Method and device for determining nail mold combination, readable storage medium and terminal equipment

Abstract

The invention belongs to the technical field of engineering, and particularly relates to a method and device for determining a nail mold combination, a computer readable storage medium and terminal equipment. The method comprises the steps that a target lap joint material combination is obtained; based on a preset prediction model, predicting an optimal nail mold combination corresponding to the target lap joint material combination; wherein the prediction model is a pre-constructed linear regression model used for predicting an optimal nail model combination corresponding to the lap joint material combination; and based on the optimal rivet die combination, determining a target rivet die combination from one or more known rivets and known rivet dies. According to the method, the proper nail mold combination can be automatically determined by using the pre-established linear regression model, and the determination process does not need personnel participation, so that the labor cost can be reduced.

Description

Technical Field

[0001] The present application belongs to the field of engineering technology, and in particular, relates to a method, device, computer-readable storage medium and terminal device for determining a nail mold combination. Background Art

[0002] Self-piercing riveting technology is a method of pressing rivets into materials through riveting dies to form a connection. Through self-piercing riveting technology, various materials can be mechanically connected. For the same overlapping material combination, the riveting quality brought about by riveting with different nail die combinations may be different. Therefore, before riveting a certain overlapping material combination, it is necessary to determine the appropriate rivets and riveting dies. However, the determination process of the nail die combination in the prior art usually requires the participation of experienced experts and is highly dependent on manual experience, resulting in high labor costs. Summary of the invention

[0003] In view of this, the embodiments of the present application provide a method, apparatus, computer-readable storage medium and terminal device for determining a nail mold combination to solve the problem that the method for determining a nail mold combination in the prior art is highly dependent on manual experience, resulting in high labor costs.

[0004] A first aspect of an embodiment of the present application provides a method for determining a nail mold combination, which may include:

[0005] Obtaining a target overlapping material combination;

[0006] Based on a preset prediction model, predict the optimal nail mold combination corresponding to the target overlapping material combination; wherein the prediction model is a pre-constructed linear regression model for predicting the optimal nail mold combination corresponding to the overlapping material combination;

[0007] Based on the optimal nail die combination, a target nail die combination is determined from one or more known rivets and known rivet dies.

[0008] A second aspect of an embodiment of the present application provides a device for determining a nail mold combination, which may include:

[0009] An acquisition module, used for acquiring a target overlapping material combination;

[0010] A prediction module, used to predict the optimal nail mold combination corresponding to the target overlapping material combination based on a preset prediction model; wherein the prediction model is a pre-constructed linear regression model for predicting the optimal nail mold combination corresponding to the overlapping material combination;

[0011] A determination module is used to determine a target nail die combination from one or more known rivets and known rivet dies based on the optimal nail die combination.

[0012] A third aspect of an embodiment of the present application 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 of any one of the above methods for determining a nail mold combination are implemented.

[0013] The fourth aspect of an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the above-mentioned methods for determining a nail mold combination when executing the computer program.

[0014] A fifth aspect of an embodiment of the present application provides a computer program product. When the computer program product is run on a terminal device, the terminal device executes any step of the above-mentioned method for determining a nail mold combination.

[0015] Compared with the prior art, the embodiments of the present application have the following beneficial effects: the embodiments of the present application obtain a target overlapping material combination; based on a preset prediction model, the optimal nail mold combination corresponding to the target overlapping material combination is predicted; wherein the prediction model is a pre-constructed linear regression model for predicting the optimal nail mold combination corresponding to the overlapping material combination; based on the optimal nail mold combination, the target nail mold combination is determined from one or more known rivets and known rivet molds. In the embodiments of the present application, the pre-established linear regression model can be used to automatically determine the appropriate nail mold combination, and the determination process does not require human involvement, thereby helping to reduce labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A schematic diagram of the metallographic structure of a self-piercing riveted section;

[0018] Figure 2 A flowchart of an embodiment of a method for determining a nail mold combination in an embodiment of the present application;

[0019] Figure 3 Another schematic diagram of the metallographic structure of a self-piercing riveted section;

[0020] Figure 4 A first schematic diagram for riveting a target overlapping material combination;

[0021] Figure 5 A second schematic diagram for riveting a target overlapping material combination;

[0022] Figure 6 A third schematic diagram of riveting the target overlapping material combination;

[0023] Figure 7 This is a structural diagram of an embodiment of a device for determining a nail mold combination in an embodiment of the present application;

[0024] Figure 8 This is a schematic block diagram of a terminal device in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described below are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] It should be understood that when used in this 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 groups thereof.

[0027] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0028] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0029] As used in this 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 phrases "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 application, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0031] Self-piercing riveting technology is a method of forming a connection by pressing a rivet into the material through a riveting die. With self-piercing riveting technology, the individual materials to be connected can be mechanically connected.

[0032] For example, see Figure 1 Material A can be used as the upper plate material and material B as the bottom plate material. Through the self-piercing riveting technology, rivets can be pressed into material A and material B, so that material A and material B can be mechanically connected.

[0033] For the same overlapping material combination, the riveting quality brought by using different nail die combinations may be different. Specifically, the riveting quality will be affected by factors such as the mechanical properties of the connecting materials, the thickness of the connecting materials, the surface state of the connecting materials, the size of the rivet, the hardness of the rivet, the type of rivet die and the size of the rivet die.

[0034] Therefore, before riveting a certain overlapping material combination, it is necessary to determine suitable rivets and riveting molds. However, the determination process of the rivet mold combination in the prior art usually requires the participation of experienced experts, which is highly dependent on manual experience, resulting in high labor costs.

