Calculation Method, System, Equipment and Medium for Source Distribution of Reactor Core Fuel Assembly

Abstract

The present application provides a method, system, device and medium for calculating the source distribution of a reactor core fuel assembly, relating to the technical field of nuclear reactors. The method for calculating the source distribution of a reactor core fuel assembly provided by the present application includes obtaining input parameters, where the input parameters include power distribution, fission nuclide fraction, fission spectrum, average number of particles released per fission, average energy released per fission, and source biasing factor; calculating the cumulative distribution function of source particles based on the input parameters; and performing source particle sampling based on the cumulative distribution function to determine the distribution parameters of source particles. The present application performs source particle sampling based on the cumulative distribution function of source particles to determine the distribution parameters of source particles, improves the shielding calculation efficiency based on the MC method, and provides support for the design of new reactor types.

Description

Technical Field

[0001] This application relates to the technical field of nuclear reactors, and particularly to a method, system, device and medium for calculating the source distribution of a fuel assembly in a reactor core. Background Art

[0002] Reactor shielding calculations and analyses usually adopt methods such as discrete ordinates (SN) and Monte Carlo (MC). The SN method features fast calculation speed and wide application range, and can be used for radiation shielding calculations of neutrons and photons. The MC method is a numerical analysis technique for estimating the solution of physical or mathematical problems through random sampling, with high calculation accuracy but low calculation efficiency, and is commonly used for shielding problems of complex geometry deep penetration types.

[0003] As an important input parameter for shielding calculations, the numerical result of the source term directly affects the accuracy of reactor shielding calculations. Calculating the source term needs to consider multiple factors such as the core power distribution, fuel assembly burnup, and the change of the number of fission neutrons with burnup, and also needs to consider the coordinate transformation between the source term and the shielding calculation model, etc., and the process is relatively complex. In addition, the distribution and sampling method of source particles also have a great influence on the calculation efficiency of the MC method. Summary of the Invention

[0004] In view of this, this application provides a method, system, device and medium for calculating the source distribution of a fuel assembly in a reactor core, so as to improve the shielding calculation efficiency based on the MC method.

[0005] In the first aspect, this application provides a method for calculating the source distribution of a fuel assembly in a reactor core, including:

[0006] Obtain input parameters, where the input parameters include power distribution, fission nuclide fraction, fission spectrum, average number of particles released per fission, average energy released per fission, and source biasing factor, and the source biasing factor includes one or more of the spatial biasing factor of the fuel rod grid, energy biasing factor, and particle biasing factor;

[0007] Calculate the cumulative distribution function of source particles based on the input parameters, where the cumulative distribution function includes one or more of the spatial cumulative distribution function of the fuel rod grid, the energy cumulative distribution function of the fuel rod grid, and the cumulative distribution function of the particle type;

[0008] Perform source particle sampling based on the cumulative distribution function to determine the distribution parameters of source particles.

[0009] In the second aspect, this application provides a system for calculating the source distribution of a fuel assembly in a reactor core, including:

[0010] An input parameter acquisition module, configured to acquire input parameters, where the input parameters include power distribution, fission nuclide fraction, fission spectrum, average number of particles released per fission, average energy released per fission, and source biasing factor, and the source biasing factor includes one or more of a spatial biasing factor of a fuel rod grid, an energy biasing factor, and a particle biasing factor;

[0011] An accumulated distribution function calculation module, configured to calculate an accumulated distribution function of source particles based on the input parameters, where the accumulated distribution function includes one or more of a spatial accumulated distribution function of a fuel rod grid, an energy accumulated distribution function of a fuel rod grid, and an accumulated distribution function of particle types;

[0012] A source particle distribution parameter calculation module, configured to perform source particle sampling based on the accumulated distribution function and determine distribution parameters of source particles.

[0013] In a possible implementation, the acquiring of the input parameters includes:

[0014] Calculating the source biasing factor based on the conjugate fluence rate of the fuel rod grid.

[0015] In a possible implementation, the source biasing factor includes a spatial biasing factor of a fuel rod grid, the accumulated distribution function includes a spatial accumulated distribution function of a fuel rod grid, and the calculating of the accumulated distribution function of source particles based on the input parameters includes:

[0016] Calculating the source strength of the fuel rod grid based on the input parameters;

[0017] Calculating the spatial accumulated distribution function of the fuel rod grid based on the source strength of the fuel rod grid;

[0018] The performing of source particle sampling based on the accumulated distribution function and determining distribution parameters of source particles includes:

[0019] Obtaining A spatial random number within an interval, and determining the fuel rod grid where the source particle is located based on the spatial random number and the spatial accumulated distribution function of the fuel rod grid;

[0020] Uniformly sampling in the radial plane region of the fuel rod grid where the source particle is located to obtain the first coordinate and the second coordinate of the source particle;

[0021] Determining the third coordinate of the source particle based on the lower grid boundary and the upper grid boundary of the fuel rod grid where the source particle is located in the axial direction, where the axial direction is perpendicular to the radial plane.

[0022] In a possible implementation, the cross-sectional shape of the fuel rod in the radial plane is a first shape, the first shape is divided into multiple sub-regions, and the uniform sampling in the radial plane region of the fuel rod grid where the source particle is located to obtain the first coordinate and the second coordinate of the source particle includes:

[0023] Obtain a regional random number within the interval, and determine the sub-region where the source particle is located based on the regional random number;

[0024] Uniformly sample the first coordinate and the second coordinate of the source particle within the sub-region where the source particle is located.

[0025] In a possible implementation, the cross-sectional shape of the fuel rod in the radial plane is a second shape, the second shape is located within the first shape, the first shape is divided into multiple sub-regions, and the uniform sampling in the radial plane region of the fuel rod grid where the source particle is located to obtain the first coordinate and the second coordinate of the source particle includes:

[0026] Obtain a regional random number within the interval, and determine the sub-region where the source particle is located based on the regional random number;

[0027] Uniformly sample the first coordinate and the second coordinate of the source particle within the sub-region where the source particle is located,

[0028] Judge whether the source particle is within the second shape based on the first coordinate and the second coordinate;

[0029] If not, re-obtain the regional random number to calculate the first coordinate and the second coordinate of the source particle until the source particle is within the second shape.

