Weight determination method and device for corrosion resistance characterization parameters, medium and electronic equipment

Abstract

The invention provides a weight determination method and device for characterization parameters of corrosion resistance, a medium and electronic equipment, and the method comprises the steps: selecting a plurality of parameters related to the corrosion resistance according to the requirements of the evaluation of the corrosion resistance of a material; testing a plurality of samples, obtaining corresponding test data of each sample under the parameter set, and integrating the test data into a multi-parameter data matrix; performing principal component analysis processing on the multi-parameter data matrix to obtain a vector comprehensive contribution value of each parameter; based on the vector comprehensive contribution value, determining the weight of each parameter through a standardization proportion; wherein the weight is used for representing the relative importance of each parameter in corrosion resistance evaluation. Through a scientific weight calculation method, the problem that weight assignment in a weighted scoring model is large in randomness is solved, and a scientific basis is provided for quantitative evaluation of sample performance.

Description

Technical Field

[0001] This application belongs to the technical field of material corrosion resistance performance analysis, and relates to a method, device, medium, and electronic device for determining the weights of corrosion resistance performance characterization parameters. Background Art

[0002] In the field of materials science and engineering, the quantitative evaluation of sample performance is an important part of product design, optimization, and quality control. Accurately evaluating material performance is crucial for ensuring the reliability and durability of products in practical applications. The weighted scoring model is widely used for the comprehensive evaluation of material performance due to its intuitive simplicity, ease of understanding, and application. However, there are some prominent problems in the actual application of the existing weighted scoring model, which limit its effectiveness and accuracy.

[0003] Firstly, the randomness of weight assignment is a key issue. In the prior art, the weights of the weighted scoring model are usually assigned based on the subjective experience of researchers. This assignment method has a large deviation due to individual differences, resulting in the evaluation results lacking consistency and repeatability, and it is difficult to form a unified evaluation standard. Secondly, the problem of complex and diverse parameters is also relatively prominent. Material performance testing usually involves multiple parameters, and the specific influence of these parameters in the model has not been scientifically quantified. Therefore, the reliability and scientificity of the existing model are limited, and it is difficult to accurately reflect the actual performance of materials.

[0004] In view of the above problems, there is an urgent need to develop a scientific, efficient, and highly applicable weight calculation method to guide the construction of the weighted scoring model. Summary of the Invention

[0005] This application provides a method, device, medium, and electronic device for determining the weights of corrosion resistance performance characterization parameters to solve the technical problems such as the randomness of weight assignment and parameter complexity in the existing weighted scoring model.

[0006] In a first aspect, this application provides a method for determining the weights of corrosion resistance performance characterization parameters, including: selecting multiple parameters related to corrosion resistance according to the requirements of material corrosion resistance performance evaluation; testing multiple samples to obtain the test data corresponding to each sample under the parameter set, and integrating the test data into a multi-parameter data matrix; performing principal component analysis on the multi-parameter data matrix to obtain the vector comprehensive contribution value of each parameter; and determining the weight of each parameter through a standardized ratio based on the vector comprehensive contribution value, where the weight is used to characterize the relative importance of each parameter in the corrosion resistance performance evaluation.

[0007] In one implementation of the first aspect, the parameters related to corrosion resistance include one or more combinations of the following parameters: the initial open-circuit potential, which is used to reflect the electrochemical activity of the material under no external force; the difference between the open-circuit potential at 400 seconds and the initial moment, which is used to evaluate the electrochemical stability of the material; the corrosion potential, which refers to the potential of the material when it reaches an equilibrium state in the corrosion environment; the radius of the capacitive reactance arc, which is used to reflect the integrity and protective performance of the surface film of the material; the logarithm of the impedance modulus, which is used to characterize the resistance of the material to the corrosion reaction; the negative logarithm of the self-corrosion current density, which is used to reflect the corrosion rate of the material in the natural corrosion state; the negative logarithm of the passive current density, which is the negative logarithm of the current density required to maintain the passivation state of the material; the width of the passivation interval, which is used to characterize the potential range in which the material can remain stable in the passivation state; the blue point test time, which is used to reflect the time during which the material does not undergo local corrosion in the blue point test.

