A method for achieving global continuous similarity prediction of coupled models by introducing deviation coefficient

Abstract

The present invention discloses a method for realizing continuous similarity prediction of the whole domain of a coupling model by introducing a deviation coefficient, the method comprising: determining the influence factor based on a least square solution model of the influence factor; the least square solution model is obtained by expanding the number of virtual samples in the solution equation of the influence factor; the solution equation of the influence factor is obtained based on the coupling model introducing the deviation coefficient, the coupling model is used to determine the prediction index of a finite domain, the prediction index of the finite domain is the product of the corrected deviation coefficient and the original prediction index, the corrected deviation coefficient is used to correct the error between the original prediction index without considering the coupling effect and the ideal value of the prediction index; determining the prediction index of the whole domain according to the influence factor, and determining the similarity criterion according to the prediction index of the whole domain; predicting the response parameters of the prototype structure based on the similarity criterion. The present invention can improve the prediction accuracy.

Description

Technical Field

[0001] The invention belongs to the technical field of data processing, and in particular relates to a method for introducing a deviation coefficient to realize global continuous similarity prediction of a coupling model. Background Art

[0002] In the early stage of large-scale engineering construction, it is necessary to reasonably predict and verify the performance of key structures in order to correct and improve the specific implementation plan of the project and avoid wasting time and resources to the greatest extent. Theoretical analysis and model testing are two conventional prediction methods. However, the theoretical analysis and calculation of mathematical models are limited by the complexity of the system, and it is not always possible to obtain a reasonable and effective mathematical form to describe complex physical phenomena. Scaled model testing based on similarity theory can make up for the shortcomings of theoretical analysis and effectively predict and verify large-scale prototype systems. The design parameters of the scaled model are determined based on the similarity criteria satisfied with the prototype design parameters.

[0003] Similarity theory is widely used in scaled model design and prediction of large-scale prototype engineering tests. However, there are still two problems: multivariate design parameters have a coupling effect on performance prediction; the scaled model size design range is strictly limited by the prediction accuracy, and it is impossible to achieve full domain continuous similarity. Therefore, the current prediction method based on scaled models has poor prediction accuracy. Summary of the invention

[0004] The embodiment of the present invention provides a method for introducing a deviation coefficient to realize global continuous similarity prediction of a coupling model, which can solve the problem of poor prediction accuracy of the current method based on scaled model prediction.

[0005] In a first aspect, an embodiment of the present invention provides a method for introducing a deviation coefficient to realize global continuous similarity prediction of a coupling model, the method comprising:

[0006] Based on the least square solution model of the influence factor, the influence factor is determined according to the scale factor of the design parameter between the prototype structure and the model structure and the scale factor of the response parameter;

[0007] Among them, the influence factor is used to describe the degree to which the scaling factor of the response parameter is affected by the scaling factor of the design parameter. The least squares solution model is obtained by expanding the number of virtual samples in the solution equation of the influence factor, and the virtual samples are virtual parameters. The solution equation of the influence factor is obtained based on the coupling model that introduces the deviation coefficient. The coupling model is used to determine the prediction index of the finite domain. The prediction index of the finite domain is the product of the corrected deviation coefficient and the original prediction index. The corrected deviation coefficient is used to correct the error between the original prediction index without considering the coupling effect and the ideal value of the prediction index.

[0008] Determine the prediction index of the whole domain according to the impact factor, and determine the similarity criterion between the prototype structure and the model structure according to the prediction index of the whole domain;

[0009] Based on the similarity criterion, the response parameters of the prototype structure are predicted according to the response parameters of the model structure.

[0010] In a second aspect, an embodiment of the present invention provides a device for introducing a deviation coefficient to realize global continuous similarity prediction of a coupling model, the device comprising:

[0011] A first processing unit, the first processing unit is used to solve the model based on the least squares of the influencing factor, and determine the influencing factor according to the scaling factor of the design parameter between the prototype structure and the model structure and the scaling factor of the response parameter;

[0012] Among them, the influence factor is used to describe the degree to which the scaling factor of the response parameter is affected by the scaling factor of the design parameter. The least squares solution model is obtained by expanding the number of virtual samples in the solution equation of the influence factor, and the virtual samples are virtual parameters. The solution equation of the influence factor is obtained based on the coupling model that introduces the deviation coefficient. The coupling model is used to determine the prediction index of the finite domain. The prediction index of the finite domain is the product of the corrected deviation coefficient and the original prediction index. The corrected deviation coefficient is used to correct the error between the original prediction index without considering the coupling effect and the ideal value of the prediction index.