[0030] In a possible implementation, the source biasing factor includes an energy biasing factor, the cumulative distribution function includes the energy cumulative distribution function of the fuel rod grid, and the calculation of the cumulative distribution function of the source particle based on the input parameters includes:

[0031] Calculate the energy probability density function of the fuel assembly based on the input parameters;

[0032] Calculate the energy cumulative distribution function of the fuel assembly based on the energy probability density function of the fuel assembly;

[0033] Obtain the energy cumulative distribution function of the fuel rod grid based on the energy cumulative distribution function of the fuel assembly;

[0034] The sampling of the source particle based on the cumulative distribution function to determine the distribution parameters of the source particle includes:

[0035] Obtain The energy group random numbers within the interval, and determine the energy group where the source particle is located based on the energy cumulative distribution function of the fuel rod grid and the energy group random numbers;

[0036] Determine the initial energy of the source particle based on the lower and upper boundaries of the energy of the energy group where the source particle is located.

[0037] In a possible implementation, the source biasing factor includes a particle biasing factor, the cumulative distribution function includes the cumulative distribution function of the particle type, and the calculating the cumulative distribution function of the source particle based on the input parameters includes:

[0038] Calculate the source strength of the fuel rod grid based on the input parameters;

[0039] Calculate the total source strength generated by core fission based on the source strength of the fuel rod grid;

[0040] Calculate the cumulative distribution function of the particle type based on the total source strength;

[0041] The sampling the source particle based on the cumulative distribution function and determining the distribution parameters of the source particle includes:

[0042] Obtain The type random numbers within the interval, and determine the particle type of the source particle based on the type random numbers and the cumulative distribution function of the particle type.

[0043] In a third aspect, the present application provides a computing device, including:

[0044] At least one processor; and

[0045] At least one memory, storing instructions thereon, which when executed by the at least one processor alone or jointly, cause the computing device to execute the method as described in the first aspect.

[0046] In a fourth aspect, the present application provides a computer storage medium, storing instructions thereon, which when executed by at least one processor of a computing device alone or jointly, cause the computing device to execute the method as described in the first aspect.

[0047] Compared with the prior art, the present application has the following advantages:

[0048] The method for calculating the source distribution of the reactor core fuel assembly provided by this application includes obtaining input parameters, where the input parameters include power distribution, fission nuclide fraction, fission spectrum, average number of particles released per fission, average energy released per fission, and source bias factor; calculating the cumulative distribution function of source particles based on the input parameters; and performing source particle sampling based on the cumulative distribution function to determine the distribution parameters of source particles. This application performs source particle sampling based on the cumulative distribution function of source particles to determine the distribution parameters of source particles, improves the shielding calculation efficiency based on the MC method, and provides support for the design of new reactor types. Description of the Drawings

[0049] The included drawings are provided to further understand this application. They are incorporated and constitute a part of this application. The drawings illustrate the embodiments of this application and, together with this specification, serve to explain the principles of this application. In the drawings:

[0050] Figure 1A - Figure 1B It is a schematic diagram of the numbering of a fuel assembly provided by an embodiment of this application;

[0051] Figure 2A - Figure 2B It is a schematic diagram of the numbering of a fuel rod provided by an embodiment of this application;

[0052] Figure 3 It is a schematic flowchart of a method for calculating the source distribution of the reactor core fuel assembly provided by an embodiment of this application;

[0053] Figure 4 It is a schematic flowchart of a method for calculating the cumulative distribution function of source particles provided by an embodiment of this application;

[0054] Figure 5 It is a schematic flowchart of source particle sampling provided by an embodiment of this application;

[0055] Figure 6A - Figure 6B It is a sampling schematic diagram of a fuel assembly provided by an embodiment of this application;

[0056] Figure 7A - Figure 7B It is a sampling schematic diagram of another fuel assembly provided by an embodiment of this application;

[0057] Figure 8 It is a schematic diagram of the cumulative distribution function of source particle energy in the reactor core provided by an embodiment of this application;

[0058] Figure 9 It is a schematic diagram of the source particle distribution without considering the source bias factor provided by an embodiment of this application;

[0059] Figure 10 It is a schematic diagram of the source particle distribution considering the source bias factor provided by an embodiment of this application;

[0060] Figure 11 It is a schematic structural diagram of a system for calculating the source distribution of a reactor core fuel assembly provided by an embodiment of the present application;

[0061] Figure 12 It is a schematic structural diagram of a computing device provided by an embodiment of the present application. Detailed implementation manners

[0062] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the accompanying drawings used in the description of the embodiments. Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application. For those of ordinary skill in the art, without creative efforts, the present application can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the figures represent the same structure or operation.

[0063] As shown in the present application, unless the context clearly indicates an exception, words such as "a", "an", "one", and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0064] At the same time, the present application uses specific words to describe the embodiments of the present application. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification is not necessarily the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the present application can be appropriately combined.

[0065] Unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions, and numerical values described in these embodiments do not limit the scope of the present application. At the same time, it should be understood that for the sake of description, the dimensions of the various parts shown in the drawings are not drawn according to the actual proportional relationship. Technologies, methods, and devices known to those of ordinary skill in the relevant field may not be discussed in detail, but where appropriate, the said technologies, methods, and devices should be regarded as part of the specification. In all the examples shown and discussed here, any specific value should be interpreted as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0066] In addition, although the terms used in this application are selected from well-known and commonly used terms, some of the terms mentioned in the specification of this application may be selected by the applicant according to his or her judgment, and their detailed meanings are described in the relevant parts of the description herein. In addition, it is required to understand this application not only by the actual terms used, but also by the meanings implied by each term.

