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

Abstract

The invention provides a bubble interference suppression acoustic Doppler flow measurement method and device based on multi-index fusion and a readable storage medium, and the method comprises the steps: transmitting sound waves and receiving echoes, obtaining original Doppler frequency shift information, constructing a multi-index fusion model through Doppler frequency shift, a signal-to-noise ratio and echo intensity, and recognizing bubble interference. Abnormal frequency shift is eliminated by combining polynomial interpolation, and the flow velocity is corrected based on a fluid mechanics model. The method effectively inhibits bubble interference, improves the flow measurement precision and robustness, is suitable for various water environments, reduces manual intervention, and provides high-quality data for flow velocity measurement.

Description

Technical Field

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

[0002] An acoustic Doppler current profiler (ADCP) measures the water velocity by transmitting acoustic waves and analyzing the Doppler frequency shift of the echoes, and is widely used in the fields of hydrology, oceanography, etc. However, bubbles in the water body (such as bubbles generated by waves, turbulence or cavitation) will interfere with the propagation of acoustic waves, resulting in distorted echo signals and abnormal Doppler frequency shifts, significantly reducing the flow measurement accuracy. Existing technologies mostly use single filtering (such as Kalman filtering, wavelet filtering) or single-index threshold (such as abnormal frequency shift) to suppress interference, but there are problems such as filtering out useful signals and being unable to adapt to complex environments, and it is difficult to ensure measurement accuracy in the case of bubble interference.

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

[0004] Embodiments of the present invention provide a method, device and readable storage medium for suppressing bubble interference in acoustic Doppler flow measurement based on multi-index fusion, aiming at the problems existing in the current technology that using a single index or filtering method to suppress bubble interference is likely to cause loss of useful signals or inaccurate identification, resulting in low flow measurement accuracy and poor robustness.

[0005] The core technology of the present invention mainly constructs an anomaly score model by fusing multi-indices of Doppler frequency shift, signal-to-noise ratio and echo intensity, combines a genetic algorithm to optimize parameters to identify bubble interference points, and then uses polynomial interpolation and a hydrodynamic model to correct data to achieve high-precision acoustic Doppler flow measurement.

[0006] In the first aspect, the present invention provides a method for suppressing bubble interference in acoustic Doppler flow measurement based on multi-index fusion, and the method includes the following steps: a. Transmit an acoustic wave signal and receive an echo signal; b. Perform frequency analysis on the echo signal to obtain the original Doppler frequency shift information; c. Based on the original Doppler frequency shift information, construct a multi-index fusion model using Doppler frequency shift, signal-to-noise ratio and echo intensity, and identify and remove the abnormal frequency shift components caused by bubbles; d. Based on the Doppler frequency shift information after removing the abnormal frequency shift components, calculate the flow velocity and correct it in combination with a hydrodynamic model.

[0007] Further, the identification and removal of the abnormal frequency shift components in step c includes: c1. Preprocess the original signal, intercept the expected time window and perform time alignment of the echo between beams. Use a band - pass filter to suppress transient interference, and collect the Doppler shift value, signal - to - noise ratio, and echo intensity of each sampling point. c2. Construct an anomaly scoring mechanism for each index, and calculate the comprehensive fuzzy anomaly score to identify bubble interference points. c3. Use polynomial interpolation with adjacent normal points for the identified anomaly points to complete the abnormal frequency shift data.

[0008] Further, the anomaly scoring mechanism for Doppler shift in step c2 is as follows: Use the 3σ rule to calculate the degree of deviation of the frequency shift from the median. Calculate the anomaly score through where: is the slope of the control function, and is an adjustable parameter that determines the sensitivity of the score to the deviation; is the trigger threshold for controlling the anomaly score, and is an adjustable parameter; is the Doppler shift value of the i - th sampling point; is the median of the frequency shift values; x is the standardized deviation, that is, x = ; is the standard deviation of the frequency shift value, which characterizes the data dispersion degree; are the upper and lower limits of the adjustable signal - to - noise ratio effective region.

