Methods for determining pass-through rate and anomaly analysis based on pass-through rate

Abstract

This application provides a method for determining first-pass yield and an anomaly analysis method based on first-pass yield. The method acquires test data from multiple test items performed on a test object within a current time window. The test data includes: the number of passes for each test item, the total number of tests for each test item, and the number of failures for each test item. The method calculates the weight corresponding to each test item based on the number of failures for each test item. Based on the weights of each test item, the number of passes for each test item, and the total number of tests for each test item, the first-pass yield of the test object within the current time window is calculated. This method enables the calculated first-pass yield to better reflect production quality.

Description

Technical Field

[0001] This application belongs to the field of detection technology, and in particular relates to a method for determining the pass rate and an anomaly analysis method based on the pass rate. Background Technology

[0002] In inverter board testing, First Pass Yield (FPY) is a key indicator for measuring production quality. Traditional FPY calculation uses a simple pass / fail count, which has the following drawbacks: unreasonable weighting; different test items have vastly different impacts on product quality. For example, failure in the insulation withstand voltage test can cause serious safety hazards, while minor cosmetic defects may not affect functionality. However, the traditional method assigns the same weight to all test items, causing FPY to fail to accurately reflect production quality; and significant calculation lag, relying on batch processing with long data update cycles, often several hours or even a day, making it difficult to meet the needs of real-time quality monitoring and rapid decision-making. This may prevent quality problems from being detected and addressed in a timely manner, increasing production costs and risks. Summary of the Invention

[0003] This application provides a method for determining first-pass yield and an anomaly analysis method based on first-pass yield. By calculating the weight of each test item based on the number of failures of each test item, the calculated first-pass yield can better reflect production quality, allowing managers to clearly understand the status of key quality links. By obtaining test data within the current time window to calculate the first-pass yield, the latest quality information can be obtained in a timely manner, enabling real-time quality monitoring. This allows for rapid response when problems occur, preventing problems from escalating and reducing production costs and quality risks.

[0004] In a first aspect, embodiments of this application provide a method for determining the pass-through rate, including: Obtain test data for multiple test items performed on the test object within the current time window, wherein the test data includes: the number of passes for each test item, the total number of tests for each test item, and the number of failures for each test item; Calculate the weight of each test item based on the number of failures of each test item; The pass rate of the test object within the current time window is calculated based on the weight of each test item, the number of passes for each test item, and the total number of tests for each test item.

[0005] In some embodiments, calculating the weight corresponding to each test item based on the number of failures of each test item includes: The total number of failures is obtained by summing the failure counts of each of the aforementioned test items. The weight of each test item is obtained by dividing the number of failures of each test item by the total number of failures.

[0006] In some embodiments, calculating the pass rate of the test object within the current time window based on the weight corresponding to each of the test items, the pass count of each of the test items, and the total number of tests for each of the test items includes: For each test item, the pass rate is obtained by dividing the number of passes by the total number of tests. Multiply the pass rate of each test item by the weight of each test item to obtain the multiplication result of each test item. The pass rate of the test object within the current time window is obtained by summing the results of multiplying the results of each test item.

[0007] In some embodiments, the method further includes: The statistics corresponding to the current time window are determined based on the pass rate and historical pass rate of the test objects within the current time window. Determine whether there are abnormal fluctuations based on the statistics corresponding to the current time window; In the event of abnormal fluctuations, an alarm message is output. Abnormal fluctuations are determined to exist when the statistics corresponding to the current time window exceed the control limit, or when the statistics of consecutive preset time windows are on the same side of the pass rate target value.

[0008] In some embodiments, the upper limit of the control limit is equal to the target pass rate plus a preset calculated value, and the lower limit of the control limit is equal to the target pass rate minus the preset calculated value. The preset calculated value is calculated based on the control limit width coefficient, the standard deviation of the pass rate determined under the condition of no abnormal fluctuations, and a preset smoothing coefficient.

[0009] Secondly, embodiments of this application provide a method for anomaly analysis of pass-through rate, the method comprising: Obtain the weight and pass rate of each test item when testing the test object within the current time window; The failure contribution of each test item is determined based on the weight of each test item and the pass rate of each test item. Anomaly analysis is performed on the test object based on the failure contribution.

