Evaluation Method and Device for Electronic Components Based on Principal Component Analysis and Topsis

Abstract

This application relates to an evaluation method, device, computer device, storage medium and computer program product for electronic components based on principal component analysis and Topsis. The method includes: obtaining evaluation indicators of an evaluation object, and performing preprocessing on the evaluation indicators to obtain a preprocessing result; the preprocessing process includes standardization processing of original data and data dimensionality reduction processing based on principal component analysis, comprehensively evaluating the preprocessing result based on the Topsis method, and obtaining the distance between the evaluation object and the ideal solution; the ideal solution includes a positive ideal solution and a negative ideal solution, obtaining the relative proximity degree between the evaluation object and the ideal solution based on the distance between the evaluation object and the ideal solution, and evaluating the evaluation object based on the relative proximity degree. Using this method can improve the authenticity and accuracy of the evaluation result.

Description

Technical Field

[0001] The present application relates to the technical field of comprehensive evaluation of environmental effect data, and particularly to an evaluation method, device, computer device, storage medium, and computer program product for electronic components based on principal component analysis and Topsis. Background Technique

[0002] Before the avionics components are put into use, field environmental tests are often carried out to assess their environmental adaptability. During the test process, a large amount of environmental effect data of the electronic components will be generated, and it contains multiple performance parameters. In the process of evaluating the performance level of such electronic components, in order to more comprehensively and accurately reflect the overall performance level of the electronic components, the following two aspects of problems need to be considered: On the one hand, as many indicators as possible need to be considered to avoid one-sidedness in the evaluation of the overall performance level of the electronic components; on the other hand, the correlation between the performance parameters needs to be considered, which leads to a certain degree of overlap in the information reflected by each parameter.

[0003] The comprehensive evaluation method is a type of data analysis method used to evaluate things covering multiple attributes. Commonly used comprehensive evaluation methods include the analytic hierarchy process, principal component analysis method, and ideal solution method, and they have been widely used in many fields. However, combined with the field environmental test data of electronic components, there are relatively few studies on the comprehensive evaluation method of environmental effect data based on electronic components. Currently, there are relevant studies in this field that use the method based on principal component analysis to comprehensively evaluate the environmental effect data of electronic components.

[0004] However, there is a strong correlation between some performance parameters of electronic components. The existing evaluation methods cannot eliminate the overlapping information of the indicators, but instead strengthen the overlapping information between the indicators, making the relationship between the evaluation results and the indicator correlation very close. Therefore, when using the principal component analysis method for comprehensive evaluation, there will be a large deviation in its evaluation results, affecting the accuracy and authenticity of the evaluation results. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide an evaluation method, device, computer device, computer-readable storage medium, and computer program product for electronic components based on principal component analysis and Topsis, which can improve the accuracy and authenticity of the evaluation results.

[0006] In the first aspect, the present application provides an evaluation method for electronic components based on principal component analysis and Topsis. The method includes:

[0007] Obtain the evaluation indicators of the evaluation object, and preprocess the evaluation indicators to obtain a preprocessing result; the preprocessing process includes standardization processing of the original data and data dimensionality reduction processing based on principal component analysis;

[0008] Based on the Topsis method, comprehensively evaluate the preprocessing result to obtain the distance between the evaluation object and the ideal solution; the ideal solution includes a positive ideal solution and a negative ideal solution;

[0009] Based on the distance between the evaluation object and the ideal solution, obtain the relative closeness degree between the evaluation object and the ideal solution, and evaluate the evaluation object based on the relative closeness degree.

[0010] In one embodiment, the preprocessing the evaluation indicators to obtain a preprocessing result includes:

[0011] Perform standardization processing on the evaluation indicators to obtain the data after standardization processing;

[0012] Based on the data after standardization processing, perform multi-dimensional performance parameter dimensionality reduction processing to obtain a preprocessing result.

[0013] In one embodiment, the performing multi-dimensional performance parameter dimensionality reduction processing to obtain a preprocessing result includes:

[0014] Calculate the correlation matrix of the evaluation indicators, and obtain the eigenvalues and eigenvectors of the correlation matrix;

[0015] Calculate the principal component contribution rate, and determine the number of principal components based on the cumulative contribution rate of the principal components;

[0016] Replace the evaluation indicators based on the principal component data set to obtain the preprocessing result.

[0017] In one embodiment, the comprehensively evaluating the preprocessing result based on the Topsis method to obtain the distance between the evaluation object and the ideal solution includes:

[0018] Obtain the positive ideal solution and the negative ideal solution based on the preprocessing result;

[0019] Through iterative calculation, obtain the distance between each evaluation object and the positive ideal solution, and obtain the distance between each evaluation object and the negative ideal solution.

[0020] In one embodiment, the obtaining the relative closeness degree between the evaluation object and the ideal solution based on the distance between the evaluation object and the ideal solution includes:

[0021] Obtain the sum of the positive ideal solution and the negative ideal solution of each evaluation object;

[0022] Calculate the quotient of the negative ideal solution of each evaluation object and the sum as the relative proximity of each evaluation object to the ideal solution.

