An ultrasonic signal common-mode noise balancing filtering system and method in a strong noise environment

Abstract

The application relates to the technical field of signal filtering, in particular to an ultrasonic signal common-mode noise balance filtering system and method in a strong noise environment, which comprises the following steps: collecting a mixed signal and a reference noise signal in a strong noise environment; dividing the reference noise signal in a preset time window into intervals, analyzing the confusion degree of the frequency spectrum of the reference noise signal in each interval, combining the propeller rotating speed of a UAV to determine the characteristic coefficient of each time window; analyzing the number distribution of the frequency spectrum peak points of the reference noise signal in each time window to determine the characteristic index of each time window; adjusting the step of the adaptive filtering algorithm; and using the adaptive filtering algorithm and a noise cancellation algorithm to obtain the ultrasonic signal after noise elimination and the balance noise signal. The application can more accurately perform noise cancellation in a strong noise environment and improve the recovery effect of the ultrasonic signal.

Description

Technical Field

[0001] This application relates to the field of signal filtering technology, specifically to a common-mode noise balancing filtering system and method for ultrasonic signals under strong noise environment. Background Technology

[0002] In the current field of industrial non-destructive testing (NDT), ultrasonic testing has become an indispensable method. It utilizes the propagation characteristics of ultrasonic waves in materials, assessing the internal structure and defects of materials by detecting information such as reflection, refraction, and scattering of ultrasonic waves. Among these methods, the technology of using unmanned aerial vehicles (UAVs) equipped with fiber optic sensing systems for NDT, as a product of the integration of aerospace technology and advanced sensing technology, is gradually demonstrating its advantages in current industrial NDT. With the maturity of multi-rotor UAV technology, the application of high-precision positioning and navigation, and the advancement of fiber Bragg grating (FBG) sensor technology, this system is gradually being put into use. However, during UAV flight, the high-speed rotation of its propellers generates strong, broadband aerodynamic noise and vibration noise. This noise energy is much greater than the target ultrasonic signal, and its spectrum may partially or completely overlap with the ultrasonic signal spectrum, resulting in common-mode noise interference with similar amplitude and phase on the signal line.

[0003] In the process of common-mode noise balancing and filtering of ultrasonic signals in high-noise environments, when using a UAV equipped with a fiber optic sensing system for target detection, it is necessary to accurately acquire weak ultrasonic signals. However, during the flight of the UAV, the high-speed rotation of its propellers generates strong, broadband aerodynamic noise and vibration noise. This noise energy is much greater than that of the target ultrasonic signal, and its spectrum may partially or completely overlap with the spectrum of the ultrasonic signal. The resulting common-mode noise manifests as interference with similar amplitude and phase on the signal line. At the same time, during flight, the propeller speed is dynamically adjusted according to flight control commands, causing the noise spectrum to vary over time. Traditional noise reduction methods are usually unable to effectively remove the above-mentioned noise interference, leading to the failure of common-mode noise filtering of ultrasonic signals in high-noise environments. Summary of the Invention

[0004] In view of the above, it is necessary to provide a common-mode noise balancing and filtering system and method for ultrasonic signals in a high-noise environment to solve the above problems.

[0005] The first aspect of this application provides a method for balancing and filtering common-mode noise of ultrasonic signals in a high-noise environment, the method comprising:

[0006] Acquire mixed signals and reference noise signals collected by a dual-channel UAV in a high-noise environment;

[0007] The reference noise signal within the preset time window is divided into intervals, the degree of disorder of the spectrum of the reference noise signal in each interval is analyzed, and the characteristic coefficients of each time window are determined in combination with the propeller speed of the UAV.

[0008] The distribution of the number of peak points in the spectrum of the reference noise signal within each time window is analyzed and compared with the distribution of the number of peak points in the overall reference noise signal to determine the peak coefficient of each time window; the distribution characteristics of peak points within each time window are compared with those within all time windows, and the characteristic index of each time window is determined in combination with the peak coefficient.

[0009] Based on the characteristic coefficients and characteristic indices of each time window, the step size of the adaptive filtering algorithm is adjusted; based on the adjusted step size, the adaptive filtering algorithm and noise cancellation algorithm are used to obtain the denoised ultrasonic signal and the balanced noise signal based on the reference noise signal and the mixed signal.