[0035] In view of this, the embodiments of the present application provide a method, apparatus, computer-readable storage medium and terminal device for determining a nail mold combination to solve the problem that the method for determining a nail mold combination in the prior art is highly dependent on manual experience, resulting in high labor costs.

[0036] It should be noted that the execution subject of the method of the present application is a terminal device, specifically, it can be a desktop computer, notebook, PDA, smart phone, tablet and other devices, or it can be other computing devices.

[0037] See also Figure 2 In an embodiment of the present application, a method for determining a nail mold combination may include:

[0038] Step S201, obtaining a target overlapping material combination.

[0039] In the embodiment of the present application, the overlapping material combination to be riveted (including the upper plate material and the bottom plate material) can be referred to as the target overlapping material combination. In actual self-piercing riveting applications, one overlapping material combination can be riveted using one nail-die combination, or a group of nail-die combinations can be used to rivet multiple overlapping material combinations (i.e., multiple overlapping material combinations have the same nail and die). Therefore, the target overlapping material combination in the embodiment of the present application can include at least one overlapping material combination to be riveted.

[0040] In a specific implementation, when it is necessary to rivet one or several overlapping material combinations, the user can input the information of the overlapping material combination that needs to be riveted into the terminal device (specifically, it can be an application on the terminal device), so that the terminal device can obtain the target overlapping material combination.

[0041] Step S202: predicting the optimal nail mold combination corresponding to the target overlapping material combination based on the prediction model.

[0042] In an embodiment of the present application, a prediction model can be used to predict the optimal nail mold combination corresponding to the target overlapping material combination; wherein the prediction model is a pre-constructed linear regression model for predicting the optimal nail mold combination corresponding to the overlapping material combination.

[0043] Before using the prediction model to predict the optimal nail mold combination, the prediction model can be constructed. The construction process of the prediction model will be introduced in detail below.

[0044] In the embodiment of the present application, an analysis sample set can be determined, and the analysis sample set can be used to perform regression analysis on the initial linear regression model. Afterwards, the initial linear regression model can be analyzed based on the analysis sample set to obtain the prediction model of the embodiment of the present application. Specifically, the analysis sample set can include a preset number of sample overlapping material combinations and corresponding recommended nail mold combinations, wherein the number of sample overlapping material combinations can be set according to actual needs, and the present application does not limit this.

[0045] In a specific implementation, the sample overlapping material combination may be an overlapping material combination actually produced in mass production, and the recommended nail mold combination corresponding to the sample overlapping material combination may also be a nail mold combination used for the overlapping material combination in actual mass production.

[0046] In another specific implementation, the sample overlapping material combination may be an overlapping material combination for laboratory application, and the recommended nail mold combination corresponding to the sample overlapping material combination may also be a nail mold combination used for the overlapping material combination in laboratory application.

[0047] In the embodiment of the present application, each overlapping material combination and nail mold combination can be characterized as corresponding features; specifically, the overlapping material combination can be characterized as material thickness t, yield strength R P , tensile strength R m There are four characteristics in total, namely, the rivet length L, the outer diameter D of the rivet body, and the elongation at break A. o , Rivet volume V R , rivet hardness H, rivet die diameter D D , Riveting die depth h, Riveting die volume V D There are 7 features in total.

[0048] In an embodiment of the present application, each sample overlapping material combination can be used as input, and the recommended nail mold combination corresponding to each sample overlapping material combination can be used as the expected output of the initial linear model. The initial linear regression model can be subjected to regression analysis to obtain a prediction model.

[0049] Specifically, the material thickness, yield strength, tensile strength, elongation at break of each sample overlap material combination and the rivet nail length of the rivet can be combined to obtain an input matrix, and the input matrix can be input into the initial linear regression model, and the initial linear regression model can predict the more suitable nail mold combination corresponding to each sample overlap material combination, and output the predicted actual output matrix composed of rivet volume, rivet hardness, rivet mold diameter, rivet mold depth, and rivet mold volume. Based on the actual output (specifically the actual output matrix) and the expected output (specifically the expected output matrix) of the initial linear regression model, the initial linear regression model can be subjected to regression analysis to obtain a prediction model; wherein the expected output matrix can be composed of the rivet volume, rivet hardness, rivet mold diameter, rivet mold depth, and rivet mold volume in the recommended nail mold combination.

[0050] In the embodiment of the present application, the initial linear regression model can be expressed as follows:

[0051]

[0052] Among them, X is the input matrix of the linear regression model, is the actual output matrix of the corresponding linear regression model, where X = [t, R P ,R m ,A,L], a is the first parameter of the linear regression model (specifically the slope of the linear regression model), b is the second parameter of the linear regression model (specifically the intercept of the linear regression model), and the initial values ​​of the first parameter and the second parameter can be set according to actual needs, and this application does not limit this.

[0053] Based on the recommended nail mold combination and the actual nail mold combination corresponding to each sample lap material, the predicted loss value Loss of the linear regression model can be calculated:

[0054]

[0055] Where n is the number of sample overlap material combinations, X i is the i-th input matrix, is the ith actual output matrix, Y i is the i-th expected output matrix.

[0056] Based on the predicted loss value, the prediction model can be determined. Specifically, the first parameter and the second parameter of the linear regression model can be determined by minimizing the loss function:

[0057]

[0058] By solving the above equations, the first parameter and the second parameter can be determined. Specifically, the first parameter can be obtained by solving the following formula:

[0059]

[0060] Among them, a′ is the first parameter obtained by solving, is the mean value of the input matrix, is the average value of the actual output matrix.

[0061] After that, the second parameter can be obtained according to the following formula:

[0062]

[0063] Wherein, b′ is the second parameter obtained by solving.

