CN114969641A - Nuclear data processing method, electronic device and computer readable storage medium - Google Patents
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Abstract
The application provides a nuclear data processing method, an electronic device and a computer readable storage medium, comprising: acquiring a first random sample space based on user input information and a Latin hypercube sampling mode; acquiring a first correlation coefficient matrix corresponding to the first random sample space; determining an upper triangular matrix based on the first correlation coefficient matrix and a predetermined Cholesky decomposition transformation mode; determining a second random sample space based on the upper triangular matrix and the first random sample space; determining a third random sample space based on the second random sample space and a predetermined linear relationship; the predetermined linear relationship is determined by a predetermined kernel data covariance matrix and mathematical expectation values for each parameter in the user input information. The method combines the Latin hypercube sampling mode with Cholseky decomposition transformation, and can obtain the sample data sampling result with lower sample correlation, higher random degree and higher sampling level and quality.
Description
Technical Field
The present application relates to the field of nuclear data processing technologies, and in particular, to a nuclear data processing method, an electronic device, and a computer-readable storage medium.
Background
The nuclear data parameters are not independent of each other, certain correlation exists, and in the process of analyzing and processing the nuclear data, the direct sampling method for the nuclear data not only needs to meet the joint probability density distribution function among the nuclear data parameters, but also needs to consider the cooperative change of the nuclear data parameters during sampling. The technical implementation difficulty is great. Therefore, at present, a general and feasible scheme often needs to obtain a random sample space which follows a multivariate standard normal distribution and is completely independent from each other, and then obtain the random sample space through a predetermined linear relationship. The smaller the correlation of each sample in the random sample space is, the more random each sample is, and the more the random sample obtained by sampling and the random kernel data sample obtained finally can represent the whole source.
For this, a circular sampling method or a latin hypercube sampling method may be generally employed. In the cyclic sampling method, firstly, a random sample correlation control standard rho is set, the correlation coefficient of the nth parameter sample vector and the first n-1 parameter sample vectors is calculated, and if the absolute value is less than rho, the sampling requirement is met. Thus, for each sample taken, the correlation coefficient is calculated with the n-1 samples preceding the sample. However, sampling is often limited by various initial parameters, and once the initial parameters are increased, the cyclic sampling method faces the problem of too long sampling time due to the sharp increase of the calculated amount, so that the calculated amount is large, the efficiency is low, and the subsequent nuclear data analysis is not facilitated.
Meanwhile, various latin hypercube sampling methods in the market have various defects. For example, for the case of the row-column gram-schmidt orthogonal latin hypercube sampling, the calculation amount is huge and the calculation speed is slow when the number of parameters or samples is large. For another example, the latin hypercube sampling combined with the evolutionary algorithm needs to artificially set the evolutionary times, and when the evolutionary times are set to be large, the computation workload is huge. Meanwhile, in order to ensure that the random degree of the core data obtained by sampling meets the actual analysis requirement, or in order to ensure that the sample data obtained by sampling has low correlation and is enough to effectively represent the whole source, the number of evolutions needs to be increased as much as possible, however, the higher the number of evolutions is, the larger the computation load is, the lower the sampling efficiency is, and the serious negative influence is brought to the subsequent core data analysis.
Therefore, how to efficiently and quickly provide a reasonable random sample space with low correlation for nuclear data analysis becomes a technical problem to be solved urgently at present.
Disclosure of Invention
The application provides a nuclear data processing method, electronic equipment and a computer readable storage medium, and aims to solve the technical problems that in the related art, the relevance of extracted data samples required by nuclear data analysis is too high, and the sampling calculation efficiency is low.
In a first aspect, an embodiment of the present application provides a core data processing method, including: acquiring a first random sample space based on user input information and a Latin hypercube sampling mode; acquiring a first correlation coefficient matrix corresponding to the first random sample space; determining an upper triangular matrix based on the first correlation coefficient matrix and a predetermined Cholesky decomposition transformation mode; determining a second random sample space based on the upper triangular matrix and the first random sample space; and determining a third random sample space based on the second random sample space and a predetermined linear relation, wherein the predetermined linear relation is determined by a predetermined kernel data covariance matrix and mathematical expected values of parameters in the user input information.
