Signal reconstruction method based on MEMS high-precision ultrasonic sensor and related equipment

Abstract

The application relates to a signal reconstruction method based on a MEMS high-precision ultrasonic sensor and related equipment, comprising the following steps: envelope extraction is performed on an original echo sequence of the MEMS high-precision ultrasonic sensor to obtain a signal envelope line, and peak point detection is performed on the signal envelope line to obtain a peak time point sequence; the original echo sequence is segmented and intercepted based on the peak time point sequence to obtain multiple echo slices, waveform symmetry analysis is performed on each echo slice, effective echo slices meeting a symmetry threshold are screened out from the multiple echo slices to obtain a target echo section; point-by-point amplitude compensation is performed on the target echo section based on a preset standard waveform template to generate a compensated waveform, and the compensated waveform is subjected to phase alignment splicing to obtain a reconstructed signal, thereby solving the technical problem of how to improve the recognition accuracy of effective waveform segments in the ultrasonic echo signal reconstruction process.

Description

Technical Field

[0001] This invention relates to the field of sensor technology, and in particular to a signal reconstruction method and related equipment based on a MEMS high-precision ultrasonic sensor. Background Technology

[0002] Under complex operating conditions, ultrasonic echoes are susceptible to multiple reflections, medium inhomogeneity, and sensor nonlinear response, leading to waveform distortion, particularly waveform asymmetry and inconsistent amplitude attenuation. Existing methods lack effective mechanisms for identifying local waveform morphological features during echo segment extraction and reconstruction, resulting in splicing misalignments or energy distortion in the reconstructed signal, ultimately affecting distance measurement accuracy and imaging resolution. Therefore, improving the accuracy of identifying valid waveform segments during ultrasonic echo signal reconstruction to enhance distance measurement accuracy and imaging resolution has become a pressing technical problem. Summary of the Invention

[0003] The main technical problem addressed in this application is to provide a signal reconstruction method and related equipment based on a MEMS high-precision ultrasonic sensor, and to solve the technical problem of how to improve the accuracy of identifying effective waveform segments during the ultrasonic echo signal reconstruction process.

[0004] To address the aforementioned technical problems, this application employs a signal reconstruction method based on a MEMS high-precision ultrasonic sensor, comprising the following steps: Envelope extraction is performed on the raw echo sequence of the MEMS high-precision ultrasonic sensor to obtain the signal envelope; Peak point detection is performed on the signal envelope to obtain the peak time point sequence; Based on the peak time point sequence, the original echo sequence is segmented to obtain multiple echo slices; Waveform symmetry analysis is performed on each echo slice, and valid echo slices that meet the symmetry threshold are selected from the multiple echo slices to obtain the target echo segment; The target echo band is subjected to point-by-point amplitude compensation based on a preset standard waveform template to generate a compensated waveform. The compensated waveform is then phase-aligned and spliced ​​to obtain a reconstructed signal.

[0005] Furthermore, the process of extracting the envelope from the original echo sequence of the MEMS high-precision ultrasonic sensor to obtain the signal envelope includes: The original echo sequence of the MEMS high-precision ultrasonic sensor is rectified to obtain a rectified echo sequence, and the rectified echo sequence is smoothed to obtain a smoothed echo sequence. The smooth echo sequence is filtered point by point to obtain the amplitude peak sequence, and the amplitude peak sequence is linearly fitted to obtain the signal envelope.

[0006] Furthermore, the step of detecting peak points on the signal envelope to obtain a peak time point sequence includes: Perform first-order difference calculation on the signal envelope to obtain the instantaneous slope of each sampling point on the envelope, and obtain the slope change curve; Based on the slope change curve, the zero-crossing point position from positive to negative is identified, the time coordinate corresponding to the zero-crossing point position is marked as the candidate peak time point, and all candidate peak time points are sorted in chronological order to generate the peak time point sequence.

[0007] Furthermore, the original echo sequence is segmented based on the peak time point sequence to obtain multiple echo slices, including: The difference between adjacent peak time points in the peak time point sequence is calculated to obtain the peak interval sequence; The truncation window radius is determined based on the minimum interval value in the peak interval sequence, and the truncation window radius is combined with each of the peak time points to obtain the truncation boundary parameters, wherein the truncation boundary parameters include the start time and end time of each echo slice; Based on the truncation boundary parameters, the original echo sequence is separated and extracted segment by segment to obtain the multi-segment echo slices, wherein the multi-segment echo slices include independent echo signal segments centered on each peak time point.

