Test signal processing method of target device and electronic device

Abstract

The application discloses a test signal processing method of a target device and an electronic device. The method comprises the following steps: at least partially intercepting a test signal of a target device to form a first signal; performing wavelet transform processing on the first signal to form a second signal; comparing the waveform of the second signal with the waveform of the test signal to determine a plurality of specific correlation numbers corresponding to the second signal, wherein the specific correlation numbers represent the approximation degree between the second signal and the compared part of the test signal; generating a third signal based on the specific correlation numbers; and determining whether the test signal is abnormal based on the waveform features of the third signal. The method utilizes the characteristics of wavelet transform, automatically analyzes the test signal generated by the test target device, accurately determines whether the test signal is abnormal, and thus accurately determines whether the tested target device is abnormal, simplifies the test process and improves the test efficiency.

Description

Technical Field

[0001] This application relates to the field of signal testing of devices, and in particular to a test signal processing method and electronic device for a target device. Background Technology

[0002] When testing target devices such as computers, it is necessary to analyze the test signals. In current automated signal testing, the test program acquires the waveform of the test signal by controlling the instrument, and then determines whether the test results meet the requirements based on the parameters measured in the waveform. However, the parameters for waveform measurement are limited, while actual waveforms vary greatly and contain a wide range of parameters, many of which lack specific defined specifications. Automated programs cannot determine whether the waveform meets the specifications, affecting the test results. Manually judging the waveform requires professional technicians, which is costly and inefficient. Summary of the Invention

[0003] The purpose of this application is to provide a test signal processing method and electronic device for a target device. This method can automatically analyze the test signals generated by the target device to accurately determine whether the test signals are abnormal, thereby improving testing efficiency.

[0004] To achieve the above objectives, embodiments of this application employ the following technical solution: a test signal processing method for a target device, characterized in that it includes:

[0005] The test signal of the target device is at least partially intercepted to form a first signal;

[0006] The first signal is subjected to wavelet transform processing to form the second signal;

[0007] The waveform of the second signal is compared with the waveform of the test signal to determine multiple specific correlation numbers corresponding to the second signal, wherein the specific correlation numbers characterize the degree of approximation between the compared portions of the second signal and the test signal;

[0008] A third signal is generated based on the specific correlation number;

[0009] Based on the waveform characteristics of the third signal, it is determined whether the test signal is abnormal.

[0010] Optionally, the step of at least partially intercepting the test signal from the target device to form a first signal includes:

[0011] Acquire the test signal generated during the testing of the target device;

[0012] Based on the waveform change characteristics of the test signal and the corresponding time information, the truncated portion of the test signal is determined;

[0013] The truncated portion is truncated to generate the first signal.

[0014] Optionally, comparing the waveform of the second signal with the waveform of the test signal to determine multiple specific correlation numbers corresponding to the second signal includes:

[0015] The waveform of the second signal is compared with a series of consecutive comparison portions of the test signal;

[0016] Determine the plurality of specific correlation numbers corresponding to the second signal.

[0017] Optionally, generating a third signal based on the specific correlation number includes:

[0018] A first specific correlation array corresponding to the second signal is determined, wherein the first specific correlation array includes: multiple specific correlation numbers generated by comparing the same waveform of the second signal with the waveform of the test signal;

[0019] The third signal is generated based on the first specific related array.

[0020] Optionally, comparing the waveform of the second signal with the waveform of the test signal to determine multiple specific correlation numbers corresponding to the second signal includes:

[0021] The waveform of the second signal is stretched or compressed according to a preset ratio;

[0022] The waveform of the second signal after stretching or compression is compared with a series of consecutive comparison portions of the test signal.

[0023] Determine the plurality of specific correlation numbers corresponding to the second signal.

[0024] Optionally, generating a third signal based on the specific correlation number includes:

[0025] Determine a second specific correlation array corresponding to the second signal, wherein the second specific correlation array includes: a plurality of specific correlation numbers generated by comparing the same waveform of the stretched or compressed second signal with the waveform of the test signal;

[0026] The third signal is generated based on the second specific correlation array.

[0027] Optionally, generating a third signal based on the specific correlation number includes:

[0028] Generate a corresponding two-dimensional array based on the first specific related array and / or the second specific related array;

[0029] Summation and transformation are performed on each row of the two-dimensional array to generate a corresponding one-dimensional array;

[0030] The waveform of the third signal is determined based on the values ​​of the one-dimensional array.

[0031] Optionally, determining whether the test signal is abnormal based on the waveform characteristics of the third signal includes:

[0032] Determine the first peak value in the third signal;

[0033] The value of the third signal is determined to be the second peak value, which is second only to the first peak value.

[0034] If a specific relationship is satisfied between the second peak value and the first peak value, the test signal is determined to be abnormal.

