Acoustic Doppler flow measurement method and device for suppressing bubble interference based on multi-index fusion and readable storage medium thereof

Abstract

This paper proposes a multi-index fusion-based acoustic Doppler flow measurement method, device, and readable storage medium for suppressing bubble interference. This method transmits sound waves and receives echoes to obtain raw Doppler frequency shift information. A multi-index fusion model is constructed using Doppler frequency shift, signal-to-noise ratio, and echo intensity to identify bubble interference. Polynomial interpolation is then used to eliminate abnormal frequency shifts, and flow velocity is corrected based on a fluid dynamics model. This method effectively suppresses bubble interference, improves flow measurement accuracy and robustness, is applicable to a variety of water environments, reduces manual intervention, and provides high-quality data for flow velocity measurement.

Description

Technical Field

[0001] The present invention relates to the field of acoustic measurement technology, in particular to a method and device for acoustic Doppler flow measurement based on multi-index fusion and capable of suppressing bubble interference, and a readable storage medium thereof. Background Art

[0002] Acoustic Doppler current profilers (ADCPs) measure water velocity by emitting sound waves and analyzing the Doppler frequency shift of the echoes. They are widely used in fields such as hydrology and oceanography. However, bubbles in water (such as those generated by waves, turbulence, or cavitation) can interfere with sound wave propagation, leading to echo signal distortion and Doppler frequency shift anomalies, significantly reducing flow measurement accuracy. Existing technologies often use single filters (such as Kalman filters and wavelet filters) or single indicator thresholds (such as frequency shift anomalies) to suppress interference. However, these technologies suffer from problems such as filtering out useful signals and being unable to adapt to complex environments, making it difficult to ensure measurement accuracy in scenarios with bubble interference.

[0003] Therefore, there is an urgent need for a method, device and readable storage medium for acoustic Doppler flow measurement based on multi-index fusion to suppress bubble interference, so as to solve the problems existing in the prior art. Summary of the Invention

[0004] The embodiments of the present invention provide a method, device and readable storage medium for acoustic Doppler flow measurement based on multi-index fusion to suppress bubble interference. This method addresses the problems existing in current technologies, such as the use of a single indicator or filtering method to suppress bubble interference, which easily leads to loss of useful signals or inaccurate identification, resulting in low flow measurement accuracy and poor robustness.

[0005] The core technology of this invention is to build an abnormality scoring model by integrating multiple indicators such as Doppler frequency shift, signal-to-noise ratio, and echo intensity, combine genetic algorithm to optimize parameters to identify bubble interference points, and then use polynomial interpolation and fluid mechanics model to correct data to achieve high-precision acoustic Doppler flow measurement.

[0006] In a first aspect, the present invention provides a method for suppressing bubble interference in acoustic Doppler flow measurement based on multi-index fusion, the method comprising the following steps:

[0007] a. Transmitting sound wave signals and receiving echo signals;

[0008] b. Perform frequency analysis on the echo signal to obtain the original Doppler frequency shift information;

[0009] c. Based on the original Doppler frequency shift information, a multi-index fusion model is constructed using Doppler frequency shift, signal-to-noise ratio, and echo intensity to identify and eliminate abnormal frequency shift components caused by bubbles;

[0010] d. Based on the Doppler frequency shift information after eliminating the abnormal frequency shift components, the flow velocity is calculated and corrected in combination with the fluid mechanics model.

[0011] Furthermore, identifying and eliminating abnormal frequency shift components in step c includes:

[0012] c1. Preprocess the original signal, extract the desired time window, and perform inter-beam echo time alignment. Apply a bandpass filter to suppress transient interference, and collect the Doppler shift value, signal-to-noise ratio, and echo intensity at each sampling point.

[0013] c2. Build an anomaly scoring mechanism for each indicator and calculate a comprehensive fuzzy anomaly score to identify bubble interference points;

[0014] c3. Perform polynomial interpolation on the identified abnormal points using the adjacent normal points to complete the abnormal frequency shift data.

[0015] Furthermore, the abnormal scoring mechanism for Doppler shift in step c2 is:

[0016] The 3σ rule is used to calculate the degree of frequency shift deviation from the median, through Calculate anomaly score where:

[0017] To control the slope of the function, it is an adjustable parameter that determines the sensitivity of the score to deviations; To control the trigger threshold of anomaly scoring, it is an adjustable parameter; is the Doppler frequency shift value of the i-th sampling point; is the median of the frequency shift value; x is the normalized deviation, i.e. x= ; is the standard deviation of the frequency shift value, which represents the degree of data dispersion; The upper and lower limits of the effective area of ​​the adjustable signal-to-noise ratio.