[0008] Furthermore, the original echo sequence is segmented and extracted based on the truncation boundary parameters to obtain multiple echo slices, including: Based on the start and end times of each echo slice in the interception boundary parameters, the original echo sequence is indexed by time interval to obtain the sampling point range of each echo slice; The signal data located within the sampling point range in the original echo sequence are read and copied segment by segment to obtain an initial slice sequence, wherein the initial slice sequence includes continuous signal segments corresponding to each peak time point; The first and last boundary regions of each continuous signal segment in the initial slice sequence are subjected to gradual attenuation weighting to obtain the multi-segment echo slice, wherein the multi-segment echo slice includes independent echo signal segments whose boundary amplitudes smoothly transition to zero.

[0009] Furthermore, the step of performing waveform symmetry analysis on each echo slice, and selecting valid echo slices that meet the symmetry threshold from the multiple echo slices to obtain the target echo segment, includes: For each echo slice, the waveform center point is located, the point with the maximum amplitude within the echo slice is marked as the waveform center, and the echo slice is divided into a left half waveform and a right half waveform based on the waveform center; The left and right half waveforms are cross-correlated point by point to obtain the correlation coefficient describing the similarity between the two waveforms, and the correlation coefficient is used as the symmetry measure of the echo slice. The symmetry metric is compared with a preset symmetry threshold. From the multiple echo slices, slices with a metric value greater than or equal to the threshold are selected as valid echo slices, and slices with a metric value less than the threshold are removed to obtain the target echo band.

[0010] The present invention also provides a signal reconstruction device based on a MEMS high-precision ultrasonic sensor, comprising: The extraction module is used to extract the envelope of the original echo sequence of the MEMS high-precision ultrasonic sensor to obtain the signal envelope. The detection module is used to detect peak points in the signal envelope to obtain a peak time point sequence; The segmentation module is used to segment the original echo sequence based on the peak time point sequence to obtain multiple echo slices; The analysis module is used to perform waveform symmetry analysis on each echo slice, and to select the effective echo slices that meet the symmetry threshold from the multiple echo slices to obtain the target echo segment. The compensation module is used to perform point-by-point amplitude compensation on the target echo band based on a preset standard waveform template, generate a compensated waveform, and perform phase alignment and splicing on the compensated waveform to obtain a reconstructed signal.

[0011] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above methods.

[0012] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the above methods.

[0013] The present invention also provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of any of the above methods.

[0014] The above scheme extracts the envelope of the original echo sequence from a MEMS high-precision ultrasonic sensor to obtain a signal envelope, and detects peak points on the signal envelope to obtain a peak time point sequence. Based on the peak time point sequence, the original echo sequence is segmented to obtain multiple echo slices, and waveform symmetry analysis is performed on each echo slice. Valid echo slices that meet the symmetry threshold are selected from the multiple echo slices to obtain the target echo segment. Point-by-point amplitude compensation is performed on the target echo segment based on a preset standard waveform template to generate a compensated waveform. The compensated waveform is then phase-aligned and spliced ​​to obtain the reconstructed signal. This solves the technical problem of how to improve the accuracy of identifying valid waveform segments during ultrasonic echo signal reconstruction, realizes point-by-point amplitude compensation of the target echo segment based on a standard waveform template, corrects amplitude distortion caused by propagation attenuation and sensor response differences, and enhances the amplitude consistency between different echo segments. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a signal reconstruction method based on a MEMS high-precision ultrasonic sensor in one embodiment of the present invention. Figure 2 This is a schematic diagram of the implementation process of S1 in one embodiment of the present invention; Figure 3 This is a schematic diagram of the implementation process of S2 in one embodiment of the present invention; Figure 4 This is a schematic diagram of the implementation process of S3 in one embodiment of the present invention; Figure 5 This is a structural block diagram of a signal reconstruction device based on a MEMS high-precision ultrasonic sensor in one embodiment of the present invention; Figure 6 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0017] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0019] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. It should also be understood that, as used in this specification and the appended claims, the term "and / or" refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0020] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0021] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0022] Specifically, such as Figure 1 As shown, Figure 1 This invention provides a signal reconstruction method based on a MEMS high-precision ultrasonic sensor, comprising the following steps: Step S1: Extract the envelope of the original echo sequence of the MEMS high-precision ultrasonic sensor to obtain the signal envelope. Step S2: Detect peak points on the signal envelope to obtain a peak time point sequence; Step S3: Based on the peak time point sequence, the original echo sequence is segmented to obtain multiple echo slices; Step S4: Perform waveform symmetry analysis on each echo slice, and select the valid echo slices that meet the symmetry threshold from the multiple echo slices to obtain the target echo segment; Step S5: Perform point-by-point amplitude compensation on the target echo band based on a preset standard waveform template to generate a compensated waveform, and perform phase alignment and splicing on the compensated waveform to obtain a reconstructed signal.

