Corrosion resistance characterization parameter screening method and system, medium and electronic equipment

Abstract

The invention provides a corrosion resistance characterization parameter screening method and system, a medium and electronic equipment, and the method comprises the steps: obtaining test data of a test sample, the test data comprising test results of a plurality of corrosion resistance test parameters; data preprocessing is conducted on the test data, then correlation coefficient analysis is conducted to obtain a correlation coefficient matrix, and the data preprocessing mode comprises data scaling; performing parameter screening on the correlation coefficient matrix based on a screening rule to obtain a target parameter, wherein the screening rule comprises a positive and negative screening rule and a correlation coefficient threshold screening rule; and extracting the target parameters, performing data association in combination with the test sample, and outputting the data to a user side. According to the method, the problems of large parameter selection randomness, multi-parameter redundancy and conflict in the corrosion resistance characterization of the stainless steel material are effectively solved, the characterization efficiency of the corrosion resistance and the reliability of the characterization result can be improved, and the practical value is very high.

Description

Technical Field

[0001] The present invention belongs to the technical field of materials science, and particularly relates to a method, system, medium and electronic device for screening characterization parameters of corrosion resistance performance. Background Art

[0002] At present, stainless steel materials are widely used in industrial, architectural and medical fields, etc., and their corrosion resistance has become an important index for material selection and optimization. In the prior art, the characterization of corrosion resistance mainly relies on a variety of test methods, such as salt spray test, blue point test, electrochemical test, etc. Although these methods can obtain a variety of parameters to characterize the corrosion resistance, there are still the following technical bottlenecks when facing multi-parameter characterization:

[0003] 1. Randomness in parameter selection. At present, screening test parameters usually rely on the experience of researchers, and different personnel may select different parameters, resulting in inconsistencies in evaluation results.

[0004] 2. Multi-parameter redundancy and conflict. There may be a high degree of correlation or contradiction between many test parameters, and unfiltered parameters will reduce the scientific nature of evaluation.

[0005] 3. Lack of standardization. There is a lack of a unified parameter screening standard, and it is impossible to ensure the accuracy and comparability of test results. Summary of the Invention

[0006] In view of the above-mentioned disadvantages of the prior art, the purpose of the present invention is to provide a method, system, medium and electronic device for screening characterization parameters of corrosion resistance performance, which is used to solve the problems of great randomness in the selection of material corrosion resistance characterization parameters, multi-parameter redundancy and lack of screening standards in the prior art.

[0007] In a first aspect, the present invention provides a method for screening characterization parameters of corrosion resistance performance, and the method includes the following steps:

[0008] Obtain test data of a test sample, where the test data includes test results of several corrosion resistance performance test parameters;

[0009] Perform data preprocessing on the test data, and then perform correlation coefficient analysis to obtain a correlation coefficient matrix, where the data preprocessing method includes data scaling;

[0010] Based on a screening rule, perform parameter screening on the correlation coefficient matrix to obtain target parameters, where the screening rule at least includes a positive and negative screening rule and a correlation coefficient threshold screening rule;

[0011] Extract the target parameters, and perform data association in combination with the test sample and output to a user terminal.

[0012] In a possible implementation manner of the present application, the obtaining of the test data of the test sample specifically includes:

[0013] Obtaining the test results of a plurality of corrosion resistance test parameters, where the test results at least include the initial open-circuit voltage, the open-circuit potential test time difference, the corrosion potential, the capacitive reactance arc radius, the logarithm value of the impedance modulus, the negative logarithm value of the self-corrosion current density, the negative logarithm value of the passivation current density, the passivation interval width, the blue point test time, and the salt spray test time, where

[0014] The open-circuit voltage represents the potential at the start of the test;

[0015] The open-circuit potential test time difference represents the difference between the open-circuit potential at the test time and the initial moment, and the test time includes 400 seconds;

[0016] The corrosion potential represents the equilibrium potential of the test sample under corrosion conditions;

[0017] The capacitive reactance arc radius represents the capacitive reactance characteristic of the electrochemical impedance;

[0018] The logarithm value of the impedance modulus represents the impedance modulus value measured at a preset frequency, and the preset frequency includes 0.01 Hz;

[0019] The negative logarithm value of the self-corrosion current density represents the corrosion rate of the test sample;

[0020] The negative logarithm value of the passivation current density represents the passivation effect of the test sample;

[0021] The passivation interval width represents the potential range of the test sample in the passivation state;

[0022] The blue point test time represents the duration of the blue point test;

[0023] The salt spray test time represents the duration of the salt spray test.

[0024] In a possible implementation manner of the present application, the data preprocessing of the test data and then the correlation coefficient analysis to obtain the correlation coefficient matrix specifically includes:

[0025] Performing data scaling on the test data, including performing data scaling using the maximum-minimum normalization method;

[0026] Calculating the correlation coefficient matrix based on the test data after data scaling, where the correlation coefficient matrix includes a lower triangular matrix.

