Correlation distance ultrasonic imaging method

Abstract

The invention provides a correlation distance ultrasonic imaging method, which belongs to the technical field of ultrasonic imaging, and specifically comprises the following steps: acquiring N standard ultrasonic signals by utilizing ultrasonic equipment according to equipment parameters of matched ultrasonic equipment based on a standard sample, generating standard self-correlation signals based on the standard ultrasonic signals, and keeping the equipment parameters unchanged, the method comprises the steps of scanning a to-be-tested workpiece to collect a test ultrasonic signal, generating a test self-correlation signal based on the test ultrasonic signal, calculating a cross-correlation signal between the test self-correlation signal and a standard self-correlation signal, and generating a workpiece image based on the slope of matching of the cross-correlation signal and a mapping function of a grayscale image, thereby improving the accuracy of imaging processing.

Description

Technical Field

[0001] The present invention belongs to the technical field of ultrasonic imaging, and particularly relates to a correlation distance ultrasonic imaging method. Background Art

[0002] Nondestructive testing generally relies on physical properties such as force, heat, sound, light, and electricity of the workpiece to be tested. These physical properties are applied to the workpiece to be inspected in a specific manner. When the material structure of the workpiece is abnormal, changes in physical quantities such as force, heat, sound, light, and electricity occur, and based on this, the material structure and internal structure of the workpiece are inferred. As a commonly used method for nondestructive testing, ultrasonic imaging uses ultrasound as the propagation medium to image the workpiece, and the common modes mainly include two types: time-of-flight imaging and voltage amplitude imaging.

[0003] However, the above technical solutions all have the following technical problems: The above methods only perform imaging based on extracting the positions of wave peaks and wave valleys (i.e., time of flight) or the characteristic values corresponding to these positions (i.e., voltage amplitude) and other characteristics from the ultrasonic time-domain waveform, without considering the internal information of the ultrasonic waveform with fine granularity. For time-series signals, only characteristic indicators such as the mean or peak value are extracted, and obviously, the collected ultrasonic waveforms are not effectively utilized.

[0004] Secondly, in order to implement the above extraction operation, a time gate is required, and information such as the position, width, and trigger threshold of the gate needs to be provided manually. Therefore, the effects of time-of-flight imaging and voltage amplitude imaging highly depend on the professional skill level of the operator in setting the gate.

[0005] To solve the above technical problems, the present application provides a correlation distance ultrasonic imaging method. Summary of the Invention

[0006] To achieve the object of the present invention, the present invention adopts the following technical solutions: Specifically, in the first aspect, the present application provides a correlation distance ultrasonic imaging method, which specifically includes: Based on a standard sample and according to the device parameters of the matching ultrasonic device, use the ultrasonic device to collect N standard ultrasonic signals, generate a standard autocorrelation signal based on the standard ultrasonic signals, keep the device parameters unchanged, scan the workpiece to be tested to collect test ultrasonic signals, and generate a test autocorrelation signal based on the test ultrasonic signals; Calculate the cross-correlation signal between the test autocorrelation signal and the standard autocorrelation signal, and generate a workpiece image based on the slope matching the mapping function between the cross-correlation signal and the grayscale image.

[0007] The beneficial effects of the present invention are as follows: By performing the generation process of the autocorrelation signal, the influence of information such as the position, width, and trigger threshold of the time gate on the ultrasonic detection result is reduced, thereby further improving the accuracy of ultrasonic imaging processing.

[0008] By determining the cross-correlation signal based on the autocorrelation signal, the technical problem of low accuracy in ultrasonic imaging processing caused by solely using a certain feature is avoided. Starting from the perspective of the detailed feature, i.e., the cross-correlation signal, more signal details are retained, so that the detection and processing result of the cross-signal can more accurately reflect the real operating state.

[0009] A further technical solution is that the value of N is not less than 14.

[0010] A further technical solution is that the device parameters of the ultrasonic device matched with the standard sample include the working distance, excitation voltage, sampling rate, and gain of the ultrasonic device.

