Abnormal Environment Chip Stability Testing Method Based on MiniLED Display

Abstract

The present invention relates to the technical field of chip testing, and specifically relates to a method for testing the stability of chips in an abnormal environment based on MiniLED display. The method includes: obtaining the first credibility and the second credibility of each power consumption data in the power consumption data sequence, obtaining the credibility weighting factor of each power consumption data according to the first credibility and the second credibility, and performing weighted fusion on all the power consumption data sequences according to the credibility weighting factor of the power consumption data to obtain a target data sequence; obtaining the stability test result of the chips to be tested in the abnormal test environment according to the target data sequence. The target data sequence obtained by the present invention through weighted fusion based on the credibility weighting factor can make the stability test result of the chips to be tested in the abnormal test environment more accurate and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of chip testing, and particularly to a method for testing the stability of chips in an abnormal environment based on MiniLED display. Background Art

[0002] MiniLED display technology is a backlight system applied to LCD displays. Moreover, by using LED chips with dimensions between 50 and 200 micrometers, MiniLED display technology achieves higher resolution, brightness, contrast, and color gamut performance, while making the device thinner, lighter, and more energy-efficient. In addition, due to the functions of MiniLED display technology, it has been widely used in various display devices, such as high-end monitors, large-size TVs, laptops, and tablets. And because the chips used in MiniLED display technology play an important role in display technology, the stability and reliability of the chips during use are crucial. Currently, in order to ensure or understand the stability and reliability of chips during use, stability tests are usually conducted on the chips used in MiniLED display technology. Since the usage environment of the chips is not constant, that is, the chips will also be used in abnormal environments, and abnormal environments particularly affect the stability and reliability of the chips. Therefore, in order to evaluate the long-term reliability and stability of the chips, various extreme environmental conditions that the chips may encounter in actual use, such as high temperature, high humidity, high pressure, and high light intensity, are simulated to discover and solve potential problems in advance to ensure the stability and reliability of the chips in actual applications.

[0003] Currently, the method of high-voltage accelerated aging test is generally used to test the stability of chips in abnormal environments. Before testing the chips, generally multiple test chips are selected first, and then the high-voltage accelerated aging test method is used to test each test chip, and a power consumption data sequence of each test chip during the test is obtained by using a collection device. Finally, the final stability test result is determined based on the average sequence of the power consumption data sequences of all test chips. However, during the test process, some factors interfering with the test process are not considered. For example, in a closed experimental environment, too strong light intensity will cause the temperature to rise, resulting in the formation of a high-pressure environment in the experimental environment. The high-pressure environment will increase electromagnetic interference and cause voltage fluctuations, and electromagnetic interference and voltage fluctuations will cause some power consumption data to be interfered, that is, electromagnetic interference and voltage fluctuations will cause some data in the collected power consumption data sequence to deviate or have deviations. When some data in the collected power consumption data sequence deviate or have deviations, if the final stability test result is determined based on the average sequence of the power consumption data sequences of all test chips, it will lead to unreliable and inaccurate problems in the obtained stability test result. Therefore, how to accurately and reliably obtain the stability test result has become an urgent problem to be solved. Summary of the Invention

[0004] To solve the above problems, the present invention provides a method for testing the stability of chips in an abnormal environment based on MiniLED display. The specific technical solutions adopted are as follows:

[0005] An embodiment of the present invention provides a method for testing the stability of chips in an abnormal environment based on MiniLED display, including the following steps:

[0006] Select A test chips from the chips to be tested in a batch, and perform high-voltage accelerated aging tests on each test chip to obtain the power consumption data sequence corresponding to each test chip in the abnormal test environment, where A is greater than 1;

[0007] Obtain the local subsequences corresponding to each power consumption data in the power consumption data sequence, and perform linear fitting on the power consumption data sequence and the local subsequences corresponding to each power consumption data respectively to obtain the first fitting line of the power consumption data sequence and the second fitting lines of each power consumption data. According to the first fitting line and the second fitting lines, obtain the first credibility of each power consumption data;

[0008] According to the adjacent power consumption data of each power consumption data in the power consumption data sequence and the difference between two adjacent power consumption data in the local subsequence, obtain the volatility index value of each power consumption data. According to the power consumption data in the power consumption data sequence and the volatility index value of each power consumption data, obtain the second credibility of each power consumption data;

[0009] According to the first credibility and the second credibility, obtain the credibility weighting factor of each power consumption data. According to the credibility weighting factor of the power consumption data, perform weighted fusion on all the power consumption data sequences to obtain the target data sequence;

[0010] According to the target data sequence, obtain the stability test result of the chips to be tested in the abnormal test environment.

[0011] Beneficial effects: First, the present invention selects A test chips from the chips of the batch to be tested, and performs high-voltage accelerated aging tests on each test chip to obtain the power consumption data sequence corresponding to each test chip in the abnormal test environment. Secondly, local subsequences corresponding to each power consumption data in the power consumption data sequence are obtained, and linear fitting is respectively performed on the power consumption data sequence and the local subsequences corresponding to each power consumption data to obtain the first fitting line of the power consumption data sequence and the second fitting lines of each power consumption data. According to the first fitting line and the second fitting lines, the first credibility of each power consumption data is obtained. Then, according to the adjacent power consumption data of each power consumption data in the power consumption data sequence and the difference between two adjacent power consumption data in the local subsequence, the volatility index value of each power consumption data is obtained. According to the power consumption data in the power consumption data sequence and the volatility index value of each power consumption data, the second credibility of each power consumption data is obtained. Then, according to the first credibility and the second credibility, the credibility weighting factor of each power consumption data is obtained. According to the credibility weighting factor of the power consumption data, all the power consumption data sequences are weighted and fused to obtain the target data sequence. Finally, according to the target data sequence, the stability test result of the chips of the batch to be tested in the abnormal test environment is obtained. Moreover, the target data sequence obtained by weighted fusion according to the credibility weighting factor in the present invention can make the stability test result of the chips of the batch to be tested in the abnormal test environment more accurate and reliable. Description of the Drawings

[0012] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0013] Figure 1 It is a flowchart of a method for testing the stability of chips in an abnormal environment based on MiniLED display according to the present invention. Detailed Embodiments

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art belong to the scope protected by the embodiments of the present invention.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art belonging to the present invention.

[0016] This embodiment provides a method for testing the stability of chips in an abnormal environment based on MiniLED display, which is described in detail as follows:

[0017] As Figure 1 shown, the method for testing the stability of chips in an abnormal environment based on MiniLED display includes the following steps:

[0018] Step S001, select A test chips from the chips of the batch to be tested, and perform high-voltage accelerated aging test on each test chip to obtain the power consumption data sequence corresponding to each test chip in the abnormal test environment.

[0019] The main purpose of this embodiment is to accurately and reliably obtain the stability test results when testing the stability of chips in an abnormal test environment, and this embodiment mainly tests the chips used in the MiniLED display technology. In addition, for the convenience of understanding and analysis, this embodiment will select any batch of chips that have not undergone stability testing for testing, and this embodiment will record the selected batch of chips as the chips of the batch to be tested. The chips in the chips of the batch to be tested belong to the same type, and A chips are randomly selected from the chips of the batch to be tested as test chips. The test chips are mainly used for subsequent stability testing; and in this embodiment, the implementer needs to set the value of A according to the actual situation. For example, in this embodiment, the value of A can be set to an empirical value, or the value of A can be determined according to the total number of chips in the batch to be tested. If the total number of chips in the batch to be tested is A1, then the integer value of one percent of A1 can be used as the value of A.