[0027] In a possible implementation manner of the present application, parameter screening is performed on the correlation coefficient matrix based on the positive and negative screening rules, specifically including:

[0028] Calculate the average value of the correlation coefficients corresponding to each test parameter and other test parameters to determine the positive and negative correlations. Among them,

[0029] If the result of calculating the average value of the correlation coefficients between the current test parameter and other test parameters is negative, it indicates that the current parameter is negatively correlated and is excluded;

[0030] Otherwise, no exclusion is performed.

[0031] In a possible implementation manner of the present application, parameter screening is performed on the correlation coefficient matrix based on the correlation coefficient threshold screening rule, which specifically includes:

[0032] Calculate the average value of the correlation coefficients corresponding to each test parameter and other test parameters to determine weak correlations. Among them,

[0033] When the absolute value of the calculation result of the average correlation coefficient is less than the weak threshold, it is determined to be weakly correlated. The weak threshold includes 0.3;

[0034] If the calculation result of the average correlation coefficient between the current test parameter and other test parameters is determined to be weakly correlated, the current test parameter is excluded.

[0035] In a possible implementation manner of the present application, parameter screening is performed on the correlation coefficient matrix. Specifically, it further includes:

[0036] Calculate the average value of the correlation coefficients corresponding to each test parameter and other test parameters to determine strong correlations. Among them,

[0037] When the absolute value of the calculation result of the average correlation coefficient is greater than the strong threshold, it is determined to be weakly correlated. The weak threshold includes 0.9;

[0038] If the calculation result of the average correlation coefficient between the current test parameter and other test parameters is determined to be strongly correlated, find all pairs of test parameters corresponding to strong correlations to form a group, and only retain one test parameter in the strongly correlated parameter group.

[0039] In a possible implementation manner of the present application, only retaining one test parameter in the strongly correlated parameter group specifically includes calculating the correlation coefficients between the test parameters within the group and the test parameters determined not to be excluded in the strongly correlated parameter group, arranging them according to the values of the current calculation results, and sequentially discarding the corresponding test parameters until the number of test parameters within the group is one and then stopping. Among them, the arrangement order includes the order from large to small.

[0040] In a second aspect, the present invention provides a screening system for corrosion resistance performance characterization parameters. The system includes:

[0041] An acquisition module for acquiring test data of a test sample, where the test data includes test results of a number of corrosion resistance test parameters;

[0042] A calculation module for performing data preprocessing on the test data and then performing correlation coefficient analysis to obtain a correlation coefficient matrix, where the data preprocessing method includes data scaling;

[0043] A screening module for screening parameters from the correlation coefficient matrix based on screening rules to obtain target parameters, where the screening rules at least include a positive / negative screening rule and a correlation coefficient threshold screening rule;

[0044] An output module for extracting the target parameters, associating the data with the test sample, and outputting the result to the user terminal.

[0045] In a third aspect, the present invention provides an electronic device, which includes: a processor and a memory;

[0046] The memory is used for storing a computer program;

[0047] The processor is used for executing the computer program stored in the memory, so that the electronic device executes the above-mentioned method for screening corrosion resistance characterization parameters.

[0048] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by an electronic device, it implements the above-mentioned method for screening corrosion resistance characterization parameters.

[0049] As described above, the method, system, medium, and electronic device for screening corrosion resistance characterization parameters according to the present invention have the following beneficial effects: effectively solving the problems of large randomness in parameter selection, redundancy and conflict of multiple parameters in the corrosion resistance characterization of stainless steel materials, being able to improve the characterization efficiency of corrosion resistance and the reliability of the characterization results, and having strong practical value. Description of the Drawings

[0050] Figure 1 Showing a schematic diagram of a scenario of the electronic device according to an embodiment of the present invention;

[0051] Figure 2 Showing a schematic diagram of steps of the method for screening corrosion resistance characterization parameters according to an embodiment of the present invention;

[0052] Figure 3 Showing a schematic diagram of a polarization curve graph in an embodiment of the method for screening corrosion resistance characterization parameters according to the present invention;

[0053] Figure 4Schematic diagram showing the application of the positive and negative screening rules in an embodiment of the screening method for the corrosion resistance characterization parameters of the present invention;

[0054] Figure 5 Schematic diagram showing the application of the correlation coefficient threshold screening in an embodiment of the screening method for the corrosion resistance characterization parameters of the present invention;

[0055] Figure 6 Schematic diagram showing the structure of the screening system for the corrosion resistance characterization parameters of the present invention in an embodiment;

[0056] Figure 7 Schematic diagram showing the structure of the electronic device of the present invention in an embodiment. Detailed implementation manners

[0057] The following specific examples illustrate the implementation manners of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention 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 invention. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0058] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention 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.