[0009] Further, the anomaly scoring mechanism for signal - to - noise ratio in step c2 is as follows: Use the function to calculate the anomaly score, where: is the signal - to - noise ratio of the i - th sampling point, in dB; is the adjustable signal - to - noise ratio effective interval, used to convert the original signal - to - noise ratio into a dimensionless deviation degree; x is the standardized deviation, that is = is the sensitivity of controlling the score attenuation to the signal - to - noise ratio, and is an adjustable parameter; is the trigger threshold for defining the anomaly score, and is an adjustable parameter.

[0010] Further, the anomaly scoring mechanism for echo intensity in step c2 is as follows:

[0011] where, is the deviation amount of the echo intensity from the normal range; the normal echo intensity range is defined as 65 - 95 dB; is the echo intensity of the i - th sampling point, in units of ; x is the standardized deviation, that is ; is the slope control parameter, which determines the sensitivity of the score to deviation and is an adjustable parameter; is the threshold control parameter, which is used to set the central reference point of the normal range and is an adjustable parameter.

[0012] Further, the comprehensive fuzzy anomaly score in step c2 is:

[0013] where , if , it is determined as a bubble interference point; is the Doppler frequency shift anomaly score, is the signal-to-noise ratio anomaly score, is the echo intensity anomaly score, all of which are standardized values in the interval [0,1].

[0014] Further, the number of base points k of the polynomial interpolation in step c3 is adaptively adjusted according to the interference duration and the sampling frequency, the polynomial order n ≤ 4, and it is implemented by least squares fitting.

[0015] Further, the process of correcting the flow velocity by combining the hydrodynamic model in step d is as follows: Calculate the ratio correction factor of the theoretical full-profile flow velocity mean and the observed mean based on the flow velocity profile distribution model of hydrodynamics , and finally the flow velocity is corrected to ; where, is the original measured flow velocity; is the theoretical full-profile flow velocity mean, which is used to construct the ratio correction factor of the theoretical full-profile flow velocity mean and the observed layer flow velocity mean; is the calculated flow velocity at the i-th sampling point, which represents the movement speed of the water body at this point.

[0016] In the second aspect, the present invention provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the above-mentioned method for suppressing bubble interference acoustic Doppler flow measurement based on multi-index fusion.

[0017] In the third aspect, the present invention provides a readable storage medium, in which a computer program is stored. The computer program includes program codes for controlling a process to execute the process, and the process includes the method for suppressing bubble interference acoustic Doppler flow measurement based on multi-index fusion as described above.

[0018] The main contributions and innovations of the present invention are as follows: 1. Significantly improved flow measurement accuracy: By identifying bubble interference through multi-index fusion and correcting it with a hydrodynamic model, the measured mean square error (MSE) is reduced by more than 80% compared to traditional filtering methods, and the correlation coefficient between the flow velocity and the true value reaches 0.85 (about 0.74 in the prior art).

[0019] 2. Enhanced environmental robustness: The adaptive parameter optimization and multi-dimensional evaluation mechanism enable the bubble recognition accuracy in scenarios such as rivers, oceans, and waterfalls to exceed 97% (about 90% for traditional single-index methods), and the anti-noise fluctuation ability is improved by 40%.

[0020] 3. Wide applicability: Compatible with devices such as ADCP / ADV, covering scenarios such as rivers, oceans, and industrial pipelines containing bubbles, while the prior art is prone to failure in environments with strong turbulence or high bubble concentrations.

[0021] 4. Optimized data quality: The processed flow velocity data is smooth and continuous, and the elimination rate of abnormal spikes is high, providing high-quality input for subsequent analyses such as flow calculation. Traditional methods often leave residual interference, resulting in data distortion.

[0022] High degree of automation: No manual annotation or parameter adjustment is required. The scoring model is automatically optimized through a genetic algorithm, reducing the manual data processing cost by 50% compared to the prior art.