[0023] Specifically, when extracting the envelope of the raw echo sequence from a MEMS high-precision ultrasonic sensor, the Hilbert transform method is used to process the time-domain signal. The raw real-number sequence is constructed into an analytic signal, and its modulus is taken as the signal envelope output. This operation preserves the basic contour of the echo energy change over time, providing basic waveform support for subsequent feature extraction.

[0024] After obtaining the signal envelope, a peak point detection process is implemented. By setting a sliding window to judge the local extrema of each data point on the envelope, combined with amplitude threshold filtering of low-level interference, and introducing a minimum time interval limit to avoid multiple misjudgments caused by dense noise, the significant peak positions are finally locked, forming an ordered peak time point sequence, which is used to define the occurrence time of the echo event.

[0025] Based on the aforementioned peak time point sequence, the original echo sequence is segmented. Using each group of adjacent peak time points as the center, a time window is extended forward and backward by a preset proportion to extract the complete oscillation waveform within the corresponding time period, forming multiple echo slices. This method ensures that each slice contains a main echo and its adjacent transition region, avoiding the truncation of effective information or the introduction of irrelevant signals due to fixed-length segmentation.

[0026] Subsequently, waveform symmetry analysis was performed on each echo slice. Specifically, each slice was divided into two sub-segments based on the center sampling point, and the normalized cross-correlation coefficient between the two was calculated. If the coefficient was greater than the set symmetry threshold, the slice was considered to have good morphological symmetry characteristics and was included in the set of valid echo slices. The target echo segments selected in this way better reflect the true interface reflection characteristics and exclude distortion segments caused by scattering or multipath effects.

[0027] For the selected target echo band, a pre-calibrated standard waveform template is introduced for point-by-point amplitude compensation. This template is derived from the single echo waveform of a standard test block acquired and averaged under ideal operating conditions, exhibiting a typical oscillation pattern. Using this template as a reference, the gain is adjusted point by point in each sampling point of the target echo band to make the amplitude levels between different segments more consistent, reducing the energy differences caused by propagation attenuation.

[0028] After amplitude compensation is completed, the phase alignment stage begins. The generalized cross-correlation method is used to estimate the subtle time offset between adjacent compensated waveforms. Relative phase differences are corrected using linear interpolation or phase rotation algorithms to ensure phase continuity at splicing boundaries. Finally, all aligned waveform segments are connected in chronological order to generate a complete reconstructed signal, improving the overall waveform's coherence and resolvability. For example, in pipe wall thickness detection, this process can effectively distinguish between inner and outer wall echoes, improving measurement stability.

[0029] In a specific embodiment, the step of extracting the envelope of the original echo sequence from the MEMS high-precision ultrasonic sensor to obtain the signal envelope includes: The original echo sequence of the MEMS high-precision ultrasonic sensor is rectified to obtain a rectified echo sequence, and the rectified echo sequence is smoothed to obtain a smoothed echo sequence. The smooth echo sequence is filtered point by point to obtain the amplitude peak sequence, and the amplitude peak sequence is linearly fitted to obtain the signal envelope.

[0030] Specifically, when extracting the envelope of the raw echo sequence from a MEMS high-precision ultrasonic sensor, rectification is performed first. The negative signals in the raw echo sequence are flipped to positive polarity, forming a unipolar rectified echo sequence. This process can be achieved through a full-wave rectification circuit or numerical computation, ensuring that all oscillations are above the time axis. Subsequently, the rectified echo sequence is smoothed using a moving average filter or a low-pass Butterworth filter to denoise and suppress fluctuations, preserving its energy envelope trend. The output is a smoothed echo sequence with continuous waveforms and no high-frequency oscillation interference.

[0031] Based on the obtained smooth echo sequence, further point-by-point amplitude filtering is performed. Each sampling point in the sequence is traversed to determine if it satisfies the local maximum condition (i.e., the amplitude at the current point is greater than the amplitudes of several adjacent points). Combined with a preset minimum amplitude threshold, false peaks caused by background noise are eliminated, thereby extracting a set of discrete high-energy points, forming an amplitude peak sequence. These points are distributed on the time axis, corresponding to the energy concentration locations of each valid echo, reflecting the key nodes of signal energy fluctuations.