[0013] A second processing unit, the second processing unit is used to determine a prediction index of the entire domain according to the influencing factor, and determine a similarity criterion between the prototype structure and the model structure according to the prediction index of the entire domain;

[0014] The third processing unit is used to predict the response parameters of the prototype structure according to the response parameters of the model structure based on a similarity criterion.

[0015] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory is used to store computer programs; the processor can be used to execute the computer program (instructions) stored in the memory to implement the method of the first aspect above.

[0016] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed, the method of the first aspect described above can be implemented.

[0017] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: according to the method provided by the present invention, by introducing a deviation coefficient in the coupling model, the error caused by ignoring the coupling effect can be corrected; by expanding the virtual samples in the solution equation of the influencing factor, the effective range of the scale factor can be expanded to achieve full-domain continuous prediction of the prototype structure, thereby improving the prediction accuracy of the prototype structure response parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of a coupling model introducing a deviation coefficient and a least squares solution model and a similarity criterion construction process based on the coupling model provided by an embodiment of the present invention;

[0019] Figure 2 A trend diagram of a prediction error changing with a scaling factor provided by an embodiment of the present invention;

[0020] Figure 3 A schematic diagram of the coupling effect of a design parameter provided by an embodiment of the present invention;

[0021] Figure 4 A flowchart of a method for introducing a deviation coefficient to realize global continuous similarity prediction of a coupling model provided by an embodiment of the present invention;

[0022] Figure 5a-5d A schematic diagram for comparing prediction effects of an embodiment of the present invention;

[0023] Figure 6a-6d A schematic diagram for comparing prediction errors provided by an embodiment of the present invention;

[0024] Figure 7 A schematic diagram of the structure of a device for introducing a deviation coefficient to realize global continuous similarity prediction of a coupling model provided by an embodiment of the present invention;

[0025] Figure 8 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0027] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0028] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0029] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.

[0030] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0031] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0032] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0033] Example 1

[0034] Figure 1 The flowchart shown is a coupling model with a deviation coefficient introduced and a least square solution model and a similarity criterion construction process provided by an embodiment of the present invention. As an example but not a limitation, the derivation process may include steps S101-S105, and each step is described below.

[0035] S101, determining the similarity criterion form and the prediction index form according to the similarity theory.

[0036] In a possible implementation, the similarity criterion form may be first determined according to the Buckingham-PI theorem in similarity theory, and then described in the form of a power function equation.

[0037] In one example, a similarity criterion of the form may be satisfied: Where S j Represents similarity criteria, is the i-th design parameter X i The scaling factor, is the i-th design parameter X i The ideal value of the prediction coefficient, T represents the functional relationship between the two.

[0038] Exemplarily, the scaling factor of the design parameter and the scaling factor of the response parameter may satisfy the following formula:

[0039]

[0040] in, is the i-th design parameter of the prototype structure, is the i-th design parameter of the model structure, is the jth response parameter Y affected by the i-th design parameter j,i The scaling factor, Y j,i (p) is the jth response parameter of the prototype structure affected by the i-th design parameter, Y j,i (m) is the jth response parameter of the model structure affected by the i-th design parameter.

[0041] Exemplarily, the design parameters of the model structure are in a certain ratio to the design parameters of the prototype structure, and based on a similarity criterion between the two, the response parameters of the prototype structure can be predicted according to the response parameters of the model structure.

[0042] In one example, if the scaling factor of each design parameter satisfies a nonlinear relationship with the scaling factor of the response parameter, the relationship between the scaling factor of the response parameter and the scaling factor of the design parameter can be described in the form of a power function equation, thereby describing the similarity criterion in the form of a power function equation.