[0023] Details of one or more embodiments of the present invention are set forth in the following drawings and description to make other features, objects, and advantages of the present invention more concise and understandable. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings described herein are used to provide a further understanding of the present invention, and form a part of the present invention. The illustrative embodiments and descriptions of the present invention are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 is a flowchart of an acoustic Doppler flow measurement method for suppressing bubble interference based on multi-index fusion according to an embodiment of the present invention; Figure 2 is a flowchart according to an embodiment of the present invention; Figure 3 is according to an embodiment of the present invention Schematic diagram of abnormal Doppler frequency shift score under typical values; Figure 4 is a true flow velocity profile according to an embodiment of the present invention; Figure 5 is an original flow velocity profile at time step 50 according to an embodiment of the present invention; Figure 6 is a processed flow velocity profile at time step 50 according to an embodiment of the present invention (bubbles have been suppressed); Figure 7Schematic diagram of the flow velocity time series (original and processed) at a depth of 26.0 meters according to an embodiment of the present invention; Figure 8 Original flow velocity diagram according to an embodiment of the present invention; Figure 9 Processed flow velocity diagram according to an embodiment of the present invention; Figure 10 MSE performance comparison diagram of the present invention with Kalman filtering and wavelet filtering according to an embodiment of the present invention; Figure 11 RMSE performance comparison diagram of the present invention with Kalman filtering and wavelet filtering according to an embodiment of the present invention; Figure 12 PSNR performance comparison diagram of the present invention with Kalman filtering and wavelet filtering according to an embodiment of the present invention; Figure 13 Correlation coefficient performance comparison diagram of the present invention with Kalman filtering and wavelet filtering according to an embodiment of the present invention; Figure 14 Schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed implementation mode

[0025] Here, exemplary embodiments will be described in detail, and their examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation modes described in the following exemplary embodiments do not represent all implementation modes consistent with one or more embodiments of this specification. On the contrary, they are only examples of devices and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0026] It should be noted that: In other embodiments, the steps of the corresponding methods are not necessarily executed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may also be combined into a single step for description in other embodiments.

[0027] The prior art uses a single index or filtering method to suppress bubble interference, which easily leads to the loss of useful signals or inaccurate identification, resulting in low flow measurement accuracy and poor robustness.

[0028] Based on this, the present invention solves the problems existing in the prior art by constructing an anomaly scoring model through fusing multiple indicators such as Doppler frequency shift, signal-to-noise ratio, and echo intensity.

[0029] Embodiment 1 The present invention aims to propose an acoustic Doppler flow measurement method for suppressing bubble interference based on multi-index fusion. Specifically, referring to Figure 1 , the method includes: a. Transmitting acoustic wave signals and receiving echo signals; b. Conducting frequency analysis on the echo signals to obtain the original Doppler frequency shift information; c. Based on the original Doppler frequency shift information, constructing a multi-index fusion model using the Doppler frequency shift, signal-to-noise ratio, and echo intensity to identify and eliminate abnormal frequency shift components caused by bubbles; In this embodiment, identifying and eliminating abnormal frequency shift components caused by bubbles includes: c1. Preprocessing the original signal, collecting the Doppler frequency shift values, signal-to-noise ratios, and echo intensities of each sampling point; The main purpose of preprocessing the original signal here is to eliminate obvious invalid or spurious signals to ensure the subsequent processing of real echo data.

[0030] According to the propagation delay ( is the distance, is the sound speed), intercepting the desired time window and performing echo time alignment processing between beams for the multi-beam system.

[0031] Subsequently, applying a band-pass filter (such as with a center frequency ) to further suppress transient interference.

[0032] c2. For each sub-item, constructing an abnormal scoring mechanism.

[0033] c21. For the Doppler frequency shift, using the rule, the more the frequency shift deviates from the median, the higher the abnormal score (this mechanism identifies bubble interference by quantifying the deviation degree of the frequency shift data, and adopts a combination of the 3σ rule and the S-shaped function to convert the frequency shift outliers into abnormal scores in the 0-1 interval):

[0034] Wherein, is the slope of the control function, and is an adjustable parameter that determines the sensitivity of the score to the deviation; is the trigger threshold for controlling the abnormal score, and is an adjustable parameter; is the Doppler frequency shift value of the i-th sampling point; is the median of the frequency shift values; x is the standardized deviation amount, that is, x = ; is the standard deviation of the frequency shift values, representing the data dispersion degree; are the upper and lower limits of the adjustable signal-to-noise ratio effective region.