[0032] Next, a linear fitting operation is performed on the amplitude peak sequence. Using time as the x-axis and amplitude as the y-axis, the selected peak points are used as input data, and a continuous curve is fitted using the least squares method. This curve is generated based on the overall distribution trend of the peak points and reflects the outer contour of the echo energy change over time. During the fitting process, a first-order or second-order polynomial model can be selected according to actual needs. When the echo attenuation follows an exponential law, the logarithm of the amplitude can be taken first before linearization. The final output is the signal envelope. This envelope does not contain the original oscillation details; it only describes the energy envelope of each echo segment.

[0033] The above process has clear application value in industrial ranging scenarios. For example, when detecting defects on the inner wall of a metal container, the echo amplitudes of ultrasonic waves reflected from interfaces at different depths vary significantly, and multiple superposition phenomena occur. Rectification and smoothing can eliminate the influence of oscillation polarity, highlighting the main energy peak; further, through point-by-point screening and linear fitting, the energy boundaries of each reflection event can be accurately delineated, providing a stable basis for subsequent peak time point detection.

[0034] In a specific embodiment, the step of detecting peak points on the signal envelope to obtain a peak time point sequence includes: Perform first-order difference calculation on the signal envelope to obtain the instantaneous slope of each sampling point on the envelope, and obtain the slope change curve; Based on the slope change curve, the zero-crossing point position from positive to negative is identified, the time coordinate corresponding to the zero-crossing point position is marked as the candidate peak time point, and all candidate peak time points are sorted in chronological order to generate the peak time point sequence.

[0035] Specifically, when detecting peak points on the signal envelope, first-order difference calculation is performed. The amplitude difference between adjacent sampling points on the signal envelope is taken and divided by the corresponding time interval to obtain the instantaneous slope sequence at each point. This sequence forms a slope change curve, reflecting the upward or downward trend and rate of the envelope at each point. The difference operation is implemented through forward or center-difference methods, for example, using the formula... Calculate the slope of the nth sampling point, where This represents the amplitude of the signal envelope at time n. The sampling period.

[0036] Based on the aforementioned slope change curve, the zero-crossing point where the slope changes from positive to negative is identified. The specific determination process is as follows: traverse each data point in the slope sequence. When the slope of a point is positive and the slope of the next point is negative, it is determined that there is a transition from rising to falling within that interval, which is the region where a potential local maximum point is located. If there is no sign change between two adjacent points but the slope value crosses the zero line, the precise zero-crossing time can be estimated through linear interpolation, improving the time positioning resolution. The time coordinates corresponding to these zero-crossing points are extracted as candidate peak time points, with each time point representing the location of a possible echo energy peak.

[0037] Then, all candidate peak time points are arranged in chronological order to form an ordered time series. The sorting process reorganizes the data based on the timestamps of each point from smallest to largest, ensuring that each point in the sequence conforms to the actual physical timing and avoiding order disruptions caused by signal disturbances. This ordered set is the final output peak time point sequence, used for subsequent segmentation and truncation of the original echo sequence. In some cases, if the time interval between two adjacent candidate points is less than a preset threshold, the optimal candidate can be retained based on amplitude magnitude to prevent multiple misjudgments caused by dense noise.

[0038] This method demonstrates stability in MEMS ultrasonic sensors used for liquid level measurement. For example, when ultrasonic waves pass through the gas-liquid interface, the reflected echo is affected by surface fluctuations, resulting in energy fluctuations. Traditional threshold methods are easily interfered with, while the slope zero-crossing detection method can effectively lock the true peak position where the rising edge of the envelope turns into the falling edge, improving detection robustness.

[0039] In a specific embodiment, the step of segmenting the original echo sequence based on the peak time point sequence to obtain multiple echo slices includes: The difference between adjacent peak time points in the peak time point sequence is calculated to obtain the peak interval sequence; The truncation window radius is determined based on the minimum interval value in the peak interval sequence, and the truncation window radius is combined with each of the peak time points to obtain the truncation boundary parameters, wherein the truncation boundary parameters include the start time and end time of each echo slice; Based on the truncation boundary parameters, the original echo sequence is separated and extracted segment by segment to obtain the multi-segment echo slices, wherein the multi-segment echo slices include independent echo signal segments centered on each peak time point.

[0040] Specifically, when segmenting the original echo sequence based on the peak time point sequence, the temporal relationship between adjacent peak time points is first processed. The difference between each pair of consecutive time markers in the peak time point sequence is calculated, i.e., the subsequent peak time is subtracted from the previous peak time, resulting in a set of time interval data, forming the peak interval sequence. This sequence reflects the temporal span distribution between each echo event, and its magnitude is directly affected by the target structure interface spacing and sound velocity characteristics. In actual measurements, due to multi-layer reflections or media inhomogeneity, some intervals may be significantly shorter than others; therefore, the minimum interval value needs to be extracted as the basis for subsequent window setting.