[0023] In one embodiment, the evaluating the evaluation object based on the relative proximity includes:

[0024] Arrange the relative proximities of each evaluation object to the ideal solution in descending order;

[0025] Obtain the evaluation object with the highest relative proximity ranking as the optimal evaluation scheme.

[0026] In a second aspect, the present application also provides an electronic component evaluation device based on principal component analysis and Topsis. The device includes:

[0027] A preprocessing module for obtaining evaluation indicators of an evaluation object and preprocessing the evaluation indicators to obtain a preprocessing result; the preprocessing process includes standardizing the original data and dimensionality reduction processing based on principal component analysis;

[0028] A distance acquisition module for comprehensively evaluating the preprocessing result based on the Topsis method to obtain the distance between the evaluation object and the ideal solution; the ideal solution includes a positive ideal solution and a negative ideal solution;

[0029] An evaluation module for obtaining the relative proximity of the evaluation object to the ideal solution based on the distance between the evaluation object and the ideal solution, and evaluating the evaluation object based on the relative proximity.

[0030] In a third aspect, the present application also provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0031] Obtain evaluation indicators of an evaluation object and preprocess the evaluation indicators to obtain a preprocessing result; the preprocessing process includes standardizing the original data and dimensionality reduction processing based on principal component analysis;

[0032] Comprehensively evaluate the preprocessing result based on the Topsis method to obtain the distance between the evaluation object and the ideal solution; the ideal solution includes a positive ideal solution and a negative ideal solution;

[0033] Obtain the relative proximity of the evaluation object to the ideal solution based on the distance between the evaluation object and the ideal solution, and evaluate the evaluation object based on the relative proximity.

[0034] Fourthly, the present application also provides a computer-readable storage medium. On the computer-readable storage medium, there is a computer program which, when executed by a processor, implements the following steps:

[0035] Obtain the evaluation indicators of the evaluation object, and perform preprocessing on the evaluation indicators to obtain a preprocessing result; the preprocessing process includes standardization processing of the original data and data dimensionality reduction processing based on principal component analysis;

[0036] Based on the Topsis method, comprehensively evaluate the preprocessing result to obtain the distance between the evaluation object and the ideal solution; the ideal solution includes a positive ideal solution and a negative ideal solution;

[0037] Based on the distance between the evaluation object and the ideal solution, obtain the relative closeness degree between the evaluation object and the ideal solution, and evaluate the evaluation object based on the relative closeness degree.

[0038] Fifthly, the present application also provides a computer program product. The computer program product includes a computer program which, when executed by a processor, implements the following steps:

[0039] Obtain the evaluation indicators of the evaluation object, and perform preprocessing on the evaluation indicators to obtain a preprocessing result; the preprocessing process includes standardization processing of the original data and data dimensionality reduction processing based on principal component analysis;

[0040] Based on the Topsis method, comprehensively evaluate the preprocessing result to obtain the distance between the evaluation object and the ideal solution; the ideal solution includes a positive ideal solution and a negative ideal solution;

[0041] Based on the distance between the evaluation object and the ideal solution, obtain the relative closeness degree between the evaluation object and the ideal solution, and evaluate the evaluation object based on the relative closeness degree.

[0042] The above-mentioned evaluation method, device, computer device, storage medium and computer program product based on principal component analysis and Topsis obtain the evaluation indicators of the evaluation object, perform preprocessing on the evaluation indicators to obtain a preprocessing result, comprehensively evaluate the preprocessing result based on the Topsis method to obtain the distance between the evaluation object and the ideal solution, and finally, based on the distance between the evaluation object and the ideal solution, obtain the relative closeness degree between the evaluation object and the ideal solution, and evaluate the evaluation object based on the relative closeness degree. The evaluation process is realized by the distances from the positive ideal solution and the negative ideal solution, and the optimal and worst values in the evaluated object are introduced into the evaluation model, so that the evaluation result can fully reflect the overall characteristics of the evaluated object, make the evaluation result closer to the actual result, and improve the authenticity and accuracy of the evaluation result. Description of the Drawings

[0043] Figure 1 An application environment diagram of the evaluation method for electronic components based on principal component analysis and TOPSIS in an embodiment;

[0044] Figure 2 A schematic flowchart of the evaluation method for electronic components based on principal component analysis and TOPSIS in an embodiment;

[0045] Figure 3 A schematic overall flowchart of the evaluation steps for electronic components in an embodiment;

[0046] Figure 4 A structural block diagram of the evaluation device for electronic components based on principal component analysis and TOPSIS in an embodiment;

[0047] Figure 5 An internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0048] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] The evaluation method for electronic components based on principal component analysis and TOPSIS provided by the embodiments of the present application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the server 104 through a network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed in the cloud or other network servers.

