Gamma radiation field reconstruction method based on Gaussian puff model parameter assimilation

Abstract

The invention relates to a gamma radiation field reconstruction method based on Gaussian puff model parameter assimilation. The method comprises the following steps: constructing a Gaussian puff model; based on a Gaussian puff model, obtaining an initial state vector of the puff when the puff passes through a preset position; on the basis of a Kalman filtering algorithm, under the condition that the plurality of puff passes through the preset position, updating the initial state vector at the preset position, and obtaining a target state vector for accumulating the plurality of puff at the preset position; through the target state vector, determining the total concentration of the superposed puff at the preset position after the plurality of puff passes; and based on the total concentration of the puff, determining a gamma dose rate at a preset position, and obtaining a gamma radiation field at the preset position. According to the method, the position of the puff is converted into the state vector, and the state vector of the puff is updated based on the Kalman filtering algorithm, so that the dynamic process of the puff changing along with time is obtained, real-time adjustment of the puff is better met, and the accuracy of the gamma radiation field is improved.

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] The Gaussian plume model is an atmospheric pollution transport model suitable for discontinuous release and unsteady diffusion processes, often used to simulate the rapid evolution of pollutants during emergencies. Compared to the Gaussian smoke plume model, the plume model more realistically reflects the time-varying release characteristics of pollution sources, offering greater adaptability and superior spatial description capabilities, especially in short- to medium-scale nuclear emergency diffusion simulations. However, this model involves numerous parameters, such as the release location, diffusion radius, and intensity of each plume, which are often difficult to obtain accurately in real-world accidents, leading to significant biases in long-term simulations. To improve the predictive accuracy of the Gaussian plume model, data assimilation methods have been gradually applied in recent years to correct model parameters using real-time observation data, thereby improving the spatial distribution characteristics of the simulation results.

[0003] Currently, in the field of nuclear accident consequence assessment system development and research, there are few reports on three-dimensional environmental gamma radiation field reconstruction methods for Gaussian plume model parameter assimilation. Especially under complex meteorological conditions, there is a critical problem that the plume properties cannot be accurately obtained and adjusted in real time, affecting the reliability of simulated gamma radiation field results.

[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 smoke model parameter assimilation, aiming to solve the technical problems existing in the prior art.

[0006] The present invention adopts the following technical solution: A Gaussian smoke cloud model is constructed. Based on the Gaussian smoke cloud model, the initial state vector of the smoke cloud at a preset location is obtained. Based on the Kalman filter algorithm, when multiple smoke clouds pass through the preset location, the initial state vector at the preset location is updated to obtain the target state vector of the accumulated multiple smoke clouds at the preset location. The total concentration of the smoke cloud after the multiple smoke clouds pass through the preset location is determined by the target state vector. Based on the total concentration of the smoke cloud, the γ dose rate at the preset location is determined to obtain the γ radiation field at the preset location.

[0007] Optionally, the construction of the Gaussian smoke model includes: constructing a coordinate system with the smoke generation location as the origin; pre-generating multiple smoke clouds and determining the concentration distribution of each smoke cloud; representing the concentration distribution of each smoke cloud using a three-dimensional Gaussian function based on the coordinate system; and linearly superimposing the three-dimensional Gaussian functions corresponding to the concentrations of multiple smoke clouds to obtain a Gaussian smoke model, wherein the Gaussian smoke model is used to represent the process of simulating the movement of multiple smoke clouds.

[0008] Optionally, obtaining the initial state vector of the smoke plume at a preset location based on the Gaussian smoke plume model includes: determining the leak location of the smoke plume and the average wind speed at the leak location, wherein the smoke plume is discharged into the upper atmosphere through a chimney, the lower end of the chimney is connected to the smoke plume's generation location, and the upper end of the chimney is connected to the smoke plume's leak location; determining the smoke plume's position and source strength at the preset location; and constructing the initial state vector of the smoke plume based on the average wind speed at the leak location, the smoke plume's position, and the source strength, wherein the initial state vector of the smoke plume is as follows: in, Let be the initial state vector of the smoke plume, u be the average wind speed at the leak location, (x, y, z) be the spatial coordinates of the smoke plume, and q be the source strength of the smoke plume.