[0011] A further technical solution is that the specific steps for constructing the standard autocorrelation signal are as follows: Using the standard ultrasonic signals at the target moment and the next moment of the target moment, and further combining the mean values of the standard ultrasonic signals at different moments, determine the correlation coefficient of the standard ultrasonic signal at the target moment; According to the correlation coefficients of the standard ultrasonic signals at different target moments, obtain the standard autocorrelation signal through filtering processing.

[0012] A further technical solution is that the filtering processing is performed using the mean filtering method.

[0013] A further technical solution is that the calculation method of the cross-correlation signal is as follows: Based on the test autocorrelation signal and the standard autocorrelation signal, determine the norm distance between the test autocorrelation signal and the standard autocorrelation signal; Take the norm distances at different moments as the cross-correlation signals at different moments.

[0014] A further technical solution is that generating a workpiece image based on the cross-correlation signal specifically includes: Based on the cross-correlation signals at different positions and different moments, convert the cross-correlation signal at each position into the corresponding gray value; Using the gray values at different positions, arrange them according to the positions to obtain the image of the workpiece to be measured.

[0015] A further technical solution is that the slope is determined according to the maximum and minimum values of the norm distances at different moments at this position.

[0016] A further technical solution lies in that the calculation formula of the mapping function of the grayscale image is as follows: g i is the grayscale value at the i-th moment, b is the intercept, k is the slope, and d i is the cross-correlation signal at the i-th moment.

[0017] A further technical solution lies in that the calculation method of the slope is as follows: where and are the maximum and minimum values of the norm distance respectively.

[0018] In a second aspect, the present invention provides a computer system, including: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor. When the processor runs the computer program, it executes the above-mentioned correlation distance ultrasonic imaging method.

[0019] Other features and advantages will be described in the subsequent specification. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification and the drawings.

[0020] To make the above objectives, features, and advantages of the present invention more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By referring to the drawings and describing its exemplary embodiments in detail, the above and other features and advantages of the present invention will become more obvious.

[0022] Figure 1 is a schematic diagram of the time of flight; Figure 2 is a flowchart of a correlation distance ultrasonic imaging method; Figure 3 is a flowchart of the specific steps for constructing a standard autocorrelation signal; Figure 4 is a flowchart of the calculation method of the cross-correlation signal. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] In order to enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this specification.

[0024] As Figure 1 shown, for the time-of-flight response depth information, if the time-of-flight is measured, the depth data can be obtained according to the propagation speed of ultrasound in the workpiece. By converting the time-of-flight at each position of the workpiece into corresponding gray-scale values point by point and arranging them according to the corresponding positions, an image of the workpiece can be obtained. Voltage amplitude imaging is to select a certain characteristic value of the signal for imaging, such as the peak amplitude, valley amplitude, or peak-to-peak value between the peak and valley. By converting this certain characteristic value into corresponding gray-scale values and arranging them according to the positions, an image of the workpiece can also be obtained.

[0025] However, the above methods have deficiencies.

[0026] First of all, the above methods only perform imaging based on extracting features such as the peak and valley positions (i.e., the time-of-flight) or the characteristic values (i.e., the voltage amplitude) corresponding to these positions from the ultrasonic time-domain waveform, without considering the internal information of the fine granularity of the ultrasonic waveform. For time-series signals, only features such as the mean or peak value are extracted, and obviously the collected ultrasonic waveforms are not effectively utilized.

[0027] Secondly, in order to implement the above extraction operation, a Figure 1 time gate as shown is required. And information such as the position, width, and trigger threshold of the gate needs to be provided manually. Therefore, the effects of time-of-flight imaging and voltage amplitude imaging highly depend on the professional skill level of the operator in setting the gate.

[0028] In addition, the above methods can only start analyzing the workpiece to be tested based on the entire image after the imaging is completely finished, and cannot evaluate the quality of the workpiece according to the ultrasonic signals at specific positions of the workpiece.

[0029] Embodiment 1 As Figure 2 shown, the present application provides a correlation distance ultrasonic imaging method, which specifically includes: Based on a standard sample and according to the device parameters of the matching ultrasonic device, using the ultrasonic device to collect N standard ultrasonic signals, generating a standard autocorrelation signal based on the standard ultrasonic signals, keeping the device parameters unchanged, scanning the workpiece to be tested to collect test ultrasonic signals, and generating a test autocorrelation signal based on the test ultrasonic signals; Calculating the cross-correlation signal between the test autocorrelation signal and the standard autocorrelation signal, and generating a workpiece image based on the slope matching the mapping function between the cross-correlation signal and the gray-scale image.

