Parameter determination method, device, storage medium and processor

Abstract

The present invention discloses a parameter determination method, device, storage medium, and processor. The method includes: obtaining initial information corresponding to a property parameter set of a target component, wherein the initial information is used to at least indicate the parameter quantity and distribution ratio of the property parameter set; determining at least one target fitness based on the initial information, wherein the target fitness is used to indicate the adaptability of the property parameter set in the current environment; determining at least one property parameter subset from the property parameter set based on the target fitness, wherein a first fitness is used to indicate the adaptability of the property parameter subset in the current environment after cross-calculation; and determining target property parameters based on the first fitness and a second fitness of the property parameter subset, wherein the second fitness is used to indicate the adaptability of the property parameter subset in the current environment before cross-calculation. The present invention solves the technical problem of low accuracy in parameter identification of electromagnetic components.

Description

Technical Field

[0001] The present invention relates to the field of electric power, and in particular to a parameter determination method, device, storage medium and processor. Background Art

[0002] Currently, accurate simulation of electromagnetic components in power systems requires precise modeling of their hysteresis characteristics. However, accurate modeling of hysteresis characteristics is closely dependent on the electromagnetic component's parameters. Furthermore, the identification of these parameters is limited in related technologies, resulting in low accuracy in electromagnetic parameter identification.

[0003] Currently, no effective solution has been proposed to the technical problem of low accuracy in parameter identification of the above-mentioned electromagnetic components. Summary of the Invention

[0004] Embodiments of the present invention provide a parameter determination method, device, storage medium, and processor to at least solve the technical problem of low accuracy in parameter identification of electromagnetic components.

[0005] According to one aspect of an embodiment of the present invention, a parameter determination method is provided. The method includes: obtaining initial information corresponding to a property parameter set of a target element, wherein the initial information is used to at least represent the parameter quantity and distribution ratio of the property parameter set; determining at least one target fitness based on the initial information, wherein the target fitness is used to represent the adaptability of the property parameter set in a current environment; determining at least one property parameter subset from the property parameter set based on the target fitness, wherein the property parameter subset converges to a target region; performing a cross calculation on the property parameter subset to obtain a first fitness of the property parameter subset, wherein the first fitness is used to represent the adaptability of the property parameter subset after the cross calculation in the current environment; and determining a target property parameter based on the first fitness and a second fitness of the property parameter subset, wherein the second fitness is used to represent the adaptability of the property parameter subset before the cross calculation in the current environment.

[0006] Optionally, determining at least one target fitness based on the initial information includes: determining the fitness of the attribute parameter set based on the initial information; and sorting the fitness to obtain the target fitness.

[0007] Optionally, based on the target fitness, at least one attribute parameter subset is determined from the attribute parameter set, including: based on the first target fitness and the second target fitness in the target fitness, determining the initial area in which the attribute parameter set is located, wherein the first target fitness is greater than any fitness in the fitness except the first target fitness, and the second target fitness is less than any fitness in the fitness except the second target fitness; based on the initial area, determining the attribute parameter subset from the attribute parameter set.

[0008] Optionally, based on the initial region, determining an attribute parameter subset from the attribute parameter set includes: iterating the initial region to obtain a target region; and deleting attribute parameters that do not converge to the target region from the attribute parameter set to obtain the attribute parameter subset.

[0009] Optionally, a cross calculation is performed on the attribute parameter subset to obtain a first fitness of the attribute parameter subset, including: performing a horizontal cross calculation on the attribute parameter subset to obtain the fitness of the attribute parameter subset after the horizontal cross calculation, and performing a vertical cross calculation on the attribute parameter subset to obtain the fitness of the attribute parameter subset after the vertical cross calculation; the fitness of the attribute parameter subset after the horizontal cross calculation and the fitness of the attribute parameter subset after the vertical cross calculation are determined as the first fitness.

[0010] Optionally, based on the first fitness and the second fitness of the attribute parameter subset, the target attribute parameter is determined, including: comparing the first fitness and the second fitness to obtain a comparison result, wherein the comparison result is used to represent the relationship between the first fitness and the second fitness; in response to the comparison result being that the first fitness is greater than or equal to the second fitness, the first attribute parameter corresponding to the first fitness is determined as the target attribute parameter; in response to the comparison result being that the first fitness is less than the second fitness, the second attribute parameter corresponding to the second fitness in the attribute parameter subset is determined as the target attribute parameter, wherein the first attribute parameter is used to represent the child parameter of the second attribute parameter, and the second attribute parameter is used to represent the parent parameter of the first attribute parameter.

[0011] According to one aspect of an embodiment of the present invention, a parameter determination device is provided. The device may include: an acquisition unit, configured to acquire initial information corresponding to a property parameter set of a target element, wherein the initial information is used to at least represent the parameter quantity and allocation ratio of the property parameter set; a first determination unit, configured to determine at least one target fitness based on the initial information, wherein the target fitness is used to represent the degree of adaptability of the property parameter set in a current environment; a second determination unit, configured to determine at least one property parameter subset from the property parameter set based on the target fitness, wherein the property parameter subset converges to a target region; a calculation unit, configured to perform a cross calculation on the property parameter subset to obtain a first fitness of the property parameter subset, wherein the first fitness is used to represent the degree of adaptability of the property parameter subset after the cross calculation in the current environment; and a third determination unit, configured to determine a target property parameter based on the first fitness and the second fitness of the property parameter subset, wherein the second fitness is used to represent the degree of adaptability of the property parameter subset before the cross calculation in the current environment.

[0012] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device containing the computer-readable storage medium is controlled to execute the parameter determination method of an embodiment of the present invention.

[0013] According to another aspect of an embodiment of the present invention, a processor is provided, which is configured to run a program, wherein when the program is run by the processor, the method for determining parameters according to an embodiment of the present invention is executed.

[0014] According to another aspect of an embodiment of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the parameter determination method of the embodiment of the present invention is implemented.

