CN113205596A - Radiation field inversion reconstruction method based on curved surface mean curvature and diffusion equation - Google Patents

Radiation field inversion reconstruction method based on curved surface mean curvature and diffusion equation Download PDF

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CN113205596A
CN113205596A CN202110582897.3A CN202110582897A CN113205596A CN 113205596 A CN113205596 A CN 113205596A CN 202110582897 A CN202110582897 A CN 202110582897A CN 113205596 A CN113205596 A CN 113205596A
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方晟
朱尚臻
曹建主
张亚军
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Abstract

The invention relates to a radiation field inversion reconstruction method based on a curved surface mean curvature and a diffusion equation, which comprises the following steps: s1, inputting and normalizing radiation field sampling data; s2, establishing an initial radiation field space matrix; s3, setting iterative computation parameters; s4, calculating a radiation field space matrix unAn average curvature matrix at a higher dimension; s5, updating u by utilizing discrete format of Cahn-Hilliard equationn+1(ii) a S6, judging whether the set iteration condition is reached, if so, entering the step S7, otherwise, returning to the step S4; and S7, performing inverse normalization on the obtained radiation field space result matrix to obtain a two-dimensional or three-dimensional radiation field inversion result. The invention can be directly carried out by sampling dataAnd (4) inverting the three-dimensional radiation field.

Description

Radiation field inversion reconstruction method based on curved surface mean curvature and diffusion equation
Technical Field
The invention relates to a radiation field inversion reconstruction method, a radiation field inversion reconstruction system, radiation field inversion reconstruction processing equipment and a computer storage medium based on curved surface mean curvature and diffusion equation, and relates to the technical field of nuclear application.
Background
In the process of safe utilization of nuclear energy, the conditions of building a nuclear radiation field and a reduction radiation field are necessary under most conditions of calculating radiation dose, searching a radiation source and the like. The nuclear radiation field may be constructed using a forward or inverse method. The current research is mainly focused on forward modeling methods, which are mainly applicable to the case of known radioactive sources. Under the environment of unknown sources, the inversion method shows superiority. Because the inversion method is irrelevant to the information of the radioactive source, the inversion method can play an important role under the condition that the radioactive substance is leaked accidentally or the radioactive source is lost and other unknown sources. In this case, the inversion method has important advantages in recovering the radiation field and determining the activity and position of the radiation source. The inversion calculation of the information such as the position of the source by using the measured value of the radiation field is difficult in the prior art, because the source position is related to a plurality of quantities in a point nuclear integration method, such as the distance between the point source and the measuring point, an accumulation factor, an exponential decay term and the like, the unknown quantity to be solved is more, and the solution of unknown parameters such as the source and the space size is a nonlinear inversion problem.
In the existing technology for calculating and reconstructing a radiation field by using an inversion method, when shielding is not considered, the dose rate at a measuring position and the distance from the measuring position to a point source are in inverse square relation, and space interpolation methods such as an inverse distance weighted average method and a kriging method are more methods used at present. The inverse distance weighted average method has the disadvantages that only distance weight is considered, the selection of weighted values is irregular, the uneven cluster data is easy to have the condition that the point data is obviously higher than the surrounding data points, and the unreasonable weighted values can cause larger deviation in calculation. The kriging method considers the rule of the spatial statistical distribution of the radiation field, and due to the statistical characteristic, the interpolation result at the position of the existing data is different from that of the existing data. The interpolation method has poor capability of restoring the geometric shapes of a plurality of radioactive source objects, and the current interpolation method for the direct inversion reconstruction of a three-dimensional radiation field also faces great difficulty. In some cases, the inversion result is not ideal due to the sparse sampling data.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a radiation field inversion reconstruction method, system, processing device and computer storage medium based on mean curvature of curved surface and diffusion equation, which can make radiation field data better restored.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a radiation field inversion reconstruction method based on a curved surface mean curvature and a diffusion equation, including:
s1, inputting and normalizing radiation field sampling data;
s2, establishing an initial radiation field space matrix;
s3, setting iterative computation parameters;
s4, calculating a radiation field space matrix unAn average curvature matrix at a higher dimension;
s5, updating u by utilizing discrete format of Cahn-Hilliard equationn+1；
S6, judging whether the set iteration condition is reached, if so, entering the step S7, otherwise, returning to the step S4;
and S7, performing inverse normalization on the obtained radiation field space result matrix to obtain a two-dimensional and/or three-dimensional radiation field inversion result.