[0009] Optionally, the Kalman filter-based algorithm, when multiple smoke plumes pass through preset positions, updates the initial state vector at the preset positions to obtain a target state vector accumulating the multiple smoke plumes at the preset positions. This includes: determining the predicted smoke plume concentration of the smoke plumes passing through the preset positions using an observation operator matrix and the initial state vector, wherein the observation operator matrix is ​​used to indicate the operator that maps the initial state vector, which indicates the system state, to the smoke plume concentration, which indicates the actual physical quantity; acquiring the detected smoke plume concentration at the preset positions using a detection device; determining the observation error by comparing the detected smoke plume concentration with the predicted smoke plume concentration; based on the initial state vector and the Gaussian smoke plume model, setting a preset smoke plume movement direction, obtaining the new position of the smoke plume after the preset movement, and obtaining a preset state vector; and correcting the preset state vector using Kalman gain to obtain the target state vector.

[0010] Optionally, the step of obtaining the preset state vector by pre-setting the smoke cloud movement direction based on the initial state vector and the Gaussian smoke cloud model, and obtaining the new position of the smoke cloud after the preset movement, includes: the prediction equation of the preset state vector is as follows: in, For the preset state vector, Let be the state transition function of the system. Let be the initial predicted state vector, u be the average wind speed at the leak location, and t be the time it takes for the plume to be released.

[0011] Optionally, the step of using Kalman gain to correct the preset state vector to obtain the target state vector includes: The target state vector is as follows: in, For the preset state vector, Let K be the target state vector, K be the Kalman gain, and d be the observation error.

[0012] Optionally, determining the total concentration of the smoke plumes after multiple smoke plumes have passed through the target state vector includes: determining the movement process of the multiple smoke plumes based on the target state vector; determining the basic data of each smoke plume based on the movement process of the multiple smoke plumes, wherein the basic data includes the average wind speed at the leak location, the spatial coordinates of each smoke plume, and the source strength of each smoke plume; determining the concentration of each smoke plume among the multiple smoke plumes based on the basic data of each smoke plume; and superimposing the smoke plume concentrations corresponding to the multiple smoke plumes to obtain the total smoke plume concentration.

[0013] Optionally, the step of summing the smoke concentrations corresponding to multiple smoke plumes to obtain the total smoke plume concentration includes: the total smoke plume concentration is as follows: Where (x, y, z) are the spatial coordinates of the smoke plume, t is the release time of the smoke plume, and i is the i-th smoke plume released in the Gaussian smoke plume model. Let u be the source strength of the i-th smoke plume, and u be the average wind speed at the leak location. The diffusion parameter on the x-axis is expressed as the standard deviation of concentration. Here, represents the diffusion parameter along the y-axis as expressed in terms of concentration standard deviation. Let be the diffusion parameter on the z-axis expressed as the standard deviation of concentration, and h be the effective height of the plume leakage location.

[0014] Optionally, based on the smoke plume concentration, determining the γ dose rate at a preset location to obtain the γ radiation field at the preset location includes: based on the smoke plume concentration, obtaining the γ-ray photon flux rate, wherein the photon flux rate is used to indicate the total energy of all rays in the air. The ray passes through the point ( The photon flux rate formed at the location; determining the energy of the radionuclide. The branching ratio is determined; the effective dose of the nuclide in the smoke concentration and the proportionality factor of the air absorbed dose are determined; based on the photon flux, the branching ratio and the proportionality factor, the γ dose rate is determined; based on the γ dose rate, the γ radiation field at the preset location is obtained.

[0015] Optionally, determining the γ dose rate based on the photon flux, the branching ratio, and the scaling factor includes: the γ dose rate is calculated as follows: in, For point ( The γ dose rate; ω is the ratio factor between the effective dose of the radionuclide in the smoke plume concentration and the air-absorbed dose. The conversion factor; The linear energy absorption coefficient; ρ is the energy of the ray; ρ is the density of air. For nuclide n at a specific energy The branch ratio below, and All the energy in the air is The ray passes through the point ( The photon flux formed at the location.