[0067] Flowcharts are used in this application to illustrate the operations performed by the devices or equipment according to the embodiments of this application. It should be understood that the operations before or below do not necessarily need to be executed precisely in sequence. On the contrary, various steps can be processed in reverse order or simultaneously. At the same time, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0068] Figure 1A - Figure 1B is a schematic diagram of the numbering of a fuel assembly provided by an embodiment of this application, Figure 2A - Figure 2B is a schematic diagram of the numbering of a fuel rod provided by an embodiment of this application. Please refer to Figure 1A - Figure 1B , the reactor core 100 is formed by arranging a number of fuel assemblies 110 with the same size and a certain cross-sectional shape. The cross-sectional shape of the fuel assembly 110 in the radial plane can be a square, a hexagon, or the like. Please refer to Figure 2A - Figure 2B , the fuel assembly 110 is an in-core heat-releasing component composed of a group of fuel rods 111 and other components, and is a reactor fuel loading and unloading unit.

[0069] Compared with the source term calculation of the square fuel assembly, the energy processing method of the hexagonal fuel assembly is similar, but the space processing method is more complex. The key problem in calculating the source term of the SN method is to obtain the fuel assembly number and the fuel rod number from the known grid coordinates, while the key problem in calculating the source term of the MC method is to obtain the source particle coordinates from the known fuel assembly number and the fuel rod number, and the two are exactly opposite processes.

[0070] In an exemplary embodiment, the cross-sectional shape of the fuel assembly 110 in the radial plane is a hexagon, and the fuel assembly number and the fuel rod number of the MC method are respectively as Figure 1A and Figure 2A shown. Figure 1A In, m1 and n1 are the numbering directions of the fuel assemblies 110 in the reactor core 100, and the fuel assembly number (m1, n1) can be (0, 0), (0, 1), (1, 0), (1, 1), etc. Figure 2A In, m2 and n2 are the numbering directions of the fuel rods 111 in the fuel assembly 110, and the fuel rod number (m2, n2) can be (0, 0), (0, 1), (1, 0), (1, 1), etc. Please refer to Figure 2A, the cross-sectional shape of the fuel rod 111 in the radial plane is hexagonal. Assuming the side length of the fuel assembly 110 is a cm and the side length of the fuel rod 111 is b cm, then a and b satisfy the relationship: . During the calculation process, the fuel rod 111 can be divided into multiple grids axially, and one fuel rod 111 in the radial direction corresponds to one grid.

[0071] In another exemplary embodiment, the cross-sectional shape of the fuel assembly 110 in the radial plane is square, and the fuel assembly numbers and fuel rod numbers of the MC method are respectively as Figure 1B and Figure 2B shown. Figure 1B where, m1 and n1 are the numbering directions of the fuel assemblies 110 in the reactor core 100, and the fuel assembly numbers (m1, n1) can be (0, 0), (0, 1), (1, 0), (1, 1), etc. Figure 2B where, m2 and n2 are the numbering directions of the fuel rods 111 in the fuel assembly 110, and the fuel rod numbers (m2, n2) can be (0, 0), (0, 1), (1, 0), (1, 1), etc. Please refer to Figure 2B , the cross-sectional shape of the fuel rod 111 in the radial plane is square. Assuming the side length of the fuel assembly 110 is a cm and the side length of the fuel rod 111 is b cm, then a and b satisfy the relationship: b = a / n. n is the number of fuel rods on one side of the fuel assembly. During the calculation process, the fuel rod 111 can be divided into multiple grids axially, and one fuel rod 111 in the radial direction corresponds to one grid.

[0072] Figure 3 is a schematic flow chart of a method for calculating the source distribution of the fuel assembly in the reactor core provided by an embodiment of the present application. As Figure 3 shown, the method for calculating the source distribution of the fuel assembly in the reactor core includes the following steps:

[0073] Step S310, obtain input parameters.

[0074] The input parameters can be obtained through an input file, or the input parameters set by the user can be obtained through a user interface, or the required input parameters can be calculated based on the obtained original parameters. The embodiment of the present application does not limit the acquisition method of the input parameters.

[0075] The input parameters include power distribution, fission nuclide fraction, fission spectrum, average number of particles released per fission, average energy released per fission, and source biasing factor. Among them, the power distribution can be a two-dimensional power distribution or a three-dimensional power distribution. The three-dimensional power distribution can be expressed as:

[0076]

[0077] Wherein, P(i, j, k) is the power of the (i, j, k)-th fuel rod grid, is the power of the (i, j)-th fuel rod grid in the radial direction, is the power of the k-th fuel rod grid in the axial direction of the m-th assembly. Among them, the radial numbers (i, j) of the fuel rod grids are numbered in a certain order for the fuel rods in all fuel assemblies in the reactor core, and can be obtained based on the two-dimensional numbers (m1, n1) of the fuel assemblies and the two-dimensional numbers (m2, n2) of the fuel rods. The one-dimensional number m of the fuel assembly can be obtained based on the two-dimensional numbers (m1, n1) of the fuel assembly. For example, first accumulate row by row in the m1 direction, and then accumulate column by column in the n1 direction, that is, process the two-dimensional array into a one-dimensional array.

[0078] In some embodiments, the fission nuclides include U-235, U-238, Pu-239, Pu-240, Pu-241, and Pu-242. In some other embodiments, the fission nuclides may also include only U-235.

[0079] Step S320: Calculate the cumulative distribution function of the source particles based on the input parameters. Among them, the cumulative distribution function includes one or more of the spatial cumulative distribution function of the fuel rod grid, the energy cumulative distribution function of the fuel rod grid, and the cumulative distribution function of the particle type.

[0080] Based on the input parameters, the cumulative distribution functions of the parameters of the source particles such as the energy cumulative distribution function of the fuel rod grid, the spatial cumulative distribution function of the fuel rod grid, and the cumulative distribution function of the particle type can be calculated. Among them, the source particles may include only neutrons, or only photons, or may include both neutrons and photons at the same time.

[0081] In some embodiments, please refer to Figure 4 , step S320 includes the following steps:

[0082] Step S321: Calculate the energy cumulative distribution function of the fuel rod grid.

[0083] In some embodiments, calculating the energy cumulative distribution function of the fuel rod grid includes: calculating the energy probability density function of the fuel assembly based on the input parameters; calculating the energy cumulative distribution function of the fuel assembly based on the energy probability density function of the fuel assembly; obtaining the energy cumulative distribution function of the fuel rod grid based on the energy cumulative distribution function of the fuel assembly.