Further, normalizing the radiation field sample data comprises: and (3) mapping the measured point data into a (0, 1) interval to be a decimal between 0 and 1 by dividing the gamma radiation dose rate data of each measured point by the maximum gamma radiation dose rate in the measured point.
Further, establishing the initial radiation field spatial matrix comprises: setting a space step length, namely the side length of a matrix grid, dispersing the whole radiation field space into a grid number m in the length direction, a grid number n in the width direction and a grid number c in the height direction, placing the normalized sampling data in the space matrix according to corresponding positions, and enabling the data of the non-sampling positions of the radiation field to be zero at the corresponding positions in the space matrix.
Further, iteratively calculating the parameter includes inputting a time stepLong and maximum number of iterations Nmax。
Further, a radiation field spatial matrix u is calculatednThe calculation of the mean curvature in the higher dimension includes:
the average curvature values c (u) of the three-dimensional matrix values in the higher dimension are:
Figure BDA0003086673370000021
the average curvature values c (u) of the two-dimensional matrix values in the higher dimension are:
Figure BDA0003086673370000022
the average curvature value c (u) of each position of different dimensional matrixes forms a corresponding average curvature matrix C (u)
Wherein u represents the initial radiation field space matrix value in the current iteration step, and ui(i ═ x, y, z) denotes the first derivative value of the spatial matrix in the i direction, uij(i ═ x, y, z; j ═ x, y, z) denotes the second derivative values of the spatial matrix in the i and j directions.
Further, u is updated using the discrete format of the Cahn-Hilliard equationn+1The method comprises the following steps:
s51, average curvature matrix C (u), radiation field space matrix unInitial spatial matrix u0Fourier transformation is carried out to obtain an average curvature matrix after Fourier transformation
Figure BDA0003086673370000031
Spatial matrix of radiation field
Figure BDA0003086673370000032
And an initial spatial matrix
Figure BDA0003086673370000033
S52 numerical solution form in discrete format using Cahn-Hilliard equationsTo obtain the (N + 1) th (N is 0,1,2 … … N)max-1) under iterative computation
Figure BDA0003086673370000034
The numerical solution is of the form:
Figure BDA0003086673370000035
wherein, delta is Laplace operator, delta t is time step length, f is initial space matrix after fast Fourier transform
Figure BDA0003086673370000036
∈，λ，C1，C2Are all constant term parameters, λ (f-U)n) Is a fidelity term obtained from the difference between the sampled measured value of the initial dose rate and the data obtained from each calculation at the sampling position;
s52, pair
Figure BDA0003086673370000037
Performing fast Fourier inverse transformation and taking a real part to obtain an updated radiation field space matrix un+1。
In a second aspect, the present invention further provides a radiation field inversion reconstruction system based on the mean curvature of a curved surface and the diffusion equation, the system comprising:
a radiation field data input module configured to input and normalize radiation field sampling data;
a radiation field spatial matrix establishing module configured to establish an initial radiation field spatial matrix;
the iteration parameter setting module is configured to set iteration calculation parameters;
a mean curvature matrix calculation module configured to calculate a radiation field spatial matrix unAn average curvature matrix at a higher dimension;
an iterative computation module configured to update u using a discrete format of a Cahn-Hilliard equationn+1；
An iteration number judging module configured to judge whether an iteration is reachedGeneration number Nmax；
And the radiation field space matrix calculation module is configured to perform inverse normalization on the obtained radiation field space result matrix u to obtain an inversion result of the two-dimensional or three-dimensional radiation field.
In a third aspect, the present invention further provides a processing apparatus, which at least includes a processor and a memory, where the memory stores a computer program, and the processor executes the computer program when executing the computer program to implement the radiation field inversion reconstruction method based on the mean curvature and the diffusion equation of the curved surface.