[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 smoke cloud model is constructed; based on the Gaussian smoke cloud model, the initial state vector of the smoke cloud at a preset position is obtained; based on the Kalman filter algorithm, when multiple smoke clouds pass through the preset position, the initial state vector at the preset position is updated to obtain the target state vector of the accumulated multiple smoke clouds at the preset position; through the target state vector, the total concentration of the smoke cloud after the multiple smoke clouds have passed through the preset position is determined; based on the total concentration of the smoke cloud, the γ dose rate at the preset position is determined to obtain the γ radiation field at the preset position. This achieves the goal of transforming the position of the smoke cloud into a state vector, updating the state vector of the smoke cloud based on the Kalman filter algorithm, thereby obtaining the dynamic process of the smoke cloud changing over time, which is more consistent with the real-time adjustment of the smoke cloud and thus improves the accuracy of the γ radiation field. 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 smoke model parameter assimilation in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the Gaussian smoke model construction in a γ-radiation field reconstruction method based on Gaussian smoke model parameter assimilation in Embodiment 1 of the present invention; Figure 3This is a schematic diagram of the Gaussian smoke model parameter assimilation result in a γ-radiation field reconstruction method based on Gaussian smoke model parameter assimilation in Embodiment 1 of the present invention; Figure 4 This refers to the change in monitoring point information of Gaussian smoke puff model parameter assimilation in a γ-radiation field reconstruction method based on Gaussian smoke puff model parameter assimilation in Embodiment 1 of the present invention. Figure 5 This is a schematic diagram of a γ-radiation field reconstruction system based on Gaussian smoke 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 Gaussian plume is a form of the Gaussian model, a concept and model used to describe the diffusion of pollutants in the atmosphere. It assumes that polluted air masses are discrete plumes, and that the concentration of pollutants within the plume follows a normal distribution (i.e., a Gaussian distribution) in both the horizontal and vertical directions.

[0022] To address the problems existing in related technologies, this application provides a method for reconstructing a γ-radiation field based on Gaussian smoke model parameter assimilation.

[0023] Example 1 This embodiment provides a method for reconstructing the γ-radiation field based on Gaussian smoke 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 smoke model parameter assimilation in Embodiment 1 of the present invention. The method includes: Step S102: Construct a Gaussian smoke model; Optionally, the Gaussian plume model models the discontinuous release process as multiple wind-drifting and diffusing pollution "plumes," with the concentration distribution of each plume represented by a three-dimensional Gaussian function. The overall pollutant concentration is a linear superposition of all plumes, such as... Figure 2 As shown, the actual smoke cloud is transformed into a computer model through the above method in order to simulate and study experiments.

[0024] In some preferred embodiments, constructing a Gaussian smoke cloud model includes: constructing a coordinate system with the smoke cloud generation location as the origin; pre-generating multiple smoke clouds and determining the concentration distribution of each smoke cloud; representing the concentration distribution of each smoke cloud using a three-dimensional Gaussian function based on the coordinate system; and linearly superimposing the three-dimensional Gaussian functions corresponding to the concentrations of multiple smoke clouds to obtain a Gaussian smoke cloud model, wherein the Gaussian smoke cloud model is used to represent the process of simulating the movement of multiple smoke clouds.

[0025] Optionally, a Gaussian plume model needs to be constructed. To simulate the plume's trajectory, a coordinate system needs to be built to characterize the pollution status of the plume in different directions during its actual generation. Secondly, it is also necessary to determine the wind speed and direction during the actual process. Wind speed affects the plume's diffusion state, and wind direction affects the plume's diffusion direction. At the same time, it is also necessary to determine the plume's generation location and the chimney height. The plume is generated by pollutants on the ground and guided into the upper atmosphere by the chimney. Different chimney heights result in different source strengths of the plume, which may cause various plume variations. Therefore, it is necessary to consider the above-mentioned factors affecting the plume to determine the Gaussian plume model so as to determine the accurate plume trajectory and thus the accurate gamma radiation field.

