Component testing method and device, electronic equipment, storage medium and program

Abstract

The invention discloses a component testing method and device, electronic equipment, a storage medium and a program. Acquiring a plurality of test data and respective corresponding standard data; the test data is obtained by testing corresponding components in the same actual test environment, and the standard data is obtained by testing components in a preset standard test environment; calculating an average value of the test data to obtain a test average value, and calculating an average value of the standard data to obtain a standard average value; the multiple pieces of test data, the multiple pieces of standard data, the test average value and the standard average value are input into a parameter prediction function for calculation, regression parameters are obtained, the regression parameters are parameters of linear regression curves of the multiple pieces of test data in the same actual test environment, and the regression parameters are curves used for fitting the test data with the standard data as the target; and inputting the test data into the linear regression curve with the regression parameters to obtain corresponding target test data. Therefore, the method can obtain the target test data close to the standard data.

Description

Technical Field

[0001] The present application belongs to the technical field of quality inspection, and in particular, relates to a method, device, electronic equipment, storage medium and program for component testing. Background Art

[0002] In actual production, during the factory's testing of electronic components, especially the testing of diodes, such as the testing of the reverse recovery time of recovery diodes, different test equipment needs to set up different test environments, such as setting up different connection lines, or setting up different gold finger lengths, etc. Therefore, the test data of the same recovery diode after being tested by different test equipment will be different. Even when the same equipment performs multiple tests on the same recovery diode, it cannot be guaranteed that the test environment is completely consistent in multiple tests. Therefore, the test data obtained for each test will also be different.

[0003] More importantly, in actual production, the test data obtained by the factory using different equipment to test the same recovery diode, or even the test data obtained by the same equipment multiple times testing the same recovery diode, are often very different from the standard data obtained by the laboratory in a standard test environment. Summary of the invention

[0004] The embodiments of the present application provide a method, device, electronic device, storage medium and program for testing a component, which can obtain target test data close to standard data by using a linear regression curve.

[0005] In a first aspect, an embodiment of the present application provides a method for testing a component, the method comprising:

[0006] Acquire multiple test data of component testing, and standard data corresponding to the test data; the test data is obtained by testing the corresponding components in the same preset actual test environment, and the standard data corresponding to the test data is obtained by testing the components in a preset standard test environment;

[0007] Calculate the average value of multiple test data to obtain the test average value, and calculate the average value of the standard data corresponding to each test data to obtain the standard average value;

[0008] Input multiple test data, multiple standard data, test average values ​​and standard average values ​​into a preset parameter prediction function for calculation to obtain regression parameters, where the regression parameters are parameters of a linear regression curve of multiple test data corresponding to the same actual test environment, and the linear regression curve is a curve used to fit each test data with the standard data as the target;

[0009] Each test data is input into a linear regression curve with regression parameters for calculation to obtain the corresponding target test data.

[0010] Furthermore, the preset parameter prediction function includes a first sub-parameter function and a second sub-parameter function; a plurality of test data, a plurality of standard data, a test average value and a standard average value are input into the preset parameter prediction function for calculation to obtain regression parameters, including:

[0011] Input multiple test data, multiple standard data, test average value and standard average value into a preset first sub-parameter function for calculation to obtain a first sub-parameter, where the first sub-parameter is the slope of a linear regression curve of multiple test data under the same actual test environment;

[0012] The first sub-parameter, the test average value and the standard average value are input into the preset second subsystem function for calculation to obtain the second sub-parameter, which is the intercept of the linear regression curve of multiple test data under the same actual test environment.

[0013] Furthermore, a plurality of test data, a plurality of standard data, a test average value and a standard average value are input into a preset first sub-parameter function for calculation to obtain a first sub-parameter, including:

[0014] Determine a first deviation between each test data and the test mean, and determine a second deviation between the corresponding standard data and the standard mean;

[0015] summing the products of each first deviation and the corresponding second deviation to obtain a first summation result;

[0016] The squares of the first deviations are summed to obtain a second summation result;

[0017] A ratio of the first summation result to the second summation result is determined as a first sub-parameter.

[0018] Furthermore, the first sub-parameter, the test average value and the standard average value are input into a preset second sub-system function for calculation to obtain the second sub-parameter, including:

[0019] Calculate the product of the first subparameter and the test mean;

[0020] The difference between the standard mean value and the product is determined as the second sub-parameter.

[0021] Furthermore, after obtaining the corresponding target test data, it also includes:

[0022] Calculate the sum of squares of the differences between each standard data and the standard mean value to obtain the overall sum of squares;

[0023] Calculate the sum of squares of the differences between each standard data and the corresponding target test data to obtain the residual sum of squares;

[0024] Input the overall sum of squares and the residual sum of squares into the fitting function to calculate the fitting degree;

[0025] When the degree of fit is greater than or equal to a preset constraint threshold, it is determined that the target test data determined by the linear regression function with regression parameters is valid.

