A high-precision automatic resonator group parameter generation method, computer device and storage medium based on particle swarm optimization

Abstract

A method for generating high-precision automated resonance subgroup parameters based on particle swarm optimization, a computer device, and a storage medium, which relate to the field of nuclear reactor physics calculations. It solves the problems that existing automated means completely eliminate the negative subgroup parameters caused by uncertainty in traditional subgroup parameter generation methods and the unstable accuracy of subgroup parameters in traditional subgroup parameter generation methods. The present invention provides the following solutions: initialize the particle swarm coordinates, particle swarm size, and number of iterations in the resonance energy group; set the domain of definition for the particle swarm coordinates; update the position of the new particles after iterative update according to the particle swarm coordinates and particle velocities; obtain the new particle swarm cross-section and particle swarm probability from the updated new particle coordinates; repeat the particle swarm parameters and particle swarm probability obtained by iterative update, and calculate the fitness function; when the fitness function is less than the convergence criterion of 1%, the iteration terminates, and the final particle swarm cross-section and particle swarm probability are obtained. It is applicable to the field of particle swarm parameter calculation.

Description

Technical Field

[0001] The invention relates to the field of nuclear reactor physics calculation. Background Art

[0002] Resonance calculations form the basis of nuclear reactor core physics calculations by providing effective multi-group cross sections for neutron transport equations. At present, the main resonance calculation methods include equivalent theory method, ultrafine group method and subgroup method. Among them, the subgroup method expresses the violently fluctuating resonance peak in the form of probability density and solves the effective resonance cross section through the cross section distribution probability. In practice, the subgroup method describes the change of cross section value through subgroup cross section and describes the probability density corresponding to the subgroup cross section through subgroup probability. Subgroup cross section and subgroup probability are collectively referred to as subgroup parameters, and their generation has an important influence on the accuracy of subgroup method implementation.

[0003] At present, the traditional method of generating subgroup parameters is to use the correspondence between the effective resonance cross section and the subgroup parameters, select different background cross section points to establish a nonlinear well-posed equation set, and then solve the subgroup parameters through the Padé approximation method. This is also the method currently used by most commercial programs. However, this method introduces a large number of approximations in the mathematical process of subgroup parameter calculation, which may cause the solved subgroup parameters to deviate from the actual physical meaning. For example, the solved subgroup cross section value may be negative, which will cause errors in the subgroup method calculation. Therefore, special processing is required for the generation of subgroup parameters to avoid this problem.

[0004] Prior art The paper "HELIOS Methods" (Stamm'ler R.HELIOS Methods[R].Sweden:Studsvik Scandpower,2008) published in 2008 proposed a trial and error method. In order to avoid negative subgroup parameters caused by ill-posedness, the method obtains different subgroup parameters by selecting different numbers of subgroups or background cross-section point combinations, and then selects the solution that conforms to the physical meaning. For I subgroups, the Pad approximation method needs to select 2I-1 background cross-section points to solve the nonlinear equations. Among the background cross-section points given by the database, all situations consisting of 2I-1 background cross sections are traversed, and the subgroup parameters are calculated respectively, and then the resonance integral table provided by the database is inferred using the subgroup parameters. Through continuous trial and error, when the inferred resonance integral table and the resonance integral table provided by the database meet the error requirements, the subgroup parameters that meet the requirements are obtained.

[0005] However, the trial-and-error method cannot avoid the ill-posedness of subgroup parameter solutions from a mathematical perspective, and it may still happen that exhaustively enumerating all background cross-section points cannot meet the calculation requirements.

[0006] The paper "Subgroup Weight Generation Based on Shielded Pin-cell Cross Section Conservation" (Joo H G, Kim G Y, Pogosbekyan L. Subgroup Weight Generation Based on Shielded Pin-cell Cross Section Conservation[J]. Annals of Nuclear Energy, 2009, 36(7):859-868.) published in the prior art in 2009 proposed a method of pre-determining subgroup cross-section values and then calculating subgroup probabilities based on the least squares method with constraints. This transforms the problem of generating subgroup parameters into a problem of solving the minimum value of a non-linear multi-variable function. Specific implementation methods include the Lagrange multiplier method proposed by Han Gyu Joo, and fitting calculations by calling commercial software such as MATHEMATICA and MATLAB. During the fitting process, conditional constraints are added, including that the subgroup cross-section is greater than zero and the subgroup probability is between 0 and 1. However, this method often requires calling commercial software such as MATHEMATICA and MATLAB and cannot be coupled in component calculation programs. In addition, due to the complexity of the energy group structure and energy spectrum characteristics, the constrained fitting method is difficult to implement in programs and the calculation accuracy is unstable. Summary of the Invention

