Control motherboard testing method and device thereof

Abstract

This application provides a method and apparatus for testing control motherboards. The method includes: retrieving cost-related attribute data and benefit-related attribute data for each test item of a first control motherboard, wherein each test item of the first control motherboard is tested in its original order; the larger the cost-related attribute data and benefit-related attribute data, the longer the total testing time for each test item; inputting the cost-related attribute data and benefit-related attribute data of each test item of the first control motherboard into a multi-attribute decision model to obtain a test decision for a target batch of control motherboards, wherein the target batch of control motherboards includes multiple second control motherboards, the second control motherboards being of the same model as the first control motherboards; and testing each of the second control motherboards in the target batch of control motherboards according to the test decision of the target batch of control motherboards. This method improves the efficiency of testing control motherboards.

Description

Technical Field

[0001] This application relates to motherboard testing technology, and more particularly to a motherboard testing method and apparatus. Background Technology

[0002] The control motherboard is one of the main components of a display device (such as an LCD TV), and its performance determines whether the display device can operate normally. Therefore, the performance of the control motherboard needs to be tested before it leaves the factory to ensure the quality of the display device.

[0003] When testing control motherboards, each motherboard is typically tested according to a predetermined set of test items. These items include testing the normal operation of image signal input and output, sound input and output, and the functionality of buttons and remote control. However, testing each motherboard according to these predetermined items usually follows a fixed order, making it difficult to quickly determine if a problem has occurred. Improving the efficiency of testing control motherboards for malfunctions remains a pressing issue. Summary of the Invention

[0004] This application provides a control motherboard testing method and apparatus to improve the efficiency of testing whether the motherboard is malfunctioning.

[0005] On the one hand, this application provides a method for controlling motherboard testing, including:

[0006] The cost-related attribute data and benefit-related attribute data of each test item of the first control motherboard are retrieved. Each test item of the first control motherboard is tested in the original order of the test items. The cost-related attribute data includes at least the test cycle time of the test item, and the benefit-related attribute data includes at least the number of test pauses of the test item. The larger the cost-related attribute data and the benefit-related attribute data are, the longer the total test time of the test item is.

[0007] The cost-related attribute data and benefit-related attribute data of each test item of the first control motherboard are input into the multi-attribute decision model to obtain the test decision of the target batch of control motherboards. The target batch of control motherboards includes multiple second control motherboards, and the second control motherboards are of the same model as the first control motherboards. The test decision is used to instruct the second control motherboards to be tested in a first order according to each test item. The multi-attribute decision model is used to sort the test items according to the weighted arithmetic mean operator calculated by the cost-related attribute data, benefit-related attribute data, the scale of the cost-related attributes, and the scale of the benefit-related attributes, and to select the initial test decision with the shortest test time from the multiple initial test decisions obtained after sorting the test items as the test decision of the target batch of control motherboards.

[0008] Each of the second control motherboards in the target batch control motherboard is tested according to the test decision of the target batch control motherboard.

[0009] In some embodiments, the multi-attribute decision model is used for:

[0010] The importance of cost-type and benefit-type attributes is scaled using the scaling method in the analytic hierarchy process, forming multiple comparison matrices A with multiple scales, and each comparison matrix A corresponds to one scale.

[0011] Calculate the weights of the cost-class attributes and the benefit-class attributes in each comparison matrix A based on the scale in each comparison matrix A;

[0012] The normalized value of the cost attribute data of the first test project is calculated based on the minimum value of the cost attribute data of the test project and the cost attribute data of the first test project. The normalized value of the benefit attribute data of the first test project is calculated based on the maximum value of the benefit attribute data of the test project and the benefit attribute data of the first test project.

[0013] Based on the normalized value of the cost attribute data of the first test item, the weight of the cost attribute in each comparison matrix A, the normalized value of the benefit attribute data of the first test item, and the weight of the benefit attribute in each comparison matrix A, the weighted arithmetic mean operator of the first test item on each scale is determined until the weighted arithmetic mean operator of each test item on each scale is obtained.

[0014] After sorting the test items from largest to smallest according to the weighted arithmetic mean operator on the same scale for each test item, an initial test decision corresponding to a scale is obtained, until an initial test decision corresponding to each scale is obtained;

[0015] The first control motherboard is tested according to the initial test decision corresponding to each scale, and the initial test decision with the shortest test completion time of the first control motherboard is selected as the test decision for the target batch of control motherboards.

[0016] In some embodiments, calculating the weights of the cost-class attributes and the benefit-class attributes in each comparison matrix A based on the scale in each comparison matrix A includes:

[0017] According to the formula Determine the weight of each cost class attribute in each of the comparison matrices A;

[0018] According to the formula Determine the weight of each benefit class attribute in each of the comparison matrices A; (a i，j ) 2x2 Let A, a represent the comparison matrix. i，j This represents the scale between the i-th and j-th attributes out of the n attributes of the test item.

[0019] In some embodiments, the step of calculating the normalized value of the cost-class attribute data of the first test project based on the minimum value of the cost-class attribute data of the test project and the cost-class attribute data of the first test project, and calculating the normalized value of the benefit-class attribute data of the first test project based on the maximum value of the benefit-class attribute data of the test project and the benefit-class attribute data of the first test project, includes:

[0020] According to the formula The normalized value of the cost attribute data of the first test item is calculated;

[0021] According to the formula The normalized values ​​of the benefit-type attribute data of the first test item are calculated;

[0022] Where minS represents the minimum value of the cost attribute data of the test item, S N R represents the cost attribute data of the first test item; maxR represents the maximum value of the benefit attribute data of the test item. N This represents the benefit-related attribute data for the first test item;

[0023] The step of determining the weighted arithmetic mean operator for the first test item on each scale based on the normalized value of the cost attribute data of the first test item, the weight of the cost attribute in each comparison matrix A, the normalized value of the benefit attribute data of the first test item, and the weight of the benefit attribute in each comparison matrix A, until the weighted arithmetic mean operator for each test item on each scale is obtained, includes:

[0024] The weighted arithmetic mean operator of the first test item on each scale is determined according to the formula WAA(m,k)=w1m+w2k, where WAA(m,k) represents the weighted average operator of each test item on the first scale, m represents the normalized value of the cost attribute data of the first test item, and k represents the normalized value of the benefit attribute data of the first test item.

[0025] In some embodiments, testing the first control motherboard according to the initial test decision corresponding to each scale, and selecting the initial test decision with the shortest test completion time for the first control motherboard as the test decision for the target batch of control motherboards, includes:

[0026] The first control motherboard is tested according to the initial test decisions corresponding to the multiple scales, and the time taken for the first control motherboard to complete the test according to each initial test decision is obtained.