[0030] Further, the value of N is not less than 14.

[0031] Specifically, the device parameters of the ultrasonic device matching the standard sample include the working distance, excitation voltage, sampling rate, and gain of the ultrasonic device.

[0032] It is understandable that, as Figure 3 shown, the specific steps for constructing the standard autocorrelation signal are as follows: Taking the standard ultrasonic signals at the target moment and the next moment of the target moment, and further combining the mean values of the standard ultrasonic signals at different moments, to determine the correlation coefficient of the standard ultrasonic signal at the target moment; According to the correlation coefficients of the standard ultrasonic signals at different target moments, the standard autocorrelation signal is obtained through filtering processing.

[0033] Furthermore, the filtering processing is performed by using the method of mean filtering.

[0034] Specifically, as Figure 4 shown, the calculation method of the cross-correlation signal is as follows: Based on the test autocorrelation signal and the standard autocorrelation signal, to determine the norm distance between the test autocorrelation signal and the standard autocorrelation signal; Taking the norm distances at different moments as the cross-correlation signals at different moments.

[0035] Furthermore, generating a workpiece image based on the cross-correlation signal specifically includes: Based on the cross-correlation signals at different positions and different moments, converting the cross-correlation signal at each position into the corresponding gray value; Using the gray values at different positions and arranging them according to the positions to obtain the image of the workpiece to be tested.

[0036] It is understandable that the slope is determined according to the maximum and minimum values of the norm distances at different moments at this position.

[0037] Furthermore, the calculation formula of the mapping function of the gray image is: g i is the gray value at the i-th moment, b is the intercept, k is the slope, and d i is the cross-correlation signal at the i-th moment.

[0038] Specifically, the calculation method of the slope is: where and are respectively the maximum and minimum values of the norm distance

[0039] ​Further, before determining the slope, it is also necessary to determine the distribution data of the norm distances at different times at the said position. When both the maximum value of the norm distance and the deviation amount of the norm distances at different times are not greater than the preset deviation amount, and the deviation amount of the norm distances at different times from the minimum value of the norm distance at the said position is not greater than the preset deviation amount, then the maximum value and the minimum value of the norm distances at different times at this position are used for determination.

[0040] Specifically, when there is a deviation amount of the maximum value of the norm distance from the norm distance at a certain time at the said position that is greater than the preset deviation amount, or there is a deviation amount of the norm distance at a certain time from the minimum value of the norm distance at the said position that is greater than the preset deviation amount, the specific steps for determining the slope are as follows: The time when the deviation amount of the norm distance from the maximum value of the norm distance is greater than the preset deviation amount or the deviation amount of the norm distance from the minimum value of the norm distance at the said position is greater than the preset deviation amount is taken as the distance deviation time. When the number of the said distance deviation times is less than the preset distance deviation time number threshold, then the maximum value and the minimum value of the norm distances at different times at this position are used for determination. When the number of the said distance deviation times is not less than the preset distance deviation time number threshold, then a preset strategy is used to determine the said slope.

[0041] It can be understood that using the preset strategy to determine the said slope specifically includes: Based on the test autocorrelation signals at different times at the said position, multiple filtering algorithms are used for filtering processing to obtain the distance deviation times that belong to interference signals under different filtering algorithms, and the filtering algorithm for which the distance deviation times belong to interference signals is taken as the autocorrelation filtering algorithm. Based on the test ultrasonic signals at different times at the said position, multiple filtering algorithms are used for filtering processing to obtain the distance deviation times that belong to interference signals under different filtering algorithms, and the filtering algorithm for which the distance deviation times belong to interference signals is taken as the ultrasonic filtering algorithm. According to the matching data of the autocorrelation filtering algorithm and the ultrasonic filtering algorithm at different distance deviation times, the method for determining the said slope is determined.