[0050] Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0051] This application aims at the problem of comprehensively evaluating the environmental effect data of electronic components. Combining the characteristics of the environmental effect data of electronic components, a comprehensive evaluation model that combines principal component analysis and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS method) is proposed. It solves the deviation of the comprehensive evaluation results caused by the correlation between the performance parameters of electronic components during the comprehensive evaluation using the principal component analysis method alone. At the same time, the principal component analysis method is used to reduce the dimension of the performance parameters of electronic components, removing the correlation between attributes. On this basis, a comprehensive evaluation is carried out based on the TOPSIS method, eliminating the influence of overlapping information between attributes on the comprehensive evaluation results, making the evaluation results more objective and accurate, so as to better reflect the comprehensive performance level of electronic components.

[0052] In one embodiment, as Figure 2 shown, a method for evaluating electronic components based on principal component analysis and TOPSIS is provided. Taking the application of this method to Figure 1 the server as an example for illustration, it includes the following steps:

[0053] Step 202, obtain the evaluation indicators of the evaluation object, and perform preprocessing on the evaluation indicators to obtain the preprocessing result; the preprocessing process includes the normalization processing of the original data and the data dimension reduction processing based on principal component analysis.

[0054] Specifically, the number of evaluation objects is not less than one, the evaluation indicators of each evaluation object are the same and the number of evaluation indicators is not less than one. For example, there are n evaluation objects, and each evaluation object has s evaluation indicators. Then the original evaluation indicator matrix can be expressed as:

[0055]

[0056] The process of performing preprocessing on the evaluation indicators to obtain the preprocessing result includes the normalization processing of the original data and the data dimension reduction processing based on principal component analysis. Since different evaluation indicators represent different properties of the samples, not only the dimensions are different, but also the numerical values may vary greatly. Therefore, it is necessary to perform normalization processing on the data. After normalization processing, the mean value of each variable of the evaluation indicators is 0, and the variance is equal to 1. The data dimension reduction processing based on principal component analysis includes calculating the correlation matrix, calculating the eigenvalues and eigenvectors of the correlation matrix, calculating the principal component contribution rate and determining the number of principal components. Through the data dimension reduction processing based on principal component analysis, the original s evaluation indicators are reduced to m principal components.

[0057] Step 204, perform a comprehensive evaluation on the preprocessing result based on the TOPSIS method to obtain the distance between the evaluation object and the ideal solution; the ideal solution includes the positive ideal solution and the negative ideal solution.

[0058] Among them, the TOPSIS method must first determine the positive ideal solution and negative ideal solution of each index; the positive ideal solution is that each of its attribute values reaches the optimal value among all candidate solutions, that is, an ideal value (solution) among all candidate solutions is assumed; while the negative ideal solution is that each of its attribute values approaches or even reaches the worst value among all candidate solutions, that is, a worst value (solution) among all candidate solutions is assumed. Subsequently, the weighted distance between each solution and the ideal solution is calculated, and then the proximity level (referred to as the closeness degree) of each solution to the ideal solution is obtained, thereby determining whether each solution is better. If a certain solution is closest to the ideal solution and farthest from the negative ideal solution, it can be determined as the optimal solution.

[0059] Specifically, based on the TOPSIS method, a comprehensive evaluation is performed on the preprocessing result, and the distance between the evaluation object and the positive ideal solution, as well as the distance between the evaluation object and the negative ideal solution, are respectively obtained; so as to be able to obtain the relative proximity degree of the corresponding evaluation object to the ideal solution according to the distance between the evaluation object and the positive ideal solution and the distance between the evaluation object and the negative ideal solution.

[0060] Step 206, based on the distance between the evaluation object and the ideal solution, obtain the relative proximity degree of the evaluation object to the ideal solution, and evaluate the evaluation object based on the relative proximity degree.

[0061] Specifically, the relative proximity degree of the evaluation object to the ideal solution is obtained according to the distance between the evaluation object and the positive ideal solution and the distance between the evaluation object and the negative ideal solution, and the evaluation effect is judged through the relative proximity degree of the evaluation object to the ideal solution. If the relative proximity degree of the evaluation object to the ideal solution is higher, the evaluation result of the evaluation object is better; conversely, if the relative proximity degree of the evaluation object to the ideal solution is lower, the evaluation result of the evaluation object is worse.

[0062] In the above-mentioned evaluation method of electronic components based on principal component analysis and TOPSIS, the evaluation indexes of the evaluation object are obtained, the evaluation indexes are preprocessed to obtain the preprocessing result, a comprehensive evaluation is performed on the preprocessing result based on the TOPSIS method, the distance between the evaluation object and the ideal solution is obtained, and finally, based on the distance between the evaluation object and the ideal solution, the relative proximity degree of the evaluation object to the ideal solution is obtained, and the evaluation object is evaluated based on the relative proximity degree. The evaluation process is realized through the distances from the positive ideal solution and the negative ideal solution, and the optimal and worst values in the evaluated object are introduced into the evaluation model, so that the evaluation result can fully reflect the overall characteristics of the evaluated object, make the evaluation result closer to the actual result, and improve the authenticity and accuracy of the evaluation result.

[0063] In one embodiment, the preprocessing the evaluation indexes to obtain the preprocessing result includes:

[0064] Performing standardization processing on the evaluation indexes to obtain the data after standardization processing;

[0065] Perform dimensionality reduction processing on the multi-dimensional performance parameters based on the data after the standardization processing to obtain a preprocessing result.