[0026] Step S104: Based on the Gaussian smoke model, obtain the initial state vector of the smoke at the preset position. In some preferred embodiments, based on a Gaussian smoke plume model, the initial state vector of the smoke plume at a predetermined location is obtained, including: determining the leak location of the smoke plume and the average wind speed at the leak location, wherein the smoke plume is discharged into the upper atmosphere through a chimney, the lower end of the chimney is connected to the smoke plume's generation location, and the upper end of the chimney is connected to the smoke plume's leak location; determining the smoke plume's position and source strength at the predetermined location; and constructing the initial state vector of the smoke plume based on the average wind speed at the leak location, the smoke plume's position, and the source strength, wherein the initial state vector of the smoke plume is as follows: in, Let be the initial state vector of the smoke plume, u be the average wind speed at the leak location, (x, y, z) be the spatial coordinates of the smoke plume, and q be the source strength of the smoke plume.

[0027] Optionally, the numerical values ​​in the Gaussian plume model need to be initialized. During initialization, the initial state of each plume needs to be determined, including its spatial coordinates (x, y, z), source strength q, and average wind speed u at the leak location. The state vectors of all plumes are then defined. It can be represented as: in, It is the transpose of the vector.

[0028] Optionally, taking a real wind field with a wind direction of 45 degrees as an example, the background wind field is set to 0 degrees during calculation. The assimilation state variables are shown in the initial state vector of the smoke plume, and the final assimilation result is as follows. Figure 3 As shown, the assimilated field is basically consistent with the real field. Specifically, after multiple experiments, the experimental data results are as follows: Figure 4 As shown.

[0029] Furthermore, a covariance matrix P needs to be set for each state vector based on the Kalman filter algorithm to represent the uncertainty of the smoke in the Gaussian smoke model. The size of the covariance matrix depends on the error of the initial state.

[0030] Step S106: Based on the Kalman filter algorithm, when multiple smoke plumes pass through the preset position, update the initial state vector at the preset position to obtain the target state vector of the accumulated multiple smoke plumes at the preset position. Optionally, Ensemble Kalman Filtering (ENKF) is a powerful algorithm for state estimation that combines observed data with a numerical model (Gaussian puff model) to dynamically correct the Gaussian puff model's predictions, thereby improving prediction accuracy. This algorithm adapts to system uncertainties by updating the states of multiple ensemble members and iteratively updates at different time steps.

[0031] In some preferred embodiments, based on the Kalman filter algorithm, when multiple smoke plumes pass through preset positions, the initial state vector at the preset positions is updated to obtain the target state vector accumulated from the multiple smoke plumes at the preset positions. This includes: determining the predicted smoke plume concentration of the smoke plumes passing through the preset positions using the observation operator matrix and the initial state vector, wherein the observation operator matrix is ​​used to indicate the operator that maps the initial state vector, which indicates the system state, to the smoke plume concentration, which indicates the actual physical quantity; acquiring the detected smoke plume concentration at the preset positions using a detection device; determining the observation error by comparing the detected smoke plume concentration with the predicted smoke plume concentration; based on the initial state vector and the Gaussian smoke plume model, setting the preset smoke plume movement direction, acquiring the new position of the smoke plume after the preset movement, and obtaining the preset state vector; and correcting the preset state vector using Kalman gain to obtain the target state vector.

[0032] Optionally, during the prediction phase, predictions are made based on the previous initial state vector and the Gaussian smoke model. For each time step t, the Gaussian smoke model calculates the new location and concentration distribution of the smoke plume, thus obtaining new state variables. The prediction equation is: in, This represents the state transition function, reflecting the changes in the smoke plume's state variables. At any given moment, each smoke plume has fixed state variables, meaning its spatial location, source strength, and wind speed are all fixed. The role of M is to predict the state variables at time t+1 based on the state variables at time t. For the preset state vector, Let be the initial predicted state vector, u be the average wind speed at the leak location, and t be the time it takes for the plume to be released.

[0033] Optionally, based on the aforementioned dynamic preset state vectors, at time step t+1, a covariance matrix P is set for each state vector to determine a series of possible generated γ-radiation dose rate fields, as shown below: Specifically, each set member generates a series of possible radiation dose rate fields by solving the above equation. Assuming there are N set members, the predicted state vector of the i-th set member is a. i The covariance of the predicted state is P. i .

[0034] Optionally, during the update phase, real-time monitoring data (such as the concentration of radionuclides in the air or the radiation dose rate) is introduced, and the introduced monitoring data is compared with the model predictions. The core of the update process is to calculate the observation error and correct the state vector and covariance matrix based on the error.