In the foregoing embodiment of the present application, optionally, the determining an upper triangular matrix based on the first correlation coefficient matrix and a predetermined Cholesky decomposition transform manner includes: and taking the first correlation coefficient matrix as the product of the upper triangular matrix and the transpose matrix of the upper triangular matrix, and decomposing the first correlation coefficient matrix through a preset Cholesky decomposition transformation mode to obtain the upper triangular matrix.
In the foregoing embodiment of the present application, optionally, the determining a second random sample space based on the upper triangular matrix and the first random sample space includes: obtaining an inverse matrix of the upper triangular matrix; determining a product of the inverse matrix and the first random sample space as the second random sample space.
In the above embodiments of the present application, optionally, the predetermined linear relationship is expressed as:
X＝A*Z * +μ
wherein A is a coefficient matrix decomposed by the predetermined kernel data covariance matrix, and Z * For the second random sample space, μ represents the mathematical expected value of the respective parameter.
In the foregoing embodiment of the present application, optionally, before the determining a third random sample space based on the second random sample space and a predetermined linear relationship, the method further includes: acquiring a second correlation number matrix corresponding to the second random sample space; and if the correlation of the second correlation coefficient matrix is smaller than that of the first correlation coefficient matrix, allowing to enter a step of determining a third random sample space based on the second random sample space and a predetermined linear relationship, wherein the correlation of any correlation coefficient matrix is determined by the similarity between the off-diagonal elements in the matrix and 0.
In a second aspect, an embodiment of the present application provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being arranged to perform the method of any of the first aspects above.
In a third aspect, an embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions for performing the method flow of any one of the first aspect.
According to the technical scheme, aiming at the technical problems that the correlation of the extracted data sample required by nuclear data analysis in the related technology is too high and the sampling calculation efficiency is low, the Latin hypercube sampling mode and Cholseky decomposition transformation are combined, the sample data sampling result with lower sample correlation, higher random degree and higher sampling level quality can be obtained, the extracted sample data is more representative, the technical problem that the sampling method in the related technology cannot give consideration to both the low correlation and the high sampling efficiency of a random sample space is solved, the sampling efficiency is improved, and the correlation of the sample in the random sample space is also reduced. Furthermore, the finally obtained random sample space can be used for representing the source entirety more truly.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 shows a flow diagram of a method of nuclear data processing according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a correlation coefficient matrix of a sample space generated by a Latin hypercube sampling method in the related art;
FIG. 3 shows a schematic diagram of a matrix of correlation coefficients for a sample space generated using a Latin hypercube sampling approach in combination with a Cholesky decomposition transform according to one embodiment of the present application;
FIG. 4 is a schematic diagram showing a correlation coefficient matrix of a sample space generated by another Latin hypercube sampling method in the related art;
FIG. 5 shows a schematic diagram of a matrix of correlation coefficients for a sample space generated using a Latin hypercube sampling approach in combination with a Cholesky decomposition transform according to another embodiment of the present application;
FIG. 6 shows a block diagram of an electronic device according to an embodiment of the application.
Detailed Description
For better understanding of the technical solutions of the present application, the following detailed descriptions of the embodiments of the present application are provided with reference to the accompanying drawings.
It should be understood that the embodiments described are only a few embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the examples of this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
FIG. 1 shows a flow diagram of a method of nuclear data processing according to an embodiment of the present application.
As shown in fig. 1, a flow of a core data processing method according to an embodiment of the present application includes:
The user input information comprises various nuclear data parameters, the number of samples required by the first random sample space needing to be sampled and the like, and when the samples are extracted, the first random sample space needs to be extracted according to the nuclear data parameters and the number of samples given by the user input information.
Latin hypercube sampling is a method for approximate random sampling from multivariate parameter distribution, belonging to the hierarchical sampling technology. Wherein, a plurality of kernel data parameters for limiting sampling behavior are available, each kernel data parameter corresponding to a dimension; then, dividing each dimension into a plurality of intervals which are not overlapped with each other, so that each interval has the same sampling probability, or dividing each dimension into a plurality of intervals which are uniformly distributed and have the same length; then, randomly extracting a point in each interval under each dimension; and finally, randomly extracting points from the plurality of points extracted in each dimension to form a vector as a first random sample space.