[0010] Preferably, the degree of disorder is calculated using spectral entropy.

[0011] Preferably, determining the characteristic coefficients for each time window specifically involves:

[0012] Obtain the permutation entropy of the spectral entropy of all interval reference noise signals for each time window;

[0013] Calculate the difference between the propeller speed of the UAV in each time window and the previous time window; determine the characteristic coefficient of each time window based on the permutation entropy corresponding to each time window and the difference; wherein the characteristic coefficient is positively correlated with the difference and the permutation entropy.

[0014] Preferably, the formula for the characteristic coefficients of each time window is: In the formula, Indicates time window eigencoefficients, , These represent time windows. Time window The propeller speed of the internal drone; As a preset constant, This represents an exponential function with the natural constant as its base. This represents the permutation entropy of the time window t.

[0015] Preferably, determining the peak coefficient for each time window specifically involves:

[0016] Obtain the percentage of peak points in each time window, denoted as the first percentage; obtain the percentage of peak points in the spectrum of the reference noise signal, denoted as the second percentage; calculate the difference between the first percentage and the second percentage, and perform normalization processing to obtain the peak coefficient of each time window.

[0017] Preferably, the determination of the characteristic indicators for each time window specifically includes:

[0018] Calculate the average height of all peak points in the spectrum of the reference noise signal. Then, calculate the difference between the height of each peak point and the average height within each time window. Accumulate the differences in the heights of all peak points in each time window and positively fuse them with the negative correlation mapping result of the peak coefficient to obtain the characteristic index of each time window.

[0019] Preferably, adjusting the step size of the adaptive filtering algorithm specifically involves:

[0020] For each time window, calculate the normalized value of the product of the characteristic coefficient and the characteristic index, and then multiply it by the base step size to obtain the adjustment step size for each time window.

[0021] Preferably, the basic step size is the total power of the input signal of the adaptive filtering algorithm.

[0022] Preferably, obtaining the denoised ultrasonic signal and the balanced noise signal specifically involves:

[0023] An adaptive filtering algorithm is used to iteratively optimize the amplitude and phase of the reference noise signal. The filtered noise signal is then subtracted from the mixed signal, and the denoised ultrasonic signal and the balanced noise signal are output.

[0024] Secondly, embodiments of this application also provide a common-mode noise balancing and filtering system for ultrasonic signals in a high-noise environment, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0025] This application has at least the following beneficial effects:

[0026] This application, by acquiring mixed signals and reference noise signals in a high-noise environment, can better capture the characteristics and changes of noise. The reference noise signal, serving as a benchmark for the noise source, helps the adaptive noise cancellation algorithm accurately identify noise components and effectively filter them. Through interval division, the characteristics of the reference noise signal at different time periods can be analyzed in more detail. The degree of spectral disorder helps determine the noise's volatility, which is crucial for dynamically adjusting adaptive filter parameters (such as step size), ensuring the filter's stability and effectiveness in complex environments. The UAV propeller speed directly affects noise characteristics; combining this information helps to more accurately understand and predict noise signal patterns. Characteristic coefficients help distinguish the different characteristics of noise and target signals, thereby optimizing the filtering process and making noise cancellation more precise. Furthermore, analyzing the distribution of the number of peak points in the reference noise signal spectrum determines the peak coefficient for each time window; the number and distribution of spectral peak points reveal the frequency characteristics and intensity of the noise. By analyzing peak coefficients, the main frequency bands of noise can be identified, providing a basis for noise suppression. This step helps adjust the algorithm, enabling the filter to more effectively model and suppress noise signals. Furthermore, by comparing local and global peak distributions, it's possible to identify which time periods are most prominent, allowing for specific noise processing during these periods. This analysis improves the algorithm's dynamic adaptability, ensuring efficient noise cancellation under varying noise environments. Dynamically adjusting the step size based on the characteristic coefficients and indicators of each time window optimizes the filtering process, improves the performance of the noise cancellation algorithm, and makes it more adaptable to environmental changes. By adjusting the step size, the algorithm can more accurately eliminate noise components in mixed signals, recovering clearer ultrasonic signals. This effectively reduces the impact of noise on signal quality, providing higher-quality denoising results and enabling more precise noise cancellation in strong noise environments, thus improving the recovery effect of ultrasonic signals. Attached Figure Description