[0020] Then, use the high-voltage accelerated aging test method to perform high-voltage accelerated aging test on each test chip, and use the acquisition device to collect the power consumption data sequence corresponding to each test chip during the test. The power consumption data sequence corresponding to any test chip is composed of all the power consumption data of the test chip collected from the start of the test to the end of the test. The high-voltage accelerated aging test refers to using a HAST high-acceleration aging test chamber to test the stability of the chip. The acquisition device can be a power analyzer; this embodiment records the environment simulated in the high-acceleration aging test chamber as the abnormal test environment, so as to record the above-obtained power consumption data sequence as the power consumption data sequence corresponding to each test chip in the abnormal test environment.

[0021] And during the test of all test chips, the test environment is not changed, that is, the parameters of the high-acceleration aging test chamber are not changed, and it is required that the test duration of each test chip is the same; it should also be noted that if the space of the high-acceleration aging test chamber is sufficient, all the test chips can be put in for testing at the same time, or they can be put in in batches.

[0022] In addition, the purpose of using the high-voltage accelerated aging test method to test the stability of the chip is that the HAST high-acceleration aging test chamber can simulate extreme environments of high temperature, high humidity, high air pressure, and high light intensity, that is, the high-acceleration aging test chamber can simulate different abnormal test environments, and the abnormal test environments refer to high temperature, high humidity, high air pressure, high light intensity, etc.; moreover, before the test, the implementer needs to set the parameters of the HAST high-acceleration aging test chamber according to the actual situation. For example, the temperature can be set to 125 degrees Celsius, the pressure can be set to 3 atm, and the test duration can be set to 2 hours, etc.; and it should also be noted that in this embodiment, the acquisition time intervals between adjacent two power consumption data in each power consumption data sequence are all the same, and the implementer can also set the acquisition time interval between adjacent two power consumption data in the sequence according to the actual situation, such as it can be set to 1 second.

[0023] Therefore, through the above process, this embodiment can obtain the power consumption data sequence corresponding to each test chip under the abnormal test environment.

[0024] Step S002: Obtain the local subsequences corresponding to each power consumption data in the power consumption data sequence, and perform linear fitting on the power consumption data sequence and the local subsequences corresponding to each power consumption data respectively to obtain the first fitting line of the power consumption data sequence and the second fitting lines of each power consumption data. According to the first fitting line and the second fitting lines, obtain the first credibility of each power consumption data.

[0025] Since some factors interfering with the testing process are not considered during the current testing process. For example, in a closed experimental environment, too strong light intensity will cause the temperature to rise, resulting in a high-pressure environment in the experimental environment. The high-pressure environment will increase electromagnetic interference and cause voltage fluctuations. Electromagnetic interference and voltage fluctuations will cause some power consumption data to be interfered. That is, electromagnetic interference and voltage fluctuations will cause some data in the collected power consumption data sequence to deviate or have deviations. When some data in the collected power consumption data sequence deviate or have deviations, if the final stability test result is determined based on the average sequence of the power consumption data sequences of all test chips, it will lead to unreliable and inaccurate stability test results. In order to make the obtained stability test result more accurate in this embodiment, this embodiment will analyze the power consumption data sequence to obtain the credibility of each power consumption data in each power consumption data sequence. The credibility can reflect the possibility of the corresponding data deviating or having deviations or can reflect the probability of the corresponding data being interfered. Then, based on the credibility of each power consumption data in the power consumption data sequence, all power consumption data sequences are weighted and fused. Subsequently, the stability test result is analyzed based on the sequence obtained after weighted fusion. And this method in this embodiment can minimize the impact of some interfered power consumption data on the stability test result.

[0026] Based on the above analysis, it can be seen that in the following, this embodiment will obtain the credibility of each power consumption data in the power consumption data sequence. In this embodiment, the first credibility of each power consumption data in the power consumption data sequence will be obtained first. However, before obtaining the first credibility, it is necessary to first obtain the local subsequences corresponding to each power consumption data in the power consumption data sequence, and linearly fit the power consumption data sequence and the local subsequences corresponding to each power consumption data respectively to obtain the first fitting line of the power consumption data sequence and the second fitting lines of each power consumption data. Then, according to the first fitting line and the second fitting lines, the first credibility of each power consumption data is obtained. It can be seen that this embodiment needs to first obtain the local subsequences corresponding to each power consumption data in the power consumption data sequence. That is, the process of obtaining the local subsequences is as follows:

[0027] First, a preset local window is constructed, and the size of the preset local window is set to 1×L, where L is the length of the preset local window. And in specific applications, the implementer needs to set the value of L according to the actual situation. However, in this embodiment, it is required that the value of L is an odd number. For example, in this embodiment, the value of L can be set to 3 or 5.

[0028] For the sake of easy understanding, in this embodiment, the process of obtaining the local subsequence corresponding to any power consumption data b in the power consumption data sequence B corresponding to any test chip is taken as an example for description. Then, the process of obtaining the local subsequence corresponding to the power consumption data b is as follows: In the power consumption data sequence B corresponding to the test chip, a window that contains the power consumption data b and is equal to the preset local window size is constructed, and it is denoted as the local window corresponding to the power consumption data b. Then, the sequence formed by all the power consumption data in the local window corresponding to the power consumption data b is denoted as the local subsequence corresponding to the power consumption data b.

[0029] And the specific process of constructing the local window corresponding to the power consumption data b is as follows: If in the power consumption data sequence B, the number of power consumption data on the left side of the power consumption data b is less than Then, in the constructed local window corresponding to the power consumption data b, the power consumption data b is located at the first position; if in the power consumption data sequence B, the number of power consumption data on the right side of the power consumption data b is less than Then, in the constructed local window corresponding to the power consumption data b, the power consumption data b is located at the last position; if in the power consumption data sequence B, the number of power consumption data on both the left and right sides of the power consumption data b is not less than Then, in the constructed local window corresponding to the power consumption data b, the power consumption data b is located at the middle position.

[0030] After obtaining the local subsequences corresponding to each power consumption data, in this embodiment, next, according to the power consumption data sequence corresponding to each test chip and the local subsequences corresponding to each power consumption data, the first fitting line of the power consumption data sequence corresponding to each test chip and the second fitting line of each power consumption data in the power consumption data sequence are obtained. And the first fitting line and the second fitting line are the key to obtaining the first credibility subsequently. Therefore, the specific process of obtaining the first fitting line of the power consumption data sequence corresponding to each test chip and the second fitting line of each power consumption data in the power consumption data sequence is as follows:

[0031] Perform linear fitting on the power consumption data sequence corresponding to each test chip respectively, and denote the obtained line as the first fitting line of the corresponding power consumption data sequence; perform linear fitting on the local subsequences corresponding to each power consumption data in the power consumption data sequence respectively, and denote the obtained line as the second fitting line of the corresponding power consumption data.