[0035] Specifically, observation values Compared with the predicted value The difference (i.e., observation error) Q is expressed as: in, The observation operator matrix is ​​then used. The state vector is then updated using the Kalman gain K. The Kalman gain K is calculated using the following formula: Where R is the observation noise covariance matrix.

[0036] Optionally, the Kalman filter algorithm iterates at each time step until the distributions of all ensemble members converge. The final ensemble mean is the optimal state estimate of the model, representing the most likely radiation dose rate field distribution.

[0037] In some preferred embodiments, based on the initial state vector and the Gaussian smoke model, the smoke movement direction is preset, the new position of the smoke after the preset movement is obtained, and the preset state vector is obtained, including: the prediction equation of the preset state vector is as follows: in, For the preset state vector, Let be the state transition function of the system. Let be the initial predicted state vector, u be the average wind speed at the leak location, and t be the time it takes for the plume to be released.

[0038] In some preferred embodiments, the preset state vector is corrected using Kalman gain to obtain the target state vector, including: The target state vector is as follows: in, For the preset state vector, Let K be the target state vector, K be the Kalman gain, and Q be the observation error.

[0039] Step S108: Determine the total concentration of smoke plumes superimposed after multiple smoke plumes pass through the target state vector; In some preferred embodiments, determining the total concentration of multiple smoke plumes superimposed after passing through a preset location using a target state vector includes: determining the movement process of multiple smoke plumes based on the target state vector; determining the basic data of each smoke plume based on the movement process of multiple smoke plumes, wherein the basic data includes the average wind speed at the leak location, the spatial coordinates of each smoke plume, and the source strength of each smoke plume; determining the concentration of each smoke plume among the multiple smoke plumes based on the basic data of each smoke plume; and superimposing the smoke plume concentrations corresponding to the multiple smoke plumes to obtain the total smoke plume concentration.

[0040] Optionally, in determining the target state vector back, It will also correspond By obtaining certain data, we can obtain the real-time changing coordinates of the smoke plume, the average wind speed at the leak location, and the source strength of the smoke plume. Based on the changes in the smoke plume data, we can change the movement state of the smoke plume in the Gaussian smoke plume model, thereby making the assimilated data of the smoke plume closer to the real state.

[0041] In some preferred embodiments, the smoke concentrations corresponding to multiple smoke plumes are summed to obtain the total smoke plume concentration, including: The total concentration of the smoke plume is as follows: Where (x, y, z) are the spatial coordinates of the smoke plume, t is the release time of the smoke plume, and i is the i-th smoke plume released in the Gaussian smoke plume model. Let u be the source strength of the i-th smoke plume, and u be the average wind speed at the leak location. The diffusion parameter on the x-axis is expressed as the standard deviation of concentration. Here, represents the diffusion parameter along the y-axis as expressed in terms of concentration standard deviation. Let be the diffusion parameter on the z-axis expressed as the standard deviation of concentration, and h be the effective height of the plume leakage location.

[0042] Step S110: Based on the total concentration of the smoke plume, determine the γ dose rate at the preset location to obtain the γ radiation field at the preset location.

[0043] Optionally, since the smoke concentration field is directly related to the radioactivity of the nuclides, the radiation dose rate field can be further calculated. For a fixed receiver point (x0, y0, z0) in space, its gamma radiation dose rate is the sum of the irradiation from different energy rays of all radionuclides in the air. The standard formula for calculating the gamma radiation dose rate is: Where G is a point The gamma radiation dose rate is Sv / s; ω is the ratio factor between the effective dose and the absorbed dose in air, Sv / Gy, representing the radiation risk to the organism; K = 1.6 × 10⁻⁶.-13 It is the conversion factor, J / MeV; It is the linear energy absorption coefficient, m- 1 ; It is the energy of the radiation, MeV; ρ is the air density, kg / m3. It is the nuclide n at a specific energy The branch ratio below, and All the energy in the air is The ray passes through the point ( Photon flux formed at position m -2 ·s -1 .