Therefore, by using a Latin hypercube sampling mode, the obtained sample can more accurately reflect the distribution of values in the input probability distribution. Of course, the manner of obtaining the first random sample space includes, but is not limited to, the latin hypercube sampling manner described above, and may also be any other manner that meets the actual sampling requirement, which is not described herein again.
And 104, acquiring a first correlation coefficient matrix corresponding to the first random sample space.
The first random sample space is Z s ＝[Z s1 ,Z s2 ,....,Z snX ] T Then, the correlation coefficient matrix C of the first random sample space is:
wherein any element rho in the correlation coefficient matrix C i,j A correlation coefficient representing a parameter i and a parameter j in the first random sample space, where p i,i ＝1,ρ i,j ＝ρ j,i And, in addition,
wherein z is si For the sample values under the parameter i,
is the average of the samples at parameter i.
And 106, determining an upper triangular matrix based on the first correlation coefficient matrix and a preset Cholesky decomposition transformation mode.
Specifically, the first correlation coefficient matrix C is used as the upper triangular matrix Q and the transpose matrix Q of the upper triangular matrix T And decomposing the first correlation coefficient matrix through a preset Cholesky decomposition transformation mode to obtain the upper triangular matrix. The Cholesky decomposition transformation, also called square root method, is a decomposition in which a symmetric positive definite matrix is expressed as the product of a lower triangular matrix and its transpose.
Specifically, an inverse Q of the upper triangular matrix is obtained -1 The inverse matrix Q -1 And the first random sample space Z s Is determined as the second random sample space Z * 。
And step 110, determining a third random sample space based on the second random sample space and a predetermined linear relation, wherein the predetermined linear relation is determined by a predetermined kernel data covariance matrix and mathematical expectation values of parameters in the user input information.
That is, the third random sample space is determined based on the second random sample space, a predetermined kernel data covariance matrix, and mathematical expected values of parameters in the user input information. For example, the third random sample space X and the second random sample space Z may be set * The predetermined linear relation between the coefficient matrix A obtained by the predetermined kernel data covariance matrix decomposition and the mathematical expected value mu of each parameter in the user input information is as follows:
X＝A*Z * +μ
therefore, the final third random sample space can be determined as the sample data sampling result through the second random sample space with higher sample random degree and higher sampling level.
Specifically, before a random sample space is obtained by a sampling mode, a plurality of kernel data parameters for limiting sampling behaviors are given, and value ranges and distribution characteristics are set for the kernel data parameters, wherein mean value information and a covariance matrix sigma are set for the kernel data parameters, and the mean value information is used for reflecting mu.
Then, the predetermined kernel data covariance matrix ∑ AA T Therefore, A can be obtained by decomposing Sigma.
When sigma is a symmetric positive definite matrix, a Cholesky decomposition change mode is adopted to decompose the sigma, and when sigma is a symmetric singular matrix, a singular value decomposition mode is adopted to decompose the sigma.
In the physical calculation of the reactor, the information amount of nuclear data is huge, and the correlation between different nuclides and different reaction channels needs to be considered, so that the uncertainty analysis work of the nuclear data calculation can be carried out by adopting a random sample spatial correlation control technology based on Cholesky decomposition transformation.
Nuclear cross-section data has two main features: 1) the device has certain uncertainty; 2) with correlation, uncertainty and correlation of the nuclear cross-sectional data can be generally represented using the nuclear cross-sectional covariance. Further, a normal distribution is generally used to describe the nuclear cross-sectional distribution characteristics. Therefore, it can be considered that the data of the multiple groups of nuclear sections follow the joint normal distribution, i.e., X to N nX (mu, v), wherein nX represents the number of nuclear cross-sectional data, and μ ═ mu 1 ,μ 2 ,....,μ nX ] T Is a cross-section mean vector, and Σ is a multi-cluster kernel cross-section covariance matrix, whose format is as follows:
in the above formula, the matrix element cov (x) i ,x j ) Represents the nuclear section x i And x j The covariance of (a). In addition, the default kernel section covariance can be used to reflect the uncertainty and correlation information of the kernel section itself, because the standard deviation of the kernel section parameters is the value of the uncertainty of the kernel section, the diagonal elements of the kernel section covariance matrix are the variances of the kernel section parameters, and the off-diagonal elements represent the correlation information between different kernel section parameters.