[0027] The weights of the cost-type attributes and the benefit-type attributes used when generating the initial test decision with the shortest test completion time for the first control motherboard are retrieved as the optimal cost-type attribute weights and the optimal benefit-type attribute weights.

[0028] After obtaining the weights of the cost-type attributes and the benefit-type attributes of the test items sorted by weight, the weights of the two cost-type attributes adjacent to the optimal cost-type attribute weight and the weights of the two benefit-type attributes adjacent to the optimal benefit-type attribute weight are obtained.

[0029] A test decision to be verified is determined based on the smaller weight of the two cost-type attribute weights to be verified, the optimal cost-type attribute weight, the optimal benefit-type attribute weight, and the larger weight of the two benefit-type attribute weights to be verified. Then, another test decision to be verified is determined based on the larger weight of the two cost-type attribute weights to be verified, the optimal cost-type attribute weight, the optimal benefit-type attribute weight, and the smaller weight of the two benefit-type attribute weights to be verified.

[0030] When the test duration for completing the first control motherboard test according to both of the two test decisions to be verified is greater than the shortest test duration for completing the first control motherboard test according to the initial test decision, the initial test decision with the shortest test completion time is determined as the test decision for the target batch control motherboard.

[0031] In some embodiments, after testing each of the second control motherboards in the target batch control motherboards according to the test decision of the target batch control motherboards, the method further includes:

[0032] Obtain the pass-through rate of the target batch of control motherboards after testing;

[0033] When the pass rate of the target batch control motherboard falls within a first value range, the test decision is updated at a first time interval. When the pass rate of the target batch control motherboard falls within a second value range, the test decision is updated at a second time interval. The maximum value of the first value range is less than the minimum value of the second value range, and the first time interval is less than the second time interval.

[0034] In some embodiments, updating the test decision includes:

[0035] Retrieve test data generated by any of the second control motherboards after the test decision test is completed according to the target batch control motherboards;

[0036] The test data generated after any one of the second control motherboards is tested is input into the multi-attribute decision model to obtain the updated test decision.

[0037] In one embodiment, it further includes:

[0038] After retrieving the first batch of control motherboards belonging to the first control motherboard and testing them in the original order of the test items, the proportion of the number of control motherboards that passed the test within the preset number of test items in the second test item to the total number of control motherboards tested.

[0039] When the proportion of the number of control motherboards that pass the test within the preset number of test attempts is greater than or equal to the preset proportion of the total number of control motherboards tested, the number of test attempts for the second test item on each control motherboard is set to the preset number of test attempts, until the number of test attempts for each test item in the test decision of the target batch of control motherboards is set.

[0040] Each of the second control motherboards in the target batch is tested according to the test decision of the target batch control motherboard and the number of tests for each test item in the test decision of the target batch control motherboard.

[0041] On the other hand, this application provides a control motherboard testing device, comprising:

[0042] The acquisition module is used to retrieve cost-related attribute data and benefit-related attribute data of each test item of the first control motherboard. Each test item of the first control motherboard is tested in the original order of the test items. The cost-related attribute data includes at least the test cycle time of the test item, and the benefit-related attribute data includes at least the number of test pauses of the test item. The larger the cost-related attribute data and the benefit-related attribute data are, the longer the total test time of the test item is.

[0043] The processing module is used to input the cost attribute data and benefit attribute data of each test item of the first control motherboard into a multi-attribute decision model to obtain the test decision of the target batch of control motherboards. The target batch of control motherboards includes multiple second control motherboards, and the second control motherboards are of the same model as the first control motherboards. The test decision is used to instruct the second control motherboards to be tested in a first order according to each test item. The multi-attribute decision model is used to sort the test items according to a weighted arithmetic mean operator calculated based on the cost attribute data, benefit attribute data, the scale of the cost attribute, and the scale of the benefit attribute, and to select the initial test decision with the shortest test time from the multiple initial test decisions obtained after sorting the test items as the test decision of the target batch of control motherboards.

[0044] The testing module is used to test each of the second control motherboards in the target batch of control motherboards according to the testing decisions of the target batch of control motherboards.

[0045] On the other hand, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0046] The memory stores computer-executed instructions;

[0047] The processor executes computer execution instructions stored in the memory to implement the motherboard control testing method as described in the first aspect.

[0048] On the other hand, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the control motherboard testing method as described in the first aspect.

[0049] On the other hand, this application provides a computer program product, including a computer program that, when executed by a processor, implements the control motherboard testing method as described in the first aspect.

[0050] The control board testing method provided in this application retrieves cost attribute data and benefit attribute data generated after the first control board has been tested in the original order of the test items and inputs them into a multi-attribute decision model to obtain the test decision for the target batch of control boards. The target batch of control boards includes multiple second control boards, which are of the same model as the first control board. The multi-attribute decision model is used to sort the test items based on a weighted arithmetic mean operator calculated from the cost attribute data, benefit attribute data, and the scaling of the cost and benefit attributes. From the multiple initial test decisions obtained after sorting the test items, the initial test decision with the shortest test time is selected as the test decision for the target batch of control boards. Compared to testing the first control board in the original order of the test items, testing the target batch of control boards based on the test decision of the target batch of control boards in this application can reduce the test time for the second control boards and the target batch of control boards. The test decision for the target batch of control motherboards applies to all control motherboards of the same model as the second control motherboard. When another control motherboard of the same model as the second control motherboard needs to be tested, the test decision for the target batch of control motherboards can be used directly for testing. Attached Figure Description

[0051] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0052] Figure 1 This is a schematic diagram illustrating the application scenario of the control motherboard testing method provided in this application.

[0053] Figure 2 This is a flowchart illustrating the control motherboard testing method provided in Embodiment 1 of this application.

[0054] Figure 3 This is a flowchart illustrating the control motherboard testing method provided in Embodiment 2 of this application.

[0055] Figure 4 This is a schematic diagram of a control motherboard testing method provided in another embodiment of this application.

[0056] Figure 5 This is a schematic diagram of the control motherboard testing device provided in Embodiment 3 of this application.

[0057] Figure 6 This is a schematic diagram of an electronic device provided in Embodiment 4 of this application.

[0058] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0059] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0060] The control motherboard is one of the main components of a display device (such as an LCD TV), and its performance determines whether the display device can operate normally. Therefore, the performance of the control motherboard needs to be tested before it leaves the factory to ensure the quality of the display device.