[0015] In an embodiment of the present invention, initial information corresponding to the attribute parameter set of the target element is obtained, for example, the parameter amount and distribution ratio of the attribute parameter set are obtained, and then the adaptability of the attribute parameter set in the current environment is determined based on the obtained initial information. Then, based on the determined adaptability of the attribute parameter set in the current environment, at least one attribute parameter subset that converges to the target area can be determined from the attribute parameter set. By cross-calculating the attribute parameter subset that converges to the target area, the first adaptability of the attribute parameter subset can be obtained, that is, the adaptability of the attribute parameter subset after the cross calculation in the current environment can be obtained, and based on the adaptability of the attribute parameter subset after the cross calculation in the current environment and the adaptability of the attribute parameter subset before the cross calculation in the current environment, the target attribute parameters can be determined, thereby achieving the purpose of accurately identifying the parameters of the electromagnetic element, solving the technical problem of low accuracy in parameter identification of the electromagnetic element, and achieving the technical effect of improving the accuracy in parameter identification of the electromagnetic element. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 is a flow chart of a parameter determination method according to an embodiment of the present invention;

[0018] Figure 2 1 is a flow chart of a JA hysteresis model parameter identification method based on a sparrow algorithm and a vertical and horizontal cross algorithm according to an embodiment of the present invention;

[0019] Figure 3 is a flow chart of a sparrow search method according to an embodiment of the present invention;

[0020] Figure 4 is a flow chart of a vertical and horizontal cross optimization method according to an embodiment of the present invention;

[0021] Figure 5 is a schematic diagram of changes in the root mean square error of each method with the number of iterations according to an embodiment of the present invention;

[0022] Figure 6 is a schematic diagram comparing a calculated result and a measured result of a saturation hysteresis loop according to an embodiment of the present invention;

[0023] Figure 7 is a schematic diagram comparing a calculated result of a saturation hysteresis loop after adding noise and a measured result according to an embodiment of the present invention;

[0024] Figure 8 2 is a schematic diagram of a parameter determination device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Example 1

[0028] According to an embodiment of the present invention, a method for determining parameters is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] Figure 1 1 is a flow chart of a method for determining parameters according to an embodiment of the present invention. The method may include the following steps:

[0030] Step S101: Acquire initial information corresponding to a property parameter set of a target component.

[0031] In the technical solution provided in the above step S101 of the present invention, the above initial information can be used to at least represent the parameter quantity, allocation ratio and safety threshold (ST) of the attribute parameter set, wherein the parameter quantity of the attribute parameter set can be used to record the total number of attribute parameters in the attribute parameter set, the allocation ratio of the attribute parameter set can be used to record the discoverer ratio (PD) and the alerter ratio (SD) in the attribute parameter set, and the safety threshold of the attribute parameter set can be used to maintain the attribute parameters within a safe area.

[0032] In this embodiment, the above attribute parameter set can be expressed as: n =(M sn , α n , a n , c n , k n ), n can be any natural number, M sn Can be used to represent the saturation magnetization of the material, α n Can be used to represent the main field component, a n The parameter that can be used to represent the non-hysteretic magnetization behavior model, c n Can be used to express the reversible magnetic susceptibility, k n It can be used to represent the magnetic domain wall concentration coefficient, which is only used as an example here and is not specifically limited.

[0033] In this embodiment, initial information corresponding to the property parameter set of the target element is obtained. For example, based on the property parameter set of the target element determined according to the hysteresis model, the initial information corresponding to the property parameter set is determined, for example, the parameter amount, allocation ratio and safety threshold of the property parameter set are determined. The target element can be an electromagnetic element (for example, commonly used ferromagnetic materials, etc.). This is only an example for illustration and is not specifically limited.

[0034] Step S102: determining at least one target fitness based on the initial information.

[0035] In the technical solution provided in the above-mentioned step S102 of the present invention, the above-mentioned target fitness can be used to represent the adaptability of the attribute parameter set in the current environment. For example, the above-mentioned target fitness can at least include: a first target fitness and a second target fitness. The first target fitness can be greater than any fitness in the fitness set except the first target fitness, and the second target fitness can be less than any fitness in the fitness set except the second target fitness.

[0036] It should be noted that the above-mentioned fitness set can be used to store one or more fitnesses, wherein the number of fitnesses stored in the fitness set corresponds one-to-one to the parameter quantity in the attribute parameter set, and the number of fitnesses stored in the fitness set can change due to changes in the parameter quantity in the attribute parameter set.

[0037] In this embodiment, after obtaining the initial information corresponding to the attribute parameter set of the target element, at least one target fitness is determined based on the initial information. For example, based on the parameter amount and distribution ratio of the obtained attribute parameter set, the adaptability of the attribute parameter set in the current environment can be determined. For example, the first target fitness and the second target fitness in the target fitness can be determined. This is only an example for illustration and is not specifically limited.

[0038] Step S103 : determining at least one attribute parameter subset from the attribute parameter set based on the target fitness.

[0039] In the technical solution provided in step S103 of the present invention, the attribute parameter subset may converge to a target region, wherein the target region may be a region obtained by iterating an initial region where the attribute parameter set is located.

[0040] In this embodiment, after determining at least one target fitness based on the initial information, at least one attribute parameter subset is determined from the attribute parameter set based on the target fitness. For example, based on the degree of adaptation of the attribute parameter set in the current environment, the initial region in which the attribute parameter set is located can be determined. Then, based on the determined initial region, at least one attribute parameter subset that converges to the target region can be determined from the attribute parameter set. That is, at least one x=(M s ,α,a,c,k).

[0041] Step S104: performing cross calculation on the attribute parameter subset to obtain a first fitness of the attribute parameter subset.

[0042] In the technical solution provided in the above step S104 of the present invention, the above first fitness can be used to indicate the adaptability of the attribute parameter subset after cross-calculation in the current environment.

[0043] In this embodiment, after determining at least one attribute parameter subset from the attribute parameter set based on the target fitness, a cross calculation is performed on the attribute parameter subset to obtain a first fitness of the attribute parameter subset. For example, by performing horizontal cross calculation and vertical cross calculation on the attribute parameter subset, the fitness of the attribute parameter subset after the above cross calculation can be obtained, and then the fitness of the attribute parameter subset after the above cross calculation is determined as the first fitness of the attribute parameter subset, thereby obtaining the adaptability of the attribute parameter subset after the cross calculation in the current environment.

[0044] Step S105 : determining target attribute parameters based on the first fitness and the second fitness of the attribute parameter subset.

[0045] In the technical solution provided in step S105 of the present invention, the second fitness can be used to indicate the degree of adaptability of the attribute parameter subset before cross calculation in the current environment.