[0032] After obtaining the first fitting line of the power consumption data sequence and the second fitting line of each power consumption data, this embodiment will obtain the first credibility of each power consumption data in each power consumption data sequence according to the first fitting line of the power consumption data sequence corresponding to each test chip and the second fitting line of each power consumption data in the power consumption data sequence. The first credibility is one of the important indicators for obtaining the credibility weighting factor for weighted fusion subsequently. Then, the specific process for obtaining the first credibility of each power consumption data in each power consumption data sequence is as follows:

[0033] First, calculate the similarity between the second fitting line of each power consumption data in each power consumption data sequence and the first fitting line of the corresponding power consumption data sequence, and record the calculation result as the fitting similarity characterization value corresponding to the power consumption data. For example, the fitting similarity characterization value corresponding to the power consumption data b in the power consumption data sequence B refers to the similarity between the first fitting line of the power consumption data sequence B and the second fitting line of the power consumption data b. Then, obtain the first marking value of each power consumption data in each power consumption data sequence, and the first marking value of the j-th power consumption data in different power consumption data sequences is j. After obtaining the fitting similarity characterization value and the first marking value corresponding to each power consumption data, for the convenience of understanding, this embodiment will describe the process of obtaining the first credibility of the power consumption data b in the power consumption data sequence B as an example based on the obtained fitting similarity characterization value and the first marking value corresponding to the power consumption data, that is, the process of obtaining the first credibility of the power consumption data b in the power consumption data sequence B is as follows:

[0034] First, in all power consumption data sequences, obtain all power consumption data with the same first marker value as the power consumption data b, and denote the set constructed by all the obtained power consumption data with the same first marker value as the power consumption data b as the characteristic power consumption data set of the power consumption data b. That is, the data in the characteristic power consumption data set of the power consumption data b comes from different sequences but are all at the same position in the sequences. Then, among the fitting similarity characterization values corresponding to all the power consumption data in the characteristic power consumption data set of the power consumption data b, select the maximum fitting similarity characterization value as the maximum characterization value of the power consumption data b. After that, calculate the absolute value of the difference between the fitting similarity characterization value corresponding to the power consumption data b and the maximum characterization value of the power consumption data b, and denote the calculation result as the first difference corresponding to the power consumption data b. Then, add the first difference corresponding to the power consumption data b to a preset first constant, and denote the reciprocal of the obtained result as the first characterization value. Immediately afterwards, in the characteristic power consumption data set of the power consumption data b, obtain all the data except the power consumption data b, and denote the set formed by all the data except the power consumption data b in the characteristic power consumption data set as the subset B1 corresponding to the power consumption data b. Then, obtain the first similarity index value corresponding to each power consumption data in the subset B1. The first similarity index value corresponding to the h-th power consumption data in the subset B1 refers to the similarity between the local subsequence corresponding to the h-th power consumption data and the local subsequence corresponding to the power consumption data b. And the greater the similarity between the local subsequence corresponding to the h-th power consumption data and the local subsequence corresponding to the power consumption data b, the more similar the local subsequence corresponding to the h-th power consumption data is to the local subsequence corresponding to the power consumption data b. Then, in the subset B1, count the number of power consumption data with the first similarity index value greater than a preset similarity threshold, and denote it as the first quantity characterization value. After that, obtain the ratio of the first quantity characterization value to the total number of data in the subset B1, and denote it as the second characterization value. Finally, denote the mean of the first characterization value and the second characterization value as the first credibility of the power consumption data b in the sequence B.

[0035] And in specific applications, the implementer needs to set the preset similarity threshold and the preset first constant according to the actual situation. For example, in this embodiment, the preset similarity threshold can be set to 0.6, and the preset first constant can be set to 1. And in this embodiment, the dynamic time warping DTW can be used to calculate the similarity between two sequences. The method of using the dynamic time warping DTW to calculate the similarity between two sequences is a well-known technology, so this embodiment will not describe it in detail.

[0036] In addition, the specific expression for obtaining the first credibility of the power consumption data b in the sequence B in this embodiment is:

[0037]

[0038] where β1 is the first credibility of the power consumption data b, SB,j is the fitting similarity characterization value corresponding to the power consumption data b, S1 j is the maximum characterization value of the power consumption data b. N1 is the number of power consumption data with fitting similarity characterization values greater than the preset similarity threshold in the subset B1, and is also the first quantity characterization value. N0 is the total number of data in the subset B1.

[0039] And when |S B,b - S1 b | is smaller, it indicates that the local subsequence corresponding to the power consumption data b, that is, the change trend in the corresponding local window, is more consistent with the overall change trend in the sequence B. And when the change trend in the local window corresponding to the power consumption data b is more consistent with the overall change trend in the sequence B, it indicates that the degree of interference of the power consumption data b is smaller, that is, it indicates that the credibility of the power consumption data b is higher; when is larger, it indicates that the change trend in the local window corresponding to the power consumption data b is more similar to the change trend in the local window corresponding to the power consumption data with the same first marker value as the power consumption data b in the remaining power consumption data sequences, and it also indicates that the degree of interference of the power consumption data b is smaller, that is, it indicates that the credibility of the power consumption data b is higher; and because when |S B,b - S1 b | is smaller and is larger, the value of β1 is larger. Based on the above analysis, it can be known that when the value of β1 is larger, it indicates that the degree of interference of the power consumption data b in the sequence B is smaller or the credibility of the power consumption data b is higher, and the higher the credibility indicates that the reference degree of the power consumption data b in the sequence B is greater when performing weighted fusion later; on the contrary, when the value of β1 is smaller, it indicates that the degree of interference of the power consumption data b in the sequence B is larger or the credibility is lower.

[0040] Therefore, according to the method of the first credibility of the power consumption data b in this embodiment, the first credibility of each power consumption data in the power consumption data sequence corresponding to each test chip can be obtained.

[0041] Step S003, according to the adjacent power consumption data of each power consumption data in the power consumption data sequence and the difference between adjacent two power consumption data in the local subsequence, obtain the volatility index value of each power consumption data. According to the power consumption data in the power consumption data sequence and the volatility index value of each power consumption data, obtain the second credibility of each power consumption data.