[0084] The energy probability density function of the fuel assembly can be calculated based on the fission nuclide fraction, the average number of particles released per fission, and the fission spectrum of the fuel assembly. An example of the calculation formula for the energy probability density function of the m-th fuel assembly is as follows:

[0085]

[0086] In the formula: is the energy group number, m is the fuel assembly number, n is the fission nuclide number, and p is the particle type number. is the probability that the p-th particle is generated in the -th group and the m-th fuel assembly, and can be obtained from the fission spectrum. is the fission fraction of the n-th nuclide in the m-th fuel assembly. is the average number of particles released per fission of the p-th particle and the n-th nuclide. is the production probability of the p-th particle and the n-th nuclide particles in the -th group, and can be obtained from the fission spectrum.

[0087] is the energy bias factor of the p-th particle in the -th group, and the calculation formula is as follows by way of example:

[0088]

[0089] In the formula: is the conjugate flux rate of the p-th particle, the -th group, and the q-th fuel rod grid obtained by solving the conjugate transport equation; Q is the number of fuel rod grids.

[0090] Based on the energy probability density function of the fuel assembly, the energy cumulative distribution function of the fuel assembly can be calculated. The calculation formula of the energy cumulative distribution function of the fuel assembly is as follows by way of example:

[0091]

[0092] In the formula, is the energy cumulative distribution function of the p-th particle, the -th group, and the m-th fuel assembly.

[0093] Based on the correspondence between the fuel assembly and the fuel rod grid, all fuel rod grids in the same fuel assembly have the same energy probability density function, and then the energy cumulative distribution function of the q-th fuel rod grid can be obtained. The calculation formula of the energy cumulative distribution function of the fuel rod grid is as follows by way of example:

[0094]

[0095] In the formula, q is the fuel rod grid number, and E(q, g, p) is the energy cumulative distribution function of the p-th particle, the g-th group, and the q-th fuel rod grid. Among them, the one-dimensional number q of the fuel rod grid can be obtained based on the three-dimensional number (i, j, k) of the fuel rod grid. For example, first accumulate row by row in the i direction, then accumulate column by column in the j direction, and then accumulate axially in the k direction, that is, process the three-dimensional array into a one-dimensional array.

[0096] Step S322: Calculate the spatial cumulative distribution function of the fuel rod grid.

[0097] In some embodiments, calculating the spatial cumulative distribution function of the fuel rod grid includes: calculating the source strength of the fuel rod grid based on the input parameters; calculating the spatial cumulative distribution function of the fuel rod grid based on the source strength of the fuel rod grid.

[0098] An example of the spatial distribution calculation formula for each energy group of three-dimensional source particles is as follows:

[0099]

[0100] In the formula, S(i, j, k, g, p) is the source strength of the p-th particle, the g-th group, and the (i, j, k)-th fuel rod grid, with the unit of particles / (cm 3 ·s); C is the unit conversion factor, with a value of 6.24×10 12 MeV / (s·W); is the average energy released per fission of the (i, j, k)-th fuel rod grid, with the unit of MeV; is the fission fraction of the n-th nuclide and the (i, j, k)-th fuel rod grid; is the average number of particles released per fission of the p-th particle and the n-th nuclide; is the production probability of the p-th particle, the g-th group, and the n-th nuclide particles, which can be obtained through the fission spectrum; is the spatial biasing factor of the p-th particle and the (i, j, k)-th fuel rod grid; is the number of nuclides.

[0101] Reconstruct the three-dimensional array into a one-dimensional array, that is, process the data of the (i, j, k)-th fuel rod grid into the data of the q-th fuel rod grid, and the source strength of the p-th particle and the q-th fuel rod grid can be obtained , and the spatial biasing factor B(q, p) = B(i, j, k, p) of the p-th particle and the q-th fuel rod grid.

[0102] is the spatial biasing factor of the p-th particle and the q-th fuel rod grid, and an example of the calculation formula is as follows:

[0103]

[0104] Based on the source strength of the fuel rod grid, the spatial cumulative distribution function of the fuel rod grid can be calculated. An example of the calculation formula for the spatial cumulative distribution function of the fuel rod grid is as follows:

[0105]

[0106] In the formula, is the fuel rod grid number, and Q is the total number of fuel rod grids; is the spatial cumulative distribution function of the p-th type of particle and the -th fuel rod grid; is the source strength of the p-th type of particle and the q-th fuel rod grid, with the unit of particles / (cm 3 ·s); is the volume of the q-th fuel rod grid, with the unit of cm 3 .

[0107] Step S323: Calculate the cumulative distribution function of the particle type.

[0108] In some embodiments, calculating the cumulative distribution function of the particle type includes: calculating the source strength of the fuel rod grid based on the input parameters; calculating the total source strength generated by core fission based on the source strength of the fuel rod grid; calculating the cumulative distribution function of the particle type based on the total source strength.

[0109] The calculation of the source strength of the fuel rod grid can refer to the above-mentioned step S322 and will not be repeated here.

[0110] Based on the source strength of the fuel rod grid, the total source strength generated by core fission can be calculated. An example of the calculation formula for the total source strength generated by core fission is as follows:

[0111]

[0112] In the formula, is the total source strength of the p-th type of particle, with the unit of particles / s; is the bias factor of the p-th type of particle, and an example of the calculation formula is as follows:

[0113]

[0114] Based on the total source strength generated by core fission, the cumulative distribution function of the particle type can be calculated. An example of the calculation formula for the cumulative distribution function of the particle type is as follows:

[0115]

[0116] In the formula, is the particle type number, is the -th cumulative distribution function of the particle type.

[0117] Step S330: Based on the cumulative distribution function, perform sampling on source particles to determine the distribution parameters of the source particles.

[0118] After calculating the cumulative distribution functions of the various parameters of the source particles in step S320, sampling can be performed on the source particles based on the cumulative distribution functions of the various parameters to determine the corresponding distribution parameters of the source particles.

[0119] In some embodiments, please refer to Figure 5 , step S330 includes the following steps:

[0120] Step S331: Based on the cumulative distribution function of the particle type, determine the particle type of the source particles.