[0066] Specifically, different indicators represent different properties of the samples. Not only are their dimensions different, but there may also be significant differences in the numerical values. Therefore, it is necessary to perform standardization processing on the data and on the evaluation indicators. The method for obtaining the data after the standardization processing can refer to the following formula:

[0067]

[0068]

[0069]

[0070] where is the data after standardization; is the arithmetic mean; S k is the sample standard deviation. After the standardization processing, the mean of each variable is 0 and the variance is equal to 1.

[0071] Perform dimensionality reduction processing on the multi-dimensional performance parameters based on the data after the standardization processing to obtain a preprocessing result. The data dimensionality reduction processing based on the principal component analysis includes calculating the correlation matrix, calculating the eigenvalues and eigenvectors of the correlation matrix, calculating the contribution rate of the principal components, and determining the number of principal components. Through the data dimensionality reduction processing based on the principal component analysis, the original s evaluation indicators are reduced to m principal components.

[0072] In this embodiment, the evaluation indicators are standardized to obtain the data after the standardization processing. Based on the data after the standardization processing, dimensionality reduction processing of the multi-dimensional performance parameters is performed to obtain a preprocessing result. Through the standardization processing and the dimensionality reduction processing of the principal component analysis, the preprocessing of the evaluation indicators is realized, and the preprocessing result is obtained, achieving the removal of the correlation between the performance parameters and eliminating the overlap of the information reflected by the performance parameters.

[0073] In one embodiment, the performing dimensionality reduction processing on the multi-dimensional performance parameters to obtain a preprocessing result includes:

[0074] Calculate the correlation matrix of the evaluation indicators and obtain the eigenvalues and eigenvectors of the correlation matrix;

[0075] Calculate the contribution rate of the principal components and determine the number of principal components based on the cumulative contribution rate of the principal components;

[0076] Replace the evaluation indicators based on the principal component data set to obtain the preprocessing result.

[0077] Specifically, calculate the correlation matrix of the evaluation indicators, and obtain the eigenvalues and eigenvectors of the correlation matrix. The correlation matrix R is as follows:

[0078]

[0079] Among them,

[0080] Obtain the eigenvectors of the correlation matrix, and find the p non - negative eigenvalues of the correlation matrix R, arranged in descending order as λ1≥λ2≥λ3≥…≥λp>0. The corresponding eigenvectors are:

[0081]

[0082] Calculate the contribution rate of the principal components, determine the number of principal components based on the cumulative contribution rate of the principal components. According to the eigenvectors, obtain the linear combination of the first m principal components as:

[0083] Y 1 =u 11 X 1 +u 12 X 2 +…+u 1p X p

[0084] Y 2 =u 21 X 1 +u 22 X 2 +…+u 2p X p

[0085] Y m =u m1 X 1 +u m2 X 2 +…+u mp X p

[0086] After that, calculate the contribution rate of the principal components, determine the number of principal components. a k is the variance contribution rate of the k - th principal component Y k ;

[0087] is the cumulative contribution rate of the principal components Y 1 ,Y 2 ,…,Y m The selection of the principal components is determined according to the contribution rate of the principal components. Usually, the number of principal components is selected so that the cumulative contribution rate reaches more than 85%, that is, when

[0088]

[0089] Through the dimensionality reduction implementation process of principal component analysis, the original parameter set that may have correlations is converted into a principal component set without correlations; in this embodiment, the original s performance parameters of electronic components are reduced to m principal components to obtain the preprocessing result. After the preprocessing process of the two parts, the final result of the preprocessing of the original data set is:

[0090]

[0091] In this embodiment, the correlation matrix of the evaluation indexes is calculated, and the eigenvalues and eigenvectors of the correlation matrix are obtained. The contribution rate of the principal components is calculated. The number of principal components is determined based on the cumulative contribution rate of the principal components. The evaluation indexes are replaced based on the principal component data set to obtain the preprocessing result, realizing the preprocessing of the evaluation indexes and obtaining the preprocessing result, removing the correlations between the performance parameters, and eliminating the overlapping of the information reflected by the performance parameters.

[0092] In one embodiment, the comprehensive evaluation of the preprocessing result based on the Topsis method to obtain the distance between the evaluation object and the ideal solution includes:

[0093] Obtaining the positive ideal solution and the negative ideal solution based on the preprocessing result;

[0094] Through iterative calculation, the distances between each evaluation object and the positive ideal solution are obtained, and the distances between each evaluation object and the negative ideal solution are obtained.

[0095] Specifically, the positive ideal solution and the negative ideal solution are obtained based on the preprocessing result, and the positive ideal solution and the negative ideal solution can be expressed as:

[0096] Z + =(Z max1 ,Z max2 ,...,Z maxn ),Z - =(Z min1 ,Z min2 ,...,Z minn )

[0097] where Z + is the positive ideal solution; Z - is the negative ideal solution.