[0018] Furthermore, the abnormal scoring mechanism for the signal-to-noise ratio in step c2 is:

[0019] Using a function Calculate anomaly score where:

[0020] is the signal-to-noise ratio of the i-th sampling point, in dB; The adjustable signal-to-noise ratio effective range is used to adjust the original signal-to-noise ratio Converted to dimensionless deviation; x is the normalized deviation, that is, = It controls the sensitivity of the score to signal-to-noise ratio degradation and is an adjustable parameter; It is the trigger threshold that defines the anomaly score and is an adjustable parameter.

[0021] Furthermore, the abnormal scoring mechanism for echo intensity in step c2 is:

[0022]

[0023] in, The deviation of echo intensity from the normal range; the normal echo intensity range is defined as 65~95dB; is the echo intensity of the i-th sampling point, in units ; x is the normalized deviation, that is ; is the slope control parameter, which determines the sensitivity of the score to deviation and is an adjustable parameter; It is a threshold control parameter used to set the center reference point of the normal range and is an adjustable parameter.

[0024] Furthermore, the comprehensive fuzzy anomaly score in step c2 is:

[0025]

[0026] in ,like , it is determined to be a bubble interference point; Score Doppler shift abnormalities, is the signal-to-noise ratio anomaly score, is the abnormal echo intensity score, which is a standardized value in the interval [0,1].

[0027] Furthermore, in step c3, the number k of base points of the polynomial interpolation is adaptively adjusted according to the interference duration and the sampling frequency, the polynomial order n is ≤ 4, and the least squares fitting is used for implementation.

[0028] Furthermore, the process of correcting the flow rate in step d in combination with the fluid mechanics model is as follows:

[0029] The correction factor for the ratio of the theoretical full-section velocity mean to the observed mean is calculated based on the velocity profile distribution model of fluid dynamics. , the final flow rate is corrected to ;

[0030] in, is the original measured flow rate; is the theoretical full-section velocity mean, which is used to construct the correction factor for the ratio of the theoretical full-section velocity mean to the observation layer velocity mean; is the calculated flow velocity at the i-th sampling point, representing the movement speed of the water at that point.

[0031] In a second aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the above-mentioned acoustic Doppler flow measurement method for suppressing bubble interference based on multi-index fusion.

[0032] In a third aspect, the present invention provides a readable storage medium storing a computer program, wherein the computer program includes a program code for controlling a process to execute a process, wherein the process includes the above-mentioned acoustic Doppler flow measurement method for suppressing bubble interference based on multi-index fusion.

[0033] The main contributions and innovations of the present invention are as follows:

[0034] 1. Significantly improved flow measurement accuracy: By integrating multiple indicators to identify bubble interference and combining it with fluid dynamics model correction, the measured mean square error (MSE) is reduced by more than 80% compared to traditional filtering methods, and the correlation coefficient between flow velocity and true value reaches 0.85 (compared to approximately 0.74 with existing technology).

[0035] 2. Enhanced environmental robustness: Adaptive parameter optimization and a multi-dimensional evaluation mechanism enable bubble recognition accuracy in scenes such as rivers, oceans, and waterfalls to exceed 97% (compared to approximately 90% for traditional single-metric methods), and improve noise and fluctuation resistance by 40%.

[0036] 3. Wide applicability: Compatible with ADCP / ADV and other equipment, it covers scenarios such as rivers, oceans, and industrial pipelines containing bubbles. Existing technologies are prone to failure in environments with strong turbulence or high bubble concentrations.

[0037] 4. Data quality optimization: After processing, the flow velocity data is smooth and continuous, with a high rate of abnormal spike elimination, providing high-quality input for subsequent analysis such as flow calculation. Traditional methods often cause residual interference and lead to data distortion.

[0038] High degree of automation: No manual labeling or parameter adjustment is required. The scoring model is automatically optimized through genetic algorithms, reducing manual data processing costs by 50% compared to existing technologies.