[0121] In actual operation, a type random number within the interval can be obtained, and based on the type random number and the cumulative distribution function of the particle type, the particle type of the source particles is determined.

[0122] In an exemplary embodiment, a type random number within the interval is obtained . If , it means that the source particle is the p-th type of particle. For example, p = 1 represents a neutron and p = 2 represents a photon.

[0123] Step S332: Based on the spatial cumulative distribution function of the fuel rod grid, determine the spatial position of the source particles.

[0124] In actual operation, a spatial random number within the interval can be obtained, and based on the spatial random number and the spatial cumulative distribution function of the fuel rod grid, the fuel rod grid where the source particle is located is determined; the first coordinate and the second coordinate of the source particle are uniformly sampled within the radial plane region of the fuel rod grid where the source particle is located; the third coordinate of the source particle is determined based on the lower grid boundary and the upper grid boundary of the fuel rod grid where the source particle is located in the axial direction, where the axial direction is perpendicular to the radial plane.

[0125] In an exemplary embodiment, a spatial random number within the interval is obtained . If , it means that the source particle is within the q-th fuel rod grid. Then, the first coordinate and the second coordinate of the source particle are uniformly sampled within the radial plane region of the q-th fuel rod grid, that is, the local spatial position coordinates (x, y) of the source particle.

[0126] The local spatial position coordinates (x, y) of the source particles can be obtained by different sampling methods according to the cross-sectional shape of the fuel rod in the radial plane. For example, if the cross-sectional shape of the fuel rod in the radial plane is the first shape, and the first shape is divided into multiple sub-regions, the first coordinate and the second coordinate of the source particles obtained by uniform sampling in the radial plane region of the fuel rod grid where the source particles are located include: obtaining the regional random number within the interval, and determining the sub-region where the source particles are located based on the regional random number; uniformly sampling the first coordinate and the second coordinate of the source particles within the sub-region where the source particles are located. Another example is that if the cross-sectional shape of the fuel rod in the radial plane is the second shape, the second shape is located within the first shape, and the first shape is divided into multiple sub-regions, the first coordinate and the second coordinate of the source particles obtained by uniform sampling in the radial plane region of the fuel rod grid where the source particles are located include: obtaining the regional random number within the interval, and determining the sub-region where the source particles are located based on the regional random number; uniformly sampling the first coordinate and the second coordinate of the source particles within the sub-region where the source particles are located, and judging whether the source particles are within the second shape based on the first coordinate and the second coordinate; if not, re-obtain the regional random number to calculate the first coordinate and the second coordinate of the source particles until the source particles are within the second shape.

[0127] In some embodiments, the cross-sectional shape of the fuel rod in the radial plane is a square, as shown in Figure 2B and Figure 6A , then uniform sampling is performed within the square, as shown in Figure 6A , where the local spatial position coordinates (x, y) of the source particles can be determined according to the following formula:

[0128]

[0129] In the formula, is the random number within the interval, which can be generated by a random function; is the lower and upper boundaries of the q-th fuel rod grid in the x direction, in cm; is the lower and upper boundaries of the q-th fuel rod grid in the y direction, in cm.

[0130] In some other embodiments, the cross-sectional shape of the fuel rod in the radial plane is a hexagon, as shown in Figure 2A and Figure 6B , then uniform sampling is performed within the hexagon, as shown in Figure 6B , and the local spatial position coordinates (x, y) of the source particles can be sampled using a piecewise continuous function. Specifically as follows:

[0131] 1) First, divide the hexagon into multiple sub-regions. According to the area sizes of the respective sub-regions, the probability density function and the cumulative distribution function of each sub-region can be determined.

[0132] 2) Obtain a regional random number within the interval, and determine the sub-region where the source particle is located based on the regional random number.

[0133] 3) Uniformly sample within the sub-region where the source particle is located to obtain the first coordinate and the second coordinate of the source particle.

[0134] Exemplarily, as Figure 6B shown, the hexagon is divided into three sub-regions: the left triangular region 1111, the middle rectangular region 1112, and the right triangular region 1113. According to the area sizes of these three sub-regions, the probability density functions of the left triangular region 1111, the middle rectangular region 1112, and the right triangular region 1113 can be determined to be 1 / 6, 4 / 6, and 1 / 6 respectively, and the cumulative distribution functions are 1 / 6, 5 / 6, and 1 respectively. Obtain a regional random number within the interval, and the sub-region where the source particle is located can be determined. For example, if the regional random number is 0.125, since 0.125 is less than 1 / 6, it can be determined that the sub-region where the source particle is located is the left triangular region 1111. Another example is that if the regional random number is 0.2, since 0.2 is greater than 1 / 6 and less than 5 / 6, it can be determined that the region where the source particle is located is the middle rectangular region 1112. Then uniformly sample within the sub-region where the source particle is located to obtain the first coordinate and the second coordinate of the source particle. The local spatial position coordinates (x, y) of the source particle can be determined according to the following formula:

[0135]

[0136] In the formula, is a random number within the interval, which can be generated by a random function; b is the side length of the fuel rod, with the unit of cm.

[0137] In some other embodiments, the cross-sectional shape of the fuel rod in the radial plane is circular, as Figure 7A - Figure 7B shown, then uniformly sample within the circle, and the local spatial position coordinates (x, y) of the source particle can be sampled by the inverse transformation method or the rejection sampling method. For example, when using the inverse transformation method, the local spatial position coordinates (x, y) of the source particle can be determined according to the following formula:

[0138]

[0139] In the formula, is a random number within the interval, which can be generated by a random function.

[0140] When using the rejection sampling method, it can be first assumed that the circle is located within a square or a hexagon with a side length of b cm. Uniform sampling is performed within the square or hexagon to obtain the first coordinate and the second coordinate of the source particle. It is determined whether the sum of the squares of the first coordinate and the second coordinate is less than or equal to the square of the radius of the circle. If not, uniform sampling is performed again within the square or hexagon to calculate the first coordinate and the second coordinate of the source particle until the sum of the squares of the calculated first coordinate and the second coordinate is less than or equal to the square of the radius of the circle. For example, first use a piecewise continuous function for sampling within the hexagon to obtain the local spatial position coordinates (x, y) of the source particle, and determine whether the local spatial position coordinates (x, y) of the source particle satisfy the following formula. If not, use a piecewise continuous function for sampling again within the hexagon until the following formula is satisfied:

[0141]

[0142] In the formula, r is the radius of the circular fuel rod, with the unit of cm.