[0059] Specifically, the evaluation and characterization of the corrosion resistance of stainless steel materials currently mainly rely on the following test methods, such as salt spray test, blue point test, electrochemical test, etc. Although these methods can obtain various parameters to characterize the corrosion resistance, there are still many technical bottlenecks when facing multi-parameter characterization. Therefore, developing a scientific and efficient method to screen the characterization parameters of corrosion resistance and quantify their relative importance is of great significance for improving the accuracy of corrosion resistance research. Among them, the screening method for the corrosion resistance characterization parameters proposed by the present invention aims to solve the problems of large randomness in parameter selection, multi-parameter redundancy, and lack of screening criteria in the prior art, and is used to determine the key parameters that can characterize the corrosion resistance of the stainless steel surface, and can be applied to fields such as the performance research of surface passivation films, such as Figure 1As shown, it is a scenario application diagram of the present invention. First, test data corresponding to stainless steel materials is obtained. Generally, there are more than 5 items of test data (such as salt spray test results, polarization potential, characteristic values in electrochemical impedance spectroscopy, etc.). Then, the test data is screened according to the screening rules set by the present invention to obtain target parameters. Finally, based on different test samples and combined with the corresponding target parameters, data association is performed and output to the user terminal for subsequent applications of characterizing and evaluating the corrosion resistance of stainless steel materials. Specifically, by using statistical analysis techniques, the present invention can quickly screen out key parameters that have a significant impact on corrosion resistance, and eliminate redundant or irrelevant parameters, providing a scientific basis for corrosion resistance evaluation, thereby improving the accuracy of testing.

[0060] Next, the technical solutions in the embodiments of the present invention will be described in detail with reference to the accompanying drawings in the embodiments of the present invention.

[0061] Specifically, please refer to Figure 2 , in an embodiment of the invention, the method for screening the corrosion resistance characterization parameters of the present invention includes the following steps:

[0062] Step S202, obtain the test data of the test sample, where the test data includes the test results of several corrosion resistance test parameters;

[0063] Step S204, perform data preprocessing on the test data, and then perform correlation coefficient analysis to obtain a correlation coefficient matrix, where the data preprocessing method includes data scaling;

[0064] Step S206, perform parameter screening on the correlation coefficient matrix based on the screening rules to obtain target parameters, where the screening rules at least include positive and negative screening rules and correlation coefficient threshold screening rules;

[0065] Step S208, extract the target parameters, and perform data association in combination with the test sample and output to the user terminal.

[0066] It should be noted that in this embodiment, it specifically describes how to perform data screening. First, test data needs to be obtained. Correspondingly, it is to obtain the test data of test samples. Among them, the test data includes the test results of several corrosion resistance test parameters. Different test samples have different test methods and corresponding test results also vary. Then, data preprocessing is performed on the test data, such as through data scaling processing, and then correlation coefficient analysis is carried out to obtain a correlation coefficient matrix. Thus, parameter screening is performed on the correlation coefficient matrix based on the screening rules to obtain target parameters. Among them, the screening rules are the core technical points. Positive and negative screening rules and correlation coefficient threshold screening rules are set to screen the data, so as to reduce redundant and conflicting parameters. And through the set screening rules, the screening criteria can be unified, reducing misjudgments caused by human interference and ensuring the accuracy of the test. Finally, the target parameters are extracted, and data association is performed in combination with the corresponding test samples and output to the user terminal.

[0067] Further, in an embodiment of the invention, the obtaining of the test data of the test sample specifically includes:

[0068] Obtaining the test results of several corrosion resistance test parameters. Among them, the test results at least include the initial open circuit voltage, the difference in open circuit potential test time, the corrosion potential, the radius of the capacitive reactance arc, the logarithm of the impedance modulus, the negative logarithm of the self-corrosion current density, the negative logarithm of the passive current density, the width of the passivation interval, the blue point test time, and the salt spray test time. Among them,

[0069] The open circuit voltage represents the potential at the start of the test;

[0070] The difference in open circuit potential test time represents the difference between the open circuit potential at the test time and the initial moment. The test time includes 400 seconds;

[0071] The corrosion potential represents the equilibrium potential of the test sample under corrosion conditions;

[0072] The radius of the capacitive reactance arc represents the capacitive reactance characteristic of the electrochemical impedance;

[0073] The logarithm of the impedance modulus represents the impedance modulus value measured at a preset frequency. The preset frequency includes 0.01 Hz;

[0074] The negative logarithm of the self-corrosion current density represents the corrosion rate of the test sample;

[0075] The negative logarithm of the passive current density represents the passivation effect of the test sample;

[0076] The width of the passivation interval represents the potential range of the test sample in the passive state;

[0077] The blue point test time represents the duration of the blue point test;

[0078] The salt spray test time represents the duration of the salt spray test.