[0042] In order to further make the subsequent obtained stability results more reliable and accurate, in this embodiment, after obtaining the first credibility of each power consumption data, the second credibility of each power consumption data will be continuously obtained, and based on the first credibility and the second credibility, the sequence obtained after weighted fusion can be made more reliable; in addition, in this embodiment, before obtaining the second credibility of each power consumption data, it is necessary to first obtain the volatility index value of each power consumption data according to the difference between the adjacent power consumption data of each power consumption data in the power consumption data sequence and the difference between two adjacent power consumption data in the local subsequence corresponding to each power consumption data, and then according to the power consumption data in the power consumption data sequence and the volatility index value of each power consumption data, the second credibility of each power consumption data is obtained; therefore, in this embodiment, the volatility index value of each power consumption data will be obtained first, and the specific obtaining process is as follows:

[0043] First, according to the adjacent power consumption data of each power consumption data in the power consumption data sequence corresponding to each test chip, the adjacent slope difference value corresponding to each power consumption data in each power consumption data sequence is obtained; then, according to the difference between two adjacent power consumption data in the local subsequence corresponding to each power consumption data, the characteristic difference sequence corresponding to each power consumption data is obtained; after obtaining the adjacent slope difference value and the characteristic difference sequence corresponding to each power consumption data, for the convenience of understanding, in this embodiment, the obtaining process of the volatility index value of the power consumption data b in the power consumption data sequence B will be described as an example based on the obtained adjacent slope difference value and the characteristic difference sequence, that is, the obtaining process of the volatility index value of the power consumption data b is as follows: First, the reciprocal of the result obtained by adding the adjacent slope difference value corresponding to the power consumption data b to the preset first constant is recorded as the first characteristic value of the power consumption data b, the result obtained by subtracting the first characteristic value from the preset first constant is recorded as the second characteristic value of the power consumption data b, the mean value of the characteristic difference sequence corresponding to the power consumption data b is recorded as the third characteristic value of the power consumption data b, and the mean value of the second characteristic value and the third characteristic value of the power consumption data b is recorded as the volatility index value of the power consumption data b.

[0044] In addition, the specific expression for calculating the volatility index value of the power consumption data b is:

[0045]

[0046] where γ is the volatility index value of the power consumption data b, and ΔK is the adjacent slope difference value corresponding to the power consumption data b, is the second eigenvalue of the power consumption data b, and D3 is the third eigenvalue of the power consumption data b; and the larger ΔK is, the greater the difference between the power consumption data b and the two adjacent data on its left and right, that is, the more likely it is that the power consumption data b belongs to the turning point. When the power consumption data b is more likely to belong to the turning point, it indicates that the volatility of the position where the power consumption data b is located may be greater; when D3 is larger, it indicates that the data change in the local window corresponding to the power consumption data b is greater, and it also indicates that the data fluctuation in the local window corresponding to the power consumption data b is more obvious. When the data fluctuation in the local window corresponding to the power consumption data b is more obvious, it indicates that the volatility of the position where the power consumption data b is located may be greater; and because when ΔK is larger and D3 is larger, the value of γ is larger, and based on the above analysis, it can be known that when the value of γ is larger, it indicates that the volatility of the position where the power consumption data b is located may be greater, and conversely, when the value of γ is smaller, it indicates that the volatility of the position where the power consumption data b is located may be smaller.

[0047] In this embodiment, the process of obtaining the adjacent slope difference value corresponding to each power consumption data in each power consumption data sequence according to the adjacent power consumption data of each power consumption data in the power consumption data sequence corresponding to each test chip is as follows:

[0048] First, construct a mapping space, and the abscissa value of the data points in the constructed mapping space is the first marker value, and the ordinate value is the power consumption data; then map all the power consumption data and the first marker value of the power consumption data in each power consumption data sequence into the mapping space, and record the data points obtained by the mapping as the data points corresponding to the corresponding power consumption data, that is, each power consumption data in the power consumption data sequence corresponds to a data point, and the abscissa value of the data point corresponding to any power consumption data is the first marker value of the power consumption data, and the ordinate value is the power consumption data.

[0049] Based on the data points corresponding to the obtained power consumption data, next, the process of obtaining the adjacent slope difference value corresponding to the j-th power consumption data in the power consumption data sequence B will be described as an example. That is, the process of obtaining the adjacent slope difference value corresponding to the j-th power consumption data in sequence B is as follows: First, determine whether j is not equal to 1 and B0. If so, obtain the slope of the line formed by the data point corresponding to the j-th power consumption data in sequence B and the data point corresponding to the (j - 1)-th power consumption data, and denote it as the first slope. Obtain the slope of the line formed by the data point corresponding to the j-th power consumption data in sequence B and the data point corresponding to the (j + 1)-th power consumption data, and denote it as the second slope. Then, take the absolute value of the difference between the first slope and the second slope as the adjacent slope difference value corresponding to the j-th power consumption data in sequence B, and B0 is the total number of data in sequence B. Otherwise, continue to determine whether j is equal to 1. If so, take the absolute value of the slope of the line formed by the data point corresponding to the j-th power consumption data in sequence B and the data point corresponding to the (j + 1)-th power consumption data as the adjacent slope difference value corresponding to the j-th power consumption data in sequence B. Otherwise, continue to determine whether j is equal to B0. If so, take the absolute value of the slope of the line formed by the data point corresponding to the j-th power consumption data in sequence B and the data point corresponding to the (j - 1)-th power consumption data as the adjacent slope difference value corresponding to the j-th power consumption data in sequence B.

[0050] In this embodiment, the method for obtaining the characteristic difference sequence corresponding to each power consumption data is as follows: For the power consumption data b in the power consumption data sequence B, obtain the normalized value of the absolute value of the difference between two adjacent power consumption data in the local subsequence corresponding to the power consumption data b. Then, the sequence constructed by the normalized values of the absolute values of the differences between all adjacent two power consumption data in the local subsequence corresponding to the power consumption data b is denoted as the characteristic difference sequence corresponding to the power consumption data b. That is, the d-th characteristic difference in the characteristic difference sequence corresponding to the power consumption data b is the normalized value of the absolute value of the difference between the (d + 1)-th power consumption data and the d-th power consumption data in the local subsequence corresponding to this power consumption data. That is, the result of normalizing the absolute value of the difference between the (d + 1)-th power consumption data and the d-th power consumption data using the normalization function norm() is the d-th characteristic difference in the characteristic difference sequence corresponding to the power consumption data b.

[0051] Therefore, according to the above process of obtaining the volatility index value of the power consumption data b, this embodiment can obtain the volatility index values of each power consumption data in each power consumption data sequence. After obtaining the volatility index values of each power consumption data, this embodiment will then obtain the second credibility of each power consumption data according to the power consumption data in the power consumption data sequence and the volatility index values of each power consumption data. The second credibility is one of the important indicators for obtaining the credibility weighting factor for weighted fusion in the future. In addition, for the sake of easy understanding, the following will describe the process of obtaining the second credibility of the j-th power consumption data in the power consumption data sequence B as an example, that is, the process of obtaining the second credibility of the j-th power consumption data in sequence B is as follows:

[0052] First, obtain the first subsequence, the second subsequence, and the third subsequence of the j-th power consumption data in sequence B; then perform linear fitting on the data points corresponding to all the power consumption data in the first subsequence, and denote the fitted straight line as the first data point fitting straight line. Perform linear fitting on the volatility index values of all the power consumption data in the first subsequence, and denote the fitted straight line as the first volatility fitting straight line; then calculate the similarity between the first data point fitting straight line and the first volatility fitting straight line, and denote the calculation result as the first similarity value. Immediately afterwards, perform linear fitting on the data points corresponding to all the power consumption data in the second subsequence, and denote the fitted straight line as the second data point fitting straight line; perform linear fitting on the volatility index values of all the power consumption data in the second subsequence, and denote the fitted straight line as the second volatility fitting straight line; then calculate the similarity between the second data point fitting straight line and the second volatility fitting straight line, and denote the calculation result as the second similarity value. Next, obtain the absolute value of the difference between the first similarity value and the second similarity value, and denote it as the first similarity difference. Obtain the reciprocal of the sum of the first similarity difference and the preset first constant, and denote it as the first characteristic ratio. Obtain the mean of the first characteristic ratio and the second similarity value, and denote it as the first characteristic mean corresponding to the j-th power consumption data in sequence B.