[0042] Further, according to the matching data of the autocorrelation filtering algorithm and the ultrasonic filtering algorithm at different distance deviation times, determining the method for determining the said slope specifically includes: Based on the matching data of the autocorrelation filtering algorithm and the ultrasonic filtering algorithm at the said distance deviation times, determine the number of the autocorrelation filtering algorithm and the ultrasonic filtering algorithm at different distance deviation times. The distance deviation moments at which both the number of autocorrelation filtering algorithms and the number of ultrasonic filtering algorithms are greater than the preset filtering algorithm threshold are regarded as suspected interference moments; When the number of distance deviation moments excluding the suspected interference moments is less than the preset quantity threshold, the maximum and minimum values of the norm distances at different moments of this position are used for determination; When the number of distance deviation moments excluding the suspected interference moments is not less than the preset quantity threshold, the maximum and minimum values of the norm distances at the moments when there are no ultrasonic filtering algorithms and autocorrelation filtering algorithms at this position are used for determination.

[0043] Embodiment 2 Autocorrelation is similar to performing two observations on the same signal at different moments, and the similarity degree of the two observations is judged by comparison. Cross - correlation is used to evaluate the signal with another different signal for similarity. Autocorrelation and cross - correlation, as digital signal processing means in the time domain, can discover valuable information hidden in complex signals; especially after a reference signal is given, the similarity degree between the measurement signal and the reference signal can be determined.

[0044] The specific method includes: (1) Collecting standard ultrasonic signals based on a standard sample . (2) Generating a standard autocorrelation signal based on the standard ultrasonic signal . (3) Scanning the workpiece to be measured to collect test ultrasonic signals (4) Generating a test autocorrelation signal based on the test ultrasonic signal . (5) Calculating the norm distance between the test autocorrelation signal and the standard autocorrelation signal. (6) Generating a workpiece image based on the relevant norm distance.

[0045] The steps are described separately as follows.

[0046] (1) Collecting standard ultrasonic signals based on a standard sample .

[0047] The standard ultrasonic signal should have normal signal characteristics and is obtained by collecting through a standard sample, such as a standard wafer stored in a constant temperature and humidity environment.

[0048] To reduce the influence of noise, the number of collections is not less than 14. For example, the standard sample is divided into 15 positions in 3 rows and 5 columns, and the moving transducer is located at these positions to obtain standard ultrasonic signals , where: is the number of collections, is the The time index of a standard ultrasonic signal.

[0049] (2) Generate a standard autocorrelation signal based on the standard ultrasonic signal .

[0050] Calculate the correlation coefficient of each standard ultrasonic signal . Where: is the mean value of the standard ultrasonic signal . Based on the calculated correlation coefficients , obtain the standard autocorrelation signal through mean filtering.

[0051] (3) Scan the workpiece to be tested and collect test ultrasonic signals .

[0052] Keep parameters such as the working distance, excitation voltage, sampling rate, and gain unchanged, and scan each position of the workpiece to be tested point by point.

[0053] Collect the test ultrasonic signal at each position, where: is the th position of the workpiece to be tested.

[0054] (4) Generate a test autocorrelation signal based on the test ultrasonic signal .

[0055] Based on the collected test ultrasonic signal and the standard autocorrelation signal , calculate the correlation coefficient where: is the mean value of the test ultrasonic signal , is the mean value of the reference signal , and j is the jth moment.

[0056] (5) Calculate the norm distance between the test autocorrelation signal and the standard autocorrelation signal.

[0057] Based on the test autocorrelation signal and the reference correlation signal , generate the norm distance . The 2-norm, i.e., the Euclidean distance, can be selected as an example. In this case, the distance is .

[0058] (6) Generate a workpiece image based on the correlation norm distance.

[0059] Point by point, the norm distance After being converted into corresponding grayscale values and arranged according to the corresponding positions, an image of the workpiece can be obtained.

[0060] Specifically, taking an 8-bit depth, i.e., 256-level grayscale image as an example, the grayscale mapping is , where: the slope , the intercept , and are respectively the maximum and minimum values of the norm distance .

[0061] The filtering algorithms include clipping filtering method, median filtering method, arithmetic mean filtering method, recursive mean filtering method (moving average), first-order lag filtering method, Kalman filter, complementary filter, wavelet transform filtering, and adaptive filtering.