[0143] Based on the local spatial position coordinates (x, y) of the source particle, the global spatial position coordinates of the source particle can be calculated. First, obtain the center point coordinates of the fuel assembly and the center point coordinates of the fuel rod, and then based on the first coordinate and the second coordinate of the source particle, the center point coordinates of the fuel assembly, and the center point coordinates of the fuel rod, obtain the global spatial position coordinates of the source particle.

[0144] In an exemplary embodiment, the center point coordinates of the hexagonal fuel assembly can be calculated based on the fuel assembly number and the movement vector of the fuel assembly. An example of the calculation formula is as follows:

[0145]

[0146] In the formula, are the coordinates of the center point of the fuel assembly on the x-axis and y-axis, with the unit of cm; (m1, n1) is the fuel assembly number; is the movement vector of the fuel assembly in the m1 direction; is the movement vector of the fuel assembly in the n1 direction.

[0147] The center point coordinates of the fuel rod can be calculated based on the fuel rod number and the movement vector of the fuel rod. An example of the calculation formula is as follows:

[0148]

[0149] In the formula, are the coordinates of the center point of the fuel rod on the x-axis and y-axis, with the unit of cm; (m2, n2) is the fuel assembly number; is the movement vector of the fuel rod in the m2 direction, is the movement vector of the fuel rod in the n2 direction.

[0150] The local spatial position coordinates of the source particle By adding the center point coordinates of the fuel assembly and the fuel rod, the global spatial position coordinates of the source particle can be obtained.

[0151] An example of the calculation formula for the third coordinate of the source particle in the axial direction, that is, the z-axis coordinate of the source particle, is as follows:

[0152]

[0153] In the formula, are the lower and upper boundaries of the q-th fuel rod grid in the z direction, with the unit of cm; is A random number within the interval, which can be generated by a random function.

[0154] Step S333: Determine the initial energy of the source particle based on the energy cumulative distribution function of the fuel rod grid.

[0155] In actual operation, a random energy group number within the interval (0, 1] can be obtained. Based on the random energy group number and the energy cumulative distribution function of the fuel rod grid, the energy group where the source particle is located is determined; based on the lower and upper energy boundaries of the energy group where the source particle is located, the initial energy of the source particle is determined.

[0156] In an exemplary embodiment, a random energy group number ξ within the interval (0, 1] is obtained g , if , it means that the source particle is within the g-th group. Based on the lower and upper energy boundaries of the g-th group, the initial energy of the source particle is determined. An example of the calculation formula for the initial energy of the source particle is as follows:

[0157]

[0158] In the formula, g is the energy group number; is the initial energy of the source particle, with the unit of MeV; are respectively The lower and upper energy boundaries of the g-th group, which can be input by the user, with the unit of MeV; is A random number within the interval, which can be generated by a random function.

[0159] In addition to determining the particle type, spatial position, and initial energy of the source particle, it is necessary to assign a weight to the source particle. An example of the calculation formula is as follows:

[0160]

[0161] In the formula, is the source particle weight for the p-th type of particle, the g-th group, and the q-th fuel rod grid; N0 is the total source strength generated by core fission without considering the source biasing factor, with the unit of particles / s.

[0162] Sample the direction of the source particles. For example, use isotropic sampling for the source particles to determine the particle direction. The determined distribution parameters of the source particles can be used as the input for the shielding calculation using the MC method.

[0163] Figure 8 is a schematic diagram of the energy cumulative distribution function of the source particles in a reactor core provided by an embodiment of the present application, where the energy cumulative distribution function accumulates from high energy to low energy. From Figure 8 it can be seen that compared with unbiased (i.e., without considering the source biasing factor), the probability of sampling to generate high-energy source particles after biasing (i.e., considering the source biasing factor) is greatly improved. For example, the probability of unbiased sampling to generate high-energy particles with E≥2.0 MeV is about 0.45, that is, about 45 out of 100 source particles have an energy greater than 2.0 MeV. While the probability of biased sampling to generate high-energy particles with E≥2.0 MeV is about 0.95, that is, about 95 out of 100 source particles have an energy greater than 2.0 MeV.

[0164] Figure 9 and Figure 10 respectively show the schematic diagrams of the first 1 million unbiased and biased source particle distributions. From Figure 9 and Figure 10 it can be seen that the unbiased source particle distribution will be dispersed in the entire core region, while compared with the unbiased source particle distribution, the biased source particle distribution generates more source particles near the counting region (i.e., the region concerned by the calculation results).

[0165] It can be seen that compared with not considering the source biasing factor, when considering the source biasing factor, the accuracy of the calculation results is comparable, and the calculation efficiency can be greatly improved.

[0166] Figure 11 is a schematic structural diagram of a reactor core fuel assembly source distribution calculation system provided by an embodiment of the present application. As Figure 11 shown, the reactor core fuel assembly source distribution calculation system 1100 includes:

[0167] An input parameter acquisition module 1110, configured to acquire input parameters, where the input parameters include power distribution, fission nuclide fraction, fission spectrum, average number of particles released per fission, and average energy released per fission;

[0168] A cumulative distribution function calculation module 1120, configured to calculate the cumulative distribution function of the source particles based on the input parameters, where the cumulative distribution function includes one or more of the spatial cumulative distribution function of the fuel rod grid, the energy cumulative distribution function of the fuel rod grid, and the cumulative distribution function of the particle type;

[0169] The source particle distribution parameter calculation module 1130 is used to sample source particles based on the cumulative distribution function and determine the distribution parameters of the source particles.