In a fourth aspect, the present invention further provides a computer storage medium having computer readable instructions stored thereon, where the computer readable instructions can be executed by a processor to execute the radiation field inversion reconstruction method based on the mean curvature of surface and the diffusion equation.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. according to the invention, because the diffusion property of the Cahn-Hilliard equation is used for inversion and the unconditionally stable convex splitting method is used for solving (please ensure that the content exists in the specific implementation mode), the radiation field data under the unknown position under iteration can be well restored, the relative deviation between the inversion data and the true value is low, and the problems that the calculation result of the inverse distance weighted average method is influenced by the cluster data and the weighted value and the interpolation result deviation at the existing data position of the Kriging method are solved;
2. according to the method, the difference between the sampling measured value and the data obtained by each calculation is added to obtain the fidelity term of each calculation, so that the problem of interpolation result deviation at the existing data position of the Kriging method cannot occur in the inversion result;
3. according to the invention, because the average curvature of the curved surface formed by radiation field data in a higher-dimensional space is used as a derivative function of a nonlinear potential flow function in a Cahn-Hilliard equation, the inversion result can better reduce the geometric shapes of one or more radioactive source objects and the space size of a source;
4. the shapes of one or more reconstructed radioactive source objects can be visually distinguished, and the problem of poor capability of an interpolation method for restoring the geometric shapes of one or more radioactive source objects is solved;
5. the invention has ideal three-dimensional radiation field inversion reconstruction results under less sampling data, and solves the problem that the three-dimensional radiation field is difficult to invert directly by interpolation and can not obtain information such as the position, the size and the like of a source;
in conclusion, the invention can directly perform ideal three-dimensional radiation field inversion by sampling data.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Like reference numerals refer to like parts throughout the drawings. In the drawings:
fig. 1 is a schematic flow chart of a radiation field inversion reconstruction method according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
It is to be understood that the terminology used herein is for the purpose of describing particular example embodiments only, and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" may be intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises," "comprising," "including," and "having" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order described or illustrated, unless specifically identified as an order of performance. It should also be understood that additional or alternative steps may be used.
For convenience of description, spatially relative terms, such as "inner", "outer", "lower", "upper", and the like, may be used herein to describe one element or feature's relationship to another element or feature as illustrated in the figures. Such spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures.
The method combines the Cahn-Hilliard equation to solve and radiate the property of the place in a plane or a space, enables the average curvature of the curved surface formed by the radiation field data calculated each time in a higher one-dimensional space (for example, for a plane radiation field, calculating the average curvature of the curved surface formed by the information in a three-dimensional space, and for a three-dimensional radiation field space, calculating the average curvature of the plane formed by the information in a four-dimensional space) space to be used as a derivative function of a nonlinear potential flow function in the Cahn-Hilliard equation, obtains Fidelity term (Fidelity term) drive of each calculation by the difference between the sampling measured value of the initial dose rate and the data obtained by each calculation at the sampling position, iteratively solves the Cahn-Hilliard equation modified by adding the Fidelity term by using fast Fourier transform, and obtains the complete inversion result of the original two-dimensional or three-dimensional radiation field after a certain number of iterations.
Example one
As shown in fig. 1, the radiation field inversion reconstruction method based on the mean curvature of curved surface and the diffusion equation provided in this embodiment includes the following steps:
s1, inputting and normalizing radiation field sampling data;
specifically, after gamma radiation dose rate measurements on different spatial positions are performed on a radiation field to be inverted and reconstructed, normalization processing is performed on the measured point data, that is, the gamma radiation dose rate data of each measured point is divided by the maximum gamma radiation dose rate in the measured point, so that the measured point data are mapped into a (0, 1) interval and become a decimal between 0 and 1.
S2, establishing an initial radiation field space matrix u0The locations where the radiation field is not sampled have zero data at corresponding locations in the spatial matrix.
Specifically, by defining the spatial range of the radiation field such as length, width, and height, a spatial step, that is, a side length of the matrix grid is set, and the entire radiation field space is discretized into m × n × c (the number of grids in the length direction × the number of grids in the width direction × the number of grids in the height direction) grid points. And placing the normalized sampling data in the spatial matrix according to the corresponding positions, wherein the positions of the radiation field which are not sampled have zero data at the corresponding positions in the spatial matrix.
For example, for a radiation field space of 10m × 10m × 10m, coordinates are represented by (x, y, z), where the measurement point is (5m,5m,5m), and the measurement point has a normalized value of 0.5. With 1m as the spatial step, the radiation field is spatially discretized into a spatial matrix having 10 × 10 × 10 grid points. The matrix coordinate (5,5,5) has a value of 0.5, and the coordinate points such as (5,5,4), (5,5,6) have a value of 0 corresponding to the positions of the non-measured points.