[0035] This application also provides an electronic device, including:

[0036] The interception module is configured to intercept at least a portion of the test signal of the target device to form a first signal;

[0037] The wavelet transform module is configured to perform wavelet transform processing on the first signal to form a second signal;

[0038] The comparison module is configured to compare the waveform of the second signal with the waveform of the test signal to determine multiple specific correlation numbers corresponding to the second signal, wherein the specific correlation numbers characterize the degree of approximation between the comparison portions of the second signal and the test signal;

[0039] A generation module, configured to generate a third signal based on the specific correlation number;

[0040] The determination module is configured to determine whether the test signal is abnormal based on the waveform characteristics of the third signal.

[0041] This application also provides another electronic device, including a memory and a processor, wherein the memory stores an executable program, and the processor executes the executable program to perform the following steps:

[0042] The test signal of the target device is at least partially intercepted to form a first signal;

[0043] The first signal is subjected to wavelet transform processing to form the second signal;

[0044] The waveform of the second signal is compared with the waveform of the test signal to determine multiple specific correlation numbers corresponding to the second signal, wherein the specific correlation numbers characterize the degree of approximation between the compared portions of the second signal and the test signal;

[0045] A third signal is generated based on the specific correlation number;

[0046] Based on the waveform characteristics of the third signal, it is determined whether the test signal is abnormal.

[0047] This application also provides a storage medium carrying one or more computer programs, characterized in that the one or more computer programs are executed by a processor as described above.

[0048] This method can utilize the characteristics of wavelet transform to automatically analyze the test signals generated by the target device to accurately determine whether the test signals are abnormal, thereby accurately determining whether the target device under test has malfunctioned, simplifying the testing process and improving testing efficiency. Attached Figure Description

[0049] Figure 1 This is a flowchart of a test signal processing method for the target device in an embodiment of this application;

[0050] Figure 2 Examples of embodiments of this application Figure 1 A flowchart of one embodiment of step S100;

[0051] Figure 3 Examples of embodiments of this application Figure 1 A flowchart of one embodiment of step S300;

[0052] Figure 4 Examples of embodiments of this application Figure 1 A flowchart of one embodiment of step S400;

[0053] Figure 5 Examples of embodiments of this application Figure 1 A flowchart of another embodiment of step S300;

[0054] Figure 6 Examples of embodiments of this application Figure 1 A flowchart of another embodiment of step S400;

[0055] Figure 7 Examples of embodiments of this application Figure 1 A flowchart of another embodiment of step S400;

[0056] Figure 8 Examples of embodiments of this application Figure 1 A flowchart of one embodiment of step S500;

[0057] Figure 9 This is a structural block diagram of the electronic device according to an embodiment of this application;

[0058] Figure 10 This is a structural block diagram of another electronic device according to an embodiment of this application. Detailed Implementation

[0059] Various embodiments and features of this application are described herein with reference to the accompanying drawings.

[0060] It should be understood that various modifications can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this application will be apparent to those skilled in the art.

[0061] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0062] These and other features of this application will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0063] It should also be understood that although this application has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this application, which have the features described in the claims and are therefore all within the scope of protection defined herein.

[0064] The above and other aspects, features and advantages of this application will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0065] Specific embodiments of this application are described thereafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this application, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the application. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as the basis and representative basis for the claims to teach those skilled in the art to use this application in a variety of substantially any suitable detailed structures.

[0066] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.

[0067] This application provides a test signal processing method for a target device. This method analyzes and processes test signals generated for testing the target device to accurately determine whether the test signals are abnormal, thereby determining whether the target device is malfunctioning. For example, testing is required during the production, maintenance, and repair of target devices such as computers. This includes testing various components within the target device, such as testing the computer's motherboard, which includes testing the various components installed on the motherboard. Corresponding test signals are generated during the specific testing process. The specific manifestation of these test signals can be in the form of signal waves.

[0068] Test signals can directly characterize the current state of the corresponding component under test. That is, the analysis and processing of test signals can determine the condition of the device under test, including whether any abnormalities have occurred. For example, analyzing the waveform of the test signal can help determine whether the corresponding device under test is malfunctioning.

[0069] The test signal processing method of this application will be described in detail below with reference to the accompanying drawings, such as... Figure 1 As shown, the method includes the following steps:

[0070] S100: At least a portion of the test signal from the target device is intercepted to form a first signal.

[0071] For example, when testing a target device such as a computer, test signals are generated. Analyzing these test signals can determine whether the target device is malfunctioning. For instance, if the waveform of the test signal is abnormal, it can be determined that the target device itself is malfunctioning.

[0072] In this embodiment, a test signal is acquired through a data acquisition device. The test signal can be a continuous waveform that reflects the characteristics of the target device. In one embodiment, a portion of the test signal exhibiting waveform changes can be extracted to form a first signal. For example, at least a portion of the test signal can be extracted within a time period of waveform rise or fall to form the first signal. In another embodiment, a portion of the waveform can be selected from the test signal to generate the first signal according to testing needs, thereby making the first signal more targeted.

[0073] S200, perform wavelet transform processing on the first signal to form the second signal.