[0053] Then, on the straight line fitted to the second data points, obtain the data point whose abscissa value is equal to that of the data point corresponding to the j-th power consumption data in sequence B, and denote it as the first fitted data point corresponding to the j-th power consumption data in sequence B. Then, calculate the Euclidean distance between the data point corresponding to the j-th power consumption data in sequence B and its corresponding first fitted data point, and denote it as the first distance characterization value. Immediately afterwards, perform linear fitting on the data points corresponding to all the power consumption data in the third subsequence, and denote the obtained straight line as the third data point fitting line. Then, on the third data point fitting line, obtain the data point whose abscissa value is equal to that of the data point corresponding to the j-th power consumption data in sequence B, and denote it as the second fitted data point corresponding to the j-th power consumption data in sequence B. Then, calculate the Euclidean distance between the data point corresponding to the j-th power consumption data in sequence B and its corresponding second fitted data point, and denote it as the second distance characterization value. Continue to obtain the absolute value of the difference between the first distance characterization value and the second distance characterization value, and denote it as the third distance characterization value. Then, obtain the reciprocal of the sum of the third distance characterization value and a preset first constant, and denote it as the second characteristic ratio. Then, obtain the mean of the second characteristic ratio and the first distance characterization value, and denote it as the second characteristic mean corresponding to the j-th power consumption data in sequence B.

[0054] Finally, obtain the mean of the first characteristic mean and the second characteristic mean corresponding to the j-th power consumption data in sequence B, and denote it as the second credibility of the j-th power consumption data in sequence B.

[0055] And in this embodiment, the processes of obtaining the first subsequence, the second subsequence, and the third subsequence of the j-th power consumption data in sequence B are as follows:

[0056] First, determine whether j is greater than a preset second constant. If so, denote the data sequence formed by the 1st to j-th power consumption data in sequence B as the first subsequence of the j-th power consumption data. At this time, the first subsequence contains the j-th power consumption data in sequence B. Denote the data sequence formed by the 1st to (j - 1)-th power consumption data in sequence B as the second subsequence of the j-th power consumption data. At this time, the second subsequence contains the (j - 1)-th power consumption data in sequence B but does not contain the j-th power consumption data. Denote the data sequence formed by the (j - 1)-th to (j - M1)-th power consumption data in sequence B as the third subsequence of the j-th power consumption data, where M1 is the preset first quantity threshold corresponding to the j-th power consumption data in sequence B. At this time, the third subsequence contains the (j - 1)-th power consumption data and the (j - M1)-th power consumption data in sequence B. And in specific applications, the implementer needs to set the preset second constant according to the actual situation. For example, the preset second constant can be set to 3 or 5. Additionally, in this embodiment, it is required that the value of the preset first quantity threshold M1 corresponding to the j-th power consumption data in sequence B belongs to the interval And the requirement is an integer. If j is 10, then the preset first quantity threshold M1 corresponding to the j-th power consumption data in sequence B can be set to 4.

[0057] When it is determined that j is not greater than the preset second constant, the data sequence formed by the j-th power consumption data to the (j + Z1)-th power consumption data in sequence B is denoted as the first subsequence of the j-th power consumption data. At this time, the first subsequence includes the j-th power consumption data and the (j + Z1)-th power consumption data in sequence B. The data sequence formed by the (j + 1)-th power consumption data to the (j + Z1)-th power consumption data in sequence B is denoted as the second subsequence corresponding to the j-th power consumption data. At this time, the second subsequence includes the (j + 1)-th power consumption data and the (j + Z1)-th power consumption data in sequence B. The data sequence formed by the (j + 1)-th power consumption data to the (j + Z2)-th power consumption data in sequence B is denoted as the third subsequence corresponding to the j-th power consumption data. At this time, the third subsequence includes the (j + 1)-th power consumption data and the (j + Z2)-th power consumption data in sequence B. Z1 is a preset first integer, and Z2 is a preset second integer, that is, both Z1 and Z2 are integers. And in specific applications, the implementer needs to set the values of Z1 and Z2 according to the actual situation, but it is required that Z2 is less than Z1. For example, in this embodiment, it can be required that the value of Z1 is greater than one-tenth of the total number of data in sequence B but less than one-third of the total number of data in sequence B, and it can be required that the value of Z2 is greater than 5 but not greater than one-tenth of the total number of data in sequence B. For example, if the total number of data in sequence B is 90, then Z1 can be set to 20 and Z2 can be set to 8.

[0058] In addition, in this embodiment, the specific expression for obtaining the second credibility of the j-th power consumption data in sequence B is:

[0059]

[0060] Among them, β2 is the second credibility of the j-th power consumption data in sequence B, U1 is the first distance characterization value, U2 is the second distance characterization value, |U1 - U2| is the third distance characterization value, is the second feature mean value, H2 is the first similarity value, H2 is the second similarity value, |H1 - H2| is the first similarity difference value, is the first feature mean value.

[0061] Moreover, when |U1 - U2| and U1 are smaller, it indicates that the trend difference of the j-th power consumption data in sequence B in a smaller range and the trend difference in a larger range are more consistent, and the j-th power consumption data in sequence B is more similar to the fitting data on the fitting line. It also indicates that the interference degree of the j-th power consumption data in sequence B may be smaller at this time. When the interference degree is smaller, it can also indicate that the credibility of the j-th power consumption data in sequence B is higher. When |H1 - H2| is smaller and H2 is larger, it indicates that the data fluctuation change trend and the data change trend of the j-th power consumption data in sequence B in a smaller range or a larger range are more similar. That is, when the change speed of the power consumption data increases, the volatility of the power consumption data also increases. It also indicates that the interference degree of the j-th power consumption data in sequence B may be smaller at this time. When the interference degree is smaller, it can also indicate that the credibility of the j-th power consumption data in sequence B is higher. Also, because when |U1 - U2| and U1 are smaller, |H1 - H2| is smaller, and H2 is larger, the value of β2 is larger. Based on the above description, when the value of β2 is larger, the interference degree of the j-th power consumption data in sequence B may be smaller, thus indicating that the credibility of the j-th power consumption data in sequence B is higher. The higher the credibility, the greater the reference degree of the j-th power consumption data in sequence B during subsequent weighted fusion. On the contrary, when the value of β2 is smaller, it indicates that the interference degree of the j-th power consumption data in sequence B may be larger, thus indicating that the credibility of the j-th power consumption data in sequence B is lower.