[0079] It should be noted that in this embodiment, the specific content of the test results is specifically described, including the initial open circuit voltage, the difference in open circuit potential test time, the corrosion potential, the radius of the capacitive arc, the logarithmic value of the impedance modulus, the negative logarithmic value of the self-corrosion current density, the negative logarithmic value of the passive current density, the width of the passivation interval, the blue point test time, and the salt spray test time. Among them, the initial open circuit potential is obtained based on the potential at the start of the electrochemical test; and the difference in open circuit potential at 400 seconds and the initial moment is calculated to obtain the 400-second difference in open circuit potential, and the logarithmic value of the impedance modulus measured at a frequency of 0.01 Hz is obtained. The corrosion potential representing the equilibrium potential of the test sample under corrosion conditions and the negative logarithmic value of the self-corrosion current density representing the corrosion rate of the test sample are obtained based on the polarization curve graph. Among them, the polarization curve graph is an important tool for studying the corrosion behavior of metals. Through the polarization curve graph, the corrosion potential and the self-corrosion current can be determined. The polarization curve graph usually takes the electrode potential (V) as the abscissa and the current density (I) or current (I) as the ordinate, showing the change in the current density on the metal surface at different potentials. Among them, the polarization curve is usually divided into an anodic polarization curve and a cathodic polarization curve. These two curves intersect at a certain point, and this point is called the corrosion potential (E corr ), and the corresponding current density is called the self-corrosion current (I corr ). Specifically, the corrosion potential (Ecorr) is the potential corresponding to the intersection point of the anodic polarization curve and the cathodic polarization curve in the polarization curve. At this point, oxidation and reduction reactions occur simultaneously on the metal surface, reaching a dynamic equilibrium state. Therefore, the corrosion potential is the potential at which the metal stably exists in the corrosion environment; and the self-corrosion current (Icorr) is the current density on the metal surface at the corrosion potential. By drawing a straight line parallel to the vertical axis at the corrosion potential, the current value corresponding to the intersection point of this straight line and the tangent of the anodic polarization curve is the self-corrosion current. As Figure 3 shown, it shows an example of a polarization curve graph, where the abscissa is the self-corrosion current J / (A·cm -2 ), and the ordinate is the corrosion potential Among them, the anodic polarization curve and the cathodic polarization curve intersect at a certain point, and the potential corresponding to this point is -0.24 V, and the corresponding current density is 10 μA / cm 2 , correspondingly, the corrosion potential Ecorr = -0.24 V; the self-corrosion current Icorr = 10 μA / cm 2 .

[0080] Further, the negative logarithm value of the passivation current density is obtained by taking the mean of all measured current density values within the width of the passivation interval, specifically representing the passivation effect of the test sample; and the width of the passivation interval is obtained by taking the maximum potential range where the current density difference is not greater than 10 0.25 A / cm 2 to represent the potential range of the test sample in the passivated state. Among them, for the potential range of the material in the passivated state, the wider the passivation interval, the better the corrosion resistance of the material; and the blue point test time is the mean of the durations of three blue point tests to reflect the tolerance time of the material to local corrosion; and the salt spray test time is obtained based on the duration of the salt spray test, and the radius of the capacitive arc is estimated using a Nyquist plot. Among them, a Nyquist plot is a graph used to represent complex impedance and is usually used for electrochemical impedance spectroscopy (EIS) analysis. Specifically, the real part (Z') of the impedance is taken as the horizontal axis and the imaginary part (Z") as the vertical axis to plot the change trajectory of the impedance at different frequencies. It should be noted that the Nyquist plot can provide information about electrode processes, material properties, etc. If the capacitive arc is semicircular, the radius R = (Zmax - Zmin) / 2 can be calculated by the formula, where Zmax is the real part value of the highest point of the capacitive arc and Zmin is the real part value of the lowest point of the capacitive arc.

[0081] Further, in an embodiment of the invention, the test data is preprocessed and then a correlation coefficient analysis is performed to obtain a correlation coefficient matrix, which specifically includes:

[0082] Data scaling is performed on the test data, including data scaling using the maximum-minimum normalization method;

[0083] Based on the test data after data scaling, a correlation coefficient calculation is performed to obtain the correlation coefficient matrix, where the correlation coefficient matrix includes a lower triangular matrix.