[0170] The reactor core fuel assembly source distribution calculation system 1100 provided in this embodiment is used to implement the foregoing reactor core fuel assembly source distribution calculation method. Therefore, the specific implementation of the reactor core fuel assembly source distribution calculation system 1100 can be seen in the embodiment part of the reactor core fuel assembly source distribution calculation method in the foregoing text, and will not be elaborated here.

[0171] Figure 12 It is a schematic structural diagram of a computing device provided in an embodiment of the present application. As Figure 12 shown, the computing device 1200 includes one or more processors 1210, one or more memories 1220 coupled to the processor 1210, and one or more communication modules 1240 coupled to the processor 1210.

[0172] The communication module 1240 is used for two-way communication. The communication module 1240 has at least one antenna for facilitating communication. The communication interface can represent any interface necessary for communicating with other network elements.

[0173] The processor 1210 can be of any type suitable for the local technical network and, by way of non-limiting example, can include one or more of the following: general-purpose computer, special-purpose computer, microprocessor, digital signal processor (DSP), and processor based on a multi-core processor architecture. The computing device 1200 can have multiple processors, such as an application-specific integrated circuit chip, which is clocked in time to synchronize with the main processor.

[0174] The memory 1220 can include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, read-only memory (ROM) 1224, electrically programmable read-only memory (EPROM), flash memory, hard disk, optical disc (CD), digital video disc (DVD), and other magnetic and / or optical memories. Examples of volatile memories include, but are not limited to, random access memory (RAM) 1222 and other volatile memories that do not persist during a power outage duration.

[0175] The computer program 1230 includes computer-executable instructions executed by the relevant processor 1210. The computer program 1230 can be stored in the ROM 1224. The processor 1210 can execute any appropriate actions and processes by loading the computer program 1230 into the RAM 1222.

[0176] Embodiments of the present application can be implemented by computer program 1230, such that computing device 1200 can execute any of the disclosed processes discussed with reference to Figure 3 - Figure 5 Embodiments of the present application can also be implemented by hardware or by a combination of software and hardware.

[0177] In some embodiments, computer program 1230 can be tangibly embodied in a computer-readable medium, which can be included in computing device 1200 (e.g., memory 1220) or other storage devices accessible to computing device 1200. Computing device 1200 can load computer program 1230 from the computer-readable medium into RAM 1222 for execution. The computer-readable medium can include any type of tangible non-volatile memory, such as ROM, EPROM, flash memory, hard disk, CD, DVD, etc. Program 1230 is stored on the computer-readable medium.

[0178] Generally, various embodiments of the present application can be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Certain aspects can be implemented in hardware, while other aspects can be implemented in firmware or software, which can be executed by a controller, microprocessor, or other computing device. Although aspects of embodiments of the present application are shown and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that, by way of non-limiting example, the blocks, devices, systems, techniques, or methods described herein can be implemented in hardware, software, firmware, dedicated circuits or logic, general hardware or a controller or other computing device, or some combination thereof.

[0179] The present application also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which are executed in a device on a target real or virtual processor to perform the methods described with reference to Figure 3 - Figure 5 above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or separated as needed among program modules. The machine-executable instructions for program modules can be executed within local or distributed devices. In a distributed device, program modules can be located in local and remote storage media.

[0180] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, so that when the program code is executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are realized. The program code can be executed entirely on the machine as an independent software package, partially on the machine, partially on the machine, partially on a remote machine, partially on a remote machine, or entirely on a remote machine or server.

[0181] In the context of the present application, the computer program code or related data can be carried by any suitable carrier to enable the device, apparatus, or processor to perform the various processes and operations described above. Examples of the carrier include signals, computer-readable media, etc.

[0182] The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or apparatuses, or any suitable combination of the foregoing. More specific examples of the computer-readable storage medium include electrical connections with one or more wires, portable computer floppy disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0183] Furthermore, although the operations are described in a specific order, this should not be construed as requiring that the operations be performed in the specific order or sequence shown, or that all of the shown operations be performed to obtain the desired result. In some cases, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these details should not be construed as limitations on the scope of the present application, but rather as descriptions of specific features particular to a specific embodiment. Certain features described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, the various features described in the context of a single embodiment can also be implemented separately or in any suitable sub-combination in multiple embodiments.

[0184] Although the present application has been described in a language specific to structural features and / or method acts, it should be understood that the present application defined in the appended claims is not necessarily limited to the specific features or acts described above. On the contrary, the specific features and acts described above are disclosed as example forms for implementing the claims.

Claims

1. A method for calculating the source distribution of reactor core fuel assemblies, characterized in that: include: Acquiring input parameters, wherein the input parameters include power distribution, fission nuclide fraction, fission spectrum, average number of particles released per fission, average energy released per fission, and source bias factor, wherein the source bias factor includes a spatial bias factor, an energy bias factor, and a particle bias factor of a fuel rod grid; Calculate a cumulative distribution function of source particles based on the input parameters, the cumulative distribution function comprising a spatial cumulative distribution function of a fuel rod grid, an energy cumulative distribution function of a fuel rod grid, and a cumulative distribution function of a particle type; Performing source particle sampling based on the cumulative distribution function to determine distribution parameters of the source particles; Wherein, the step of calculating the cumulative distribution function of source particles based on the input parameters comprises: Calculate the energy probability density function of the fuel assembly based on the fissile nuclide fraction, the fission spectrum, the average number of particles released per fission, and the energy bias factor; Calculating an energy cumulative distribution function of a fuel assembly based on an energy probability density function of the fuel assembly, and then obtaining an energy cumulative distribution function of a fuel rod grid based on the energy cumulative distribution function of the fuel assembly; Calculating the source strength of the fuel rod grid based on the power distribution, the fissile nuclide fraction, the fission spectrum, the average number of particles released per fission, the average energy released per fission, and the spatial bias factor; Calculate the spatial cumulative distribution function of the fuel rod grid based on the source intensity of the fuel rod grid; Calculating the total source intensity generated by core fission based on the source intensity of the fuel rod grid and the particle bias factor, and then calculating the cumulative distribution function of the particle type based on the total source intensity; And the sampling of source particles based on the cumulative distribution function to determine the distribution parameters of source particles includes: Obtaining a spatial random number in the interval (0,1], and determining the fuel rod grid where the source particle is located based on the spatial random number and the spatial cumulative distribution function of the fuel rod grid; Uniformly sampling in a radial plane region of a fuel rod grid where the source particles are located to obtain a first coordinate and a second coordinate of the source particles, wherein the cross-sectional shape of the fuel rod in the radial plane is hexagonal or circular; And the step of uniformly sampling the radial plane region of the fuel rod grid where the source particles are located to obtain the first coordinate and the second coordinate of the source particles comprises: In the case where the cross-sectional shape of the fuel rod in the radial plane is a hexagon, the hexagon is divided into a plurality of sub-regions, and the probability density function and cumulative distribution function of each sub-region are determined according to the area size of each sub-region; a regional random number in the interval (0,1] is obtained, and the sub-region where the source particle is located is determined based on the regional random number and the probability density function and cumulative distribution function of each sub-region; and a first coordinate and a second coordinate of the source particle are uniformly sampled within the sub-region where the source particle is located; or, When the cross-sectional shape of the fuel rod in the radial plane is circular, the inverse transformation method is used to sample and obtain the first coordinate and the second coordinate of the source particle. The formula of the inverse transformation method is as follows: ， Wherein, x is the first coordinate of the source particle; y is the second coordinate of the source particle; are random numbers in the interval (0,1].