S3, inputting a time step and a maximum iteration number Nmax；
The time step of numerical calculation and the maximum iteration number are input, so that the calculation is iterated in a limited time and a limited number.
S4, calculating a radiation field space matrix unThe average curvature matrix in the higher dimension.
In particular unDenotes the nth (N is 0,1,2 … … N)max1) radiation field spatial matrix resulting from a first iteration, e.g. the first iteration through an initial radiation field spatial matrix u0Further obtaining u by iterative calculation of average curvature matrix under higher dimension1Second iterative calculation of u1Further iterative computation of the average curvature matrix under a higher dimension is carried out to obtain u2…, and so on, where the radiation field spatial matrix u is calculatednThe average curvature in the higher dimension is calculated as:
let u denote the initial radiation field spatial matrix value in this iteration step, ui(i＝x, y, z) represent the first derivative values of the spatial matrix in the i direction. u. ofij(i ═ x, y, z; j ═ x, y, z) denotes the second derivative values of the spatial matrix in the i and j directions.
The average curvature values c (u) of the three-dimensional matrix values in the higher dimension are:
Figure BDA0003086673370000061
for a matrix in a two-dimensional plane, the average curvature value c (u) of the two-dimensional matrix in the higher dimension is:
Figure BDA0003086673370000062
and forming a corresponding average curvature matrix C (u) by the average curvature values c (u) of all the positions of the different dimensional matrices.
S5, updating u by utilizing discrete format of Cahn-Hilliard equationn+1The specific process comprises the following steps:
s51, obtaining the average curvature matrix C (u) and the radiation field space matrix u in the S4 stepnInitial spatial matrix u0Fourier transformation is carried out to obtain an average curvature matrix after Fourier transformation
Figure BDA0003086673370000063
Spatial matrix of radiation field
Figure BDA0003086673370000064
And an initial spatial matrix
Figure BDA0003086673370000065
S52 finding the (N + 1) th order using numerical solution in discrete format of Cahn-Hilliard equation (N is 0,1,2 … … N)max-1) under iterative computation
Figure BDA0003086673370000066
The numerical solution is of the form:
Figure BDA0003086673370000067
wherein, delta is Laplace operator, delta t is time step length, f is initial space matrix after fast Fourier transform
Figure BDA0003086673370000068
∈，λ，C1，C2Are all constant term parameters, λ (f-U)n) Is a fidelity term derived from the difference between the actual sampled value of the initial dose rate and the data obtained at each calculation at the sampling location.
S52, pair
Figure BDA0003086673370000069
Performing fast Fourier inverse transformation and taking a real part to obtain an updated radiation field space matrix un+1。
S6, judging whether the iteration number N is reachedmaxIf yes, go to step S7, otherwise return to step S4;
and S7, performing inverse normalization on the obtained radiation field space result matrix u to obtain a two-dimensional or three-dimensional complete inversion result of the radiation field.
Specifically, all the numerical values of the obtained radiation field space result matrix u are multiplied by the maximum gamma radiation dose rate in the measuring point in the step S1, and inverse normalization is performed to obtain a two-dimensional or three-dimensional complete inversion matrix value of the radiation field, where the numerical value of the matrix point is the gamma radiation dose rate inverted at the corresponding space position. The inversion result can be used for grasping the radiation field distribution situation in nuclear activity places such as nuclear material disposal and nuclear waste radiochemical treatment after the nuclear power station is retired through rapid inversion, and planning production scenes such as nuclear personnel working routes.
Example two
The first embodiment provides a radiation field inversion reconstruction method based on the mean curvature of a curved surface and a diffusion equation, and correspondingly, the first embodiment provides a radiation field inversion reconstruction system. The radiation field inversion reconstruction system provided in this embodiment can implement the radiation field inversion reconstruction based on the curved surface mean curvature and the diffusion equation in the first embodiment, and the system can be implemented by software, hardware, or a combination of software and hardware. For example, the system may comprise integrated or separate functional modules or units to perform the corresponding steps in the method of an embodiment. Since the radiation field inversion reconstruction system of the present embodiment is substantially similar to the method embodiment, the description process of the present embodiment is relatively simple, and reference may be made to part of the description of the first embodiment for relevant points.