[0062] Therefore, according to the above process of obtaining the second credibility of the j-th power consumption data in sequence B in this embodiment, the second credibility of each power consumption data in each power consumption sequence can be obtained. Additionally, in this embodiment, linear fitting is performed multiple times and the similarity between any two lines is calculated multiple times. In specific applications, the implementer needs to select the methods for linear fitting and calculating the similarity between any two lines according to the actual situation. For example, in this embodiment, the least squares method can be selected for linear fitting, and the angle between two lines can be used to measure the similarity between two lines. It should be noted that when the angle between two lines is used to measure the similarity between two lines, the angle between two lines and the similarity between two lines are negatively correlated. That is, the angle between two lines needs to be normalized first, and the purpose of normalization is to eliminate the dimension. Then, a negative correlation mapping is performed on the normalized angle, and the mapping result is used as the similarity between the corresponding two lines. For example, if the angle between two lines is 30 degrees, first normalize the angle between these two lines using 180 degrees, that is, the ratio of 30 degrees to 180 degrees is the normalization result. Then, exp(-X) is used as the similarity between these two lines, where exp() is the exponential function with the constant e as the base, and X is the normalized value of the angle between these two lines.

[0063] In step S004, according to the first credibility and the second credibility, credibility weighting factors of the respective power consumption data are obtained, and all the power consumption data sequences are weighted and fused according to the credibility weighting factors of the power consumption data to obtain a target data sequence.

[0064] After the first credibility and the second credibility of each power consumption data are obtained in this embodiment, weighted fusion will be performed next. Since both the first credibility and the second credibility can reflect the credibility of the power consumption data, for the convenience of subsequent weighted fusion, this embodiment will first integrate the first credibility and the second credibility of each power consumption data to obtain a credibility weighting factor for subsequent weighted fusion, that is, in this embodiment, the credibility weighting factors of the respective power consumption data are obtained according to the first credibility and the second credibility of each power consumption data. Specifically:

[0065] For the power consumption data b in the power consumption data sequence B: First, obtain the sum of the target credibilities corresponding to all the power consumption data in the characteristic power consumption data set of the power consumption data b, and denote it as the comprehensive credibility. Then, obtain the ratio of the target credibility corresponding to the power consumption data b to the comprehensive credibility, and denote it as the credibility weighting factor of the power consumption data b. And the target credibility corresponding to the power consumption data refers to the average of the first credibility and the second credibility of the corresponding power consumption data.

[0066] According to the above process of obtaining the credibility weighting factor of the power consumption data b, this embodiment can obtain the credibility weighting factor of each power consumption data. After obtaining the credibility weighting factor of the power consumption data, this embodiment will perform weighted fusion on all the power consumption data sequences according to the credibility weighting factors of the respective power consumption data in the power consumption data sequence, and obtain a target data sequence according to the weighted fusion result. The specific process of obtaining the target data sequence is as follows: First, obtain the weighted values of the respective power consumption data in each power consumption data sequence, and denote the sequence composed of the weighted values of the respective power consumption data in the power consumption data sequence as the weighted value sequence of the corresponding power consumption data sequence. Then, add all the obtained weighted value sequences, and denote the sequence obtained after the sequence addition as the target data sequence. And the weighted value of any power consumption data refers to the product of the credibility weighting factor of the power consumption data and the power consumption data.

[0067] Therefore, this embodiment can obtain the target data sequence according to the above process, and denote all the data in the target data sequence as target data.

[0068] In step S005, according to the target data sequence, obtain the stability test result of the chip under test in the abnormal test environment.

[0069] After obtaining the target data sequence, this embodiment will analyze the target data sequence to obtain the stability test result of the chips to be tested in the abnormal test environment. Specifically:

[0070] First, obtain the sequence to be analyzed in the target data sequence. The specific process of obtaining the sequence to be analyzed is as follows: If the (t - 1)-th target data in the target data sequence is not greater than the preset limit power consumption threshold, and all the data after the (t - 1)-th data are greater than the preset limit power consumption threshold, where all the data after the (t - 1)-th data refer to the t-th target data in the target data sequence and all the target data after the t-th target data in the target data sequence, then the sequence formed by the t-th target data and all the target data after the t-th target data in the target data sequence is denoted as the sequence to be analyzed, and all the data in the sequence to be analyzed exceed the maximum power consumption value of the tested chip type. Additionally, in specific applications, the implementer needs to determine the preset limit power consumption threshold according to the type of the chips to be tested, and the preset limit power consumption threshold also refers to the maximum power consumption of the specified chip. For example, if the chips to be tested are medium-power chips, the preset limit power consumption threshold can be set to 0.5 watts.

[0071] After that, obtain the unit time length, where the unit time length is the acquisition time interval between two adjacent data in the power consumption data sequence, and in this embodiment, the acquisition time interval between two adjacent data in each power consumption data sequence is the same; then obtain the product of the total number of data in the sequence to be analyzed and the unit time length, and denote it as the abnormal time length, where the abnormal time length refers to the time length during which the chip power consumption during the test exceeds the preset limit power consumption threshold.

[0072] Immediately afterwards, obtain the ratio of the abnormal time length to the total test duration of any test chip, and denote it as the first ratio. In this embodiment, the total test duration of each test chip is equal; then obtain the difference between the preset first constant and the first ratio, and denote it as the first index value; then obtain the normalized ratio sequence corresponding to the sequence to be analyzed, denote the mean value of the normalized ratio sequence as the first mean value, and denote the difference between the preset first constant and the first mean value as the second index value; and the r-th normalized ratio in the normalized ratio sequence corresponding to the sequence to be analyzed refers to the normalized value of the second ratio corresponding to the r-th target data in the sequence to be analyzed, and the second ratio corresponding to the r-th target data refers to the ratio of the analysis difference of the r-th target data to the preset limit power consumption threshold. Then the analysis difference of the r-th target data refers to the absolute value of the difference between the r-th target data and the preset limit power consumption threshold, and here the normalization function norm() is used for normalization processing.

[0073] Finally, obtain the average value of the first index value and the second index value, and use it as the stability index value of the chips to be tested in the abnormal test environment; in addition, the specific expression for obtaining the stability index value of the chips to be tested in the abnormal test environment is:

[0074]

[0075] where W is the stability index value of the chips to be tested in the abnormal test environment, T1 is the abnormal time length, T2 is the total test duration, n1 is the first average value, is the first index value, (1 - n1) is the second index value; and when is smaller and n1 is smaller, it indicates that the value of W is larger, and the larger the value of W, the longer the time the chips to be tested in the abnormal test environment are in normal operation, and it also indicates that the stability of the chips to be tested in the abnormal test environment is better. On the contrary, when the value of W is smaller, it indicates that the stability of the chips to be tested in the abnormal test environment is worse.

[0076] Therefore, in this embodiment, the stability index value of the chips to be tested in the abnormal test environment can be obtained through the above process, and based on the obtained stability index value of the chips to be tested in the abnormal test environment, the stability of the chips to be tested in the abnormal test environment can be analyzed. For example, obtain a preset stability threshold. When the obtained stability index value of the chips to be tested in the abnormal test environment is less than the preset stability threshold, it is determined that the stability of the chips to be tested in the abnormal test environment is poor, or it is determined that the stability of the chips to be tested does not meet the requirements. On the contrary, when the obtained stability index value of the chips to be tested in the abnormal test environment is not less than the preset stability threshold, it is determined that the stability of the chips to be tested in the abnormal test environment is good, or it is determined that the stability of the chips to be tested meets the requirements; and in specific applications, the implementer needs to set the preset stability threshold according to the actual situation, such as it can be set to 0.4; in addition, it should be noted that the obtained sequence to be analyzed may be an empty set, and when it is an empty set, it indicates that the data in the target data sequence does not exceed the preset limit power consumption threshold, then it indicates that the power consumption data in the target data sequence meets the specified power consumption requirements, so it can be directly determined that the stability of the chips to be tested meets the requirements.