[0026] Furthermore, after obtaining the corresponding target test data, it also includes:

[0027] Calculate the sum of squares of the differences between each standard data and the standard mean value to obtain the overall sum of squares;

[0028] Calculate the sum of squares of the difference between the standard mean and each target test data to obtain the regression sum of squares;

[0029] Calculate the ratio of the regression sum of squares to the overall sum of squares to get the goodness of fit;

[0030] When the degree of fit is greater than or equal to a preset constraint threshold, it is determined that the target test data determined by the linear regression function with regression parameters is valid.

[0031] Furthermore, after obtaining the corresponding compensation data, the method further includes:

[0032] Determine the difference between each target test data and the corresponding standard data;

[0033] When the difference is less than a preset difference threshold, it is determined that the target test data determined by the linear regression function with regression parameters is valid.

[0034] In a second aspect, an embodiment of the present application provides a device for testing components, the device comprising:

[0035] An acquisition module is used to acquire multiple test data of component testing and standard data corresponding to the test data; the test data is obtained by testing the corresponding components in the same preset actual test environment, and the standard data corresponding to the test data is obtained by testing the components in a preset standard test environment;

[0036] An average value calculation module is used to calculate the average values ​​of multiple test data to obtain a test average value, and calculate the average value of standard data corresponding to each test data to obtain a standard average value;

[0037] A parameter calculation module is used to input multiple test data, multiple standard data, test average values ​​and standard average values ​​into a preset parameter prediction function for calculation to obtain regression parameters. The regression parameters are parameters of a linear regression curve corresponding to multiple test data under the same actual test environment. The linear regression curve is a curve used to fit each test data with the standard data as the target.

[0038] The target test data determination module is used to input each test data into a linear regression curve with regression parameters for calculation to obtain the corresponding target test data.

[0039] In a third aspect, an embodiment of the present application provides an electronic device, the device comprising:

[0040] a processor and a memory storing computer program instructions;

[0041] When a processor executes computer program instructions, a component testing method as described in any of the above items is implemented.

[0042] In a fourth aspect, an embodiment of the present application provides a computer storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, a component testing method as described in any of the above items is implemented.

[0043] In a fifth aspect, an embodiment of the present application provides a method for causing the electronic device to perform any of the above component tests when instructions in a computer program product are executed by a processor of the electronic device.

[0044] The component testing method, device, electronic device, storage medium and program of the embodiments of the present application are based on various test data obtained by testing various components in an actual test environment, and based on various standard data obtained by testing various components in a standard test environment. After calculating the test average value of each test data and the standard average value of each standard data, in order to fit each test data with the corresponding standard data as the target, each test data, each standard data, the test average value and the standard average value can be input into a parameter prediction function, so that the parameters of the linear regression curve used to fit the test data, that is, the regression parameters, can be predicted. Furthermore, when the linear regression curve is used to fit each test data, target test data close to the standard data can be obtained, making the target test data more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0046] Figure 1 It is a flowchart of a component testing method provided in an embodiment of the present application;

[0047] Figure 2 It is a schematic diagram of the fitting of the linear regression curve provided in the embodiment of the present application;

[0048] Figure 3It is a screening comparison diagram of low yield batches provided in the embodiment of the present application;

[0049] Figure 4 It is a screening comparison diagram of a normal yield batch provided in an embodiment of the present application;

[0050] Figure 5 It is a structural schematic diagram of a device for testing components provided in an embodiment of the present application;

[0051] Figure 6 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0053] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0054] As described in the background technology section, the related component testing technology is still difficult to meet the needs of actual work.

[0055] In the process of implementing this application, the applicant discovered that in actual work, in order to verify whether the components are qualified, the factory needs to test the components. When testing the components in the factory, different testing equipment will be used. On the one hand, the testing equipment needs to use conductive wires and gold fingers to connect to the components under test, and the sizes and characteristics of the conductive wires and gold fingers of different testing equipment cannot be guaranteed to be completely consistent, which leads to the factory using different testing equipment to test the same component, and the test data obtained are very different.

[0056] On the other hand, when testing components, the test equipment needs to set up a test environment. Since the hardware of different test equipment is different, it is impossible to set up exactly the same test environment. After using different test equipment to test the same component, the difference between the test data is further exacerbated.

[0057] In addition, even if the same test equipment is used to test the same component multiple times, the test environment is different for each test, so the test data obtained for each test is also different. Therefore, when the same test equipment is used to test different components, the test data obtained for different components cannot accurately represent the condition of the component, let alone the overall yield rate of multiple different components.

[0058] On the other hand, each test equipment is different from the test equipment in the laboratory. Therefore, after the standard test environment is set up in the laboratory, the standard data obtained by testing the same component in the standard test environment is often very different from the test data tested by various equipment in the factory, and the two cannot match.