[0061] When testing control motherboards, each motherboard is typically tested according to a predetermined set of test items. These items include testing the normality of image signal input and output, the normality of sound input and output, and the normality of button and remote control operation. Testing each motherboard according to these predetermined test items usually follows a fixed order. If a failure occurs only during the last test, it indicates a problem with the control motherboard. For example, if there are 20 test items, and the problem is only discovered during the 20th test, then executing the first 19 tests has wasted a significant amount of time. Therefore, the existing method of testing control motherboards according to a fixed order of test items cannot quickly determine whether a control motherboard is faulty. How to improve the efficiency of testing control motherboards for malfunctions remains a pressing issue.

[0062] Based on this, this application provides a control motherboard testing method and apparatus, applied to an electronic device for testing the control motherboard of a display device. The control motherboard testing method retrieves cost-type attribute data and benefit-type attribute data for each test item generated after a first control motherboard has completed testing according to the original order of the test items. This data is then input into a multi-attribute decision model to obtain a test decision for the target batch of control motherboards. A second control motherboard, of the same model as the first control motherboard, is then tested based on this test decision. The multi-attribute decision model is used to sort test items based on a weighted arithmetic mean operator calculated from the minimum value of the cost-type attribute data and the maximum value of the benefit-type attribute data. A larger weighted arithmetic mean operator indicates that the test item is more likely to fail quickly.

[0063] Therefore, this method prioritizes the tests most likely to fail quickly, placing them first in the testing order, and then arranging the other tests in ascending order of test duration. This allows the system to determine if the control motherboard is malfunctioning while the first test is being performed, reducing testing time and improving efficiency in identifying malfunctioning control motherboards.

[0064] The control motherboard testing method provided in this application is applied to computer equipment, such as computers and laboratory servers. Please see [link to application]. Figure 1The computer equipment retrieves the cost-related and effect-related attribute data for each test item generated after the first control motherboard has completed testing in the original order of the test items. This data is then input into the multi-attribute decision model to obtain the test decision for the target batch of control motherboards. The second control motherboard is then tested based on this test decision. The computer equipment includes an industrial control computer, an artificial intelligence (AI) platform, a display platform, and a storage platform. The industrial control computer collects test data (cost-related and effect-related attribute data) and uploads it to the AI ​​platform. The AI ​​platform then models and trains the multi-attribute decision model to calculate the test decision. The industrial control computer then requests the latest test decision generated by the AI ​​platform and stores it locally for use in testing the control motherboards. The storage platform stores the test data uploaded by the AI ​​platform. The display platform displays the test data after the industrial control computer has performed the tests according to the test decisions.

[0065] Please see Figure 2 Embodiment 1 of this application provides a method for controlling motherboard testing, including:

[0066] S210, retrieve the cost attribute data and benefit attribute data of each test item of the first control motherboard. Each test item of the first control motherboard is tested in the original order of the test items. The cost attribute data includes at least the test cycle time of the test item, and the benefit attribute data includes at least the number of test pauses of the test item. The larger the cost attribute data and the benefit attribute data are, the longer the total test time of the test item is.

[0067] For example, as shown in Table 1, the first control motherboard has 14 test items (test items 1 to 14 shown in Table 1). Each test item has its own test cycle duration (the cycle shown in Table 1) and the number of test pauses (the number of NGs shown in Table 1). The total test time for each test item (the total NG time shown in Table 1) is equal to the product of the cumulative test cycles of the previous few test items and its own number of test pauses. For example, the total test time for test item 2 in Table 1 is 2.44 * 26 = 63, the total test time for test item 3 is (2.44 + 4.45) * 0 = 0, the total test time for test item 4 is (2.44 + 4.45 + 4.54) * 1 = 11, and so on. There are a total of 14 test items, and the total test time spent by the control motherboard after completing the test is 3172 seconds.

[0068] Table 1:

[0069] Test order code name Test Project Beat (s) NG count Total NG duration (s) 1 x1 Test Item 1 2.44 22 0 2 x2 Test Item 2 4.45 26 63 3 x3 Test Item 3 4.52 0 0 4 x4 Test Item 4 4.43 1 11 5 x5 Test Item 5 12.26 35 554 6 x6 Test Item 6 1.61 5 141 7 x7 Test Item 7 4.68 0 0 8 x8 Test Item 8 3.7 5 172 9 x9 Test Item 9 3.16 7 267 10 x10 Test Item 10 3.39 19 784 11 x11 Test Item 11 2.79 3 134 12 x12 Test Item 12 2.04 21 996 13 x13 Test Item 13 9.33 1 49 14 x14 Test Item 14 3.03 0 0 3172

[0070] S220, the cost attribute data and benefit attribute data of each test item of the first control motherboard are input into the multi-attribute decision model to obtain the test decision of the target batch of control motherboards. The target batch of control motherboards includes multiple second control motherboards, which are of the same model as the first control motherboard. The test decision is used to instruct the second control motherboards to be tested in the first order of each test item. The multi-attribute decision model is used to sort the test items according to the weighted arithmetic mean operator calculated by the cost attribute data, benefit attribute data, the scale of the cost attribute, and the scale of the benefit attribute, and to select the initial test decision with the shortest test time from the multiple initial test decisions obtained after sorting the test items as the test decision of the target batch of control motherboards.

[0071] For example, as shown in Table 1, the cost attribute data generated after the first control motherboard test is the beat rate of each test item, and the benefit attribute data generated after the first control motherboard test is the number of NGs for each test item.

[0072] After inputting the cost attribute data and benefit attribute data of each test item of the first control motherboard into the multi-attribute decision model, the multi-attribute decision model first uses the scaling method in the analytic hierarchy process to scale the importance of the cost attribute and benefit attribute, forming multiple comparison matrices A with multiple scales, and one comparison matrix A corresponds to one scale.

[0073] The Analytic Hierarchy Process (AHP) uses the scaling method shown in Table 2 to provide quantitative scales for comparisons under different conditions. If the test item data has n attributes, pairwise comparisons are performed according to the scaling method shown in Table 2 to form a comparison matrix A = (a i，j ) nxn a i，j This represents the scale between the i-th and j-th attributes out of n attributes possessed by the test item. In this embodiment, the test item data has two attributes: cost attributes and benefit attributes. Therefore, this comparison matrix... a 1，1 This refers to the scale between cost-class attributes, a 2，2 This refers to the scale between benefit-type attributes, a 1，2 This refers to the scale between cost-based attributes and benefit-based attributes, a 2，1 This refers to the scale between benefit-type attributes and cost-type attributes. As shown in Table 2, the scale between cost-type attributes and benefit-type attributes has multiple scale values, each representing a different ratio of importance. For example, in Table 2, "1" means that cost-type attributes and benefit-type attributes have the same importance, "2" means that cost-type attributes are slightly more important than benefit-type attributes, and so on.

