the environment is crucial for formulating emergency response strategies. Gaussian plume models, due to their simple structure and high computational efficiency, are widely used in the initial stages of nuclear emergencies to simulate radioactive material transport. These models can quickly generate spatial distribution maps of pollutants based on input conditions such as emission source characteristics and meteorological parameters, making them suitable for real-time forecasting and rapid response. However, limited by the model's inherent simplification assumptions, the accuracy of Gaussian plume models decreases when dealing with unsteady processes or complex terrain, and they are also highly sensitive to input parameters. In practical applications, key parameters such as source term intensity and diffusion coefficient are often difficult to obtain, leading to significant uncertainties in the simulation results.
[0003] Especially in early nuclear accident emergency scenarios, existing research mainly focuses on source term inversion estimation, while three-dimensional environmental gamma radiation field reconstruction methods based on key parameter correction in Gaussian plume models are still relatively scarce, resulting in large deviations in the gamma radiation field obtained from Gaussian plume models.
[0004] The above problems urgently need to be addressed. Summary of the Invention
[0005] This invention discloses a method for reconstructing a γ-radiation field based on Gaussian plume model parameter assimilation, aiming to solve the technical problems existing in the prior art.
[0006] The present invention adopts the following technical solution: This invention provides a method for reconstructing a gamma radiation field based on Gaussian plume model parameter assimilation, comprising: constructing a Gaussian plume model and determining the source term and wind field parameters in the Gaussian plume model; determining assimilation state variables using the source term and wind field parameters to obtain assimilation vector values; using a three-dimensional variational assimilation method with the objective function minimization as the optimization objective to correct the assimilation vector values and determine the corrected assimilation vector values, wherein the minimum value of the objective function is solved iteratively using the gradient descent method; based on the corrected assimilation vector values, dynamically correcting and reconstructing the three-dimensional space of the Gaussian plume model to determine the pollutant correction concentration at a preset location, wherein the preset location is any location to which the plume can diffuse; and based on the pollutant correction concentration, determining the gamma dose rate at the preset location to obtain the gamma radiation field.
[0007] Optionally, the step of constructing a Gaussian plume model and determining the source terms and wind field parameters in the Gaussian plume model includes: constructing a coordinate system with the plume generation location as the origin; representing the concentration distribution of the plume using a three-dimensional Gaussian function based on the coordinate system to obtain a Gaussian plume model; determining the pollutant concentration calculation equation at a preset location based on the Gaussian plume model; and determining the source terms and wind field parameters related to pollutant concentration diffusion based on the pollutant concentration calculation equation, wherein the source terms and wind field parameters are source term intensity, dominant wind speed, and effective emission height.
[0008] Optionally, the pollutant concentration calculation equation is as follows: Where C(x,y,z) is the pollutant concentration at the preset location (x,y,z), Q is the source term intensity, U is the prevailing wind speed, and h is the effective emission height. Let be the diffusion coefficient in the y-direction. denoted as the diffusion coefficient in the z-direction.
[0009] Optionally, determining the assimilation state variables using the source term and wind field parameters to obtain the assimilation vector value includes: The assimilation vector value is calculated as follows: Where a is the assimilation vector value, It is the transpose of the vector.
[0010] Optionally, a three-dimensional variational assimilation method is employed, with the minimization of the objective function as the optimization objective, to correct the assimilation vector value and determine the corrected assimilation vector value. The minimum value of the objective function is solved iteratively using the gradient descent method, including: using the three-dimensional variational assimilation method, with real-time environmental γ-dose rate observations as constraints and the assimilation vector value as a variable, to determine the objective function; using the gradient descent method, to determine the gradient formula corresponding to the objective function; based on the gradient formula, performing gradient iterative solutions on the assimilation vector value to obtain the optimal analytical solution; and based on the optimal analytical solution, obtaining the corrected assimilation vector value.
[0011] Optionally, the three-dimensional variational assimilation method, using real-time environmental γ dose rate observations as constraints and assimilation vector values as variables, determines the objective function, including: The objective function is as follows: Where J(a) is the objective function, and a is the assimilation vector value. Let represent the values to be assimilated after each iteration, B be the parameter background error covariance matrix, o be the actual environmental γ dose rate vector, R be the observation error covariance matrix, and H be the observation operator.