[0059] Based on this, factories find it difficult to obtain accurate test data close to standard data when using various test equipment, and are thus unable to accurately determine whether components are qualified.

[0060] In order to solve the problems of the prior art, the embodiments of the present application provide a method, device, electronic device, storage medium and program for testing components.

[0061] The component testing method provided in the embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0062] refer to Figure 1 A component testing method according to an embodiment of the present application comprises the following steps:

[0063] Step S101, obtaining multiple test data of component testing, and standard data corresponding to the test data; the test data is obtained by testing the corresponding components in the same preset actual test environment, and the standard data corresponding to the test data is obtained by testing the components in a preset standard test environment.

[0064] In this embodiment, an actual test environment is set up using a test device. Under the actual test environment, multiple preset components are tested separately using the same test device to obtain test data corresponding to each component. Each component is tested in a standard test environment to obtain standard test data corresponding to each component.

[0065] The actual test environment includes the values ​​of the test environment set by the test equipment, and the specifications of the test auxiliary materials such as conductive wires and gold fingers used when testing each component.

[0066] Based on this, the test data and corresponding standard data of each earphone cover under the same test equipment and the same test environment can be obtained, and then the following data compensation can be performed on each test data on this basis to make each test data close to the corresponding standard data after compensation.

[0067] In a specific example, a diode is taken as a specific example of a component, and a reverse recovery time (TRR) test is taken as a specific example of a component test.

[0068] Based on this, after setting the values ​​of the actual test environment for the test equipment, the same test equipment and the same auxiliary materials are used to perform TRR tests on multiple diodes, and the test data of each diode as shown in Table 1 below are obtained.

[0069] Furthermore, in a standard test environment, each of the above diodes was subjected to a TRR test to obtain standard data of each diode as shown in Table 1 below.

[0070] Table 1. Test data and standard data table.

[0071] Diode number Test Data Standard data 1 131.50 132.69 2 139.00 152.50 3 138.33 151.44 4 138.00 150.60 5 130.00 139.30 6 131.33 128.00 7 125.00 124.42 8 131.00 131.08 9 133.00 130.83 10 122.00 123.33

[0072] It can be seen that in Table 1, for each diode, its test data is greatly different from the standard data.

[0073] Among them, taking diodes No. 1, 2, 3 and 4 as examples, the difference between the test data and the standard data of diode No. 1 is 1.19, while the difference between the test data and the standard data of diode No. 2 is 13.5, the difference between the test data and the standard data of diode No. 3 is 13.11, and the difference between the test data and the standard data of diode No. 4 is 12.6.

[0074] Based on this, when the preset difference threshold is 5% of the standard data, the differences of the above diodes No. 2, No. 3 and No. 4 all exceed 5%, and it can be considered that each of the above test data has a huge difference from the corresponding standard data.

[0075] At the same time, it can be seen that even when the same test equipment is used and the same test environment is set, after the TRR test is performed on each diode, the gap between the test data of each diode and the standard data is not the same.

[0076] Step S102: Calculate the average values ​​of the plurality of test data to obtain a test average value, and calculate the average value of the standard data corresponding to each test data to obtain a standard average value.

[0077] In this embodiment, based on the test data and standard data of each diode obtained in the above steps, the respective average values ​​can be further calculated, and the calculated average values ​​are used as the basis for performing the following compensation operation.

[0078] Specifically, each test data may be accumulated and then an average value may be obtained, and the obtained average value may be used as the test average value.

[0079] Furthermore, each standard data is accumulated to obtain an average value, and the obtained average value is used as the standard average value.

[0080] In a specific example, taking the above Table 1 as an example, after accumulating each test data, the test average value is obtained. It is 131.92.

[0081] Furthermore, after accumulating the various standard data, the standard average value is obtained It is 136.42.

[0082] Step S103, input the multiple test data, multiple standard data, the test average value and the standard average value into a preset parameter prediction function for calculation to obtain regression parameters, wherein the regression parameters are parameters of a linear regression curve of the multiple test data under the same actual test environment, and the linear regression curve is a curve used to fit each test data with the standard data as the target.

[0083] In this embodiment, based on the various test data, various standard data, test average values ​​and standard average values ​​obtained in the aforementioned steps, each data can be input into a parameter prediction function, and the regression parameters of the linear regression curve can be calculated using the parameter prediction function, thereby obtaining a linear regression curve corresponding to the same test equipment and the same actual test environment and having the regression parameters.

[0084] Specifically, the regression parameter includes a first sub-parameter and a second sub-parameter, and the parameter prediction function specifically includes a first sub-parameter function and a second sub-parameter function.

[0085] The first sub-parameter specifically represents the slope of the linear regression curve, and the second sub-parameter specifically represents the intercept of the linear regression curve.

