Test instrument data acquisition methods, devices, electronic equipment and storage media

Abstract

This application discloses a method, apparatus, electronic device, and storage medium for acquiring test instrument data. The method includes: acquiring a first test instrument dataset; the first test instrument dataset includes multiple test instrument data; selecting a portion of the test instrument data from the first test instrument dataset to obtain a second test instrument dataset; the data density of the second test instrument dataset is greater than the data density of the remaining test instrument data in the first test instrument dataset; and determining target test instrument data based on the test instrument data included in the second test instrument dataset. Implementing this application embodiment can improve the accuracy of determining mobile device parameters based on test instrument data acquired from test instruments, thereby improving the testing accuracy of mobile devices.

Description

Technical Field

[0001] This application relates to the field of equipment testing, specifically to a data acquisition method, device, electronic equipment, and storage medium for testing instruments. Background Technology

[0002] Today, mobile devices such as smartphones undergo extensive testing before leaving the factory to confirm and optimize product performance. This extensive testing often requires the use of testing instruments, such as radio frequency (RF) performance testing and baseband testing. Obtaining performance parameters from these instruments provides a clear visual representation of the device's performance, facilitating quantification and subsequent optimization comparisons.

[0003] Automated testing methods can be used to obtain the performance parameters of mobile devices through testing instruments. Existing automated testing methods mainly determine the current performance parameters of mobile devices by taking the average of multiple samples. However, when the testing power is high, the parameters obtained by the testing instruments fluctuate greatly and are prone to extreme values, which can cause the parameters obtained by the testing instruments to be biased and lead to inaccurate test results. Summary of the Invention

[0004] This application discloses a test instrument data acquisition method, device, electronic device, and storage medium, which can improve the accuracy of determining the parameters of a mobile device based on the test instrument data acquired by the test instrument, thereby improving the testing accuracy of the mobile device.

[0005] This application discloses a method for acquiring test instrument data, characterized in that the method includes:

[0006] Obtain the first test instrument dataset; the first test instrument dataset includes data from multiple test instruments.

[0007] A portion of the test instrument data is selected from the first test instrument dataset to obtain the second test instrument dataset; the data density of the second test instrument dataset is greater than the data density of the remaining test instrument data in the first test instrument dataset.

[0008] The target test instrument data is determined based on the test instrument data included in the second test instrument dataset.

[0009] As an optional implementation, the step of selecting a portion of the test instrument data from the first test instrument dataset to obtain the second test instrument dataset includes:

[0010] The first test instrument dataset is divided into multiple first test instrument data subsets; each first test instrument data subset includes the same number of test instrument data; each first test instrument data subset includes at least one different test instrument data.

[0011] The subset of first test instrument data with the highest data density is selected from the plurality of first test instrument data subsets as the second test instrument dataset.

[0012] As an optional implementation, selecting the first test instrument data subset with the highest data density from the plurality of first test instrument data subsets as the second test instrument dataset includes:

[0013] Calculate the range of the individual test instrument data included in each of the first subsets of test instrument data;

[0014] The first test instrument data subset with the smallest range is selected from the plurality of first test instrument data subsets; the first test instrument data subset with the smallest range corresponds to the largest data density;

[0015] The first test instrument data subset with the smallest range is used as the second test instrument dataset.

[0016] As an optional implementation, the step of selecting a portion of the test instrument data from the first test instrument dataset to obtain the second test instrument dataset includes:

[0017] The test instrument data included in the first test instrument dataset are sorted in ascending order to obtain the third test instrument dataset.

[0018] A portion of the test instrument data is selected from the third test instrument dataset to obtain the second test instrument dataset.

[0019] As an optional implementation, the step of selecting a portion of the test instrument data from the third test instrument dataset to obtain the second test instrument dataset includes:

[0020] The test instrument data included in the third test instrument dataset are subtracted pairwise at preset intervals to obtain multiple pairwise subtraction results;

[0021] Determine the minimum value among the multiple pairwise subtraction results;

[0022] The two test instrument data used for pairwise subtraction to obtain the minimum value, and the test instrument data in the third test instrument dataset located between the two test instrument data, are determined as the second test instrument dataset.