[0008] In one implementation of the first aspect, the integration of the test data into a multi-parameter data matrix includes: classifying the test data according to the sample number and parameter type; constructing a matrix structure, where the rows of the matrix correspond to different samples and the columns correspond to different parameters; filling the test data of each sample under each parameter into the corresponding positions of the matrix to form a multi-parameter data matrix; and performing data preprocessing on the multi-parameter data matrix, including unifying the data format and handling missing values.

[0009] In one implementation of the first aspect, the principal component analysis of the multi-parameter data matrix to obtain the vector comprehensive contribution value of each parameter includes: performing standardization processing on the multi-parameter data matrix to make the parameter data have the same dimension and comparability; calculating the covariance matrix of the multi-parameter data matrix to reflect the correlation between parameters; solving the eigenvalues and eigenvectors of the covariance matrix, where the eigenvalues represent the variance contribution rate of each principal component, and the eigenvectors represent the linear combination relationship between the principal components and each parameter; and calculating the vector comprehensive contribution value of each parameter based on the eigenvalues and eigenvectors of the principal components.

[0010] In one implementation of the first aspect, the vector comprehensive contribution value is calculated by the following formula: where k i is the vector comprehensive contribution value of the i-th parameter, λ j is the eigenvalue of the j-th principal component, a ij is the eigenvector value of the i-th parameter on the j-th principal component, and m is the number of selected principal components.

[0011] In an implementation of the first aspect, it further includes: screening the principal components, and the specific steps are: arranging the principal components in descending order according to the magnitudes of the eigenvalues; performing normalization processing on the eigenvalues to calculate the normalized eigenvalues of each principal component; accumulating the normalized eigenvalues until the cumulative contribution rate reaches a preset threshold; selecting the principal components whose cumulative contribution rate reaches the preset threshold as effective principal components for subsequent calculation of the comprehensive contribution value of the vectors.

[0012] In an implementation of the first aspect, the formula for determining the normalization ratio is: where K i represents the weight of the i-th parameter, k i represents the comprehensive contribution value of the vector of the i-th parameter, and n represents the total number of parameters.

[0013] In a second aspect, the present application provides an apparatus for determining the weights of corrosion resistance characterization parameters, including: a parameter selection module for selecting a plurality of parameters related to corrosion resistance according to the requirements of evaluating the corrosion resistance of materials; a matrix construction module for testing a plurality of samples, obtaining the test data corresponding to each sample under the parameter set, and integrating the test data into a multi-parameter data matrix; an analysis and processing module for performing principal component analysis on the multi-parameter data matrix to obtain the comprehensive contribution value of the vector of each parameter; a weight determination module for determining the weight of each parameter through the normalization ratio based on the comprehensive contribution value of the vector; wherein the weight is used to characterize the relative importance of each parameter in the evaluation of corrosion resistance.

[0014] In a third aspect, the present application provides an electronic device, including: one or more processors; and one or more memories, wherein computer-readable code is stored in the memories, and when the computer-readable code is run by the one or more processors, the method for determining the weights of corrosion resistance characterization parameters as described above is implemented.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for determining the weights of corrosion resistance characterization parameters as described above is implemented.

[0016] The above embodiments have at least one of the following technical effects:

[0017] (1) By adopting a data-driven method and calculating the weights of each parameter through the principal component analysis method, it is completely based on the test data, avoiding the uncertainty brought by subjective human assignment. This data-based weight calculation method can more accurately reflect the actual contribution of each parameter to the corrosion resistance, improving the scientificity and objectivity of the evaluation results.

[0018] (2) By performing principal component analysis, parameters that have a significant impact on the corrosion resistance performance were screened out, and redundant parameters were eliminated. This process not only simplifies the model structure but also significantly improves the computational efficiency of the model while ensuring the accuracy of the evaluation results. By reducing redundant parameters, the model can process data more efficiently and reduce the computational complexity.

[0019] (3) It is not only applicable to the evaluation of corrosion resistance performance but also can be extended to the comprehensive evaluation of various material properties. Its generality and extensibility enable this method to be widely applied in different types of materials science and engineering fields, providing a unified solution for the quantitative evaluation of various material properties.

[0020] (4) The effective application of the principal component analysis method significantly reduces the complexity of the model and improves the computational efficiency. At the same time, through scientific weight calculation and parameter screening, this application can provide more reliable and accurate evaluation results, providing strong support for the comprehensive evaluation of material properties. Description of the Drawings

[0021] Figure 1 Shown is an exemplary flow diagram of the method for determining the weights of the corrosion resistance characterization parameters described in the embodiments of the present application.