[0086] Based on this, after obtaining the first sub-parameter and the second sub-parameter, a corresponding linear regression curve can be constructed according to the slope and the intercept.

[0087] In this embodiment, the first sub-parameter may be calculated using the first parameter sub-function, and the second sub-parameter may be calculated using the second parameter sub-function.

[0088] Among them, when calculating the first sub-parameter, the test data obtained above, the standard data corresponding to each test data, the test average value and the standard average value can be used for calculation, and each data is input into the first sub-parameter function to calculate the first sub-parameter.

[0089] Specifically, for each diode, the difference between the corresponding test data and the test average value is calculated and used as the first deviation, and the difference between the corresponding standard data and the standard average value is calculated and used as the second deviation.

[0090] Further, the first deviation and the second deviation are multiplied to obtain a product, and the products of the first deviation and the second deviation corresponding to each diode are summed to obtain a first summation result.

[0091] Furthermore, the first deviations of the respective diodes are squared and then accumulated to obtain a second summation result.

[0092] Further, a ratio of the first summation result to the second summation result is used as the first sub-parameter.

[0093] In a specific example, the first sub-parameter may be calculated according to the following formula (1):

[0094]

[0095] Where b represents the first sub-parameter, n represents the number of diodes tested by TRR, i represents the i-th diode, and x i represents the test data of the i-th diode, y i Represents the standard data of the i-th diode.

[0096] In some other embodiments, the first sub-parameter may also be calculated using the following formula (2):

[0097]

[0098] Based on this, in the specific example of Table 1, the value of the first sub-parameter b calculated according to the above formula (1) or formula (2) is 1.813741.

[0099] Furthermore, when calculating the second sub-parameter, the first sub-parameter, the test average value and the standard average value obtained above can be used for calculation, and each data is input into the second sub-parameter function to calculate the second sub-parameter.

[0100] Specifically, based on the first sub-parameter determined above, since the first sub-parameter represents the slope of the linear regression curve and the second sub-parameter represents the intercept of the linear regression curve, there is a fixed calculation relationship between the slope and the intercept. Therefore, the second sub-parameter, the test average value and the standard average value can be used to calculate the second sub-parameter.

[0101] The product between the first sub-parameter and the test average value may be calculated, and the difference between the standard average value and the product may be used as the second sub-parameter.

[0102] In a specific example, the second sub-parameter may be calculated according to the following formula (3):

[0103]

[0104] Wherein, a represents the second sub-parameter.

[0105] Based on this, in the specific example of Table 1, the value of the second sub-parameter a calculated according to the above formula (3) is -102.842.

[0106] Furthermore, based on the first sub-parameter and the second sub-parameter obtained above, a linear regression curve for each test data may be constructed.

[0107] The linear regression curve can be specifically expressed as the following formula (4):

[0108]

[0109] Among them, x represents the test data, It represents the target test data obtained by fitting any test data with a linear regression curve.

[0110] In the specific example of Table 1, the linear regression curve can be specifically expressed as the following formula (5):

[0111]

[0112] Among them, Figure 2 As shown, Figure 2 The relationship between each test data and the corresponding standard data in the specific example of Table 1 is shown, wherein the x-axis represents the test data, the y-axis represents the standard data, and each data point in the coordinate axis represents the intersection of each test data and the corresponding standard data. Figure 2 The linear regression curve in the formula (5) shows that It can be seen that the linear regression curve specifically represents the calculation relationship between each test data and the corresponding standard data.

[0113] It can be seen that the linear regression curve of this embodiment corresponds to the actual test environment of the test device, so that the linear regression curve can be used to fit various test data and obtain the target test data corresponding to the same test device in the same test environment.

[0114] Step S104: input each test data into a linear regression curve having the regression parameters for calculation to obtain corresponding target test data.

[0115] In this embodiment, based on the linear regression curve obtained above, each test data is fitted using the linear regression curve, and when fitting each test data, the corresponding standard data is used as the target, so that the target test data corresponding to each test data is obtained after fitting.

[0116] Specifically, after the linear regression curve is obtained, each test data can be input into the formula of the linear regression curve respectively, and the target test data can be calculated.

[0117] Based on this, a preset difference threshold can be used to determine whether each target test data is valid.

[0118] Specifically, the difference between each target test data and the corresponding standard data can be calculated, and it can be determined whether the difference is less than the difference threshold. When the difference is less than the difference threshold, it is considered that the difference between the target test data and the standard data is small, and the target test data is valid. When the difference is greater than or equal to the difference threshold, it is considered that the target test data is invalid.

[0119] The difference threshold may be, for example, 5% of the corresponding standard data.

[0120] In the specific example of Table 1, each test data can be input into the formula (5) And the target test data shown in Table 2 below is obtained:

[0121] Table 2. Target test data table.