[0074] Wavelet transform (WT) is a transform analysis method that inherits and develops the localization idea of ​​short-time Fourier transform while overcoming the shortcomings such as the window size not changing with frequency. It provides a "time-frequency" window that changes with frequency, making it a tool for time-frequency analysis and processing of signals. The characteristics of wavelet transform include its ability to fully highlight certain features of a problem through transformation, its ability to perform localized analysis of time (or space) and frequency, and its ability to progressively refine the signal (or function) at multiple scales through scaling and translation operations, ultimately achieving time subdivision at high frequencies and frequency subdivision at low frequencies. It can automatically adapt to the requirements of time-frequency signal analysis, thus focusing on any detail of the signal.

[0075] In this embodiment, the prominent features of wavelet transform, including its ability to perform localized analysis of time (or space) frequency, are utilized. A wavelet transform is applied to the first signal, resulting in a second signal that is a waveform with a zero-mean point over a finite distance. This second signal can represent the characteristics of the test signal through waveform representation.

[0076] S300, compare the waveform of the second signal with the waveform of the test signal to determine a plurality of specific correlation numbers corresponding to the second signal, wherein the specific correlation numbers characterize the degree of approximation between the comparison portion of the second signal and the test signal.

[0077] For example, the test signal has a large amount of data and a long waveform, while the second signal has a shorter waveform. When comparing the waveform of the second signal with the waveform of the test signal, the waveform of the second signal can be compared step by step with the entire waveform of the test signal. For example, a part of the waveform of the test signal can be compared first, then another part of the waveform of the test signal, and so on, until all the waveforms of the test signal have been compared.

[0078] During waveform comparison, the original waveform of the second signal can be compared with the test signal. Alternatively, the original waveform of the second signal can be processed, such as stretched or compressed, before comparison with the test signal. This allows for accurate determination of the waveform differences between the second signal and the test signal, and consequently, the determination of multiple specific correlation numbers corresponding to the second signal. In one embodiment, comparing the second signal with the test signal each time determines the corresponding specific correlation number. If the second signal is compared with the test signal multiple times, multiple corresponding specific correlation numbers can be generated.

[0079] The specific correlation number characterizes the degree of approximation between the contrasting portions of the second signal and the test signal. It can be calculated using a specific formula, which is as follows:

[0080]

[0081]

[0082] Among them, WT u,a : These are specific correlation numbers, i.e., the results of wavelet coefficient calculations. These coefficients represent the relationship between the wavelet and the local signal. This represents a continuous wavelet (there are multiple types), where u represents the offset and a represents the maximum value of the scale. express Conjugate complex number; S(t): the test signal, which is a continuous signal, where t can be a time parameter.

[0083] S400, based on the specific correlation number, generates a third signal.

[0084] For example, by comparing the second signal with the test signal each time, a specific correlation number can be determined. In this embodiment, the second signal is compared with different waveform portions of the test signal multiple times to obtain multiple corresponding specific correlation numbers. Each specific correlation number represents the degree of approximation between the corresponding waveform portion of the test signal and the second signal. By using a large number of specific correlation numbers, the degree of approximation between the waveform of the entire test signal and the waveform of the second signal can be determined.

[0085] In this embodiment, all specific related numbers can be processed to obtain an array that is easy to represent and process. Then, the array is waveform-processed to obtain a third signal.

[0086] S500, based on the waveform characteristics of the third signal, determine whether the test signal is abnormal.

[0087] For example, the waveform of the third signal has corresponding characteristics, including waveform variation characteristics. The waveform characteristics of the third signal can be compared with a preset feature library to determine whether the waveform characteristics represent abnormal information. For instance, analyzing the peak variation pattern in the waveform characteristics of the third signal, if this variation pattern is the same as or similar to the waveform information recorded in the feature library, it can be determined that the waveform characteristics of the third signal represent an abnormal phenomenon, thus determining that the test signal is abnormal, indicating that the target device is abnormal. Otherwise, it can be determined that the waveform characteristics of the third signal are not abnormal, thus determining that the test signal is not abnormal, indicating that the target device is not abnormal. Alternatively, the characteristics of the third signal's waveform can be analyzed to determine whether the test signal is abnormal.

[0088] This method can utilize the characteristics of wavelet transform to automatically analyze the test signals generated by the target device to accurately determine whether the test signals are abnormal, thereby accurately determining whether the target device under test has malfunctioned, simplifying the testing process and improving testing efficiency.

[0089] In one embodiment of this application, when analyzing the test signal of the target device, a comprehensive analysis of the entire test signal is required to obtain a complete and accurate analysis result. In this embodiment, a representative waveform of the test signal can be extracted, subjected to wavelet transform to highlight the characteristics of the extracted portion, and then compared with all parts of the test signal to perform a comprehensive analysis of the test signal based on the comparison results.

[0090] In this embodiment, the test signal of the target device is at least partially intercepted to form a first signal, such as... Figure 2 As shown, it includes the following steps:

[0091] S110, acquire the test signal generated by testing the target device.

[0092] For example, during the production, maintenance, and installation of target equipment, testing of the target equipment is required. This can be carried out using relevant testing equipment to generate corresponding test signals. These test signals are then acquired using acquisition equipment and processed.