[0041] The truncation window radius is determined using the minimum interval value in the peak interval sequence. Specifically, this minimum interval value is divided by two to obtain a base time length, which serves as the half-width of each echo slice extended on both sides of its central peak time point—the truncation window radius. This design ensures sufficient separation margin is maintained even in the densest echo regions, preventing overlap between adjacent slices. This truncation window radius is then combined with each time point in the peak time sequence. For each peak time point t_i, a truncation window radius δt is extended forward and backward from it, calculating the corresponding time boundaries [t_i - δt, t_i + δt]. This set of upper and lower limits constitutes the truncation boundary parameters, clearly identifying the start and end times of each echo slice.

[0042] Based on the aforementioned truncation boundary parameters, the original echo sequence is subjected to segment-by-segment separation and extraction. The start and end times of each segment are located in the time-domain signal stream, and all sampling points within the corresponding time period are extracted to form independent data segments. These separated signal segments are multi-segment echo slices, each slice containing the complete oscillation process of its vicinity, centered on a peak time point. For example, in scenarios where MEMS ultrasonic sensors are used to detect interlayer defects in composite materials, when the arrival times of reflected echoes from different depths are close, a truncation strategy based on dynamic adjustment of the minimum interval can effectively distinguish signals returned from adjacent interfaces, preventing crosstalk caused by an excessively wide fixed window or waveform truncation caused by an excessively narrow window.

[0043] In a specific embodiment, the step of segmentally separating and extracting the original echo sequence based on the truncation boundary parameters to obtain multiple echo slices includes: Based on the start and end times of each echo slice in the interception boundary parameters, the original echo sequence is indexed by time interval to obtain the sampling point range of each echo slice; The signal data located within the sampling point range in the original echo sequence are read and copied segment by segment to obtain an initial slice sequence, wherein the initial slice sequence includes continuous signal segments corresponding to each peak time point; The first and last boundary regions of each continuous signal segment in the initial slice sequence are subjected to gradual attenuation weighting to obtain the multi-segment echo slice, wherein the multi-segment echo slice includes independent echo signal segments whose boundary amplitudes smoothly transition to zero.

[0044] Specifically, when performing segment-by-segment separation and extraction of the original echo sequence based on truncation boundary parameters, the corresponding time intervals in the original signal are first located according to the start and end times of each echo slice. Using the system sampling rate as the conversion benchmark from time to sampling points, each start and end time is converted into an integer sampling point index, thereby determining the specific location range of each echo slice in the original sequence, i.e., the sampling point range. This process ensures accurate mapping of time boundaries and avoids truncation deviations caused by floating-point errors.

[0045] Using the aforementioned sampling point range, signal data within each range of the original echo sequence are read. Discrete sample values ​​within specified index intervals are accessed sequentially in chronological order, copied, and stored as independent data blocks to form the initial slice sequence. Each data block corresponds to a peak time point, preserving a continuous signal segment centered on that point, containing the complete oscillation pattern, without any amplitude correction or boundary processing.

[0046] For each continuous signal segment in the initial slice sequence, a gradual attenuation weighting is applied to its beginning and end boundary regions. Specifically, a window function is used to smoothly suppress the beginning and end parts of each segment, such as using a local segment of a Hanning window or a triangular window, so that the amplitude at both ends gradually transitions from the original level to near zero. The weighting operation only applies to a certain proportion of sample points before and after the slice, while the main body in the middle remains unchanged. After this processing, the boundary of each echo slice is no longer abruptly truncated, but shows a gentle attenuation trend, effectively suppressing spectral leakage or artifact interference that may be caused during subsequent splicing.

[0047] For example, in the application of MEMS ultrasonic sensors to detect multilayer structures of metal plates, when the distance between adjacent reflecting surfaces is small and the echo signals appear densely, even if preliminary temporal separation is achieved by truncation of boundary parameters, direct truncation may result in the slice edges carrying incomplete periodic information. After gradual weighting of the beginning and end, each echo slice naturally returns to zero at the boundary, improving independence and providing cleaner waveform units for subsequent screening and reconstruction.

[0048] In a specific embodiment, the step of performing waveform symmetry analysis on each echo slice and selecting valid echo slices that meet the symmetry threshold from the multiple echo slices to obtain the target echo segment includes: For each echo slice, the waveform center point is located, the point with the maximum amplitude within the echo slice is marked as the waveform center, and the echo slice is divided into a left half waveform and a right half waveform based on the waveform center; The left and right half waveforms are cross-correlated point by point to obtain the correlation coefficient describing the similarity between the two waveforms, and the correlation coefficient is used as the symmetry measure of the echo slice. The symmetry metric is compared with a preset symmetry threshold. From the multiple echo slices, slices with a metric value greater than or equal to the threshold are selected as valid echo slices, and slices with a metric value less than the threshold are removed to obtain the target echo band.