[0062] Optionally, a preset strategy is adopted to determine the slope, which specifically includes: S1 Use the maximum and minimum values of the norm distance at different times at this position to determine the time of the maximum distance and the time of the minimum distance. Based on the test autocorrelation signals at the time of the maximum distance and the time of the minimum distance, multiple filtering algorithms are used for filtering processing to obtain the times that belong to the interference signals under different filtering algorithms, and the filtering algorithm whose time belongs to the interference signal is used as the autocorrelation filtering algorithm; S2 Based on the test ultrasonic signals at different times at the time of the maximum distance and the time of the minimum distance, multiple filtering algorithms are used for filtering processing to obtain the times that belong to the interference signals under different filtering algorithms, and the filtering algorithm whose time belongs to the interference signal is used as the ultrasonic filtering algorithm; S3 Determine the interference outliers according to the matching data of the autocorrelation filtering algorithm and the ultrasonic filtering algorithm at different distance deviation times, and use the endpoint interference outliers and the interference outliers to determine the method for determining the slope.

[0063] Furthermore, the matching data is the number of the autocorrelation filtering algorithm and the ultrasonic filtering algorithm at different distance deviation times.

[0064] It should be noted that using the endpoint interference outliers and the interference outliers to determine the method for determining the slope specifically includes: When the endpoint interference outlier is less than the preset endpoint outlier threshold, the maximum and minimum values of the norm distance at different times at this position are used for determination; When the endpoint interference outlier is not less than the preset endpoint anomaly threshold, determine whether the endpoint interference outlier is greater than the endpoint anomaly threshold. If so, determine it using the maximum and minimum of the norm distances at the moments when the ultrasonic filtering algorithm and the autocorrelation filtering algorithm do not exist at this position. If not, when the interference outlier is greater than the preset interference threshold, determine it using the maximum and minimum of the norm distances at different moments at this position. When the interference outlier is not greater than the preset interference threshold, determine it using the maximum and minimum of the norm distances at the moments when the ultrasonic filtering algorithm and the autocorrelation filtering algorithm do not exist at this position.

[0065] Optionally, the above step S1 includes the following content: Determine the moment with the maximum distance and the moment with the minimum distance based on the maximum and minimum of the norm distances at different moments at this position. Based on the matching data of the autocorrelation filtering algorithm and the ultrasonic filtering algorithm at the moment with the maximum distance and the moment with the minimum distance, when it is determined that there is no autocorrelation filtering algorithm at both the moment with the maximum distance and the moment with the minimum distance, then determine it using the maximum and minimum of the norm distances at different moments at this position. When it is determined that there is an autocorrelation filtering algorithm at either the moment with the maximum distance or the moment with the minimum distance, then proceed to step S2.

[0066] It can be understood that when there is no autocorrelation filtering algorithm and ultrasonic filtering algorithm at both the moment with the maximum distance and the moment with the minimum distance, it means that the data at the moment with the maximum distance and the moment with the minimum distance are reliable under different filtering algorithms. Therefore, other data do not need to be considered, and the maximum and minimum of the norm distances at different moments at this position can be used to determine the slope.

[0067] By determining the slope, the gray-scale distribution of the image at this position can be made more accurate, so as to more accurately reflect the gray-scale image of the actual workpiece to be measured.

[0068] It should be noted that when there is an autocorrelation filtering algorithm at either the moment with the maximum distance or the moment with the minimum distance, there is a probability of interference signal. Therefore, using the original slope calculation method will inevitably bring interference signals, resulting in inaccurate formation results of the gray-scale image. Therefore, it is necessary to proceed to the next step to determine the ultrasonic filtering algorithm.