[0023] As an optional implementation, the third test instrument dataset includes a first data subset and a second data subset; the test instrument data included in the first data subset and the second data subset are sorted in ascending order; the number of test instrument data included in the first data subset and the second data subset are equal; the step of selecting a portion of the test instrument data from the third test instrument dataset to obtain the second test instrument dataset includes:

[0024] Determine the median value of the test instrument data corresponding to the first data subset and the second data subset respectively; the median value of the first data subset is less than the median value of the second data subset;

[0025] Calculate the difference between the median values ​​of the test instrument data corresponding to the first data subset and the second data subset, respectively;

[0026] Calculate the product of the difference and the first weight to obtain the second weight;

[0027] Calculate the difference between the median value of the first data subset and the second weight to obtain the first threshold;

[0028] The sum of the median value of the second data subset and the second weight is calculated to obtain the second threshold.

[0029] The test instrument data in the third test instrument dataset that are less than the first threshold and greater than the second threshold are removed from the third test instrument dataset to obtain the second test instrument dataset.

[0030] As an optional implementation, determining the target test instrument data based on the test instrument data included in the second test instrument dataset includes:

[0031] Calculate the average value of the data from each test instrument included in the second test instrument dataset to obtain the target test instrument data.

[0032] This application discloses a test instrument data acquisition device, the device comprising:

[0033] The acquisition module is used to acquire a first test instrument dataset; the first test instrument dataset includes data from multiple test instruments.

[0034] The selection module is used to select a portion of the test instrument data from the first test instrument dataset to obtain a second test instrument dataset; the data density of the second test instrument dataset is greater than the data density of the remaining test instrument data in the first test instrument dataset;

[0035] The determination module is used to determine the target test instrument data based on the test instrument data included in the second test instrument dataset.

[0036] This application discloses an electronic device, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor enables the processor to implement any of the test instrument data acquisition methods disclosed in this application.

[0037] This application discloses a computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute any of the test instrument data acquisition methods disclosed in this application.

[0038] Compared with related technologies, the embodiments of this application have the following beneficial effects:

[0039] A first test instrument dataset is obtained; a portion of the instrument data is selected from the multiple test instrument data included in the first test instrument dataset to obtain a second test instrument dataset with a data density greater than that of the remaining test instrument data in the first test instrument dataset; the target test instrument data is determined based on the test instrument data included in the second test instrument dataset. This embodiment of the application improves the accuracy of determining the mobile device parameters based on the test instrument data collected by the test instrument by selecting the portion of test instrument data with the highest data density from the acquired multiple test instrument data, effectively avoiding interference from extreme data, thereby improving the testing accuracy of the mobile device. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the embodiments 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.

[0041] Figure 1 This is a schematic flowchart of a test instrument data acquisition method disclosed in an embodiment of this application;

[0042] Figure 2 This is a flowchart illustrating another test instrument data acquisition method disclosed in the embodiments of this application;

[0043] Figure 3 This is a flowchart illustrating another test instrument data acquisition method disclosed in the embodiments of this application;

[0044] Figure 4This is a waveform diagram of test instrument data disclosed in an embodiment of this application;

[0045] Figure 5 This is a schematic diagram of the structure of a test instrument data acquisition device disclosed in an embodiment of this application;

[0046] Figure 6 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. Detailed Implementation

[0047] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0048] It should be noted that the terms "comprising" and "having," and any variations thereof, in the embodiments and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0049] In related technologies, testing methods for mobile devices such as smartphones are mainly divided into two categories: manual testing and automated testing. Manual testing involves manually reading data collected by testing instruments, resulting in more accurate parameters and allowing for subjective judgment and selection of more precise parameters. However, manual testing is time-consuming and incurs high labor costs. Automated testing, on the other hand, uses computer programs to automatically read data collected by testing instruments, offering more precise and efficient time control. However, computer programs are somewhat rigid in their approach to reading parameters, typically reading the instrument's current parameters at short intervals and calculating the average of all reads to obtain the accurate final parameters of the mobile device.

[0050] However, the parameters collected by testing instruments fluctuate constantly, especially when the testing power of the instruments is high, the fluctuations are drastic, and extreme values ​​are easily generated. Automated testing methods that use multiple samples and averages do not take this factor into account, making them susceptible to the influence of extreme values. This results in the final determined parameters of the mobile device deviating significantly from the actual test results. In contrast, manual testing, where data is manually read from testing instruments, typically takes values ​​within a stable range, making it relatively accurate. Automated testing methods that use multiple samples and averages indiscriminately select values, easily leading to the selection of extreme values. This results in biased parameters for the mobile device, causing inaccurate test results and requiring re-verification, thus impacting testing efficiency and quality.