The nuclear section parameters are not mutually independent and have certain correlation, so when a sampling method is used for directly extracting a nuclear section sample space based on the nuclear section covariance matrix and the mean vector information, a joint probability density distribution function among the nuclear section parameters is met, and the cooperative change of the nuclear section parameters during sampling is considered, thereby having great difficulty in technical realization. Currently, a common and feasible solution is to first obtain compliance N nX (0, I) multivariate standard normal distribution and completely mutually independent sample spaceBased on the known covariance matrix of the kernel section and the mean vector information, effective sampling of the correlation parameters is realized by simple mathematical change, as shown in the following formula:
X＝A*Z * +μ
from the above analysis, the key to successfully carry out the uncertainty analysis of the nuclear section data by using the sampling method is focused on that the determined correlation coefficient matrix of the sampling random sample space is as close to the identity matrix as possible.
For the correlation coefficient matrix of the random sample space obtained by the Latin hypercube sampling mode, the correlation coefficient is large and does not meet the requirement. Therefore, the random sample space can be changed one by one based on the Cholesky decomposition transformation random sample space correlation control technology to obtain a new random sample space, and the correlation coefficient matrix of the new random sample space is closer to the unit diagonal matrix than the correlation coefficient matrix of the original random sample space, which shows that the Cholesky decomposition transformation random sample space correlation control technology can effectively reduce the statistical correlation among the sample parameters to enable the parameters in the sample space to be more independent.
To extract compliance N 44 (0,I 44 ) The distributed sample space is taken as an example, the number of the selected samples is 200, and fig. 2 shows a correlation coefficient matrix of the sample space generated by adopting a latin hypercube sampling mode in the related art, which is equivalent to the first random sample space Z mentioned above in the present application s 。
In order to make the presentation of the correlation coefficient matrix more intuitive, all diagonal elements of the correlation coefficient matrix are set to zero, and only the lower triangular part of the correlation coefficient matrix is given.
Fig. 3 shows a schematic diagram of a correlation coefficient matrix of a second random sample space generated after combining a latin hypercube sampling mode and Cholesky decomposition transformation in the present application, that is, a schematic diagram of a correlation coefficient matrix of a second random sample space in a latin hypercube coupling Cholesky decomposition transformation sampling (LHS-CDC) mode. As can be seen from comparison with fig. 2, the second random sample space Z obtained in the present application is compared with the related art * To 10 -3 On the left and right sides, the samples under each parameter are more independent, namely the samples in the second random sample space have lower sample correlation relative to the samples in the first random sample space and have higher randomness, so that the finally obtained uncertainty of the nuclear data sampling result is more reasonable and accurate.
Since the correlation coefficient matrix corresponding to the random sample space can reflect the correlation between samples in the random sample space, the correlation between samples in the second random sample space and the first random sample space can be determined after the second random sample space is obtained and determined.
Specifically, after step 108, a second correlation number matrix corresponding to the second random sample space may be obtained. The manner of obtaining the second correlation coefficient matrix is the same as the principle of obtaining the first correlation coefficient matrix, and is not described herein again.
Then, if the correlation of the second correlation coefficient matrix is smaller than the correlation of the first correlation coefficient matrix, the step 110 is allowed to be performed to determine the final third random sample space. In any correlation coefficient matrix, the off-diagonal elements in the ith row and the jth column show the correlation between the parameter i and the parameter j in the random sample space to which the correlation coefficient matrix belongs, so that the closer the off-diagonal elements are to 0, the lower the correlation between corresponding samples is. And the correlation of the second correlation coefficient matrix is smaller than that of the first correlation coefficient matrix, which shows that the second random sample space corresponding to the second correlation coefficient matrix has higher sample randomness and better sampling level than the first random sample space corresponding to the first correlation coefficient matrix.