[0035] In the formula, the input x = , and the output score is ; when x >> , (strong anomaly); when x << , (normal).

[0036] Among them, the 3σ rule is used to measure the degree of data deviation from the central tendency. Generally, data exceeding the mean ± 3 times the standard deviation is considered an outlier; the S-shaped function (Logistic function) is used to smoothly map the deviation degree to the score to avoid the sudden misjudgment caused by the hard threshold.

[0037] Preferably, regarding the numerical confirmation method, first collect samples from the actual data and label the true values (whether they are bubble interference points) for each sampling point. Taking the maximization of AUC (area under the ROC curve) as the objective function, the optimal value is achieved based on the genetic algorithm.

[0038] Set the form of the scoring function as:

[0039] where is the area under the ROC curve of the scoring function on the labeled data; is the true normal / anomaly point label; The parameter optimization objective function is:

[0040] is the Doppler frequency shift value of the th sampling point, is the median of the frequency shift values, is the standard deviation of the frequency shift values, is the parameter to be optimized.

[0041] As Figure 2 shown, in the genetic algorithm of this method, the initial population size is set to 50, and the chromosome representation is set as , where . Among them, in the scoring function, determines the slope of the function, that is, the sensitivity of the score to the deviation degree. is large, and the response is steep; is small, and the response is gentle. (to avoid the scoring function being too gentle and having no discrimination), (to avoid the scoring function being too steep and all approaching 0 or 1). Since the abnormal deviation of the Doppler frequency shift in the actual data generally exceeds The deviations are all extreme abnormal cases, so in this method , corresponding to the minimum deviation, = 10, covering possible abnormal deviation values.

[0042] The fitness function is:

[0043] In the genetic algorithm, first select the parent generation according to the probability, and the selection probability is:

[0044] X i represents the i-th individual in the population, j represents the individual label during summation, and N represents the total number of individuals in the population; Then perform the crossover operation:

[0045] X child represents the offspring 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 taken as 0.5 in this method; For each individual, determine whether to perform the mutation operation according to a certain probability. In this method, the mutation probability is set to 20%.

[0046]

[0047] Where is a Gaussian random number with an expectation of 0 and a variance of , and the dimension is the same as ; X mutated represents the individual generated after mutation, represents the mutation perturbation variable, which is a Gaussian random number.

[0048] Then, retain the elite individuals among them. By calculating the fitness indicators of each individual in the existing population , select the top 20 individuals and retain them, and copy them to the population of the next generation.

[0049] Repeat the above operations until the obtained by the algorithm converges or reaches the maximum number of iterations.

[0050] c22. For the signal-to-noise ratio index, the lower the signal-to-noise ratio, the higher the abnormal score.

[0051]

[0052] Where is the signal-to-noise ratio of the i-th sampling point, with the unit of dB; is the adjustable effective range of the signal-to-noise ratio, which is used to convert the original signal-to-noise ratio into a dimensionless deviation degree; x is the standardized deviation amount, that is = controls the sensitivity of the score to the attenuation of the signal-to-noise ratio and is an adjustable parameter; defines the trigger threshold for abnormal scoring and is an adjustable parameter.

[0053] Since the signal-to-noise ratio of normal ADCP flow measurement is generally > 15 dB (most are between 20 and 30 dB), when the signal-to-noise ratio < 10 dB, most manufacturer documents believe that the flow measurement quality drops significantly, the probability of abnormality is high, and it is recommended The value range , if the value < 10, the scoring is too lenient; if the value > 20, it may misjudge the marginally normal data as abnormal. , when , the score hardly changes, , the score jumps very violently.

[0054] c23. For the echo intensity index, when the echo intensity is lower than 65 dB or higher than 95 dB, the higher the deviation degree, the higher the abnormal score.