[0064] According to the solved first parameter and second parameter, the prediction model of the embodiment of the present application can be determined. After the prediction model is constructed, the prediction model can be applied to the actual process of determining the nail mold combination.

[0065] In a specific implementation of the embodiment of the present application, before performing regression analysis on the initial linear regression model, each feature of the sample overlap material combination and the recommended nail mold combination can be normalized to obtain a dimensionless normalized input matrix and a corresponding expected output matrix. Specifically, the input matrix X i Normalized input matrix X i 'for:

[0066]

[0067] Among them, X max is the maximum value in each input matrix, Xmin is the minimum value of each input matrix, and the expected output matrix Y after normalization i 'for:

[0068]

[0069] Among them, Y max is the maximum value in each expected output matrix, Y min is the minimum value in each expected output matrix; after that, the linear regression model can be determined by referring to the above method.

[0070] In another specific implementation of the embodiment of the present application, the obtained prediction model can also be analyzed and verified to determine whether the prediction model meets the preset standard; specifically, part of the analysis sample set can be used as a regression analysis data set for the above-mentioned regression analysis process, and the remaining part of the analysis sample set can be used as a verification analysis data set for the analysis and verification process; here, the R-squared value (R 2 ) and mean square error (MSE) values ​​to verify the effect of the prediction model. 2 It can be used to measure the degree of fit of the prediction model to the data. The specific calculation formula is:

[0071]

[0072] Where m is the number of data in the validation analysis data set, To verify the jth actual output matrix in the analysis data set, Y j To verify the jth expected output matrix in the analysis data set, To verify the average value of each expected output matrix in the analysis data set. In addition, the MSE of the expected output matrix and the actual output matrix can also be calculated:

[0073]

[0074] If the R calculated by the analysis and verification process 2 If the R-squared value is less than or equal to the preset R-squared threshold, and / or the MSE calculated in the analysis and verification process is less than or equal to the preset mean square error threshold, the performance of the prediction model can be considered good, and the prediction model can be applied to the actual nail model combination determination application; if the R-squared value calculated in the analysis and verification process is less than or equal to the preset mean square error threshold, the prediction model can be considered to have good performance, and the prediction model can be applied to the actual nail model combination determination application; 2If the R-squared value is greater than the preset R-squared threshold, and / or the MSE calculated by the analysis and verification process is greater than the preset mean square error threshold, it can be considered that the performance of the prediction model is poor. At this time, the prediction model can be optimized by referring to the regression analysis process in the previous text. Among them, the R-squared threshold and the mean square error threshold can be set according to actual needs, and this application does not limit this.

[0075] In an embodiment of the present application, the optimal nail mold combination corresponding to the target overlapping material combination can be predicted based on the prediction model; specifically, the characteristics of the target overlapping material combination and the rivet length can be used as an input matrix, and the input matrix can be used as an input of the prediction model to obtain an output matrix of the prediction model; based on the various characteristics in the output matrix, the optimal nail mold combination can be determined.

[0076] Specifically, the outer diameter of the nail body can be determined based on the tensile strength and total plate thickness of the target overlapping material combination; the rivet length can be determined based on the outer diameter of the nail body; wherein the total plate thickness is the material thickness of the upper plate material in the target overlapping material combination (the upper plate thickness t U ) and the material thickness of the base material (base thickness t L ) and.

[0077] Here, if the tensile strength of the target overlapping material is less than the preset strength threshold, and the total plate thickness is less than the total plate thickness threshold, the outer diameter of the nail body can be determined to be the preset first outer diameter; if the tensile strength of the target overlapping material is greater than or equal to the strength threshold, and / or the total plate thickness is greater than or equal to the total plate thickness threshold, the outer diameter of the nail body can be determined to be the preset second outer diameter.

[0078] Among them, the strength threshold, the total plate thickness threshold, the first outer diameter and the second outer diameter can all be set according to actual needs, and the embodiments of the present application do not limit this.

[0079] For example, the strength threshold can be set to 600 MPa, the total plate thickness threshold can be set to 2.5 mm, the first outer diameter can be set to 3 mm, and the second outer diameter can be set to 5 mm; that is, if the tensile strength of the target overlapping material is less than 600 MPa and the total plate thickness is less than 2.5 mm, then the outer diameter of the nail body can be determined to be 3 mm; if the tensile strength of the target overlapping material is greater than or equal to 600 MPa, and / or the total plate thickness is greater than or equal to 2.5 mm, then the outer diameter of the nail body can be determined to be 5 mm.

[0080] After determining the outer diameter of the rivet body, the rivet length can be determined based on the outer diameter of the rivet body and the total plate thickness. For example, the value of the rivet length needs to satisfy: 0.7*nail body outer diameter ≤ rivet length ≤ total plate thickness + 3; accordingly, any value between 0.7*nail body outer diameter and total plate thickness + 3 can be selected as the rivet length.

[0081] Afterwards, the corresponding input matrix can be combined according to the characteristics of the target overlapping material combination and the outer diameter of the nail body, and the input matrix can be used as the input of the prediction model to obtain the output matrix of the prediction model; based on the rivet die diameter, rivet die depth, and rivet die volume in the output matrix, the corresponding optimal rivet can be determined, and based on the rivet volume and rivet hardness in the output matrix, as well as the determined nail body outer diameter and rivet nail length, the corresponding optimal rivet can be determined; based on the determined optimal rivet and optimal rivet die, the optimal nail die combination can be combined.

[0082] Step S203: Based on the optimal nail mold combination, determine a target nail mold combination from one or more known rivets and known rivet molds.