[0046] In this embodiment, after cross-calculation is performed on the attribute parameter subset to obtain the first fitness of the attribute parameter subset, the target attribute parameter is determined based on the first fitness and the second fitness of the attribute parameter subset. For example, a comparison result can be obtained by comparing the fitness of the attribute parameter subset after the cross-calculation in the current environment with the fitness of the attribute parameter subset before the cross-calculation in the current environment. Then, by analyzing the obtained comparison result, it can be determined whether the obtained comparison result is: the fitness of the attribute parameter subset after the cross-calculation in the current environment is greater than or equal to the fitness of the attribute parameter subset before the cross-calculation in the current environment, or: the fitness of the attribute parameter subset after the cross-calculation in the current environment is less than the fitness of the attribute parameter subset before the cross-calculation in the current environment. Then, based on the analyzed comparison result, the target attribute parameter can be determined.

[0047] In the above steps S101 to S105 of the present application, initial information corresponding to the attribute parameter set of the target element is obtained, for example, the parameter amount and distribution ratio of the attribute parameter set are obtained, and then the adaptability of the attribute parameter set in the current environment is determined based on the obtained initial information. Then, based on the determined adaptability of the attribute parameter set in the current environment, at least one attribute parameter subset that converges to the target area can be determined from the attribute parameter set. By cross-calculating the attribute parameter subset that converges to the target area, the first adaptability of the attribute parameter subset can be obtained, that is, the adaptability of the attribute parameter subset after cross-calculation in the current environment can be obtained, and the target attribute parameters can be determined based on the adaptability of the attribute parameter subset after cross-calculation in the current environment and the adaptability of the attribute parameter subset before cross-calculation in the current environment, thereby achieving the purpose of accurately identifying the parameters of the electromagnetic element, solving the technical problem of low accuracy of parameter identification of the electromagnetic element, and achieving the technical effect of improving the accuracy of parameter identification of the electromagnetic element.

[0048] The above method of this embodiment is further introduced below.

[0049] As an optional embodiment, step S102, determining at least one target fitness based on the initial information, includes: determining the fitness of the attribute parameter set based on the initial information; and sorting the fitness to obtain the target fitness.

[0050] In this embodiment, the above sorting method can be ascending sorting or descending sorting, which is only used as an example and is not specifically limited.

[0051] In this embodiment, after obtaining initial information corresponding to the attribute parameter set of the target element, the fitness of the attribute parameter set is determined based on the initial information; the fitness is sorted to obtain the target fitness. For example, based on the parameter amount and distribution ratio of the obtained attribute parameter set, the fitness of each attribute parameter in the attribute parameter set can be calculated, and then by sorting the fitness of each attribute parameter in ascending or descending order, the adaptability of each attribute parameter in the attribute parameter set in the current environment can be obtained. For example, the first target fitness and the second target fitness in the target fitness can be determined. This is only an example and is not specifically limited.

[0052] As an optional implementation method, step S103 determines at least one attribute parameter subset from the attribute parameter set based on the target fitness, including: determining the initial area where the attribute parameter set is located based on the first target fitness and the second target fitness in the target fitness; and determining the attribute parameter subset from the attribute parameter set based on the initial area.

[0053] In this embodiment, the target fitness may include at least a first target fitness and a second target fitness. The first target fitness may be greater than any fitness in the fitness set except the first target fitness, and the second target fitness may be less than any fitness in the fitness set except the second target fitness. For example, the first target fitness may be expressed as f best To express it, the second objective fitness can be expressed as f worst Here, it is only used as an example and is not a specific limitation.

[0054] It should be noted that the above-mentioned fitness set can be used to store one or more fitnesses, wherein the number of fitnesses stored in the fitness set corresponds one-to-one to the parameter quantity in the attribute parameter set, and the number of fitnesses stored in the fitness set can change due to changes in the parameter quantity in the attribute parameter set.

[0055] Optionally, after determining at least one target fitness based on the initial information, an initial region in which the attribute parameter set is located is determined based on the first target fitness and the second target fitness in the target fitness; based on the initial region, an attribute parameter subset is determined from the attribute parameter set. For example, based on determining the first target fitness and the second target fitness in the target fitness, the initial region in which the attribute parameter set is located can be determined, that is, the original safe region in which the attribute parameter set is located can be determined. Then, based on the original safe region in which the attribute parameter set is located, an attribute parameter subset that converges to the target region can be determined from the attribute parameter set, that is, at least one x=(M) that converges to the target region is determined. s ,α,a,c,k).

[0056] Optionally, the first target fitness is determined as the upper boundary value of the initial region, and the second target fitness is determined as the lower boundary value of the initial region. On the basis of determining the upper boundary value and the lower boundary value of the initial region, the range of the initial region can be determined. Then, based on the range of the initial region, a subset of attribute parameters that converge to the target region can be determined from the attribute parameter set, that is, at least one x=(M s ,α,a,c,k).

[0057] As an optional embodiment, based on the initial area, an attribute parameter subset is determined from the attribute parameter set, including: iterating the initial area to obtain a target area; deleting attribute parameters that do not converge to the target area from the attribute parameter set to obtain the attribute parameter subset.

[0058] In this embodiment, the number of iterations may vary according to different target elements.

[0059] In this embodiment, after determining the initial region where the attribute parameter set is located based on the first target fitness and the second target fitness in the target fitness, the initial region is iterated to obtain the target region; from the attribute parameter set, the attribute parameters that have not converged to the target region are deleted to obtain an attribute parameter subset. For example, the target region can be obtained by iterating the initial region where the attribute parameter set is located, and then the attribute parameter subset can be obtained by deleting the attribute parameters that have not converged to the target region from the attribute parameter set, wherein the range of the target region is smaller than the range of the initial region.

[0060] As an optional implementation method, step S104 performs a cross calculation on the attribute parameter subset to obtain a first fitness of the attribute parameter subset, including: performing a horizontal cross calculation on the attribute parameter subset to obtain the fitness of the attribute parameter subset after the horizontal cross calculation, and performing a vertical cross calculation on the attribute parameter subset to obtain the fitness of the attribute parameter subset after the vertical cross calculation; the fitness of the attribute parameter subset after the horizontal cross calculation and the fitness of the attribute parameter subset after the vertical cross calculation are determined as the first fitness.

[0061] In this embodiment, after determining at least one attribute parameter subset from the attribute parameter set based on the target fitness, the fitness of the attribute parameter subset after the horizontal cross calculation can be obtained by performing a horizontal cross calculation on the attribute parameter subset, and the fitness of the attribute parameter subset after the vertical cross calculation can be obtained by performing a vertical cross calculation on the attribute parameter subset. The fitness of the attribute parameter subset after the horizontal cross calculation and the fitness of the attribute parameter subset after the vertical cross calculation are then determined as the adaptability of the attribute parameter subset after the above cross calculation in the current environment, thereby obtaining the first fitness.

