Gamma radiation field reconstruction method based on Lagrange particle model parameter assimilation
By employing Lagrange particle model parameter assimilation techniques and ensemble Kalman filtering algorithms for radioactive aerosol particles, this approach addresses existing technical problems, improves upon existing technologies, and provides a flexible and precise tool for reconstructing radiation fields.
Patent Information
- Application Number
- CN202511654726.1
- 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 Lagrange particle models are sensitive to initial conditions and particle parameters in assessing the consequences of nuclear accidents, leading to inaccurate simulation results of gamma radiation fields, especially in complex terrain environments where it is difficult to reconstruct gamma radiation fields effectively.
An ensemble Kalman filter algorithm is used to dynamically update the state parameters of radioactive aerosol particles. The particle trajectory is optimized by correcting the assimilation state. A local concentration field is constructed by combining the Gaussian kernel function to determine the gamma dose rate and reconstruct the gamma radiation field.
It improves the accuracy of three-dimensional radiation field reconstruction, is applicable to nuclear emergency scenarios under complex terrain and unsteady meteorological conditions, and provides a flexible and sophisticated radiation field assessment tool.
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Figure CN121706530A_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 parameter assimilation of a Lagrange particle model. Background Technology
[0002] The Lagrange particle model, by tracking the trajectories of a large number of particles, can meticulously simulate the transport processes of radioactive contaminants under complex terrain and unstable weather conditions, and is widely used for environmental atmospheric diffusion modeling and nuclear emergency scenario analysis. This model is suitable for complex terrain and has a greater advantage in handling small- to medium-scale diffusion problems. However, the Lagrange model is highly sensitive to initial conditions and particle properties; inaccurate estimations of parameters such as particle release location, mass, and velocity can easily lead to accumulated biases in the simulation results. With the development of radiation monitoring methods, more and more high-resolution dose rate data can be acquired in real time, providing a reliable data source for model correction.
[0003] Currently, in nuclear accident consequence assessment systems, the Lagrange model is often used to directly generate gamma radiation fields. However, due to particle motion, the particle parameters are inaccurate and cannot be adjusted, especially in complex terrain environments, ultimately resulting in inaccurate gamma radiation fields.
[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 parameter assimilation of a Lagrange particle model, aiming to solve the technical problems existing in the prior art.
[0006] This invention employs the following technical solution: determining the spatial positions of multiple radioactive aerosol particles based on a Lagrange particle model; constructing state parameters based on the spatial positions of the multiple radioactive aerosol particles and the source strength of each aerosol particle; constructing a background state vector using the state parameters of all radioactive aerosol particles, and dynamically updating the background state vector using an ensemble Kalman filter algorithm to obtain a corrected assimilation state, wherein the corrected assimilation state includes the updated spatial positions of the radioactive aerosol particles; determining the optimized transport trajectory of the radioactive aerosol particles based on the spatial positions of the radioactive aerosol particles in the corrected assimilation state; constructing the local concentration field corresponding to the radioactive aerosol particles using a Gaussian kernel function based on the transport trajectory; and determining the gamma dose rate corresponding to each radioactive aerosol particle based on the local concentration field to obtain the gamma radiation field.
[0007] Optionally, a background state vector is constructed using the state parameters of all radioactive aerosol particles. An ensemble Kalman filter algorithm is used to dynamically update the background state vector to obtain a corrected assimilation state. This includes: constructing a background state vector using the state parameters of all radioactive aerosol particles; setting multiple sets of samples to obtain multiple background state vectors and a sample state vector set; and in the case of multiple background state vectors and a sample state vector set, each background state vector is iteratively updated based on the ensemble Kalman filter algorithm to obtain multiple corrected assimilation states.