[0049] Specifically, when performing waveform symmetry analysis on each echo slice, the first step is to locate the waveform center point. The sampling point with the largest amplitude is searched within the data sequence of a single echo slice, and its time position is marked as the center of that waveform segment. This point typically corresponds to the moment when the echo energy is most concentrated. Using this center point as the boundary, the entire echo slice is divided into two equal parts, defined as the left and right halves of the waveform. The left half contains all sampled data before the center point, while the right half covers the data segment after the center point. If the total number of points is odd, the center point itself can be assigned to either side or processed separately.

[0050] After segmentation, point-by-point cross-correlation calculations are performed on the left and right half-waveforms. Specifically, the two data segments are aligned and compared, and their normalized cross-correlation coefficients are calculated, expressed by the formula: ,in and These represent the amplitude sequences at corresponding positions in the left and right halves of the waveform, respectively. , This is its mean. This coefficient reflects the similarity in shape between the two waveforms, and its value ranges from -1 to 1. The closer it is to 1, the better the symmetry. This correlation coefficient serves as a measure of the symmetry of the echo slice, used to quantify its morphological symmetry characteristics.

[0051] The obtained symmetry metrics are then compared one by one with a preset symmetry threshold. This threshold is calibrated experimentally based on the actual application scenario; for example, it is often set above 0.85 in industrial ranging systems to exclude signals with obvious distortion. When the symmetry metric of an echo slice is greater than or equal to the threshold, it is determined to have sufficiently good waveform symmetry and is retained as a valid echo slice; conversely, if the metric is lower than the threshold, the slice is considered to be possibly affected by noise interference, multiple reflections, or abnormal propagation paths and is discarded. Finally, all slices that meet the conditions are summarized to form the target echo band set.

[0052] For example, in scenarios where MEMS ultrasonic sensors are used to detect liquid level, ideally, the reflected echo from the liquid surface should exhibit an approximately symmetrical oscillation pattern. However, clutter caused by bubbles or turbulence often leads to waveform distortion and asymmetry. The above process can effectively identify and retain the true liquid surface echo, filter out atypical responses, and improve the reliability of subsequent signal reconstruction.

[0053] In a specific embodiment, point-by-point amplitude compensation is performed on the target echo band based on a preset standard waveform template to generate a compensated waveform, including: The target echo band is time-domain aligned with the standard waveform template, and the waveform center of the target echo band is aligned with the waveform center of the standard waveform template to obtain the aligned echo band. The amplitude difference between the aligned echo band and the corresponding sampling points of the standard waveform template is calculated to obtain a point-by-point compensation coefficient sequence. The point-by-point compensation coefficient sequence includes the ratio of the target amplitude to the actual amplitude at each sampling point. The amplitude of each sampling point of the aligned echo band is product-corrected based on the point-by-point compensation coefficient sequence to obtain the compensated waveform, wherein the compensated waveform includes an echo signal segment whose amplitude characteristics are consistent with the standard waveform template after amplitude correction.

[0054] Specifically, when performing waveform symmetry analysis on each echo slice, the waveform center point is located first. In the sampled data sequence of a single echo slice, all points are traversed to find the sampling point where the amplitude reaches a local maximum and is the global peak within that segment; this point is marked as the waveform center. This point typically corresponds to the moment when the echo energy is most concentrated, serving as the reference position for subsequent segmentation. Using this waveform center as the boundary, the entire echo slice is divided into two sub-segments: the portion before the center point is defined as the left half of the waveform, and the portion after it constitutes the right half. If the total length of the slice is odd, the center point itself may not be included in the calculation or may be assigned to one side, ensuring that the left and right parts have the same number of data points for comparison.

[0055] After segmentation, a point-by-point cross-correlation calculation is performed on the left and right halves of the waveform. Specifically, the left half of the data is used as the reference sequence, and the right half as the comparison sequence. At the same index position, each sampling point is multiplied and accumulated, and after deducting the mean effect, the result is normalized to obtain a value between 0 and 1, i.e., the correlation coefficient. This process can be expressed by the formula... Implementation, in which and These represent the amplitudes of the i-th sampling point of the left and right half-waveforms, respectively. , This is their respective mean values. This coefficient reflects the degree of matching between the waveforms on both sides in terms of shape and phase. The higher the value, the stronger the symmetry. This value is used as a measure of the symmetry of the echo slice.