[0077] In summary, in this embodiment, first, A test chips are selected from the chips of the batch to be tested, and each test chip is subjected to high-voltage accelerated aging test to obtain the power consumption data sequence corresponding to each test chip in the abnormal test environment; secondly, the local subsequences corresponding to each power consumption data in the power consumption data sequence are obtained, and the power consumption data sequence and the local subsequences corresponding to each power consumption data are linearly fitted respectively to obtain the first fitting line of the power consumption data sequence and the second fitting lines of each power consumption data. According to the first fitting line and the second fitting lines, the first credibility of each power consumption data is obtained; then, according to the adjacent power consumption data of each power consumption data in the power consumption data sequence and the difference between two adjacent power consumption data in the local subsequence, the volatility index value of each power consumption data is obtained. According to the power consumption data in the power consumption data sequence and the volatility index value of each power consumption data, the second credibility of each power consumption data is obtained; then, according to the first credibility and the second credibility, the credibility weighting factor of each power consumption data is obtained. According to the credibility weighting factor of the power consumption data, all the power consumption data sequences are weighted and fused to obtain the target data sequence; finally, according to the target data sequence, the stability test result of the chips of the batch to be tested in the abnormal test environment is obtained. Moreover, the target data sequence obtained by weighted fusion according to the credibility weighting factor in this embodiment can make the stability test result of the chips of the batch to be tested in the abnormal test environment more accurate and reliable.

[0078] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for testing the stability of a chip in an abnormal environment based on MiniLED display, characterized in that, The method includes the following steps: Select A test chips from the chips of the batch to be tested, and perform high-voltage accelerated aging tests on each test chip to obtain the power consumption data sequence corresponding to each test chip in the abnormal test environment, where A>1; Obtain the local subsequences corresponding to each power consumption data in the power consumption data sequence, and perform linear fitting on the power consumption data sequence and the local subsequences corresponding to each power consumption data respectively to obtain the first fitting line of the power consumption data sequence and the second fitting lines of each power consumption data. According to the first fitting line and the second fitting lines, obtain the first credibility of each power consumption data; According to the adjacent power consumption data of each power consumption data in the power consumption data sequence and the difference between two adjacent power consumption data in the local subsequence, obtain the volatility index value of each power consumption data. According to the power consumption data in the power consumption data sequence and the volatility index value of each power consumption data, obtain the second credibility of each power consumption data; According to the first credibility and the second credibility, obtain the credibility weighting factor of each power consumption data. According to the credibility weighting factor of the power consumption data, perform weighted fusion on all the power consumption data sequences to obtain the target data sequence; According to the target data sequence, obtain the stability test result of the chips of the batch to be tested in the abnormal test environment.

2. The method for testing the stability of a chip in an abnormal environment based on MiniLED display according to claim 1, wherein, The method for obtaining the local subsequences corresponding to each power consumption data includes: For any power consumption data b in any power consumption data sequence B, construct a window on the power consumption data sequence B that contains the power consumption data b and is equal to the preset local window size, and denote it as the local window corresponding to the power consumption data b. Denote the sequence formed by all the data in the local window corresponding to the power consumption data b as the local subsequence corresponding to the power consumption data b.

3. The method for testing the stability of an abnormal environment chip based on MiniLED display according to claim 1, characterized in that, The method for obtaining the first credibility of each power consumption data includes: Calculate the similarity between the second fitting line of each power consumption data in the power consumption data sequence and the first fitting line of the corresponding power consumption data sequence, and denote the calculation result as the fitting similarity characterization value corresponding to the power consumption data; obtain the first marker value of each power consumption data in the power consumption data sequence. The first marker value of the jth power consumption data in the power consumption data sequence is j; For any power consumption data b in any power consumption data sequence B: Among all the power consumption data sequences, the set constructed by all the power consumption data with the same first marker value as the power consumption data b is denoted as the characteristic power consumption data set of the power consumption data b; Among the fitting similarity representation values corresponding to all the power consumption data in the characteristic power consumption data set, select the maximum fitting similarity representation value as the maximum representation value of the power consumption data b; Denote the absolute value of the difference between the fitting similarity representation value corresponding to the power consumption data b and the maximum representation value as the first difference; Denote the reciprocal of the value obtained by adding the first difference and a preset first constant as the first representation value; Denote the set formed by the data in the characteristic power consumption data set except the power consumption data b as the subset B1; Obtain the first similarity index value corresponding to each power consumption data in the subset B1, and the first similarity index value corresponding to the h-th power consumption data in the subset B1 is the similarity between the local subsequence corresponding to the h-th power consumption data and the local subsequence corresponding to the power consumption data b; In the subset B1, count the number of power consumption data with the first similarity index value greater than a preset similarity threshold, and denote it as the first quantity representation value; Denote the ratio of the first quantity representation value to the total number of data in the subset B1 as the second representation value; Denote the mean of the first representation value and the second representation value as the first credibility of the power consumption data b.

4. The method for testing the stability of an abnormal environment chip based on MiniLED display according to claim 3, wherein, The method for obtaining the volatility index value of each power consumption data includes: According to the adjacent power consumption data of each power consumption data in the power consumption data sequence, obtain the adjacent slope difference value corresponding to each power consumption data; Obtain the characteristic difference sequence corresponding to each power consumption data, and the d-th characteristic difference in the characteristic difference sequence corresponding to the power consumption data is the normalized value of the absolute value of the difference between the (d + 1)-th data and the d-th data in the local subsequence corresponding to the corresponding power consumption data; For any power consumption data b in any power consumption data sequence B, denote the reciprocal of the result obtained by adding the adjacent slope difference value corresponding to the power consumption data b and a preset first constant as the first characteristic value of the power consumption data b, denote the result obtained by subtracting the first characteristic value from the preset first constant as the second characteristic value of the power consumption data b, denote the mean of the characteristic difference sequence corresponding to the power consumption data b as the third characteristic value of the power consumption data b, and denote the mean of the second characteristic value and the third characteristic value as the volatility index value of the power consumption data b.