[0122] Diode number Test Data Standard data Target test data 1 131.50 132.69 134.76 2 139.00 152.50 149.27 3 138.33 151.44 145.64 4 138.00 150.60 147.45 5 130.00 139.30 132.94 6 131.33 128.00 131.13 7 125.00 124.42 123.88 8 131.00 131.08 134.76 9 133.00 130.83 134.76 10 122.00 123.33 121.15

[0123] Similarly, taking diodes 1, 2, 3 and 4 as examples, the difference between the test data and the standard data of diode No. 1 is 2.07, while the difference between the test data and the standard data of diode No. 2 is 3.23, the difference between the test data and the standard data of diode No. 3 is 5.8, and the difference between the test data and the standard data of diode No. 4 is 3.15.

[0124] Based on this, when the difference threshold is 5% of the standard data, the differences between the above-mentioned diodes No. 1, No. 2, No. 3 and No. 4 are all less than 5%. It can be considered that there are small differences between the test data of diodes No. 1, No. 2, No. 3 and No. 4 and the corresponding standard data, and each target test data is valid.

[0125] It can be seen that the component testing method of the present embodiment is based on various test data obtained by testing various components in an actual test environment, and based on various standard data obtained by testing various components in a standard test environment. After calculating the test average value of each test data and the standard average value of each standard data, in order to fit each test data with the corresponding standard data as the target, each test data, each standard data, the test average value and the standard average value can be input into a parameter prediction function, so that the parameters of the linear regression curve used to fit the test data, that is, the regression parameters, can be predicted. Furthermore, when the linear regression curve is used to fit each test data, target test data close to the standard data can be obtained.

[0126] In a specific example, to demonstrate the beneficial effects of the present solution, multiple batches of diodes with known yields may be selected for verification, wherein each batch contains multiple diode products.

[0127] Specifically, a batch of diodes with normal yield is selected, and a batch of diodes with low yield is selected, wherein the average TRR of each diode in a batch is 133 as an evaluation standard for the normal yield. When the average TRR is less than 133, the diodes in this batch have a normal yield, and when the average TRR is greater than or equal to 133, the diodes in this batch have a low yield.

[0128] like Figure 3 As shown, Figure 3 A schematic diagram of the screening comparison of low yield batches is shown.

[0129] in, Figure 3 The histogram in shows the frequency of occurrence of different TRRs of the diodes in the batch. For example, the diode with a TRR of 132 appears 15 times, that is, there are 15 diodes with a TRR of 132 in the batch.

[0130] Furthermore, by using the above-mentioned component testing method, the test data of each diode is fitted with the corresponding standard data as the target to obtain the target test data corresponding to each diode. Figure 3The curve in represents the distribution probability of the target test data. It can be seen that in the batches with low yield rate, the distribution probability of the target test data is consistent with the histogram. The target test data can be used to accurately screen out batches with an average TRR greater than or equal to 133, that is, batches with low yield rate can be screened out.

[0131] Furthermore, if Figure 4 As shown, Figure 4 A screening comparison diagram of a normal yield batch is shown.

[0132] in, Figure 4 The histogram in shows the frequency of occurrence of different TRRs of diodes in the batch. For example, a diode with a TRR of 120 appears about 22 times, that is, there are 22 diodes with a TRR of 120 in the batch.

[0133] Furthermore, by using the above-mentioned component testing method, the test data of each diode is fitted with the corresponding standard data as the target to obtain the target test data corresponding to each diode. Figure 4 The curve in represents the distribution probability of the target test data. It can be seen that in the batches with normal yield rate, the distribution probability of the target test data is consistent with the histogram. The target test data can be used to accurately screen out batches with an average TRR less than 133, that is, batches with normal yield rate can be screened out.

[0134] In another embodiment of the present application, after obtaining the above-mentioned target test data, preset constraint conditions may be used to determine whether the calculated target test data and / or the obtained linear regression curve are valid.

[0135] In this embodiment, the overall sum of squares and the residual sum of squares may be used to construct a fitness function, and the fitness function may be used to calculate the fitness of the linear regression curve.

[0136] Specifically, the difference between each standard data and the standard average value can be calculated and then squared, and the sum of the squares of each difference can be calculated to obtain the overall sum of squares TSS.

[0137] Specifically, the total sum of squares TSS can be calculated according to the following formula (6):

[0138]

[0139] Furthermore, the difference between each standard data and the corresponding target test data may be calculated and then squared, and the sum of the squares of each difference may be calculated to obtain the residual sum of squares RSS.

[0140] Specifically, the residual sum of squares RSS can be calculated according to the following formula (7):

[0141]

[0142] Based on this, the fitness function shown in the following formula (8) can be constructed, and the total sum of squares TSS and residual sum of squares RSS obtained above are input into the fitness function to calculate the fitness:

[0143]

[0144] Among them, R 2 Indicates the goodness of fit.