[0062] Optionally, the above-mentioned horizontal cross calculation can be implemented by the following formulas (1) and (2):

[0063]

[0064]

[0065] in, and Can be used to represent and For the d-dimensional individual generated after horizontal crossover, r1 and r2 can be random numbers in the range of (0, 1), and c1 and c2 can be random numbers in the range of (-1, 1).

[0066] Optionally, the above longitudinal cross calculation can be implemented by the following formula (3):

[0067]

[0068] in, Can be used to represent SM ij1 and SM ij2 For the offspring generated after vertical crossover, r can be a random number in the range of (0, 1).

[0069] As an optional embodiment, step S105 determines the target attribute parameter based on the first fitness and the second fitness of the attribute parameter subset, including: comparing the first fitness and the second fitness to obtain a comparison result, wherein the comparison result is used to represent the relationship between the first fitness and the second fitness; in response to the comparison result being that the first fitness is greater than or equal to the second fitness, determining the first attribute parameter corresponding to the first fitness as the target attribute parameter; in response to the comparison result being that the first fitness is less than the second fitness, determining the second attribute parameter corresponding to the second fitness in the attribute parameter subset as the target attribute parameter.

[0070] In this embodiment, the above comparison result can be used to represent the relationship between the first fitness and the second fitness, the above first attribute parameter can be used to represent the child parameter of the second attribute parameter, and the above second attribute parameter can be used to represent the parent parameter of the first attribute parameter. For example, the above comparison result can be: the first fitness is greater than or equal to the second fitness, and the above comparison result can also be: the first fitness is less than the second fitness. This is only an example for illustration and is not specifically limited.

[0071] In this embodiment, after cross-calculation is performed on the attribute parameter subset to obtain the first fitness of the attribute parameter subset, a comparison result can be obtained by comparing the first fitness and the second fitness, and then the obtained comparison result can be determined by analyzing the obtained comparison result. If the obtained comparison result is that the first fitness is greater than or equal to the second fitness, the first attribute parameter corresponding to the first fitness is determined as the target attribute parameter. If the obtained comparison result is that the first fitness is less than the second fitness, the second attribute parameter corresponding to the second fitness in the attribute parameter subset is determined as the target attribute parameter.

[0072] In this embodiment, initial information corresponding to the attribute parameter set of the target element is obtained, for example, the parameter amount and distribution ratio of the attribute parameter set are obtained, and then the adaptability of the attribute parameter set in the current environment is determined based on the obtained initial information. Then, based on the determined adaptability of the attribute parameter set in the current environment, at least one attribute parameter subset that converges to the target area can be determined from the attribute parameter set. By cross-calculating the attribute parameter subset that converges to the target area, the first fitness of the attribute parameter subset can be obtained, that is, the adaptability of the attribute parameter subset after the cross calculation in the current environment can be obtained, and based on the adaptability of the attribute parameter subset after the cross calculation in the current environment and the adaptability of the attribute parameter subset before the cross calculation in the current environment, the target attribute parameters can be determined, thereby achieving the purpose of accurately identifying the parameters of the electromagnetic element, solving the technical problem of low accuracy in parameter identification of the electromagnetic element, and achieving the technical effect of improving the accuracy in parameter identification of the electromagnetic element.

[0073] Example 2

[0074] The technical solutions of the embodiments of the present invention are described below with reference to preferred implementation methods.

[0075] In power systems, accurate simulation of electromagnetic components requires precise modeling of their hysteresis characteristics. However, accurate modeling of hysteresis characteristics is closely dependent on the electromagnetic component's parameters. Furthermore, in related technologies, the identification of electromagnetic component parameters is relatively limited, resulting in low accuracy in electromagnetic parameter identification.

[0076] However, an embodiment of the present invention proposes a parameter determination method, which quickly locates the area of ​​the optimal solution, then uses cross calculation to perform local search, and then determines the optimal solution, thereby achieving the purpose of accurately identifying the parameters of the electromagnetic component, solving the technical problem of low accuracy in parameter identification of the electromagnetic component, and achieving the technical effect of improving the accuracy of parameter identification of the electromagnetic component.

[0077] Figure 2 1 is a flow chart of a JA hysteresis model parameter identification method based on a sparrow algorithm and a cross-cross algorithm according to an embodiment of the present invention. The method may include the following steps:

[0078] Step S201, obtaining the formula of the hysteresis model (JA model).

[0079] In the technical solution provided in step S201 of the present invention, the above formulas can be shown as follows (4) to (6):

[0080]

[0081]

[0082]

[0083] Among them, M can be used to represent the magnetization intensity, H can be used to represent the external magnetic field intensity, B can be used to represent the magnetic induction intensity, μ0 can be used to represent the vacuum magnetic permeability, M an Can be used to express the hysteresis-free magnetization, M irr Can be used to represent the irreversible magnetization, M s It can be used to represent the saturation magnetization intensity of the material, α can be used to represent the main field component, a can be used to represent the parameters of the non-hysteretic magnetization behavior model, c can be used to represent the reversible magnetization coefficient, k can be used to represent the magnetic domain wall concentration coefficient, δ can be used to represent the direction coefficient, when the magnetic field intensity increases, δ can take the value of 1, when the magnetic field intensity decreases, δ can take the value of -1.

[0084] Alternatively, using M as an intermediate variable, the relationship between BH can be obtained as shown in the following formula (7):

[0085] B=μ0(M+H) (7)

[0086] It should be noted that, from the above, the JA hysteresis model involves M s Therefore, for each ferromagnetic material, in order to describe its hysteresis characteristics, it is necessary to have M s , α, a, c, k five parameters for accurate identification.

[0087] Step S202, determine the parameters to be identified: M s ,α,a,c,k.

[0088] In the technical solution provided in step S202 of the present invention, after obtaining the formula of the hysteresis model (JA model), the parameters to be identified are determined: M s ,α,a,c,k.

[0089] Step S203: determine the objective function as:

[0090] In the technical solution provided in step S203 of the present invention, after determining the parameter to be identified: M s After ,α,a,c,k, the objective function is determined as:

[0091] In this embodiment, by determining the objective function, the optimization goal can be set to minimize the error between the overall hysteresis loop generated by the model and the actual measurement, where N is the number of sampling points, B is the number of sampling points, and i and can be respectively the i-th particle x i (M si , α i , a i , c i , k i )Calculated values ​​obtained by JA model and their corresponding theoretical values.