[0043] Exemplarily, the functional relationship between the scaling factor of the response parameter and the scaling factor of the design parameter can be expressed in the form of a power function equation as follows:

[0044]

[0045] in, is the ideal value of the prediction index, which can be expressed as right The influence weight of; N is the total number of samples of design parameters.

[0046] Exemplarily, the similarity criterion described in the form of a power function equation can be expressed as:

[0047] In a possible implementation, a function equation satisfied by the prediction index and the scaling factor of the design parameter may be obtained according to a trend of the prediction accuracy changing with the scaling factor.

[0048] In one example, see Figure 2 The trend diagram of the prediction error changing with the scaling factor; the part before the origin on the horizontal axis indicates that the model structure is enlarged based on the prototype structure, and the part after the origin indicates that the model structure is reduced based on the prototype structure. The origin λ = 1 indicates that the design parameters of the prototype structure P and the model structure M are exactly the same, that is, The vertical axis is the prediction error.

[0049] See also Figure 2 , when the scaling factor λ = 1, the design parameters of the prototype structure and the model structure are completely consistent, the response parameters are also the same, and there is no prediction error in theory; as the scaling factor increases, the error also increases synchronously. Therefore, the prediction index and the scaling factor of the design parameters can satisfy the nonlinear functional relationship g′, that is, When the model structure is exactly the same as the prototype structure, that is, hour, Also, see Figure 2 ,when When a small change is made within the neighborhood U(1,ε) of 1, it is not enough to cause a drastic change in the response parameter. The prediction error can be less than the preset error threshold E in this interval. b According to the above formula (1.2), there can be Make Therefore, the prediction index can satisfy the following formula, In the neighborhood U(1,ε) of 1, Always holds true:

[0050]

[0051] Among them, α j , β j For description right The influencing factor of the degree of influence.

[0052] S102, introducing a deviation coefficient, and determining an expression satisfied by the deviation coefficient.

[0053] In a possible implementation, the original prediction index without considering the coupling effect can be first obtained based on the central difference approximation, and then the deviation coefficient is introduced to obtain the expression of the deviation coefficient based on the full increment method.

[0054] In one example, see Figure 3 Schematic diagram of the coupling effect of the design parameters shown in . If the design parameters are linearly independent, X i Each change in ΔX i , response Y i Corresponding change ΔY i , the change of global response is When the coupling effect is considered, the change in the global response is ΔY, and See also Figure 3 , the prediction index can affect the accuracy of the similarity criterion. Assuming that the design parameters are independent of each other, the original prediction index without considering the coupling effect can be obtained based on sensitivity analysis.

[0055] Specifically, we can take the logarithm of both sides of formula (1.2) to obtain:

[0056]

[0057] Then, a sensitivity analysis is performed on formula (1.5), and the central difference approximate expression of the original prediction index is obtained when the coupling effect is not considered:

[0058]

[0059] in, is the i-th design parameter X i The original prediction index.

[0060] In an example, considering the coupling effect between the design parameters and introducing the deviation coefficient, we can get:

[0061] Substituting formula (1.7) into formula (1.5) we can obtain:

[0062]

[0063] If the scaling factors between the design parameters change equally but Response parameter changes Then the deviation coefficient can be obtained by using the full increment method to satisfy the following formula:

[0064]

[0065] S103, correcting the deviation coefficient, and determining a coupling model for introducing the deviation coefficient according to the corrected deviation coefficient.

[0066] For example, the virtual sample factor can be extracted to perform parameter transformation on the above formula (1.5) to obtain:

[0067]

[0068] Let x i Change Δx i Substituting formula (1.10) into formula (1.5) we can get:

[0069]

[0070] Combining formulas (1.5), (1.10), and (1.11) we can obtain:

[0071]

[0072] Here, Δy represents the coupling effect.

[0073] For example, z i With x i Can satisfy:

[0074] Right i =f(x i ) can be expanded into the first-order Taylor formula to obtain:

[0075]

[0076] Refer to formula (1.14), there exists As the left end point of the scale factor range. Let Δx i =Δx, combining formulas (1.12), (1.13), and (1.14), we can get:

[0077]

[0078] By transposing the terms in formula (1.15), we can obtain:

[0079]

[0080] Substituting formula (1.8) into formula (1.16) we can get the corrected deviation coefficient:

[0081] ξ j c =ξ j +Δ ξ (1.17)

[0082] Among them, ξj c is the jth corrected deviation coefficient, ξ j is the jth deviation coefficient, Δ ξ is the coupling effect correction term.