[0083] In the embodiment of the present application, the optimal nail mold combination is a theoretically optimal nail mold combination obtained by prediction. After obtaining the optimal nail mold combination, the rivets and rivet molds that are most similar to the optimal rivet combination can be determined from the known rivets and rivet molds to save production costs. Here, the known rivets and rivet molds can be rivets and rivet molds with known parameters and models.

[0084] In an embodiment of the present application, a rivet that is most similar to an optimal rivet can be determined from known rivets; specifically, a rivet parameter difference between the optimal rivet and each known rivet can be calculated to obtain each rivet parameter difference, and a known rivet corresponding to the minimum value of each rivet parameter difference can be determined as a target rivet; wherein the rivet parameter difference may include a difference between the optimal rivet and the known rivets in rivet volume, rivet hardness, rivet body outer diameter or rivet nail length.

[0085] For example, there are 20 known rivets, so the rivet parameter gap between the optimal rivet and the 20 known rivets can be calculated to obtain 20 rivet parameter gaps, and the minimum value of the 20 rivet parameter gaps is the rivet parameter gap between the optimal rivet and the known rivet A. Therefore, the known rivet A can be determined as the target rivet.

[0086] In addition, a rivet die that is most similar to the optimal rivet die can be determined from among the known rivet dies; specifically, a rivet die parameter difference between the optimal rivet die and each known rivet die can be calculated to obtain each rivet die parameter difference, and a known rivet die corresponding to a minimum value of each rivet die parameter difference can be determined as a target rivet die; wherein the rivet die parameter difference may include a difference between the optimal rivet die and the known rivet die in rivet die diameter, rivet die depth or rivet die volume.

[0087] For example, there are 15 known riveting dies, so the riveting die parameter gap between the optimal riveting die and the 15 known riveting dies can be calculated to obtain 15 riveting die parameter gaps, and the minimum value of the 15 riveting die parameter gaps is the riveting die parameter gap between the optimal riveting die and the known riveting die B. Therefore, the known riveting die B can be determined as the target riveting die.

[0088] Based on the target rivet and the target rivet die, the corresponding target rivet die combination can be determined.

[0089] In a specific implementation, after the target nail mold combination is determined, the target overlapping material combination can be riveted using the target nail mold combination.

[0090] In a specific implementation of the embodiment of the present application, the riveting conditions of each known overlapping material combination can be scored to obtain scoring data, and a riveting quality database can be constructed based on the scoring data, so that when a known overlapping material combination needs to be riveted, the scoring data in the riveting quality database can be referred to to determine the appropriate nail mold combination, and when an unknown overlapping material combination needs to be riveted, the nail mold combination can still be determined by referring to the above process. Accordingly, data support can be provided for determining the nail mold combination, which can greatly simplify the complexity of the nail mold combination determination process.

[0091] The above implementation method will be introduced in detail below.

[0092] For a certain overlapping material combination, the quality scores of the working conditions after riveting the overlapping material combination with each known nail mold combination can be performed to obtain the riveting quality scores of each known nail mold combination with the overlapping material combination.

[0093] Specifically, the riveting conditions may include the interlocking value u after riveting, the minimum bottom thickness t r and the final head height P h ,like Figure 3 As shown, based on the preset quality scoring standard, the riveting condition can be scored to obtain the riveting quality score corresponding to the riveting condition.

[0094] As just an example, when a certain overlapping material combination includes two or more materials, that is, when two or more layers of materials are overlapped, if the bottom plate thickness in the overlapping material combination is less than or equal to the preset bottom plate thickness threshold, and the known riveting condition corresponding to the overlapping material combination meets the preset first quality scoring standard, the riveting quality score of the known riveting condition can be calculated based on the preset quality scoring calculation formula; otherwise, the riveting quality score of the known riveting condition can be set to the preset quality score value, and the quality score can be used to indicate that the known riveting condition does not meet the preset quality scoring standard. If the bottom plate thickness in the overlapping material combination is greater than the preset bottom plate thickness threshold, and the known riveting condition corresponding to the overlapping material combination meets the preset second quality scoring standard, the riveting quality score of the known riveting condition can be calculated based on the preset quality scoring calculation formula; otherwise, the riveting quality score of the known riveting condition can be set to the preset quality score value, and the quality score can be used to indicate that the known riveting condition does not meet the preset quality scoring standard. The above-mentioned bottom plate thickness threshold, first quality scoring standard, second quality scoring standard, quality score, and quality score calculation formula can all be set according to actual needs, and the embodiments of the present application do not limit this.

[0095] For example, the bottom plate thickness threshold can be set to 1.5 mm, the first quality scoring standard can be set to the interlocking value u being greater than or equal to the preset first interlocking value, and the minimum bottom thickness t r If the first bottom thickness is greater than or equal to the preset one, and the final head height falls within the preset head height range, the value of the first interlocking value can be set to k1D o , the value of the first bottom thickness can be set to 0.5k1D o , when the tensile strength of the base material R m When the tensile strength of the base material is greater than or equal to 600 MPa, k1 is 0.02. m When it is less than 600 MPa, k1 is 0.04; the head height range can be set to [-0.5, 0.3] (in millimeters), the second quality scoring standard can be set to the interlocking value being greater than or equal to the preset second interlocking value, the minimum bottom thickness being greater than or equal to the preset second bottom thickness, the final head height falling within the preset head height range, and the value of the second interlocking value can be set to k2D o , the value of the second bottom thickness can be set to 0.5k2D o , when the tensile strength of the underlying material R m When the tensile strength of the underlying material is greater than or equal to 600 MPa, k2 is 0.02. m When the pressure is less than 600 MPa, k2 is set to 0.08, the quality score can be set to -1, and the quality score calculation formula can be set to: Q = αu + βt r -|(1-α-β)P h|; Wherein, Q is the riveting quality score, α is the first quality score parameter, β is the second quality score parameter, the value of the first quality score parameter is greater than or equal to the second quality score parameter, and the value range of the first quality score is [0.6, 0.7].