[0092] Step S204: outputting the fitted magnetic flux (B)-magnetic field intensity (H) curve.

[0093] In the technical solution provided in step S204 of the present invention, when determining the objective function: Afterwards, the fitted BH curve is output.

[0094] Step S205: Initialize the sparrow population and set algorithm parameters.

[0095] In the technical solution provided by the above step S205 of the present invention, the population position is randomly initialized, the number of individuals N, the safety threshold ST, the proportion of discoverers PD, the proportion of alerters SD, the maximum number of iterations iter in the sparrow algorithm are set. max and the initial value of the state position of the sparrow individual {X0}=(x1,x2,...,x n ), where x n =(M sn , α n , a n , c n , k n ).

[0096] Step S206: Calculate the fitness of individuals in the sparrow group.

[0097] In the technical solution provided in the above step S206 of the present invention, after the sparrow population is initialized and the algorithm parameters are set, the fitness of the individuals in the sparrow population is calculated.

[0098] In this embodiment, by sorting the fitness of all sparrow individuals, the global optimal fitness value and the global worst fitness value can be found, and then the initial global optimal position can be calculated.

[0099] In this embodiment, the fitness of the sparrow's location is calculated based on the fitness function, that is, the fitness value of the objective function is calculated, the fitness of the individuals is sorted, and according to the proportion of discoverers, the ones with larger fitness are selected as discoverers, and the rest are followers. In addition, according to the proportion of vigilants, a certain number of individuals in the population are randomly selected as vigilants.

[0100] Step S207: updating the positions of the discoverer, follower, and sentinel.

[0101] In the technical solution provided in the above step S207 of the present invention, after calculating the fitness of individuals in the sparrow group, the positions of the discoverer, follower and sentinel are updated.

[0102] In this embodiment, the process of updating the positions of the discoverer, follower, and sentinel can be expressed as follows (8) to (10):

[0103]

[0104]

[0105]

[0106] Among them, the above formula (8) is the formula for updating the position of the discoverer, t can be used to represent the number of iterations, x i,j It can be used to represent the position information of the i-th sparrow individual in the j-th dimension, Q can be used to represent a random number that obeys the normal distribution, and L can be used to represent the unit matrix of size (1, d). When R2 < ST, it means that there are no predators around, and the producer conducts a large-scale search. When R2 ≥ ST, it means that the sparrow has found a predator, so the sparrow needs to fly to a safe area. The above formula (9) is the formula for updating the follower's position, x p Can be used to represent the best position occupied by the producer, x worst It can be used to represent the current global worst position. A can be used to represent a matrix of size (1, d). Each element in the matrix can be randomly assigned a value of 1 or -1, and A + =A T (AA T ) -1 The above formula (10) is the formula for updating the position of the guard, x best It can be used to represent the current global optimal position, β can be used to represent the step size control parameter, which obeys the normal distribution with mean 0 and variance 1, K∈(-1,1) and is a random number, f i It can be used to represent the fitness value of the current sparrow individual, f g and f w They can be used to represent the current global optimal fitness value and the current global worst fitness value respectively. ε can be a constant to avoid the denominator being zero.

[0107] Step S208: Update individual optimal information and group optimal information.

[0108] In the technical solution provided in the above step S208 of the present invention, after the positions of the discoverer, follower and sentinel are updated, the individual optimal information and the group optimal information are updated.

[0109] In this embodiment, after the expert or operator hangs up the real-time communication of the guidance process, the superimposed video data R1 is saved in the relay protection operation information library. In addition, the superimposed video data R1 can be classified as a typical case or archived in daily operations as needed.

[0110] Step S209: determine whether the current region converges to the local optimal region.

[0111] In the technical solution provided in the above step S209 of the present invention, after the individual optimal information and the group optimal information are updated, it is determined whether the current region converges to the local optimal region.

[0112] In this embodiment, whether the current region converges to the local optimal region can be determined by the following formula (11):

[0113] |f best -f worst |<ξ (11)

[0114] Among them, f best and f worst They can be used to represent the optimal and worst fitness values ​​of the population obtained through iteration, respectively. ξ can be a pre-set small positive number. When the absolute value of the difference between the above fitness values ​​satisfies ξ, it can be considered that the population variability has decreased, the sparrow algorithm has reached its optimal range, and a more refined search is required. The 50% of individuals with higher fitness in the population are then selected for the crossover algorithm.

[0115] If the current region converges to the local optimal region, the process proceeds to step S210 to select the top 50% optimal individuals. If the current region does not converge to the local optimal region, the process returns to step S206.

[0116] Step S211: perform horizontal crossover on the two paired individuals.

[0117] In the technical solution provided in the above step S211 of the present invention, after selecting the top 50% best individuals, a horizontal crossover is performed on the two paired individuals.

[0118] In this embodiment, individuals in the population are first randomly paired, and then a horizontal crossover is performed on the two paired individuals, wherein the horizontal crossover can be implemented by formulas (1) and (2).

[0119] Step S212: perform vertical cross-sectioning on all individuals in two different dimensions.

[0120] In the technical solution provided in the above step S212 of the present invention, after the two paired individuals are horizontally crossed, all individuals are vertically crossed in two different dimensions.

[0121] In this embodiment, vertical crossover can be an arithmetic crossover in which all individuals operate on two different dimensions. When each individual performs a vertical crossover, only one dimension is updated, while the other dimensions remain unchanged, providing an opportunity for the stagnant dimension to escape the local optimum. Because the parameters represented by different dimensions have different meanings, normalization and unification of dimensions are required before crossover. Vertical crossover can be implemented using formula (3).

[0122] Step S213, judging whether the individuals after vertical and horizontal intersection meet the constraint conditions.

[0123] In the technical solution provided in the above step S213 of the present invention, after all individuals are vertically crossed in two different dimensions, it is determined whether the individuals after the vertical and horizontal crossing meet the constraint conditions.

[0124] In this embodiment, if the individual after the vertical and horizontal crossing meets the constraint condition, the process proceeds to step S204; if the individual after the vertical and horizontal crossing meets the unconstrained condition, the process returns to step S211.

[0125] Figure 3 : is a flow chart of a sparrow search method according to an embodiment of the present invention. Figure 3 As shown, the method may include the following steps:

[0126] Step S301: Obtain the parameter x to be identified i (M si , α i , a i , c i , k i ).