[0012] Optionally, determining the gradient formula corresponding to the objective function using gradient descent includes: the gradient formula is as follows: in, This is used to decrease the gradient of the objective function containing assimilation vector values.
[0013] Optionally, based on the gradient formula, the assimilation vector values are iteratively solved to obtain the optimal analytical solution, including: the iterative formula is as follows: Where k is the number of iterations. Let be the assimilation vector value of the k-th iteration. Let α be the assimilation vector value for the (k+1)th iteration, and α be the iteration step size.
[0014] Optionally, the step of dynamically correcting and reconstructing the Gaussian plume model based on the corrected assimilation vector value to determine the corrected pollutant concentration at a preset location includes: determining the corrected source term and wind field parameters based on the assimilation vector value 'a', wherein the corrected diffusion parameter is the corrected source term intensity. Corrected prevailing wind speed and the revised effective emission height Based on the corrected diffusion parameters, the three-dimensional space of the Gaussian plume model is reconstructed to obtain a new Gaussian plume model; using the new Gaussian plume model and the pollutant concentration calculation equation, the corrected pollutant concentration at the preset position of the plume is obtained.
[0015] Optionally, determining the γ dose rate at the preset location based on the pollutant correction concentration to obtain the γ radiation field includes: the γ dose rate at the preset location is calculated as follows: in, ) represents the γ dose rate at the preset location, F is the operator that converts the pollutant correction concentration into the γ dose rate, and C( ; () represents the corrected concentration of pollutants.
[0016] The technical solution adopted in this invention can achieve at least one of the following beneficial effects: In this embodiment of the invention, a Gaussian plume model is constructed, and the source term and wind field parameters in the Gaussian plume model are determined. The assimilation state variables are determined using the source term and wind field parameters to obtain assimilation vector values. A three-dimensional variational assimilation method is used, with the minimum objective function as the optimization objective, to correct the assimilation vector values, determining the corrected assimilation vector values. The minimum value of the objective function is iteratively solved using the gradient descent method. Based on the corrected assimilation vector values, the Gaussian plume model is dynamically corrected and its three-dimensional space is reconstructed to determine the corrected pollutant concentration at a preset location, where the preset location is any location the plume can diffuse to. Based on the corrected pollutant concentration, the γ-dose rate at the preset location is determined, obtaining the γ-radiation field. This achieves the goal of assimilating key parameters, constructing an objective function based on real-time environmental γ-dose rate observations using a three-dimensional variational assimilation method, and dynamically correcting and reconstructing the γ-radiation field output by the Gaussian plume model, thereby obtaining more accurate key parameters and making the simulation results more accurate. Attached Figure Description
[0017] 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 γ-radiation field reconstruction method based on Gaussian plume model parameter assimilation in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the Gaussian plume model parameter assimilation result of a γ-radiation field reconstruction method based on Gaussian plume model parameter assimilation in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of a γ-radiation field reconstruction system based on Gaussian plume model parameter assimilation in Embodiment 2 of the present invention. Detailed Implementation
[0018] 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.
[0019] 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.
[0020] 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.
[0021] First, to facilitate understanding of the embodiments of the present invention, some terms or nouns involved in the present invention will be explained below: A smoke plume is a continuous stream of smoke emitted from pollution sources such as factory chimneys. It can be viewed as a series of countless smoke plumes emitted sequentially at infinitely short intervals. After each plume is emitted, it spreads and migrates in the atmosphere with the wind, and the aggregation of numerous smoke plumes forms a smoke plume.
[0022] Parameter assimilation is a technique that integrates observational data with mathematical models. Its core is to adjust model parameters or initial conditions to make the model simulation results closer to actual observations, thereby improving the accuracy and reliability of the model.
[0023] To address the problems existing in related technologies, this application provides a method for reconstructing a γ-radiation field based on Gaussian plume model parameter assimilation.