[0096] In the above example, when a certain overlapping material combination includes two or more materials, if the bottom plate thickness in the overlapping material combination is less than or equal to 1.5 mm, and the interlocking value in the known riveting working condition corresponding to the overlapping material combination is greater than or equal to the first interlocking value, the minimum bottom thickness is greater than or equal to the first bottom thickness, and the final head height falls within the head height range, then according to αu+βt r -|(1-α-β)P h | Calculate the riveting quality score Q of the overlapping material combination; otherwise, the riveting quality score of the known riveting condition can be set to -1; if the base plate thickness in the overlapping material combination is greater than 1.5 mm, and the interlocking value in the known riveting condition corresponding to the overlapping material combination is greater than or equal to the second interlocking value, the minimum base thickness is greater than or equal to the second base thickness, and the final head height falls within the head height range, then according to αu+βt r -|(1-α-β)P h |The riveting quality score Q of the overlapping material combination is calculated; otherwise, the riveting quality score of the known riveting condition can be set to -1.

[0097] In an embodiment of the present application, a riveting condition of a known lap material combination and a known nail mold combination can be called a known riveting condition, and the riveting quality score of a known riveting condition can be used as a data unit in a riveting quality database. Based on each data unit, a riveting quality database can be constructed.

[0098] If there is no unknown overlapping material combination in the target overlapping material combination, the target nail mold combination can be determined based on a preset riveting quality database and the target overlapping material combination.

[0099] If there is at least one unknown overlapping material combination in the target material type, the optimal nail mold combination corresponding to the target overlapping material combination can be predicted based on the prediction model with reference to the method described above.

[0100] For example, the riveting quality database includes each riveting quality score corresponding to the lap combination consisting of material A and material B, and each riveting quality score corresponding to the lap combination consisting of material A and material D, while the riveting quality database does not include each riveting quality score corresponding to the lap combination consisting of material A and material C; if the target lap material combination is combination 1 consisting of material A and material B, it can be determined that there is no unknown lap material combination in the target lap material combination; if the target lap material combination includes combination 2 consisting of material A and material C, and combination 3 consisting of material A and material D, it can be determined that there is an unknown lap material combination in the target lap material combination.

[0101] If there is no unknown overlapping material combination in the target material type, each first target riveting quality score can be determined based on the riveting quality database; wherein the first target riveting quality score is the riveting quality score of any target known riveting condition, and the target known riveting condition is the riveting quality score of the target overlapping combination and a known nail mold combination.

[0102] For example, the target lap joint combination only includes the lap joint material combination consisting of material A and material B, and the riveting quality database includes the riveting quality scores of the working conditions of the lap joint material combination being riveted with known nail die combination 1, known nail die combination 2, known nail die combination 3, known nail die combination 4, known nail die combination 5, and known nail die combination 6, respectively, then the 6 riveting quality scores can be determined as the first target riveting quality scores.

[0103] For another example, the target lap joint combination includes lap joint material combination 1 consisting of material A and material B, and lap joint material combination 2 consisting of material A and material C. For lap joint material combination 1, the riveting quality database includes the riveting quality scores of the riveting conditions in which lap joint material combination 1 is riveted with known nail mold combination 1, known nail mold combination 2, known nail mold combination 3, and known nail mold combination 4 respectively; for lap joint material combination 2, the riveting quality database includes the riveting quality scores of the riveting conditions in which lap joint material combination 2 is riveted with known nail mold combination 1, known nail mold combination 2, known nail mold combination 3, and known nail mold combination 4 respectively. The above 8 riveting quality scores can be determined as the first target riveting quality scores.

[0104] Afterwards, a target nail die combination may be determined based on each first target riveting quality score.

[0105] Specifically, if the target overlapping material combination includes only one overlapping material combination, the known nail mold combination corresponding to the maximum value of each first target riveting quality score may be determined as the target nail mold combination.

[0106] For example, the target overlapping material combination only includes the overlapping material combination composed of material A and material B. It can be determined from the riveting quality database that the overlapping material combination corresponds to 8 known riveting conditions, that is, 8 first target riveting quality scores can be determined, and the known riveting condition corresponding to the maximum value of the 8 first target riveting quality scores is the riveting condition of the overlapping material combination and the known nail mold combination 3. Then the known nail mold combination 3 can be determined as the target nail mold combination.

[0107] If the target overlapping material combination includes more than two overlapping material combinations, it is necessary to comprehensively consider the first target riveting quality scores corresponding to each overlapping material combination to determine the corresponding second target riveting quality score. Specifically, for a known nail die combination, the riveting quality database may include the first target riveting quality scores of the riveting conditions of the known nail die combination and each overlapping material combination in the target overlapping material combination, and then the first target riveting quality scores corresponding to the known nail die combination may be comprehensively considered to determine the second target riveting quality score corresponding to the known nail die combination.

[0108] In a specific implementation, each first target riveting quality score corresponding to a known nail die combination may be added together to obtain a second target riveting quality score corresponding to the known nail die combination.

[0109] In another specific implementation, each first target riveting quality score corresponding to a known nail die combination may be multiplied to obtain a second target riveting quality score corresponding to the known nail die combination.