[0098] Through iterative calculation, the distances between each evaluation object and the positive ideal solution are obtained, and the distances between each evaluation object and the negative ideal solution are obtained. The distance between the i-th evaluation object and the positive ideal solution and the distance between the i-th evaluation object and the negative ideal solution can be expressed as:

[0099]

[0100] Among them, S i + is the distance between the i-th evaluation object and the positive ideal solution; S i - is the distance between the i-th evaluation object and the negative ideal solution.

[0101] In this embodiment, based on the preprocessing results, the positive ideal solution and the negative ideal solution are obtained. Through iterative calculation, the distances between each evaluation object and the positive ideal solution are obtained, and the distances between each evaluation object and the negative ideal solution are obtained. Moreover, the degree of closeness between the evaluation object and the ideal solution can be further obtained. According to the degree of closeness between the evaluation object and the ideal solution, not only can the evaluation objects be reasonably sorted, but also the gaps between each evaluation scheme can be fully reflected, and the advantages and disadvantages of different evaluation objects can be quantitatively displayed, making the comprehensive performance evaluation results of the electronic components more real, intuitive and reliable.

[0102] In one embodiment, obtaining the relative closeness between the evaluation object and the ideal solution based on the distance between the evaluation object and the ideal solution includes:

[0103] Obtain the sum of the positive ideal solution and the negative ideal solution of each evaluation object;

[0104] Calculate the quotient of the negative ideal solution of each evaluation object and the sum as the relative closeness between each evaluation object and the ideal solution.

[0105] Specifically, the method for obtaining the relative closeness between each evaluation object and the ideal solution can refer to the following formula:

[0106] C i = S i - / (S i + + S i - ), i = 1, 2,..., n

[0107] Among them, C i is the relative closeness between the i-th evaluation object and the ideal solution. The relative closeness between the evaluation object and the ideal solution can indicate the degree of closeness between the evaluation unit and the ideal state. The higher the relative closeness between the evaluation object and the ideal solution, the better the evaluation result.

[0108] In this embodiment, the sum of the positive ideal solution and the negative ideal solution of each evaluation object is obtained, and the quotient of the negative ideal solution of each evaluation object and the sum is calculated as the relative closeness between each evaluation object and the ideal solution. By obtaining the relative closeness between each evaluation object and the ideal solution, the judgment of the evaluation effect of the evaluation object is realized, and the authenticity and accuracy of the evaluation result are improved.

[0109] In one embodiment, the evaluation of the evaluation object based on the relative proximity includes:

[0110] Arrange the relative proximity of each evaluation object to the ideal solution in descending order;

[0111] Obtain the evaluation object with the highest relative proximity ranking as the optimal evaluation scheme.

[0112] Specifically, arrange the relative proximity of each evaluation object to the ideal solution in descending order. The evaluation object with a higher ranking has a better evaluation effect. Obtain the evaluation object with the highest relative proximity ranking as the optimal evaluation scheme to achieve optimal evaluation.

[0113] In this embodiment, by arranging the relative proximity of each evaluation object to the ideal solution in descending order and obtaining the evaluation object with the highest relative proximity ranking as the optimal evaluation scheme, not only can the evaluation objects be reasonably ranked, but also the gaps between various evaluation schemes can be fully reflected, the advantages and disadvantages of different evaluation objects can be quantitatively displayed, making the comprehensive performance evaluation results of electronic components more real, intuitive, and reliable.

[0114] Figure 3 FIG. is a schematic diagram of the overall process of the evaluation steps for electronic components in one embodiment. As Figure 3 shown, in this embodiment, first, the principal component analysis method is used to perform dimensionality reduction processing on the parameter set composed of each performance parameter of the electronic component, aiming to remove the correlation between each performance parameter to eliminate the overlap of the information reflected by each performance parameter. On this basis, the Topsis model is used to comprehensively evaluate the environmental effect data of the electronic component. Compared with the simple comprehensive evaluation method based on Topsis, this application adds a process for dealing with the correlation between each performance parameter of the electronic component, eliminates the repeated influence of some index parameters on the evaluation result, and simplifies the calculation process of the comprehensive evaluation model based on Topsis. In the related research on environmental adaptability, some people have also proposed a comprehensive performance evaluation method for electronic devices based on principal component analysis. Compared with this method, this application combines the principal component analysis method and the method based on Topsis to make the evaluation result more interpretable.

[0115] In addition, in this application, the closeness can accurately measure the advantages and disadvantages of the overall performance of electronic components, not only can the evaluation objects be reasonably ranked, but also the gaps between various evaluation schemes can be fully reflected, the advantages and disadvantages of different evaluation objects can be quantitatively displayed, making the comprehensive performance evaluation results of electronic components more real, intuitive, and reliable.

[0116] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limitation, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0117] Based on the same inventive concept, an embodiment of the present application further provides an evaluation device for electronic components based on principal component analysis and TOPSIS for implementing the above-mentioned evaluation method for electronic components based on principal component analysis and TOPSIS. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the evaluation device for electronic components based on principal component analysis and TOPSIS provided below can refer to the limitations on the evaluation method for electronic components based on principal component analysis and TOPSIS in the above text, and will not be repeated here.