The radiation field inversion reconstruction system based on the mean curvature of the curved surface and the diffusion equation provided by the embodiment includes:
a radiation field data input module configured to input and normalize radiation field sampling data;
a radiation field spatial matrix building module configured to build an initial radiation field spatial matrix u0The non-sampled position of the radiation field has zero data at the corresponding position in the space matrix
An iteration parameter setting module configured to input a time step and a maximum iteration number Nmax；
An average curvature matrix calculation module for calculating a radiation field space matrix unThe average curvature matrix in the higher dimension.
An iterative computation module configured to update u using a discrete format of a Cahn-Hilliard equationn+1
An iteration number judging module configured to judge whether the iteration number N is reachedmax；
And the radiation field space matrix calculation module is configured to perform inverse normalization on the obtained radiation field space result matrix u to obtain a two-dimensional or three-dimensional complete inversion result of the radiation field.
EXAMPLE III
The present embodiment provides a processing device for implementing the radiation field inversion reconstruction method based on the mean curvature of curved surface and the diffusion equation provided in the first embodiment, where the processing device may be a processing device for a client, such as a mobile phone, a notebook computer, a tablet computer, a desktop computer, and the like, so as to execute the radiation field inversion reconstruction method in the first embodiment.
The processing equipment comprises a processor, a memory, a communication interface and a bus, wherein the processor, the memory and the communication interface are connected through the bus so as to complete mutual communication. The memory stores a computer program executable on the processor, and the processor executes the radiation field inversion reconstruction method based on the mean curvature of the curved surface and the diffusion equation provided in the embodiment.
Preferably, the Memory may be a high-speed Random Access Memory (RAM), and may also include a non-volatile Memory, such as at least one disk Memory.
Preferably, the processor may be various general processors such as a Central Processing Unit (CPU), a Digital Signal Processor (DSP), and the like, which are not limited herein.
Example four
The radiation field inversion reconstruction method based on the mean curvature of surface and the diffusion equation in this embodiment is embodied as a computer program product, which may include a computer readable storage medium having computer readable program instructions for executing the radiation field inversion reconstruction method based on the mean curvature of surface and the diffusion equation in this embodiment.
The computer readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any combination of the foregoing.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: it is to be understood that modifications may be made to the above-described arrangements in the embodiments or equivalents may be substituted for some of the features of the embodiments without departing from the spirit or scope of the present invention.

Claims (9)

1. A radiation field inversion reconstruction method based on the mean curvature and the diffusion equation of a curved surface is characterized by comprising the following steps:
s1, inputting and normalizing radiation field sampling data;
s2, establishing an initial radiation field space matrix;
s3, setting iterative computation parameters;
s4, calculating a radiation field space matrix unAn average curvature matrix at a higher dimension;
s5, updating u by utilizing discrete format of Cahn-Hilliard equationn+1；
S6, judging whether the set iteration condition is reached, if so, entering the step S7, otherwise, returning to the step S4;
and S7, performing inverse normalization on the obtained radiation field space result matrix to obtain a two-dimensional or three-dimensional radiation field inversion result.
2. The radiation field inversion reconstruction method based on the mean curvature of surface and the diffusion equation as claimed in claim 1, wherein normalizing the radiation field sampling data comprises:
and (3) mapping the measured point data into a (0, 1) interval to be a decimal between 0 and 1 by dividing the gamma radiation dose rate data of each measured point by the maximum gamma radiation dose rate in the measured point.
3. The radiation field inversion reconstruction method based on the mean curvature of surface and the diffusion equation as claimed in claim 1, wherein the establishing of the initial radiation field spatial matrix comprises:
setting a space step length, namely the side length of a matrix grid, dispersing the whole radiation field space into a grid number m in the length direction, a grid number n in the width direction and a grid number c in the height direction, placing the normalized sampling data in the space matrix according to corresponding positions, and enabling the data of the non-sampling positions of the radiation field to be zero at the corresponding positions in the space matrix.