[0007] The object of the present invention is to solve the problems that in the existing traditional subgroup parameter generation methods using automated means, the subgroup parameters are negative due to uncertainty and the subgroup parameter accuracy is unstable in traditional subgroup parameter generation methods.

[0008] To achieve the above object, the present invention provides the following technical solution: A high-precision automated resonance subgroup parameter generation method based on particle swarm optimization, comprising the following steps: S1. Read the correspondence table of background cross-section and resonance integral or resonance cross-section according to the resonance energy group;

[0009] S2. Initialize the particle swarm coordinates, particle swarm size and number of iterations in the resonance energy group;

[0010] S3. Set the domain of definition for the particle swarm coordinates;

[0011] S4. Set the particle velocity within the domain of definition;

[0012] S5. Update the position of the new particles after iterative update according to the particle swarm coordinates and particle velocity;

[0013] S6. Obtain the new particle swarm cross-section and particle swarm probability based on the updated new particle coordinates;

[0014] S7. Repeat the particle swarm parameters and particle swarm probability obtained by the update iteration, and calculate the fitness function f n (t); when the fitness function f n (t) is less than the convergence criterion of 1%, the iteration terminates, and the final particle swarm cross-section and particle swarm probability are obtained.

[0015] Furthermore, a preferred implementation manner is provided. The process of initializing the particle swarm coordinates, particle swarm size, and number of iterations in the resonance energy group in S2 is as follows:

[0016] For the resonance energy group containing I particle swarms, the coordinate dimension of the particle swarm is 2I, where the first I dimensions are the subgroup probabilities and the last I dimensions are the subgroup cross-sections; for the nth particle in the tth iteration, its position coordinates are:

[0017]

[0018] Among them, the superscript j represents the spatial dimension, is the position coordinate of the nth particle in the tth iteration, is the subgroup probability of the nth particle in the tth iteration, is the subgroup cross-section of the nth particle in the tth iteration, and int is the cross-section defined by the intermediate resonance approximation;

[0019] The particle velocity V has the same dimensional structure as the particle position X, and the particle velocity is updated as:

[0020]

[0021] Among them, the inertia factor w takes a value of 0.6, the two acceleration constants c1 and c2 take values of 2.0, and r1 and r2 are random numbers, is the particle velocity, is the position of the local optimal solution, G j (t) is the position of the global optimal solution, is the velocity of the nth particle updated to the (t + 1)th iteration;

[0022] Judge whether the current resonance group needs resonance calculation according to the following formula:

[0023]

[0024] In the formula: σ t,g,10 and σ t,g,∞ respectively represent the background cross-section value of 10 10 b and the total cross-section under infinite dilution conditions; if R fIf it is greater than 0.01, then first specify that the number of subgroups is 2.

[0025] Furthermore, a preferred implementation is provided. The method for setting the domain of definition of the particle swarm coordinates in S3 is as follows:

[0026] For the i-th subgroup, when the number of particle swarms of i does not exceed I subgroups, the particle swarm probability is the i-th coordinate of the particle position, and its lower limit of value is 0, and the upper limit is 1 minus the sum of the subgroup probabilities of the previous i - 1 subgroups; the particle swarm probability of the last particle swarm is obtained by subtracting the sum of the probabilities of the previous I - 1 subgroups from 1;

[0027] For the subgroup cross-section, its lower limit of value is 0, and the maximum value of the subgroup cross-section is limited to 10 10 b; The particle swarm cross-section and the particle swarm probability need to satisfy the following formula:

[0028]

[0029] The particle swarm cross-section of the last particle swarm is obtained by back-calculating from the sum of all particle swarm probabilities and the cross-sections of the previous I - 1 particle swarms; the value range of the particle swarm position coordinates is:

[0030]

[0031] Furthermore, a preferred implementation is provided. In S4, the process of setting the particle velocity is as follows: For the particle swarm probability, the value range of the particle movement velocity is 0 - 1; for the particle swarm cross-section, the value range of the change in the particle movement velocity in this dimension is set to 0 - 10 2 :

[0032]

[0033] Furthermore, a preferred implementation is provided. The fitness function f n (t) in S7 is as follows:

[0034]

[0035] Furthermore, a preferred implementation is provided. In S7, if the update iteration reaches the set maximum number of iterations, and the corresponding fitness function f n (t) still does not meet the requirements, then return to S2, increase the number of particle swarms by 1, and re-initialize the particle positions and velocities.

[0036] Furthermore, a preferred implementation is provided. In S7, the maximum number of particle swarms is restricted to 5. If the fitness function f n (t) that meets the requirements is still not obtained in the case of the maximum number of particle swarms, then increase the convergence criterion by 0.1%; continuously loop the above process until the calculated particle swarm parameters meet the requirements.

[0037] Solution 3: A computer device, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor runs the computer program stored in the memory, the processor implements the method described in any one of the above.

[0038] Solution 4: A computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0039] The advantages of the present invention are as follows: Since the particle swarm algorithm constrains the value range of particle coordinates, the situation of negative values of subgroup parameters is completely avoided; First, compared with the trial-and-error method in the prior art, the present invention has the characteristic of automatic optimization, can automatically generate subgroup parameters, and the algorithm has better robustness; Second, compared with the fitting method, the implementation process of the present invention is simple, and the high precision of particle swarm parameters is ensured through the convergence of the fitness function.

[0040] Compared with the traditional Padé approximation calculation method, the subgroup parameters obtained by the high-precision automatic resonance subgroup parameter generation method based on particle swarm optimization have the following two advantages: First, since the particle swarm algorithm constrains the value range of particle coordinates, the calculated subgroup parameters no longer appear negative, avoiding the phenomenon that the subgroup fixed source equation cannot converge caused by the traditional method; Second, the subgroup parameters fitted by the particle swarm algorithm have high calculation accuracy. Generally speaking, the high-precision automatic resonance subgroup parameter generation method based on particle swarm optimization solves the ill-posed problem of the traditional Padé approximation method, and the calculated subgroup parameters conform to physical meaning and have high calculation accuracy.

[0041] The present invention is also applicable to the field of particle swarm parameter calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flowchart of a high-precision automatic resonance subgroup parameter generation method based on particle swarm optimization described in Embodiment 1.

[0043] Figure 2 It is a schematic diagram of the correspondence table between the background cross section and the effective absorption cross section of 238U in the energy range of 6.48 - 7.34 eV described in S1 of Embodiment 1.

[0044] Figure 3 It is a schematic diagram of the change trend of the fitness function of the 6.48 - 7.34 eV energy group with the number of particle swarms described in S7 of Embodiment 1.

[0045] Figure 4For the 6.48 - 7.34 eV energy range based on the particle swarm algorithm of the present invention 238 Table of subgroup parameters of U.

[0046] Figure 5 Relative deviation of the cross - section of 238U in the 6.48 - 7.34 eV energy range based on the particle swarm algorithm of the present invention. Specific implementation manner

[0047] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, rather than all of them.

[0048] Embodiment 1. Refer to Figure 1 and Figure 2 to illustrate this embodiment. This embodiment provides a high - precision automated resonance subgroup parameter generation method based on particle swarm optimization. The method includes the following steps:

[0049] S1. Read the correspondence table of background cross - section and resonance integral or resonance cross - section according to the resonance energy group;

[0050] S2. Initialize the particle swarm coordinates, particle swarm size, and number of iterations in the resonance energy group;

[0051] S3. Set the domain of definition for the particle swarm coordinates;

[0052] S4. Set the particle velocity within the domain of definition and set the particle velocity;

[0053] S5. Update the position of the new particle after iterative update according to the particle swarm coordinates and particle velocity;

[0054] S6. Obtain the new particle swarm cross - section and particle swarm probability according to the updated new particle coordinates;

[0055] S7. Repeat the particle swarm parameters and particle swarm probability obtained by iterative update, and calculate the fitness function f n (t); when the fitness function f n (t) is less than the convergence criterion of 1%, the iteration terminates, and the final particle swarm cross - section and particle swarm probability are obtained.