[0145] Based on this, the constraint condition can be set to be that the fitness is greater than or equal to the constraint threshold.

[0146] Furthermore, when the degree of fit is greater than or equal to the constraint threshold, the constraint condition is satisfied, and the obtained linear regression function with the above regression parameters is considered valid, and / or the target test data calculated with the linear regression function with the above regression parameters is valid.

[0147] Furthermore, when the degree of fit is less than the constraint threshold, the constraint condition is not satisfied, and the obtained linear regression function with the above regression parameters is considered invalid, and the target test data calculated with the linear regression function with the above regression parameters is invalid.

[0148] In the specific example of Table 2, the constraint threshold may be set to 0.8.

[0149] Based on this, TSS=1153.45 is calculated according to the above formula (6), and RSS=230 is calculated according to the above formula (7).

[0150] Based on this, R is calculated according to the above formula (8): 2 =0.8.

[0151] It can be seen that the fit satisfies the constraint conditions, and it is considered that the obtained linear regression function formula (5) with the above regression parameters is valid, and / or the target test data calculated by the linear regression function formula (5) with the above regression parameters is valid.

[0152] It can be seen that the fitting function constructed by using the overall sum of squares and the residual sum of squares in this embodiment can characterize the degree of fit between each target test data and the corresponding labeled data. Through the constraint threshold, when the degree of fit does not meet expectations, poor target test data can be determined, thereby ensuring the accuracy of the target test data.

[0153] In another embodiment of the present application, after obtaining the above-mentioned target test data, the overall sum of squares and the regression sum of squares can also be used to calculate the degree of fit, so as to use the constraint conditions corresponding to the degree of fit to determine whether the calculated target test data and / or the obtained linear regression curve are valid.

[0154] Specifically, the difference between each standard data and the standard average value can be calculated and then squared, and the sum of the squares of each difference can be calculated to obtain the overall sum of squares TSS.

[0155] Furthermore, the difference between each target test data and the standard average value may be calculated and then squared, and the sum of the squares of each difference may be calculated to obtain the regression sum of squares ESS.

[0156] Specifically, the residual sum of squares RSS can be calculated according to the following formula (9):

[0157]

[0158] Based on this, a goodness of fit function as shown in the following formula (10) can be constructed, and the above-obtained total sum of squares TSS and total sum of squares ESS are input into the goodness of fit function to calculate the goodness of fit:

[0159]

[0160] Based on this, the constraint condition can still be set to be that the fitness is greater than or equal to the constraint threshold.

[0161] Furthermore, when the degree of fit is greater than or equal to the constraint threshold, the constraint condition is satisfied, and the obtained linear regression function with the above regression parameters is considered valid, and / or the target test data calculated with the linear regression function with the above regression parameters is valid.

[0162] Furthermore, when the degree of fit is less than the constraint threshold, the constraint condition is not satisfied, and the obtained linear regression function with the above regression parameters is considered invalid, and the target test data calculated with the linear regression function with the above regression parameters is invalid.

[0163] In the specific example of Table 2, the constraint threshold may be set to 0.8.

[0164] Based on this, according to the formula (6) of the above embodiment, TSS=1153.45 is calculated, and according to the above formula (9), ESS=922.76 is calculated.

[0165] Based on this, R is calculated according to the above formula (10): 2 =0.8.

[0166] It can be seen that the fit satisfies the constraint conditions, and it is considered that the obtained linear regression function formula (5) with the above regression parameters is valid, and / or the target test data calculated by the linear regression function formula (5) with the above regression parameters is valid.

[0167] It can be seen that the fitting function constructed by using the overall sum of squares and the regression sum of squares in this embodiment can characterize the degree of fit between each target test data and the corresponding labeled data. Through the constraint threshold, when the degree of fit does not meet expectations, poor target test data can be determined, thereby ensuring the accuracy of the target test data.

[0168] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, an embodiment of the present application further provides a device for testing components.

[0169] refer to Figure 5 , the device for testing the components includes:

[0170] The acquisition module 501 is used to acquire multiple test data of component testing and standard data corresponding to the test data; the test data is obtained by testing the corresponding components in the same preset actual test environment, and the standard data corresponding to the test data is obtained by testing the components in a preset standard test environment;

[0171] An average value calculation module 502 is used to calculate the average values ​​of multiple test data to obtain a test average value, and calculate the average value of standard data corresponding to each test data to obtain a standard average value;

[0172] The parameter calculation module 503 is used to input multiple test data, multiple standard data, test average values ​​and standard average values ​​into a preset parameter prediction function for calculation to obtain regression parameters. The regression parameters are parameters of a linear regression curve corresponding to multiple test data under the same actual test environment. The linear regression curve is a curve used to fit each test data with the standard data as the target.

[0173] The target test data determination module 504 is used to input each test data into a linear regression curve with regression parameters for calculation to obtain corresponding target test data.