[0069] Optionally, the above step S2 includes the following content: S21 When the sum of the number of autocorrelation filtering algorithms and ultrasonic filtering algorithms at the moment of maximum distance or the sum of the number of autocorrelation filtering algorithms and ultrasonic filtering algorithms at the moment of minimum distance does not meet the requirements, the maximum and minimum values of the norm distance at the moment when there are no ultrasonic filtering algorithms and autocorrelation filtering algorithms at the position are used to determine the slope. When both the sum of the number of autocorrelation filtering algorithms and ultrasonic filtering algorithms at the moment of maximum distance and the sum of the number of autocorrelation filtering algorithms and ultrasonic filtering algorithms at the moment of minimum distance meet the requirements, proceed to step S22; Exemplarily, when either the sum of the number of autocorrelation filtering algorithms and ultrasonic filtering algorithms at the moment of maximum distance or the sum of the number of autocorrelation filtering algorithms and ultrasonic filtering algorithms at the moment of minimum distance does not meet the requirements, it indicates that there may be interference risks at the moment of maximum distance or minimum distance. Therefore, it can be directly determined that no other data needs to be considered when determining the slope.

[0070] By implementing the screening of extreme abnormal situations, the moments of maximum distance and minimum distance with interference risks can be quickly identified, thereby improving the efficiency of slope identification processing and further avoiding the influence of interference signals on the slope.

[0071] S22 Use the matching data of the autocorrelation filtering algorithms and ultrasonic filtering algorithms at the moment of maximum distance and minimum distance to determine the endpoint interference outliers. When the endpoint interference outliers are less than the preset endpoint anomaly threshold, the maximum and minimum values of the norm distance at different moments at this position are used for determination. When the endpoint interference outliers are not less than the preset endpoint anomaly threshold, proceed to step S23; It should be further noted that in the case of relatively small endpoint interference outliers, whether it is the moment of maximum distance or minimum distance, the risk of it being an interference signal is relatively small. Therefore, in this state, no other data needs to be considered, and the maximum and minimum values of the norm distance at different moments at this position can be directly used for determination, thus ensuring the accuracy of slope identification processing.

[0072] When the endpoint interference outliers are relatively large, it is necessary to further determine the degree of deviation, that is, the difference in the risk of being an interference signal, so as to further improve the efficiency of slope identification processing.

[0073] S23 Determine whether the endpoint interference outliers are greater than the endpoint anomaly threshold. If so, use the maximum and minimum values of the norm distance at the moment when there are no ultrasonic filtering algorithms and autocorrelation filtering algorithms at the position for determination. If not, proceed to step S3.

[0074] Embodiment 3 In a second aspect, the present invention provides a computer system, comprising: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor, wherein when the processor runs the computer program, it executes the above-mentioned related distance ultrasonic imaging method.

[0075] Optionally, the above step S3 includes the following content: S31 determines interference outliers according to the matching data of the autocorrelation filtering algorithm and the ultrasonic filtering algorithm at different distance deviation times. When the number of the autocorrelation filtering algorithm and the ultrasonic filtering algorithm at different distance deviation times are both within a preset number range, the maximum and minimum values of the norm distance at different times of this position are used for determination. When the number of the autocorrelation filtering algorithm and the ultrasonic filtering algorithm at different distance deviation times are not both within the preset number range, it proceeds to step S32; It can be understood that when the number of the autocorrelation filtering algorithm and the ultrasonic filtering algorithm at different distance deviation times are both within a preset number range, it indicates that the number of the autocorrelation filtering algorithm and the ultrasonic filtering algorithm at the distance deviation times are both relatively large. Therefore, the risk of it being an interference signal is greater.

[0076] When the risk of being an interference signal is relatively large, even if there is a distance deviation, since the distance deviation times are all risk deviations of the interference signal, at this time, the maximum and minimum values of the norm distance at different times of this position can be used for determination, so as to ensure a more uniform distribution of grayscale.

[0077] By evaluating whether the distance deviation times are interference signals, the influence of the distance deviation times can be excluded, thereby ensuring the uniformity and reliability of the grayscale image distribution.

[0078] S32 takes the distance deviation times when the number of the autocorrelation filtering algorithm and the ultrasonic filtering algorithm are both greater than a preset filtering algorithm threshold as suspected interference times. When the number of the distance deviation times excluding the suspected interference times is less than a preset number threshold, the maximum and minimum values of the norm distance at different times of this position are used for determination. When the number of the distance deviation times excluding the suspected interference times is not less than the preset number threshold, it proceeds to step S33; It can be understood that when the number of the distance deviation times excluding the suspected interference times is less than a preset number threshold, it indicates that the number of the distance deviation times without the risk of interference signals is relatively small at this time. Therefore, even if there is a deviation, the maximum and minimum values of the norm distance at different times of this position can still be used for determining the slope, further reducing the influence of interference signals.