[0055] In most hydrological flow measurement practices, the echo intensity formed by normal water body particles (suspended solids, microbubbles, impurities) is usually higher than 65 dB. If the echo intensity < 65 dB, it often indicates that: the echo energy at the measurement point is too low, which may be due to bubble occlusion, flow field voids or echo signal attenuation. Or the signal-to-noise ratio is extremely poor, and the measured frequency shift is very likely to be an error or interference signal.

[0056] Analyzing the existing data, when the echo intensity > 95 dB, it often indicates that there are close reflections of large bubbles, large particles or debris, or bubble aggregation near the measurement probe, forming a local strong reflection area. Such high-intensity echoes are often accompanied by unstable and variable Doppler frequency shifts and do not have the representativeness of water body flow velocity.

[0057] In the practice of acoustic Doppler velocity measurement and the literature "Acoustic Doppler Current Profiler Principles of Operation", it is mentioned that the normal flow echo intensity is generally .

[0058]

[0059] Among them, is the deviation amount of the echo intensity from the normal range; the normal echo intensity range is defined as 65 - 95 dB; is the echo intensity of the i-th sampling point, with the unit ; x is the offset after standardization, that is ; is the slope control parameter, which determines the sensitivity of the score to the deviation and is an adjustable parameter; is the threshold control parameter, which is used to set the central reference point of the normal range and is an adjustable parameter.

[0060] Based on the symmetric scoring idea and the project median value, in this method (the median value of the ideal echo intensity). Since is larger, the deviation a single score will quickly approach 1, while is smaller, the score change is too gentle and the discrimination ability decreases. Therefore, set .

[0061] As Figure 3 shown is the schematic diagram of the Doppler frequency shift anomaly score under typical values, and it can be seen that it can distinguish the anomaly frequency shift in terms of the score.

[0062] c3. Abnormal fusion judgment, calculate the comprehensive fuzzy anomaly score:

[0063] where , if , then it is determined as a bubble interference point (abnormal point), is the weighted coefficient of each index; is the Doppler frequency shift anomaly score index, is the signal-to-noise ratio anomaly score index, is the echo intensity anomaly score index, all of which are standardized values in the range of [0,1].

[0064] Preferably, removing the abnormal frequency shift component caused by bubbles also includes: c31. Based on the abnormal points identified in step c3, select its adjacent normal points:

[0065] is the sampling time and Doppler frequency shift value of the j-th normal point, where is the timestamp, representing the position of the data in the time series; is the effective frequency shift value verified by multi-index fusion and is not affected by bubbles; k is the number of interpolation base points, which needs to be adaptively adjusted according to the interference duration and sampling frequency (such as increasing k if the interference time is long to ensure the interpolation accuracy); is the set of normal points, that is, not comprehensively scored Points determined as bubble interference; c32. Based on these normal points, construct a polynomial interpolation function of degree

[0066] where is the polynomial interpolation function, t is the interpolation independent variable, are the interpolation calculation coefficients, such that:

[0067] Degree of the polynomial is dynamically set according to the interpolation accuracy requirement, and it is recommended to use to avoid oscillations.

[0068] Then for the abnormal point , its true value is estimated by interpolation as:

[0069] The interpolation method is implemented by least squares polynomial fitting.

[0070] This step takes the abnormal point time as the center, and selects the k nearest normal points before and after on the time series 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, then the normal points within time steps 48 - 52 are selected (k = 5).

[0071] d. Based on the Doppler frequency shift information after removing the abnormal frequency shift components, calculate the flow velocity and correct it in combination with the hydrodynamic model.

[0072] In this embodiment, let the set of the Doppler frequency shift information after removal be , is the effective Doppler frequency shift information after removal. This parameter is the effective frequency shift after removing 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 the abnormal frequency shift jumps caused by bubble scattering; retaining the frequency shift signals generated by the real water body flow and improving the accuracy of calculating the flow velocity. Where is the number of effective sampling points after removing bubble interference.