[0010] In some embodiments, determining the failure contribution of each test item based on its corresponding weight and pass rate includes: The failure rate of each test item is determined based on the pass rate of each test item. The calculated failure rate for each test item is obtained by multiplying the failure rate of each test item by the weight of each test item. The total computational failure rate is obtained by summing the calculated failure rates for each test item. Divide the calculated failure rate of each test item by the total calculated failure rate to obtain the failure contribution of each test item.

[0011] Thirdly, embodiments of this application provide a device for determining the throughput, comprising: The data acquisition module is used to acquire test data of multiple test items performed on the test object within the current time window. The test data includes: the number of passes for each test item, the total number of tests for each test item, and the number of failures for each test item. The dynamic weighting module is used to calculate the weight of each test item based on the number of failures of each test item. The calculation module is used to calculate the pass rate of the test object within the current time window based on the weight corresponding to each of the test items, the number of passes for each of the test items, and the total number of tests for each of the test items.

[0012] Fourthly, embodiments of this application provide an anomaly analysis device based on pass-through rate, comprising: The acquisition module is used to acquire the weights of each test item and the pass rates of each test item when testing the test object within the current time window. An anomaly monitoring module is used to determine the failure contribution of each test item based on the weight of each test item and the pass rate of each test item. An anomaly detection module is used to perform anomaly analysis on the test object based on the failure contribution.

[0013] Fifthly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in any of the above-mentioned embodiments.

[0014] Sixthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the preceding claims.

[0015] In a seventh aspect, embodiments of this application provide a computer program product that, when run on a terminal device, causes an electronic device to execute any of the methods described above.

[0016] The beneficial effects of the embodiments in this application compared with the prior art are: This application provides a method for determining first-pass yield. The method acquires test data from multiple test items performed on a test object within a current time window. The test data includes: the number of passes for each test item, the total number of tests for each test item, and the number of failures for each test item. A weight is calculated for each test item based on its failure count. The first-pass yield of the test object within the current time window is calculated based on the weights of each test item, the number of passes for each test item, and the total number of tests for each test item. This method, by calculating the weights for each test item based on its failure count, makes the calculated first-pass yield more reflective of production quality. It allows managers to clearly understand the status of key quality processes. Calculating the first-pass yield using test data within the current time window enables timely access to the latest quality information, achieving real-time quality monitoring. This allows for rapid response when problems arise, preventing escalation and reducing production costs and quality risks. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic diagram illustrating the implementation process of a method for determining throughput provided for the purposes of this application; Figure 2 A schematic diagram illustrating the implementation process of an anomaly analysis method based on pass-through rate provided in this application embodiment; Figure 3 A schematic diagram of the data flow for an anomaly analysis method based on pass-through rate provided in an embodiment of this application; Figure 4 A schematic diagram of a through-pass rate determination device provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0019] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0020] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0021] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0022] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrases "if determined" or "if detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once detected," or "in response to detection."

[0023] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0025] Based on the problems in related technologies, this application provides a method for determining pass rate that can be applied to electronic devices. Electronic devices may include: mobile phones, tablets, wearable devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), test monitoring systems, etc. This application does not impose any restrictions on the specific type of electronic device.

[0026] Figure 1 A schematic diagram illustrating the implementation process of a method for determining throughput provided for the purposes of this application is shown below. Figure 1 As shown, the methods for determining the pass rate include: Step S101: Obtain test data for multiple test items performed on the test object within the current time window, wherein the test data includes: the number of passes for each test item, the total number of tests for each test item, and the number of failures for each test item.

[0027] In this embodiment, test data refers to the raw data generated when performing various tests on the test object (such as inverter board, energy storage converter) within a specific time window. Test items may include: insulation withstand voltage test, appearance inspection, communication test, etc. Test data includes: number of passes, total number of tests, and number of failures. The number of passes is the number of qualified products that actually pass a certain test item within the time window (e.g., 90 pieces pass the insulation withstand voltage test); the total number of tests is the total number of times the same test item is tested within the time window (e.g., the insulation withstand voltage test is performed 100 times); the number of failures is the number of times the same test item fails within the time window (e.g., the insulation withstand voltage test fails 10 times, and the number of failures = total number of tests - number of passes). The length of the time window can be configured, for example, it can be configured to 15 minutes, or it can be configured to 1 hour, etc.