[0118] In one embodiment, as Figure 4 shown, an evaluation device for electronic components based on principal component analysis and TOPSIS is provided, including: a preprocessing module 401, a distance acquisition module 402, and an evaluation module 403, where:

[0119] The preprocessing module 401 is configured to obtain evaluation indexes of an evaluation object, and perform preprocessing on the evaluation indexes to obtain a preprocessing result; the preprocessing process includes standardization processing of original data and data dimensionality reduction processing based on principal component analysis;

[0120] The distance acquisition module 402 is configured to perform a comprehensive evaluation on the preprocessing result based on the TOPSIS method, and obtain the distance between the evaluation object and the ideal solution; the ideal solution includes a positive ideal solution and a negative ideal solution;

[0121] The evaluation module 403 is configured to obtain the relative closeness between the evaluation object and the ideal solution based on the distance between the evaluation object and the ideal solution, and evaluate the evaluation object based on the relative closeness.

[0122] In one embodiment, the preprocessing module 401 is specifically configured to: perform standardization processing on the evaluation indexes to obtain the data after standardization processing; based on the data after standardization processing, perform dimensionality reduction processing on multi-dimensional performance parameters to obtain a preprocessing result.

[0123] In one embodiment, the preprocessing module 401 is further configured to: calculate the correlation matrix of the evaluation metrics, and obtain the eigenvalues and eigenvectors of the correlation matrix; calculate the contribution rate of the principal components, and determine the number of principal components based on the cumulative contribution rate of the principal components; replace the evaluation metrics based on the principal component dataset to obtain the preprocessing result.

[0124] In one embodiment, the distance acquisition module 402 is specifically configured to: obtain the positive ideal solution and the negative ideal solution based on the preprocessing result; through iterative calculation, obtain the distance between each evaluation object and the positive ideal solution, and obtain the distance between each evaluation object and the negative ideal solution.

[0125] In one embodiment, the evaluation module 403 is specifically configured to: obtain the sum of the positive ideal solution and the negative ideal solution of each evaluation object; calculate the quotient of the negative ideal solution of each evaluation object and the sum as the relative closeness degree of each evaluation object to the ideal solution.

[0126] In one embodiment, the evaluation module 403 is further configured to: arrange the relative closeness degrees of each evaluation object to the ideal solution in descending order; obtain the evaluation object with the highest relative closeness degree ranking as the optimal evaluation scheme.

[0127] The above-mentioned electronic component evaluation device based on principal component analysis and Topsis obtains the evaluation metrics of the evaluation object, preprocesses the evaluation metrics to obtain the preprocessing result, comprehensively evaluates the preprocessing result based on the Topsis method, obtains the distance between the evaluation object and the ideal solution, and finally, based on the distance between the evaluation object and the ideal solution, obtains the relative closeness degree of the evaluation object to the ideal solution, evaluates the evaluation object based on the relative closeness degree, realizes the evaluation process through the distances from the positive ideal solution and the negative ideal solution, introduces the optimal and inferior values in the object to be evaluated into the evaluation model, enables the evaluation result to fully reflect the overall characteristics of the object to be evaluated, makes the evaluation result closer to the actual result, and improves the authenticity and accuracy of the evaluation result.

[0128] Each module in the above-mentioned electronic component evaluation device based on principal component analysis and Topsis can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in hardware form or be independent of it, or be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0129] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 5As shown in the figure. The computer device includes a processor, a memory, and a network interface connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements an evaluation method for electronic components based on principal component analysis and TOPSIS.

[0130] Those skilled in the art can understand that Figure 5 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0131] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0132] Obtain the evaluation indicators of the evaluation object, and perform preprocessing on the evaluation indicators to obtain a preprocessing result; the preprocessing process includes standardization processing of the original data and data dimensionality reduction processing based on principal component analysis;

[0133] Based on the TOPSIS method, comprehensively evaluate the preprocessing result to obtain the distance between the evaluation object and the ideal solution; the ideal solution includes a positive ideal solution and a negative ideal solution;

[0134] Based on the distance between the evaluation object and the ideal solution, obtain the relative closeness between the evaluation object and the ideal solution, and evaluate the evaluation object based on the relative closeness.

[0135] In one embodiment, when the processor executes the computer program, the following steps are also implemented: perform standardization processing on the evaluation indicators to obtain the data after standardization processing; based on the data after standardization processing, perform multi-dimensional performance parameter dimensionality reduction processing to obtain a preprocessing result.

[0136] In one embodiment, when the processor executes the computer program, the following steps are also implemented: calculate the correlation matrix of the evaluation indicators, and obtain the eigenvalues and eigenvectors of the correlation matrix; calculate the principal component contribution rate, and determine the number of principal components based on the cumulative contribution rate of the principal components; replace the evaluation indicators based on the principal component dataset to obtain a preprocessing result.

[0137] In one embodiment, when the processor executes the computer program, the following steps are further implemented: obtaining a positive ideal solution and a negative ideal solution based on the preprocessing result; obtaining the distance between each evaluation object and the positive ideal solution through iterative calculation, and obtaining the distance between each evaluation object and the negative ideal solution.