[0174] In one embodiment, the parameter calculation module 503 is specifically used for:

[0175] Input multiple test data, multiple standard data, test average value and standard average value into a preset first sub-parameter function for calculation to obtain a first sub-parameter, where the first sub-parameter is the slope of a linear regression curve of multiple test data under the same actual test environment;

[0176] The first sub-parameter, the test average value and the standard average value are input into the preset second subsystem function for calculation to obtain the second sub-parameter, which is the intercept of the linear regression curve of multiple test data under the same actual test environment.

[0177] The multiple test data, the multiple standard data, the test average value and the standard average value are input into a preset first sub-parameter function for calculation to obtain the first sub-parameter, including:

[0178] Determine a first deviation between each test data and the test mean, and determine a second deviation between the corresponding standard data and the standard mean;

[0179] summing the products of each first deviation and the corresponding second deviation to obtain a first summation result;

[0180] The squares of the first deviations are summed to obtain a second summation result;

[0181] A ratio of the first summation result to the second summation result is determined as a first sub-parameter.

[0182] Furthermore, the first sub-parameter, the test average value and the standard average value are input into a preset second sub-system function for calculation to obtain the second sub-parameter, including:

[0183] Calculate the product of the first subparameter and the test mean;

[0184] The difference between the standard mean value and the product is determined as the second sub-parameter.

[0185] In one embodiment, the target test data determination module 504 is specifically configured to:

[0186] After obtaining the corresponding target test data, execute:

[0187] Calculate the sum of squares of the differences between each standard data and the standard mean value to obtain the overall sum of squares;

[0188] Calculate the sum of squares of the differences between each standard data and the corresponding target test data to obtain the residual sum of squares;

[0189] Input the overall sum of squares and the residual sum of squares into the fitting function to calculate the fitting degree;

[0190] When the degree of fit is greater than or equal to a preset constraint threshold, it is determined that the target test data determined by the linear regression function with regression parameters is valid.

[0191] Or, after getting the corresponding target test data, execute:

[0192] Calculate the sum of squares of the differences between each standard data and the standard mean value to obtain the overall sum of squares;

[0193] Calculate the sum of squares of the difference between the standard mean and each target test data to obtain the regression sum of squares;

[0194] Calculate the ratio of the regression sum of squares to the overall sum of squares to get the goodness of fit;

[0195] When the degree of fit is greater than or equal to a preset constraint threshold, it is determined that the target test data determined by the linear regression function with regression parameters is valid.

[0196] After obtaining the corresponding compensation data, execute:

[0197] Determine the difference between each target test data and the corresponding standard data;

[0198] When the difference is less than a preset difference threshold, it is determined that the target test data determined by the linear regression function with regression parameters is valid.

[0199] For the convenience of description, the above devices are described in terms of functions and are divided into various modules. Of course, when implementing the embodiments of the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0200] The device of the above embodiment is used to implement the corresponding component testing method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0201] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, an embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a method for testing components as in any of the above-mentioned embodiments is implemented.

[0202] Figure 6 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0203] The electronic device may include a processor 601 and a memory 602 storing computer program instructions.

[0204] Specifically, the processor 601 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0205] The memory 602 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 602 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory 602 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 602 may be inside or outside the electronic device. In a particular embodiment, the memory 602 is a non-volatile solid-state memory.

[0206] The memory 602 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Therefore, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0207] The processor 601 implements any one of the component testing methods in the above embodiments by reading and executing computer program instructions stored in the memory 602 .

[0208] In one example, the electronic device may further include a communication interface 603 and a bus 610. Figure 6 As shown, the processor 601, the memory 602, and the communication interface 603 are connected via a bus 610 and communicate with each other.

[0209] The communication interface 603 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0210] Bus 610 includes hardware, software or both, and the parts of electronic equipment are coupled to each other.For example, but not limitation, bus may include accelerated graphics port (Accelerated Graphics Port, AGP) or other graphics bus, enhanced industry standard architecture (Extended Industry Standard Architecture, EISA) bus, front-side bus (FSB), hypertransmission (Hyper Transport, HT) interconnection, industry standard architecture (Industry Standard Architecture, ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 310 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0211] The electronic device can perform the component testing method in the embodiment of the present application based on the linear regression curve, thereby realizing the combination Figure 1 Describes the method of component testing.

[0212] In addition, in combination with the component testing method in the above embodiment, the embodiment of the present application can provide a computer storage medium to implement. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any component testing method in the above embodiment is implemented.

[0213] An embodiment of the present application also provides a computer program product, including a computer program, which implements any one of the component testing methods in the above embodiments when the computer program is processed and executed.

[0214] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0215] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0216] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides a vehicle, which includes a component testing device and / or electronic device of any of the above-mentioned embodiments, and the electronic device executes any of the above-mentioned component testing methods.