[0051] This application discloses a data acquisition method, device, electronic device, and storage medium for test instruments, which can improve the accuracy of test instruments in acquiring parameters from mobile devices.

[0052] The test instrument data acquisition method disclosed in this application belongs to the category of automated test methods, which will be described in detail below.

[0053] Please see Figure 1 , Figure 1 This is a flowchart illustrating a data acquisition method for a test instrument disclosed in an embodiment of this application. Wherein, Figure 1 The data acquisition method for the test instrument described herein is applicable to electronic devices such as personal computers, laptops, smartphones, and tablets, but is not limited to these devices in the embodiments of this application.

[0054] like Figure 1 As shown, the data acquisition method for this test instrument may include the following steps:

[0055] 101. Obtain the first test instrument dataset.

[0056] The first test instrument dataset includes data from multiple test instruments.

[0057] Test instruments may include digital multimeters, digital oscilloscopes, spectrum analyzers, network analyzers, etc., and are not specifically limited to any one of them. Test instruments can collect test data from mobile devices such as mobile phones and tablets. The test data from mobile devices may include, but is not limited to, temperature, voltage, current, etc.

[0058] Mobile devices often require extensive testing before production or shipment to determine and optimize their performance parameters. For example, test instruments can measure the radio frequency (RF) performance of mobile devices, allowing electronic devices to calibrate the RF performance of the mobile device and generate a compensation table after acquiring the RF parameters collected by the test instruments. Alternatively, test instruments can measure the battery parameters of mobile devices, allowing electronic devices to compensate for the battery parameters after acquiring the battery parameters collected by the test instruments. Or, test instruments can measure the current and power parameters of mobile devices, which are related to the lifespan of the mobile device's components.

[0059] The first test instrument dataset includes data from multiple test instruments, which are data collected by the test instruments from mobile devices. The number of test instrument data included in the first test instrument dataset can be 10 or 15, and there is no specific limitation.

[0060] The electronic device can acquire multiple test instrument data collected by the test instrument according to the sampling interval to obtain the first test instrument dataset. The sampling interval can be 1 second, and the specific interval is not limited.

[0061] 102. Select a portion of the test instrument data from the first test instrument dataset to obtain the second test instrument dataset.

[0062] The data density of the second test instrument dataset is greater than the data density of the remaining test instrument data in the first test instrument dataset.

[0063] Data density can be the amount of data within a unit of measurement. In this embodiment, data density can be the amount of data within a unit of test instrument data. For example, if the test instrument data is current, the data density can be the amount of data within a unit of current. The higher the data density of a dataset, the larger the amount of data within a unit of measurement, indicating that the data in the dataset is more densely packed and that there are fewer extreme data points.

[0064] Optionally, the electronic device can select a portion of the test instrument data from the first test instrument dataset each time. There must be at least one different test instrument data among the selected portions of the test instrument data each time. The electronic device can calculate the variance of the selected portions of the test instrument data each time, and take the portion of the test instrument data with the smallest variance as the portion of the test instrument data with the largest data density to obtain the second test instrument dataset.

[0065] Optionally, the electronic device can select a portion of the test instrument data from the first test instrument dataset each time. There must be at least one different test instrument data among the selected portions of the test instrument data each time. The electronic device can calculate the range of the selected portions of the test instrument data each time, and take the portion of the test instrument data with the smallest range as the portion of the test instrument data with the largest data density to obtain the second test instrument dataset.

[0066] In some embodiments, the test instrument data is voltage values. The electronic device calculates the average of multiple voltage values ​​included in the first test instrument dataset. When the average of the multiple voltage values ​​is greater than a voltage threshold, a portion of the voltage values ​​is selected from the first test instrument dataset to obtain the second test instrument dataset. This is because when the test instrument tests the voltage of the mobile device, if the measured voltage of the mobile device is greater than the voltage threshold, it indicates that the collected voltage fluctuations are relatively drastic and extreme values ​​are easily generated. Therefore, a portion of the test instrument data needs to be selected from the first test instrument dataset to obtain the second test instrument dataset.