[0079] The specific preset quantity threshold can be determined according to the scanning duration at this position. When the scanning duration is longer, the preset quantity threshold is larger, so that the dynamic evaluation of the interference risk can be realized, and the reliability of the overall recognition and processing can be improved.

[0080] By excluding the suspected interference moments, the screening of the distance deviation moments without real interference risk can be realized, and the accuracy of the slope recognition and processing is further improved.

[0081] S33 determines the interference outliers according to the matching data of the autocorrelation filtering algorithm and the ultrasonic filtering algorithm at different distance deviation moments. When the interference outlier is greater than the preset interference threshold, the maximum and minimum values of the norm distances at different moments of this position are used for determination. When the interference outlier is not greater than the preset interference threshold, the maximum and minimum values of the norm distances at the moments when there is no ultrasonic filtering algorithm and autocorrelation filtering algorithm at this position are used for determination.

[0082] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the embodiments of the device, equipment, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0083] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the figures do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0084] The above is only one or more embodiments of this specification and is not used to limit this specification. For those skilled in the art, there can be various changes and modifications to one or more embodiments of this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A related distance ultrasonic imaging method, characterized in that, Specifically, it includes: Based on the standard sample and according to the device parameters of the matching ultrasonic device, collect N standard ultrasonic signals using the ultrasonic device, generate a standard autocorrelation signal based on the standard ultrasonic signals, keep the device parameters unchanged, scan the workpiece to be tested to collect test ultrasonic signals, and generate a test autocorrelation signal based on the test ultrasonic signals; Calculate the cross-correlation signal between the test autocorrelation signal and the standard autocorrelation signal, and generate a workpiece image based on the slope matched with the mapping function of the cross-correlation signal and the grayscale image.

2. The relevant distance ultrasonic imaging method according to claim 1, wherein The value of N is not less than 14.

3. The relevant distance ultrasonic imaging method according to claim 1, characterized in that, The device parameters of the ultrasonic device matched with the standard sample include the working distance, excitation voltage, sampling rate, and gain of the ultrasonic device.

4. The relevant distance ultrasonic imaging method according to claim 1, characterized in that, The specific steps for constructing the standard autocorrelation signal are as follows: Use the standard ultrasonic signals at the target moment and the next moment of the target moment, and further combine the mean value of the standard ultrasonic signals at different moments to determine the correlation coefficient of the standard ultrasonic signal at the target moment; According to the correlation coefficients of the standard ultrasonic signals at different target moments, obtain the standard autocorrelation signal through filtering processing.

5. The related-distance ultrasonic imaging method according to claim 4, characterized in that, The filtering processing is performed using the method of mean filtering.

6. The related distance ultrasonic imaging method according to claim 1, characterized in that The calculation method of the cross-correlation signal is as follows: Based on the test autocorrelation signal and the standard autocorrelation signal, determine the norm distance between the test autocorrelation signal and the standard autocorrelation signal; Take the norm distances at different moments as the cross-correlation signals at different moments.

7. The relevant distance ultrasonic imaging method according to claim 1, wherein Generating a workpiece image based on the cross-correlation signal specifically includes: Based on the slope matched with the mapping function of the cross-correlation signal and the grayscale image at different positions and different moments, convert the cross-correlation signal at each position into the corresponding grayscale value; Use the grayscale values at different positions and arrange them according to the positions to obtain the image of the workpiece to be tested.

8. The related distance ultrasonic imaging method according to claim 7, characterized in that, The slope is determined according to the maximum and minimum values of the norm distances at different moments at this position.

9. The relative distance ultrasonic imaging method according to claim 7, wherein The calculation formula of the mapping function of the grayscale image is as follows: g i is the grayscale value at the i-th moment, b is the intercept, k is the slope, and d i is the cross-correlation signal at the i-th moment.

10. The relevant distance ultrasonic imaging method according to claim 9, characterized in that, The calculation method of the slope is as follows: where and are the maximum value and the minimum value of the norm distance respectively.

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