[0024] Example 1 This embodiment provides a method for reconstructing the γ-radiation field based on Gaussian plume model parameter assimilation, such as... Figure 1 As shown, Figure 1 This is a flowchart of a γ-radiation field reconstruction method based on Gaussian plume model parameter assimilation in Embodiment 1 of the present invention. The method includes: Step S102: Construct a Gaussian plume model and determine the source term and wind field parameters in the Gaussian plume model; Optionally, the Gaussian plume model is an analytical pollutant diffusion model, and its basic form is as follows: Where: C(x,y,z) is the pollutant concentration at point (x,y,z); Q is the source term intensity (release rate); U is the prevailing wind speed; and h is the effective emission height. The diffusion coefficient is in the y-direction (lateral direction). The diffusion coefficient is in the z (vertical) direction. and It is a function of the downwind distance x.
[0025] Optionally, the Gaussian plume model needs to consider diffusion in the y and z directions as well as migration in the x direction. The plume model is the simplest atmospheric diffusion model and is used to calculate the case of long-term uniform release of pollutants.
[0026] Optionally, for ease of practical operation, F is defined as an operator that converts pollutant concentration into γ dose rate, in which case the γ dose rate is as follows: In some preferred embodiments, a Gaussian plume model is constructed, and the source terms and wind field parameters in the Gaussian plume model are determined, including: constructing a coordinate system with the plume generation location as the origin; representing the plume concentration distribution using a three-dimensional Gaussian function based on the coordinate system to obtain the Gaussian plume model; determining the pollutant concentration calculation equation at a preset location based on the Gaussian plume model; and determining the source terms and wind field parameters related to pollutant concentration diffusion based on the pollutant concentration calculation equation, wherein the source terms and wind field parameters are source term intensity, dominant wind speed, and effective emission height.
[0027] In some preferred embodiments, the equations for calculating pollutant concentration are as follows: Where C(x,y,z) is the pollutant concentration at the preset location (x,y,z), Q is the source term intensity, U is the prevailing wind speed, and h is the effective emission height. Let be the diffusion coefficient in the y-direction. denoted as the diffusion coefficient in the z-direction.
[0028] Step S104: Determine the assimilation state variables using the source term and wind field parameters to obtain the assimilation vector value; Optionally, the three parameters in the Gaussian plume model that have the most significant impact on the γ dose rate field can be used as assimilation state variables: By modifying the above parameters, the γ dose rate distribution D(x,y,z) in the entire space is indirectly affected, and the result is compared and corrected with the observed values. Full field reconstruction is achieved through the optimization of a few key parameters, which greatly improves the computational efficiency.
[0029] like Figure 2As shown, the actual wind field was set with a wind direction of 45 degrees, and the background wind field was assimilated using 0-degree data. The assimilated state variables are as shown above, and the final assimilation result is as follows. Figure 2 As shown, the assimilated field is basically consistent with the real field.
[0030] In some preferred embodiments, the assimilation state variables are determined using source terms and wind field parameters to obtain assimilation vector values, including: the assimilation vector values are calculated as follows: Where a is the assimilation vector value, It is the transpose of the vector.
[0031] Step S106: Using a three-dimensional variational assimilation method, with the objective function minimization as the optimization objective, the assimilation vector value is corrected, and the corrected assimilation vector value is determined. The minimum value of the objective function is solved iteratively by the gradient descent method. Optionally, a three-dimensional variational assimilation method can be used, with the following objective function as the optimization target: Wherein: the first term on the right half of the equal sign is the background constraint term, which represents the difference between the background field and the analysis field (the background field is the initial preset result of the model, which is the basic data before the assimilation process begins, and its core source is the simulation result of the model itself; the analysis field is the final output of the three-dimensional variational assimilation, which is the "optimal pollutant concentration field" obtained by minimizing the objective function and fusing the background field and the observation data); the second term on the right half of the equal sign is the observation constraint term, which represents the difference between the analysis field and the observation data (the data actually detected). is the initial parameter background value (i.e., the initial preset result and the value to be assimilated in each iteration); B is the parameter background error covariance matrix; o is the actual observed γ dose rate vector; R is the observation error covariance matrix (here it is a diagonal matrix, indicating that the errors of the observation sites are independent); H is a nonlinear observation operator that maps the parameters to the dose rate prediction values at all observation points.
[0032] Optionally, since H is a nonlinear function calculated using the Gaussian plume formula, the minimum point of the objective function J(x) is usually solved iteratively using the gradient descent method.