[0067] Similarly, the test instrument data is the current value; the electronic device calculates the average of multiple current values ​​included in the first test instrument dataset. When the average of multiple current values ​​is greater than the current threshold, a portion of the current values ​​are selected from the first test instrument dataset to obtain the second test instrument dataset.

[0068] In some embodiments, the electronic device generates a graph corresponding to the first test instrument dataset based on multiple test instrument data included in the first test instrument dataset; the electronic device divides the graph into multiple curve segments, removes the test instrument data corresponding to curve segments whose waveform amplitude is greater than the waveform amplitude threshold, and determines the remaining test instrument data as the second test instrument dataset.

[0069] In some embodiments, the electronic device stores standard value ranges corresponding to multiple performance parameters of the mobile device; the electronic device can be connected to multiple test instruments; each test instrument collects different performance parameters of the mobile device; after acquiring the first test instrument dataset collected by the test instruments, the electronic device parses out the performance parameters corresponding to the test instruments; obtains the standard value ranges corresponding to the performance parameters; and selects a portion of the test instrument data in the first test instrument dataset that falls within the standard value range to obtain the second test instrument dataset.

[0070] 103. Determine the target test instrument data based on the test instrument data included in the second test instrument dataset.

[0071] The target test instrument data is the final value of the performance parameters of the mobile device determined from multiple test instrument data collected from the test instrument.

[0072] Electronic devices can determine the target test instrument data by calculating the mode, median, mean, etc. of the test instrument data included in the second test instrument dataset.

[0073] This application embodiment improves the accuracy of determining mobile device parameters based on test instrument data by selecting the portion of test instrument data with the highest data density from multiple acquired test instrument data. This effectively avoids interference from extreme data points, thereby enhancing the testing accuracy of mobile devices. Compared to automated testing methods that use multiple sampling and averaging, the automated testing method proposed in this application embodiment improves accuracy.

[0074] Please see Figure 2 , Figure 2 This is a schematic flowchart illustrating another test instrument data acquisition method disclosed in an embodiment of this application. This test instrument data acquisition method can be applied to any of the aforementioned electronic devices. For example... Figure 2 As shown, the method includes the following steps:

[0075] 201. Obtain the first test instrument dataset.

[0076] The first test instrument dataset includes data from multiple test instruments.

[0077] 202. Divide the first test instrument dataset into multiple first test instrument data subsets.

[0078] Each first subset of test instrument data includes the same number of test instrument data; each first subset of test instrument data includes at least one different set of test instrument data.

[0079] For example, if each first test instrument data subset includes 5 test instrument data, and the first test instrument dataset includes multiple test instrument data of 1, 2, 4, 5, 6, 3, 4, 2, 7, 8 respectively, then the first test instrument data subset can be [1, 2, 4, 5, 6], [1, 2, 4, 5, 3], [1, 2, 4, 5, 4], [2, 4, 5, 6, 3], [4, 5, 6, 3, 8], etc., without any specific limitation.

[0080] The subset of first test instrument data with the highest data density is selected from multiple subsets of first test instrument data as the second test instrument dataset, as shown in steps 203 to 305.

[0081] If a subset of first-test instrument data has the highest data density, it means that the amount of data within a unit of test instrument data in that subset is the largest. For example, if the test instrument data is current, and the subset of first-test instrument data has the highest data density, it means that the amount of data within a unit of current in that subset is the largest.

[0082] 203. Calculate the range of the data of each test instrument included in each subset of the first test instrument data.

[0083] Calculate the range of each test instrument data included in each first test instrument data subset, that is, calculate the difference between the maximum value and the minimum value of each test instrument data included in each first test instrument data subset. By using the range of each test instrument data included in each first test instrument data subset, determine the data density of each first test instrument data subset.

[0084] 204. Select the first test instrument data subset with the smallest range from multiple first test instrument data subsets.

[0085] The data density is highest for the first subset of test instrument data with the smallest range.

[0086] If multiple subsets of first test instrument data with equal and smallest ranges are selected, these subsets can be used as the second test instrument dataset. Alternatively, one subset of first test instrument data with equal and smallest ranges can be selected as the second test instrument dataset. The selection method can include calculating variance, calculating average, selecting median values, etc., and is not specifically limited.

[0087] 205. The subset of data from the first test instrument with the smallest range is taken as the dataset of the second test instrument.