[0110] In another specific implementation, each first target riveting quality score corresponding to a known nail mold combination may be weighted averaged according to a preset weight to obtain a second target riveting quality score corresponding to the known nail mold combination.

[0111] Afterwards, the known nail die combination corresponding to the maximum value of each second target riveting quality score may be determined as the target nail die combination.

[0112] For example, eight second target riveting quality scores are calculated according to the above method, and the known rivet combination corresponding to the maximum value of the eight second target riveting quality scores is known rivet combination 4, so the known rivet combination 4 can be determined as the target nail mold combination.

[0113] Through the riveting quality database and prediction model, the nail mold combination can be automatically determined without human intervention, which helps to reduce labor costs and reduce analytical errors in the nail mold combination determination process caused by human factors, making the determination method more scientific and reliable.

[0114] To facilitate understanding, the above process will be described below with reference to specific examples.

[0115] In a specific example, the target overlapping material combination includes overlapping material combination A and overlapping material combination B; for overlapping material combination A, the upper plate material is a certain brand of aluminum alloy, and the bottom plate material is the same brand of aluminum alloy. The material thickness of the upper plate material is 1.5 mm, the yield strength is 150 MPa, and the elongation at fracture is 23%; the material thickness of the bottom plate material is 2.0 mm, the yield strength is 150 MPa, and the elongation at fracture is 23%; for overlapping material combination B, the upper plate material is a certain brand of aluminum alloy, and the bottom plate material is the same brand of aluminum alloy. The material thickness of the upper plate material is 2.0 mm, the yield strength is 150 MPa, and the elongation at fracture is 23%; the material thickness of the bottom plate material is 2.0 mm, the yield strength is 150 MPa, and the elongation at fracture is 23%.

[0116] The riveting quality database includes overlapping material combination A and overlapping material combination B. Therefore, a suitable nail mold combination can be determined based on the riveting quality database.

[0117] Specifically, for a known nail mold combination in the riveting quality database, the riveting quality score of the known nail mold combination riveted with overlapping material combination A (i.e., the first target riveting quality score corresponding to overlapping material combination A) and the riveting quality score of the known nail mold combination riveted with overlapping material combination B (i.e., the first target riveting quality score corresponding to overlapping material combination B) can be determined in the riveting quality database. Based on the two first target riveting quality scores, the second target riveting quality score corresponding to the known nail mold combination can be determined.

[0118] Here, it can be determined that the outer diameter of the nail body is 5 mm. If the tensile strength of the base material is less than 600 MPa, and the minimum base thickness is greater than or equal to 0.2 mm, the interlocking value is greater than or equal to 0.4 mm, and the final head height falls within the interval [-0.5, 0.3] (in mm), or if the tensile strength of the base material is greater than or equal to 600 MPa, the base thickness is less than or equal to 1.5 mm, the minimum thickness is greater than or equal to 0.1 mm, the interlocking value is greater than or equal to 0.2 mm, and the final head height falls within the interval [-0.5, 0.3] (in mm), then the formula Q = 0.6u + 0.3t r -|0.1P h |The first target riveting quality score Q is calculated, otherwise, the first target riveting quality score can be set to -1.

[0119] If the tensile strength of the base plate is greater than or equal to 600 MPa and the base plate thickness is greater than 1.5 mm, and the minimum thickness is greater than or equal to 0.05 mm, the interlocking value is greater than or equal to 0.1 mm, and the final head height falls within the interval [-0.5, 0.3] (in mm), then the formula Q = 0.6u + 0.3t r -|0.1Ph |The first target riveting quality score Q is calculated, otherwise, the first target riveting quality score can be set to -1.

[0120] Some data of riveting quality scoring can be shown in the following table:

[0121]

[0122] It can be seen that the maximum value of each second target riveting quality score is 0.47, and the known nail mold combination corresponding to the maximum value is the known nail mold combination 1, so the known nail mold combination 1 can be determined as the target nail mold combination; the parameters of the rivets and rivet molds corresponding to the above-mentioned known rivet combinations can be shown in the following table:

[0123]

[0124] From the above table, we can know the rivet parameters and rivet die parameters corresponding to the known nail die combination 1.

[0125] The metallographic diagram of riveting the overlapping material combination A using the known nail mold combination 1 can be shown as follows: Figure 4 As shown, the metallographic diagram of riveting using the known nail mold combination 1 and the overlapping material combination B can be shown as Figure 5 shown.

[0126] In another specific example, the target overlapping material combination includes overlapping material combination C. The upper plate material of overlapping material combination C is a certain brand of aluminum alloy, and the bottom plate material is another brand of heat-treatment-free cast aluminum. The material thickness of the upper plate material is 2.0 mm, the yield strength is 340 MPa, and the elongation at break is 22%; the material thickness of the bottom plate material is 3.0 mm, the yield strength is 120 MPa, and the elongation at break is 10%. Since overlapping material combination C is an unknown overlapping material combination, the prediction model can be used to predict the optimal nail mold combination corresponding to overlapping material combination C. Here, the predicted rivet length of the optimal rivet corresponding to overlapping material combination C is 6.56 1 mm, rivet volume is 120.412 cubic millimeters, rivet hardness level is 4.675, it can be determined that the rivet length of the target rivet corresponding to the optimal rivet is 6.5 mm, the rivet volume is 124.3 cubic millimeters, and the rivet hardness level is 5; the predicted optimal rivet die diameter corresponding to the overlapping material combination C is 111.6 mm, the rivet die depth is 147.403 mm, and the rivet die volume is 140.904 cubic millimeters. It can be determined that the rivet die diameter of the target rivet die corresponding to the optimal rivet die is 110 mm, the rivet die depth is 140 mm, and the rivet die volume is 133.9 cubic millimeters; accordingly, the target nail die combination can be determined. After determining the target nail die combination, the target overlapping material combination can be riveted using the target nail die combination. The metallographic diagram of riveting overlapping material combination C using the target nail die combination can be shown as follows Figure 6 shown.