[0028] In this embodiment of the application, test results can be generated in real time by testing equipment (such as voltage tester, power analyzer, appearance inspection equipment, communication testing equipment, etc.). The testing equipment transmits the test result data to the electronic device via Kafka message queue. The electronic device performs format standardization (such as unifying the time format) and anomaly filtering (such as removing invalid data that interrupts the test) to obtain the test data.

[0029] Step S102: Calculate the weight corresponding to each test item based on the number of failures of each test item.

[0030] In this embodiment, the weight is a dynamic coefficient reflecting the degree of impact of the test item on the overall quality, and is automatically adjusted based on the failure frequency. High-frequency failure items (such as insulation withstand voltage failure accounting for 60%) will receive a higher weight, while low-frequency failure items (such as visual inspection failure accounting for 5%) will receive a lower weight. The sum of the weights corresponding to all test items is 1.

[0031] In this embodiment, the number of failures of a test item within the current time window can be obtained, and a normalized formula can be used to dynamically allocate weights. For example, if the insulation withstand voltage failure rate is 60% and the visual inspection failure rate is 5%, then the weight allocation is w withstand voltage = 0.65 and w visual inspection = 0.15, significantly increasing the weight of high-frequency failure items.

[0032] Step S103: Calculate the pass rate of the test object within the current time window based on the weight corresponding to each test item, the number of passes for each test item, and the total number of tests for each test item.

[0033] In this embodiment of the application, the pass rate of the test object within the current time window is the weighted comprehensive pass rate. In this embodiment of the application, the weighted pass rate of the current time window can be calculated based on dynamic weights and test data.

[0034] This application provides a method for determining first-pass yield. This method acquires test data from multiple test items performed on a test object within a current time window. The test data includes: the number of passes for each test item, the total number of tests for each test item, and the number of failures for each test item. A weight is calculated for each test item based on its failure count. The first-pass yield of the test object within the current time window is calculated based on the weights of each test item, the number of passes for each test item, and the total number of tests for each test item. By calculating the weights for each test item based on its failure count, the calculated first-pass yield better reflects production quality, allowing managers to clearly understand the status of key quality processes. Calculating the first-pass yield using test data within the current time window enables timely access to the latest quality information, achieving real-time quality monitoring. This allows for rapid response when problems arise, preventing escalation and reducing production costs and quality risks.

[0035] In some embodiments, step S102 can be implemented by the following steps: Step S1021: Sum the number of failures for each of the test items to obtain the total number of failures.

[0036] In this embodiment, the total number of failures is the sum of the failures of all test items within the same time window. For example, if there are 5 insulation withstand voltage failures and 2 visual inspection failures, then the total number of failures = 5 + 2 = 7.

[0037] In this embodiment, the failure count of each test item can be collected in real time from the test equipment (e.g., through database query or message queue). The failure counts of all test items are added together to obtain the total failure count.

[0038] Step S1022: Divide the number of failures of each test item by the total number of failures to obtain the weight corresponding to each test item.

[0039] In this embodiment of the application, the weight corresponding to each test item can be obtained by dividing the number of failures of each test item by the total number of failures, which can be expressed by the formula: The weight of a test item = the number of failures of that item / the total number of failures.

[0040] To prevent the weight of zero-failure items from becoming zero, a base weight can be set. The formula for calculating the weight of each test item can then be expressed as: ; Where α is the normalization coefficient and β is the base weight (e.g., 0.1) to avoid zeroing the weight of zero failure items.

[0041] The method provided in this application dynamically allocates weights based on the number of failures, with high-frequency failure items automatically receiving higher weights. This makes the first-pass yield calculation more focused on actual quality issues. In addition, the weights are automatically calculated from the data without manual intervention, making it highly adaptable (e.g., automatically included in the calculation when new test items are added).

[0042] In some embodiments, step S103 can be implemented by the following steps: Step S1031: For each test item, divide the number of passes by the total number of tests to obtain the pass rate corresponding to each test item.