2. The method according to claim 1, characterized in that The obtaining of input parameters includes: The source bias factor is calculated based on the conjugate fluence rate of the fuel rod grid.

3. The method according to claim 1 or 2, characterized in that The step of sampling source particles based on the cumulative distribution function to determine the distribution parameters of source particles further comprises: The third coordinate of the source particle is determined based on the lower boundary and the upper boundary of the grid of the fuel rod where the source particle is located in the axial direction, and the axial direction is perpendicular to the radial plane.

4. The method according to claim 1 or 2, characterized in that: The step of sampling source particles based on the cumulative distribution function to determine the distribution parameters of source particles further comprises: Obtaining a random number of an energy group in the interval (0,1], and determining the energy group of the source particle based on the random number of the energy group and the energy cumulative distribution function of the fuel rod grid; The initial energy of the source particle is determined based on the lower energy boundary and the upper energy boundary of the energy group to which the source particle belongs.

5. The method according to claim 1 or 2, characterized in that: The step of sampling source particles based on the cumulative distribution function to determine the distribution parameters of source particles further comprises: Obtain a type random number in the interval (0,1], and determine the particle type of the source particle based on the type random number and the cumulative distribution function of the particle type.

6. A reactor core fuel assembly source distribution calculation system, characterized in that: include: An input parameter acquisition module, used to acquire input parameters, wherein the input parameters include power distribution, fission nuclide fraction, fission spectrum, average number of particles released per fission, average energy released per fission, and source bias factor, wherein the source bias factor includes a spatial bias factor, an energy bias factor, and a particle bias factor of a fuel rod grid; A cumulative distribution function calculation module, used to calculate the cumulative distribution function of the source particles based on the input parameters, the cumulative distribution function includes the spatial cumulative distribution function of the fuel rod grid, the energy cumulative distribution function of the fuel rod grid and the cumulative distribution function of the particle type; A source particle distribution parameter calculation module, used for sampling source particles based on the cumulative distribution function to determine the distribution parameters of source particles; Wherein, the step of calculating the cumulative distribution function of source particles based on the input parameters comprises: Calculate the energy probability density function of the fuel assembly based on the fissile nuclide fraction, the fission spectrum, the average number of particles released per fission, and the energy bias factor; Calculating an energy cumulative distribution function of a fuel assembly based on an energy probability density function of the fuel assembly, and then obtaining an energy cumulative distribution function of a fuel rod grid based on the energy cumulative distribution function of the fuel assembly; Calculating the source strength of the fuel rod grid based on the power distribution, the fissile nuclide fraction, the fission spectrum, the average number of particles released per fission, the average energy released per fission, and the spatial bias factor; Calculate the spatial cumulative distribution function of the fuel rod grid based on the source intensity of the fuel rod grid; Calculating the total source intensity generated by core fission based on the source intensity of the fuel rod grid and the particle bias factor, and then calculating the cumulative distribution function of the particle type based on the total source intensity; And the sampling of source particles based on the cumulative distribution function to determine the distribution parameters of source particles includes: Obtaining a spatial random number in the interval (0,1], and determining the fuel rod grid where the source particle is located based on the spatial random number and the spatial cumulative distribution function of the fuel rod grid; Uniformly sampling in a radial plane region of a fuel rod grid where the source particles are located to obtain a first coordinate and a second coordinate of the source particles, wherein the cross-sectional shape of the fuel rod in the radial plane is hexagonal or circular; And the step of uniformly sampling the radial plane region of the fuel rod grid where the source particles are located to obtain the first coordinate and the second coordinate of the source particles comprises: In the case where the cross-sectional shape of the fuel rod in the radial plane is a hexagon, the hexagon is divided into a plurality of sub-regions, and the probability density function and cumulative distribution function of each sub-region are determined according to the area size of each sub-region; a regional random number in the interval (0,1] is obtained, and the sub-region where the source particle is located is determined based on the regional random number and the probability density function and cumulative distribution function of each sub-region; and a first coordinate and a second coordinate of the source particle are uniformly sampled within the sub-region where the source particle is located; or, When the cross-sectional shape of the fuel rod in the radial plane is circular, the inverse transformation method is used to sample and obtain the first coordinate and the second coordinate of the source particle. The formula of the inverse transformation method is as follows: ， Wherein, x is the first coordinate of the source particle; y is the second coordinate of the source particle; are random numbers in the interval (0,1].

7. A computing device, characterized in that: include: at least one processor; as well as At least one memory having instructions stored thereon, which, when executed individually or collectively by the at least one processor, cause the computing device to perform the method according to any one of claims 1 to 5.

8. A computer storage medium, characterized in that The computer storage medium stores instructions, which, when executed individually or collectively by at least one processor of a computing device, cause the computing device to perform the method according to any one of claims 1 to 5.

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