[0093] S120, Based on the waveform change characteristics of the test signal and the corresponding time information, determine the truncated portion of the test signal.

[0094] For example, waveform variation characteristics can reflect the data characteristics of the test signal. The waveform of the test signal fluctuates with changes in the time parameter, and changes may occur during the fluctuation. In one embodiment, when truncating the signal, a portion of the test signal within a time period of waveform rise and / or fall can be truncated to form the truncated portion. In another embodiment, the test signal can be truncated uniformly according to the time parameter; in one embodiment, a customized truncation of the test signal can be performed.

[0095] S130, perform a truncation operation on the truncated portion to generate the first signal.

[0096] Based on the waveform and signal content of the extracted portion, a first signal is generated. The generated first signal can reflect the signal portion of the test signal that may exhibit abnormalities.

[0097] Continuing with the above embodiments, the test signal is truncated according to its waveform variation characteristics and corresponding time information, and then subjected to wavelet transform to form a second signal with prominent features. The second signal can be compared step-by-step with the test signal. For example, a portion of the second signal can be compared first, and then other parts of the second signal can be compared until all waveforms of the test signal are compared. This yields a complete comparison result.

[0098] In one embodiment of this application, the waveform of the second signal is compared with the waveform of the test signal to determine multiple specific correlation numbers corresponding to the second signal, such as... Figure 3 As shown, it includes:

[0099] S310, compare the waveform of the second signal with a plurality of consecutive comparison portions of the test signal.

[0100] For example, the test signal is a signal with a relatively long wavelength, and the second signal is a signal with a relatively short wavelength. When comparing the waveform of the second signal with the waveform of the test signal, the waveform of the second signal can be compared with multiple consecutive comparison portions of the test signal. For example, wavelets... The starting points of the second signal and the test signal S(t) are aligned and compared, and then the wavelet is... The second signal is shifted along the waveform extension direction of the test signal (the shift distance can be the wavelength of the second signal), and then compared with the corresponding part of the waveform of the test signal S(t). Then the wavelet is... The second signal is translated along the waveform extension direction of the test signal and then compared with the corresponding part of the waveform of the test signal S(t).

[0101] S320, determine the plurality of specific correlation numbers corresponding to the second signal.

[0102] For example, after each waveform comparison between the second signal and the test signal, a specific correlation number can be obtained according to a specific formula. The specific formula, as described above, is:

[0103]

[0104]

[0105] After multiple comparisons, several specific correlation numbers can be obtained.

[0106] In one embodiment of this application, the generation of a third signal based on the specific correlation number is as follows: Figure 4 As shown, it includes the following steps:

[0107] S410, determine a first specific correlation array corresponding to the second signal, wherein the first specific correlation array includes: a plurality of specific correlation numbers generated by comparing the same waveform of the second signal with the waveform of the test signal;

[0108] S420, the third signal is generated based on the first specific correlation array.

[0109] For example, without changing the waveform of the second signal, the second signal can be compared with the test signal multiple times, generating multiple specific correlation numbers accordingly. In one embodiment, after the entire waveform of the second signal and the test signal is continuously compared step by step, multiple specific correlation numbers are generated. These specific correlation numbers can be determined as a first specific correlation array. The first specific correlation array is processed, such as plotting points, i.e., plotting the values ​​of the first specific correlation array on the coordinate axis to generate a third signal. The waveform peak value corresponding to the first specific correlation array is calibrated using a correlation function; a judgment is made based on the calibrated peak value characteristics. For example, if there is only one peak value, it indicates that the waveform is normal; if there are multiple peak values, and the second largest peak value is > 0.5 * the peak value of the highest point of the waveform, it can be determined that there is an anomaly in this segment of the waveform, and the program pops up a window to prompt the tester.

[0110] In one embodiment of this application, the waveform of the second signal is compared with the waveform of the test signal to determine multiple specific correlation numbers corresponding to the second signal, such as... Figure 5 As shown, it includes the following steps:

[0111] S330, stretch or compress the waveform of the second signal according to a preset ratio;

[0112] S340, compare the waveform of the stretched or compressed second signal with a plurality of consecutive comparison portions of the test signal;

[0113] S350, determine the plurality of specific correlation numbers corresponding to the second signal.

[0114] For example, the waveform of the second signal can be stretched or compressed by increasing or decreasing the wavelength by a multiple, such as by doubling or decreasing the wavelength of the second signal.

[0115] The waveform of the stretched or compressed second signal is compared with multiple consecutive contrast portions of the test signal, including comparing the waveform of one wavelength of the second signal at a time, thus progressively and continuously comparing the test signal. For example, in the first comparison, the second signal is compared with the contrast portion of the test signal at the same wavelength. In the second comparison, the waveform of the second signal can be shifted one wavelength along the extension direction of the test signal waveform before being compared with the test signal. After the comparison is completed, the waveform of the second signal is shifted one wavelength along the extension direction of the test signal waveform again and compared with the test signal, and so on. Each comparison can generate a specific correlation number according to a specific formula.