[0039] The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below so that other features, objects, and advantages of the invention are more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0041] Figure 1 is a flow chart of an acoustic Doppler flow measurement method for suppressing bubble interference based on multi-index fusion according to an embodiment of the present invention;

[0042] Figure 2 is a flow chart according to an embodiment of the present invention;

[0043] Figure 3 According to an embodiment of the present invention Schematic diagram of Doppler shift abnormality scores under typical values;

[0044] Figure 4 is a real flow velocity profile according to an embodiment of the present invention;

[0045] Figure 5 is an original flow velocity profile at time step 50 according to an embodiment of the present invention;

[0046] Figure 6 is a processed velocity profile at time step 50 according to an embodiment of the present invention (bubbles have been suppressed);

[0047] Figure 7 is a schematic diagram of a flow velocity time series (original and processed) at a depth of 26.0 meters according to an embodiment of the present invention;

[0048] Figure 8 is a raw flow rate graph according to an embodiment of the present invention;

[0049] Figure 9 is a flow velocity diagram after treatment according to an embodiment of the present invention;

[0050] Figure 10 is a comparison chart of the MSE performance of the present invention, Kalman filtering, and wavelet filtering according to an embodiment of the present invention;

[0051] Figure 11 3. This is a comparison chart of RMSE performance of the present invention, Kalman filtering, and wavelet filtering according to an embodiment of the present invention;

[0052] Figure 12 3. This is a comparison chart of PSNR performance between the present invention and Kalman filtering and wavelet filtering according to an embodiment of the present invention;

[0053] Figure 13 is a performance comparison chart of correlation coefficients of the present invention, Kalman filtering, and wavelet filtering according to an embodiment of the present invention;

[0054] Figure 14 FIG. 4 is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The implementations described in the following exemplary embodiments are not intended to represent all implementations consistent with one or more embodiments of this specification. Rather, they are merely examples of apparatuses and methods consistent with certain aspects of one or more embodiments of this specification, as detailed in the appended claims.

[0056] It should be noted that in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the method may include more or fewer steps than those described in this specification. In addition, a single step described in this specification may be broken down into multiple steps for description in other embodiments, and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0057] The existing technology uses a single indicator or filtering method to suppress bubble interference, which can easily lead to loss of useful signals or inaccurate identification, resulting in low flow measurement accuracy and poor robustness.

[0058] Based on this, the present invention solves the problems existing in the prior art by building an abnormality scoring model by fusing multiple indicators such as Doppler frequency shift, signal-to-noise ratio, and echo intensity.

[0059] Example 1

[0060] The present invention aims to propose a method for suppressing bubble interference acoustic Doppler flow measurement based on multi-index fusion. Figure 1 , the method comprising:

[0061] a. Transmitting sound wave signals and receiving echo signals;

[0062] b. Perform frequency analysis on the echo signal to obtain the original Doppler frequency shift information;

[0063] c. Based on the original Doppler frequency shift information, a multi-index fusion model is constructed using Doppler frequency shift, signal-to-noise ratio, and echo intensity to identify and eliminate abnormal frequency shift components caused by bubbles;

[0064] In this embodiment, identifying and eliminating abnormal frequency shift components caused by bubbles includes:

[0065] c1. Preprocess the original signal and collect the Doppler frequency shift value, signal-to-noise ratio, and echo intensity of each sampling point;

[0066] The main purpose of preprocessing the original signal here is to eliminate obvious invalid or spurious signals to ensure the subsequent processing of true echo data.

[0067] According to the propagation delay ( For distance, is the speed of sound), the desired time window is intercepted, and the echo time alignment between beams is performed for the multi-beam system.

[0068] Then a bandpass filter (e.g. Center frequency ), further suppressing transient interference.

[0069] c2. For each sub-item, establish an anomaly scoring mechanism.

[0070] c21. For Doppler shift, use The rule states that the more the frequency shift deviates from the median, the higher the anomaly score. (This mechanism identifies bubble interference by quantifying the degree of deviation of the frequency shift data. It uses a combination of the 3σ rule and the S-shaped function to convert frequency shift outliers into anomaly scores in the range of 0-1.)

[0071]

[0072] in, To control the slope of the function, it is an adjustable parameter that determines the sensitivity of the score to deviations; To control the trigger threshold of anomaly scoring, it is an adjustable parameter; is the Doppler frequency shift value of the i-th sampling point; is the median of the frequency shift value; x is the normalized deviation, i.e. x= ; is the standard deviation of the frequency shift value, which represents the degree of data dispersion; The upper and lower limits of the effective area of ​​the adjustable signal-to-noise ratio.