[0138] In one embodiment, when the processor executes the computer program, the following steps are further implemented: obtaining the sum of the positive ideal solution and the negative ideal solution of each evaluation object; calculating the quotient of the negative ideal solution of each evaluation object and the sum as the relative closeness degree of each evaluation object to the ideal solution.

[0139] In one embodiment, when the processor executes the computer program, the following steps are further implemented: arranging the relative closeness degrees of each evaluation object to the ideal solution in descending order; obtaining the evaluation object with the highest relative closeness degree as the optimal evaluation scheme.

[0140] The above computer device obtains the evaluation indexes of the evaluation object, preprocesses the evaluation indexes to obtain a preprocessing result, comprehensively evaluates the preprocessing result based on the Topsis method, obtains the distance between the evaluation object and the ideal solution, and finally obtains the relative closeness degree of the evaluation object to the ideal solution based on the distance between the evaluation object and the ideal solution, evaluates the evaluation object based on the relative closeness degree, realizes the evaluation process through the distances from the positive ideal solution and the negative ideal solution, introduces the optimal and inferior values in the evaluated object into the evaluation model, enables the evaluation result to fully reflect the overall characteristics of the evaluated object, makes the evaluation result closer to the actual result, and improves the authenticity and accuracy of the evaluation result.

[0141] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0142] Obtaining the evaluation indexes of the evaluation object, and preprocessing the evaluation indexes to obtain a preprocessing result; the preprocessing process includes standardization processing of the original data and data dimension reduction processing based on principal component analysis;

[0143] Comprehensively evaluating the preprocessing result based on the Topsis method to obtain the distance between the evaluation object and the ideal solution; the ideal solution includes a positive ideal solution and a negative ideal solution;

[0144] Based on the distance between the evaluation object and the ideal solution, obtaining the relative closeness degree of the evaluation object to the ideal solution, and evaluating the evaluation object based on the relative closeness degree.

[0145] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: performing a normalization process on the evaluation metrics to obtain the data after the normalization process; based on the data after the normalization process, performing a dimensionality reduction process on the multi-dimensional performance parameters to obtain a preprocessing result.

[0146] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: calculating the correlation matrix of the evaluation metrics, and obtaining the eigenvalues and eigenvectors of the correlation matrix; calculating the contribution rate of the principal components, and determining the number of principal components based on the cumulative contribution rate of the principal components; replacing the evaluation metrics based on the principal component dataset to obtain the preprocessing result.

[0147] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: obtaining the positive ideal solution and the negative ideal solution based on the preprocessing result; through iterative calculation, obtaining the distance between each evaluation object and the positive ideal solution, and obtaining the distance between each evaluation object and the negative ideal solution.

[0148] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: obtaining the sum of the positive ideal solution and the negative ideal solution of each evaluation object; calculating the quotient of the negative ideal solution of each evaluation object and the sum as the relative closeness degree of each evaluation object to the ideal solution.

[0149] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: arranging the relative closeness degrees of each evaluation object to the ideal solution in descending order; obtaining the evaluation object with the highest relative closeness degree ranking as the optimal evaluation scheme.

[0150] The above storage medium obtains the evaluation metrics of the evaluation object, preprocesses the evaluation metrics to obtain a preprocessing result, comprehensively evaluates the preprocessing result based on the TOPSIS method, obtains the distance between the evaluation object and the ideal solution, and finally, based on the distance between the evaluation object and the ideal solution, obtains the relative closeness degree of the evaluation object to the ideal solution, evaluates the evaluation object based on the relative closeness degree, realizes the evaluation process through the distances from the positive ideal solution and the negative ideal solution, introduces the optimal and inferior values in the evaluated object into the evaluation model, enables the evaluation result to fully reflect the overall characteristics of the evaluated object, makes the evaluation result closer to the actual result, and improves the authenticity and accuracy of the evaluation result.

[0151] In one embodiment, a computer program product is provided, including a computer program, which when executed by a processor, implements the following steps:

[0152] Obtaining the evaluation metrics of the evaluation object, and preprocessing the evaluation metrics to obtain a preprocessing result; the preprocessing process includes the normalization process of the original data and the dimensionality reduction process based on the principal component analysis;

[0153] Comprehensively evaluate the preprocessing result based on the TOPSIS method to obtain the distance between the evaluation object and the ideal solution; the ideal solution includes the positive ideal solution and the negative ideal solution;

[0154] Based on the distance between the evaluation object and the ideal solution, obtain the relative closeness degree between the evaluation object and the ideal solution, and evaluate the evaluation object based on the relative closeness degree.

[0155] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: perform standardization processing on the evaluation index to obtain the data after standardization processing; based on the data after standardization processing, perform dimensionality reduction processing on multi-dimensional performance parameters to obtain the preprocessing result.

[0156] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: calculate the correlation matrix of the evaluation index, and obtain the eigenvalues and eigenvectors of the correlation matrix; calculate the contribution rate of the principal component, and determine the number of principal components based on the cumulative contribution rate of the principal components; replace the evaluation index based on the principal component dataset to obtain the preprocessing result.