[0088] 206. Determine the target test instrument data based on the test instrument data included in the second test instrument dataset.

[0089] This application embodiment divides the first test instrument dataset into multiple first test instrument data subsets, and selects the first test instrument data subset with the smallest range from these subsets. This subset with the smallest range is then determined as the second test instrument dataset with the highest data density. The target test instrument data is then determined based on this second test instrument dataset. This improves the accuracy of determining mobile device parameters from the test instrument data collected by the test instruments and effectively avoids interference from extreme data. Compared to automated testing methods that involve multiple sampling and averaging, the automated testing method proposed in this application embodiment improves the testing accuracy of mobile devices.

[0090] Please see Figure 3 , Figure 3 This is a schematic flowchart of another test instrument data acquisition method disclosed in an embodiment of this application. This test instrument data acquisition method can be applied to any of the aforementioned electronic devices.

[0091] 301. Obtain the first test instrument dataset.

[0092] The first test instrument dataset includes data from multiple test instruments.

[0093] 302. Sort the multiple test instrument data included in the first test instrument dataset in ascending order to obtain the third test instrument dataset.

[0094] In some embodiments, the third test instrument dataset includes a first data subset and a second data subset; the multiple test instrument data included in the first data subset and the second data subset are sorted in ascending order; the number of test instrument data included in the first data subset and the second data subset are equal; a portion of the test instrument data is selected from the third test instrument dataset to obtain the second test instrument dataset, including:

[0095] Determine the median value of the test instrument data corresponding to the first data subset and the second data subset respectively; the median value of the first data subset is less than the median value of the second data subset; calculate the difference between the median values ​​of the test instrument data corresponding to the first data subset and the second data subset respectively; calculate the product of the difference and the first weight to obtain the second weight; calculate the difference between the median value of the first data subset and the second weight to obtain the first threshold; calculate the sum of the median value of the second data subset and the second weight to obtain the second threshold; remove test instrument data in the third test instrument dataset that is less than the first threshold and greater than the second threshold from the third test instrument dataset to obtain the second test instrument dataset.

[0096] For example, the first test instrument dataset includes multiple test instrument data points numbered 4, 3, 5, 7, 6, 8, 10, 9, 11, and 27. These test instrument data points are sorted in ascending order to obtain the third test instrument dataset, which includes multiple test instrument data points numbered 3, 4, 5, 6, 7, 8, 9, 10, 11, and 27. Therefore, the first data subset includes multiple test instrument data points numbered 3, 4, 5, 6, and 7, and the second data subset includes multiple test instrument data points numbered 8, 9, 10, 11, and 27. The median value of the test instrument data points in the first data subset is 5, and the median value of the test instrument data points in the second data subset is... The first weight is 10, and the difference between the median values ​​of the test instrument data corresponding to the first and second data subsets is 5. If the first weight is 0.5, the product of the difference and the first weight is the second weight, which is 2.5. The difference between the median value corresponding to the first data subset and the second weight is the first threshold, which is 2.5. The sum of the median value corresponding to the second data subset and the second weight is the second threshold, which is 12.5. Test instrument data that are less than the first threshold and test instrument data that are greater than the second threshold are removed from the third test instrument dataset to obtain the second test instrument dataset. The test instrument data included in the second test instrument dataset are 3, 4, 5, 6, 7, 8, 9, 10, and 11.

[0097] In some embodiments, selecting a portion of the test instrument data from the third test instrument dataset to obtain the second test instrument dataset may further include steps 303 to 305.

[0098] 303. Subtract the data from multiple test instruments in the third test instrument dataset at preset intervals to obtain multiple results of pairwise subtraction.

[0099] The preset interval can be 3, and the specific interval is not limited.

[0100] For example, the electronic device can acquire data from 10 test instruments collected by the test instruments and store it in the first test instrument dataset. The first test instrument dataset can be a collection. The data in the first test instrument dataset is sorted in ascending order to obtain the third test instrument dataset. The 10 test instrument data included in the third test instrument dataset are sorted in ascending order as A[0], A[1], A[2], A[3], A[4], A[5], A[6], and A[7]. ]、A[7]、A[8]、A[9];If the preset interval is 3, the multiple test instrument data included in the third test instrument dataset are subtracted from each other according to the preset interval to obtain a total of 6 results of pairwise subtraction of B1, B2, B3, B4, B5, B6, where B1=A[4]-A[0]、B2=A[5]-A[1]、B3=A[6]-A[2]、B4=A[7]-A[3]、B5=A[8]-A[4]、B6=A[9]-A[5].