[0043] In this embodiment of the application, the pass rate (FPY_i) corresponding to the test item refers to the pass rate of a single test item within a specific time window. The calculation formula is: FPYi = number of passes / total number of tests. Step S1032: Multiply the pass rate corresponding to each test item by the weight corresponding to each test item to obtain the multiplication result corresponding to each test item.

[0044] In this embodiment, the multiplication result is the product of the pass rate of a single test item and its weight, representing the contribution of the test item to the overall pass rate.

[0045] Step S1033: Summing the multiplication results of each test item to obtain the pass rate of the test object within the current time window.

[0046] In this embodiment, the pass rate of the test object within the current time window is the result of multiplying all test items, reflecting the overall pass rate of the test object within the current time window.

[0047] The formula for calculating the pass rate of the test object within the current time window is: ; Where N is the total number of test items; The dynamic weight coefficient for the i-th test item; This represents the number of tests that passed within the current time window for the i-th test item. This represents the total number of tests for the i-th test item within the current time window.

[0048] In some embodiments, after step S103, the method further includes: Step S104: Determine the statistics corresponding to the current time window based on the pass rate and historical pass rate of the test object within the current time window.

[0049] In this embodiment, the historical pass rate is the pass rate data for past time windows. The statistic is a quantitative indicator calculated based on the current pass rate and the historical pass rate, used to determine whether quality fluctuations are abnormal.

[0050] In this embodiment of the application, the statistic can be calculated using the following formula: zt = λyt + (1-λ)zt-1; yt is the input FPY (weighted pass rate) for the current time window (e.g., minute t). ).

[0051] zt-1 is the statistic for the historical time window (time t-1), which is the smoothed value of historical accumulation.

[0052] The initial value z is usually set as the target mean of the process, or initialized using the mean of historical first-pass yields. λ is a smoothing coefficient (0 < λ ≤ 1), used to control the weighting of new and old data. The smaller λ is (e.g., 0.05), the higher the weight of historical data, and the less sensitive it is to short-term fluctuations; the larger λ is (e.g., 0.3), the more attention is paid to recent changes, making it suitable for rapid detection of sudden changes. For example, in inverter testing, if the FPY fluctuation is small (e.g., mature process), λ = 0.1 is used; if rapid response to anomalies is required (e.g., trial production of a new production line), λ = 0.2~0.3 is used.

[0053] For example, assuming the target FPY is 95%, λ=0.2, historical zt-1=94%, and current yt=90%, then... zt = 0.2 × 90% + 0.8 × 94% = 93.2%.

[0054] Step S105: Determine whether there are abnormal fluctuations based on the statistics corresponding to the current time window.

[0055] Step S106: In the event of abnormal fluctuations, an alarm message is output. Abnormal fluctuations are determined to exist when the statistic corresponding to the current time window exceeds the control limit, or when the statistic of consecutive preset time windows is on the same side of the pass rate target value.

[0056] In this embodiment, control limits are the upper and lower boundaries of the statistical process. If the statistic exceeds the control limits, it is determined to be an abnormal fluctuation.

[0057] In this embodiment of the application, the upper limit of the control limit is equal to the target pass rate plus a preset calculated value, and the lower limit of the control limit is equal to the target pass rate minus the preset calculated value. The preset calculated value is calculated based on the control limit width coefficient, the standard deviation of the pass rate determined under the condition of no abnormal fluctuations, and a preset smoothing coefficient.

[0058] The upper limit of the control limit can be expressed as: The lower limit of the control limit can be expressed as: ;in, The process target first-pass yield is the average (e.g., 95%), typically the expected FPY under steady-state conditions. σ is the process standard deviation, calculated using historical data fluctuations under steady-state conditions. L is the control limit width coefficient (usually 2.5~3.0); where L=3 (default strict level, low false negative rate); L=2.5 (relaxed level, suitable for initial process debugging). A preset time window (N) is the number of consecutive observation time windows used to detect continuous deviations (e.g., first-pass yield below the target value for three consecutive windows).