[0033] The goal of three-dimensional variational assimilation (3D-Var) is to minimize the cost function J(x) in the background field. Under the constraint of the observed data o, the optimal analysis field a is obtained.
[0034] Taking the gradient of the cost function J(x) with respect to x, we get: This formula is the core gradient formula for the three-dimensional variational assimilation problem, consisting of two parts: a background term (prediction term) and an observation term (actual measurement term).
[0035] The optimal analytical field is solved iteratively using the gradient descent method.
[0036] Iteration formula: Substituting the gradient, we get: Where: α is the iteration step size. Let be the analysis field for the k-th iteration.
[0037] Optionally, when the gradient is zero, i.e. With J(x)=0, the theoretical optimal solution can be obtained directly: therefore: This formula is the standard analytical solution for three-dimensional variational assimilation.
[0038] For each observation point i, the corresponding predicted γ dose rate is calculated by the Gaussian plume model controlled by the current state variables: in, ( This refers to the operator that maps state variables to monitored values, which is equivalent to the value calculated using the concentration and dose rate operators at this monitoring point. and ( The values are equal. When the observation point and the model grid point do not coincide, a spatial interpolation method (such as three-dimensional inverse distance weighting or nearest neighbor method) is used to extract the dose rate value from the Gaussian analytical function to realize the mapping of the Gaussian plume model output to the observation space.
[0039] Optionally, based on the above objective function, both the background error covariance matrix and the observation error covariance matrix need to be modeled. The specific modeling is as follows: Specifically, to accurately reflect the uncertainty of the model's background field and its spatial correlation, the parametric 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. and , i and j, are the three-dimensional spatial coordinates of the i-th and j-th grid points, respectively. 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.
[0040] In practical calculations, since the parameter background error covariance matrix B is huge (an n×n matrix), the computational complexity can be reduced by controlling the effective correlation range, localization methods, or principal component dimensionality reduction techniques (such as EOF expansion).
[0041] Specifically, considering the differences in sampling methods, equipment performance, and environmental conditions at different observation stations, the observation error covariance matrix R is set to be 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.
[0042] In some preferred embodiments, a three-dimensional variational assimilation method is employed, with the minimization of the objective function as the optimization objective. The assimilation vector value is then corrected to determine the corrected assimilation vector value. The minimum value of the objective function is solved iteratively using the gradient descent method, including: using the three-dimensional variational assimilation method, with the real-time environmental γ dose rate observation as a constraint and the assimilation vector value as a variable, to determine the objective function; using the gradient descent method, to determine the gradient formula corresponding to the objective function; based on the gradient formula, to perform gradient iterative solution on the assimilation vector value to obtain the optimal analytical solution; and based on the optimal analytical solution, to obtain the corrected assimilation vector value.
[0043] In some preferred embodiments, a three-dimensional variational assimilation method is employed, using real-time environmental γ dose rate observations as constraints and assimilation vector values as variables, to determine the objective function, including: the objective function as follows: Where J(a) is the objective function, and a is the assimilation vector value. Let represent the values to be assimilated after each iteration, B be the parameter background error covariance matrix, o be the actual environmental γ dose rate vector, R be the observation error covariance matrix, and H be the nonlinear observation operator.
[0044] In some preferred embodiments, the gradient formula corresponding to the objective function is determined using the gradient descent method, including the following gradient formula: in, This is used to decrease the gradient of the objective function containing assimilation vector values.
[0045] In some preferred embodiments, the assimilation vector values are solved iteratively based on the gradient formula to obtain the optimal analytical solution, including the following iterative formula: Where k is the number of iterations. Let be the assimilation vector value of the k-th iteration. Let α be the assimilation vector value for the (k+1)th iteration, and α be the iteration step size.
[0046] Step S108: Based on the corrected assimilation vector value, dynamically correct and reconstruct the three-dimensional space of the Gaussian plume model to determine the corrected pollutant concentration at a preset location, where the preset location is any location that the plume can diffuse to. In some preferred embodiments, the Gaussian plume model is dynamically corrected and its three-dimensional space reconstructed based on the corrected assimilation vector value to determine the corrected pollutant concentration at a preset location. This includes: determining the corrected source term and wind field parameters based on the assimilation vector value 'a', wherein the corrected diffusion parameter is the corrected source term intensity. Corrected prevailing wind speed and the revised effective emission height Based on the corrected diffusion parameters, the three-dimensional space of the Gaussian plume model is reconstructed to obtain a new Gaussian plume model. Using the new Gaussian plume model and the pollutant concentration calculation equation, the corrected pollutant concentration at the preset location of the plume is obtained.