4. The radiation field inversion reconstruction method based on the mean curvature of surface and the diffusion equation as claimed in claim 1, wherein the iterative computation parameters include input time step and maximum iteration number Nmax。
5. The radiation field inversion reconstruction method based on the mean curvature of surface and the diffusion equation as claimed in one of claims 1 to 4, wherein a radiation field space matrix u is calculatednThe calculation of the mean curvature in the higher dimension includes:
the average curvature values c (u) of the three-dimensional matrix values in the higher dimension are:
Figure FDA0003086673360000011
the average curvature values c (u) of the two-dimensional matrix values in the higher dimension are:
Figure FDA0003086673360000012
the average curvature value c (u) of each position of different dimensional matrixes forms a corresponding average curvature matrix C (u)
Wherein u represents the initial radiation field space matrix value in the current iteration step, and ui(i ═ x, y, z) denotes the first derivative value of the spatial matrix in the i direction, uij(i ═ x, y, z; j ═ x, y, z) denotes the second derivative values of the spatial matrix in the i and j directions.
6. The radiation field inversion reconstruction method based on the mean curvature of surface and the diffusion equation as claimed in claim 5, wherein the u is updated by using a discrete format of Cahn-Hilliard equationn+1The method comprises the following steps:
s51, average curvature matrix C (u), radiation field space matrix unInitial spatial matrix u0Fourier transformation is carried out to obtain an average curvature matrix after Fourier transformation
Figure FDA0003086673360000021
Spatial matrix of radiation field
Figure FDA0003086673360000022
And an initial spatial matrix
Figure FDA0003086673360000023
S52 finding the (N + 1) th order using numerical solution in discrete format of Cahn-Hilliard equation (N is 0,1,2 … … N)max-1) under iterative computation
Figure FDA0003086673360000024
The numerical solution is of the form:
Figure FDA0003086673360000025
wherein, delta is Laplace operator, delta t is time step length, f is initial space matrix after fast Fourier transform
Figure FDA0003086673360000026
∈，λ，C1，C2Are all constant term parameters, λ (f-U)n) Is a fidelity term obtained from the difference between the sampled measured value of the initial dose rate and the data obtained from each calculation at the sampling position;
s52, pair
Figure FDA0003086673360000027
Performing fast Fourier inverse transformation and taking a real part to obtain an updated radiation field space matrix un+1。
7. A radiation field inversion reconstruction system based on the mean curvature and the diffusion equation of a curved surface is characterized by comprising:
a radiation field data input module configured to input and normalize radiation field sampling data;
a radiation field spatial matrix establishing module configured to establish an initial radiation field spatial matrix;
the iteration parameter setting module is configured to set iteration calculation parameters;
a mean curvature matrix calculation module configured to calculate a radiation field spatial matrix unAn average curvature matrix at a higher dimension;
an iterative computation module configured to update u using a discrete format of a Cahn-Hilliard equationn+1；
An iteration number judging module configured to judge whether the iteration number N is reachedmax；
And the radiation field space matrix calculation module is configured to perform inverse normalization on the obtained radiation field space result matrix u to obtain an inversion result of the two-dimensional or three-dimensional radiation field.
8. A processing apparatus comprising at least a processor and a memory having a computer program stored thereon, wherein the processor executes when executing the computer program to implement the method of radiation field inversion reconstruction based on mean curvature of surface and diffusion equation according to any of claims 1 to 6.
9. A computer storage medium having computer readable instructions stored thereon which are executable by a processor to implement the method of radiation field inversion reconstruction based on mean curvature of surface and diffusion equations of any one of claims 1 to 6.
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KR20180080638A (en) * 2017-01-04 2018-07-12 강릉원주대학교산학협력단 Validation method of outgoing longwave radiation at top-of-atmosphere retrieved from Himawari-8 AHI sensor data
CN111667571A (en) * 2020-06-08 2020-09-15 南华大学 Nuclear facility source item three-dimensional distribution rapid reconstruction method, device, equipment and medium

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* Cited by examiner, † Cited by third party
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KR20180080638A (en) * 2017-01-04 2018-07-12 강릉원주대학교산학협력단 Validation method of outgoing longwave radiation at top-of-atmosphere retrieved from Himawari-8 AHI sensor data
CN111667571A (en) * 2020-06-08 2020-09-15 南华大学 Nuclear facility source item three-dimensional distribution rapid reconstruction method, device, equipment and medium

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