[0008] Optionally, when there are multiple background state vectors and a set of sample state vectors, each background state vector is iteratively updated based on an ensemble Kalman filter algorithm to obtain multiple corrected assimilation states. Each corrected assimilation state includes: determining the average value of the set of sample state vectors; obtaining the background field error based on the average value; determining the contribution of the radioactive aerosol particles to the dose rate at the actual monitoring point to obtain the observation operator; obtaining the dose rate at the actual monitoring point to determine the observation error covariance matrix; determining the Kalman gain matrix based on the background field error, the observation operator, and the observation error covariance matrix; and using the Kalman gain matrix to fuse the dose rate at the actual monitoring point to update the radioactive aerosol particle states in the background state vectors to obtain the corrected assimilation state.
[0009] Optionally, determining the average value of the sample state vector set and obtaining the background field error based on the average value includes: the background field error is calculated as follows: Where B is the background field error. This is the nth background state vector. Let i be the background state vector. This represents the total number of sample state vectors in the set.
[0010] Optionally, determining the dose rate contribution of the radioactive aerosol particles to the actual monitoring point to obtain the observation operator includes: the observation operator is calculated as follows: in, For the observation operator, The observation operator contributing the dose rate to the j-th actual monitoring point by radioactive aerosol particles. Let be the source strength of the i-th radioactive aerosol particle. For kernel function, Let j be the location of the actual monitoring point. Let represent the spatial location of the i-th radioactive aerosol particle, N be the total number of radioactive aerosol particles, and A be the set of sample state vectors of radioactive aerosol particles.
[0011] Optionally, determining the Kalman gain matrix based on the background field error, the observation operator, and the observation error covariance matrix includes: the Kalman gain matrix is calculated as follows: Where K is the Kalman gain matrix, B is the background field error, H is the observation operator, and R is the observation error covariance matrix.
[0012] Optionally, the step of using the Kalman gain matrix to fuse the dose rate at the actual monitoring point and updating the radioactive aerosol particle state in the background state vector to obtain the corrected assimilation state includes: the corrected assimilation state is calculated as follows: in, For the nth modified assimilation state, Let K be the nth background state vector, and K be the Kalman gain matrix. This represents the dose rate at the actual monitoring point. for The observation operator.
[0013] Optionally, the optimized transport trajectory of radioactive aerosol particles is determined based on the spatial position of the radioactive aerosol particles in the modified assimilation state. Where multiple modified assimilation states exist, the transport trajectory of each group of radioactive aerosol samples includes: in, For the description of the transportation trajectory, Let represent the spatial location of the i-th radioactive aerosol particle, and u represent the dominant wind field. This represents the random perturbation term in the random diffusion process.
[0014] Optionally, based on the transport trajectory, a Gaussian kernel function is used to construct the local concentration field corresponding to the radioactive aerosol particles. Where multiple radioactive aerosol transport trajectories exist, the local concentration field of each radioactive aerosol sample includes: the local concentration field is calculated as follows: in, In order to be in The local concentration field of radioactive aerosol particles at location t at time t. The source strength of the i-th radioactive aerosol particle For kernel function, It is a vector representation of the spatial location (x, y, z).
[0015] Optionally, determining the gamma dose rate corresponding to each radioactive aerosol particle based on the local concentration field to obtain the gamma radiation field includes: determining multiple sets of gamma dose rates corresponding to each radioactive aerosol particle based on each local concentration field; averaging the multiple sets of gamma dose rates to obtain an average gamma radiation dose rate field; the average gamma dose rate is calculated as follows: in, The average gamma dose rate, The total number of sample state vectors. denoted as the gamma dose rate for group i.