[0056] Refer to Figure 1 and Figure 2 to illustrate this embodiment. This embodiment uses the particle swarm algorithm to calculate 238 the particle swarm parameters of U in the 6.48 - 7.34 eV energy group. Since the resonance effect is strong here, the present invention directly takes the number of 5 particle swarms as an example for illustration. Refer to the following embodiments for details.Figure 2 Indicates 6.48~7.34eV energy range 238 The trend of the effective absorption cross section of U versus the background cross section, where the background cross section is 10 5 At b, the effective absorption cross-section reaches 1000b over a large area, and gradually tends to a stable value of 1000b.

[0057] Implementation method 2: This implementation method is a further limitation of the high-precision automatic resonance subgroup parameter generation method based on particle swarm optimization provided in implementation method 1. The process of initializing the particle swarm coordinates, particle swarm size and iteration number in the resonance energy group in S2 is:

[0058] Assuming that a certain resonance energy group contains I particle groups, the coordinate dimension of the particle group is 2I, where the first I dimensions are the subgroup probability and the last I dimensions are the subgroup cross section; for the nth particle of the tth iteration, its position coordinate is:

[0059]

[0060] Among them, j represents the spatial dimension, is the position coordinate of the nth particle in the tth iteration, is the subgroup probability of the nth particle in the tth iteration, is the subgroup cross section of the nth particle at the tth iteration, int is the cross section defined by the intermediate resonance approximation;

[0061] The particle velocity V has the same dimensional structure as the particle position X, and the particle velocity is updated as:

[0062]

[0063] Among them, the inertia factor w is 0.6, the two acceleration constants c1 and c2 are 2.0, and r1 and r2 are random numbers. is the particle speed, is the location of the local optimal solution, G j (t) is the position of the global optimal solution, is the speed of the nth particle updated to the t+1th iteration;

[0064] Determine whether the current resonance group needs resonance calculation according to the following formula:

[0065]

[0066] Where: t,g,10 and σ t,g,∞ Respectively represent the background cross section value of 10 10 b and the total cross section under infinite dilution conditions; if R fIf it is greater than 0.01, then the number of subgroups is first specified as 2.

[0067] Embodiment 3: This embodiment further limits the method for generating high-precision automated resonance subgroup parameters based on particle swarm optimization provided in Embodiment 1. The method for setting the domain of definition of the particle swarm coordinates in S3 is as follows:

[0068] For the i-th (i < I) subgroup, its particle swarm probability is the i-th coordinate of the particle position, and its lower limit of value is 0, and the upper limit is 1 minus the sum of the subgroup probabilities of the previous i - 1 subgroups; the particle swarm probability of the last particle swarm is directly obtained by subtracting the sum of the probabilities of the previous I - 1 subgroups from 1.

[0069] For the subgroup cross-section, its lower limit of value is 0. To avoid the flux approaching 0 due to an overly large cross-section, the maximum value of the subgroup cross-section is limited to 10 10 b; The particle swarm cross-section and the subgroup probability need to satisfy the following formula:

[0070]

[0071] The particle swarm cross-section of the last particle swarm will be deduced from the sum of all particle swarm probabilities and the cross-sections of the previous I - 1 subgroups; Considering the above constraints comprehensively, the value range of the particle swarm position coordinates can be expressed by the following formula:

[0072]

[0073] In Embodiment 3, the judgment criterion of formula (3) is much greater than 0.01. The present invention directly takes the number of 5 particle swarms as an example for illustration. The particle swarm probability is initialized to a random value between 0 and 1, and it is ensured that the sum of the particle swarm probabilities is 1. The particle swarm cross-section σ int,i is taken as a random value between 0 and σ int,∞ and the particle swarm cross-section of the last particle swarm will be deduced from the sum of all particle swarm probabilities and the cross-sections of the previous I - 1 subgroups. The particle swarm size is set to 10,000, and the maximum number of iterations is limited to 300.