In a database with a SCALE program 44 238 The correlation coefficient matrix of the random sample space obtained by adopting the latin hypercube sampling mode is shown in fig. 4 by taking the U (n, r) section as an example. The correlation coefficient matrix is a symmetric matrix, the off-diagonal elements represent the correlation among different parameters, and generally, only the correlation coefficient is givenThe upper triangular part or the lower triangular part of the number matrix can completely express all the correlation information of the correlation coefficient matrix, and the diagonal element is set to be 0 to realize the intuition of observation.
In addition, a new random sample space is generated after the Latin hypercube sampling mode is combined with Cholesky decomposition transformation, a correlation coefficient matrix of the new random sample space is shown in FIG. 5, and similarly, in order to realize the intuitiveness of observation, a diagonal element is set to be 0.
Compared with the random sample space obtained by the related technology, the random sample space obtained by the method is lower in correlation coefficient, the samples under all parameters are more independent, and the randomness is higher, so that the uncertainty of the finally obtained nuclear data sampling result is more reasonable and accurate.
By the technical scheme, a Latin hypercube sampling mode is combined with Cholseky decomposition transformation, sample data sampling results with lower sample correlation, higher random degree and higher sampling level quality can be obtained, the sampled sample data is more representative, the technical problem that a sampling method in the related technology cannot give consideration to both low correlation and high sampling efficiency of a random sample space is solved, the sampling efficiency is improved, and the correlation of samples in the random sample space is also reduced. In addition, because the statistical correlation among all parameters introduced in the sampling process is reduced, the propagation and quantification of uncertainty are more accurate, and the method is suitable for later uncertainty analysis. Furthermore, the finally obtained random sample space can be used for representing the source entirety more truly.
FIG. 6 shows a block diagram of an electronic device of an embodiment of the present application.
As shown in FIG. 6, an electronic device 400 of one embodiment of the present application includes at least one memory 402; and a processor 404 communicatively coupled to the at least one memory 402; wherein the memory stores instructions executable by the at least one processor 404, the instructions being configured to perform the scheme described in any of the embodiments above. Therefore, the electronic device 400 has the same technical effects as any of the above embodiments, and will not be described herein again.
The electronic device of the embodiments of the present application exists in various forms, including but not limited to:
(1) mobile communication devices, which are characterized by mobile communication capabilities and are primarily targeted at providing voice and data communications. Such terminals include smart phones (e.g., iphones), multimedia phones, functional phones, and low-end phones, among others.
(2) The ultra-mobile personal computer equipment belongs to the category of personal computers, has calculation and processing functions and generally has the characteristic of mobile internet access. Such terminals include PDA, MID, and UMPC devices, such as ipads.
(3) Portable entertainment devices such devices may display and play multimedia content. Such devices include audio and video players (e.g., ipods), handheld game consoles, electronic books, as well as smart toys and portable car navigation devices.
(4) The server is similar to a general computer architecture, but has higher requirements on processing capability, stability, reliability, safety, expandability, manageability and the like because of the need of providing highly reliable services.
(5) And other electronic devices with data interaction functions.
In addition, an embodiment of the present application provides a computer-readable storage medium, which stores computer-executable instructions for executing the method flow described in any of the above embodiments.
The technical scheme of the application is explained in detail with the accompanying drawings, and by the technical scheme of the application, a Latin hypercube sampling mode and Cholseky decomposition transformation are combined, so that a sample data sampling result with lower sample correlation, higher random degree and higher sampling level quality can be obtained. Furthermore, the finally obtained random sample space can be used for more truly characterizing the whole source.
It should be understood that although the terms first, second, etc. may be used to describe random sample spaces in the embodiments of the present application, these random sample spaces should not be limited to these terms. These terms are only used to distinguish random sample spaces from each other. For example, the first random sample space may also be referred to as a second random sample space, and similarly, the second random sample space may also be referred to as a first random sample space without departing from the scope of embodiments of the present application.