[0016] The technical solution adopted in this invention can achieve at least one of the following beneficial effects: In this embodiment of the invention, by using the parameters of radioactive aerosol particles as the core, and reconstructing the three-dimensional radiation field based on the background state vector and real-time monitoring data, the background state variables are corrected to obtain the corrected assimilation variables. This achieves the technical effect of improving the accuracy of the three-dimensional radiation field reconstruction by correcting the particle parameters of radioactive aerosols. 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 Lagrange particle model parameter assimilation in Embodiment 1 of the present invention; Figure 2 This is a particle distribution and concentration field distribution diagram before assimilation correction in a γ-radiation field reconstruction method based on Lagrange particle model parameter assimilation in Embodiment 1 of the present invention. Figure 3 This is a particle distribution and concentration field distribution diagram after assimilation correction in a γ-radiation field reconstruction method based on Lagrange particle model parameter assimilation in Embodiment 1 of the present invention. Figure 4 This is a schematic diagram of a γ-radiation field reconstruction device based on Lagrange particle 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: Parameter assimilation is a data analysis method that integrates observational data with model predictions to optimize model parameters and states. Its core purpose is to make the model output more closely resemble the real system.
[0022] Lagrange particles are the research vehicle for the "Lagrange method" in fluid mechanics. The core of this method is to track the motion of individual particles to describe the behavior of the entire fluid.
[0023] To address the problems existing in related technologies, this application provides a method for reconstructing a γ-radiation field based on parameter assimilation of a Lagrange particle model.
[0024] Example 1 This embodiment provides a method for reconstructing a γ-radiation field based on parameter assimilation of the Lagrange particle model, such as... Figure 1 As shown, Figure 1 This is a flowchart of a γ-radiation field reconstruction method based on Lagrange particle model parameter assimilation in Embodiment 1 of the present invention. The method includes: Step S102: Based on the Lagrange particle model, determine the spatial positions of multiple radioactive aerosol particles; Optionally, the Lagrange particle model is a model used for simulating atmospheric diffusion. It treats radionuclides in the air as a series of particles, each representing a small cluster of radioactive aerosols. The motion of these radioactive aerosol particles is influenced by the atmospheric flow field, diffusion effects, and environmental factors such as temperature, humidity, and wind speed. This Lagrange particle model tracks the trajectories of these particles and uses their state variables to calculate the concentration field and radiation dose rate field of the entire diffusion region.
[0025] Step S104: Based on the spatial positions of multiple radioactive aerosol particles and the source strength of each aerosol particle, construct state parameters. Optionally, the state parameters of each radioactive aerosol particle can be modeled as follows: in, Let i be the state parameter vector of the i-th radioactive aerosol. Let be the spatial position of the i-th radioactive aerosol particle. Let be the source strength of the i-th radioactive aerosol particle, i.e., the radioactivity intensity (or source term mass).
[0026] Optionally, the background state vector consisting of all radioactive aerosol particles is: Where A is a background state vector of a set of radioactive aerosol particle samples. Let be the state parameter vector of the Nth radioactive aerosol particle.
[0027] This background state vector can fully describe the distribution characteristics and radioactive properties of particles in three-dimensional space, facilitating model-driven prediction and assimilation correction, such as... Figure 2 As shown, Figure 2 This is a particle distribution and concentration field distribution diagram before assimilation correction in a γ-radiation field reconstruction method based on Lagrange particle model parameter assimilation in Embodiment 1 of the present invention.
[0028] Step S106: The background state vector is constructed using the state parameters of all radioactive aerosol particles. The ensemble Kalman filter algorithm is used to dynamically update the background state vector to obtain the corrected assimilation state, which includes the updated spatial position of the radioactive aerosol particles. In some preferred embodiments, a background state vector is constructed using the state parameters of all radioactive aerosol particles. An ensemble Kalman filter algorithm is used to dynamically update the background state vector to obtain a corrected assimilation state. This includes: constructing a background state vector using the state parameters of all radioactive aerosol particles; setting multiple sets of samples to obtain multiple background state vectors and a sample state vector set; and in the case of multiple background state vectors and a sample state vector set, each background state vector is iteratively updated based on the ensemble Kalman filter algorithm to obtain multiple corrected assimilation states.
[0029] Optionally, multiple sample sets need to be initialized first, that is, an initial state set containing m samples needs to be constructed: in, Let m be the background state vector of the m-th sample.