[0083] in:

[0084]

[0085] Therefore, the prediction index of the finite domain (i.e. The theoretical value of the prediction index when the value is [1, +∞) can satisfy:

[0086]

[0087] Where ξ=[ξ 1 c ,ξ 2 c ,…,ξ N c ] is the deviation coefficient vector, is the original prediction index vector.

[0088] The coupling model with the deviation coefficient introduced can be expressed as:

[0089] Z=ξ·Z s T (1.20).

[0090] S104, expanding and adjusting the number of design parameters, and converting the solution of the influencing factors into a least squares problem.

[0091] In one possible implementation, see Figure 2 , the effective range of the scaling factor in the above coupling model is small, and it is only applicable in a small range, and cannot achieve continuous prediction of the entire domain. Therefore, the total number of samples N of the design parameters can be virtually expanded to expand the range of the effective range.

[0092] Exemplarily, the values ​​of the scaling factors that make the prediction error less than or equal to the preset error threshold may be the valid interval of the scaling factors, and the values ​​of the scaling factors that make the prediction error greater than the preset error threshold may be the invalid interval of the scaling factors.

[0093] In one example, if the scale factor of the design parameter varies within The solution equation of the impact factor can be expressed as: G(α j ,β j ,N)=0. When the scale factor of the design parameter changes within a certain range When As the scale factor variation interval The left boundary point; then Δx=x i , and x i ≠0. In actual processing, Δx can be a given constant, and Δy can be obtained indirectly. Combining formulas (1.13) and (1.14) can obtain the solution equation of the influencing factor:

[0094]

[0095] Among them, b = Δy / Δx is the relative change of the scale factor, which is a constant, and c i is also a known constant, and when When c i Always positive.

[0096] For example, c i The following formula can be satisfied:

[0097]

[0098] In one example, the virtual sample size N can be extracted from the sample size N V and the actual sample size N R , and for N V Perform expansion adjustments.

[0099] Specifically, by decomposing formula (1.21) and performing qualitative analysis based on the function graph, we can obtain:

[0100]

[0101] Among them, the function P 1 (x i ) and P 2 (x i )’s image intersection (x k ,P 1,2 k ) can be expressed as formula (1.21) at point x k A set of solutions (α j ,β j ). N and c i The value of is always non-negative, and b, α j , β j The positive or negative value determines P 1 (x i ) and P 2 (x i ) is determined by the quadrant that the image traverses in the rectangular coordinate system.

[0102] Referring to formula (1.23), we can see that N is P 1 (x i ), and the larger the N, the larger the slope, P1 (x i ) and P 2 (x i )The closer the intersection of the image is to 0, that is In the effective interval (λ - ,λ + ), the lower the prediction error. Therefore, by increasing the value of N, the effect of virtually expanding the number of samples can be achieved, which greatly suppresses the trend of the prediction error increasing with the increase of the scale factor, expands the range of the effective interval, meets the design and test requirements of large-scale scale models, and achieves effective continuous similarity in the entire domain.

[0103] P 1 (x i ) is determined as the virtual sample size N V , and P 2 (x i ) is determined as the actual sample size N R , and substituting it into formula (1.10) and formula (1.21), we can get the solution equation of the impact factor after expanding the virtual sample:

[0104]

[0105] In one example, the least squares solution model of the impact factor can be obtained by converting the solution equation of the impact factor into a least squares problem according to formula (1.24).

[0106] Exemplarily, the least squares solution model may satisfy the following formula:

[0107]

[0108] in:

[0109]

[0110] Among them, b k is the relative change of the scaling factor of the kth real sample, is the i-th design parameter X i The scaling factor, α j , β j For description right The influencing factor of the degree of influence, is the jth response parameter Y affected by the i-th design parameter j,i The scaling factor, Δ represents the change, g * Represents the least squares solution model.