[0022] Figure 2 Shown is an exemplary flow diagram of performing principal component analysis on a multi-parameter data matrix described in the embodiments of the present application.

[0023] Figure 3 Shown is an exemplary flow diagram of screening the principal components described in the embodiments of the present application.

[0024] Figure 4 Shown is a flow diagram of the parameter weight determination described in the embodiments of the present application.

[0025] Figure 5 Shown is an exemplary structural diagram of the device for determining the weights of the corrosion resistance characterization parameters described in the embodiments of the present application.

[0026] Figure 6 Shown is an exemplary structural diagram of the electronic device described in the embodiments of the present application. Detailed Description of the Embodiments

[0027] The following describes the implementation manners of the present application through specific specific examples. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0028] It should be noted that the diagrams provided in the following embodiments only schematically illustrate the basic concept of the present application. Therefore, only the components related to the present application are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0029] In the field of corrosion resistance performance, the quantitative evaluation of sample performance is crucial for material selection and application. Due to its intuitiveness and ease of use, the weighted scoring model is widely used in the comprehensive evaluation of corrosion resistance performance. However, there are some prominent problems in the actual application of the existing weighted scoring models. First, the randomness of weight assignment is relatively large, usually based on the subjective experience of researchers for assignment, resulting in significant deviations in evaluation results due to individual differences and making it difficult to form a unified evaluation standard. Second, the corrosion resistance performance test involves multiple parameters, and the complexity and diversity of these parameters make their specific influence on the model not scientifically quantified, resulting in a decrease in the reliability and scientificity of the model. In addition, there is a high correlation between some test parameters. Directly introducing them into the model will increase the computational complexity and is not helpful for improving the results.

[0030] Therefore, a scientific, efficient, and highly applicable weight calculation method is needed to guide the construction of the weighted scoring model. This method should be able to objectively quantify the influence of each parameter on the corrosion resistance performance, reduce the subjectivity of weight assignment, and at the same time reduce the complexity of the model and improve the accuracy and reliability of the evaluation results.

[0031] Next, the technical solutions in the embodiments of the present application will be described in detail with reference to the accompanying drawings in the embodiments of the present application.

[0032] As Figure 1 shown, the embodiments of the present application provide a method for determining the weights of corrosion resistance performance characterization parameters, including the following steps S110 to S140.

[0033] S110, according to the requirements of material corrosion resistance performance evaluation, select multiple parameters related to corrosion resistance.

[0034] Specifically, in the field of corrosion resistance evaluation, in order to comprehensively and accurately evaluate the corrosion resistance of materials, multiple parameters related to corrosion resistance need to be selected. The selection of these parameters is based on a deep understanding of the corrosion behavior of materials and actual application requirements, ensuring that the performance of materials in different corrosion environments can be reflected from multiple perspectives.

[0035] First, considering the diversity of corrosion environments, the selected parameters should cover the performance of materials in different corrosion media. For example, in a corrosion environment containing hydrogen sulfide (H 2 S) and carbon dioxide (CO 2 ), the corrosion rate of materials will be affected by temperature, pressure, and the partial pressures of H 2 S and CO 2 . Therefore, the selected parameters should include but are not limited to corrosion rate, the composition and structure of corrosion products, and the temperature, pressure, and gas partial pressures of the corrosion environment, etc.

[0036] Secondly, to ensure the reliability of evaluation results, the selected parameters should have clear physical meanings and measurability. For example, the corrosion rate can be measured by methods such as immersion tests and electrochemical tests, and the composition and structure of corrosion products can be analyzed by techniques such as scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD).

[0037] In addition, the selected parameters should also consider the actual application conditions of the materials. For example, under high-temperature and high-pressure conditions, the corrosion behavior of materials may be different from that under normal temperature and pressure. Therefore, parameters that can reflect the corrosion resistance of materials in the actual use environment need to be selected.

[0038] In some embodiments, the parameters related to corrosion resistance include one or more combinations of the following parameters:

[0039] The initial open-circuit potential, which is used to reflect the electrochemical activity of the material without external force.

[0040] The difference in open-circuit potential at 400 seconds from the initial moment is used to evaluate the electrochemical stability of the material, and this parameter can reveal the dynamic changes of the material in the initial stage of corrosion.

[0041] The corrosion potential refers to the potential of the material when it reaches an equilibrium state in the corrosion environment, and this parameter directly reflects the corrosion resistance of the material.