[0074] It should be noted that a 1，2 =1 / a 2，1 a 1，2 It can be any number from 1 to 9 shown in Table 2. Correspondingly, one comparison matrix A corresponds to one scale, and there will be 17 comparison matrices A for one test item. Among the 17 comparison matrices A, there are 9 comparison matrices A when the cost class attribute is attribute i and the benefit class attribute is attribute j, and 9 comparison matrices A when the benefit class attribute is attribute i and the cost class attribute is attribute j. Because there is one duplicate comparison matrix A (scale 1) among these 18 comparison matrices A, there are 17 comparison matrices A in this case.

[0075] Table 2:

[0076]

[0077] The multi-attribute decision model then calculates the weights of the cost-class attribute and the benefit-class attribute in each comparison matrix A based on the scale in each matrix A. Specifically, the multi-attribute decision model uses the formula... Determine the weight of each cost class attribute in each comparison matrix A according to the formula. Determine the weight of each benefit class attribute in each comparison matrix A. Assume a i，j =3, then compare the matrices When the cost attribute is more important, its weight is also greater. When there are 17 comparison matrices A, there are also 17 sets of weights (each set of weights includes the weight of a cost attribute and the weight of a benefit attribute), and each set of weights corresponds to one comparison matrix A.

[0078] This multi-attribute decision model is used to calculate the normalized value of the cost attribute data of the first test project based on the minimum value of the cost attribute data of the test project and the cost attribute data of the first test project, and to calculate the normalized value of the benefit attribute data of the first test project based on the maximum value of the benefit attribute data of the test project and the benefit attribute data of the first test project.

[0079] Specifically, when this multi-attribute decision model calculates the normalized value of the cost attribute data of the first test project based on the minimum value of the cost attribute data of the test project and the cost attribute data of the first test project, it uses the formula... The normalized value of the cost attribute data for the first test item is calculated. Here, minS represents the minimum value of the cost attribute data for the test item, and S... NThis represents the cost attribute data for the first test item. For example, in Table 1, minS equals 1.61 (the cycle time of test item 6). This first test item can be any one of test items 1 to 12. Assuming the first test item is test item 1, then m = 1.61 / 2.44 = 0.66.

[0080] This multi-attribute decision model is used to calculate the normalized value of the benefit attribute data of the first test project based on the maximum value of the benefit attribute data of the test project and the benefit attribute data of the first test project, according to the formula... The normalized values ​​of the benefit-type attribute data for the first test item are calculated. Here, maxR represents the maximum value of the benefit-type attribute data for the test item, and R... N This represents the benefit-related attribute data for the first test item. For example, in Table 1, maxR equals 35 (the number of NGs for test item 5). This first test item can be any one of test items 1 to 12. Assuming the first test item is test item 1, then k = 22 / 35 = 0.63.

[0081] This multi-attribute decision model is used to determine the weighted arithmetic mean operator for the first test item on each scale based on the normalized values ​​of the cost attribute data, the weights of the cost attribute in each comparison matrix A, the normalized values ​​of the benefit attribute data, and the weights of the benefit attribute in each comparison matrix A, until the weighted arithmetic mean operator for each test item on each scale is obtained. Specifically, the multi-attribute decision model is used to determine the weighted arithmetic mean operator for the first test item on each scale according to the formula WAA(m,k) = w1m + w2k. Here, WAA(m,k) represents the weighted average operator for each test item on the first scale, m represents the normalized value of the cost attribute data, and k represents the normalized value of the benefit attribute data. Assuming the first test item is test item 1, then the WAA(m,k) for test item 1 is 0.75 * 0.66 + 0.25 * 0.63 = 0.6525.

[0082] After sorting the test items from largest to smallest using the weighted arithmetic mean operator on the same scale for each test item, an initial test decision is obtained corresponding to one scale. This process continues until an initial test decision is obtained for each scale. In other words, an initial test decision is obtained by calculating the weighted arithmetic mean operator for each test item according to one scale and then sorting the test items. For example, if Table 2 has 9 scales and the two attributes form 17 comparison matrices A, then there will be 17 corresponding initial test decisions.

[0083] Then, the first control motherboard is tested according to the initial test decision corresponding to each scale, and the initial test decision with the shortest test completion time for the first control motherboard is selected as the test decision for the target batch of control motherboards. For example, among the 17 initial test decisions described above, the initial test decision with the shortest test completion time when testing the first control motherboard is selected as the test decision for the target batch of control motherboards.

[0084] S230, each of the second control boards in the target batch control board is tested according to the test decision of the target batch control board.

[0085] After testing the second control motherboard based on the testing decision of the target batch control motherboard, the time spent testing each second control motherboard in the target batch control motherboard is less than the time spent testing the second control motherboard in the original order of the test items. In other words, the time spent testing the target batch control motherboard is less than the time spent testing the target batch control motherboard in the original order of the test items.

[0086] For example, Table 3 shows the order, cycle time, number of NGs, and total NG duration of the test items when testing the second control motherboard according to the test decision of the target batch control motherboard. It can be seen that the total NG duration shown in Table 3 (1714s) is much smaller than the total NG duration shown in Table 1 (3172s).

[0087] Table 3:

[0088] Test order code name Test Project Beat (s) NG count Total NG duration (s) 1 x12 Test Item 12 2.04 21 2 x1 Test Item 1 2.44 22 45 3 x6 Test Item 6 1.61 5 22 4 x5 Test Item 5 12.26 35 213 5 x2 Test Item 2 4.45 26 477 6 x10 Test Item 10 3.39 19 433 7 x9 Test Item 9 3.16 7 183 8 x11 Test Item 11 2.79 3 88 9 x8 Test Item 8 3.7 5 161 10 x14 Test Item 14 3.03 0 0 11 x4 Test Item 4 4.43 1 39 12 x3 Test Item 3 4.52 0 0 13 x7 Test Item 7 4.68 0 0 14 x13 Test Item 13 9.33 1 53 1714

[0089] In some embodiments, to determine whether the initial test decision with the shortest test completion time for the first control motherboard is the test decision with the shortest test completion time, further test decisions may be generated, and these test decisions may be compared with the test decision with the shortest test completion time to determine whether a test decision with a shorter test completion time has appeared. When no test decision with a shorter test completion time has appeared, the test decision with the shortest test completion time is determined to be the test decision for the target batch of control motherboards.

[0090] Specifically, this multi-attribute decision model is used to test the first control board according to multiple initial test decisions corresponding to this scale, obtaining the time it takes for the first control board to complete the test according to each initial test decision. The weights of the cost attribute and the benefit attribute used when generating the initial test decision with the shortest test completion time are taken as the optimal cost attribute weight and the optimal benefit attribute weight. For example, when the test items are sorted according to Table 2, the optimal cost attribute weight is 0.67, and the optimal benefit attribute weight is 0.33.