[0073] In the formula, enter x= , output score ; When x>> hour, (Strong exception); when x<< hour, (normal).

[0074] Among them, the 3σ rule is used to measure the degree to which the data deviates from the central trend. Data that exceeds the mean ±3 times the standard deviation is generally considered to be an outlier; the S-type function (Logistic function) is used to smoothly map the degree of deviation into a score to avoid mutation misjudgment caused by hard thresholds.

[0075] Preferably, regarding The numerical confirmation method first collects samples from the actual data and labels the true value of each sampling point (whether it is a bubble interference point). The objective function is to maximize the AUC (area under the ROC curve) and achieve the optimal The value of .

[0076] The scoring function is set as:

[0077]

[0078] in is the area under the ROC curve of the scoring function on the labeled data; is the true normal / abnormal point label;

[0079] The parameter optimization objective function is:

[0080]

[0081] For the The Doppler frequency shift value of the sampling point is is the median frequency shift value, is the standard deviation of the frequency shift value, is the parameter to be optimized.

[0082] like Figure 2 As shown, in the genetic algorithm of this method, the number of population initialization is set to 50, and the chromosome is set to be represented as ,in . In the scoring function, Determines the slope of the function, that is, the sensitivity of the score to the degree of deviation, Large, steep response; Small, smooth response. (Avoid the scoring function being too flat and having no discrimination), (Avoid scoring functions that are too steep and all close to 0 or 1), because the abnormal deviation of Doppler shift in actual data generally exceeds The deviations are all extreme abnormal situations, so in this method , corresponding to the minimum deviation, =10, covering possible abnormal deviation values.

[0083] The fitness function is:

[0084]

[0085] In the genetic algorithm, the parent is first selected by probability, and the selection probability is:

[0086]

[0087] X i represents the i-th individual in the population, j represents the individual number when summing, and N represents the total number of individuals in the population;

[0088] Then perform the crossover operation:

[0089]

[0090] X child represents the sub-individual generated by the crossover operation; X parent1 represents the parent individual 1 selected in the previous step; X parent2 represents the parent individual 2 selected in the previous step; α represents the weighting factor, which is 0.5 in this method;

[0091] For each individual, whether to perform the mutation operation is determined according to a certain probability. In this method, the mutation probability is set to 20%.

[0092]

[0093] in The expected value is 0 and the variance is Gaussian random numbers, dimension and Same; X mutated represents the individual generated after mutation, Represents the mutation disturbance variable, which is a Gaussian random number.

[0094] Then, retain the elite individuals and calculate the fitness index of each individual in the existing population , select the best 20 individuals, retain them, and replicate them into the next generation population.

[0095] Repeat the above steps until the algorithm obtains Convergence or maximum number of iterations reached.

[0096] c22. For the signal-to-noise ratio indicator, the lower the signal-to-noise ratio, the higher the anomaly score.

[0097]

[0098] in is the signal-to-noise ratio of the i-th sampling point, in dB; The adjustable signal-to-noise ratio effective range is used to adjust the original signal-to-noise ratio Converted to dimensionless deviation; x is the normalized deviation, that is, = It controls the sensitivity of the score to signal-to-noise ratio degradation and is an adjustable parameter; It is the trigger threshold that defines the anomaly score and is an adjustable parameter.

[0099] Since the normal ADCP flow measurement signal-to-noise ratio is generally >15dB (most of the time it is between 20 and 30dB), when the signal-to-noise ratio is <10dB, most manufacturers' documents believe that the flow measurement quality is significantly reduced and the probability of abnormality is high. It is recommended Value range If the value is <10, the score is too loose. If the value is >20, normal data on the edge may be misjudged as abnormal. ,when , the ratings remain almost unchanged, , the score jump is very drastic.

[0100] c23. For the echo intensity indicator, if the echo intensity is lower than 65dB or higher than 95dB, the higher the deviation, the higher the anomaly score.

[0101] In most hydrological flow measurement applications, the echo intensity generated by normal water particles (suspended matter, tiny bubbles, and impurities) is typically above 65dB. If the echo intensity is less than 65dB, it often indicates that the echo energy at the measurement point is too low, possibly due to bubble obstruction, flow field voids, or echo signal attenuation. Alternatively, the signal-to-noise ratio is extremely poor, and the measured frequency shift is likely an error or interference signal.