[0027] Figure 1 A flowchart illustrating the steps of a common-mode noise balancing and filtering method for ultrasonic signals in a high-noise environment, as provided in one embodiment of this application;

[0028] Figure 2 This is a flowchart illustrating the adjustment of the step size of an adaptive filtering algorithm according to one embodiment of this application. Detailed Implementation

[0029] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application's specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0031] It should also be noted that the terms "first" and "second" in this application and its accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the method. Without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged, and some steps can also be deleted.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0033] The following description, in conjunction with the accompanying drawings, details the specific scheme of the ultrasonic signal common-mode noise balancing filtering system and method provided in this application under strong noise conditions.

[0034] Please see Figure 1 The diagram illustrates a flowchart of a common-mode noise balancing and filtering method for ultrasonic signals in a high-noise environment, according to an embodiment of this application. The method includes the following steps:

[0035] The first step: Acquire the mixed signal and reference noise signal collected by the dual channels of the UAV in a high-noise environment.

[0036] When performing common-mode noise filtering of ultrasonic signals in a high-noise environment, the target scenario is non-destructive testing using a fiber optic sensing system mounted on a drone. Because the propellers rotate at high speed during drone flight, they generate strong, broadband aerodynamic and vibration noise, which can interfere with the acquisition of weak ultrasonic signals. Therefore, to ensure accurate signal detection, a dual-channel fiber optic sensor is used to simultaneously capture ultrasonic signals, effectively reducing the impact of noise.

[0037] First, a dual-channel fiber optic sensing probe is configured on the UAV. This probe integrates two physically adjacent fiber optic sensing units with different acoustic coupling characteristics. The two channels are designated as the first channel (signal channel) and the second channel (noise reference channel). The sensing unit corresponding to the first channel is designed to be sensitive to both the target ultrasonic signal and environmental noise (including UAV propeller noise). The sensing unit corresponding to the second channel is physically shielded, making it insensitive to the target ultrasonic signal, but it has similar response characteristics to environmental noise (especially common-mode noise from the UAV itself) as the first channel. Simultaneously, the UAV is equipped with an optical signal demodulation and acquisition unit. This unit is responsible for emitting light to the sensing probe and receiving and demodulating the sensing optical signals from both channels, synchronously converting them into digitized electrical signals. It also acquires the mixed signal (ultrasonic and noise signals) obtained from the first channel, as well as the reference noise signal obtained from the second channel. During ultrasonic flaw detection, the center frequency of the ultrasonic probe is 5MHz. According to the Nyquist theorem, the signal sampling frequency is set to 20MHz to complete signal data acquisition.

[0038] Simultaneously, a differential Hall effect sensor on the UAV is used to acquire the rotational speed signal, and the sampling frequency of the rotational speed signal is calculated according to the Nyquist sampling theorem. ,in, Indicates the drone's maximum rotational speed. This represents the number of pulses per revolution of the drone, which is twice the number of pole pairs of the motor and is determined based on the actual equipment used. When acquiring the rotational speed, the frequency F is obtained by calculating the number of rising edges of a square wave within a preset time period and dividing by the corresponding time. In this embodiment, the preset time period is 1 second, but the implementer can adjust it according to the actual situation. Then, through... Calculate and obtain the rotational speed. This indicates the number of pulses per revolution. If the number of pole pairs of the motor is 1, then... =2 (two pulses are generated per revolution). The timing of all signals is calibrated using the same master clock source.

[0039] The second step is to divide the reference noise signal within the preset time window into intervals, analyze the degree of disorder in the spectrum of the reference noise signal within each interval, and determine the characteristic coefficients of each time window by combining the propeller speed of the UAV.