The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrase "if determined" or "if detected (a stated condition or event)" may be interpreted as "upon determining" or "in response to determining" or "upon detecting (a stated condition or event)" or "in response to detecting (a stated condition or event)", depending on the context.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions in actual implementation, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a Processor (Processor) to execute some steps of the method described in the embodiments of the present application, that is, include modules corresponding to the flow of the method. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
The above description is only a preferred embodiment of the present application and should not be taken as limiting the present application, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (8)
1. A method of nuclear data processing, comprising:
acquiring a first random sample space based on user input information and a Latin hypercube sampling mode;
acquiring a first correlation coefficient matrix corresponding to the first random sample space;
determining an upper triangular matrix based on the first correlation coefficient matrix and a predetermined Cholesky decomposition transformation mode;
determining a second random sample space based on the upper triangular matrix and the first random sample space;
and determining a third random sample space based on the second random sample space and a predetermined linear relation, wherein the predetermined linear relation is determined by a predetermined kernel data covariance matrix and mathematical expected values of parameters in the user input information.
2. The method of claim 1, wherein determining an upper triangular matrix based on the first correlation coefficient matrix and a predetermined Cholesky decomposition transformation comprises:
and taking the first correlation coefficient matrix as the product of the upper triangular matrix and the transpose matrix of the upper triangular matrix, and decomposing the first correlation coefficient matrix through a preset Cholesky decomposition transformation mode to obtain the upper triangular matrix.
3. The nuclear data processing method of claim 1 or 2, wherein the determining a second random sample space based on the upper triangular matrix and the first random sample space comprises:
acquiring an inverse matrix of the upper triangular matrix;
determining a product of the inverse matrix and the first random sample space as the second random sample space.
4. The nuclear data processing method of claim 3, wherein the predetermined linear relationship is expressed as:
X＝A*Z * +μ
wherein A is a coefficient matrix decomposed by the predetermined kernel data covariance matrix, and Z * For the second random sample space, μ represents the mathematical expected value of the respective parameter.
5. The nuclear data processing method of claim 1, wherein prior to said determining a third random sample space based on the second random sample space and a predetermined linear relationship, further comprising:
acquiring a second correlation number matrix corresponding to the second random sample space;
allowing entry into the step of determining a third random sample space based on the second random sample space and a predetermined linear relationship if the correlation of the second correlation coefficient matrix is less than the correlation of the first correlation coefficient matrix, wherein,
the correlation of any matrix of correlation coefficients is determined by how close the off-diagonal elements in the matrix are to 0.
6. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions being arranged to perform the method of any of the preceding claims 1 to 5.
7. A computer-readable storage medium having stored thereon computer-executable instructions for performing the method flow of any of claims 1-5.
8. The computer-readable storage medium according to claim 7, characterized in that it comprises modules corresponding to the method flows according to any one of claims 1 to 5.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116483881A (en) * | 2023-04-26 | 2023-07-25 | 北京远舢智能科技有限公司 | Data sampling method, device, electronic equipment and medium based on pull Ding Chao cube |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111881591A (en) * | 2020-07-31 | 2020-11-03 | 贵州大学 | SVD-based large-scale relevance random variable twice-sequencing sampling method |
| CN113221955A (en) * | 2021-04-15 | 2021-08-06 | 哈尔滨工程大学 | Uncertainty propagation method for high-dimensional input parameters in reactor physical analysis |
| CN114707310A (en) * | 2022-03-19 | 2022-07-05 | 哈尔滨工程大学 | Physical simulation method and device for nuclear reactor based on singular value decomposition transformation, computer equipment and storage medium |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111881591A (en) * | 2020-07-31 | 2020-11-03 | 贵州大学 | SVD-based large-scale relevance random variable twice-sequencing sampling method |
| CN113221955A (en) * | 2021-04-15 | 2021-08-06 | 哈尔滨工程大学 | Uncertainty propagation method for high-dimensional input parameters in reactor physical analysis |
| CN114707310A (en) * | 2022-03-19 | 2022-07-05 | 哈尔滨工程大学 | Physical simulation method and device for nuclear reactor based on singular value decomposition transformation, computer equipment and storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116483881A (en) * | 2023-04-26 | 2023-07-25 | 北京远舢智能科技有限公司 | Data sampling method, device, electronic equipment and medium based on pull Ding Chao cube |
| CN116483881B (en) * | 2023-04-26 | 2024-05-03 | 北京远舢智能科技有限公司 | Data sampling method and device based on pull Ding Chao cube, electronic equipment and medium |
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