[0111] S105, solving the influencing factors based on finite element and sensitivity analysis to obtain a global prediction index, and determining a similarity criterion between the prototype structure and the model structure according to the global prediction index.

[0112] In a possible implementation, the finite element theory can be used to calculate the performance response caused by the scaling factor of different design parameters, and the data fitting method can be used to calculate the influencing factors to determine the prediction index of the entire domain.

[0113] In an example, according to formula (1.6), we can get:

[0114]

[0115] Among them, Y j,i (0) is the scale factor affected by the design parameters The response parameters affected by Y j,i (+) Scale factor corresponding to the design parameters The response parameters affected by k=1,2,...,N R , δ represents the unit increment of the scaling factor of the design parameter. The value of each change should be as small as possible and be able to cause a change in the system response.

[0116] When δ=0.05,N R =2, when the value with lower computational cost is taken, expanding b = Δy / Δx yields:

[0117]

[0118] Calculate N by finite element theory R Substituting the sample data into formula (1.27) and formula (1.28) we can get N R Group

[0119] exist Redefining the prediction index within the interval can obtain the prediction index of the entire domain to satisfy:

[0120] In one example, in order to satisfy the global effective continuous similarity, formula (1.25) can be solved by Levenberg-Marquardt method to obtain α j and β j Substituting it into the above formula (1.29) can obtain the solution of the global prediction index. Then substituting the solution of the global prediction index into formula (1.3) can obtain the similarity criterion that satisfies the following formula:

[0121]

[0122] It can be expressed in the form of power function:

[0123]

[0124] Example 2

[0125] The method for introducing a deviation coefficient to realize global continuous similarity prediction of a coupling model provided in an embodiment of the present invention can be applied to electronic devices such as mobile terminals, personal laptops, supercomputers, etc. The embodiment of the present invention does not impose any restrictions on the specific type of electronic devices.

[0126] Figure 4 The flowchart shown is a method for implementing a method of introducing a deviation coefficient to realize global continuous similarity prediction of a coupling model provided by an embodiment of the present invention. As an example but not a limitation, the prediction method may include steps S401-S404, and each step is described below.

[0127] S401, based on the least square solution model of the influencing factor, the influencing factor is determined according to the scaling factor of the design parameter between the prototype structure and the model structure and the scaling factor of the response parameter.

[0128] Exemplarily, the influence factor may be used to describe the degree to which the scaling factor of the response parameter is affected by the scaling factor of the design parameter.

[0129] In one example, the least squares solution model (see formula (1.25) in Example 1) can be obtained by expanding the number of virtual samples in the solution equation of the influencing factor (see formula (1.22) in Example 1).

[0130] Exemplarily, the virtual sample may be a virtual parameter. The solution equation of the influencing factor after the virtual sample is expanded may be expressed by the formula (1.24) in the first embodiment.

[0131] Exemplarily, the solution equation of the influencing factor can be obtained based on a coupling model that introduces a deviation coefficient (see formula (1.20) in Example 1).

[0132] Exemplarily, the coupling model may be used to determine a prediction index of a finite domain, where the prediction index of the finite domain is the product of the corrected deviation coefficient and the original prediction index.

[0133] Exemplarily, the corrected deviation coefficient (see formula (1.17) in Example 1) is used to correct the error between the original prediction index without considering the coupling effect and the theoretical value of the prediction index.

[0134] S402, determining a prediction index of the entire domain according to the impact factor, and determining a similarity criterion between the prototype structure and the model structure according to the prediction index of the entire domain.

[0135] Exemplarily, the prediction index of the entire domain can be determined according to the impact factor based on formula (1.29) in the above embodiment 1. Based on formula (1.30) in the above embodiment 1, the similarity criterion between the prototype structure and the model structure can be determined according to the prediction index of the entire domain.

[0136] S403, predicting the response parameters of the prototype structure according to the response parameters of the model structure based on a similarity criterion.

[0137] Exemplarily, the values ​​of the response parameters of the model structure may be measured first, and then substituted into the similarity criterion to obtain the values ​​of the response parameters of the prototype structure.