[0042] In addition, the radius of the capacitive reactance arc is used to reflect the integrity and protective performance of the surface film of the material. This parameter is obtained through electrochemical impedance spectroscopy (EIS) testing and can provide important information about the quality of the surface film.

[0043] The logarithm value of the impedance modulus is used to characterize the resistance of the material to the corrosion reaction. This parameter evaluates the corrosion resistance of the material by measuring the modulus value of the electrochemical impedance.

[0044] The negative logarithm value of the self-corrosion current density is used to reflect the corrosion rate of the material in the natural corrosion state. This parameter is obtained through polarization curve testing and can directly reflect the corrosion tendency of the material.

[0045] The negative logarithm value of the passive current density is the negative logarithm of the current density required to maintain the passivation state of the material. This parameter reflects the passivation ability of the material in the corrosion environment.

[0046] The passivation interval width is used to characterize the potential range within which the material can maintain stability in the passivation state. This parameter is obtained through polarization curve testing and can evaluate the passivation stability of the material.

[0047] The blue point test time is used to reflect the time during which the material does not undergo local corrosion in the blue point test. This parameter is obtained through the blue point test and can evaluate the tolerance of the material to local corrosion.

[0048] S120, test multiple samples, obtain the test data corresponding to each sample under the parameter set, and integrate the test data into a multi-parameter data matrix.

[0049] Specifically, first, in the test stage, test multiple representative samples to obtain the test data corresponding to each sample under the parameter set. These data include but are not limited to the initial open circuit potential, corrosion potential, negative logarithm value of the self-corrosion current density, etc. The test data for each parameter is obtained through standardized test methods to ensure the accuracy and reliability of the data.

[0050] Subsequently, organize the obtained test data according to the samples and parameters to form a multi-parameter data matrix.

[0051] Exemplarily, classify the test data according to the sample number and parameter type. The test results of each sample under different parameters are clearly identified and classified to ensure the organization and traceability of the data. Next, construct a matrix structure where the rows of the matrix correspond to different samples and the columns correspond to different parameters. Accurately fill in the test data of each sample under each parameter in the corresponding positions of the matrix. This process requires accurate data entry to ensure that each data point can correctly reflect the performance of the sample under a specific parameter.

[0052] To improve the quality and usability of data, data preprocessing is performed on the multi-parameter data matrix. The preprocessing steps include unifying the data format to ensure that all data is stored in a consistent format for easy reading and processing by analysis tools. At the same time, missing value processing is carried out to fill or correct blanks or abnormal points in the data to ensure the integrity and accuracy of the data.

[0053] S130, perform principal component analysis on the multi-parameter data matrix to obtain the comprehensive contribution value of each parameter vector.

[0054] Specifically, in the field of corrosion resistance performance evaluation, the principal component analysis method (PCA) is an important data analysis tool that transforms multiple related variables into a set of uncorrelated principal components through linear transformation. In the evaluation of corrosion resistance performance, the application of PCA can effectively solve the complexity problem of multi-parameter data.

[0055] In some embodiments, as Figure 2 shown, the S130 specifically includes steps S131 to S134.

[0056] S131, perform standardization on the multi-parameter data matrix to make the parameter data have the same dimension and comparability.

[0057] Specifically, perform Z-score standardization on the multi-parameter data matrix to make the parameter data have the same dimension and comparability. The formula is as follows:

[0058]

[0059] where X is the original data, μ is the mean of the parameter, and σ is the standard deviation of the parameter.

[0060] Exemplarily, for the initial open circuit potential parameter, its original data is [-0.0213, -0.0088, 0.0787,...], and its mean is calculated to be -0.005 and the standard deviation is 0.05. Then the standardized data is [0.5, 0.1, 1.7,...].

[0061] Although Z-score standardization can eliminate the dimension difference, through a large number of experimental verifications, this method may produce negative values in the standardized results in corrosion data analysis, resulting in the loss of the positive characterization ability of parameters with clear physical meanings such as corrosion rate. Or, the numerical range after standardization is not fixed, affecting the horizontal comparability of different batches of data. And when there are extreme outliers in the sample data (such as abnormal current peaks caused by local corrosion), the standard deviation σ will increase abnormally, resulting in the over-compression of the normal data distribution in the standardized results.