[0059] Example: Upper Control Limit (UCL) = 97.5%, Lower Control Limit (LCL) = 94.5%. The first-pass yield target (FPY_Target) is the expected first-pass yield standard (e.g., 96%) for the production process. If the statistic is continuously on the same side of the target value (e.g., continuously below 96%), it may indicate a systematic problem. If the statistic corresponding to the current time window exceeds the control limit, it is determined that there is abnormal fluctuation.

[0060] In some embodiments, the relative position (above / below) of the pass rate of each window to the target value can be recorded. If N consecutive windows are all below the target value (e.g., FPY), the pass rate is recorded. _Target = 96%, and if FPY < 96% for 3 consecutive windows, it is considered abnormal.

[0061] In this embodiment, the alarm information may include key data such as: anomaly type, current pass rate, historical average, and control limits. Alarms can be issued using an alarm device.

[0062] The method provided in this application embodiment quantifies the degree of fluctuation through statistical measures. Even if the first pass rate does not exceed a fixed threshold, abnormal trends can be detected. By quantifying fluctuations through statistical measures, real-time and accurate monitoring of production quality is achieved.

[0063] In related technologies, anomaly analysis is based on manual analysis of each test item sequentially to determine the problem, and the cause cannot be automatically located by associating test items.

[0064] In view of this, this application embodiment further provides an anomaly analysis method based on pass rate. This anomaly analysis method based on pass rate can be applied to electronic devices, which may include: mobile phones, tablets, wearable devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), test monitoring systems, etc. This application embodiment does not impose any restrictions on the specific type of electronic device. Figure 2 A schematic diagram illustrating the implementation flow of an anomaly analysis method based on pass-through rate provided in this application embodiment is shown below. Figure 2 As shown, it includes: Step S201: Obtain the weights of each test item and the pass rates of each test item when testing the test object within the current time window.

[0065] In this embodiment of the application, the number of failures of each test item can be summed to obtain the total number of failures; the number of failures of each test item can be divided by the total number of failures to obtain the weight corresponding to each test item.

[0066] In this embodiment of the application, the pass rate for each test item can be obtained by dividing the number of passes by the total number of tests.

[0067] Step S202: Determine the failure contribution of each test item based on the weight of each test item and the pass rate of each test item.

[0068] In this embodiment of the application, the failure contribution rate is used to quantify the responsibility ratio of each test item for the overall failure and to locate the key failure source.

[0069] In this embodiment, the failure rate of each test item can be determined based on the pass rate of each test item; the failure rate of each test item is multiplied by the weight of each test item to obtain the calculated failure rate of each test item; the calculated failure rates of each test item are summed to obtain the total calculated failure rate; the calculated failure rate of each test item is divided by the total calculated failure rate to obtain the failure contribution of each test item. The failure contribution can be calculated using the following formula: ; To test the weight of item i, This represents the pass rate of test item i within the current time window. (Molecular) The weighted failure rate of test item i reflects its absolute impact. The denominator represents the sum of the weighted failure rates of all test items, used for normalization.

[0070] For example, suppose test items A (weight 0.5, FPY = 80%) and B (weight 0.3, FPY = 90%): Then C A =0.5×0.2 / (0.3×0.1+0.5×0.2)=0.13 / 0.1≈76.9%.

[0071] Step S202: Perform anomaly analysis on the test object based on the failure contribution.

[0072] In this embodiment, failure contribution can be sorted from high to low to identify dominant failure items and thus locate key failure sources. Continuing the example above, it shows that test item A contributes 76.9% to the current failure and needs to be investigated first.

[0073] The method provided in this application embodiment measures the impact of each project by measuring the failure contribution, and quickly locates projects with high weight and low pass rate.

[0074] In this embodiment, the electronic device executing steps S101 to S103 and the device executing steps S201 to S203 can be the same electronic device. When the electronic devices are the same, steps S201 to S203 can be executed after steps S101 to S103. In some embodiments, they may not be the same electronic device; in this case, the electronic device executing steps S101 to S103 can be a first electronic device, and the electronic device executing steps S201 to S203 can be a second electronic device.