[0030] In some preferred embodiments, when there are multiple background state vectors and a set of sample state vectors, each background state vector is iteratively updated based on an ensemble Kalman filter algorithm to obtain multiple corrected assimilation states. Each corrected assimilation state includes: determining the average value of the set of sample state vectors; obtaining the background field error based on the average value; determining the contribution of radioactive aerosol particles to the dose rate at the actual monitoring point to obtain the observation operator; obtaining the dose rate at the actual monitoring point to determine the observation error covariance matrix; determining the Kalman gain matrix based on the background field error, the observation operator, and the observation error covariance matrix; and using the Kalman gain matrix to fuse the dose rate at the actual monitoring point to update the radioactive aerosol particle state in the background state vector to obtain the corrected assimilation state.
[0031] Optionally, the background field error is obtained by subtracting the average value from the above sample set, specifically: Where B is the background field error. This is the nth background state vector. Let i be the background state vector. This represents the total number of sample state vectors in the set.
[0032] Optionally, the predicted particle distribution and corresponding dose rate field are obtained by advancing each set of samples using the Lagrange formula; the measured dose rate data are then fused using Kalman gain to update the particle state of each set member. Where: H is the observation operator, used to convert the state of the monitored radioactive aerosol particles into the observed dose rate; is the sample of observations; K is the Kalman gain matrix estimated using the sample covariance.
[0033] Wherein, the Kalman gain matrix K represents the assimilation weights, and the calculation formula is as follows: Where K is the Kalman gain matrix, B is the background field error, H is the observation operator, and R is the observation error covariance matrix.
[0034] Optionally, this method does not require linearization of the Lagrange particle model, is suitable for handling the highly nonlinear relationship between the Lagrange particle trajectory and the dose rate, and has strong stability and physical consistency.
[0035] Optionally, the observation operator and kernel function are constructed as follows: The observation operator H expresses the contribution of particles to the dose rate at the monitoring point, and the calculation formula is as follows: in Let j be the location of the j-th monitoring station, and let the kernel function be... Gaussian or inverse distance weighting is employed to adapt to any measurement point in three-dimensional space. To enhance spatial resolution and suppress spatial discrepancies caused by insufficient particle number, a local weighting strategy and interpolation mechanism are introduced to ensure the continuity and smoothness of the radiation field estimation. Through the observation operator H, the state vector A of the radioactive aerosol particles can be converted into the dose rate value at the monitoring point, where the source strength in the right-hand side of the equation is... and coordinates , are all part of vector A.
[0036] Optional strategies for constructing and adjusting the error covariance are as follows: The observation error covariance matrix R is set as a diagonal matrix to account for differences in sampling methods, equipment performance, and environmental conditions at different monitoring points, indicating that each observation error is independent of the others. in, This represents the measurement error variance of the m-th monitoring point. The error variance can be obtained through analysis of historical observation data, equipment calibration records, or empirical settings.
[0037] In some preferred embodiments, the average value of the sample state vector set is determined, and the background field error is obtained based on the average value, including: the background field error is calculated as follows: Where B is the background field error. This is the nth background state vector. Let i be the background state vector. This represents the total number of sample state vectors in the set.
[0038] In some preferred embodiments, the dose rate contribution of radioactive aerosol particles to the actual monitoring point is determined to obtain an observation operator, including: the observation operator is calculated as follows: in, For the observation operator, The observation operator contributing the dose rate to the j-th actual monitoring point by radioactive aerosol particles. Let be the source strength of the i-th radioactive aerosol particle. For kernel function, Let j be the location of the actual monitoring point. Let N be the spatial location of the i-th radioactive aerosol particle, and N be the total number of radioactive aerosol particles.
[0039] In some preferred embodiments, the Kalman gain matrix is determined based on the background field error, the observation operator, and the observation error covariance matrix, including: the Kalman gain matrix is calculated as follows: Where K is the Kalman gain matrix, B is the background field error, H is the observation operator, and R is the observation error covariance matrix.