[0127] Step S302, initialize the sparrow algorithm, assign values ​​to the sparrow population, set the number of particles, safety threshold and the ratio of producers to guards in the algorithm.

[0128] In the technical solution provided in step S302 of the present invention, after obtaining the parameter to be identified x i (M s , α, a, c, k), the sparrow algorithm is initialized, the sparrow population is assigned, the number of particles, the safety threshold and the ratio of producers to guards in the algorithm are set.

[0129] Step S303: Calculate the individual fitness of the sparrows, and sort the calculated individual fitness of the sparrows from large to small.

[0130] In the technical solution provided in the above step S303 of the present invention, the sparrow algorithm is initialized, the individual fitness of the sparrows is calculated, and the calculated individual fitness of the sparrows is sorted from large to small.

[0131] Step S304: select the individual with higher fitness as the discoverer.

[0132] In the technical solution provided in the above step S304 of the present invention, after calculating the individual fitness of the sparrows, the calculated individual fitness of the sparrows are sorted from large to small, and the individual with the higher fitness is selected as the discoverer.

[0133] Step S305: determine whether RT is greater than ST.

[0134] In the technical solution provided in the above step S305 of the present invention, after the individual with higher fitness is selected as the discoverer, it is determined whether RT is greater than ST.

[0135] In this embodiment, if RT is greater than ST, the process proceeds to step S306, where the finder enters a safe area. If RT is not greater than ST, the process proceeds to steps S307 and S308, where the producer conducts a wide-range search and updates individual sparrow information.

[0136] Step S309: select individuals with lower fitness as followers.

[0137] In the technical solution provided in the above step S309 of the present invention, after calculating the individual fitness of the sparrows, the calculated individual fitness of the sparrows are sorted from large to small, and then the estimation result of the residual magnetic flux is output.

[0138] Step S310, determining whether the number of individuals is greater than n / 2.

[0139] In the technical solution provided in the above step S310 of the present invention, after taking individuals with lower fitness as followers, it is determined whether the number of individuals is greater than n / 2.

[0140] In this embodiment, it is determined whether the number of individuals is greater than n / 2. If the number of individuals is greater than n / 2, step S311 is entered, and the followers are allowed to forage at other locations. If the number of individuals is not greater than n / 2, step S312 and step S308 are entered, and the followers compete with the discoverers for positions and update the individual information of the sparrows.

[0141] Step S313: randomly select some individuals as sentinels.

[0142] In the technical solution provided in the above step S313 of the present invention, after calculating the individual fitness of the sparrows, the calculated individual fitness of the sparrows are sorted from large to small, and then some individuals are randomly selected as sentinels.

[0143] Step S314: determine whether the sentinel is at the edge of the population.

[0144] In the technical solution provided in the above step S314 of the present invention, after randomly selecting some individuals as sentinels, it is determined whether the sentinels are at the edge of the population.

[0145] In this embodiment, if the alerter is at the edge of the population, step S315 is entered to move the alerter closer to the optimal sparrow individual. If the alerter is not at the edge of the population, steps S316 and S308 are entered to move closer to other sparrows and update the sparrow individual information.

[0146] Step S317: determine whether the updated sparrow individual information meets the preset conditions.

[0147] In the technical solution provided by the above step S317 of the present invention, after the sparrow individual information is updated, it is determined whether the updated sparrow individual information meets a preset condition.

[0148] In this embodiment, if the updated sparrow individual information meets the preset condition, the process proceeds to step S318 to output the updated sparrow individual information; if the updated sparrow individual information does not meet the preset condition, the process returns to step S303.

[0149] Figure 4 is a flow chart of a vertical and horizontal cross optimization method according to an embodiment of the present invention. Figure 4 As shown, the method may include the following steps:

[0150] Step S401: Obtain the parameter x to be identified i (M si , α i , a i , c i , k i ).

[0151] In the technical solution provided in the above step S401 of the present invention, the relay protection operation information database can receive the issued operation ticket.

[0152] Step S402 : setting the vertical cross probability and the horizontal cross probability in the vertical and horizontal cross optimization method.

[0153] In the technical solution provided in step S402 of the present invention, after obtaining the parameter to be identified x i (M s , α, a, c, k), the vertical cross probability and the horizontal cross probability in the vertical and horizontal cross optimization method are set.

[0154] Step S403: Generate random numbers r1 and r2.

[0155] In the technical solution provided in the above step S403 of the present invention, after the vertical cross probability and the horizontal cross probability in the vertical and horizontal cross optimization method are set, random numbers r1 and r2 are generated.

[0156] Step S404: determine whether r1 is greater than PC.

[0157] In the technical solution provided in the above step S404 of the present invention, after generating the random numbers r1 and r2, it is determined whether r1 is greater than PC.

[0158] In this embodiment, if r1 is greater than PC, the process proceeds to step S405, where the parent generation performs a horizontal crossover to generate offspring, and the parent and offspring compete with each other to retain individuals with higher fitness. If r1 is not greater than PC, the process proceeds to step S406 to determine whether r2 is greater than PH.

[0159] If r2 is greater than PH, then go to step S407, and the offspring are generated by vertical crossover of the parent generation. The parent and offspring compete with each other and individuals with higher fitness are retained. If r2 is not greater than PH, then go to steps S408 and S409 to generate a new population and determine whether the generated new population meets the output conditions.

[0160] If the generated new population meets the output condition, the process proceeds to step S410 to output identification information of the new population. If the generated new population does not meet the output condition, the process returns to step S403.

[0161] Figure 5 FIG. 1 is a schematic diagram showing how the root mean square error of each method changes with the number of iterations according to an embodiment of the present invention. Figure 5 As shown in the figure, the Sparrow Search Algorithm (SSA) quickly converges to the global optimal solution after 7 iterations, but the accuracy of the local solution is insufficient in the later stage, converging only to about 1.6A / m. The Crossing-Based Optimization Algorithm (CSO) has a higher accuracy than the SSA algorithm due to the existence of the crossover operator, which allows the population to search for the optimal solution in a wide range. After 60 iterations, the error reaches 0.5A / m and is still decreasing, proving that it has not fallen into the local optimum. However, the CSO algorithm has a long computational time and cannot obtain the best result within a limited time. The hybrid algorithm based on SSA and CSO uses the SSA algorithm in the first 20 iterations. After locking the range of the optimal solution, the population difference decreases and it starts to switch to the CSO algorithm. Within this range, the global optimal solution is found through local search, thus achieving the technical effect of improving the convergence speed while ensuring the convergence accuracy.