[0062] To solve the above problems, this application preferably adopts the maximum-minimum normalization method, and its calculation formula is:

[0063]

[0064] where x is the original corrosion characteristic parameter value; X min corresponds to the theoretical minimum value of the characteristic parameter in the training dataset; X max is the theoretical maximum value of the corresponding characteristic parameter in the training dataset; x norm is the dimensionless parameter after normalization.

[0065] In this implementation, the maximum-minimum normalization maps the original data to the [0,1] interval through a linear transformation, ensuring that all parameter values maintain non-negative characteristics and are consistent with the positive correlation of actual physical quantities such as corrosion rate and potential change. Uniformly converting parameters with different dimensions into fixed interval values improves the reliability of multi-source data fusion analysis.

[0066] S132. Calculate the covariance matrix of the multi-parameter data matrix to reflect the correlation between parameters.

[0067] Specifically, the covariance matrix is a square matrix, and its elements represent the covariance between different parameters. The formula is as follows:

[0068]

[0069] where X and Y are two parameters, and μ X and μ Y are their means respectively. The covariance matrix can reveal the linear relationship between parameters and provide a basis for subsequent principal component analysis.

[0070] Exemplarily, the covariance between the initial open-circuit potential and the corrosion potential is calculated to be 0.002, indicating a certain positive correlation between these two parameters.

[0071] It should be noted that when there are significant dimensional differences between parameters and no normalization processing is performed, the correlation coefficient matrix can be alternatively calculated for analysis. The correlation coefficient matrix is calculated by the following formula:

[0072] where R xy represents the correlation coefficient between parameters X and Y, and σ X and σ Y are the standard deviations of parameters X and Y respectively. The correlation coefficient matrix can eliminate the influence of dimensional differences and is particularly suitable for situations where the physical meanings and magnitudes of parameters are significantly different.

[0073] Exemplarily, when the dimensional differences of the test parameters are significant (such as simultaneously including potential values (V) and the logarithm of current density (A / cm 2)) The correlation coefficient matrix can be used to replace the covariance matrix for principal component analysis.

[0074] S133. Solve the eigenvalues and eigenvectors of the covariance matrix, where the eigenvalue represents the variance contribution rate of each principal component, and the eigenvector represents the linear combination relationship between the principal component and each parameter.

[0075] Specifically, solve the eigenvalues and eigenvectors of the covariance matrix. The eigenvalue represents the variance contribution rate of each principal component, while the eigenvector represents the linear combination relationship between the principal component and each parameter. By solving the characteristic equation:

[0076] det(C - λI) = 0

[0077] The eigenvalue λ can be obtained, where C is the covariance matrix and I is the identity matrix. For each eigenvalue, the corresponding eigenvector can be found by solving the linear equation system (C - λI)v = 0.

[0078] Exemplarily, assume that the eigenvalues of the first two principal components obtained are 4.0816 and 0.6695 respectively, and the corresponding eigenvectors are [0.3349, 0.4743,..., 0.4848] and [0.8950, 0.0536,...,-0.0461]. The eigenvalue 4.0816 indicates that the first principal component has the highest variance contribution rate and can capture most of the variation information in the data.

[0079] S134. Calculate the vector comprehensive contribution value of each parameter based on the eigenvalues and eigenvectors of the principal component.

[0080] Exemplarily, the vector comprehensive contribution value is calculated by the following formula:

[0081]

[0082] where k i is the vector comprehensive contribution value of the i-th parameter, λ j is the eigenvalue of the j-th principal component, a ij is the eigenvector value of the i-th parameter on the j-th principal component, and m is the number of selected principal components.

[0083] In the evaluation of corrosion resistance, in order to further optimize the results of principal component analysis (PCA) and ensure that the selected principal components can effectively reflect the main information of the original data, it is necessary to screen the principal components, as Figure 3 shown, specifically including steps S210 to S240.

[0084] S210. Arrange the principal components in descending order according to the magnitude of the eigenvalues.

[0085] Specifically, the magnitude of the eigenvalue reflects the importance of each principal component in the data. The larger the eigenvalue, the more data variance the principal component can explain. By arranging them in descending order, it ensures that the most important principal components are ranked first, providing a basis for subsequent screening.

[0086] S220, standardize the eigenvalues and calculate the standardized eigenvalues of each principal component.