[0075] Based on the foregoing embodiments, this application further provides an anomaly analysis method based on pass-through rate. This anomaly analysis based on pass-through rate can be applied to an anomaly analysis system based on pass-through rate. The anomaly analysis system based on pass-through rate includes: a testing device, a data acquisition module, a streaming computing engine, a dynamic weight module, a calculation module, a visualization dashboard, an anomaly monitoring module, and an alarm engine. Figure 3 A schematic diagram of the data flow for an anomaly analysis method based on pass-through rate provided in an embodiment of this application, as shown below. Figure 3 As shown, it includes: Test equipment: Performs various FT tests on the inverter board (such as voltage test, power test, etc.), and generates each test result, including the test item name, pass / fail status, timestamp, and other information.

[0076] Data Acquisition Layer: Receives raw test data in real time from test devices (e.g., via Kafka messages), performs format standardization on the test data, filters invalid or abnormal data (e.g., test interruption), and stores it in Elasticsearch.

[0077] Streaming computing engine: Continuously receives test data streams from the data acquisition layer, groups the data according to a preset time window (e.g., every 15 minutes), and sends the processed data to the dynamic weight module.

[0078] Dynamic weighting module: Counts the number of failures of each test item in the most recent time period, automatically calculates and updates the weight value of each test item based on the failure frequency, and test items with high failure frequency get higher weight, while test items with low failure frequency get lower weight.

[0079] Calculation module: Receives weight data from the dynamic weight module and uses a weighted formula to calculate the pass rate for the current time window, which better reflects the impact of key test items compared to traditional methods.

[0080] Anomaly monitoring module: Performs statistical analysis on the calculated FPY value, uses the exponentially weighted moving average algorithm to monitor the FPY change trend, and automatically triggers an early warning mechanism when abnormal fluctuations are detected.

[0081] Visual dashboard: Real-time display of FPY curve changes, showing the weight distribution of each test item, and highlighting anomaly warning information.

[0082] Alarm Engine: Receives abnormal signals from the anomaly monitoring module, automatically analyzes the cause of the anomaly (such as identifying the test item with the highest contribution), and notifies relevant personnel through various means (screen prompts, message pushes, etc.).

[0083] The method provided in this application embodiment is processed in real time throughout the entire process. Through dynamic adjustment of weights, the FPY calculation is made to better reflect the actual quality situation. The intelligent early warning system can automatically identify abnormal patterns and reduce manual intervention.

[0084] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0085] According to the foregoing embodiments, this application provides a through-pass rate determination device. The various modules and units included in the device can be implemented by a processor in a computer device; of course, they can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor unit (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0086] This application provides a device for determining the throughput. Figure 4 A schematic diagram of a through-pass rate determination device provided in an embodiment of this application is shown below. Figure 4 As shown, the straight-through rate determining device 400 includes: The data acquisition module 401 is used to acquire test data of multiple test items performed on the test object within the current time window, wherein the test data includes: the number of passes for each test item, the total number of tests for each test item, and the number of failures for each test item; The dynamic weighting module 402 is used to calculate the weight corresponding to each test item based on the number of failures of each test item. The calculation module 403 is used to calculate the pass rate of the test object within the current time window based on the weight corresponding to each of the test items, the number of passes for each of the test items, and the total number of tests for each of the test items.

[0087] In some embodiments, the dynamic weighting module includes: The first calculation unit is used to sum the number of failures for each of the test items to obtain the total number of failures; The second calculation unit is used to divide the number of failures of each test item by the total number of failures to obtain the weight corresponding to each test item.

[0088] In some embodiments, the computing module includes: The third calculation unit is used to divide the number of passes by the total number of tests for each test item to obtain the pass rate corresponding to each test item. The fourth calculation unit is used to multiply the pass rate corresponding to each test item by the weight corresponding to each test item to obtain the multiplication result corresponding to each test item. The fifth calculation unit is used to sum the multiplication results of each test item to obtain the pass rate of the test object within the current time window.

[0089] In some embodiments, the through-pass determination device 400 further includes: The anomaly monitoring module is used to determine the statistics corresponding to the current time window based on the pass rate and historical pass rate of the test object within the current time window; The judgment module is used to determine whether there are abnormal fluctuations based on the statistics corresponding to the current time window; An alarm engine is used to output alarm information when there are abnormal fluctuations. Specifically, abnormal fluctuations are determined when the statistics corresponding to the current time window exceed the control limit, or when the statistics of consecutive preset time windows are on the same side of the pass rate target value.