[0162] Figure 6 FIG. 1 is a schematic diagram comparing the calculated result and the measured result of a saturation hysteresis loop according to an embodiment of the present invention. Figure 6 As shown in the figure, the fitting curve of the hybrid algorithm based on SSA_CSO almost completely coincides with the theoretical (real) curve, and its convergence accuracy is significantly better than that of other single algorithms.

[0163] Figure 7 FIG. 1 is a schematic diagram comparing the calculated result of the saturation hysteresis loop after adding noise and the measured result according to an embodiment of the present invention. Figure 7 As shown in the figure, when noise with a signal-to-noise ratio of 50dB is introduced on the basis of the theoretical hysteresis loop, the fitting curves of the SSA algorithm and the CSO algorithm after adding noise are less consistent with the theoretical curve. While the fitting curve of the hybrid algorithm based on SSA_CSO deviates, it is still within the allowable error range. This shows that when the signal-to-noise ratio is 50dB, the parameter determination method adopted in the embodiment of the present application still has good accuracy performance.

[0164] In this embodiment, the formula of the JA model is obtained to determine the parameters to be identified: M s , α, a, c, k and objective function, then output the fitted BH curve, initialize the sparrow population, set the algorithm parameters, calculate the individual fitness in the sparrow population, update the positions of the discoverer, follower and sentinel, and update the individual optimal information and the group optimal information, then judge whether the current area converges to the local optimal area, if the current area converges to the local optimal area, select the top 50% optimal individuals, perform horizontal crossover on the two paired individuals, perform vertical crossover on all individuals in two different dimensions, and then judge whether the individuals after the vertical and horizontal crossover meet the constraints, if the individuals after the vertical and horizontal crossover meet the constraints, then output the fitted BH curve, thereby achieving the purpose of accurately identifying the parameters of the electromagnetic components, solving the technical problem of low accuracy of parameter identification of the electromagnetic components, and achieving the technical effect of improving the accuracy of parameter identification of the electromagnetic components.

[0165] Example 3

[0166] According to an embodiment of the present invention, a parameter determination device is further provided. It should be noted that the parameter determination device can be used to execute a parameter determination method in embodiment 1.

[0167] Figure 8 FIG is a schematic diagram of a parameter determination device according to an embodiment of the present invention. Figure 8 As shown, the parameter determination device 800 may include: an acquisition unit 801 , a first determination unit 802 , a second determination unit 803 , a calculation unit 804 and a third determination unit 805 .

[0168] The acquisition unit 801 is configured to acquire initial information corresponding to a property parameter set of a target component, wherein the initial information is used to at least represent a parameter quantity and a distribution ratio of the property parameter set.

[0169] The first determining unit 802 is configured to determine at least one target fitness based on the initial information, wherein the target fitness is used to indicate the degree of adaptation of the attribute parameter set in the current environment.

[0170] The second determining unit 803 is configured to determine at least one attribute parameter subset from the attribute parameter set based on the target fitness, wherein the attribute parameter subset converges to the target area.

[0171] The calculation unit 804 is configured to perform a cross calculation on the attribute parameter subset to obtain a first fitness of the attribute parameter subset, wherein the first fitness is used to indicate the adaptability of the attribute parameter subset after the cross calculation in the current environment.

[0172] The third determining unit 805 is configured to determine the target attribute parameter based on the first fitness and the second fitness of the attribute parameter subset, wherein the second fitness is used to indicate the adaptability of the attribute parameter subset in the current environment before cross calculation.

[0173] Optionally, the first determining unit 802 may include: a first determining module, configured to determine the fitness of the attribute parameter set based on initial information; and a sorting module, configured to sort the fitness to obtain a target fitness.

[0174] Optionally, the second determination unit 803 may include: a second determination module, used to determine the initial area in which the attribute parameter set is located based on the first target fitness and the second target fitness in the target fitness, wherein the first target fitness is greater than any fitness in the fitness except the first target fitness, and the second target fitness is less than any fitness in the fitness except the second target fitness; a third determination module, used to determine the attribute parameter subset from the attribute parameter set based on the initial area.

[0175] Optionally, the third determination module may include: an iteration submodule for iterating the initial area to obtain the target area; and a deletion submodule for deleting attribute parameters that do not converge to the target area from the attribute parameter set to obtain an attribute parameter subset.

[0176] Optionally, the calculation unit 804 may include: a calculation module, used to perform horizontal cross calculation on the attribute parameter subset to obtain the fitness of the attribute parameter subset after the horizontal cross calculation, and perform vertical cross calculation on the attribute parameter subset to obtain the fitness of the attribute parameter subset after the vertical cross calculation; a fourth determination module, used to determine the fitness of the attribute parameter subset after the horizontal cross calculation and the fitness of the attribute parameter subset after the vertical cross calculation as the first fitness.

[0177] Optionally, the third determination unit 805 may include: a comparison module, used to compare the first fitness and the second fitness to obtain a comparison result, wherein the comparison result is used to represent the relationship between the first fitness and the second fitness; a first response module, used to determine the first attribute parameter corresponding to the first fitness as the target attribute parameter in response to the comparison result that the first fitness is greater than or equal to the second fitness; a second response module, used to determine the second attribute parameter corresponding to the second fitness in the attribute parameter subset as the target attribute parameter in response to the comparison result that the first fitness is less than the second fitness, wherein the first attribute parameter is used to represent the child parameter of the second attribute parameter, and the second attribute parameter is used to represent the parent parameter of the first attribute parameter.

[0178] In this embodiment, an acquisition unit is used to acquire initial information corresponding to a property parameter set of a target component, wherein the initial information is used to at least represent the parameter amount and distribution ratio of the property parameter set; a first determination unit is used to determine at least one target fitness based on the initial information, wherein the target fitness is used to represent the degree of adaptability of the property parameter set in a current environment; a second determination unit is used to determine at least one property parameter subset from the property parameter set based on the target fitness, wherein the property parameter subset converges to a target area; a calculation unit is used to perform a cross calculation on the property parameter subset to obtain a first fitness of the property parameter subset, wherein the first fitness is used to represent the degree of adaptability of the property parameter subset after the cross calculation in the current environment; and a third determination unit is used to determine the target property parameter based on the first fitness and the second fitness of the property parameter subset, wherein the second fitness is used to represent the degree of adaptability of the property parameter subset before the cross calculation in the current environment, thereby achieving the purpose of accurately identifying the parameters of the electromagnetic component, solving the technical problem of low accuracy in parameter identification of the electromagnetic component, and achieving the technical effect of improving the accuracy of parameter identification of the electromagnetic component.