[0138] According to the method provided by the present invention, by introducing a deviation coefficient in the coupling model, the error caused by ignoring the coupling effect can be corrected; by expanding the virtual sample in the solution equation of the influencing factor, the effective range of the scale factor can be expanded to achieve full-domain continuous prediction of the prototype structure, thereby improving the prediction effect of the prototype structure response parameters.

[0139] In order to better illustrate the beneficial effects of the present invention, the following simulation experiments were carried out:

[0140] Simulation experiment 1

[0141] In simulation experiment 1, the natural frequency of the four-side clamped rectangular thin plate shown in Figure 5 is predicted by the method provided by the present invention and the prediction method (abbreviated as LM-VPM) in the existing literature (L.Li, Z.Luo, F.He, et al. Animated partial similitude method for dynamic characteristic of rotorsystems based on Levenberg-Marquardt method[J]. Mech. Syst. Signal Process. 2022, 165.).

[0142] Exemplarily, the design parameters of the four-side clamped rectangular thin plate are shown in Table 1. According to the design parameters in Table 1, the finite element model is established and simulated to obtain the first four natural frequencies and the maximum deflection of the four-side clamped rectangular thin plate as shown in Table 2. Then, six groups of scaled models are set as shown in Table 3, and the natural frequencies corresponding to each group of scaled models are predicted based on the method provided by the present invention and the LM-VPM method, respectively, to obtain the values ​​of the prediction indexes shown in Table 4.

[0143] Table 1 Design parameters of rectangular thin plates with four sides clamped

[0144] parameter a b h q Numeric 1850mm 470mm 2mm <![CDATA[50N / m 2 ]]>

[0145] Table 2 Simulation data of the prototype structure of a rectangular thin plate with four sides clamped

[0146] parameter <![CDATA[f 1st ]]> <![CDATA[f 2nd ]]> <![CDATA[f 3rd ]]> <![CDATA[f 4th ]]> Numeric 51.203Hz 54.328Hz 59.990Hz 68.611Hz

[0147] Table 3 Scaled model

[0148]

[0149] Table 4 Prediction index of natural frequency

[0150]

[0151]

[0152] FIG. 5 is a schematic diagram showing a comparison of prediction effects according to an embodiment of the present invention.

[0153] For example, see Figure 5. Figure 5a , Figure 5b , Figure 5c , Figure 5d The following are comparison diagrams of the prediction effects of the 1st, 2nd, 3rd and 4th order natural frequencies of a rectangular thin plate with four sides clamped. The circles represent the prediction values ​​obtained by the method provided by the present invention, the diamonds represent the prediction values ​​obtained by the LM-VPM method, and the stars represent the theoretical values.

[0154] FIG. 6 is a schematic diagram showing a comparison of prediction errors provided by an embodiment of the present invention.

[0155] For example, see Figure 6. Figure 6a , Figure 6b , Figure 6c , Figure 6d The comparison diagrams are respectively the prediction errors of the 1st, 2nd, 3rd and 4th order natural frequencies of the rectangular thin plate with four sides clamped. The circles represent the prediction errors of the method provided by the present invention, and the diamonds represent the prediction errors of the LM-VPM method.

[0156] Referring to Figures 5 and 6, it can be seen that the predicted value of the natural frequency of the thin plate obtained by the method provided by the present invention is highly consistent with the theoretical value, and the prediction accuracy is significantly higher than that of the LM-VPM method. In addition, it can be seen that the prediction accuracy of the LM-VPM method decreases significantly with the increase of the scale factor; while the prediction error of the method provided by the present invention is stably maintained at a low level.

[0157] Therefore, according to the method provided by the present invention, by introducing a deviation coefficient in the coupling model, the error caused by ignoring the coupling effect can be corrected; by expanding the virtual sample in the solution equation of the influencing factor, the effective range of the scale factor can be expanded to achieve full-domain continuous prediction of the prototype structure, thereby improving the prediction effect of the prototype structure response parameters.

[0158] Example 3

[0159] Figure 7 The structure diagram of a device for introducing a deviation coefficient to realize global continuous similarity prediction of a coupling model provided by an embodiment of the present invention is shown. As an example but not limitation, the device 700 may include a first processing unit 710, a second processing unit 720 and a third processing unit 730.