[0116] In one embodiment of this application, the generation of a third signal based on the specific correlation number is as follows: Figure 6 As shown, it includes the following steps:

[0117] S430, determine the second specific correlation array corresponding to the second signal, wherein the second specific correlation array includes: a plurality of specific correlation numbers generated by comparing the same waveform of the stretched or compressed second signal with the waveform of the test signal;

[0118] S440, the third signal is generated based on the second specific correlation array.

[0119] For example, when stretching or compressing the waveform of the second signal, the second signal can be compared with the test signal multiple times, generating multiple specific correlation numbers accordingly. In one embodiment, after the entire waveform of the second signal and the test signal is continuously compared step by step, multiple specific correlation numbers are generated accordingly. These specific correlation numbers can be determined as a second specific correlation array. The second specific correlation array is processed, such as plotting points, i.e., plotting the values ​​of the second specific correlation array on the coordinate axis to generate a third signal. The waveform peak value of the third signal is calibrated using a correlation function; a judgment is made based on the calibrated peak value characteristics. For example, if there is only one peak value, it indicates that the waveform is normal; if there are multiple peak values, and the second largest peak value is > 0.5 * the peak value of the highest point of the waveform, it can be determined that there is an anomaly in this segment of the waveform, and the program pops up a window to prompt the tester.

[0120] In one embodiment of this application, the generation of a third signal based on the specific correlation number is as follows: Figure 7 As shown, it includes the following steps:

[0121] S450, based on the first specific related array and / or the second specific related array, generate a corresponding two-dimensional array;

[0122] S460, sum and transform each row of data in the two-dimensional array to generate a corresponding one-dimensional array;

[0123] S470, Based on the values ​​of the one-dimensional array, determine the waveform of the third signal.

[0124] For example, in one specific embodiment, the waveform of the second signal that has not been changed can be compared with the waveform of the test signal to determine the first specific correlation array corresponding to the second signal; then the waveform of the second signal that has undergone compression and stretching changes can be compared with the waveform of the test signal to determine the second specific correlation array corresponding to the second signal; then, based on the first specific correlation array and the second specific correlation array, a corresponding two-dimensional array is generated; and then, after performing one-dimensional transformation and plotting operations on the two-dimensional array, the third signal is obtained.

[0125] For example, the first step is to process the second signal wavelet. Aligning with the starting point of the original test signal S(t), WT is calculated according to a specific formula. u,a The second step is to expand / compress the wavelet. For example, expanding it by a factor of 1 yields the wavelet function as follows: WT at this scale is calculated using a specific formula. u,a Repeat steps one and two until all scales are completed, and then add WT. u,a The calculation results are stored in the columns of a two-dimensional array. The third step is to process the wavelet... Shifting right by a distance u, we obtain the wavelet function as follows: Calculate the WT on the u-series at intervals. u,a First, store the calculation results in the rows of the two-dimensional array. Repeat steps one through three until the waveform of the comparison test signal ends. Second, sum each row of the two-dimensional array to obtain a new one-dimensional array. Plot the values ​​of the new one-dimensional array on the coordinate axis to form the third signal. For a normal third signal, there should only be one peak point during the rise or fall of a waveform; otherwise, signal abnormalities may occur.

[0126] In one embodiment of this application, the determination of whether the test signal is abnormal based on the waveform characteristics of the third signal is as follows: Figure 8 As shown, it includes the following steps:

[0127] S510, determine the first peak value in the third signal.

[0128] The waveform of the third signal may be undulating, and sharp protrusions may appear in the waveform to form peaks. In this embodiment, the highest peak value of the third signal is determined as the first peak value.

[0129] S520, determine the second peak value in the third signal that is second only to the first peak value.

[0130] In addition to the first peak, the waveform of the third signal may also have a second peak. The peak that is second only to the first peak will be identified as the second peak.

[0131] S530, if a specific relationship is satisfied between the second peak value and the first peak value, it is determined that the test signal is abnormal.

[0132] For example, if there is only one peak value (the first peak value) in the waveform of the third signal, it means that the waveform is normal; if there are multiple peaks (all with peak values), and the peak value of the second peak is greater than 0.5 * the peak value of the first peak, it can be determined that there is an abnormality in this segment of the waveform, and the program will pop up a window to prompt the tester.

[0133] Based on the same inventive concept, embodiments of this application also provide an electronic device, such as... Figure 9 As shown, it includes:

[0134] The interception module is configured to intercept at least a portion of the test signal from the target device to form a first signal.

[0135] For example, when testing a target device such as a computer, test signals are generated. Analyzing these test signals can determine whether the target device is malfunctioning. For instance, if the waveform of the test signal is abnormal, it can be determined that the target device itself is malfunctioning.

[0136] In this embodiment, a test signal is acquired through a data acquisition device. The test signal can be a continuous waveform that reflects the characteristics of the target device. In one embodiment, the interception module can intercept the waveform portion of the test signal that shows waveform changes to form a first signal. For example, within a time period of waveform rise or fall, the interception module intercepts at least a portion of the test signal to form the first signal. In another embodiment, the interception module can customize the selection of a portion of the waveform in the test signal to generate the first signal according to test needs, thereby making the first signal more targeted.