[0179] Example 4

[0180] According to an embodiment of the present invention, a computer-readable storage medium is further provided. The storage medium includes a stored program, wherein the program executes the parameter determination method in embodiment 1.

[0181] Example 5

[0182] According to an embodiment of the present invention, a processor is further provided. The processor is configured to run a program. When the program is run by the processor, the method for determining the parameters in embodiment 1 is executed.

[0183] Example 6

[0184] According to an embodiment of the present invention, a computer program product is further provided. The computer program product includes a computer program. When the computer program is executed by a processor, the method for determining parameters in embodiment 1 of the present invention is implemented.

[0185] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0186] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0187] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0188] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected to achieve the purpose of the present embodiment according to actual needs.

[0189] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0190] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the relevant technology, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0191] The above are only preferred embodiments of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining a parameter, characterized in that: include: Acquiring initial information corresponding to the property parameter set of the target component, wherein the initial information is used to at least represent the parameter amount and allocation ratio of the property parameter set; Determining at least one target fitness based on the initial information, wherein the target fitness is used to represent the degree of adaptation of the attribute parameter set in the current environment; Based on the target fitness, determining at least one attribute parameter subset from the attribute parameter set, wherein the attribute parameter subset converges to a target area; Performing a horizontal cross calculation on the attribute parameter subset to obtain the attribute parameter subset after the horizontal cross calculation; determining the fitness of the attribute parameter subset after the horizontal cross calculation; performing a vertical cross calculation on the attribute parameter subset to obtain the attribute parameter subset after the vertical cross calculation; determining the fitness of the attribute parameter subset after the horizontal cross calculation and the fitness of the attribute parameter subset after the vertical cross calculation as a first fitness, wherein the first fitness is used to represent the adaptability of the attribute parameter subset after the cross calculation in the current environment; The first fitness and the second fitness are compared to obtain a comparison result, wherein the second fitness is used to represent the fitness level of the attribute parameter subset in the current environment before cross calculation; in response to the comparison result being that the first fitness is greater than or equal to the second fitness, a first attribute parameter corresponding to the first fitness is determined as a target attribute parameter, wherein the first attribute parameter is used to represent a descendant parameter of the second attribute parameter; in response to the comparison result being that the first fitness is less than the second fitness, a second attribute parameter in the attribute parameter subset corresponding to the second fitness is determined as the target attribute parameter, wherein the second attribute parameter is used to represent a parent parameter of the first attribute parameter; Wherein, based on the target fitness, at least one attribute parameter subset is determined from the attribute parameter set, including: determining the first target fitness in the target fitness as the upper boundary value of the initial area where the attribute parameter set is located, and determining the second target fitness in the target fitness as the lower boundary value of the initial area, to obtain the range of the initial area, wherein the first target fitness is greater than any fitness in the fitness of the attribute parameter set except the first target fitness, and the second target fitness is less than any fitness in the fitness except the second target fitness; based on the range of the initial area, the attribute parameter subset is determined from the attribute parameter set.

2. The method according to claim 1, characterized in that Determining at least one target fitness based on the initial information includes: Determining the fitness of the attribute parameter set based on the initial information; The fitnesses are sorted to obtain the target fitness.

3. The method according to claim 1, characterized in that Determining the attribute parameter subset from the attribute parameter set based on the range of the initial area includes: Iterate the range of the initial area to obtain the target area; The attribute parameters that do not converge to the target area are deleted from the attribute parameter set to obtain the attribute parameter subset.

4. A parameter determination device, characterized in that: include: an acquiring unit, configured to acquire initial information corresponding to a property parameter set of a target component, wherein the initial information is used to at least represent a parameter quantity and a distribution ratio of the property parameter set; a first determining unit, configured to determine at least one target fitness based on the initial information, wherein the target fitness is used to represent a degree of adaptation of the attribute parameter set in a current environment; a second determining unit, configured to determine at least one attribute parameter subset from the attribute parameter set based on the target fitness, wherein the attribute parameter subset converges to a target area; a calculation unit, configured to perform a horizontal cross calculation on the attribute parameter subset to obtain the attribute parameter subset after the horizontal cross calculation; determine the fitness of the attribute parameter subset after the horizontal cross calculation; perform a vertical cross calculation on the attribute parameter subset to obtain the attribute parameter subset after the vertical cross calculation; determine the fitness of the attribute parameter subset after the horizontal cross calculation and the fitness of the attribute parameter subset after the vertical cross calculation as a first fitness, wherein the first fitness is used to represent the degree of adaptability of the attribute parameter subset after the cross calculation in the current environment; a third determining unit, configured to compare the first fitness and the second fitness to obtain a comparison result, wherein the second fitness is used to represent the fitness level of the attribute parameter subset in the current environment before cross calculation; in response to the comparison result being that the first fitness is greater than or equal to the second fitness, determining the first attribute parameter corresponding to the first fitness as a target attribute parameter, wherein the first attribute parameter is used to represent a descendant parameter of the second attribute parameter; in response to the comparison result being that the first fitness is less than the second fitness, determining the second attribute parameter in the attribute parameter subset corresponding to the second fitness as the target attribute parameter, wherein the second attribute parameter is used to represent a parent parameter of the first attribute parameter; Wherein, the second determination unit is used to determine at least one attribute parameter subset from the attribute parameter set based on the target fitness by performing the following steps: determining the first target fitness in the target fitness as the upper boundary value of the initial area where the attribute parameter set is located, and determining the second target fitness in the target fitness as the lower boundary value of the initial area to obtain the range of the initial area, wherein the first target fitness is greater than any fitness in the fitness of the attribute parameter set except the first target fitness, and the second target fitness is less than any fitness in the fitness except the second target fitness; based on the range of the initial area, determine the attribute parameter subset from the attribute parameter set.

5. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the parameter determination method according to any one of claims 1 to 3.

6. A processor, characterized in that: The processor is configured to run a program, wherein the program, when run by the processor, executes the parameter determination method according to any one of claims 1 to 3.

7. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for determining the parameters according to any one of claims 1 to 3 is implemented.

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