[0091] After obtaining the weights of the cost-related attributes and the benefit-related attributes of the test items in order of weight, the weights of the two cost-related attributes adjacent to the optimal cost-related attribute weight and the two benefit-related attributes adjacent to the optimal benefit-related attribute weight are then obtained. As shown in Table 4 (where S represents cost-related attributes and R represents benefit-related attributes), the weights of the cost-related attributes are sorted from smallest to largest, and the weights of the benefit-related attributes are sorted from largest to smallest.

[0092] Table 4:

[0093]

[0094] The weights of the two cost-related attributes adjacent to the optimal cost-related attribute weight are 0.5 and 0.75, and the weights of the two benefit-related attributes adjacent to the optimal benefit-related attribute weight are 0.5 and 0.25.

[0095] Next, the weights of two unverified cost attributes are calculated using a bisection method between the optimal cost attribute weight and the weights of two adjacent cost attributes. Similarly, the weights of two unverified benefit attributes are calculated using a bisection method between the optimal benefit attribute weight and the weights of two adjacent benefit attributes. That is, a test decision to be verified is determined based on the smaller weight of the two unverified cost attribute weights, the optimal cost attribute weight, the optimal benefit attribute weight, and the larger weight of the two unverified benefit attribute weights. Then, another test decision to be verified is determined based on the larger weight of the two unverified cost attribute weights, the optimal cost attribute weight, the optimal benefit attribute weight, and the smaller weight of the two unverified benefit attribute weights. Specifically, a new cost attribute weight of 0.585 is determined based on 0.5 and 0.67, and a new benefit attribute weight of 0.415 is determined based on 0.5 and 0.33. A test decision to be verified is then determined based on the cost attribute weight of 0.585 and the benefit attribute weight of 0.415. Based on 0.75 and 0.67, a summed average weight of 0.71 is determined as the new cost attribute weight. Then, based on 0.25 and 0.33, a summed average weight of 0.29 is determined as the new benefit attribute weight. A test decision to be verified is then determined based on the cost attribute weight of 0.71 and the benefit attribute weight of 0.29. The process of determining the test decision based on the new cost attribute weight and the new benefit attribute weight is the same as the process of determining the initial test decision, and will not be repeated here.

[0096] If the test duration for completing the first control motherboard test according to both of the two test decisions to be verified is greater than the shortest test duration for completing the first control motherboard test according to the initial test decision, then the initial test decision with the shortest test completion time is determined as the test decision for the target batch of control motherboards. If one of the test durations for completing the first control motherboard test according to the two test decisions to be verified is less than the shortest test duration for completing the first control motherboard test according to the initial test decision, then the binary search method described above is used to determine whether the test decision with the shorter test duration than the shortest test duration for completing the first control motherboard test according to the initial test decision is the test decision with the shortest test duration.

[0097] Assuming that the test decision with the shortest test duration is determined by using the binary search method when the weight of the cost attribute is 0.575 and the weight of the benefit attribute is 0.415, then the test decision determined based on the weight of the cost attribute (0.575) and the weight of the benefit attribute (0.415) is the test decision for the target batch control motherboard. Table 5 shows the test item order and total NG (no good) time in the test decision determined when the weight of the cost attribute is 0.575 and the weight of the benefit attribute is 0.415. In this case, the total NG time is 1437 seconds.

[0098] Table 5:

[0099]

[0100] In summary, the control board testing method provided in this embodiment retrieves cost attribute data and benefit attribute data generated by the first control board after testing in the original order of the test items and inputs them into the multi-attribute decision model to obtain the test decision for the target batch of control boards. The target batch of control boards includes multiple second control boards, which are of the same model as the first control board. The multi-attribute decision model is used to sort the test items based on a weighted arithmetic mean operator calculated from the cost attribute data, benefit attribute data, and the scales of the cost and benefit attributes. It then selects the initial test decision with the shortest test duration from the multiple initial test decisions obtained after sorting the test items as the test decision for the target batch of control boards. Compared to testing the second control boards in the original order of the test items, this embodiment reduces the test duration of the second control boards by testing them according to the test decision for the target batch of control boards. The test decision for the target batch of control motherboards applies to all control motherboards of the same model as the second control motherboard. When another control motherboard of the same model as the second control motherboard needs to be tested, the test decision for the target batch of control motherboards can be used directly for testing.

[0101] In some embodiments, see Figure 3 This application's second embodiment provides a control motherboard testing method based on embodiment one. After embodiment one has determined the testing decisions for the target batch of control motherboards and tested each of the second control motherboards according to these decisions, the testing decisions are adjusted based on the actual testing results of the control motherboards. Specifically, the method provided in this embodiment includes:

[0102] S310, obtain the pass rate of the target batch of control motherboards after testing.

[0103] Once the testing decisions for the target batch of control motherboards are determined, the testing decisions applicable to all control motherboards of the same model as the second control motherboard are also determined. The first-pass yield refers to the parameter that a product passes all stages of production from the first process to the last. The higher the first-pass yield of the target batch of control motherboards, the fewer defective second control motherboards there are in that target batch.

[0104] S320, when the pass rate of the target batch of control motherboards falls within a first value range, the test decision is updated at a first time interval; when the pass rate of the target batch of control motherboards falls within a second value range, the test decision is updated at a second time interval; the maximum value of the first value range is less than the minimum value of the second value range, and the first time interval is less than the second time interval.

[0105] The first numerical range is, for example, [0.5, 0.85]. When the pass rate of the target batch of control motherboards falls within this range, it indicates that there are many problematic control motherboards in the target batch. In this case, the test decision is updated according to the first time interval set by the tester. The second numerical range is, for example, (0.85, 0.9). When the pass rate of the target batch of control motherboards falls within this range, it indicates that there are few problematic control motherboards in the target batch. In this case, the test decision is updated according to the second time interval set by the tester. The first time interval is shorter than the second time interval. When the pass rate of the target batch of control motherboards meets the requirements (e.g., pass rate greater than or equal to 0.9), the test decision is not updated. Optionally, when the pass rate of the target batch of control motherboards is lower than a certain threshold, an alarm operation is directly executed.

[0106] like Figure 4 As shown, when updating the test decision of the target batch control motherboard, the test data generated after any of the second control motherboards completes the test according to the test decision of the target batch control motherboard is retrieved, and the test data generated after any of the second control motherboards completes the test is input into the multi-attribute decision model to obtain the updated test decision.

[0107] Furthermore, it is also possible to retrieve the proportion of the number of control motherboards that passed the test within the preset number of tests on the second test item after the first batch of control motherboards to which the first control motherboard belongs, after testing in the original order of the test items.