[0127] In summary, the embodiment of the present application obtains a target overlapping material combination; based on a preset prediction model, predicts the optimal nail mold combination corresponding to the target overlapping material combination; wherein the prediction model is a pre-constructed linear regression model for predicting the optimal nail mold combination corresponding to the overlapping material combination; based on the optimal nail mold combination, determines the target nail mold combination from one or more known rivets and known rivet molds. In the embodiment of the present application, the pre-established linear regression model can be used to automatically determine the appropriate nail mold combination, and the determination process does not require human participation, thereby helping to reduce labor costs.

[0128] It should be understood that the size of the serial numbers of the steps in the above embodiments does not 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 embodiments of the present application.

[0129] Corresponding to the method for determining the nail mold combination described in the above embodiment, Figure 7 A structural diagram of an embodiment of a device for determining a nail mold combination provided in an embodiment of the present application is shown.

[0130] In an embodiment of the present application, a device for determining a nail mold combination may include:

[0131] An acquisition module 701 is used to acquire a target overlapping material combination;

[0132] A prediction module 702 is used to predict the optimal nail mold combination corresponding to the target overlapping material combination based on a preset prediction model; wherein the prediction model is a pre-constructed linear regression model for predicting the optimal nail mold combination corresponding to the overlapping material combination;

[0133] The determination module 703 is used to determine a target nail mold combination from one or more known rivets and known rivet molds based on the optimal nail mold combination.

[0134] In a specific implementation of the embodiment of the present application, the device further includes:

[0135] A sample set determination module, used to determine an analysis sample set; wherein the analysis sample set includes a preset number of sample overlapping material combinations and corresponding recommended nail mold combinations;

[0136] The regression analysis module is used to take each of the sample overlapping material combinations as input, take the corresponding recommended nail mold combination as expected output, perform regression analysis on the initial linear regression model, and obtain the prediction model.

[0137] In a specific implementation of the embodiment of the present application, the regression analysis module includes:

[0138] An actual output acquisition submodule is used to use each of the sample overlapping material combinations as an input of an initial linear regression model to obtain an actual nail mold combination output by the initial linear regression model;

[0139] A predicted loss value calculation submodule, used to calculate the predicted loss value based on each of the recommended nail mold combinations and each of the actual nail mold combinations;

[0140] The prediction model determination submodule is used to determine the prediction model based on the predicted loss value.

[0141] In a specific implementation of the embodiment of the present application, the determining module includes:

[0142] A rivet parameter difference calculation submodule, used for calculating the rivet parameter difference between the optimal rivet and one or more known rivets;

[0143] A target rivet determination submodule, used for determining the known rivet corresponding to the minimum value of the rivet parameter difference as the target rivet;

[0144] A riveting die parameter difference calculation submodule, used for calculating the riveting die parameter difference between the optimal riveting die and one or more known riveting dies;

[0145] The first submodule for determining a target riveting die is used to determine the known riveting die corresponding to the minimum value of the riveting die parameter differences as the target riveting die.

[0146] In a specific implementation of an embodiment of the present application, the determination module in the device for determining the nail mold combination is also used to predict the optimal nail mold combination corresponding to the target overlapping material combination based on a preset prediction model if there is at least one unknown overlapping material combination in the target overlapping material combination.

[0147] In a specific implementation of the embodiment of the present application, the device for determining the nail mold combination further includes:

[0148] The target nail mold combination determines the second module, which is used to determine the target nail mold combination based on a preset riveting quality database and the target lap material combination if there is no unknown lap material combination in the target lap material combination; wherein the riveting quality database includes the riveting quality scores of each known riveting condition, and the known riveting condition is the condition after riveting a known lap material combination with a known nail mold combination.

[0149] In a specific implementation of the embodiment of the present application, the target nail module combination determines the second module including:

[0150] A first quality score determination submodule is used to determine each first target riveting quality score based on the riveting quality database; wherein the first target riveting quality score is the riveting quality score of any target known riveting working condition, and the target known riveting working condition is the riveting quality score of the target overlapping material combination and one of the known nail mold combinations;

[0151] The target nail die combination determination second submodule is used to determine the target nail die combination based on each of the first target riveting quality scores.

[0152] In a specific implementation of the embodiment of the present application, the target nail model combination determines the second submodule including:

[0153] a target nail mold combination determining first unit, configured to determine the known nail mold combination corresponding to the maximum value of each of the first target riveting quality scores as the target nail mold combination if the target overlapping material combination includes an overlapping material combination;

[0154] a second quality score determination unit, configured to calculate a second target riveting quality score based on each of the first target riveting quality scores of the known nail die combination if the target overlapping material combination includes two or more overlapping material combinations;

[0155] The target nail die combination determining second unit is used to determine the known nail die combination corresponding to the maximum value of each of the second target riveting quality scores as the target nail die combination.

[0156] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, modules and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0157] 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.

[0158] Figure 8 A schematic block diagram of a terminal device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0159] like Figure 8 As shown, the terminal device 8 of this embodiment includes: a processor 80, a memory 81, and a computer program 82 stored in the memory 81 and executable on the processor 80. When the processor 80 executes the computer program 82, the steps in the above-mentioned method for determining the combination of nail molds are implemented, for example Figure 2Alternatively, when the processor 80 executes the computer program 82, the functions of each module / unit in the above-mentioned device embodiments are realized, for example Figure 7 The functions of modules 701 to 703 are shown.