[0084] It should be noted that in this embodiment, in order to make the evaluation scores concentrate on positive values and be easy to locate and score when new data is added, this embodiment uses the maximum-minimum normalization method to scale the test data, that is, uses the Min-Max normalization method for data scaling. Among them, the Min-Max normalization method is a commonly used data preprocessing method for scaling data to a specific range, usually [0,1][0,1][0,1] or [-1,1][-1,1][-1,1]. The normalization method maintains the original distribution form of the data through linear transformation, but scales it to a new numerical interval. Since the maximum-minimum normalization method is a technical method that can be adopted by those skilled in the art, it will not be elaborated in this embodiment. Further, based on the test data after data scaling, the correlation coefficient matrix is calculated, where the correlation coefficient matrix includes a lower triangular matrix. Among them, correlation coefficient analysis is a statistical method used to measure and describe the strength and direction of the relationship between two or more variables, which is also a technical method that can be adopted by those skilled in the art and will not be elaborated in this embodiment.

[0085] Further, in an embodiment of the invention, parameter screening is performed on the correlation coefficient matrix based on the positive-negative screening rule, specifically including:

[0086] Calculate the average value of the correlation coefficients corresponding to each test parameter and other test parameters to judge positive-negative correlation, where

[0087] If the result of calculating the average value of the correlation coefficients between the current test parameter and other test parameters is negative, it indicates that the current parameter is negatively correlated and is excluded;

[0088] Otherwise, no exclusion is performed.

[0089] It should be noted that in this embodiment, as Figure 4 shown, regarding the application of the positive-negative screening rule, specifically, calculate the average value of the correlation coefficients corresponding to each test parameter and other test parameters to examine positive-negative nature. Specifically, if the result of calculating the average value of the correlation coefficients between a certain test parameter and other test parameters is negative, it indicates that the current test parameter is negatively correlated and this parameter is discarded. When the test parameter is negatively correlated, it indicates that the selection of the current test parameter is not reasonable enough, and the correlation does not match the theoretical expectation and needs to be excluded.

[0090] Further, in an embodiment of the invention, parameter screening is performed on the correlation coefficient matrix based on the correlation coefficient threshold screening rule, specifically including:

[0091] Calculate the average value of the correlation coefficients corresponding to each test parameter and other test parameters to judge weak correlation, where

[0092] When the absolute value of the calculation result of the average value of the correlation coefficients is less than the weak threshold, it is determined as weakly correlated, and the weak threshold includes 0.3;

[0093] If the calculation result of the average value of the correlation coefficients between the current test parameter and other test parameters is determined to be weakly correlated, then the current test parameter is removed.

[0094] It should be noted that in this embodiment, as Figure 5 shown, regarding the application of the correlation coefficient threshold screening, the application of the weak threshold is described. Specifically, the average value of the correlation coefficients corresponding to each test parameter and other test parameters is calculated to determine weak correlation. Specifically, when the absolute value of the calculation result of the average value of the correlation coefficients is less than the weak threshold, it is determined as weakly correlated, and the weak threshold includes 0.3. Further, if the calculation result of the average value of the correlation coefficients between the current test parameter and other test parameters is determined to be weakly correlated, then the current test parameter is removed. It should be noted that when the absolute value of the calculation result of the average value of the correlation coefficients is less than 0.3, it indicates that the correlation between the test parameters is very weak, and such test parameters have less direct or smaller influence on the final performance result of the characterization. To reduce the measurement workload, such test parameters are removed.

[0095] Further, in an embodiment of the invention, parameter screening is performed on the correlation coefficient matrix. Parameter screening of the correlation coefficient matrix specifically further includes:

[0096] Calculating the average value of the correlation coefficients corresponding to each test parameter and other test parameters to determine strong correlation, where

[0097] When the absolute value of the calculation result of the average value of the correlation coefficients is greater than the strong threshold, it is determined as strongly correlated, and the strong threshold includes 0.9;

[0098] If the calculation result of the average value of the correlation coefficients between the current test parameter and other test parameters is determined to be strongly correlated, then find all pairs of test parameters corresponding to strong correlation to form a group, and only retain one test parameter in the strongly correlated parameter group.

[0099] It should be noted that in this embodiment, as Figure 5 shown, regarding the application of the correlation coefficient threshold screening, the application of the strong threshold is described. Specifically, the average value of the correlation coefficients corresponding to each test parameter and other test parameters is calculated to determine strong correlation. Specifically, when the absolute value of the calculation result of the average value of the correlation coefficients is greater than the strong threshold, it is determined as weakly correlated, and the weak threshold includes 0.9. Furthermore, when the calculation result of the average value of the correlation coefficients between the current test parameter and other test parameters is determined to be strongly correlated, find all pairs of test parameters corresponding to strong correlation to form a group, and only retain one test parameter in the strongly correlated parameter group.

[0100] Further, in an embodiment of the invention, only one test parameter is retained in the strongly correlated parameter group, which specifically includes calculating the correlation coefficient between the test parameters within the group and the determined non-excluded test parameters in the strongly correlated parameter group, arranging them according to the value of the current calculation result, and successively discarding the corresponding test parameters until the number of test parameters within the group is one, wherein the arrangement order includes the order from large to small.