[0108] Assuming the first batch of control motherboards contains 100 motherboards, 90 of them pass the second test item within a preset number of tests (assuming the preset number of tests is 1), meaning these 90 motherboards pass the second test item on the first test, and 10 motherboards fail within the preset number of tests. Therefore, the proportion of the first batch of control motherboards that failed the second test item within the preset number of tests to the total number of tested control motherboards is equal to 90 / 100 = 0.9. The preset number of tests can be set according to actual needs and is not limited in this application. For example, the preset number of tests could also be 2 or 3.

[0109] When the proportion of control motherboards that pass the second test item within a preset number of tests in the first batch of control motherboards is greater than or equal to a preset proportion among the total number of control motherboards tested, the number of tests for the second test item on each control motherboard is set to the preset number of tests, until the number of tests for each test item in the test items of the target batch of control motherboards is set. For example, if the preset proportion is 90%, and the proportion of control motherboards that fail the second test item within the preset number of tests in the first batch of control motherboards is equal to 90% among the total number of control motherboards tested, then the number of tests for the second test item is set to 1. That is, each control motherboard in the target batch of control motherboards is tested once for the second test item. If the second test item fails after one test, it is determined that the second test item has failed, and the control motherboard that failed the second test item is identified as an abnormal control motherboard. The test decision of the target batch of control motherboards is used to instruct the second control motherboard to be tested in the first order of each test item. The number of tests for each test item indicated by the test decision can be determined according to the method for determining the number of tests for the second test item. When determining the number of tests for each test item, it can be manually set according to the different test items.

[0110] After the test decision for the target batch control motherboard has been determined in the above description, each of the second control motherboards in the target batch control motherboard is then tested according to the test decision for the target batch control motherboard and the number of tests for each test item in the test decision for the target batch control motherboard.

[0111] Table 6 shows the impact of the number of tests for each test item on the completion time of the second control motherboard test (taking an initial test count of 3 for each test item as an example). Table 6 shows that the overall test time with 1 test item is 2642 seconds shorter than the overall test time with an initial test count of 3, which is 44 minutes. The overall test time with 2 test items is 1312 seconds shorter than the overall test time with an initial test count of 3, which is 22 minutes.

[0112] Table 6:

[0113]

[0114] Furthermore, when the pass rate of the target batch of control motherboards falls within the first numerical range, the percentage of control motherboards that failed the test within the preset number of tests for the third test item can be retrieved after the target batch of control motherboards has completed the tests according to the test items. When the percentage of control motherboards that failed the test within the preset number of tests reaches the preset percentage, the number of tests for the third test item on each control motherboard is set to the preset number of tests, until the number of tests for each test item in the test decision of the second batch of control motherboards is set. That is, the number of tests for each test item in the test decision of the second batch of control motherboards is calculated again using the same method as calculating the number of tests for each test item in the test decision of the target batch of control motherboards.

[0115] The method provided in this embodiment dynamically adjusts the test decisions that the next batch of control motherboards (of the same model as the target batch control motherboards) should use by monitoring the pass rate of the target batch control motherboards. This achieves real-time adjustment of test decisions based on actual test conditions, thereby maximizing the saving of test time.

[0116] Please see Figure 5 Embodiment 3 of this application also provides a control motherboard testing device 10, comprising:

[0117] The acquisition module 11 is used to retrieve cost attribute data and benefit attribute data of each test item of the first control motherboard. Each test item of the first control motherboard is tested in the original order of the test items. The cost attribute data includes at least the test cycle time of the test item, and the benefit attribute data includes at least the number of test pauses of the test item. The larger the cost attribute data and the benefit attribute data are, the longer the total test time of the test item is.

[0118] Processing module 12 is used to input the cost attribute data and benefit attribute data of each test item of the first control motherboard into a multi-attribute decision model to obtain the test decision of the target batch control motherboard. The target batch control motherboard includes multiple second control motherboards, which are of the same model as the first control motherboard. The test decision is used to instruct the second control motherboard to be tested in the first order of each test item. The multi-attribute decision model is used to sort the test items according to the weighted arithmetic mean operator calculated by the cost attribute data, benefit attribute data, the scale of the cost attribute, and the scale of the benefit attribute, and to select the initial test decision with the shortest test time from the multiple initial test decisions obtained after sorting the test items as the test decision of the target batch control motherboard.

[0119] Test module 13 is used to test each of the second control motherboards in the target batch of control motherboards according to the test decision of the target batch control motherboard.

[0120] This multi-attribute decision model utilizes the scaling method in the Analytic Hierarchy Process (AHP) to scale the importance of cost and benefit attributes, forming multiple comparison matrices A with multiple scales, each matrix A corresponding to one scale. Based on the scale in each comparison matrix A, the weights of the cost and benefit attributes in each matrix A are calculated. The normalized value of the cost attribute data for the first test project is calculated based on the minimum value of the cost attribute data for the test project and the cost attribute data for the first test project. Similarly, the normalized value of the benefit attribute data for the first test project is calculated based on the maximum value of the benefit attribute data for the test project and the benefit attribute data for the first test project. Finally, the normalized value of the cost attribute data for the first test project is calculated based on the value of the cost attribute data for the first test project. The normalized value, the weight of the cost attribute in each comparison matrix A, the normalized value of the benefit attribute data of the first test item, and the weight of the benefit attribute in each comparison matrix A are used to determine the weighted arithmetic mean operator of the first test item on each scale, until the weighted arithmetic mean operator of each test item on each scale is obtained; the test items are sorted from largest to smallest according to the weighted arithmetic mean operator of each test item on the same scale to obtain the initial test decision corresponding to a scale, until the initial test decision corresponding to each scale is obtained; the first control motherboard is tested according to the initial test decision corresponding to each scale, and the initial test decision with the shortest test completion time of the first control motherboard is selected as the test decision of the target batch control motherboard.

[0121] This multi-attribute decision model is used based on the formula Determine the weight of each cost class attribute in each comparison matrix A; according to the formula... Determine the weight of each benefit class attribute in each comparison matrix A; (a i，j ) 2x2This indicates that the comparison matrix A, a i，j This represents the scale between the i-th and j-th attributes out of the n attributes of the test item.