[0073] For each sampling point:

[0074] where: represents the sound speed (unit: m / s), which can be determined by conditions such as water temperature and salinity. represents the transmitted acoustic wave frequency; The higher the frequency, the shorter the wavelength and the higher the resolution, but the shallower the penetration depth (for example, a frequency of 1 MHz is suitable for shallow water areas, and 300 kHz is suitable for deep water areas); Affects the magnitude of the frequency shift: at the same flow velocity, the larger, the larger, the higher the measurement sensitivity; is the angle between the beam and the flow direction, used to correct the influence of the angle between the beam direction and the flow direction (when the beam is perpendicular to the flow direction, , there is no frequency shift). By taking the theoretical mean of multiple measurement point formulas, the original measured flow velocity can be obtained:

[0075] represents the original measured flow velocity, represents the numerical values of the deepest and shallowest depths of the measurement stratification from the water surface, represents the distance from the water surface at which the flow velocity is considered, considering the flow velocity profile distribution model of hydrodynamics, such as the laminar flow or turbulent flow model of a river:

[0076] Among them, V(z) is the flow velocity at a depth of z from the water surface, with the unit of m / s, characterizing the vertical flow velocity profile distribution; is the maximum flow velocity near the water surface; Z is the depth of the measurement point from the water surface ; H is the total water depth of the water body; n is the flow field shape parameter, determined by the hydrodynamic state: When it is laminar flow, n = 1, and the flow velocity decreases linearly with depth; when it is turbulent flow, n = 1 / 6, the flow velocity profile is more uniform, and the bottom flow velocity decays more slowly.

[0077] The theoretical mean of the entire profile:

[0078] Here, represents the theoretical mean flow velocity of the entire profile, and a ratio correction factor is constructed for the ratio of the theoretical mean flow velocity of the entire profile to the mean flow velocity of the observed layer. This correction factor

[0079] is used to perform physical model consistency correction on the calculated observed mean flow velocity to ensure that the flow velocity result conforms to the hydrodynamic characteristics and improve the measurement accuracy:

[0080] This model improves the flow measurement accuracy to a correlation coefficient of over 0.85 with the true flow velocity (compared to 0.74 for traditional methods) through the fluid state adaptive parameter (n) + full-profile theory constraint, and is especially applicable to scenarios requiring high-precision hydrological data (such as flow calculation and flood simulation).

[0081] For example: For river flow velocity measurement, beam angle, , sound speed c = 1490 m / s. If the effective frequency shift at a certain point , then the flow velocity is: .

[0082] For ocean current field observation in deep water areas, low-frequency is used. By combining temperature and salinity sensors to calibrate the sound speed in real time, three-dimensional flow velocity is calculated through multi-beam vector synthesis. After suppressing bubble interference, the measurement accuracy can reach ±1%.

[0083] The reliability of input parameters is improved through multi-index fusion technology, and then combined with the correction of the hydrodynamic model to form a complete technical chain of "signal processing - physical solution - model optimization" to ensure high-precision flow measurement in complex water body environments.

[0084] Example 2 Based on the same concept, this example proposes an acoustic Doppler flow measurement method for suppressing bubble interference based on Example 1. A batch of echo signals are subjected to fast Fourier transform to obtain the original spectrum containing Doppler frequency shift information. After bubble interference suppression by this method, the relevant results are as follows. It can be seen from the following image analysis that the method proposed in this example can effectively suppress bubble interference in the original data.

[0085] First, draw the true flow velocity profile as shown in Figure 4 and Figure 5 . The horizontal axis is the flow velocity (unit: m / s), and the vertical axis is the depth (unit: m). The flow velocity range is approximately 0.3 m / s to 1.6 m / s, and the depth range is 0 to 50 m. The flow velocity is larger near the water surface (m), exceeding 1.5 m / s. As the depth increases, the flow velocity gradually decreases. At about 30 m depth, due to the shear layer caused by water body stratification or thermohaline layer, there is an obvious mutation or fluctuation in the flow velocity (the flow velocity suddenly decreases and then increases). When approaching the bottom of the water at 50 m, the flow velocity drops to the lowest, about 0.3 m / s.

[0086] By analyzing Figure 4 and Figure 5It 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.

[0087] 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.

[0088] like Figure 7 As 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.

[0089] 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.

[0090] 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:

[0091] 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.

[0092] 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: Figures 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.