[0040] This application processes the acquired ultrasonic signal using an adaptive noise cancellation algorithm. It generates an inverse noise signal that matches the ambient noise in the mixed signal in amplitude and phase using a reference noise signal. Then, this inverse noise signal is subtracted from the mixed signal to filter out common-mode noise in the ultrasonic signal, thereby recovering a clear ultrasonic signal. The specific operation steps are as follows:

[0041] First, a short-time Fourier transform is performed on the signal acquired from the second channel to output the spectrum of the signal. The signal obtained through the short-time Fourier transform is then divided into non-overlapping windows in the time dimension. The size of each window is set to 100ms. When the window is less than 100ms, the window is supplemented by mean.

[0042] Based on the noise scenario of ultrasonic flaw detection by UAV, each window is divided into 100 intervals, each interval is 1ms. The spectral entropy corresponding to the amplitude spectrum of each interval is calculated. The spectral entropy values ​​of each interval are constructed into a sequence, with the embedding dimension set to 3 and the delay set to 1. The permutation entropy of the sequence is then calculated.

[0043] When the drone's flight is more stable, the noise generated by the rotor fluctuates more steadily. At this time, the entropy value within the corresponding time interval is relatively small, and the entropy value of different intervals within the window will be close to constant due to the stable state of the drone. The calculated permutation entropy is small, and a smaller step size is needed for more precise fine-tuning. However, when the drone moves and changes angle, the noise generated by the rotor changes more rapidly. The spectral entropy of the time interval is more chaotic, and the elements in the sequence are disordered. The permutation entropy is larger, and a larger step size is needed for rapid convergence.

[0044] When using an adaptive noise cancellation algorithm to denoise the signal data acquired by a drone, it is important to consider that the noise source changes drastically during rapid ascent and descent. When the drone performs maneuvers such as acceleration, climbing, and turning, the noise statistical characteristics change rapidly. In other words, the faster the scene changes, the more prone the filter coefficients are to momentary mismatch, requiring an increased step size to accelerate convergence. During stable flight, the drone's rotational speed changes very little, and the noise situation is relatively stable, requiring a smaller step size for more precise convergence.

[0045] Based on the above analysis, taking each time window as an example, the impact of rotational speed changes and arrangement entropy is analyzed, and characteristic coefficients of the influence of rotational speed changes and arrangement entropy on filtering within each time window are constructed. Specifically, the difference between the UAV's propeller speed in each time window and the previous time window is calculated; based on the arrangement entropy corresponding to each time window and the difference, the characteristic coefficients of each time window are determined. The characteristic coefficients are positively correlated with both the difference and the arrangement entropy.

[0046] In this embodiment, the time window The characteristic coefficients are denoted as Its formula is as follows: In the formula, Indicates time window eigencoefficients, , These represent time windows. Time window The propeller rotation speed of the UAV is obtained by using a differential Hall probe on the UAV to acquire the rotation speed signal, and the rotation speed corresponding to each window is output by frequency measurement method. This is a preset constant; in this embodiment, it is set to 0.1 to ensure calculation accuracy. The value is not 0. This represents an exponential function with the natural constant as its base. This represents the permutation entropy of the time window t.

[0047] It should be understood that when the drone's propeller speed changes rapidly, it indicates that the drone is performing maneuvers such as acceleration, climb, or turning, resulting in drastic changes in noise. The larger the value, the better, especially when there are also drastic changes within the corresponding window. The value is relatively small. As the value increases, the step size needs to be increased further; conversely, when the drone's propeller speed changes little and it is in stable flight, the noise is relatively stable. The value becomes smaller, while remaining stable within the corresponding window. The value is relatively large. The value is therefore relatively small, requiring further reduction in the step size for more precise convergence.

[0048] The third step is to analyze the distribution of the number of peak points in the spectrum of the reference noise signal within each time window, and compare it with the distribution of the number of peak points in the overall reference noise signal to determine the peak coefficient of each time window; compare the distribution characteristics of peak points within each time window with those within all time windows, and combine the peak coefficients to determine the characteristic index of each time window.