[0137] The wavelet transform module is configured to perform wavelet transform processing on the first signal to form the second signal.

[0138] Wavelet transform (WT) is a transform analysis method that inherits and develops the localization idea of ​​short-time Fourier transform while overcoming the shortcomings such as the window size not changing with frequency. It provides a "time-frequency" window that changes with frequency, making it a tool for time-frequency analysis and processing of signals. The characteristics of wavelet transform include its ability to fully highlight certain features of a problem through transformation, its ability to perform localized analysis of time (or space) and frequency, and its ability to progressively refine the signal (or function) at multiple scales through scaling and translation operations, ultimately achieving time subdivision at high frequencies and frequency subdivision at low frequencies. It can automatically adapt to the requirements of time-frequency signal analysis, thus focusing on any detail of the signal.

[0139] In this embodiment, the wavelet transform module utilizes the prominent features of wavelet transform in certain aspects, and its ability to perform localized analysis of time (or space) frequency. The first signal is processed by wavelet transform, resulting in a second signal that is a waveform with a zero-mean point over a finite distance. This second signal can represent the characteristics of the test signal through waveform representation.

[0140] The comparison module is configured to compare the waveform of the second signal with the waveform of the test signal to determine a plurality of specific correlation numbers corresponding to the second signal, wherein the specific correlation numbers characterize the degree of approximation between the comparison portions of the second signal and the test signal.

[0141] For example, the test signal has a large amount of data and a long waveform, while the second signal has a shorter waveform. When the comparison module compares the waveform of the second signal with the waveform of the test signal, it can perform a step-by-step comparison. For example, the comparison module can first compare a part of the waveform of the test signal, then compare another part of the waveform of the test signal, and so on, until all the waveforms of the test signal have been compared.

[0142] During waveform comparison, the comparison module can compare the original waveform of the second signal with the test signal, or it can perform specific processing on the original waveform of the second signal, such as stretching or compressing it, before comparing it with the test signal. This allows for accurate determination of the waveform differences between the second signal and the test signal, and subsequently, the determination of multiple specific correlation numbers corresponding to the second signal. In one embodiment, the comparison module compares the second signal with the test signal each time to determine the corresponding specific correlation number. If the second signal is compared with the test signal multiple times, multiple corresponding specific correlation numbers can be generated.

[0143] The specific correlation number characterizes the degree of approximation between the contrasting portions of the second signal and the test signal. It can be calculated using a specific formula, which is as follows:

[0144]

[0145]

[0146] Among them, WT u,a : These are specific correlation numbers, i.e., the results of wavelet coefficient calculations. These coefficients represent the relationship between the wavelet and the local signal. This represents a continuous wavelet (there are multiple types), where u represents the offset and a represents the maximum value of the scale. express Conjugate complex number; S(t): the test signal, which is a continuous signal, where t can be a time parameter.

[0147] The generation module is configured to generate a third signal based on the specific correlation number.

[0148] For example, by comparing the second signal with the test signal each time, a specific correlation number can be determined. In this embodiment, the second signal is compared with different waveform portions of the test signal multiple times to obtain multiple corresponding specific correlation numbers. Each specific correlation number represents the degree of approximation between the corresponding waveform portion of the test signal and the second signal. By using a large number of specific correlation numbers, the generation module can determine the degree of approximation between the waveform of the entire test signal and the waveform of the second signal.

[0149] In this embodiment, the generation module can process all the specific related numbers to obtain an array that is easy to represent and process, and then perform waveform processing on the array to obtain a third signal.

[0150] The determination module is configured to determine whether the test signal is abnormal based on the waveform characteristics of the third signal.

[0151] For example, the waveform of the third signal has corresponding characteristics, including waveform variation characteristics. The determination module can compare the waveform characteristics of the third signal with a preset feature library to determine whether the waveform characteristics represent abnormal information. For example, by analyzing the change pattern of the peaks in the waveform characteristics of the third signal, if the change pattern is the same as or similar to the waveform information recorded in the feature library, it can be determined that the waveform characteristics of the third signal represent an abnormal phenomenon, and thus determine that the test signal is abnormal, indicating that the target device is abnormal. Otherwise, it can be determined that the waveform characteristics of the third signal are not abnormal, and thus determine that the test signal is not abnormal, indicating that the target device is not abnormal. The determination module can also analyze the characteristics of the waveform of the third signal to determine whether the test signal is abnormal.

[0152] In one embodiment of this application, the interception module is further configured as follows:

[0153] Acquire the test signal generated during the testing of the target device;

[0154] Based on the waveform change characteristics of the test signal and the corresponding time information, the truncated portion of the test signal is determined;

[0155] The truncated portion is truncated to generate the first signal.

[0156] In one embodiment of this application, the comparison module is further configured as follows:

[0157] The waveform of the second signal is compared with a series of consecutive comparison portions of the test signal;

[0158] Determine the plurality of specific correlation numbers corresponding to the second signal.