5. The method for testing the stability of an abnormal environment chip based on MiniLED display according to claim 4, wherein The method for obtaining the adjacent slope difference value corresponding to each power consumption data includes: Construct a mapping space, where the abscissa value of the data point in the mapping space is the first marker value and the ordinate value is the power consumption data; Map all the power consumption data and the first marker value of the power consumption data in the power consumption data sequence into the mapping space to obtain the data points corresponding to each power consumption data in the power consumption data sequence; For the j-th power consumption data in the power consumption data sequence B: If j equals 1, the absolute value of the slope of the straight line formed by the two data points corresponding to the j-th power consumption data and the (j + 1)-th power consumption data in the sequence B is used as the adjacent slope difference value corresponding to the j-th power consumption data in the sequence B; If j equals B0, where B0 is the total number of data in the sequence B, the absolute value of the slope of the straight line formed by the two data points corresponding to the j-th power consumption data and the (j - 1)-th power consumption data in the sequence B is used as the adjacent slope difference value corresponding to the j-th power consumption data in the sequence B; If j is not equal to 1 and B0, the slope of the straight line formed by the two data points corresponding to the j-th power consumption data and the (j - 1)-th power consumption data in the sequence B is denoted as the first slope, the slope of the straight line formed by the two data points corresponding to the j-th power consumption data and the (j + 1)-th power consumption data in the sequence B is denoted as the second slope, and the absolute value of the difference between the first slope and the second slope is used as the adjacent slope difference value corresponding to the j-th power consumption data in the sequence B.

6. The method for testing the stability of an abnormal environment chip based on MiniLED display according to claim 5, wherein, The method for obtaining the second credibility of the power consumption data includes: For the j-th power consumption data in any power consumption data sequence B: Obtain the first subsequence, the second subsequence, and the third subsequence of the j-th power consumption data in the power consumption data sequence B; The straight line obtained by linearly fitting the data points corresponding to all the power consumption data in the first subsequence is denoted as the first data point fitting straight line, the straight line obtained by linearly fitting the volatility index values of all the power consumption data in the first subsequence is denoted as the first volatility fitting straight line, calculate the similarity between the first data point fitting straight line and the first volatility fitting straight line, and denote the calculation result as the first similarity value; The straight line obtained by linearly fitting the data points corresponding to all the power consumption data in the second subsequence is denoted as the second data point fitting straight line, the straight line obtained by linearly fitting the volatility index values of all the power consumption data in the second subsequence is denoted as the second volatility fitting straight line, calculate the similarity between the second data point fitting straight line and the second volatility fitting straight line, and denote the calculation result as the second similarity value; Denote the absolute value of the difference between the first similarity value and the second similarity value as the first similarity difference, and denote the reciprocal of the sum of the first similarity difference and a preset first constant as the first characteristic ratio; Denote the mean of the first characteristic ratio and the second similarity value as the first characteristic mean; Denote the data point corresponding to the \(j\)-th power consumption data in the sequence B as a characteristic data point; on the second data point fitting line, obtain the data point with the same abscissa value as the characteristic data point, and denote it as the first fitting data point; denote the Euclidean distance between the characteristic data point and the first fitting data point as the first distance characterization value; denote the line obtained by linearly fitting all the data points corresponding to the power consumption data in the third subsequence as the third data point fitting line, on the third data point fitting line, obtain the data point with the same abscissa value as the characteristic data point, and denote it as the second fitting data point; denote the Euclidean distance between the characteristic data point and the second fitting data point as the second distance characterization value; denote the absolute value of the difference between the first distance characterization value and the second distance characterization value as the third distance characterization value, and denote the reciprocal of the sum of the third distance characterization value and a preset first constant as the second characteristic ratio; denote the mean of the second characteristic ratio and the first distance characterization value as the second characteristic mean; Denote the mean of the first characteristic mean and the second characteristic mean as the second credibility of the \(j\)-th power consumption data in the sequence B.

7. The method for testing the stability of an abnormal environment chip based on MiniLED display according to claim 6, wherein, The method for obtaining the first subsequence, the second subsequence, and the third subsequence includes: If \(j\) is greater than a preset second constant, then denote the sequence formed by the 1st to \(j\)-th power consumption data in the sequence B as the first subsequence corresponding to the \(j\)-th power consumption data, denote the sequence formed by the 1st to \(j - 1\)-th power consumption data in the sequence B as the second subsequence corresponding to the \(j\)-th power consumption data, denote the sequence formed by the \((j - 1)\)-th to \((j - M1)\)-th power consumption data in the sequence B as the third subsequence corresponding to the \(j\)-th power consumption data, where M1 is a preset first quantity threshold corresponding to the \(j\)-th power consumption data in the sequence B; if \(j\) is not greater than the preset second constant, then denote the sequence formed by the \(j\)-th to \((j + Z1)\)-th power consumption data in the sequence B as the first subsequence corresponding to the \(j\)-th power consumption data, denote the sequence formed by the \((j + 1)\)-th to \((j + Z1)\)-th power consumption data in the sequence B as the second subsequence corresponding to the \(j\)-th power consumption data, denote the sequence formed by the \((j + 1)\)-th to \((j + Z2)\)-th power consumption data in the sequence B as the third subsequence corresponding to the \(j\)-th power consumption data, where Z1 is a preset first integer and Z2 is a preset second integer.

8. The method for testing the stability of an abnormal environment chip based on MiniLED display according to claim 3, characterized in that, The method for obtaining the credibility weighting factor includes: For the power consumption data b: Denote the sum of the target credibilities corresponding to all the power consumption data in the characteristic power consumption data set of the power consumption data b as the comprehensive credibility; denote the ratio of the target credibility corresponding to the power consumption data b to the comprehensive credibility as the credibility weighting factor of the power consumption data b, and the target credibility corresponding to the power consumption data is the mean of the first credibility and the second credibility of the corresponding power consumption data.

9. The method for testing the stability of a chip in an abnormal environment based on MiniLED display according to claim 1, wherein The method for obtaining the target data sequence includes: Denote the product of the credibility weighting factor of the power consumption data and the corresponding power consumption data as the weighted value of the corresponding power consumption data; denote the sequence composed of the weighted values of each power consumption data in the power consumption data sequence as the weighted value sequence of the corresponding power consumption data sequence; denote the sequence obtained by adding all the weighted value sequences as the target data sequence.

10. The method for testing the stability of an abnormal environment chip based on MiniLED display according to claim 1, wherein, The method for obtaining the stability test result of the chips to be tested in the abnormal test environment includes: If the (t - 1)-th data in the target data sequence is not greater than the preset limit power consumption threshold, and all the data behind the (t - 1)-th data are greater than the preset limit power consumption threshold, then denote the sequence formed by all the data behind the (t - 1)-th data in the target data sequence as the sequence to be analyzed; obtain the unit time length, where the unit time length is the acquisition time interval between two adjacent data in the power consumption data sequence; denote the product of the total number of data in the sequence to be analyzed and the unit time length as the abnormal time length. Denote the ratio of the abnormal time length to the total test duration of the test chips as the first ratio; denote the result of subtracting the first ratio from the preset first constant as the first index value; obtain the normalized ratio sequence corresponding to the sequence to be analyzed, denote the mean value of the normalized ratio sequence as the first mean value, and denote the result of subtracting the first mean value from the preset first constant as the second index value; the r-th normalized ratio in the normalized ratio sequence corresponding to the sequence to be analyzed is the normalized value of the second ratio corresponding to the r-th data in the sequence to be analyzed, the second ratio corresponding to the r-th data is the ratio of the difference to be analyzed of the r-th data to the preset limit power consumption threshold, and the difference to be analyzed of the r-th data is the absolute value of the difference between the r-th data and the preset limit power consumption threshold; denote the mean value of the first index value and the second index value as the stability index value of the chips to be tested in the abnormal test environment.

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