[0160] Exemplarily, the computer program 82 may be divided into one or more modules / units, which are stored in the memory 81 and executed by the processor 80 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 82 in the terminal device 8.

[0161] Those skilled in the art will understand that Figure 8 It is only an example of the terminal device 8 and does not constitute a limitation of the terminal device 8. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device 8 may also include input and output devices, network access devices, buses, etc.

[0162] The processor 80 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0163] The memory 81 may be an internal storage unit of the terminal device 8, such as a hard disk or memory of the terminal device 8. The memory 81 may also be an external storage device of the terminal device 8, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 8. Further, the memory 81 may also include both an internal storage unit of the terminal device 8 and an external storage device. The memory 81 is used to store the computer program and other programs and data required by the terminal device 8. The memory 81 may also be used to temporarily store data that has been output or is to be output.

[0164] The technicians in the relevant field 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 in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in 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 this application. 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.

[0165] 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.

[0166] Those of ordinary skill 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 this application.

[0167] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, 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 devices or units, which can be electrical, mechanical or other forms.

[0168] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0169] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0170] If the integrated module / 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 application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. 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 storage medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0171] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for determining a nail mold combination, characterized in that: include: Obtaining target overlapping material combination; Based on a preset prediction model, predict the optimal nail mold combination corresponding to the target overlapping material combination; wherein the prediction model is a pre-constructed linear regression model for predicting the optimal nail mold combination corresponding to the overlapping material combination; Based on the optimal nail die combination, a target nail die combination is determined from one or more known rivets and known rivet dies.

2. The method for determining a nail mold combination according to claim 1, characterized in that: The construction process of the prediction model includes: Determine an analysis sample set; wherein the analysis sample set includes a preset number of sample overlap material combinations and corresponding recommended nail mold combinations; Each of the sample overlapping material combinations is used as input, and the corresponding recommended nail mold combination is used as the expected output, and regression analysis is performed on the initial linear regression model to obtain the prediction model.

3. The method for determining a nail mold combination according to claim 2, characterized in that: The method uses each sample overlap material combination as input, uses the corresponding recommended nail mold combination as expected output, performs regression analysis on the initial linear regression model, and obtains the prediction model, including: Using each of the sample overlapping material combinations as an input of an initial linear regression model to obtain an actual nail mold combination output by the initial linear regression model; Calculating a predicted loss value based on each of the recommended nail mold combinations and each of the actual nail mold combinations; Based on the predicted loss value, the prediction model is determined.

4. The method for determining a nail mold combination according to claim 1, characterized in that: The optimal nail die combination includes an optimal rivet and an optimal rivet die, and the target rivet combination includes a target rivet and a target rivet die; The step of determining a target nail die combination from one or more known rivets and known rivet dies based on the optimal nail die combination includes: Calculating the difference between the rivet parameters of the optimal rivet and one or more known rivets; Determine the known rivet corresponding to the minimum value of the rivet parameter differences as the target rivet; Calculating the difference between the riveting die parameters of the optimal riveting die and one or more known riveting dies; The known riveting die corresponding to the minimum value of the riveting die parameter differences is determined as the target riveting die.

5. The method for determining a nail mold combination according to claim 1, characterized in that: The predicting, based on a preset prediction model, the optimal nail mold combination corresponding to the target overlapping material combination includes: If there is at least one unknown overlapping material combination in the target overlapping material combination, the optimal nail mold combination corresponding to the target overlapping material combination is predicted based on a preset prediction model.

6. The method for determining a nail mold combination according to claim 5, characterized in that: After obtaining the target overlapping material combination, the method further includes: If there is no unknown overlapping material combination in the target overlapping material combination, the target nail mold combination is determined based on a preset riveting quality database and the target overlapping material combination; wherein the riveting quality database includes riveting quality scores of various known riveting conditions, and the known riveting conditions are the conditions after riveting a known overlapping material combination and a known nail mold combination.

7. The method for determining a nail mold combination according to claim 6, characterized in that: The step of determining a target nail mold combination based on a preset riveting quality database and the target overlapping material combination includes: Based on the riveting quality database, each first target riveting quality score is determined; wherein the first target riveting quality score is the riveting quality score of any target known riveting working condition, and the target known riveting working condition is the riveting quality score of the target overlapping material combination and one of the known nail mold combinations; The target nail die combination is determined based on each of the first target riveting quality scores.

8. The method for determining a nail mold combination according to claim 7, characterized in that: The step of determining the target nail die combination based on each of the first target riveting quality scores comprises: If the target overlapping material combination includes one overlapping material combination, the known nail mold combination corresponding to the maximum value of each of the first target riveting quality scores is determined as the target nail mold combination; If the target overlapping material combination includes two or more overlapping material combinations, a second target riveting quality score is calculated based on each of the first target riveting quality scores of the known nail mold combination; The known nail die combination corresponding to the maximum value of each of the second target riveting quality scores is determined as the target nail die combination.

9. A device for determining a nail mold combination, characterized in that: include: An acquisition module, used for acquiring a target overlapping material combination; A prediction module, used to predict the optimal nail mold combination corresponding to the target overlapping material combination based on a preset prediction model; wherein the prediction model is a pre-constructed linear regression model for predicting the optimal nail mold combination corresponding to the overlapping material combination; A determination module is used to determine a target nail die combination from one or more known rivets and known rivet dies based on the optimal nail die combination.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for determining a nail mold combination according to any one of claims 1 to 8 are implemented.

11. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for determining a nail mold combination according to any one of claims 1 to 8 are implemented.

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