[0047] Step S110: Based on the pollutant correction concentration, determine the γ dose rate at the preset location to obtain the γ radiation field.
[0048] Optionally, the optimal parameter set can be obtained by optimizing the solution 'a' of the state variables. The dose rate of all grid points within the complete region was recalculated by substituting the Gaussian plume model into the data. Furthermore, a three-dimensional gamma radiation field is constructed within the entire impact area of the nuclear accident. This radiation field can be used for various downstream analyses, such as public dose assessment, contaminated zone delineation, evacuation route analysis, and emergency resource allocation.
[0049] In some preferred embodiments, the gamma dose rate at a preset location is determined based on the pollutant correction concentration to obtain the gamma radiation field, including: the gamma dose rate at the preset location is calculated as follows: in, ) represents the γ dose rate at the preset location, F is the operator that converts the pollutant correction concentration into the γ dose rate, and C( ; () represents the corrected concentration of pollutants.
[0050] Through steps S102 to S110, by using key source terms and wind field parameters (including source height h, source strength Q, and average wind speed U) in the Gaussian plume model as assimilated state variables, a three-dimensional variational data assimilation method is introduced. With real-time environmental γ-dose rate observations as constraints, dynamic correction and three-dimensional spatial reconstruction of the model's output radiation field are achieved. This method establishes a nonlinear observation operator through the transfer relationship between the parameter space and physical space, integrating parameter correction and dose rate reconstruction processes. While ensuring computational efficiency, it significantly improves simulation accuracy, providing efficient and practical technical support for rapid assessment in the initial stages of nuclear emergency scenarios. This method achieves the goal of assimilating key parameters, constructing an objective function based on the three-dimensional variational assimilation method with real-time environmental γ-dose rate observations as constraints, and dynamically correcting and reconstructing the three-dimensional spatial radiation field of the Gaussian plume model's output radiation field. This results in more accurate key parameters and more accurate simulation results.
[0051] Example 2 This embodiment also provides a γ-radiation field reconstruction system based on Gaussian plume model parameter 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.
[0052] According to an embodiment of the present invention, a system embodiment for implementing the above-described method for reconstructing a γ-radiation field based on Gaussian plume model parameter assimilation is also provided. Figure 3 This is a schematic diagram of a γ-radiation field reconstruction system based on Gaussian plume model parameter assimilation in Embodiment 2 of the present invention, as shown below. Figure 3 As shown, the above system includes: a parameter determination module 201, a data assimilation module 202, a numerical correction module 203, a model correction module 204, and a radiation field determination module 205, wherein: Parameter determination module 201 constructs a Gaussian plume model and determines the source term and wind field parameters in the Gaussian plume model; The assimilation data module 202 is connected to the parameter determination module 201. It determines the assimilation state variables using the source terms and wind field parameters, and obtains the assimilation vector values. The numerical correction module 203 is connected to the assimilation data module 202. It adopts a three-dimensional variational assimilation method, takes the minimization of the objective function as the optimization objective, corrects the assimilation vector value, and determines the corrected assimilation vector value. The minimum value of the objective function is solved iteratively by the gradient descent method. The model correction module 204, connected to the numerical correction module 203, performs dynamic correction and three-dimensional spatial reconstruction on the Gaussian plume model based on the corrected assimilation vector value, and determines the corrected pollutant concentration at a preset position, where the preset position is any position that the plume can diffuse to. The radiation field determination module 205 is connected to the model correction module 204. Based on the pollutant correction concentration, it determines the γ dose rate at a preset location and obtains the γ radiation field.
[0053] 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.
[0054] It should be noted that the parameter determination module 201, assimilation data module 202, numerical correction module 203, model correction module 204, and radiation field determination module 205 mentioned above correspond to steps S102 to S110 in the embodiments. The instances and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the system, can run on a computer terminal.
[0055] 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.