[0160] The first processing unit 710 is used to solve the model based on the least squares of the influencing factor, and determine the influencing factor according to the scaling factor of the design parameter between the prototype structure and the model structure and the scaling factor of the response parameter;

[0161] Among them, the influence factor is used to describe the degree to which the scaling factor of the response parameter is affected by the scaling factor of the design parameter. The least squares solution model is obtained by expanding the number of virtual samples in the solution equation of the influence factor, and the virtual samples are virtual parameters. The solution equation of the influence factor is obtained based on the coupling model that introduces the deviation coefficient. The coupling model is used to determine the prediction index of the finite domain. The prediction index of the finite domain is the product of the corrected deviation coefficient and the original prediction index. The corrected deviation coefficient is used to correct the error between the original prediction index without considering the coupling effect and the theoretical value of the prediction index.

[0162] The second processing unit 720 is used to determine the prediction index of the entire domain according to the influencing factor, and determine the similarity criterion between the prototype structure and the model structure according to the prediction index of the entire domain;

[0163] The third processing unit 730 is used to predict the response parameters of the prototype structure according to the response parameters of the model structure based on the similarity criterion.

[0164] According to the device provided by the present invention, by introducing a deviation coefficient in the coupling model, the error caused by ignoring the coupling effect can be corrected; by expanding the virtual sample in the solution equation of the influencing factor, the effective range of the scale factor can be expanded to achieve full-domain continuous prediction of the prototype structure, thereby improving the prediction effect of the prototype structure response parameters.

[0165] Figure 8 FIG. 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 8 The electronic device 800 shown may include: at least one processor 810 ( Figure 8Only one processor is shown in the figure), a memory 820, and a computer program 830 stored in the memory 820 and executable on the at least one processor 810, wherein the processor 810 implements the steps of any of the above-mentioned method embodiments when executing the computer program 830.

[0166] The electronic device 800 may be a processing device such as a robot that can implement the above method. The embodiment of the present invention does not impose any limitation on the specific type of the electronic device.

[0167] Those skilled in the art will understand that Figure 8 The electronic device 800 is merely an example and does not constitute a limitation on the electronic device. The electronic device 800 may include more or fewer components than shown in the figure, or may combine certain components, or may include different components. For example, the electronic device 800 may also include an input and output interface.

[0168] The processor 810 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASTC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0169] The memory 820 may be an internal storage unit in some embodiments, such as a hard disk or a memory. The memory 820 may also be an external storage device in other embodiments, such as a plug-in hard disk, a smart memory card (SmartMemory Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Further, the memory 820 may also include both an internal storage unit and an external storage device. The memory 820 is used to store an operating system, an application program, a boot loader (Boot Loader), data, and other programs, such as the program code of the computer program, etc. The memory 820 may also be used to temporarily store data that has been output or is to be output.

[0170] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0171] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0172] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0173] An embodiment of the present invention provides a computer program product. When the computer program product runs on an electronic device, the electronic device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0174] 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 present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0175] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0176] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

Claims

1. A method for realizing global continuous similarity prediction of a coupled model by introducing a deviation coefficient, characterized in that: include: Based on the least squares solution model of the influencing factor, the influencing factor is determined according to the scaling factor of the design parameters between the prototype structure and the model structure and the scaling factor of the response parameter, wherein the design parameters include the length and thickness of the rectangular thin plate with four sides clamped; Wherein, the influencing factor is used to describe the degree to which the scaling factor of the response parameter is affected by the scaling factor of the design parameter, the least squares solution model is obtained by expanding the number of virtual samples in the solution equation of the influencing factor, and the virtual samples are virtual parameters; the solution equation of the influencing factor is obtained based on a coupling model that introduces a deviation coefficient, and the coupling model is used to determine the prediction index of a finite field, and the prediction index of the finite field is the product of the corrected deviation coefficient and the original prediction index, and the corrected deviation coefficient is used to correct the error between the original prediction index without considering the coupling effect and the ideal value of the prediction index; The coupling model satisfies the following formula: Z=ξ·ZsT Z is the prediction index matrix of the finite field, ξ=[ξ1c,ξ2c,…,ξNc] is the deviation coefficient vector, ξjc is the jth corrected deviation coefficient, and N is the total number of samples of the design parameters; is the original prediction index vector, is the i-th design parameter X i The original prediction coefficient of ; T represents transposition; The corrected deviation coefficient satisfies the following formula: ξjc=ξj+Δξ ξj is the jth deviation coefficient, Δξ is the coupling effect correction term; in: in, is the jth response parameter Y affected by the i-th design parameter j,i The scaling factor, λX i is the i-th design parameter X i The scaling factor, Δ represents the change; αj, βj are used to describe right The influencing factor of the degree of influence, Determining a prediction index of the entire domain according to the influencing factor, and determining a similarity criterion between the prototype structure and the model structure according to the prediction index of the entire domain; Based on the similarity criterion, the response parameters of the prototype structure are predicted according to the response parameters of the model structure.