[0122] The normalized value of the cost attribute data of the first test project is calculated based on the minimum value of the cost attribute data of the test project and the cost attribute data of the first test project. Similarly, the normalized value of the benefit attribute data of the first test project is calculated based on the maximum value of the benefit attribute data of the test project and the benefit attribute data of the first test project. This includes: according to the formula... The normalized value of the cost attribute data for the first test item was calculated; according to the formula... The normalized values ​​of the benefit-type attribute data of the first test item are calculated; where minS represents the minimum value of the cost-type attribute data of the test item, S N R represents the cost attribute data of the first test item; maxR represents the maximum value of the benefit attribute data of the test item. N This represents the benefit-related attribute data for the first test item;

[0123] The weighted arithmetic mean operator for the first test item on each scale is determined based on the normalized value of the cost attribute data of the first test item, the weight of the cost attribute in each comparison matrix A, the normalized value of the benefit attribute data of the first test item, and the weight of the benefit attribute in each comparison matrix A, until the weighted arithmetic mean operator for each test item on each scale is obtained. This includes determining the weighted arithmetic mean operator for the first test item on each scale according to the formula WAA(m,k)=w1m+w2k, where WAA(m,k) represents the weighted average operator for each test item on the first scale, m represents the normalized value of the cost attribute data of the first test item, and k represents the normalized value of the benefit attribute data of the first test item.

[0124] The process involves testing the first control motherboard according to the initial test decisions corresponding to each scale, and selecting the initial test decision with the shortest test completion time for the first control motherboard as the test decision for the target batch of control motherboards. This includes: testing the first control motherboard according to multiple initial test decisions corresponding to the scale, obtaining the test completion time of the first control motherboard according to each initial test decision; using the weights of cost-type attributes and benefit-type attributes used when generating the initial test decision with the shortest test completion time for the first control motherboard as the optimal cost-type attribute weights and optimal benefit-type attribute weights; obtaining the weights of cost-type attributes and benefit-type attributes of test items sorted by weight, and then selecting the two cost-type attribute weights adjacent to the optimal cost-type attribute weight and the weights of the optimal benefit-type attribute weight. The weights of two adjacent benefit-type attributes are used; a test decision to be verified is determined based on the smaller weight of the two cost-type attribute weights to be verified, the optimal cost-type attribute weight, the optimal benefit-type attribute weight, and the larger weight of the two benefit-type attribute weights to be verified; another test decision to be verified is determined based on the larger weight of the two cost-type attribute weights to be verified, the optimal cost-type attribute weight, the optimal benefit-type attribute weight, and the smaller weight of the two benefit-type attribute weights to be verified; when the test time for completing the first control motherboard test according to both test decisions to be verified is greater than the shortest test time for completing the first control motherboard test according to the initial test decision, the initial test decision with the shortest test completion time is determined as the test decision for the target batch of control motherboards.

[0125] The acquisition module 11 is also used to acquire the pass rate of the target batch of control motherboards after testing;

[0126] The processing module 12 is further configured to update the test decision at a first time interval when the pass rate of the target batch control motherboard is within a first numerical range, and update the test decision according to a second time interval when the pass rate of the target batch control motherboard is within a second numerical range; the maximum value of the first numerical range is less than the minimum value of the second numerical range, and the first time interval is less than the second time interval.

[0127] The processing module 12 is also used to retrieve test data generated after any of the second control motherboards has completed the test according to the test decision of the target batch control motherboards; input the test data generated after any of the second control motherboards has completed the test into the multi-attribute decision model to obtain the updated test decision.

[0128] The acquisition module 11 is also used to retrieve the proportion of the number of control motherboards that passed the test within a preset number of test times on the second test item after the first batch of control motherboards to which the first control motherboard belongs were tested in the original order of the test items.

[0129] The processing module 12 is further configured to set the number of times the second test item is tested on each control motherboard to the preset number of times when the proportion of the number of control motherboards that have passed the test within the preset number of test times is greater than or equal to the preset proportion of the number of control motherboards tested, until the number of test items for each test item in the test decision of the target batch of control motherboards is set.

[0130] The processing module 12 is also used to test each of the second control motherboards in the target batch control motherboard according to the test decision of the target batch control motherboard and the number of tests for each test item in the test decision of the target batch control motherboard.

[0131] Please see Figure 6 Embodiment 4 of this application also provides an electronic device 20, including: a processor 21, and a memory 22 communicatively connected to the processor 21, the memory 22 storing computer execution instructions; the processor 21 executes the computer execution instructions stored in the memory 22 to implement the control motherboard testing method as described in Embodiments 1 and 2.

[0132] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, cause the computer-executable instructions to be executed by a processor to implement the control motherboard testing method provided in any of the preceding embodiments.

[0133] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the control motherboard testing method as provided in any of the preceding embodiments.

[0134] It should be noted that the aforementioned computer-readable storage media can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM), etc. It can also be various electronic devices that include one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0135] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0136] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0137] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0138] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0139] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0141] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for controlling motherboard testing, characterized in that, include: The cost-related attribute data and benefit-related attribute data of each test item of the first control motherboard are retrieved. Each test item of the first control motherboard is tested in the original order of the test items. The cost-related attribute data includes the test cycle time of the test item, and the benefit-related attribute data includes the number of test pauses of the test item. The larger the cost-related attribute data and the benefit-related attribute data are, the longer the total test time of the test item is. The cost-related attribute data and benefit-related attribute data of each test item of the first control motherboard are input into a multi-attribute decision model to obtain the test decision for the target batch of control motherboards. The target batch of control motherboards includes multiple second control motherboards, which are of the same model as the first control motherboard. The test decision is used to instruct the second control motherboards to be tested in a first order according to each test item. The multi-attribute decision model is used to sort the test items according to a weighted arithmetic mean operator calculated based on the cost-related attribute data, benefit-related attribute data, and the scales of the cost-related attributes and benefit-related attributes. From the multiple initial test decisions obtained after sorting the test items, the initial test decision with the shortest test duration is selected as the test decision for the target batch of control motherboards. The scales of the cost-related attributes and benefit-related attributes are obtained by using the scaling method in the analytic hierarchy process to scale the importance of the cost-related attributes and benefit-related attributes. Each of the second control motherboards in the target batch control motherboard is tested according to the test decision of the target batch control motherboard.

2. The method according to claim 1, characterized in that, The multi-attribute decision model is used for: The importance of cost-type and benefit-type attributes is scaled using the scaling method in the analytic hierarchy process, forming multiple comparison matrices A with multiple scales, and each comparison matrix A corresponds to one scale. Calculate the weights of the cost-class attributes and the benefit-class attributes in each comparison matrix A based on the scale in each comparison matrix A; The normalized value of the cost attribute data of the first test project is calculated based on the minimum value of the cost attribute data of the test project and the cost attribute data of the first test project. The normalized value of the benefit attribute data of the first test project is calculated based on the maximum value of the benefit attribute data of the test project and the benefit attribute data of the first test project. Based on the normalized value of the cost attribute data of the first test item, the weight of the cost attribute in each comparison matrix A, the normalized value of the benefit attribute data of the first test item, and the weight of the benefit attribute in each comparison matrix A, the weighted arithmetic mean operator of the first test item on each scale is determined until the weighted arithmetic mean operator of each test item on each scale is obtained. After sorting the test items from largest to smallest according to the weighted arithmetic mean operator on the same scale for each test item, an initial test decision corresponding to a scale is obtained, until an initial test decision corresponding to each scale is obtained; The first control motherboard is tested according to the initial test decision corresponding to each scale, and the initial test decision with the shortest test completion time of the first control motherboard is selected as the test decision for the target batch of control motherboards.