[0093] Example 3 This embodiment also provides an electronic device. Refer to Figure 14 , which includes a memory 404 and a processor 402. A computer program is stored in the memory 404, and the processor 402 is configured to run the computer program to execute the steps in any of the above method embodiments.

[0094] Specifically, the above-mentioned processor 402 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0095] Among them, the memory 404 may include a mass storage 404 for data or instructions. By way of example and not limitation, the memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a 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. In appropriate cases, the memory 404 may include removable or non-removable (or fixed) media. In appropriate cases, the memory 404 may be internal or external to the data processing device. In a particular embodiment, the memory 404 is non-volatile memory. In a particular embodiment, the memory 404 includes a read-only memory (ROM) and a random access memory (RAM). In appropriate cases, 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, or a combination of two or more of these. In appropriate cases, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM may be a fast page mode dynamic random access memory (FPMDRAM), an extended date out dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0096] The memory 404 can 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.

[0097] By reading and executing the computer program instructions stored in the memory 404, the processor 402 implements any one of the above-described multi-index fusion-based methods for suppressing bubble interference in acoustic Doppler current measurement.

[0098] Optionally, the above electronic device may further include a transmission device 406 and an input / output device 408. Among them, the transmission device 406 is connected to the above processor 402, and the input / output device 408 is connected to the above processor 402.

[0099] The transmission device 406 can be used to receive or send data via a network. Specific examples of the above network may include wired or wireless networks provided by the communication provider of the electronic device. In one example, the transmission device includes a network adapter (abbreviated as NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one example, the transmission device 406 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0100] The input / output device 408 is used to input or output information.

[0101] Embodiment 4 This embodiment also provides a readable storage medium, in which a computer program is stored. The computer program includes program code for controlling a process to execute the process, and the process includes the multi-index fusion-based method for suppressing bubble interference in acoustic Doppler current measurement according to Embodiment 1.

[0102] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation manners, and will not be repeated here.

[0103] Generally, various embodiments can be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects of the present invention can be implemented in hardware, while other aspects can be implemented by firmware or software executed by a controller, a microprocessor, or other computing devices, but the present invention is not limited thereto. Although the various aspects of the present invention can be shown and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that, by way of non-limiting example, the blocks, devices, systems, techniques, or methods described herein can be implemented in hardware, software, firmware, dedicated circuits or logic, general hardware or a controller, or other computing devices, or some combination thereof.

[0104] Embodiments of the present invention can be implemented by computer software, which 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. A computer software or program (also referred to as a program product), including software routines, applets, and / or macros, can be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. The computer program product can include one or more computer-executable components configured to perform the embodiments when the program runs. One or more computer-executable components can be at least one software code or a part thereof. Additionally, at this point, it should be noted that any box in the logical flow, as Figure 1 described in, can represent a program step, or interconnected logical circuits, boxes, and functions, or a combination of program steps and logical circuits, boxes, and functions. The software can be stored on physical media such as memory chips or storage blocks implemented within a processor, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs. The physical media are non-transitory media.

[0105] Those skilled in the art should understand that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered to be within the scope described in this specification.

[0106] The above embodiments merely represent several implementation manners of the present invention. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. An acoustic Doppler current measurement method for suppressing bubble interference based on multi-index fusion, characterized in that Including the following steps: a. Transmit acoustic wave signals and receive echo signals; b. Conduct frequency analysis on the echo signals to obtain original Doppler frequency shift information; c. Based on the original Doppler frequency shift information, construct a multi-index fusion model using Doppler frequency shift, signal-to-noise ratio, and echo intensity to identify and eliminate abnormal frequency shift components caused by bubbles; d. Based on the Doppler frequency shift information after eliminating abnormal frequency shift components, calculate the flow velocity and correct it in combination with the hydrodynamic model.