[0217] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0218] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0219] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for testing components, characterized in that: include: Acquire multiple test data of component testing, and standard data corresponding to the test data; The test data is obtained by testing corresponding components in the same preset actual test environment, and the standard data corresponding to the test data is obtained by testing components in a preset standard test environment; Calculating an average value of the plurality of test data to obtain a test average value, and calculating an average value of standard data corresponding to each test data to obtain a standard average value; Input the multiple test data, multiple standard data, the test average value and the standard average value into a preset parameter prediction function for calculation to obtain regression parameters, wherein the regression parameters are parameters of a linear regression curve of the multiple test data under the same actual test environment, and the linear regression curve is a curve used to fit each test data with the standard data as the target; Each test data is input into a linear regression curve having the regression parameters for calculation to obtain the corresponding target test data.

2. The method for testing components according to claim 1, characterized in that: The preset parameter prediction function includes a first sub-parameter function and a second sub-parameter function; the inputting the plurality of test data, the plurality of standard data, the test average value and the standard average value into the preset parameter prediction function for calculation to obtain the regression parameter includes: Input the plurality of test data, the plurality of standard data, the test average value and the standard average value into a preset first sub-parameter function for calculation to obtain a first sub-parameter, wherein the first sub-parameter is the slope of a linear regression curve of the plurality of test data under the same actual test environment; The first sub-parameter, the test average value and the standard average value are input into a preset second subsystem function for calculation to obtain a second sub-parameter, where the second sub-parameter is the intercept of a linear regression curve of the multiple test data under the same actual test environment.

3. The component testing method according to claim 2, characterized in that: The step of inputting the plurality of test data, the plurality of standard data, the test average value and the standard average value into a preset first sub-parameter function for calculation to obtain a first sub-parameter includes: Determine a first deviation between each test data and the test average value, and determine a second deviation between the corresponding standard data and the standard average value; summing the products of each first deviation and the corresponding second deviation to obtain a first summation result; The squares of the first deviations are summed to obtain a second summation result; A ratio of the first summation result to the second summation result is determined as the first sub-parameter.

4. The method for testing components according to claim 2, characterized in that: The step of inputting the first sub-parameter, the test average value, and the standard average value into a preset second sub-system function for calculation to obtain a second sub-parameter includes: Calculating the product of the first sub-parameter and the test average value; The difference between the standard average value and the product is determined as the second sub-parameter.

5. The method for testing components according to claim 1, characterized in that: After obtaining the corresponding target test data, the method further includes: Calculate the sum of squares of the differences between each standard data and the standard mean value to obtain the overall sum of squares; Calculate the sum of squares of the differences between each standard data and the corresponding target test data to obtain the residual sum of squares; Input the overall sum of squares and the residual sum of squares into a fitness function to calculate and obtain fitness; In a case where the degree of fit is greater than or equal to a preset constraint threshold, it is determined that the target test data determined by the linear regression function having the regression parameters is valid.

6. The method for testing components according to claim 1, characterized in that: After obtaining the corresponding target test data, the method further includes: Calculate the sum of squares of the differences between each standard data and the standard mean value to obtain the overall sum of squares; Calculate the sum of squares of the differences between the standard mean value and each target test data to obtain a regression sum of squares; Calculate the ratio of the regression sum of squares to the overall sum of squares to obtain a goodness of fit; In a case where the degree of fit is greater than or equal to a preset constraint threshold, it is determined that the target test data determined by the linear regression function having the regression parameters is valid.

7. The component testing method according to claim 1, characterized in that: After obtaining the corresponding compensation data, the method further includes: Determine the difference between each target test data and the corresponding standard data; When the difference is less than a preset difference threshold, it is determined that the target test data determined by the linear regression function having the regression parameters is valid.

8. A device for testing components, characterized in that: The device comprises: An acquisition module, used to acquire multiple test data of component testing, and standard data corresponding to the test data; the test data is obtained by testing the corresponding components in the same preset actual test environment, and the standard data corresponding to the test data is obtained by testing the components in a preset standard test environment; An average value calculation module, used to calculate the average values ​​of the plurality of test data to obtain a test average value, and calculate the average value of the standard data corresponding to each test data to obtain a standard average value; A parameter calculation module, used for inputting the plurality of test data, the plurality of standard data, the test average value and the standard average value into a preset parameter prediction function for calculation to obtain regression parameters, wherein the regression parameters are parameters of a linear regression curve of the plurality of test data corresponding to the same actual test environment, and the linear regression curve is a curve used for fitting each test data with the standard data as the target; The target test data determination module is used to input each test data into a linear regression curve having the regression parameters for calculation to obtain the corresponding target test data.

9. An electronic device, characterized in that: The electronic device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the method for testing components as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for testing components according to any one of claims 1 to 7 is implemented.

11. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the component testing method as described in any one of claims 1 to 7.

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