[0040] In some preferred embodiments, the dose rate at the actual monitoring point is fused using the Kalman gain matrix to update the radioactive aerosol particle states in the background state vector, resulting in a corrected assimilation state. This corrected assimilation state is calculated as follows: in, For the nth modified assimilation state, Let K be the nth background state vector, and K be the Kalman gain matrix. This represents the dose rate at the actual monitoring point. for The observation operator.
[0041] Step S108: Based on the spatial position of the radioactive aerosol particles in the modified assimilation state, determine the optimized transport trajectory of the radioactive aerosol particles. In some preferred embodiments, an optimized transport trajectory for radioactive aerosol particles is determined based on the spatial position of the particles in the modified assimilation state. Where multiple modified assimilation states exist, the transport trajectory for each group of radioactive aerosol samples includes: in, For the description of the transportation trajectory, Let represent the spatial location of the i-th radioactive aerosol particle, and u represent the dominant wind field. This represents the random perturbation term in the random diffusion process.
[0042] Optionally, the transport of radioactive aerosol particles in the atmosphere is described by the following equation: in: Represents the spatial position of the i-th particle; u is the dominant wind field; This represents the random perturbation term in the random diffusion process.
[0043] Step S110: Based on the transport trajectory, a Gaussian kernel function is used to construct the local concentration field corresponding to the radioactive aerosol particles; In some preferred embodiments, a Gaussian kernel function is used to construct the local concentration field corresponding to the radioactive aerosol particles based on the transport trajectory. Where multiple radioactive aerosol transport trajectories exist, the local concentration field of each radioactive aerosol sample includes: The local concentration field is calculated as follows: in, In order to be in The local concentration field of radioactive aerosol particles at location t at time t. The source strength of the i-th radioactive aerosol particle For kernel function, It is a vector representation of the spatial location (x, y, z).
[0044] Optionally, during the simulation, radioactive aerosol particles continuously move according to the influence of wind field and turbulence, while maintaining their respective radioactive intensities. The contribution of radioactive aerosol particles to their spatial location can be determined through the kernel function. Spatial smoothing can be achieved, such as using a Gaussian kernel function to construct a local concentration field: in, For local concentration fields, it should be noted that... , Unlike coordinates (x, y, z), The vector in bold represents the position (x, y, z).
[0045] Step S112: Based on the local concentration field, determine the gamma dose rate corresponding to each radioactive aerosol particle to obtain the gamma radiation field.
[0046] In some preferred embodiments, the gamma dose rate corresponding to each radioactive aerosol particle is determined based on the local concentration field to obtain the gamma radiation field. This includes: determining multiple sets of gamma dose rates corresponding to each radioactive aerosol particle based on each local concentration field; averaging the multiple sets of gamma dose rates to obtain the average gamma radiation dose rate field; the average gamma dose rate is calculated as follows: in, The average gamma dose rate, The total number of sample state vectors. denoted as the gamma dose rate for group i.
[0047] Optional, the optimal set of particles after assimilation correction. The data is input into a Lagrange particle model, undergoes subsequent evolution, and the final dose rate D at any point in three-dimensional space is calculated. ,t) is expressed by the dose conversion operator F as: This modeling method can capture the spatial non-uniformity of pollutant transport under complex terrain and non-uniform atmospheric conditions, and is an important three-dimensional diffusion tool in nuclear accident emergency simulation.
[0048] Optionally, based on the final dose rates corresponding to the obtained multiple sets of samples, the average value is calculated to obtain the average gamma radiation dose rate field: The three-dimensional gamma radiation field results are visualized as isodose surfaces, isopleth maps, or heat maps, and can be applied to tasks such as emergency evacuation, dose early warning, and pollution source tracing. Figure 3 As shown, Figure 3 This is a particle distribution and concentration field distribution diagram after assimilation correction in a γ-radiation field reconstruction method based on Lagrange particle model parameter assimilation in Embodiment 1 of the present invention.