[0044] Optionally, for any three-dimensional distribution C of radionuclides n (x,y,z), at point ( The photon flux generated by monoenergetic rays at point () can be calculated using the following formula: in, B ( E γ , μ · d The cumulative factor () represents the flux contribution of a single photon to the computation point. B ( E γ , μ · d These may have different forms, but they are all composed of functions containing photon energy, linear attenuation coefficient, and distance. They can be taken as... B ( E γ , μ · d )=1+ kμd ,in, k =( μ-μ a ) / μ a μ is the linear attenuation factor of air determined by photon energy, and d is the value from the calculation point (μ is the linear attenuation factor of air). The distance from the location (x, y, z) of the radioactive nuclide.

[0045] In the standard dose rate calculation method described above, the equation for photon flux rate can be converted into a convolutional form. This can be achieved using vector... r o =[ x o , y o , z o ] Tand r =[ x , y , z ] T Let represent the point in the three-dimensional coordinate calculation and any point in the integration space, that is, represent the spatial position in vector form, then we have: Where ||·||2 represents the 2-norm. Substituting d into the above formula for photon flux, the formula for calculating photon flux is expressed as: When a specific photon energy Eγ is applied, the linear decay is constant, and the photon flux can be expressed as: r o - r Functions: in: Based on the above calculation process, for ease of practical operation, F is defined as the operator that converts pollutant concentration into γ dose rate G, and the formula for calculating γ radiation dose rate is obtained as follows: Optional, based on gamma radiation dose rate This allows us to determine the gamma radiation field.

[0046] In some preferred embodiments, determining the γ dose rate at a preset location based on the smoke plume concentration to obtain the γ radiation field at the preset location includes: obtaining the photon flux rate of γ rays based on the smoke plume concentration, wherein the photon flux rate is used to indicate the total energy of all rays in the air. The ray passes through the point ( The photon flux rate formed at the location; determining the energy of the radionuclide. The branching ratio is determined; the effective dose of the nuclide in the smoke concentration and the proportionality factor of the air absorbed dose are determined; the γ dose rate is determined based on the photon flux, branching ratio and proportionality factor; and the γ radiation field at the preset location is obtained based on the γ dose rate.

[0047] In some preferred embodiments, the γ dose rate is determined based on the photon flux, branching ratio, and scaling factor, including: the γ dose rate is calculated as follows: in, For point ( The γ dose rate; ω is the ratio factor between the effective dose of the radionuclide in the smoke plume concentration and the air-absorbed dose. The conversion factor; The linear energy absorption coefficient; ρ is the energy of the ray; ρ is the density of air. For nuclide n at a specific energy The branch ratio below, and All the energy in the air is The ray passes through the point ( The photon flux formed at the location.

[0048] Through the above steps S102 to S110, the position of the smoke plume is transformed into a state vector, and the state vector of the smoke plume is updated based on the Kalman filter algorithm, thereby obtaining the dynamic process of the smoke plume changing over time, which is more in line with the real-time adjustment of the smoke plume and thus improves the accuracy of the γ radiation field.

[0049] Example 2 This embodiment also provides a γ-radiation field reconstruction system based on Gaussian smoke 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.

[0050] According to embodiments of the present invention, a system embodiment for implementing the above-described γ-radiation field reconstruction method based on Gaussian smoke model parameter assimilation is also provided. Figure 5 This is a schematic diagram of a γ-radiation field reconstruction system based on Gaussian smoke model parameter assimilation in Embodiment 2 of the present invention, as shown below. Figure 5 As shown, the above system includes: a model construction module 201, an initial vector module 202, a target vector module 203, a total concentration module 204, and a radiation field module 205, wherein: Model module 201 is used to construct a Gaussian smoke model; The initial vector module 202 and the model construction module 201, based on the Gaussian smoke model, obtain the initial state vector of the smoke when it passes through a preset position; The target vector module 203 and the initial vector module 202, based on the Kalman filter algorithm, update the initial state vector at the preset position when multiple smoke clouds pass through the preset position, so as to obtain the target state vector of multiple smoke clouds at the preset position. The total concentration module 204 and the target vector module 203 determine the total concentration of the smoke plumes after multiple smoke plumes have passed through the preset position by using the target state vector. Radiation field module 205 and total concentration module 204 determine the γ dose rate at a preset location based on the total concentration of the smoke plume, and obtain the γ radiation field at the preset location.