[0101] 304. Determine the minimum value among multiple pairwise subtraction results.

[0102] 305. The two test instrument data used for pairwise subtraction to obtain the minimum value, and the test instrument data in the third test instrument data set located between the two test instrument data, are determined as the second test instrument data set.

[0103] Determine the minimum value among B1, B2, B3, B4, B5, and B6. For example, if the minimum value is B1, then A[4] and A[0] are the two test instrument data used for pairwise subtraction to obtain the minimum value. The test instrument data between A[4] and A[0] are A[1], A[2], and A[3]. Therefore, the five test instrument data A[0], A[1], A[2], A[3], and A[4] are determined as the second test instrument dataset. The data density of the second test instrument dataset is greater than the data density of the remaining test instrument data in the first test instrument dataset.

[0104] 306. Calculate the average value of the data from each test instrument included in the second test instrument dataset to obtain the target test instrument data.

[0105] Calculate the average value of the five test instrument data A[0], A[1], A[2], A[3], and A[4] in the second test instrument dataset, and use the average value as the target test instrument data.

[0106] This application embodiment processes multiple test instrument data collected by the test instruments by first filtering out the test instrument data and then averaging the data from the portion of the test instrument data with the highest data density. This effectively avoids interference from extreme test instrument data, significantly improving the accuracy of the final target test instrument data. The accuracy of the target test instrument data determined by the method of this application embodiment is comparable to that determined by manual testing methods, but is significantly more efficient. Furthermore, compared to directly averaging the test instrument data, this application embodiment filters out multiple test instrument data with the highest data density and determines the target test instrument data from them, effectively improving data accuracy and thus enhancing the testing accuracy of mobile devices.

[0107] Please see Figure 4 , Figure 4 This is a waveform diagram of test instrument data disclosed in an embodiment of this application. The test instrument data is current, and the data density can refer to the amount of data per unit current on the vertical axis. As can be seen, the current fluctuations are quite drastic.

[0108] If the test instrument data is read manually, since the test instrument data is mostly concentrated between 1.360 and 1.363, that is, the data density of the test instrument data between 1.360 and 1.363 is relatively high, the target test instrument data determined manually is 1.3615;

[0109] If the method of directly calculating the average value through multiple sampling is used, 10 test instrument data points are obtained: 1.3592, 1.3614, 1.3615, 1.3598, 1.3615, 1.3637, 1.3563, 1.3611, 1.3612, and 1.3580. The average value of these 10 test instrument data points is taken to obtain the target test instrument data of 1.36037.

[0110] If the method of first screening and then averaging in the embodiment of this application is adopted, 10 test instrument data points are obtained by sampling: 1.3592, 1.3614, 1.3615, 1.3598, 1.3615, 1.3637, 1.3563, 1.3611, 1.3612, and 1.3580. After screening and averaging these 10 test instrument data points, the target test instrument data is obtained as 1.36134.

[0111] Therefore, it is evident that the target test instrument data determined by the method in this application embodiment is closer to the target test instrument data read manually, and can effectively simulate the values ​​read manually. Moreover, the target test instrument data obtained by directly taking the average value is obviously not the correct value and has a large deviation, while the target test instrument data determined by the embodiment of this application is closer to the true value and can be used as the true value, which is an effective data.

[0112] In summary, the method of first screening and then averaging, as used in this solution, yields more accurate target test instrument data that is closer to the true value than the direct averaging method. This effectively avoids obtaining invalid data and thus improves the testing accuracy of mobile devices.

[0113] Please see Figure 5 , Figure 5 This is a schematic diagram of a test instrument data acquisition device disclosed in an embodiment of this application. This device can be applied to electronic devices such as personal computers, laptops, smartphones, and tablets, and is not specifically limited thereto. Figure 5 As shown, the test instrument data acquisition device 500 may include: an acquisition module 510, a selection module 520, and a determination module 530.

[0114] The acquisition module 510 is used to acquire a first test instrument dataset; the first test instrument dataset includes data from multiple test instruments.

[0115] The selection module 520 is used to select a portion of the test instrument data from the first test instrument dataset to obtain a second test instrument dataset; the data density of the second test instrument dataset is greater than the data density of the remaining test instrument data in the first test instrument dataset.