[0090] In some embodiments, the upper limit of the control limit is equal to the target pass rate plus a preset calculated value, and the lower limit of the control limit is equal to the target pass rate minus the preset calculated value. The preset calculated value is calculated based on the control limit width coefficient, the standard deviation of the pass rate determined under the condition of no abnormal fluctuations, and a preset smoothing coefficient.

[0091] Based on the foregoing embodiments, this application further provides an anomaly analysis device based on pass-through rate, comprising: The acquisition module is used to acquire the weights of each test item and the pass rates of each test item when testing the test object within the current time window. An anomaly monitoring module is used to determine the failure contribution of each test item based on the weight of each test item and the pass rate of each test item. An anomaly detection module is used to perform anomaly analysis on the test object based on the failure contribution.

[0092] In some embodiments, determining the failure contribution of each test item based on its corresponding weight and pass rate includes: The failure rate of each test item is determined based on the pass rate of each test item. The calculated failure rate for each test item is obtained by multiplying the failure rate of each test item by the weight of each test item. The total computational failure rate is obtained by summing the calculated failure rates for each test item. Divide the calculated failure rate of each test item by the total calculated failure rate to obtain the failure contribution of each test item.

[0093] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0094] In addition, the device described above can be a software unit, a hardware unit, or a combination of software and hardware. It can also be integrated into electronic devices as an independent component, or exist as an independent terminal device.

[0095] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0096] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 300 of this embodiment may include: at least one processor 30 ( Figure 5Only one processor 30, memory 31, and computer program 32 stored in memory 31 and executable on at least one processor 30 are shown. When the processor 30 executes the computer program 32, it implements the steps in any of the above method embodiments, or the processor 30 executes the computer program 32 to implement the functions of each module / unit in the above device or system embodiments.

[0097] For example, computer program 32 may be divided into one or more modules / units, one or more of which are stored in memory 31 and executed by processor 30 to complete this application. One or more modules / units may be a series of computer program 32 instruction segments capable of performing a specific function, which describe the execution process of computer program 32 in electronic device 300.

[0098] This application also provides a computer-readable storage medium storing a computer program 32, which, when executed by a processor 30, implements the steps described in the above-described method embodiments.

[0099] This application provides a computer program product that, when run on an electronic device, enables the electronic device to perform the steps described in the various method embodiments above.

[0100] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program 32 instructing related hardware. The computer program 32 can be stored in a computer-readable storage medium, and when executed by the processor 30, it can implement the steps of the various method embodiments described above. The computer program 32 includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium can include at least: any entity or device capable of carrying computer program code to a terminal, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0101] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0102] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0103] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0104] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0105] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for determining the straight-through rate, characterized in that, include: Obtain test data for multiple test items performed on the test object within the current time window, wherein the test data includes: the number of passes for each test item, the total number of tests for each test item, and the number of failures for each test item; Calculating the weight of each test item based on the number of failures for each test item includes: summing the number of failures for each test item to obtain the total number of failures; dividing the number of failures for each test item by the total number of failures to obtain the weight of each test item, wherein the formula for calculating the weight of each test item is as follows: Where α is the normalization coefficient and β is the basic weight; The pass rate of the test object within the current time window is calculated based on the weights corresponding to each test item, the pass counts of each test item, and the total number of tests for each test item. This includes: for each test item, dividing the pass count by the total number of tests to obtain the pass rate for each test item; multiplying the pass rate for each test item by its corresponding weight to obtain the product result for each test item; and summing the product results for each test item to obtain the pass rate of the test object within the current time window. The formula for calculating the pass rate of the test object is as follows: Where N is the total number of test items; The dynamic weight coefficient for the i-th test item; This represents the number of tests that passed within the current time window for the i-th test item. This represents the total number of tests for the i-th test item within the current time window.