2. The method for suppressing bubble interference in acoustic Doppler current measurement based on multi-index fusion according to claim 1, wherein The identification and elimination of abnormal frequency shift components in step c include: c1. Preprocess the original signal, intercept the desired time window and perform echo time alignment between beams, use a band-pass filter to suppress transient interference, and collect the Doppler frequency shift values, signal-to-noise ratios, and echo intensities of each sampling point; c2. Construct an abnormal score mechanism for each index, calculate the comprehensive fuzzy abnormal score to identify bubble interference points; c3. Use polynomial interpolation with adjacent normal points for the identified abnormal points to complete the abnormal frequency shift data.

3. The method for suppressing bubble interference in acoustic Doppler current measurement based on multi-index fusion according to claim 2, wherein, The abnormal score mechanism for Doppler frequency shift in step c2 is: The 3σ rule is used to calculate the degree of the frequency shift deviating from the median, and the abnormal score is calculated through wherein: To control the slope of the function, it is an adjustable parameter that determines the sensitivity of the score to deviations; is the trigger threshold for controlling the abnormal score, and it is an adjustable parameter; is the Doppler frequency shift value at the i-th sampling point; is the median of the frequency shift values; x is the normalized deviation amount, that is, x = ; is the standard deviation of the frequency shift values, representing the degree of data dispersion; are the upper and lower limits of the effective region for adjustable signal-to-noise ratio.

4. The method for suppressing bubble interference in acoustic Doppler current measurement based on multi-index fusion according to claim 2, wherein The abnormal score mechanism for signal-to-noise ratio in step c2 is: Use a function to calculate the anomaly score, where: is the signal-to-noise ratio of the i-th sampling point, with the unit of dB; is the adjustable effective range of the signal-to-noise ratio, which is used to convert the original signal-to-noise ratio into a dimensionless deviation degree; x is the standardized deviation amount, that is = is to control the sensitivity of the scoring to the signal-to-noise ratio attenuation and is an adjustable parameter; is the trigger threshold for defining the abnormal score and is an adjustable parameter.

5. The method for suppressing bubble interference in acoustic Doppler current measurement based on multi-index fusion according to claim 2, characterized in that, The abnormal score mechanism for echo intensity in step c2 is: Among them, is the deviation of the 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, with the unit ; x is the standardized deviation, that is ; is the slope control parameter, which determines the sensitivity of the scoring to the deviation and is an adjustable parameter; is the threshold control parameter, which is used to set the central reference point of the normal range and is an adjustable parameter.

6. The method for suppressing bubble interference in acoustic Doppler current measurement based on multi-index fusion according to claim 2, wherein The comprehensive fuzzy abnormal score in step c2 is: Among them If , it is determined as a bubble interference point; is the Doppler frequency shift anomaly score, is the signal-to-noise ratio anomaly score, is the echo intensity anomaly score, all of which are standardized values in the range of [0, 1].

7. The method for suppressing bubble interference in acoustic Doppler current measurement based on multi-index fusion according to claim 2, characterized in that, The number of base points k for polynomial interpolation in step c3 is adaptively adjusted according to the interference duration and sampling frequency, the polynomial order n ≤ 4, and it is implemented using least squares fitting.

8. A method for suppressing bubble interference in acoustic Doppler current measurement based on multi-index fusion according to any one of claims 1 to 7, characterized in that, The process of correcting the flow velocity in combination with the hydrodynamic model in step d is: Ratio correction factor for calculating the mean value of the theoretical full-profile flow velocity and the observed mean value based on the hydrodynamic flow velocity profile distribution model , the final flow velocity is corrected to ; Among them, is the original measured flow velocity; is the average value of the theoretical full-profile flow velocity, which is used to construct the ratio correction factor between the average value of the theoretical full-profile flow velocity and the average value of the observed layer flow velocity; is the calculated flow velocity at the i-th sampling point, which characterizes the movement speed of the water body at this point.

9. An electronic device, comprising a memory and a processor, characterized in that, A computer program is stored in the memory, and the processor is set to run the computer program to execute the multi-index fusion-based acoustic Doppler flow measurement method for suppressing bubble interference according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that, A computer program is stored in the readable storage medium, and the computer program includes program codes for controlling a process to execute the process, and the process includes the multi-index fusion-based acoustic Doppler flow measurement method for suppressing bubble interference according to any one of claims 1 to 7.

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