[0049] Through steps S102 to S112, the core properties of each radioactive aerosol particle in the Lagrange particle model (such as particle position and release intensity) are used as assimilation state variables. The Ensemble Kalman Filter (EnKF) method is employed, fusing measured environmental gamma dose rate data to dynamically adjust the particle trajectory and radioactivity intensity, thereby achieving three-dimensional reconstruction and refined assessment of the nuclear accident radiation field. This method is applicable to nuclear emergency scenarios under complex terrain and unsteady meteorological conditions, exhibiting high flexibility and scalability.
[0050] Example 2 This embodiment also provides a gamma radiation field reconstruction device based on Lagrange particle model parameter assimilation. This device 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 "device" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.
[0051] According to an embodiment of the present invention, an apparatus embodiment for implementing the above-described method for reconstructing a γ-radiation field based on Lagrange particle model parameter assimilation is also provided. Figure 4 This is a schematic diagram of a γ-radiation field reconstruction device based on Lagrange particle model parameter assimilation in Embodiment 2 of the present invention, as shown below. Figure 4 As shown, the above-mentioned device includes: a spatial position module 201, a state parameter module 202, an update module 203, a trajectory determination module 204, a concentration field module 205, and a radiation field module 206, wherein: Spatial location module 201, based on the Lagrange particle model, determines the spatial location of multiple radioactive aerosol particles; The state parameter module 202 constructs state parameters based on the spatial positions of multiple radioactive aerosol particles and the source strength of each aerosol particle. The update module 203 constructs a background state vector using the state parameters of all radioactive aerosol particles and uses an ensemble Kalman filter algorithm to dynamically update the background state vector to obtain the corrected assimilation state, which includes the updated spatial position of the radioactive aerosol particles. The trajectory determination module 204 determines the optimized transport trajectory of radioactive aerosol particles based on the spatial position of the radioactive aerosol particles in the corrected assimilation state. Concentration field module 205, based on the transport trajectory, uses a Gaussian kernel function to construct the local concentration field corresponding to the radioactive aerosol particles; Radiation field module 206, based on the local concentration field, determines the gamma dose rate corresponding to each radioactive aerosol particle, and obtains the gamma radiation field.
[0052] 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.
[0053] It should be noted that the spatial location module 201, state parameter module 202, update module 203, trajectory determination module 204, concentration field module 205, and radiation field module 206 mentioned above correspond to steps S102 to S112 in the embodiments. The instances and application scenarios implemented by the above modules and their corresponding steps are the same, but they are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run on a computer terminal.
[0054] 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.
[0055] The aforementioned gamma radiation field reconstruction device based on Lagrange particle model parameter assimilation may further include a processor and a memory. The aforementioned spatial position module 201, state parameter module 202, update module 203, trajectory determination module 204, concentration field module 205, and radiation field module 206 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.
[0056] 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.
[0057] 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 γ-radiation field reconstruction methods based on Lagrange particle model parameter assimilation.
[0058] 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.
[0059] Optionally, during program execution, the device containing the non-volatile storage medium performs the following functions: Based on the Lagrange particle model, determine the spatial positions of multiple radioactive aerosol particles; construct state parameters based on the spatial positions of the multiple radioactive aerosol particles and the source strength of each aerosol particle; construct a background state vector using the state parameters of all radioactive aerosol particles, and dynamically update the background state vector using an ensemble Kalman filter algorithm to obtain a corrected assimilation state, where the corrected assimilation state includes the updated spatial positions of the radioactive aerosol particles; determine the optimized transport trajectory of the radioactive aerosol particles based on the spatial positions of the radioactive aerosol particles in the corrected assimilation state; construct the local concentration field corresponding to the radioactive aerosol particles using a Gaussian kernel function based on the transport trajectory; and determine the gamma dose rate corresponding to each radioactive aerosol particle based on the local concentration field to obtain the gamma radiation field.
[0060] 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 gamma radiation field reconstruction methods based on Lagrange particle model parameter assimilation.
[0061] 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 Lagrange particle model parameter assimilation.