[0087] Specifically, the standardization process is to divide each eigenvalue by the sum of all eigenvalues, thereby obtaining the variance contribution rate of each principal component. This process ensures that the contributions of different principal components can be fairly compared. The formula is as follows:

[0088]

[0089] where λ j is the eigenvalue of the j-th principal component, and m is the total number of principal components. Through the standardization process, the variance contribution rate of each principal component can be obtained, and the sum of these contribution rates is 1.

[0090] S230, accumulate the standardized eigenvalues until the cumulative contribution rate reaches a preset threshold.

[0091] Specifically, the cumulative contribution rate refers to the sum of the variance contribution rates of the first k principal components, which is used to evaluate the proportion of data variance that these principal components can explain. The preset threshold is usually selected as 85% or 90%, which means that the selected principal components can explain at least 85% or 90% of the data variance. The formula for calculating the cumulative contribution rate is as follows:

[0092]

[0093] where k is the number of selected principal components. By calculating the cumulative contribution rate, it can be determined how many principal components are needed to reach the preset variance explanation rate.

[0094] S240, select the principal components whose cumulative contribution rate reaches the preset threshold as effective principal components for subsequent calculation of the vector comprehensive contribution value.

[0095] In this implementation, the most important principal components for evaluating the corrosion resistance performance can be scientifically screened, thereby improving the accuracy and reliability of the evaluation results. This method can not only effectively reduce the complexity of the data but also ensure that the selected principal components can comprehensively reflect the corrosion resistance performance of the material.

[0096] S140, based on the vector comprehensive contribution value, determine the weight of each parameter through the standardization ratio; where the weight is used to characterize the relative importance of each parameter in the evaluation of the corrosion resistance performance.

[0097] Specifically, the vector comprehensive contribution value of each parameter is normalized. The purpose of normalization is to convert the contribution values of different parameters into comparable relative values, ensuring that the calculation of weights is not affected by the dimension and order of magnitude.

[0098] In some embodiments, the formula for determining the normalization ratio is:

[0099]

[0100] where K i represents the weight of the i-th parameter, k i represents the vector comprehensive contribution value of the i-th parameter, and n represents the total number of parameters.

[0101] The determined weights can be used to construct a weighted scoring model for comprehensively evaluating the corrosion resistance of materials. By assigning corresponding weights to each parameter, the model can more accurately reflect the performance of materials in different corrosion environments.

[0102] Exemplarily, for the initial open circuit potential parameter, its vector comprehensive contribution value is:

[0103] k1 = 4.0816 × 0.3349 + 0.6695 × 0.8950 = 1.37

[0104] This indicates that the comprehensive contribution value of the initial open circuit potential in the principal component is 1.37, reflecting the importance of this parameter in the evaluation of corrosion resistance.

[0105] To better illustrate the technical solution of the present application, as Figure 4 shown below, a complete application example will be presented.

[0106] First, 9 stainless steel nitric acid passivation film samples are selected. These samples are obtained using different preparation parameters to ensure the diversity and representativeness of the samples.

[0107] Then, the corrosion resistance of the 9 samples is tested, and 5 parameters related to corrosion resistance are tested, numbered from 1 to 5 from left to right. The specific parameters are as follows: initial open circuit potential (V), corrosion potential (V), logarithm of impedance modulus (at 0.01 Hz), negative logarithm of self - corrosion current density (A / cm 2 ), and blue - point test time (s). The test results are shown in Table 1 below:

[0108] Table 1 Raw data obtained from the test of each parameter

[0109]

[0110] After performing maximum-minimum normalization on the original data, principal component analysis (PCA) is carried out to calculate the eigenvalues and eigenvectors. The results are shown in Tables 2 and 3 below:

[0111] Table 2 Eigenvalues of Principal Component Analysis

[0112]

[0113] Table 3 Eigenvectors of Principal Component Analysis

[0114]

[0115] The cumulative contribution rate of the first two principal components (PC1 and PC2) is 95.02%, which has reached over 90% and has sufficient representativeness. Therefore, these two principal components and their corresponding eigenvalues and eigenvectors are selected for calculation, and then normalization processing is carried out to obtain the weight values of 5 parameters. The weight empirical assignments of each parameter in the weighted scoring are listed together for comparison, and the calculation results are shown in Table 4 below:

[0116] Table 4 Comparison of Weight Values of Two Methods

[0117]

[0118] As can be seen from Table 4, there are obvious differences between the weights obtained through principal component analysis and the weights of empirical assignments.