[0116] The determination module 530 is used to determine the target test instrument data based on the test instrument data included in the second test instrument dataset.

[0117] In one embodiment, the selection module 520 is further configured to divide the first test instrument dataset into multiple first test instrument data subsets; each first test instrument data subset includes the same number of test instrument data; each first test instrument data subset includes at least one different test instrument data; and the first test instrument data subset with the highest data density is selected from the multiple first test instrument data subsets as the second test instrument dataset.

[0118] In one embodiment, the selection module 520 is further configured to calculate the range of each test instrument data included in each first test instrument data subset; select the first test instrument data subset with the smallest range from multiple first test instrument data subsets; the first test instrument data subset with the smallest range corresponds to the largest data density; and use the first test instrument data subset with the smallest range as the second test instrument dataset.

[0119] In one embodiment, the selection module 520 is further configured to sort the multiple test instrument data included in the first test instrument dataset in ascending order to obtain a third test instrument dataset; and select a portion of the test instrument data from the third test instrument dataset to obtain a second test instrument dataset.

[0120] In one embodiment, the selection module 520 is further configured to subtract multiple test instrument data included in the third test instrument dataset from each other at a preset interval to obtain multiple results of pairwise subtraction; determine the minimum value among the multiple results of pairwise subtraction; and determine the two test instrument data used to perform pairwise subtraction to obtain the minimum value, as well as the test instrument data in the third test instrument dataset located between the two test instrument data, as the second test instrument dataset.

[0121] In one embodiment, the third test instrument dataset includes a first data subset and a second data subset; the test instrument data included in the first data subset and the second data subset are sorted in ascending order; the number of test instrument data included in the first data subset and the second data subset is equal; selecting a portion of the test instrument data from the third test instrument dataset to obtain the second test instrument dataset includes: determining the median value of the test instrument data corresponding to the first data subset and the second data subset respectively; the median value of the first data subset is less than the median value of the second data subset; calculating the difference between the median values ​​of the test instrument data corresponding to the first data subset and the second data subset respectively; calculating the product of the difference and the first weight to obtain the second weight; calculating the difference between the median value of the first data subset and the second weight to obtain the first threshold; calculating the difference between the median value of the second data subset and the second weight to obtain the second threshold; removing test instrument data in the third test instrument dataset that is less than the first threshold and greater than the second threshold from the third test instrument dataset to obtain the second test instrument dataset.

[0122] In one embodiment, the determining module 530 is further configured to calculate the average value of the individual test instrument data included in the second test instrument dataset to obtain the target test instrument data.

[0123] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application.

[0124] like Figure 6 As shown, the electronic device 600 may include:

[0125] Memory 610 storing executable program code;

[0126] Processor 620 coupled to memory 610;

[0127] The processor 620 calls the executable program code stored in the memory 610 to execute any of the test instrument data acquisition methods disclosed in the embodiments of this application.

[0128] This application discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by the processor, the processor implements any of the test instrument data acquisition methods disclosed in this application.

[0129] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Those skilled in the art should also recognize that the embodiments described in the specification are optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0130] In the various embodiments of this application, it should be understood that the sequence number of each process does not necessarily 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.

[0131] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they can 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.

[0132] Furthermore, the functional units in the various embodiments of this application 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.

[0133] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-accessible memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several requests to cause a computer device (which can be a personal computer, server, or network device, specifically a processor in the computer device) to execute some or all of the steps of the methods described in the various embodiments of this application.