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

[0052] It should be noted that the aforementioned model construction module 201, initial vector module 202, target vector module 203, total concentration module 204, and radiation field module 205 correspond to steps S102 to S110 in the embodiments. The instances and application scenarios implemented by these modules and their corresponding steps are the same, but they are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the system, can run on a computer terminal.

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

[0054] The aforementioned γ-radiation field reconstruction system based on Gaussian smoke model parameter assimilation may further include a processor and a memory. The aforementioned model construction module 201, initial vector module 202, target vector module 203, total concentration module 204, and radiation field 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.

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

[0056] According to an embodiment of this application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program runs, it controls the device where the non-volatile storage medium is located to execute any of the above-mentioned γ-radiation field reconstruction methods based on Gaussian smoke model parameter assimilation.

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

[0058] Optionally, during program execution, the device containing the non-volatile storage medium is controlled to perform the following functions: based on the Gaussian smoke model, obtain the initial state vector of the smoke at the preset position; based on the Kalman filter algorithm, update the initial state vector at the preset position when multiple smokes pass through the preset position to obtain the target state vector of the accumulated multiple smokes at the preset position; determine the total concentration of the smoke after the multiple smokes pass through the preset position using the target state vector; and determine the γ dose rate at the preset position based on the total smoke concentration to obtain the γ radiation field at the preset position.

[0059] According to an embodiment of this application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any of the above-mentioned γ-radiation field reconstruction methods based on Gaussian smoke model parameter assimilation.

[0060] According to an embodiment of this application, an embodiment of a computer program product is also provided. Optionally, in this embodiment, the computer program product includes a computer program that, when executed by a processor, implements the steps of any of the above-described gamma radiation field reconstruction methods based on Gaussian smoke model parameter assimilation.

[0061] Optionally, when the above-mentioned computer program product is executed on a data processing device, it is suitable to execute an initialization program with the following method steps: based on a Gaussian smoke model, obtain the initial state vector of the smoke at a preset position; based on a Kalman filter algorithm, update the initial state vector at the preset position when multiple smokes pass through the preset position to obtain the target state vector of the accumulated multiple smokes at the preset position; determine the total concentration of the smoke after the multiple smokes have passed through the preset position using the target state vector; and determine the γ dose rate at the preset position based on the total smoke concentration to obtain the γ radiation field at the preset position.

[0062] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: based on a Gaussian smoke model, it obtains the initial state vector of the smoke cloud at a preset position; based on a Kalman filter algorithm, it updates the initial state vector at the preset position when multiple smoke clouds pass through the preset position, thereby obtaining a target state vector accumulating the multiple smoke clouds at the preset position; using the target state vector, it determines the total concentration of the smoke cloud after the multiple smoke clouds have passed through the preset position; based on the total smoke cloud concentration, it determines the γ dose rate at the preset position, thereby obtaining the γ radiation field at the preset position.

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

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

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

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

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

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

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

Claims

1. A method for reconstructing a gamma radiation field based on assimilation of Gaussian plume model parameters, characterized in that, The method comprises the following steps: constructing a Gaussian plume model; based on the Gaussian plume model, obtaining the initial state vector of the plume passing through the preset position; based on the Kalman filtering algorithm, updating the initial state vector at the preset position in the case of multiple plumes passing through the preset position to obtain the target state vector of the multiple plumes at the preset position; determining the total concentration of the plume superimposed after the multiple plumes pass through the preset position by using the target state vector; based on the total concentration of the plume, determining the gamma dose rate at the preset position to obtain the gamma radiation field at the preset position.

2. The method of claim 1, wherein the method is characterized by, The method comprises the following steps: constructing a coordinate system with the generation position of the plume as the origin; determining the concentration distribution of each plume by presetting multiple plumes; based on the coordinate system, representing the concentration distribution of each plume by using a three-dimensional Gaussian function; linearly superimposing the three-dimensional Gaussian functions corresponding to the concentrations of multiple plumes to obtain a Gaussian plume model, wherein the Gaussian plume model is used to represent the process of simulating the movement of multiple plumes.