[0101] It should be noted that in this embodiment, the constructed strongly correlated parameter group is specifically formed by dividing the above-mentioned strongly correlated parameter pairs into one or several strongly correlated parameter groups. The strongly correlated parameter group needs to meet the following conditions: ① Among the parameters within the group, each pair is in a strongly correlated relationship; ② The number of groups is as small as possible. When selecting the parameters to be discarded, the following conditions need to be met simultaneously: in each strongly correlated group, only one parameter needs to be retained and only one parameter is retained, and the number of discarded parameters is as small as possible. Specifically, when it is impossible to determine which parameters need to be discarded among several parameters, the maximum correlation coefficient between these several test parameters and all the determined non-discarded parameters is examined respectively. Among them, the correlation coefficient between the test parameters within the group and the determined non-excluded test parameters is calculated, arranged according to the value of the current calculation result, and the corresponding test parameters are successively discarded until the number of test parameters within the group is one, wherein the arrangement order includes the order from large to small.

[0102] Specifically, in an embodiment of the invention, taking the corrosion resistance test of 9 316 stainless steel nitric acid passivation specimens in 3.5 wt% NaCl as an example of the test sample, first obtain the test data, test 9 parameters for each sample, test the following 9 parameters that may affect the corrosion resistance, and construct a correlation coefficient matrix (9×9). The test data includes:

[0103] Parameter 1, Initial open circuit potential / V: The potential at the start of the electrochemical test.

[0104] Parameter 2, Difference in open circuit potential at 400 s / V: The difference between the open circuit potential at 400 seconds and the initial moment, characterizing the electrochemical stability.

[0105] Parameter 3, Corrosion potential / V: The equilibrium potential of the sample under corrosion conditions.

[0106] Parameter 4, Capacitance arc radius / Ω: Reflecting the capacitance characteristics of the electrochemical impedance.

[0107] Parameter 5, Logarithm of impedance modulus (at 0.01 Hz): The impedance modulus value measured at a frequency of 0.01 Hz, which is logarithmically transformed to reflect the resistance of the corrosion reaction.

[0108] Parameter 6, Negative logarithm of self-corrosion current density (A / cm2): The negative logarithm of the current density during the corrosion process, indicating the corrosion rate.

[0109] Parameter 7, negative logarithm of passivation current density (A / cm2): negative logarithm of current density in the passivation state, reflecting the passivation effect.

[0110] Parameter 8, passivation interval width / V: The potential range of the material in the passivation state. The wider the passivation interval, the better the corrosion resistance of the material.

[0111] Parameter 9, Blue spot test time / s: The duration of the blue spot test reflects the material's tolerance to local corrosion and is tested in accordance with GB / T-25150-2010.

[0112] The test parameters in the test data are tabulated in Table 1.

[0113] Table 1. Test parameter details

[0114]

[0115]

[0116] Furthermore, after the test data is subjected to maximum-minimum normalization, the correlation coefficient matrix is ​​calculated, and the results are shown in Table 2.

[0117] Table 2. Correlation coefficient table

[0118]

[0119] Among them, the data in Table 2 are screened according to the positive and negative screening rules and the correlation coefficient threshold screening rules. Among them, the correlation coefficients of parameter 8 and the other eight parameters are inconsistent in positive and negative, and are negatively correlated, so it is eliminated; and the internal correlation coefficients of parameter groups 6-7-9 and 3-9 are both greater than 0.9, then parameters 6, 7, and 9 are strongly correlated with each other, and two of the parameters need to be eliminated. Among them, eliminating 9 and then eliminating one of 6 and 7 can minimize the number of eliminated parameters. Here, the more important and easier to measure parameter 6 is retained, and parameters 7 and 9 are eliminated. In this way, there is no longer a parameter pair with a correlation coefficient greater than 0.9. After performing the above steps, the average correlation coefficient of parameter 2 with other parameters is 0.2895, which is less than 0.3, and parameter 2 is eliminated. So far, parameters 1, 3, 4, 5, and 6 are retained.

[0120] Furthermore, when verifying the optimization, the evaluation scores based on the retained parameters 1, 3, 4, 5, 6 after deletion and before screening are shown in Table 3.

[0121] Table 3. Comparison of scores before and after parameter screening

[0122]

[0123] Among them, the scores in Table 3 range from 0 to 1, corresponding to the worst and the best respectively. Although the scores are slightly different before and after screening, the result ranking from the best to the worst is exactly the same, verifying the effectiveness of the screening parameters in the corrosion resistance test of 9 316 stainless steel nitric acid passivation specimens in 3.5wt% NaCl.