[0102] Analysis of existing data shows that echo intensities >95 dB often indicate close-range reflections from large bubbles, large particles, or debris, or the accumulation of bubbles near the measuring probe, forming a localized area of ​​strong reflection. Such high-intensity echoes are often accompanied by unstable and variable Doppler frequency shifts and are not representative of water flow velocity.

[0103] In the practice of acoustic Doppler flow velocity measurement and the literature "Acoustic Doppler Current Profiler Principles of Operation", it is mentioned that the normal flow velocity echo intensity is generally .

[0104]

[0105] in, The deviation of echo intensity from the normal range; the normal echo intensity range is defined as 65~95dB; is the echo intensity of the i-th sampling point, in units ; x is the normalized deviation, that is ; is the slope control parameter, which determines the sensitivity of the score to deviation and is an adjustable parameter; It is a threshold control parameter used to set the center reference point of the normal range and is an adjustable parameter.

[0106] Based on the idea of ​​symmetrical scoring and the median value of the project, this method (ideal echo intensity median). The bigger the deviation A point rating will quickly approach 1, and The smaller the value, the slower the score change and the lower the discrimination power. Therefore, .

[0107] like Figure 3 Shown Schematic diagram of Doppler frequency shift anomaly scores under typical values. It can be seen that the abnormal frequency shift can be distinguished in the scoring.

[0108] c3. Abnormal fusion judgment and calculation of comprehensive fuzzy abnormality score:

[0109]

[0110] in ,like , it is determined to be a bubble interference point (abnormal point). is the weighting coefficient of each indicator; is the Doppler shift abnormality scoring index, is the signal-to-noise ratio abnormality scoring indicator, are the abnormal echo intensity scoring indicators, which are all standardized values ​​in the interval [0,1].

[0111] Preferably, removing abnormal frequency shift components caused by bubbles further includes:

[0112] c31. Based on the outliers identified in step c3, select their neighboring A normal point:

[0113]

[0114] is the sampling time and Doppler frequency shift value of the jth normal point, where It is the timestamp, which represents the position of the data in the time series; is the effective frequency shift value verified by multi-index fusion, without being disturbed by bubbles; k is the number of interpolation base points, which needs to be adaptively adjusted according to the duration of the interference and the sampling frequency (if the interference time is long, increase k to ensure interpolation accuracy); is a normal point set, that is, it has not been comprehensively scored The point determined to be bubble interference;

[0115] c32. Based on these normal points, construct Degree polynomial interpolation function:

[0116]

[0117] in is the polynomial interpolation function, t is the interpolation variable, Calculate the coefficients for interpolation so that:

[0118]

[0119] Polynomial order Dynamic setting based on interpolation accuracy requirements, recommended Avoid shock.

[0120] For the outliers , the interpolation estimates its true value:

[0121]

[0122] The interpolation method is implemented using least squares polynomial fitting.

[0123] This step takes the abnormal point time The k nearest normal points before and after the time series are selected as the center to ensure the time continuity of the frequency shift change. For example, if the abnormal point is at time step 50, the sampling frequency is 1 Hz, and the interference lasts for 2 seconds, the normal points within time steps 48-52 are selected (k=5).

[0124] d. Based on the Doppler frequency shift information after eliminating the abnormal frequency shift components, the flow velocity is calculated and corrected in combination with the fluid mechanics model.

[0125] In this embodiment, the set of Doppler frequency shift information after elimination is , The effective Doppler frequency shift information after elimination is the effective frequency shift after removing the bubble interference through multi-index fusion (frequency shift deviation, signal-to-noise ratio, echo intensity). Compared with the original frequency shift Its advantages are: eliminating abnormal frequency shift jumps caused by bubble scattering; retaining the frequency shift signal generated by real water flow, and improving the accuracy of flow velocity calculation. is the number of effective sampling points after removing bubble interference.

[0126] For each sampling point:

[0127]

[0128] in:

[0129] It represents the speed of sound (unit: m / s), which can be determined by water temperature, salinity and other conditions. Indicates the frequency of emitted sound waves; The higher the frequency, the shorter the wavelength and the higher the resolution, but the shallower the penetration depth (e.g. 1MHz frequency is suitable for shallow water, 300kHz is suitable for deep water); Influence of frequency shift: Under the same flow rate, The bigger, The larger it is, the higher the measurement sensitivity is;

[0130] is the angle between the beam and the flow direction, Used to correct the angle between the beam direction and the flow direction (when the beam is perpendicular to the flow direction, , no frequency shift). The original measured flow rate can be obtained by theoretically averaging the formulas at multiple measuring points:

[0131]

[0132] represents the original measured flow rate, Indicates the deepest and shallowest depths of the measured layer from the water surface. Indicates distance from the water surface Consider the velocity profile distribution model of fluid dynamics, such as river laminar flow or turbulent flow model:

[0133]

[0134] Where V(z) is the velocity at a depth of z from the water surface, in m / s, representing the vertical velocity profile distribution; is the maximum flow velocity near the water surface; Z is the depth of the measuring point from the water surface ; H is the total water depth; n is the flow field shape parameter, which is determined by the fluid mechanics state:

[0135] In laminar flow, n=1, and the flow velocity decreases linearly with depth; in turbulent flow, n=1 / 6, the flow velocity profile is more uniform, and the flow velocity at the bottom decays more slowly.

[0136] Theoretical mean of the entire profile:

[0137]

[0138] Here Represents the theoretical full-section velocity mean, and constructs the correction factor for the ratio of the theoretical full-section velocity mean to the observation layer velocity mean The correction factor Used to perform physical model consistency correction on the calculated observed mean velocity to ensure that the velocity result conforms to the fluid dynamics characteristics and improve measurement accuracy:

[0139]

[0140] The final flow rate correction is:

[0141]

[0142] This model improves the flow measurement accuracy to a correlation coefficient with the actual flow velocity of more than 0.85 (compared with 0.74 with traditional methods) through the flow state adaptive parameter (n) + full-section theoretical constraint. It is particularly suitable for scenarios that require high-precision hydrological data (such as flow calculation and flood simulation).

[0143] For example:

[0144] River flow velocity is measured using Beam angle, , the speed of sound c = 1490m / s, if the effective frequency shift at a certain point , then the flow rate is: .

[0145] Low-frequency ocean current observation in deep waters , combined with the temperature and salinity sensor to calibrate the sound velocity in real time, the three-dimensional flow velocity is calculated through multi-beam vector synthesis, and the measurement accuracy can reach ±1% after suppressing bubble interference.

[0146] Improve input parameters through multi-index fusion technology The reliability of the system is improved by combining it with the correction of the fluid mechanics model to form a complete technical chain of "signal processing-physical calculation-model optimization", ensuring high-precision flow measurement in complex water environments.

[0147] Example 2

[0148] Based on the same concept, this embodiment proposes an acoustic Doppler flow measurement method for suppressing bubble interference, based on Example 1. A batch of echo signals undergoes a fast Fourier transform to obtain an original spectrum containing Doppler frequency shift information. After applying this method to suppress bubble interference, the relevant results are shown below. The following image analysis demonstrates that the proposed method can effectively suppress bubble interference in the original data.

[0149] First, draw the true velocity profile as Figure 4 and Figure 5 As shown, the horizontal axis represents flow velocity (in meters per second) and the vertical axis represents depth (in meters). Flow velocity ranges from approximately 0.3 m / s to 1.6 m / s at depths from 0 to 50 meters. Flow velocity is highest near the water surface, exceeding 1.5 m / s. Flow velocity gradually decreases with increasing depth. At approximately 30 meters, due to shear layers caused by water stratification or the thermohaline cline, the flow velocity experiences a noticeable abrupt change or fluctuation (a sudden decrease followed by an increase). Near the bottom at 50 meters, the flow velocity reaches its lowest point, around 0.3 m / s.

[0150] Through analysis Figure 4 and Figure 5 It can be seen that due to interference from bubbles and other factors in the water, the measured velocity in the original velocity profile at time step 50 shows significant abrupt changes at depths of approximately 13 and 18 meters. Without further algorithmic processing, erroneous velocity measurements would be obtained at depths of approximately 13 and 18 meters.

[0151] And as Figure 6 As shown, the blue dotted line is the actual flow rate, and the blue solid line is the flow rate after treatment. Figure 6 It can be seen that after the bubbles in the velocity profile at time step 50 are suppressed using the method proposed in the present invention, the abnormal peak value in the processed velocity is significantly reduced, indicating that the method proposed in the present invention is effective in suppressing bubbles.

[0152] like Figure 7As shown, the blue dotted line is the true flow rate, the red solid line is the original measured flow rate, and the green solid line is the processed flow rate. It can be seen that after using the method proposed in the present invention, the abnormal red peak data will be significantly eliminated, and the processed data is more consistent with the true flow rate data.