[0119] To verify the effectiveness of the method of this application, weighted scoring calculations are respectively carried out using the weights obtained through principal component analysis and the weights of empirical assignments (the scoring range is from 0 to 1). The calculation results are shown in Table 5 below:

[0120] Table 5 Comparison of Weighted Scoring Based on Two Weights

[0121]

[0122] As can be seen from Table 5, for Sample 1 and Sample 6, there are slight differences in the scoring rankings of the two weights, and the scoring rankings of the remaining samples are consistent. This verifies the improvement and rationality of the weight calculation method obtained through principal component analysis.

[0123] As Figure 5 shown, an apparatus 500 for determining the weights of corrosion resistance performance characterization parameters provided by an embodiment of this application includes a parameter selection module 501, a matrix construction module 502, an analysis and processing module 503, and a weight determination module 504. Among them,

[0124] The parameter selection module 501 is configured to select multiple parameters related to corrosion resistance according to the requirements of material corrosion resistance performance evaluation;

[0125] The matrix construction module 502 is configured to test multiple samples, obtain the test data corresponding to each sample under the parameter set, and integrate the test data into a multi-parameter data matrix;

[0126] The analysis and processing module 503 is configured to perform principal component analysis on the multi-parameter data matrix to obtain the comprehensive contribution value of the vector of each parameter;

[0127] The weight determination module 504 is configured to determine the weight of each parameter based on the comprehensive contribution value of the vector through a standardized ratio; wherein, the weight is used to characterize the relative importance of each parameter in the evaluation of corrosion resistance performance.

[0128] The module / unit described as a separation component may or may not be physically separated. The component shown as a module / unit may or may not be a physical module, that is, it may be located in one place or distributed to multiple network units. Some or all of the modules / units can be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, in various embodiments of the present application, each functional module / unit can be integrated in a processing module, or each module / unit can exist physically alone, or two or more modules / units can be integrated in one module / unit.

[0129] In one implementation manner of the present application, the embodiments of the present application provide an electronic device, such as Figure 6 shown, the electronic device includes one or more processors and one or more memories; computer-readable code is stored in the memory, and when the computer-readable code is run by the one or more processors, the method for determining the weight of the corrosion resistance performance characterization parameters as described above is implemented. In addition, the electronic device may further include conventional electronic devices such as an I / O interface and a communication module, which will not be elaborated here.

[0130] The descriptions of the processes or structures corresponding to the above respective drawings have their own emphases. For parts not detailed in a certain process or structure, reference can be made to the relevant descriptions of other processes or structures.

[0131] In one implementation manner of the present application, the embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for determining the weight of the corrosion resistance performance characterization parameters as described above is implemented.

[0132] Those of ordinary skill in the art can understand that all or part of the steps in the methods of the above embodiments can be completed by instructing a processor through a program. The program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid state drive, magnetic tape, floppy disk, optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid state disk (SSD)), etc.

[0133] In one implementation manner of the present application, the embodiments of the present application can further provide a computer program product, and the computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computing device, the processes or functions described in the embodiments of the present application are fully or partially generated. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, a computer, or a data center to another website, a computer, or a data center in a wired manner (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (e.g., infrared, wireless, microwave, etc.). When the computer program product is executed by a computer, the computer executes the method described in the foregoing method embodiments. The computer program product can be a software installation package. In the case where the foregoing method needs to be used, the computer program product can be downloaded and executed on the computer.

[0134] In several embodiments provided by the present application, it should be understood that the disclosed system, device, or method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules / units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or modules or units can be in an electrical, mechanical, or other form.

[0135] Those of ordinary skill in the art should further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0136] The above embodiments are only illustrative of the principles and effects of this application and are not intended to limit this application. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or changes completed by those of ordinary skill in the art in the technical field without departing from the spirit and technical ideas disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for determining the weight of corrosion resistance characterization parameters, characterized in that: include: According to the requirements of material corrosion resistance evaluation, multiple parameters related to corrosion resistance are selected; Testing a plurality of samples, obtaining test data corresponding to each sample under the parameter set, and integrating the test data into a multi-parameter data matrix; Performing principal component analysis on the multi-parameter data matrix to obtain a vector comprehensive contribution value of each parameter; Based on the comprehensive contribution value of the vector, the weight of each parameter is determined by a standardized ratio; wherein the weight is used to characterize the relative importance of each parameter in the corrosion resistance evaluation.