[0159] In one embodiment of this application, the generation module is further configured as follows:

[0160] A first specific correlation array corresponding to the second signal is determined, wherein the first specific correlation array includes: multiple specific correlation numbers generated by comparing the same waveform of the second signal with the waveform of the test signal;

[0161] The third signal is generated based on the first specific related array.

[0162] In one embodiment of this application, the generation module is further configured as follows:

[0163] The waveform of the second signal is stretched or compressed according to a preset ratio;

[0164] The waveform of the second signal after stretching or compression is compared with a series of consecutive comparison portions of the test signal.

[0165] Determine the plurality of specific correlation numbers corresponding to the second signal.

[0166] In one embodiment of this application, the generation module is further configured as follows:

[0167] Determine a second specific correlation array corresponding to the second signal, wherein the second specific correlation array includes: a plurality of specific correlation numbers generated by comparing the same waveform of the stretched or compressed second signal with the waveform of the test signal;

[0168] The third signal is generated based on the second specific correlation array.

[0169] In one embodiment of this application, the generation module is further configured as follows:

[0170] Generate a corresponding two-dimensional array based on the first specific related array and / or the second specific related array;

[0171] Summation and transformation are performed on each row of the two-dimensional array to generate a corresponding one-dimensional array;

[0172] The waveform of the third signal is determined based on the values ​​of the one-dimensional array.

[0173] In one embodiment of this application, the determining module is further configured as follows:

[0174] Determine the first peak value in the third signal;

[0175] The value of the third signal is determined to be the second peak value, which is second only to the first peak value.

[0176] If a specific relationship is satisfied between the second peak value and the first peak value, the test signal is determined to be abnormal.

[0177] Based on the same inventive concept, this application also provides another electronic device, such as... Figure 10 As shown, the system includes a memory and a processor. The memory stores an executable program, and the processor executes the executable program to perform the following steps:

[0178] The test signal of the target device is at least partially intercepted to form a first signal;

[0179] The first signal is subjected to wavelet transform processing to form the second signal;

[0180] The waveform of the second signal is compared with the waveform of the test signal to determine multiple specific correlation numbers corresponding to the second signal, wherein the specific correlation numbers characterize the degree of approximation between the compared portions of the second signal and the test signal;

[0181] A third signal is generated based on the specific correlation number;

[0182] Based on the waveform characteristics of the third signal, it is determined whether the test signal is abnormal.

[0183] Based on the same inventive concept, embodiments of this application also provide a storage medium carrying one or more computer programs, which, when executed by a processor, implement the steps of the method described in any embodiment.

[0184] The storage medium in this embodiment may be included in an electronic device / system; or it may exist independently and not assembled into an electronic device / system. The storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0185] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0186] The above embodiments are merely exemplary embodiments of this application and are not intended to limit this application. The scope of protection of this application is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions to this application within its substance and scope of protection, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of this application.

Claims

1. A test signal processing method for a target device, characterized in that, include: The test signal of the target device is at least partially intercepted to form a first signal; The first signal is subjected to wavelet transform processing to form the second signal; The waveform of the second signal is compared with the waveform of the test signal to determine multiple wavelet coefficients corresponding to the second signal, wherein the wavelet coefficients characterize the degree of approximation between the compared portions of the second signal and the test signal; Based on the wavelet coefficients, a third signal is generated; Based on the waveform characteristics of the third signal, it is determined whether the test signal exhibits any abnormality; wherein, The generation of the third signal based on the wavelet coefficients includes: Determine the first wavelet coefficient group corresponding to the second signal, wherein the first wavelet coefficient group includes: multiple wavelet coefficients generated by comparing the same waveform of the second signal with the waveform of the test signal; Based on the first wavelet coefficient set, a third signal is generated, including: generating a corresponding two-dimensional array based on the first wavelet coefficient set; summing and transforming the data in each row of the two-dimensional array to generate a corresponding one-dimensional array; determining the waveform of the third signal based on the values ​​of the one-dimensional array, including storing the wavelet coefficients obtained by expanding / compressing the second signal wavelet in the columns of the two-dimensional array, storing the wavelet coefficients obtained by translation in the rows of the two-dimensional array, summing the rows to obtain a new one-dimensional array, and plotting the values ​​of the new one-dimensional array on the coordinate axis to form the third signal; Determining whether the test signal is abnormal based on the waveform characteristics of the third signal includes: Determine the first peak value in the third signal; The value of the third signal is determined to be the second peak value, which is second only to the first peak value. If the value of the second peak is greater than 0.5 times the value of the first peak, the test signal is determined to be abnormal.

2. The method according to claim 1, characterized in that, The step of at least partially intercepting the test signal from the target device to form a first signal includes: Acquire the test signal generated during the testing of the target device; Based on the waveform change characteristics of the test signal and the corresponding time information, the truncated portion of the test signal is determined; The truncated portion is truncated to generate the first signal.