2. The method according to claim 1, characterized in that, The method further includes: The statistics corresponding to the current time window are determined based on the pass rate and historical pass rate of the test objects within the current time window. Determine whether there are abnormal fluctuations based on the statistics corresponding to the current time window; In the event of abnormal fluctuations, an alarm message is output. Abnormal fluctuations are determined to exist when the statistics corresponding to the current time window exceed the control limit, or when the statistics of consecutive preset time windows are on the same side of the pass rate target value.

3. The method according to claim 2, characterized in that, The upper limit of the control limit is equal to the target pass rate plus a preset calculated value, and the lower limit of the control limit is equal to the target pass rate minus the preset calculated value. The preset calculated value is calculated based on the control limit width coefficient, the standard deviation of the pass rate determined under the condition of no abnormal fluctuations, and a preset smoothing coefficient.

4. An anomaly analysis method based on pass-through rate, characterized in that, The method includes: Get the weight of each test item and the pass rate of each test item when testing the test object within the current time window; Determining the failure contribution of each test item based on its corresponding weight and pass rate includes: determining the failure rate of each test item based on its pass rate; multiplying the failure rate of each test item by its corresponding weight to obtain the calculated failure rate of each test item; summing the calculated failure rates of each test item to obtain the total calculated failure rate; and dividing the calculated failure rate of each test item by the total calculated failure rate to obtain the failure contribution of each test item. The formula for calculating the weight of each test item is as follows: Where α is the normalization coefficient and β is the base weight, the formula for calculating the pass rate of the test object is: Where N is the total number of test items; The dynamic weight coefficient for the i-th test item; This represents the number of tests that passed within the current time window for the i-th test item. The formula for calculating the failure contribution is: where represents the total number of tests for the i-th test item within the current time window. ,in, To test the weight of item i, The pass rate of test item i within the current time window; Anomaly analysis is performed on the test object based on the failure contribution.

5. A device for determining the throughput, characterized in that, include: The data acquisition module is used to acquire test data of multiple test items performed on the test object within the current time window. The test data includes: the number of passes for each test item, the total number of tests for each test item, and the number of failures for each test item. The dynamic weighting module is used to calculate the weight corresponding to each test item based on the number of failures of each test item, including: summing the number of failures of each test item to obtain the total number of failures; dividing the number of failures of each test item by the total number of failures to obtain the weight corresponding to each test item, wherein the calculation formula for the weight corresponding to each test item is: Where α is the normalization coefficient and β is the basic weight; The calculation module is used to calculate the pass rate of the test object within the current time window based on the weights corresponding to each test item, the pass counts of each test item, and the total number of tests for each test item. The calculation includes: for each test item, dividing the pass count by the total number of tests to obtain the pass rate for each test item; multiplying the pass rate for each test item by the weights corresponding to each test item to obtain the product results for each test item; and summing the product results for each test item to obtain the pass rate of the test object within the current time window. The formula for calculating the pass rate of the test object is: Where N is the total number of test items; The dynamic weight coefficient for the i-th test item; This represents the number of tests that passed within the current time window for the i-th test item. This represents the total number of tests for the i-th test item within the current time window.

6. An anomaly analysis device based on pass-through rate, characterized in that, include: The acquisition module is used to obtain the weights and pass rates of each test item when testing the test object within the current time window. An anomaly monitoring module is used to determine the failure contribution of each test item based on its corresponding weight and pass rate. This includes: determining the failure rate of each test item based on its pass rate; multiplying the failure rate of each test item by its corresponding weight to obtain the calculated failure rate of each test item; summing the calculated failure rates of each test item to obtain the total calculated failure rate; and dividing the calculated failure rate of each test item by the total calculated failure rate to obtain the failure contribution of each test item. The formula for calculating the weight of each test item is as follows: Where α is the normalization coefficient and β is the base weight, the formula for calculating the pass rate of the test object is: Where N is the total number of test items; The dynamic weight coefficient for the i-th test item; This represents the number of tests that passed within the current time window for the i-th test item. The formula for calculating the failure contribution is: where represents the total number of tests for the i-th test item within the current time window. ,in, To test the weight of item i, The pass rate of test item i within the current time window; An anomaly detection module is used to perform anomaly analysis on the test object based on the failure contribution.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 3 and / or claim 4.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 3 and / or claim 4.

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