2. The method for determining the weight of corrosion resistance performance characterization parameters according to claim 1, characterized in that: The parameters related to corrosion resistance include one or more combinations of the following parameters: Initial open circuit potential, used to reflect the electrochemical activity of the material without external force; The difference between the open circuit potential at 400 seconds and the initial moment is used to evaluate the electrochemical stability of the material; Corrosion potential refers to the potential when a material reaches equilibrium in a corrosive environment; The radius of the capacitive reactance arc is used to reflect the integrity and protective performance of the material surface film; Impedance modulus logarithm, used to characterize the material's resistance to corrosion reactions; The negative logarithm of the self-corrosion current density is used to reflect the corrosion rate of the material under natural corrosion conditions; The negative logarithm of the passivation current density is used to maintain the negative logarithm of the current density required to maintain the material in the passivation state; The width of the passivation interval is used to characterize the potential range in which the material can remain stable in the passivation state; The blue spot test time is used to reflect the time that the material remains free from local corrosion in the blue spot test.

3. The method for determining the weight of corrosion resistance characterization parameters according to claim 1, characterized in that: The step of integrating the test data into a multi-parameter data matrix comprises: Classifying the test data according to sample number and parameter type; Construct a matrix structure, where the rows of the matrix correspond to different samples and the columns correspond to different parameters; Fill the test data of each sample under each parameter into the corresponding position of the matrix to form a multi-parameter data matrix; The multi-parameter data matrix is ​​subjected to data preprocessing, including data format unification and missing value processing.

4. The method for determining the weight of corrosion resistance characteristic parameters according to claim 1, characterized in that: The performing principal component analysis on the multi-parameter data matrix to obtain a vector comprehensive contribution value of each parameter includes: Performing standardization on the multi-parameter data matrix; Calculating a covariance matrix of the multi-parameter data matrix; Solving the eigenvalues ​​and eigenvectors of the covariance matrix, wherein the eigenvalues ​​represent the variance contribution rate of each principal component, and the eigenvectors represent the linear combination relationship between the principal component and each parameter; Based on the eigenvalues ​​and eigenvectors of the principal components, the vector comprehensive contribution value of each parameter is calculated.

5. The method for determining the weight of corrosion resistance characteristic parameters according to claim 4, characterized in that: The vector comprehensive contribution value is calculated by the following formula: Among them, k i is the vector comprehensive contribution value of the i-th parameter, λ j is the eigenvalue of the jth principal component, a ij is the eigenvector value of the i-th parameter on the j-th principal component, and m is the number of selected principal components.

6. The method for determining the weight of corrosion resistance characteristic parameters according to claim 4, characterized in that: Also includes: The specific steps for screening the main components are as follows: Arrange the principal components in descending order according to the size of the eigenvalues; Performing standardization processing on the eigenvalues ​​to calculate the standardized eigenvalue of each principal component; Accumulating the standardized characteristic values ​​until the cumulative contribution rate reaches a preset threshold; The principal component whose cumulative contribution rate reaches the preset threshold is selected as the effective principal component for subsequent calculation of the vector comprehensive contribution value.

7. The method for determining the weight of corrosion resistance characteristic parameters according to claim 1, characterized in that: Also includes: The formula for determining the normalized ratio is: Among them, K i represents the weight of the i-th parameter, k i represents the vector comprehensive contribution value of the i-th parameter, and n represents the total number of parameters.

8. A device for determining the weight of corrosion resistance performance characterization parameters, characterized in that: include: A parameter selection module is used to select multiple parameters related to corrosion resistance according to the requirements of material corrosion resistance evaluation; A matrix construction module, used to test multiple samples, obtain test data corresponding to each sample under the parameter set, and integrate the test data into a multi-parameter data matrix; An analysis and processing module is used to perform principal component analysis on the multi-parameter data matrix to obtain a vector comprehensive contribution value of each parameter; A weight determination module, used to determine the weight of each parameter through a standardized ratio based on the vector comprehensive contribution value; The weight is used to characterize the relative importance of each parameter in the corrosion resistance evaluation.

9. An electronic device, characterized in that: include: one or more processors; and One or more memories, wherein the memories store computer-readable codes, and when the computer-readable codes are executed by the one or more processors, the method for determining the weight of the corrosion resistance characterization parameter according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for determining the weight of the corrosion resistance characterization parameter according to any one of claims 1 to 7 is implemented.

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