[0049] Considering that the relative interaction direction between the rotor and the air changes when the drone's flight direction changes, leading to a change in the noise spectrum distribution (especially the intensity ratio of low-frequency and high-frequency components), the spectral peaks may shift, resulting in brief peak disturbances in the spectrum, manifested as random fluctuations in peak size. Furthermore, due to rapid frequency changes, multiple peaks with similar or time-varying frequencies may merge into a single broad peak. To address these abrupt changes in the signal, a larger step size is needed for rapid adjustment and tracking of new noise statistical characteristics. When using the AMPD peak-finding algorithm to acquire peak values ​​within a window, the peak coefficient for each time window is calculated. Specifically: the proportion of peak points in each time window is obtained, denoted as the first proportion; the proportion of peak points in the reference noise signal spectrum is obtained, denoted as the second proportion; the difference between the first and second proportions is calculated and normalized to obtain the peak coefficient for each time window. In this embodiment, the sigmoid normalization function is used.

[0050] Furthermore, based on the peak point data obtained in each time window, a feature index is constructed. Specifically, the mean height of all peak points in the reference noise signal spectrum is calculated. Then, the difference between the height of each peak point in each time window and the mean height is calculated. The differences in the heights of all peak points in each time window are accumulated and positively fused with the negative correlation mapping result of the peak coefficient to obtain the feature index for each time window.

[0051] In this embodiment, the characteristic index of time window t is marked as Its formula is as follows: In the formula, This represents the total number of peak points within the time window t. This represents the height of the i-th peak point within the time window t. This represents the average height of all peak points in the spectrum of the reference noise signal. This represents the peak coefficient for the time window t.

[0052] It should be understood that when a drone changes direction, the changes caused by the airflow and rotor blades result in chaotic spectral peaks. The peak coefficients within a local window fluctuate randomly, causing a significant difference from the average height of the peak points in the overall signal spectrum. Furthermore, due to the presence of wide peaks, the total number of peak points within the window decreases, resulting in smaller calculated peak coefficients. If the peak value is relatively large, a larger step size should be used; conversely, when the peak value fluctuation of the spectrum during normal drone flight is small, the difference between the peak coefficient within the local window and the average height of the peak points of the overall signal spectrum is small, and there are no merged broad peaks, the calculated value is... Larger, obtained The error is relatively small, so a smaller step size should be used to reduce the error in steady state.

[0053] The fourth step is to adjust the step size of the adaptive filtering algorithm based on the characteristic coefficients and characteristic indices of each time window; based on the adjusted step size, the reference noise signal is filtered, and the mixed signal is combined to obtain the denoised ultrasonic signal and the balanced noise signal.

[0054] Furthermore, the step size of the LMS algorithm is adaptively adjusted by combining the obtained feature coefficients and feature indices:

[0055]

[0056] In the formula, This represents the adaptive step size of the time window t; This is the normalization function; Based on step size, The range of values ​​is , This represents the total power of the input signal, which in this embodiment will be... As the base step size.

[0057] The flowchart for adjusting the step size of the adaptive filtering algorithm is as follows: Figure 2 As shown.

[0058] After constructing the adaptive step-size LMS algorithm, adaptive noise cancellation technology is used. The weight vector of the adaptive filter is initialized to zero by taking the mixed signal (containing the target ultrasonic signal and noise) acquired by the first channel and the reference noise signal acquired by the second channel as input. Then, the adaptive step-size LMS algorithm is used iteratively to optimize the amplitude and phase of the reference noise signal, making it highly matched with the noise component in the mixed signal in amplitude and phase. Finally, the filtered noise signal is subtracted from the mixed signal, resulting in a noise-removed ultrasonic signal and a balanced noise signal that matches the noise signal of the second channel. The LMS algorithm is a well-known technique in this field, and the specific steps will not be elaborated further. This ultimately achieves efficient filtering of common-mode noise in ultrasonic signals during UAV ultrasonic flaw detection.

[0059] Based on the same inventive concept as the above method, this application embodiment also provides an ultrasonic signal common-mode noise balance filtering system under strong noise environment, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the methods described above for ultrasonic signal common-mode noise balance filtering under strong noise environment.

[0060] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0061] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from its essential characteristics. Therefore, the embodiments described above should be considered exemplary and non-limiting in all respects; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some technical features, without causing the essence of the corresponding technical solutions to deviate from the scope of the technical solutions in the embodiments of this application, should all be included within the protection scope of this application.