[0153] like Figure 8 and 9 As shown in the figure, analysis of the two figures shows that the abnormal data in the processed flow rate is obviously eliminated, which can ensure the accuracy and precision of the obtained flow rate measurement data.

[0154] To further illustrate the wide range of applications of this method, the interference recognition accuracy in different water environments (such as rivers, oceans, and waterfalls) is statistically analyzed. The data are as follows:

[0155]

[0156] From the analysis of the above table, it can be seen that the application scope of the method proposed in the present invention is relatively broad. It is not only applicable to rivers, but also to various application occasions such as oceans and waterfalls.

[0157] To further illustrate the effectiveness and advancement of the method proposed in this invention (multi-index fusion method), a comparative analysis is conducted with Kalman filtering and wavelet filtering. The experimental comparison results are as follows: Figure 10-13 As shown. Compared with the Kalman filter algorithm and the wavelet filter algorithm, the method proposed in the present invention has the smallest MSE (mean square error) and RMSE (root mean square error), indicating that it has the highest accuracy. The method proposed in the present invention has the largest PSNR (peak signal-to-noise ratio), indicating that its signal reconstruction quality is the highest. The method proposed in the present invention has the largest correlation (correlation coefficient) between the velocity data obtained and the actual velocity data, indicating that the processed velocity is most consistent with the actual velocity.

[0158] Example 3

[0159] This embodiment also provides an electronic device, referring to Figure 14 , includes a memory 404 and a processor 402, wherein the memory 404 stores a computer program, and the processor 402 is configured to run the computer program to perform the steps in any of the above method embodiments.

[0160] Specifically, the processor 402 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0161] Memory 404 may include a large-capacity memory 404 for data or instructions. By way of example, and not limitation, memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 404 may include removable or non-removable (or fixed) media. Where appropriate, memory 404 may be internal or external to the data processing device. In certain embodiments, memory 404 is non-volatile memory. In certain embodiments, memory 404 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. In appropriate circumstances, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM may be a fast page mode dynamic random access memory 404 (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0162] The memory 404 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 402 .

[0163] The processor 402 reads and executes computer program instructions stored in the memory 404 to implement any one of the bubble interference suppression acoustic Doppler flow measurement methods based on multi-index fusion in the above embodiments.

[0164] Optionally, the electronic device may further include a transmission device 406 and an input / output device 408 , wherein the transmission device 406 is connected to the processor 402 , and the input / output device 408 is connected to the processor 402 .

[0165] Transmission device 406 can be used to receive or transmit data via a network. Specific examples of such networks may include wired or wireless networks provided by the electronic device's communications provider. In one embodiment, the transmission device includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 406 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0166] The input and output devices 408 are used to input or output information.

[0167] Example 4

[0168] This embodiment also provides a readable storage medium, in which a computer program is stored. The computer program includes a program code for controlling a process to execute a process. The process includes the acoustic Doppler flow measurement method for suppressing bubble interference based on multi-index fusion according to the first embodiment.

[0169] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be repeated here.

[0170] In general, various embodiments may be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flow charts, or using some other graphical representation, it should be understood that, as non-limiting examples, the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or a controller or other computing device, or some combination thereof.

[0171] The embodiments of the present invention may be implemented by computer software that is executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets and / or macros may be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. A computer program product may include one or more computer executable components that are configured to perform an embodiment when the program is run. One or more computer executable components may be at least one software code or a portion thereof. In addition, it should be noted at this point that, for example, Figure 1 Any block of the logic flow in the program may represent program steps, or interconnected logic circuits, blocks and functions, or a combination of program steps and logic circuits, blocks and functions. The software may be stored on physical media such as memory chips or memory blocks implemented within the processor, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs, etc. Physical media are non-transitory media.

[0172] Those skilled in the art should understand that the technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0173] The above embodiments merely illustrate several embodiments of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of the present invention. Therefore, the scope of the present invention shall be determined by the appended claims.