3. The method of claim 1, wherein the method is characterized by, The method comprises the following steps: determining the leakage position of the plume and the average wind speed at the leakage position, wherein the plume is discharged into the upper atmosphere through a chimney, the lower end of the chimney is connected to the generation position of the plume, and the upper end of the chimney is connected to the leakage position of the plume; determining the plume position and the plume source intensity of the plume passing through the preset position; based on the average wind speed at the leakage position, the plume position, and the plume source intensity, constructing the initial state vector of the plume, wherein the initial state vector of the plume is as follows: where, is the initial state vector of the puff, u is the average wind speed at the leak location, (x, y, z) is the puff spatial coordinate location, and q is the puff source strength.

4. The method of claim 1, wherein the method is characterized by, The method comprises the following steps: determining the plume concentration prediction value of the plume passing through the preset position by using an observation operator matrix and the initial state vector, wherein the observation operator matrix is used to indicate an operator that maps the initial state vector of the marker system state to the plume concentration of the marker real physical quantity; obtaining the plume concentration detection value of the plume at the preset position based on a detection device; determining the observation error by using the plume concentration detection value and the plume concentration prediction value; based on the initial state vector and the Gaussian plume model, presetting the moving direction of the plume, obtaining the new position of the plume after presetting the movement, and obtaining the preset state vector; correcting the preset state vector by using the Kalman gain to obtain the target state vector.

5. The method of claim 4, wherein the method is characterized by, The method comprises the following steps: The prediction equation of the preset state vector is as follows: wherein, is the preset state vector, is the state transition function of the system, is the initial predicted state vector, u is the average wind speed at the leak location, and t is the plume release time.

6. The method of claim 5, wherein the method is characterized by, The method comprises the following steps: The target state vector is as follows: wherein, is a preset state vector, is a target state vector, K is a Kalman gain, and d is an observation error.

7. The method of claim 1, wherein the method is characterized by, The method comprises the following steps: based on the target state vector, determining the movement process of the multiple plumes by backstepping; Based on the moving process of the plurality of plumes, the basic data of each plume is determined reversely, wherein the basic data comprises an average wind speed at a leakage position, a spatial coordinate position of each plume and a source intensity of each plume; Based on the basic data of each plume, a concentration of each plume in the plurality of plumes is determined; The plume concentrations corresponding to the plurality of plumes are superimposed to obtain a total plume concentration.

8. The method of claim 7, wherein the method is characterized by, The plume concentrations corresponding to the plurality of plumes are superimposed to obtain a total plume concentration, comprising: The total plume concentration is as follows: wherein (x, y, z) is the puff spatial coordinate position, t is the puff release time, i is the i-th puff in the Gaussian puff model, is the i-th puff source strength, and u is the average wind speed at the leak location, is the x-axis diffusion parameter expressed in terms of concentration standard deviation, is the y-axis diffusion parameter expressed in terms of concentration standard deviation, is the z-axis diffusion parameter expressed in terms of concentration standard deviation, and h is the effective height of the puff leak location.

9. The method of claim 1, wherein the method is characterized by, Based on the plume concentration, a gamma dose rate at a preset position is determined to obtain a gamma radiation field at the preset position, comprising: Based on the smoke concentration, a photon flux rate of the gamma rays is obtained, wherein the photon flux rate is used to indicate a photon flux rate formed by rays with all energies passing through a point ( ) position in the air; determining branching ratios of radionuclides at energies below; A ratio factor of an effective dose of a nuclide in the plume concentration and an air absorbed dose is determined; Based on the photon flux rate, the branching ratio and the ratio factor, the gamma dose rate is determined; Based on the gamma dose rate, the gamma radiation field at the preset position is obtained.

10. The method of claim 9, wherein the method is based on assimilation of Gaussian plume model parameters. The gamma dose rate is determined based on the photon flux rate, the branching ratio and the ratio factor, comprising: The gamma dose rate is calculated as follows: The gamma dose rate is calculated as follows: where, is the gamma dose rate at point ( ); ω is the effective dose of the nuclide in the plume and the proportionality factor for air absorbed dose; is the conversion factor; is the linear energy absorption coefficient; is the photon energy; p is the air density; is the branching ratio for nuclide n at a specific energy ; and is the photon fluence rate at point ( ) due to all photons with energy in the air.

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