[0062] 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: determining the spatial positions of multiple radioactive aerosol particles based on the Lagrange particle model; constructing state parameters based on the spatial positions of the multiple radioactive aerosol particles and the source strength of each aerosol particle; constructing a background state vector using the state parameters of all radioactive aerosol particles, and dynamically updating the background state vector using an ensemble Kalman filter algorithm to obtain a corrected assimilation state, wherein the corrected assimilation state includes the updated spatial positions of the radioactive aerosol particles; determining the optimized transport trajectory of the radioactive aerosol particles based on the spatial positions of the radioactive aerosol particles in the corrected assimilation state; constructing the local concentration field corresponding to the radioactive aerosol particles using a Gaussian kernel function based on the transport trajectory; and determining the gamma dose rate corresponding to each radioactive aerosol particle based on the local concentration field to obtain the gamma radiation field.
[0063] 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: determining the spatial positions of multiple radioactive aerosol particles based on a Lagrange particle model; constructing state parameters based on the spatial positions of the multiple radioactive aerosol particles and the source strength of each aerosol particle; constructing a background state vector using the state parameters of all radioactive aerosol particles, and dynamically updating the background state vector using an ensemble Kalman filter algorithm to obtain a corrected assimilation state, wherein the corrected assimilation state includes the updated spatial positions of the radioactive aerosol particles; determining the optimized transport trajectory of the radioactive aerosol particles based on the spatial positions of the radioactive aerosol particles in the corrected assimilation state; constructing a local concentration field corresponding to the radioactive aerosol particles based on the transport trajectory using a Gaussian kernel function; and determining the gamma dose rate corresponding to each radioactive aerosol particle based on the local concentration field to obtain the gamma radiation field.
[0064] 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.
[0065] 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.
[0066] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device 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.
[0067] 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.
[0068] 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.
[0069] 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.
[0070] 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 parameter assimilation of a Lagrange particle model, characterized in that, include: Based on the Lagrange particle model, the spatial positions of multiple radioactive aerosol particles were determined. State parameters are constructed based on the spatial positions of the multiple radioactive aerosol particles and the source strength of each aerosol particle. A background state vector is constructed using the state parameters of all radioactive aerosol particles. An ensemble Kalman filter algorithm is used to dynamically update the background state vector to obtain a corrected assimilation state, wherein the corrected assimilation state includes the updated spatial position of the radioactive aerosol particles. Based on the spatial position of the radioactive aerosol particles in the modified assimilation state, the optimized transport trajectory of the radioactive aerosol particles is determined. Based on the transport trajectory, a local concentration field corresponding to the radioactive aerosol particles is constructed using a Gaussian kernel function. Based on the local concentration field, the gamma dose rate corresponding to each radioactive aerosol particle is determined, and the gamma radiation field is obtained.
2. The method for reconstructing a γ-radiation field based on Lagrange particle model parameter assimilation according to claim 1, characterized in that, A background state vector is constructed using the state parameters of all radioactive aerosol particles. An ensemble Kalman filter algorithm is then used to dynamically update this background state vector to obtain a corrected assimilation state, including: The background state vector is constructed using the state parameters of all radioactive aerosol particles. By setting multiple sets of samples, multiple background state vectors are obtained, resulting in a set of sample state vectors. In the case of multiple background state vectors and a set of sample state vectors, each background state vector is iteratively updated based on the ensemble Kalman filter algorithm to obtain multiple corrected assimilation states.
3. The method for reconstructing a γ-radiation field based on Lagrange particle model parameter assimilation according to claim 2, characterized in that, In the case of multiple background state vectors and a set of sample state vectors, each background state vector is iteratively updated based on an ensemble Kalman filter algorithm to obtain multiple corrected assimilation states, wherein each corrected assimilation state includes: Determine the average value of the sample state vector set, and obtain the background field error based on the average value; The dose rate contribution of the radioactive aerosol particles to the actual monitoring point is determined, and the observation operator is obtained. Obtain the dose rate at the actual monitoring point and determine the observation error covariance matrix; Based on the background field error, the observation operator, and the observation error covariance matrix, the Kalman gain matrix is determined. Using the Kalman gain matrix, the dose rate at the actual monitoring point is fused to update the radioactive aerosol particle state in the background state vector, thus obtaining the corrected assimilation state.