3. The method according to claim 1, characterized in that, The step of comparing the waveform of the second signal with the waveform of the test signal to determine multiple wavelet coefficients corresponding to the second signal includes: The waveform of the second signal is compared with a series of consecutive comparison portions of the test signal; Determine the multiple wavelet coefficients corresponding to the second signal.

4. The method according to claim 1, characterized in that, The step of comparing the waveform of the second signal with the waveform of the test signal to determine multiple wavelet coefficients corresponding to the second signal includes: The waveform of the second signal is stretched or compressed according to a preset ratio; The waveform of the second signal after stretching or compression is compared with a series of consecutive comparison portions of the test signal. Determine the multiple wavelet coefficients corresponding to the second signal.

5. The method according to claim 4, characterized in that, The generation of the third signal based on the wavelet coefficients includes: Determine the second wavelet coefficient group corresponding to the second signal, wherein the second wavelet coefficient group includes: multiple wavelet coefficients generated by comparing the same waveform of the stretched or compressed second signal with the waveform of the test signal; The third signal is generated based on the second set of wavelet coefficients.

6. The method according to claim 5, characterized in that, The generation of the third signal based on the wavelet coefficients further includes: Based on the second wavelet coefficient set, a corresponding two-dimensional array is generated; Summation and transformation are performed on each row of the two-dimensional array to generate a corresponding one-dimensional array; The waveform of the third signal is determined based on the values ​​of the one-dimensional array.

7. An electronic device, characterized in that, include: The interception module is configured to intercept at least a portion of the test signal of the target device to form a first signal; The wavelet transform module is configured to perform wavelet transform processing on the first signal to form a second signal; The comparison module is configured to compare the waveform of the second signal with the waveform of the test signal to determine multiple wavelet coefficients corresponding to the second signal, wherein the wavelet coefficients characterize the degree of approximation between the comparison portion of the second signal and the test signal; A generation module, configured to generate a third signal based on the wavelet coefficients; The determination module is configured to determine whether the test signal is abnormal based on the waveform characteristics of the third signal; wherein, The generation module is further configured as follows: Determine the first wavelet coefficient group corresponding to the second signal, wherein the first wavelet coefficient group includes: multiple wavelet coefficients generated by comparing the same waveform of the second signal with the waveform of the test signal; Based on the first wavelet coefficient set, a third signal is generated, including: generating a corresponding two-dimensional array based on the first wavelet coefficient set; summing and transforming the data in each row of the two-dimensional array to generate a corresponding one-dimensional array; determining the waveform of the third signal based on the values ​​of the one-dimensional array, including storing the wavelet coefficients obtained by expanding / compressing the second signal wavelet in the columns of the two-dimensional array, storing the wavelet coefficients obtained by translation in the rows of the two-dimensional array, summing the rows to obtain a new one-dimensional array, and plotting the values ​​of the new one-dimensional array on the coordinate axis to form the third signal; The determining module is further configured as follows: Determine the first peak value in the third signal; The value of the third signal is determined to be the second peak value, which is second only to the first peak value. If the value of the second peak is greater than 0.5 times the value of the first peak, the test signal is determined to be abnormal.

8. An electronic device, characterized in that, Includes a memory and a processor, wherein the memory stores an executable program, and the processor executes the executable program to perform the following steps: The test signal of the target device is at least partially intercepted to form a first signal; The first signal is subjected to wavelet transform processing to form the second signal; The waveform of the second signal is compared with the waveform of the test signal to determine multiple wavelet coefficients corresponding to the second signal, wherein the wavelet coefficients characterize the degree of approximation between the compared portions of the second signal and the test signal; Based on the wavelet coefficients, a third signal is generated; Based on the waveform characteristics of the third signal, determine whether the test signal is abnormal; The generation of the third signal based on the wavelet coefficients includes: Determine the first wavelet coefficient group corresponding to the second signal, wherein the first wavelet coefficient group includes: multiple wavelet coefficients generated by comparing the same waveform of the second signal with the waveform of the test signal; Based on the first wavelet coefficient set, a third signal is generated, including: generating a corresponding two-dimensional array based on the first wavelet coefficient set; summing and transforming the data in each row of the two-dimensional array to generate a corresponding one-dimensional array; determining the waveform of the third signal based on the values ​​of the one-dimensional array, including storing the wavelet coefficients obtained by expanding / compressing the second signal wavelet in the columns of the two-dimensional array, storing the wavelet coefficients obtained by translation in the rows of the two-dimensional array, summing the rows to obtain a new one-dimensional array, and plotting the values ​​of the new one-dimensional array on the coordinate axis to form the third signal; Determining whether the test signal is abnormal based on the waveform characteristics of the third signal includes: Determine the first peak value in the third signal; The value of the third signal is determined to be the second peak value, which is second only to the first peak value. If the value of the second peak is greater than 0.5 times the value of the first peak, the test signal is determined to be abnormal.

9. A storage medium carrying one or more computer programs, characterized in that, When the one or more computer programs are executed by a processor, they implement the steps of the method according to any one of claims 1 to 6.

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