[0157] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: obtain the positive ideal solution and the negative ideal solution based on the preprocessing result; through iterative calculation, obtain the distance between each evaluation object and the positive ideal solution, and obtain the distance between each evaluation object and the negative ideal solution.

[0158] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: obtain the sum of the positive ideal solution and the negative ideal solution of each evaluation object; calculate the quotient of the negative ideal solution of each evaluation object and the sum as the relative closeness degree between each evaluation object and the ideal solution.

[0159] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: arrange the relative closeness degrees between each evaluation object and the ideal solution in descending order; obtain the evaluation object with the highest relative closeness degree ranking as the optimal evaluation scheme.

[0160] The above computer program product obtains the evaluation indexes of the evaluation object, preprocesses the evaluation indexes to obtain a preprocessing result, comprehensively evaluates the preprocessing result based on the Topsis method, obtains the distance between the evaluation object and the ideal solution, and finally, based on the distance between the evaluation object and the ideal solution, obtains the relative closeness degree between the evaluation object and the ideal solution, and evaluates the evaluation object based on the relative closeness degree. The evaluation process is realized by the distances from the positive ideal solution and the negative ideal solution. The optimal and inferior values in the object to be evaluated are introduced into the evaluation model, so that the evaluation result can fully reflect the overall characteristics of the object to be evaluated, make the evaluation result closer to the actual result, and improve the authenticity and accuracy of the evaluation result.

[0161] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties.

[0162] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memories can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0163] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0164] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. An evaluation method for electronic components based on principal component analysis and TOPSIS, characterized in that, the method includes: Obtain the evaluation indicators of the evaluation object, and perform preprocessing on the evaluation indicators to obtain the preprocessing result; the preprocessing process includes standardization processing of the original data and data dimensionality reduction processing based on principal component analysis; among them, obtaining the evaluation indicators of the evaluation object includes: obtaining the electronic component environment effect data set; extracting relevant parameters characterizing the performance of the electronic components to form a data set; selecting evaluation indicators from the data set; Based on the TOPSIS method, comprehensively evaluate the preprocessing result to obtain the distance between the evaluation object and the ideal solution; the ideal solution includes the positive ideal solution and the negative ideal solution; Based on the distance between the evaluation object and the ideal solution, obtain the relative closeness between the evaluation object and the ideal solution, and evaluate the evaluation object based on the relative closeness.

2. The method according to claim 1, characterized in that, the obtaining the preprocessing result by preprocessing the evaluation indicators includes: Perform standardization processing on the evaluation indicators to obtain the data after standardization processing; Based on the data after the standardization processing, perform multi-dimensional performance parameter dimensionality reduction processing to obtain the preprocessing result.

3. The method according to claim 2, characterized in that, the performing multi-dimensional performance parameter dimensionality reduction processing to obtain the preprocessing result includes: Calculate the correlation matrix of the evaluation indicators, and obtain the eigenvalues and eigenvectors of the correlation matrix; Calculate the principal component contribution rate, and determine the number of principal components based on the cumulative contribution rate of the principal components; Replace the evaluation indicators based on the principal component data set to obtain the preprocessing result.

4. The method according to claim 1, characterized in that, the comprehensively evaluating the preprocessing result based on the TOPSIS method to obtain the distance between the evaluation object and the ideal solution includes: Obtain the positive ideal solution and the negative ideal solution based on the preprocessing result; Through iterative calculation, obtain the distance between each evaluation object and the positive ideal solution, and obtain the distance between each evaluation object and the negative ideal solution.

5. The method according to claim 1, characterized in that, the obtaining the relative closeness between the evaluation object and the ideal solution based on the distance between the evaluation object and the ideal solution includes: Obtain the sum of the positive ideal solution and the negative ideal solution of each evaluation object; Calculate the quotient of the negative ideal solution of each evaluation object and the sum as the relative closeness between each evaluation object and the ideal solution.

6. The method according to claim 1, characterized in that, the evaluating the evaluation object based on the relative closeness includes: Arrange the relative closeness between each evaluation object and the ideal solution in descending order; Obtain the evaluation object with the highest relative closeness ranking as the optimal evaluation scheme.

7. An evaluation device for electronic components based on principal component analysis and TOPSIS, characterized in that, the device includes: A preprocessing module, configured to obtain evaluation indexes of an evaluation object, and perform preprocessing on the evaluation indexes to obtain a preprocessing result; the preprocessing process includes standardization processing of original data and data dimension reduction processing based on principal component analysis; wherein, obtaining the evaluation indexes of the evaluation object includes: obtaining an electronic component environmental effect data set; extracting relevant parameters characterizing the performance of the electronic component to form a data set; and selecting evaluation indexes from the data set. A distance obtaining module, configured to comprehensively evaluate the preprocessing result based on the Topsis method, and obtain the distance between the evaluation object and the ideal solution; the ideal solution includes a positive ideal solution and a negative ideal solution. An evaluation module, configured to obtain the relative closeness degree between the evaluation object and the ideal solution based on the distance between the evaluation object and the ideal solution, and evaluate the evaluation object based on the relative closeness degree.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, wherein, when the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, having a computer program stored thereon, wherein, when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, wherein, when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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