[0124] The embodiment of the present application also provides a screening system for corrosion resistance characterization parameters. The screening system for corrosion resistance characterization parameters can implement the screening method for corrosion resistance characterization parameters described in the present application. However, the implementation device of the screening method for corrosion resistance characterization parameters described in the present application includes, but is not limited to, the structure of the screening system for corrosion resistance characterization parameters listed in this embodiment. Any structural deformation and replacement of the prior art made according to the principle of the present application are included in the protection scope of the present application.

[0125] Please refer to Figure 6 , in an embodiment, a screening system 60 for corrosion resistance characterization parameters provided in this embodiment, the system includes:

[0126] An acquisition module 61, configured to acquire test data of a test sample, where the test data includes test results of several corrosion resistance test parameters;

[0127] A calculation module 62, configured to perform data preprocessing on the test data, and then perform correlation coefficient analysis to obtain a correlation coefficient matrix, where the data preprocessing method includes data scaling;

[0128] A screening module 63, configured to perform parameter screening on the correlation coefficient matrix based on a screening rule to obtain target parameters, where the screening rule at least includes a positive and negative screening rule and a correlation coefficient threshold screening rule;

[0129] An output module 64, configured to extract the target parameters, and perform data association with the test sample to output to the user terminal.

[0130] Since the specific implementation manner of this embodiment corresponds to the foregoing method embodiment, the same details will not be repeated here. Those skilled in the art should also understand that Figure 6 The division of each module in the embodiment is only a logical function division. In actual implementation, it can be fully or partially integrated into one or more physical entities, and these modules can all be implemented in the form of software called by processing elements, or all be implemented in the form of hardware, or some modules be implemented in the form of software called by processing elements and some modules be implemented in the form of hardware.

[0131] In several embodiments provided by the present invention, 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 only 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 coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or modules or units can be in electrical, mechanical or other forms.

[0132] The modules / units described as separate components may or may not be physically separated. The components shown as modules / units may or may not be physical modules, that is, they can 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 objectives of the embodiments of the present invention. For example, in each embodiment of the present invention, the functional modules / units 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.

[0133] 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 the two. 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 the present invention.

[0134] An embodiment of the present invention also provides a computer-readable storage medium. Those of ordinary skill in the art can understand that all or part of the steps in the method 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. 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 integrating one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a digital video disc (DVD)), or a semiconductor medium (such as a solid-state disk (SSD)).

[0135] An embodiment of the present invention also provides an electronic device. The electronic device includes a processor and a memory.

[0136] The memory is used to store a computer program.

[0137] The memory includes various media that can store program codes, such as ROM, RAM, magnetic disk, USB flash drive, memory card, or optical disc.

[0138] The processor is connected to the memory and is used to execute the computer program stored in the memory, so that the electronic device executes the above method for screening corrosion resistance performance characterization parameters.

[0139] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it can also be a digital signal processor (DSP for short), an application-specific integrated circuit (ASIC for short), a field programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0140] Such as Figure 7As shown, the electronic device of the present invention is presented in the form of a general computing device. The components of the electronic device may include, but are not limited to: one or more processors or processing units 71, a memory 72, and a bus 73 that connects different system components (including the memory 72 and the processing unit 71).

[0141] The bus 73 represents one or more of several types of bus architectures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. By way of example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0142] The electronic device typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0143] The memory 72 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 721 and / or cache memory 722. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a storage system 723 may be used for reading and writing on non-removable, non-volatile magnetic media ( Figure 7 not shown, commonly referred to as a "hard disk drive"). Although Figure 7 not shown in the figure, a disk drive for reading and writing on a removable non-volatile disk (such as a "floppy disk") and an optical disk drive for reading and writing on a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 73 through one or more data media interfaces. The memory 72 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the embodiments of the present invention.

[0144] A program / utility 724 having a set (at least one) of program modules 7241 may be stored, for example, in the memory 72. Such program modules 7241 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules 7241 generally perform the functions and / or methods in the embodiments described in the present invention.

[0145] The electronic device can also communicate with one or more external devices (such as a keyboard, a pointing device, a display, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device, and / or communicate with any device that enables the electronic device to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 74. Moreover, the electronic device can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 75. As Figure 7 shown, the network adapter 75 communicates with other modules of the electronic device through the bus 73. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0146] The above embodiments are only illustrative of the principles and effects of the present invention, rather than limiting the present invention. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed by the present invention should still be covered by the claims of the present invention.

Claims

1. A method for screening corrosion resistance characterization parameters, characterized in that: include: Acquiring test data of the test sample, wherein the test data includes test results of several corrosion resistance test parameters; Performing data preprocessing on the test data, and then performing correlation coefficient analysis to obtain a correlation coefficient matrix, wherein the data preprocessing method includes data scaling; Performing parameter screening on the correlation coefficient matrix based on screening rules to obtain target parameters, wherein the screening rules at least include positive and negative screening rules and correlation coefficient threshold screening rules; The target parameters are extracted, combined with the test samples for data association and output to the user end.