[0056] The aforementioned γ-radiation field reconstruction system based on Gaussian plume model parameter assimilation may further include a processor and a memory. The parameter determination module 201, assimilation data module 202, numerical correction module 203, model correction module 204, and radiation field determination module 205 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.
[0057] 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.
[0058] 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 above-mentioned γ-radiation field reconstruction methods based on Gaussian plume model parameter assimilation.
[0059] 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.
[0060] Optionally, during program execution, the device containing the non-volatile storage medium may perform the following functions: construct a Gaussian plume model and determine the source terms and wind field parameters in the Gaussian plume model; determine the assimilation state variables using the source terms and wind field parameters to obtain the assimilation vector value; use a three-dimensional variational assimilation method, with the minimum objective function as the optimization objective, to correct the assimilation vector value and determine the corrected assimilation vector value, wherein the minimum value of the objective function is solved iteratively using the gradient descent method; based on the corrected assimilation vector value, dynamically correct the Gaussian plume model and reconstruct its three-dimensional space to determine the pollutant correction concentration at a preset location, wherein the preset location is any location to which the plume can diffuse; based on the pollutant correction concentration, determine the γ dose rate at the preset location to obtain the γ radiation field.
[0061] 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-mentioned γ-radiation field reconstruction methods based on Gaussian plume model parameter assimilation.
[0062] 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 gamma radiation field reconstruction methods based on Gaussian plume model parameter assimilation.
[0063] 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 steps: constructing a Gaussian plume model and determining the source terms and wind field parameters in the Gaussian plume model; determining the assimilation state variables using the source terms and wind field parameters to obtain the assimilation vector value; using a three-dimensional variational assimilation method with the objective function minimization as the optimization objective, correcting the assimilation vector value and determining the corrected assimilation vector value, wherein the minimum value of the objective function is solved iteratively using the gradient descent method; based on the corrected assimilation vector value, dynamically correcting and reconstructing the three-dimensional space of the Gaussian plume model to determine the pollutant correction concentration at a preset location, wherein the preset location is any location to which the plume can diffuse; and based on the pollutant correction concentration, determining the γ dose rate at the preset location to obtain the γ radiation field.
[0064] This invention provides an electronic device including 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 a Gaussian plume model and determining the source terms and wind field parameters in the Gaussian plume model; determining assimilation state variables using the source terms and wind field parameters to obtain assimilation vector values; using a three-dimensional variational assimilation method with the objective function minimization as the optimization objective to correct the assimilation vector values and determine the corrected assimilation vector values, wherein the minimum value of the objective function is iteratively solved using the gradient descent method; based on the corrected assimilation vector values, dynamically correcting the Gaussian plume model and reconstructing its three-dimensional space to determine the corrected pollutant concentration at a preset location, wherein the preset location is any location to which the plume can diffuse; and based on the corrected pollutant concentration, determining the γ dose rate at the preset location to obtain the γ radiation field.
[0065] 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.
[0066] 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.
[0067] 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.
[0068] 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.
[0069] 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.
[0070] 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.
[0071] 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 a γ-radiation field based on Gaussian plume model parameter assimilation, characterized in that, include: Construct a Gaussian plume model and determine the source term and wind field parameters in the Gaussian plume model; The assimilation state variables are determined using the source terms and wind field parameters, and the assimilation vector values are obtained. A three-dimensional variational assimilation method is adopted, with the minimization of the objective function as the optimization objective, to correct the assimilation vector value and determine the corrected assimilation vector value. The minimum value of the objective function is solved iteratively by the gradient descent method. Based on the corrected assimilation vector value, the Gaussian plume model is dynamically corrected and reconstructed in three dimensions to determine the corrected pollutant concentration at a preset location, wherein the preset location is any location to which the plume can diffuse. Based on the corrected concentration of the pollutants, the γ dose rate at the preset location is determined, and the γ radiation field is obtained.
2. The γ-radiation field reconstruction method based on Gaussian plume model parameter assimilation according to claim 1, characterized in that, The construction of the Gaussian plume model and the determination of the source term and wind field parameters in the Gaussian plume model include: Construct a coordinate system with the origin at the location where the plume is generated; Based on the coordinate system, the concentration distribution of the plume is represented by a three-dimensional Gaussian function to obtain a Gaussian plume model; Based on the Gaussian plume model, the equation for calculating pollutant concentration at a preset location in the plume is determined. Based on the pollutant concentration calculation equation, the source term and wind field parameters related to the diffusion of pollutant concentration are determined, wherein the source term and wind field parameters are source term intensity, dominant wind speed, and effective emission height.