2. The method according to claim 1, characterized in that The least squares solution model satisfies the following formula: NV is the number of virtual samples, N R is the number of real samples; in: Among them, bk is the relative change of the scaling factor of the kth real sample, is the i-th design parameter X i The scaling factor, αj, βj are used to describe right The influencing factor of the degree of influence, is the jth response parameter Y affected by the i-th design parameter j,i The scaling factor, Δ represents the amount of change, and g* represents the least squares solution model.

3. The method according to claim 2, characterized in that The solution equation of the impact factor satisfies the following formula: Wherein, b is the relative change of the scale factor, and N is the total number of samples of the design parameters, including the virtual samples and real samples; The i-th design parameter X i The original prediction coefficients of .

4. The method according to claim 3, characterized in that The solution equation of the impact factor after expanding the virtual sample satisfies the following formula:

5. The method according to claim 2, characterized in that: The similarity criterion satisfies the following formula: Wherein, Sj represents the similarity criterion, Yj,i(p) is the jth response parameter of the prototype structure affected by the i-th design parameter, Yj,i(m) is the jth response parameter of the model structure affected by the i-th design parameter, is the prediction index of the entire domain of the i-th design parameter.

6. A device for introducing a deviation coefficient to realize global continuous similarity prediction of a coupling model, characterized in that: include: A first processing unit, the first processing unit is used to solve the least squares model based on the influencing factor, and determine the influencing factor according to the scaling factor of the design parameters between the prototype structure and the model structure and the scaling factor of the response parameter, wherein the design parameters include the length and thickness of the rectangular thin plate with four sides clamped; Wherein, the influencing factor is used to describe the degree to which the scaling factor of the response parameter is affected by the scaling factor of the design parameter, the least squares solution model is obtained by expanding the number of virtual samples in the solution equation of the influencing factor, and the virtual samples are virtual parameters; the solution equation of the influencing factor is obtained based on a coupling model that introduces a deviation coefficient, and the coupling model is used to determine the prediction index of a finite field, and the prediction index of the finite field is the product of the corrected deviation coefficient and the original prediction index, and the corrected deviation coefficient is used to correct the error between the original prediction index without considering the coupling effect and the ideal value of the prediction index; The coupling model satisfies the following formula: Z=ξ·ZsT Z is the prediction index matrix of the finite field, ξ=[ξ1c,ξ2c,…,ξNc] is the deviation coefficient vector, ξjc is the jth corrected deviation coefficient, and N is the total number of samples of the design parameters; is the original prediction index vector, is the i-th design parameter X i The original prediction coefficient of ; T represents transposition; The corrected deviation coefficient satisfies the following formula: ξjc=ξj+Δξ ξj is the jth deviation coefficient, Δξ is the coupling effect correction term; in: in, is the jth response parameter Y affected by the i-th design parameter j,i The scaling factor, is the i-th design parameter X i The scaling factor, Δ represents the change; αj, βj are used to describe right The influencing factor of the degree of influence, A second processing unit, the second processing unit is used to determine a prediction index of the entire domain according to the influencing factor, and determine a similarity criterion between the prototype structure and the model structure according to the prediction index of the entire domain; A third processing unit is used to predict the response parameters of the prototype structure according to the response parameters of the model structure based on the similarity criterion.

7. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the electronic device, the method according to any one of claims 1 to 5 is implemented.

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