2. The method for screening corrosion resistance characterization parameters according to claim 1, characterized in that: The obtaining of the test data of the test sample specifically includes: Obtain test results of several corrosion resistance test parameters, wherein the test results at least include the starting open circuit voltage, the open circuit potential test time difference, the corrosion potential, the capacitive reactance arc radius, the logarithm of the impedance modulus, the negative logarithm of the self-corrosion current density, the negative logarithm of the passivation current density, the width of the passivation interval, the blue dot test time, and the salt spray test time, wherein the open circuit voltage represents the potential at the beginning of the test; The open circuit potential test time difference represents the difference between the open circuit potential when the test time is reached and the initial time, and the test time includes 400 seconds; The corrosion potential represents the equilibrium potential of the test sample under corrosion conditions; The capacitive reactance arc radius represents the capacitive reactance characteristic of the electrochemical impedance; The impedance modulus logarithm value represents the impedance modulus value measured at a preset frequency, and the preset frequency includes 0.01 Hz; The negative logarithmic value of the self-corrosion current density represents the corrosion rate of the test sample; The negative logarithm of the passivation current density indicates the passivation effect of the test sample; The passivation interval width represents the potential range of the test sample in the passivation state; The blue dot test time indicates the duration of the blue dot test; The salt spray test time indicates the duration of the salt spray test.

3. The method for screening corrosion resistance characteristic parameters according to claim 1, characterized in that: The data preprocessing of the test data and then the correlation coefficient analysis to obtain the correlation coefficient matrix specifically includes: Performing data scaling on the test data, including performing data scaling using a maximum-minimum normalization method; The correlation coefficient matrix is ​​obtained by performing correlation coefficient calculation based on the test data after data scaling, wherein the correlation coefficient matrix includes a lower triangular matrix.

4. The method for screening corrosion resistance characteristic parameters according to claim 1, characterized in that: The correlation coefficient matrix is ​​subjected to parameter screening based on the positive and negative screening rules, specifically including: Calculate the average value of the correlation coefficients of each test parameter and other test parameters to determine the positive and negative correlation, where: If the result of calculating the average value of the correlation coefficient between the current test parameter and other test parameters is negative, it indicates that the current parameter is negatively correlated and will be eliminated; Otherwise, no culling is performed.

5. The method for screening corrosion resistance characteristic parameters according to claim 1, characterized in that: The correlation coefficient matrix is ​​subjected to parameter screening based on the correlation coefficient threshold screening rule, specifically including: Calculate the average value of the correlation coefficients of each test parameter and other test parameters to determine weak correlation, wherein a weak correlation is determined when the absolute value of the result of the average value calculation of the correlation coefficient is less than a weak threshold, and the weak threshold includes 0.3; If the average value of the correlation coefficient between the current test parameter and other test parameters is determined to be weakly correlated, the current test parameter is eliminated.

6. The method for screening corrosion resistance characteristic parameters according to claim 1, characterized in that: Performing parameter screening on the correlation coefficient matrix Performing parameter screening on the correlation coefficient matrix specifically also includes: Calculate the average value of the correlation coefficients of each test parameter and other test parameters to determine the strong correlation, wherein when the absolute value of the result of the average value calculation of the correlation coefficient is greater than the strong threshold, it is determined to be weakly correlated, and the weak threshold includes 0.9; If the average value of the correlation coefficient between the current test parameter and other test parameters is determined to be strongly correlated, all corresponding strongly correlated test parameter pairs are found to form a group, and only one test parameter is retained in the strongly correlated parameter group.

7. The method for screening corrosion resistance characteristic parameters according to claim 6, characterized in that: The method of retaining only one test parameter in the strongly correlated parameter group specifically includes calculating the correlation coefficients of the test parameters in the group and determining the test parameters not to be eliminated in the strongly correlated parameter group, arranging them according to the values ​​of the current calculation results, and discarding the corresponding test parameters in turn until the number of test parameters in the group is one, wherein the arrangement order includes the order from large to small.

8. A screening system for corrosion resistance performance characterization parameters, characterized in that: include: An acquisition module, used to acquire test data of a test sample, wherein the test data includes test results of several corrosion resistance test parameters; A calculation module, used for performing data preprocessing on the test data, and then performing correlation coefficient analysis to obtain a correlation coefficient matrix, wherein the data preprocessing method includes data scaling; A screening module, used for performing parameter screening on the correlation coefficient matrix based on screening rules to obtain target parameters, wherein the screening rules at least include positive and negative screening rules and correlation coefficient threshold screening rules; The output module is used to extract the target parameters, perform data association with the test samples and output them to the user end.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for screening the corrosion resistance characterization parameters described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The electronic device comprises: a processor and a memory; wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device executes the method for screening corrosion resistance characterization parameters as described in any one of claims 1 to 7.

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