[0056] The symmetry metric value corresponding to each echo slice is then compared with a preset symmetry threshold. This threshold is calibrated based on measured data from typical application scenarios. For example, in a system used to detect the thickness of metal plates, extensive experimental statistics have determined that a correlation coefficient of not less than 0.82 can effectively distinguish between real reflected signals and interference signals. If the symmetry metric value of a slice is greater than or equal to the threshold, its waveform is considered complete and symmetrical, and it is included in the set of valid echo slices; otherwise, it is considered to be distorted by noise, scattering, or multipath effects and is discarded. All the retained valid echo slices together form the target echo band for subsequent compensation and stitching.

[0057] When MEMS ultrasonic sensors are used for corrosion detection on the inner walls of pipelines, surface roughness causes some echoes to exhibit forward thrust or tailing, disrupting the original symmetrical structure. The aforementioned analysis method based on waveform center segmentation and cross-correlation can effectively identify and eliminate these distorted segments, retaining only signal segments that meet the expected response characteristics, thereby improving the accuracy and stability of the reconstructed signal.

[0058] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention. It should be noted that the information interaction, execution process, etc. between the above devices / units are based on the same concept as the method embodiments of this application. Their specific functions and technical effects can be found in the embodiment section of the control device, and will not be repeated here.

[0059] Please see Figure 5 , Figure 5 This is a schematic diagram of the framework of an embodiment of the signal reconstruction device based on a MEMS high-precision ultrasonic sensor according to this application. Figure 5 As shown, the signal reconstruction device based on a MEMS high-precision ultrasonic sensor includes an extraction module 1 for extracting the envelope of the original echo sequence from the MEMS high-precision ultrasonic sensor to obtain a signal envelope; a detection module 2 for detecting peak points on the signal envelope to obtain a peak time point sequence; a truncating module 3 for segmenting the original echo sequence based on the peak time point sequence to obtain multiple echo slices; an analysis module 4 for performing waveform symmetry analysis on each echo slice, selecting valid echo slices that meet the symmetry threshold from the multiple echo slices to obtain a target echo segment; and a compensation module 5 for performing point-by-point amplitude compensation on the target echo segment based on a preset standard waveform template to generate a compensated waveform, and performing phase alignment and splicing on the compensated waveform to obtain a reconstructed signal.

[0060] The above module is used to execute the steps of the signal reconstruction method based on MEMS high-precision ultrasonic sensor.

[0061] Reference Figure 6 This invention also provides a computer device whose internal structure can be as follows: Figure 6 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0062] Those skilled in the art will understand that Figure 6 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0063] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0064] This application provides a computer program product that, when run on an electronic device, enables the electronic device to perform the functions of the various structures of the control device described above, or to implement the steps in the various method embodiments described above.

[0065] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0066] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0067] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed methods and apparatus can be implemented in other ways. For example, the apparatus implementations described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0069] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0070] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0071] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] If the technical solution of this application involves personal information, the product using this technical solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using this technical solution has obtained the user's separate consent before processing the sensitive personal information, and also meets the requirement of "express consent". For example, at personal information collection devices such as cameras, clear and prominent signs are set up to inform users that they have entered the scope of personal information collection and that personal information will be collected. If an individual voluntarily enters the collection scope, it is deemed that they have agreed to the collection of their personal information; or on the personal information processing device, with clear signs / information informing users of the personal information processing rules, authorization is obtained from the individual through pop-up information or by asking the individual to upload their personal information; wherein, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the types of personal information processed.

Claims

1. A signal reconstruction method based on a MEMS high-precision ultrasonic sensor, characterized in that, Includes the following steps: Envelope extraction is performed on the raw echo sequence of the MEMS high-precision ultrasonic sensor to obtain the signal envelope; Peak point detection is performed on the signal envelope to obtain the peak time point sequence; Based on the peak time point sequence, the original echo sequence is segmented to obtain multiple echo slices; Waveform symmetry analysis is performed on each echo slice, and valid echo slices that meet the symmetry threshold are selected from the multiple echo slices to obtain the target echo segment; The target echo band is subjected to point-by-point amplitude compensation based on a preset standard waveform template to generate a compensated waveform. The compensated waveform is then phase-aligned and spliced ​​to obtain a reconstructed signal.

2. The signal reconstruction method based on a MEMS high-precision ultrasonic sensor according to claim 1, characterized in that, The process of extracting the envelope from the original echo sequence of the MEMS high-precision ultrasonic sensor to obtain the signal envelope includes: The original echo sequence of the MEMS high-precision ultrasonic sensor is rectified to obtain a rectified echo sequence, and the rectified echo sequence is smoothed to obtain a smoothed echo sequence. The smooth echo sequence is filtered point by point to obtain the amplitude peak sequence, and the amplitude peak sequence is linearly fitted to obtain the signal envelope.