[0134] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0135] The foregoing has provided a detailed description of a test instrument data acquisition method, apparatus, electronic device, and storage medium disclosed in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for acquiring data from a test instrument, characterized in that, The method includes: Obtain the first test instrument dataset; the first test instrument dataset includes data from multiple test instruments. A portion of the test instrument data is selected from the first test instrument dataset to obtain the second test instrument dataset; The data density of the second test instrument dataset is greater than the data density of the remaining test instrument data in the first test instrument dataset; The target test instrument data is determined based on the test instrument data included in the second test instrument dataset; The step of selecting a portion of test instrument data from the first test instrument dataset to obtain the second test instrument dataset includes: sorting the multiple test instrument data included in the first test instrument dataset in ascending order to obtain a third test instrument dataset; and selecting a portion of test instrument data from the third test instrument dataset to obtain the second test instrument dataset. The third test instrument dataset includes a first data subset and a second data subset; the test instrument data included in the first data subset and the second data subset are sorted in ascending order; the number of test instrument data included in the first data subset and the second data subset are equal; the step of selecting a portion of the test instrument data from the third test instrument dataset to obtain the second test instrument dataset includes: The intermediate values ​​of the test instrument data corresponding to the first data subset and the second data subset are determined respectively; the intermediate value of the first data subset is less than the intermediate value of the second data subset; the difference between the intermediate values ​​of the test instrument data corresponding to the first data subset and the second data subset is calculated; the product of the difference and the first weight is calculated to obtain the second weight; the difference between the intermediate value of the first data subset and the second weight is calculated to obtain the first threshold; the sum of the intermediate value of the second data subset and the second weight is calculated to obtain the second threshold; test instrument data in the third test instrument dataset that are less than the first threshold and greater than the second threshold are removed from the third test instrument dataset to obtain the second test instrument dataset.

2. The method according to claim 1, characterized in that, The step of selecting a portion of the test instrument data from the first test instrument dataset to obtain the second test instrument dataset includes: The first test instrument dataset is divided into multiple first test instrument data subsets; each first test instrument data subset includes the same number of test instrument data; each first test instrument data subset includes at least one different test instrument data. The subset of first test instrument data with the highest data density is selected from the plurality of first test instrument data subsets as the second test instrument dataset.

3. The method according to claim 2, characterized in that, The step of selecting the first test instrument data subset with the highest data density from the plurality of first test instrument data subsets as the second test instrument dataset includes: Calculate the range of the individual test instrument data included in each of the first subsets of test instrument data; Select the first test instrument data subset with the smallest range from the plurality of first test instrument data subsets; The data density is the largest corresponding to the first subset of test instrument data with the smallest range. The first test instrument data subset with the smallest range is used as the second test instrument dataset.

4. The method according to claim 1, characterized in that, The step of selecting a portion of the test instrument data from the third test instrument dataset to obtain the second test instrument dataset also includes: The test instrument data included in the third test instrument dataset are subtracted pairwise at preset intervals to obtain multiple pairwise subtraction results; Determine the minimum value among the multiple pairwise subtraction results; The two test instrument data used for pairwise subtraction to obtain the minimum value, and the test instrument data in the third test instrument dataset located between the two test instrument data, are determined as the second test instrument dataset.

5. The method according to any one of claims 1-4, characterized in that, The step of determining the target test instrument data based on the test instrument data included in the second test instrument dataset includes: Calculate the average value of the data from each test instrument included in the second test instrument dataset to obtain the target test instrument data.

6. A test instrument data acquisition device, characterized in that, include: The acquisition module is used to acquire the first test instrument dataset; The first test instrument dataset includes data from multiple test instruments; The selection module is used to select a portion of the test instrument data from the first test instrument dataset to obtain the second test instrument dataset. The data density of the second test instrument dataset is greater than the data density of the remaining test instrument data in the first test instrument dataset; The determination module is used to determine the target test instrument data based on the test instrument data included in the second test instrument dataset; Specifically, the selection module is used to: sort the multiple test instrument data included in the first test instrument dataset in ascending order to obtain a third test instrument dataset; and select a portion of the test instrument data from the third test instrument dataset to obtain a second test instrument dataset. The third test instrument dataset includes a first data subset and a second data subset; the test instrument data included in the first data subset and the second data subset are sorted in ascending order; the number of test instrument data included in the first data subset and the second data subset are equal; the selection module is specifically used for: The intermediate values ​​of the test instrument data corresponding to the first data subset and the second data subset are determined respectively; the intermediate value of the first data subset is less than the intermediate value of the second data subset; the difference between the intermediate values ​​of the test instrument data corresponding to the first data subset and the second data subset is calculated; the product of the difference and the first weight is calculated to obtain the second weight; the difference between the intermediate value of the first data subset and the second weight is calculated to obtain the first threshold; the sum of the intermediate value of the second data subset and the second weight is calculated to obtain the second threshold; test instrument data in the third test instrument dataset that are less than the first threshold and greater than the second threshold are removed from the third test instrument dataset to obtain the second test instrument dataset.

7. An electronic device, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, 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 5.

Images