[0074] See Figure 5 To illustrate this embodiment, compared with the traditional Padé approximation calculation method, the subgroup parameters calculated based on the particle swarm algorithm in this embodiment have the following two advantages: First, since the particle swarm algorithm constrains the value range of the particle coordinates, the calculated subgroup parameters no longer appear as negative values, avoiding the phenomenon that the subgroup fixed source equation cannot converge caused by the traditional method; Second, the subgroup parameters fitted by the particle swarm algorithm have high calculation accuracy.

[0075] Figure 5 Gives Figure 4The calculation deviation of the effective resonance cross section under different background cross sections is deduced from the subgroup parameters shown, and the fitness function value when the particle swarm iteration converges is Figure 5 the point with the largest deviation in Figure 5 , which appears in the absorption cross section when the background cross section is 10 b. The calculation deviation of this subgroup parameter for various types of resonance cross sections under most background cross section conditions is within ±0.5%, with a high fitting accuracy. Overall, the subgroup parameter calculation method based on the particle swarm algorithm solves the ill-posed problem of the traditional Padé approximation method. The calculated subgroup parameters conform to the physical meaning and have a high calculation accuracy.

[0076] Embodiment 4: This embodiment further limits the high-precision automatic resonance subgroup parameter generation method based on particle swarm optimization described in Embodiment 1. The process of setting the particle velocity in S4 is as follows: For the particle swarm probability, the value range of the defined particle movement velocity is 0 - 1; for the particle swarm cross section, the change range of the particle movement velocity in this dimension is set to 0 - 10 2 , and the formula is:

[0077]

[0078] Embodiment 5: This embodiment further limits the high-precision automatic resonance subgroup parameter generation method based on particle swarm optimization provided in Embodiment 1. In S7, the calculation of the fitness function f n (t) formula is:

[0079]

[0080] The fitness function is the relative deviation of the effective resonance cross section deduced from the particle swarm parameters under each background cross section.

[0081] Embodiment 6: This embodiment further limits the high-precision automatic resonance subgroup parameter generation method based on particle swarm optimization provided in Embodiment 1. In S7, if the update iteration reaches the maximum number of iterations, and the corresponding fitness function f n (t) still does not meet the requirements, return to S2, increase the number of the particle swarm by 1, and re-initialize the particle position and velocity.

[0082] Embodiment 7: This embodiment further limits the high-precision automatic resonance subgroup parameter generation method based on particle swarm optimization provided in Embodiment 1. In S7, the maximum number of the particle swarm is limited to 5. If the fitness function f n (t) that meets the requirements still cannot be obtained under the condition of the maximum number of the particle swarm, then increase the convergence criterion by 0.1%; continuously loop the above process until the calculated particle swarm parameters meet the requirements.

[0083] See Figure 3Regarding this embodiment, the fitness function of the subgroup parameters initialized randomly in this embodiment, that is, the relative deviation of the effective resonance cross-section under each background cross-section deduced from the particle swarm parameters is 76.1%. During the implementation process, f n (t) decreases as the number of iterations of the particle swarm increases. It decreases most significantly in the initial stage of iteration and drops below 2% at the 10th generation of particles. f n (t) drops to 0.94% at the 18th generation of particles, meeting the convergence criterion of the particle swarm calculation, and the iteration ends. The particle swarm parameters are obtained. For details, see Figure 4 the description. It can be seen that there are no negative numbers in the particle swarm cross-section.

[0084] Embodiment 8. This embodiment proposes a computer device including a memory and a processor. A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the method described in any one of the above methods.

[0085] Embodiment 9. This embodiment proposes a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the method described in any one of Embodiments 1 to 7 are implemented.