4. The method for reconstructing a γ-radiation field based on Lagrange particle model parameter assimilation according to claim 3, characterized in that, Determining the average value of the sample state vector set, and obtaining the background field error based on the average value, includes: The background field error is calculated as follows: Where B is the background field error. This is the nth background state vector. Let i be the background state vector. This represents the total number of sample state vectors in the set.
5. The method for reconstructing a γ-radiation field based on Lagrange particle model parameter assimilation according to claim 3, characterized in that, The determination of the dose rate contribution of the radioactive aerosol particles to the actual monitoring point, resulting in the observation operator, includes: The observation operator converts the background state vector of the particle into the dose rate value at the monitoring point, calculated as follows: in, For the observation operator, The observation operator contributes to the dose rate of radioactive aerosol particles at the j-th actual monitoring point. Let be the source strength of the i-th radioactive aerosol particle. For kernel function, Let j be the location of the actual monitoring point. Let represent the spatial location of the i-th radioactive aerosol particle, N be the total number of radioactive aerosol particles, and A be the set of sample state vectors of radioactive aerosol particles.
6. A method for reconstructing a γ-radiation field based on Lagrange particle model parameter assimilation according to any one of claims 4 or 5, characterized in that, The determination of the Kalman gain matrix based on the background field error, the observation operator, and the observation error covariance matrix includes: The Kalman gain matrix is calculated as follows: Where K is the Kalman gain matrix, B is the background field error, H is the observation operator, and R is the observation error covariance matrix.
7. The method for reconstructing a γ-radiation field based on Lagrange particle model parameter assimilation according to claim 6, characterized in that, The step of using the Kalman gain matrix to fuse the dose rate at the actual monitoring point and updating the radioactive aerosol particle state in the background state vector to obtain the corrected assimilation state includes: The corrected assimilation state is calculated as follows: in, For the nth modified assimilation state, Let K be the nth background state vector, and K be the Kalman gain matrix. This represents the dose rate at the actual monitoring point. for The observation operator.
8. The method for reconstructing a γ-radiation field based on Lagrange particle model parameter assimilation according to claim 3, characterized in that, The optimized transport trajectory of radioactive aerosol particles is determined based on the spatial position of the radioactive aerosol particles under the modified assimilation state. In the case of multiple modified assimilation states, the transport trajectory of each group of radioactive aerosol samples includes: in, For the description of the transportation trajectory, Let represent the spatial location of the i-th radioactive aerosol particle, and u represent the dominant wind field. This represents the random perturbation term in the random diffusion process.
9. The method for reconstructing a γ-radiation field based on Lagrange particle model parameter assimilation according to claim 8, characterized in that, Based on the transport trajectory, a Gaussian kernel function is used to construct the local concentration field corresponding to the radioactive aerosol particles. In the case of multiple radioactive aerosol transport trajectories, the local concentration field of each radioactive aerosol sample includes: The local concentration field is calculated as follows: in, In order to be in The local concentration field of radioactive aerosol particles at location t at time t. The source strength of the i-th radioactive aerosol particle For kernel function, It is a vector representation of the spatial location (x, y, z).
10. The method for reconstructing a γ-radiation field based on Lagrange particle model parameter assimilation according to claim 9, characterized in that, The step of determining the gamma dose rate corresponding to each radioactive aerosol particle based on the local concentration field to obtain the gamma radiation field includes: Based on each of the local concentration fields, the gamma dose rates corresponding to multiple groups of radioactive aerosol particles are determined. The average gamma dose rate of the multiple groups is averaged to obtain the average gamma radiation dose rate field. The average gamma dose rate is calculated as follows: in, The average gamma dose rate, The total number of sample state vectors. denoted as the gamma dose rate for group i.