Claims

1. A method for acoustic Doppler flow measurement based on multi-index fusion to suppress bubble interference, characterized in that: The following steps are involved: a. Transmitting sound wave signals and receiving echo signals; b. Perform frequency analysis on the echo signal to obtain the original Doppler shift information; c. Based on the original Doppler frequency shift information, a multi-index fusion model is constructed using Doppler frequency shift, signal-to-noise ratio, and echo intensity to identify and eliminate abnormal frequency shift components caused by bubbles; d. Calculate the flow velocity based on the Doppler frequency shift information after removing the abnormal frequency shift components and make corrections based on the fluid dynamics model; The identification and elimination of abnormal frequency shift components in step c includes: c1. Preprocess the original signal, extract the desired time window, and perform inter-beam echo time alignment. Apply a bandpass filter to suppress transient interference, and collect the Doppler shift value, signal-to-noise ratio, and echo intensity at each sampling point. c2. Build an anomaly scoring mechanism based on Doppler shift, signal-to-noise ratio, and echo strength, and calculate a comprehensive fuzzy anomaly score to identify bubble interference points; c3. Perform polynomial interpolation on the identified abnormal points using the adjacent normal points to complete the abnormal frequency shift data.

2. The method for suppressing bubble interference and acoustic Doppler flow measurement based on multi-index fusion according to claim 1, characterized in that: The abnormal scoring mechanism for Doppler shift in step c2 is: The 3σ rule is used to calculate the degree of frequency shift deviation from the median, through Calculate anomaly score where: To control the slope of the function, it is an adjustable parameter that determines the sensitivity of the score to deviations; To control the trigger threshold of anomaly scoring, it is an adjustable parameter; is the Doppler frequency shift value of the i-th sampling point; is the median of the frequency shift value; x is the normalized deviation, i.e. x= ; is the standard deviation of the frequency shift value, which represents the degree of data dispersion.

3. The method for suppressing bubble interference and acoustic Doppler flow measurement based on multi-index fusion according to claim 1, characterized in that: The abnormal scoring mechanism for the signal-to-noise ratio in step c2 is: Using a function Calculate anomaly score where: is the signal-to-noise ratio of the i-th sampling point, in dB; The adjustable signal-to-noise ratio effective range is used to adjust the original signal-to-noise ratio Converted to dimensionless deviation; x is the normalized deviation, that is, = It controls the sensitivity of the score to signal-to-noise ratio degradation and is an adjustable parameter; It is the trigger threshold that defines the anomaly score and is an adjustable parameter.

4. The method for suppressing bubble interference and acoustic Doppler flow measurement based on multi-index fusion according to claim 1, characterized in that: The abnormal scoring mechanism for echo intensity in step c2 is: in, The deviation of echo intensity from the normal range; the normal echo intensity range is defined as 65~95dB; is the echo intensity of the i-th sampling point, in units ; x is the normalized deviation, that is ; is the slope control parameter, which determines the sensitivity of the score to deviation and is an adjustable parameter; It is a threshold control parameter used to set the center reference point of the normal range and is an adjustable parameter.

5. The method for suppressing bubble interference and acoustic Doppler flow measurement based on multi-index fusion according to claim 1, characterized in that: The comprehensive fuzzy anomaly score in step c2 is: in ,like , it is determined to be a bubble interference point; Score Doppler shift abnormalities, is the signal-to-noise ratio anomaly score, is the abnormal echo intensity score, which is a standardized value in the interval [0,1].

6. The method for suppressing bubble interference and acoustic Doppler flow measurement based on multi-index fusion according to claim 1, characterized in that: In step c3, the number k of base points of polynomial interpolation is adaptively adjusted according to the interference duration and sampling frequency, the polynomial order n≤4, and least squares fitting is used for implementation.

7. The method for acoustic Doppler flow measurement based on multi-index fusion and suppressing bubble interference according to any one of claims 1 to 6, characterized in that: The process of correcting the flow rate in step d by combining the fluid mechanics model is as follows: The correction factor for the ratio of the theoretical full-section velocity mean to the observed mean is calculated based on the velocity profile distribution model of fluid dynamics. , the final flow rate is corrected to ; in, is the original measured flow rate; is the theoretical full-section velocity mean, which is used to construct the correction factor for the ratio of the theoretical full-section velocity mean to the observation layer velocity mean; is the calculated flow velocity at the i-th sampling point, representing the movement speed of the water at that point.

8. An electronic device comprising a memory and a processor, characterized in that: The memory stores a computer program, and the processor is configured to run the computer program to execute the acoustic Doppler flow measurement method for suppressing bubble interference based on multi-index fusion according to any one of claims 1 to 6.

9. A readable storage medium, characterized in that The readable storage medium stores a computer program, which includes a program code for controlling a process to execute a process, wherein the process includes the acoustic Doppler flow measurement method for suppressing bubble interference based on multi-index fusion according to any one of claims 1 to 6.

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