[0086] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code. The above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as the combination of blocks in the block diagram or flowchart, can be used to perform the specified function

[0087] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0088] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn the basic inventive concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art can make various changes and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A high-precision automated resonator subgroup parameter generation method based on particle swarm optimization, characterized in that It includes the following steps: S1. Read the correspondence table of background cross-section and resonance integral or resonance cross-section according to the resonance energy group; S2. Initialize the particle swarm coordinates, particle swarm size and number of iterations in the resonance energy group; S3. Set the domain of definition for the particle swarm coordinates; S4. Set the particle velocity within the domain of definition; S5. Update the position of the new particle after iterative update according to the particle swarm coordinates and particle velocity; S6. Obtain the new particle swarm cross-section and particle swarm probability according to the updated new particle coordinates; S7. Repeat the particle swarm parameters and particle swarm probabilities obtained by updating and iteration, and calculate the fitness function f n (t); when the fitness function f n (t) is less than the convergence criterion of 1%, the iteration terminates, and the final particle swarm cross-section and particle swarm probability are obtained; The method for setting the domain of definition of the particle swarm coordinates in S3 is: For the i-th subgroup, when the number of particle swarms of i does not exceed I subgroups, the particle swarm probability is the i-th coordinate of the particle position, the lower limit of its value is 0, and the upper limit is 1 minus the sum of the subgroup probabilities of the previous i-1 subgroups; the particle swarm probability of the last particle swarm is obtained by 1 minus the sum of the probabilities of the previous I-1 subgroups; For the subgroup cross-section, its lower limit of value is 0, and the maximum value of the subgroup cross-section is limited to 10 10 b; The particle group cross-section and the particle group probability need to satisfy the following formula: （4） The particle swarm cross-section of the last particle swarm is deduced from the sum of all particle swarm probabilities and the cross-sections of the previous I-1 particle swarms; the value range of the particle swarm position coordinates is: （5）。 2. The high-precision automated resonator group parameter generation method based on particle swarm optimization according to claim 1, wherein The process of initializing the particle swarm coordinates, particle swarm size and number of iterations in the resonance energy group in S2 is: For the resonance energy group containing I particle swarms, the coordinate dimension of the particle swarm is 2I, where the first I dimensions are subgroup probabilities and the last I dimensions are subgroup cross-sections; for the n-th particle in the t-th iteration, its position coordinate is: （1） where j represents the spatial dimension, is the position coordinate of the nth particle in the t-th iteration, is the subgroup probability of the nth particle in the t-th iteration, is the subgroup cross-section of the nth particle in the t-th iteration, and int is the cross-section defined by the intermediate resonance approximation; Particle velocity V has the same dimensional structure as the particle position X, and the particle velocity is updated as follows: （2） Among them, the inertia factor w takes a value of 0.6, the two acceleration constants c1 and c2 take values of 2.0, r1 and r2 are random numbers, is the particle velocity, is the position of the local optimal solution, is the position of the global optimal solution, is the velocity of the nth particle updated to the (t + 1)-th iteration; Judge whether resonance calculation is required for the current resonance group according to the following formula: （3） Wherein: σ t,g,10 and σ t,g,∞ respectively represent the background cross-section value of 10 10 b and the total cross-section under infinite dilution conditions; if R f is greater than 0.01, then the subgroup number is first specified as 2.

3. The high-precision automated resonator group parameter generation method based on particle swarm optimization according to claim 1, wherein In S4, the process of setting the particle velocity is as follows: for the particle swarm probability, the value range of the particle movement velocity is 0 - 1; for the particle swarm cross-section, the change range of the particle movement velocity in this dimension is set to 0 - 10 2 : 。（6） 4. The method for generating high-precision automated resonator group parameters based on particle swarm optimization according to claim 1, characterized in that The fitness function f n (t) described in S7 is as follows: 。（7） 5. The high-precision automatic resonator group parameter generation method based on particle swarm optimization according to claim 1, wherein In S7, if the update iteration reaches the set maximum number of iterations, the corresponding fitness function f n ( t ) still does not meet the requirements, then return to S2, increase the number of particle swarms by 1, and re-initialize the particle positions and velocities.

6. The high-precision automated resonator group parameter generation method based on particle swarm optimization according to claim 1, wherein In S7, the maximum number of particle swarms is limited to 5. If the fitness function f n (t) that meets the requirements is still not obtained under the maximum number of particle swarms, the convergence criterion will be increased by 0.1%; continuously loop the above process until the calculated particle swarm parameters meet the requirements.

7. A computer device, comprising a memory and a processor, wherein, A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the method according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program. When the computer program is executed by the processor, the steps of the method according to any one of claims 1-6 are implemented.

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