Claims

1. A method for balanced filtering of common-mode noise in ultrasonic signals under high-noise environments, characterized in that, The method includes: Acquire mixed signals and reference noise signals collected by a dual-channel UAV in a high-noise environment; The reference noise signal within the preset time window is divided into intervals, the degree of disorder of the spectrum of the reference noise signal in each interval is analyzed, and the characteristic coefficients of each time window are determined in combination with the propeller speed of the UAV. The distribution of the number of peak points in the spectrum of the reference noise signal within each time window is analyzed and compared with the distribution of the number of peak points in the overall reference noise signal to determine the peak coefficient of each time window; the distribution characteristics of peak points within each time window are compared with those within all time windows, and the characteristic index of each time window is determined in combination with the peak coefficient. Based on the characteristic coefficients and characteristic indices of each time window, the step size of the adaptive filtering algorithm is adjusted; based on the adjusted step size, the adaptive filtering algorithm and noise cancellation algorithm are used to obtain the denoised ultrasonic signal and the balanced noise signal based on the reference noise signal and the mixed signal. The step size of the adaptive filtering algorithm is adjusted as follows: For each time window, calculate the normalized value of the product of the characteristic coefficient and the characteristic index, and then multiply it by the base step size to obtain the adjustment step size for each time window.

2. The method for common-mode noise balancing and filtering of ultrasonic signals under strong noise environment as described in claim 1, characterized in that, The degree of disorder is calculated using spectral entropy.

3. The method for common-mode noise balancing and filtering of ultrasonic signals under strong noise environment as described in claim 1, characterized in that, The determination of the characteristic coefficients for each time window is specifically as follows: Obtain the permutation entropy of the spectral entropy of all interval reference noise signals for each time window; Calculate the difference between the propeller speed of the UAV in each time window and the previous time window; determine the characteristic coefficient of each time window based on the permutation entropy corresponding to each time window and the difference; wherein the characteristic coefficient is positively correlated with the difference and the permutation entropy.

4. The method for common-mode noise balancing and filtering of ultrasonic signals under strong noise environment as described in claim 3, characterized in that, The formula for the characteristic coefficient of each time window is: In the formula, Indicates time window eigencoefficients, , These represent time windows. Time window The propeller speed of the internal drone; As a preset constant, This represents an exponential function with the natural constant as its base. This represents the permutation entropy of the time window t.

5. The method for common-mode noise balancing and filtering of ultrasonic signals under strong noise environment as described in claim 1, characterized in that, The determination of the peak coefficient for each time window is specifically as follows: Obtain the percentage of peak points in each time window, denoted as the first percentage; obtain the percentage of peak points in the spectrum of the reference noise signal, denoted as the second percentage; calculate the difference between the first percentage and the second percentage, and perform normalization processing to obtain the peak coefficient of each time window.

6. The method for common-mode noise balancing and filtering of ultrasonic signals under strong noise environment as described in claim 1, characterized in that, The specific method for determining the characteristic indicators for each time window is as follows: Calculate the average height of all peak points in the spectrum of the reference noise signal. Then, calculate the difference between the height of each peak point and the average height within each time window. Accumulate the differences in the heights of all peak points in each time window and positively fuse them with the negative correlation mapping result of the peak coefficient to obtain the characteristic index of each time window.

7. The method for common-mode noise balancing and filtering of ultrasonic signals under strong noise environment as described in claim 1, characterized in that, The basic step size is specifically the total power of the input signal to the adaptive filtering algorithm.

8. The method for common-mode noise balancing and filtering of ultrasonic signals under strong noise environment as described in claim 1, characterized in that, The obtained denoised ultrasonic signal and balanced noise signal are specifically as follows: An adaptive filtering algorithm is used to iteratively optimize the amplitude and phase of the reference noise signal. The filtered noise signal is then subtracted from the mixed signal, and the denoised ultrasonic signal and the balanced noise signal are output.

9. A common-mode noise balancing and filtering system for ultrasonic signals in a high-noise environment, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-8.

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