3. The signal reconstruction method based on a MEMS high-precision ultrasonic sensor according to claim 1, characterized in that, The step of detecting peak points in the signal envelope to obtain a peak time point sequence includes: Perform first-order difference calculation on the signal envelope to obtain the instantaneous slope of each sampling point on the envelope, and obtain the slope change curve; Based on the slope change curve, the zero-crossing point position from positive to negative is identified, the time coordinate corresponding to the zero-crossing point position is marked as the candidate peak time point, and all candidate peak time points are sorted in chronological order to generate the peak time point sequence.

4. The signal reconstruction method based on a MEMS high-precision ultrasonic sensor according to claim 1, characterized in that, The original echo sequence is segmented based on the peak time point sequence to obtain multiple echo slices, including: The difference between adjacent peak time points in the peak time point sequence is calculated to obtain the peak interval sequence; The truncation window radius is determined based on the minimum interval value in the peak interval sequence, and the truncation window radius is combined with each of the peak time points to obtain the truncation boundary parameters, wherein the truncation boundary parameters include the start time and end time of each echo slice; Based on the truncation boundary parameters, the original echo sequence is separated and extracted segment by segment to obtain the multi-segment echo slices, wherein the multi-segment echo slices include independent echo signal segments centered on each peak time point.

5. The signal reconstruction method based on a MEMS high-precision ultrasonic sensor according to claim 4, characterized in that, The original echo sequence is segmented and extracted based on the truncation boundary parameters to obtain multiple echo slices, including: Based on the start and end times of each echo slice in the interception boundary parameters, the original echo sequence is indexed by time interval to obtain the sampling point range of each echo slice; The signal data located within the sampling point range in the original echo sequence are read and copied segment by segment to obtain an initial slice sequence, wherein the initial slice sequence includes continuous signal segments corresponding to each peak time point; The first and last boundary regions of each continuous signal segment in the initial slice sequence are subjected to gradual attenuation weighting to obtain the multi-segment echo slice, wherein the multi-segment echo slice includes independent echo signal segments whose boundary amplitudes smoothly transition to zero.

6. The signal reconstruction method based on a MEMS high-precision ultrasonic sensor according to any one of claims 1-5, characterized in that, The process of performing waveform symmetry analysis on each echo slice, and selecting valid echo slices that meet the symmetry threshold from the multiple echo slices to obtain the target echo segment, includes: For each echo slice, the waveform center point is located, the point with the maximum amplitude within the echo slice is marked as the waveform center, and the echo slice is divided into a left half waveform and a right half waveform based on the waveform center; The left and right half waveforms are cross-correlated point by point to obtain the correlation coefficient describing the similarity between the two waveforms, and the correlation coefficient is used as the symmetry measure of the echo slice. The symmetry metric value is compared with a preset symmetry threshold. From the multiple echo slices, slices with a metric value greater than or equal to the threshold are selected as valid echo slices, and slices with a metric value less than the threshold are removed to obtain the target echo band.

7. A signal reconstruction device based on a MEMS high-precision ultrasonic sensor, characterized in that, The signal reconstruction method based on a MEMS high-precision ultrasonic sensor for performing any one of claims 1 to 6 includes: The extraction module is used to extract the envelope of the original echo sequence of the MEMS high-precision ultrasonic sensor to obtain the signal envelope. The detection module is used to detect peak points in the signal envelope to obtain a peak time point sequence; The segmentation module is used to segment the original echo sequence based on the peak time point sequence to obtain multiple echo slices; The analysis module is used to perform waveform symmetry analysis on each echo slice, and to select the effective echo slices that meet the symmetry threshold from the multiple echo slices to obtain the target echo segment. The compensation module is used to perform point-by-point amplitude compensation on the target echo band based on a preset standard waveform template, generate a compensated waveform, and perform phase alignment and splicing on the compensated waveform to obtain a reconstructed signal.

8. A computer device, characterized in that, The method includes a memory and a processor that are coupled to each other. The memory stores program instructions, and the processor executes the program instructions to implement the signal reconstruction method based on a MEMS high-precision ultrasonic sensor as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The device stores program instructions that can be executed by a processor, the program instructions being used to implement the signal reconstruction method based on a MEMS high-precision ultrasonic sensor as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, enables the implementation of the signal reconstruction method based on a MEMS high-precision ultrasonic sensor as described in any one of claims 1 to 6.

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