3. The γ-radiation field reconstruction method based on Gaussian plume model parameter assimilation according to claim 2, characterized in that, The equation for calculating pollutant concentration is as follows: Where C(x,y,z) is the pollutant concentration at the preset location (x,y,z), Q is the source term intensity, U is the prevailing wind speed, and h is the effective emission height. Let be the diffusion coefficient in the y-direction. denoted as the diffusion coefficient in the z-direction.
4. The γ-radiation field reconstruction method based on Gaussian plume model parameter assimilation according to claim 3, characterized in that, The step of determining the assimilation state variables and obtaining the assimilation vector values using the source term and wind field parameters includes: The assimilation vector value is calculated as follows: Where a is the assimilation vector value, It is the transpose of the vector.
5. The γ-radiation field reconstruction method based on Gaussian plume model parameter assimilation according to claim 1, characterized in that, A three-dimensional variational assimilation method is employed, with the minimization of the objective function as the optimization objective. The assimilation vector values are then corrected to determine the corrected assimilation vector values. The minimum value of the objective function is solved iteratively using gradient descent, including: A three-dimensional variational assimilation method is adopted, with real-time environmental γ dose rate observations as constraints and assimilation vectors as variables, to determine the objective function; The gradient formula corresponding to the objective function is determined using the gradient descent method. Based on the gradient formula, the assimilation vector values are solved by gradient iteration to obtain the optimal analytical solution; Based on the optimal analytical solution, the corrected assimilation vector value is obtained.
6. The γ-radiation field reconstruction method based on Gaussian plume model parameter assimilation according to claim 5, characterized in that, The method employs a three-dimensional variational assimilation approach, using real-time environmental gamma dose rate observations as constraints and assimilation vector values as variables to determine the objective function, including: The objective function is as follows: Where J(a) is the objective function, and a is the assimilation vector value. Let represent the values to be assimilated after each iteration, B be the parameter background error covariance matrix, o be the actual environmental γ dose rate vector, R be the observation error covariance matrix, and H be the observation operator.
7. The γ-radiation field reconstruction method based on Gaussian plume model parameter assimilation according to claim 6, characterized in that, The step of determining the gradient formula corresponding to the objective function using gradient descent includes: The gradient formula is as follows: in, This is used to decrease the gradient of the objective function containing assimilation vector values.
8. The γ-radiation field reconstruction method based on Gaussian plume model parameter assimilation according to claim 5, characterized in that, Based on the gradient formula, the assimilation vector values are solved iteratively to obtain the optimal analytical solution, including: The iterative formula is as follows: Where k is the number of iterations. Let be the assimilation vector value of the k-th iteration. Let α be the assimilation vector value for the (k+1)th iteration, and α be the iteration step size.
9. The γ-radiation field reconstruction method based on Gaussian plume model parameter assimilation according to claim 1, characterized in that, The step of dynamically correcting and reconstructing the Gaussian plume model based on the corrected assimilation vector value to determine the corrected pollutant concentration at a preset location includes: Based on the assimilation vector value 'a', the corrected source term and wind field parameters are determined, where the corrected diffusion parameter is the corrected source term intensity. Corrected prevailing wind speed and the revised effective emission height ; Based on the corrected diffusion parameters, the three-dimensional space of the Gaussian plume model is reconstructed to obtain a new Gaussian plume model. Using the new Gaussian plume model and the pollutant concentration calculation equation, the corrected pollutant concentration at the preset location of the plume is obtained.
10. The γ-radiation field reconstruction method based on Gaussian plume model parameter assimilation according to claim 1, characterized in that, The step of determining the γ dose rate at the preset location based on the pollutant correction concentration to obtain the γ radiation field includes: The γ dose rate at the preset location is calculated as follows: in, ) represents the γ dose rate at the preset location, F is the operator that converts the pollutant correction concentration into the γ dose rate, and C( ; () represents the corrected concentration of pollutants.