3. The method according to claim 2, characterized in that, The step of calculating the weights of the cost-class attributes and the benefit-class attributes in each comparison matrix A based on the scale in each comparison matrix A includes: According to the formula Determine the weight of each cost class attribute in each of the comparison matrices A; According to the formula Determine the weight of each benefit class attribute in each of the comparison matrices A; Let A represent the comparison matrix. This represents the scale between the i-th and j-th attributes out of the n attributes of the test item.

4. The method according to claim 3, characterized in that, The step of calculating the normalized value of the cost attribute data of the first test project based on the minimum value of the cost attribute data of the test project and the cost attribute data of the first test project, and calculating the normalized value of the benefit attribute data of the first test project based on the maximum value of the benefit attribute data of the test project and the benefit attribute data of the first test project, includes: According to the formula The normalized value of the cost attribute data of the first test item is calculated; According to the formula The normalized values ​​of the benefit-type attribute data of the first test item are calculated; Wherein, minS represents the minimum value of the cost attribute data of the test item. This represents the cost-related attribute data for the first test item; This represents the maximum value of the benefit-related attribute data of the test project. This represents the benefit-related attribute data for the first test item; The step of determining the weighted arithmetic mean operator for the first test item on each scale based on the normalized value of the cost attribute data of the first test item, the weight of the cost attribute in each comparison matrix A, the normalized value of the benefit attribute data of the first test item, and the weight of the benefit attribute in each comparison matrix A, until the weighted arithmetic mean operator for each test item on each scale is obtained, includes: According to the formula WAA Determine the weighted arithmetic mean operator for the first test item on each scale, where WAA represents the weighted average operator for each test item on the first scale, m represents the normalized value of the cost attribute data of the first test item, and k represents the normalized value of the benefit attribute data of the first test item.

5. The method according to any one of claims 2-4, characterized in that, The step of testing the first control motherboard according to the initial test decision corresponding to each scale, and selecting the initial test decision with the shortest test completion time for the first control motherboard as the test decision for the target batch of control motherboards, includes: The first control motherboard is tested according to the initial test decisions corresponding to the multiple scales, and the time taken for the first control motherboard to complete the test according to each initial test decision is obtained. The weights of the cost-type attributes and the benefit-type attributes used when generating the initial test decision with the shortest test completion time for the first control motherboard are taken as the optimal cost-type attribute weights and the optimal benefit-type attribute weights. After obtaining the weights of the cost-type attributes and the benefit-type attributes of the test items sorted by weight, the weights of the two cost-type attributes adjacent to the optimal cost-type attribute weight and the weights of the two benefit-type attributes adjacent to the optimal benefit-type attribute weight are obtained. A test decision to be verified is determined based on the smaller weight of the two cost-type attribute weights to be verified, the optimal cost-type attribute weight, the optimal benefit-type attribute weight, and the larger weight of the two benefit-type attribute weights to be verified. Then, another test decision to be verified is determined based on the larger weight of the two cost-type attribute weights to be verified, the optimal cost-type attribute weight, the optimal benefit-type attribute weight, and the smaller weight of the two benefit-type attribute weights to be verified. When the test duration for completing the first control motherboard test according to both of the two test decisions to be verified is greater than the shortest test duration for completing the first control motherboard test according to the initial test decision, the initial test decision with the shortest test completion time is determined as the test decision for the target batch control motherboard.

6. The method according to any one of claims 2-4, characterized in that, After testing each of the second control motherboards in the target batch control motherboard according to the test decision of the target batch control motherboard, the method further includes: Obtain the pass-through rate of the target batch of control motherboards after testing; When the pass rate of the target batch control motherboard falls within a first value range, the test decision is updated at a first time interval. When the pass rate of the target batch control motherboard falls within a second value range, the test decision is updated at a second time interval. The maximum value of the first value range is less than the minimum value of the second value range, and the first time interval is less than the second time interval.

7. The method according to claim 6, characterized in that, The updated test decision includes: Retrieve test data generated by any of the second control motherboards after the test decision test is completed according to the target batch control motherboards; The test data generated after any one of the second control motherboards is tested is input into the multi-attribute decision model to obtain the updated test decision.

8. The method according to claim 6, characterized in that, Also includes: After retrieving the first batch of control motherboards belonging to the first control motherboard and testing them in the original order of the test items, the proportion of the number of control motherboards that passed the test within the preset number of test items in the second test item to the total number of control motherboards tested. When the proportion of the number of control motherboards that pass the test within the preset number of test attempts is greater than or equal to the preset proportion of the total number of control motherboards tested, the number of test attempts for the second test item on each control motherboard is set to the preset number of test attempts, until the number of test attempts for each test item in the test decision of the target batch of control motherboards is set. Each of the second control motherboards in the target batch is tested according to the test decision of the target batch control motherboard and the number of tests for each test item in the test decision of the target batch control motherboard.

9. A control motherboard testing device, characterized in that, include: The acquisition module is used to retrieve cost attribute data and benefit attribute data of each test item of the first control motherboard. Each test item of the first control motherboard is tested in the original order of the test items. The cost attribute data includes the test cycle time of the test item, and the benefit attribute data includes the number of test pauses of the test item. The larger the cost attribute data and the benefit attribute data are, the longer the total test time of the test item is. The processing module is used to input the cost attribute data and benefit attribute data of each test item of the first control motherboard into a multi-attribute decision model to obtain the test decision of the target batch of control motherboards. The target batch of control motherboards includes multiple second control motherboards, and the second control motherboards are of the same model as the first control motherboard. The test decision is used to instruct the second control motherboards to be tested in a first order according to each test item. The multi-attribute decision model is used to sort the test items according to a weighted arithmetic mean operator calculated based on the cost attribute data, benefit attribute data, and the scales of the cost attribute and benefit attribute, and to select the initial test decision with the shortest test time from the multiple initial test decisions obtained after sorting the test items as the test decision of the target batch of control motherboards. The scales of the cost attribute and benefit attribute are obtained by using the scaling method in the analytic hierarchy process to scale the cost attribute and benefit attribute based on their importance. The testing module is used to test each of the second control motherboards in the target batch of control motherboards according to the testing decisions of the target batch of control motherboards.

10. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the control motherboard testing method as described in any one of claims 1-7.

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