Online self-sensing monitoring method and device for steady-state ultrasonic cavitation intensity

Abstract

The invention discloses a steady-state ultrasonic cavitation intensity online self-sensing monitoring method and device, and belongs to the technical field of ultrasonic cavitation. The method comprises the following steps: firstly, acquiring a transducer voltage signal, and preprocessing the acquired signal to obtain a steady-state cavitation noise signal; secondly, dimension expansion is carried out on the frequency tracking model, and real-time tracking of multi-frequency steady-state cavitation noise signals is achieved; thirdly, estimating the steady-state cavitation noise signal in real time based on a cubature Kalman filter (CKF) frequency tracking algorithm to realize online filtering of the steady-state cavitation noise signal; and finally, calculating the steady-state cavitation intensity according to the real-time filtering signal. According to the invention, on-line self-sensing monitoring of the steady-state ultrasonic cavitation intensity is realized without additional sensors, and the steady-state ultrasonic cavitation intensity can be accurately described.

Description

Technical Field

[0001] The invention belongs to the technical field of supercavitation, and relates to an online self-sensing monitoring method and device for steady-state ultrasonic cavitation intensity. Background Art

[0002] In the ultrasonic cavitation process, cavitation intensity is usually selected as the key physical quantity reflecting the processing quality and efficiency. The ultrasonic transducer contains piezoelectric materials. The pulsation of cavitation bubbles under the action of sound waves will cause the transducer surface to vibrate and generate a voltage signal. Therefore, by collecting the transducer voltage signal during the ultrasonic cavitation process, self-sensing monitoring of ultrasonic cavitation intensity can be achieved. The frequency component related to steady-state cavitation activity in the voltage signal is called the acoustic index of steady-state cavitation intensity. The steady-state cavitation intensity is obtained by calculating the acoustic index of steady-state cavitation intensity. These indicators are mainly related to the driving frequency. Related subharmonics ( ) and superharmonic ( ). At present, the self-sensing monitoring technology of steady-state ultrasonic cavitation intensity is still in an offline state, and online monitoring of steady-state cavitation intensity has not yet been realized. In order to realize online self-sensing monitoring of steady-state cavitation intensity, it is necessary to track the frequency and estimate the steady-state cavitation noise signal in the transducer voltage signal online.

[0003] Kalman filtering is accurate and robust in system state estimation, so it is widely used in frequency estimation. In the ultrasonic cavitation process, due to the nonlinear pulsation of cavitation bubbles and their interaction, cavitation noise has strong nonlinear characteristics. Commonly used nonlinear Kalman filter algorithms include Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Volumetric Kalman Filter. Among them, CKF can solve the problem of large estimation error and even divergence of results of Extended Kalman Filter and Unscented Kalman Filter in high-dimensional nonlinear systems, so it shows better stability and accuracy when dealing with high-dimensional nonlinear systems. The application of CKF frequency tracking algorithm can realize online filtering estimation of steady-state cavitation noise signal, improve the accuracy of frequency tracking and signal filtering effect.

[0004] The patent "An Ultrasonic Cavitation Intensity Monitoring Method" (CN 116558625 A) provides an ultrasonic cavitation intensity monitoring method, which integrates the signal spectrum to obtain the cavitation intensity. However, this method does not extract the steady-state cavitation noise signal and calculate the steady-state cavitation intensity.

[0005] The patent "A method and system for calculating the intensity of ultrasonic cavitation in melt" (CN 113312797 B) invented a method for calculating the sound intensity of steady-state cavitation noise and transient cavitation noise. However, this method does not realize online self-sensing monitoring of cavitation intensity and requires additional sensors.

[0006] Therefore, the current ultrasonic cavitation intensity monitoring technology basically relies on sensors, which increases the cost and complexity of the system. The existing ultrasonic cavitation intensity self-sensing monitoring technology is still in an offline state and cannot respond to cavitation intensity changes in real time to achieve online monitoring of cavitation intensity. Therefore, it has obvious limitations in engineering applications. Summary of the invention

[0007] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method and device for online self-sensing monitoring of steady-state ultrasonic cavitation intensity to solve the problem that the self-sensing monitoring technology of ultrasonic cavitation intensity in the prior art is difficult to achieve online monitoring of cavitation intensity.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions: A method for online self-sensing monitoring of steady-state ultrasonic cavitation intensity comprises the following steps: Collecting cavitation noise signals and obtaining cavitation noise signal spectrum; Based on the spectrum of the cavitation noise signal, the corresponding frequency component of the steady-state cavitation noise signal is obtained; based on the frequency component of the steady-state cavitation noise signal, the frequency tracking model of the cavitation noise signal is expanded to obtain the state equation and measurement equation of the frequency tracking model after the expansion; Through the CKF frequency tracking algorithm, the expanded state equation and the expanded measurement equation are tracked and estimated to obtain the real-time filtering signal; Based on the real-time filtered signal, the steady-state cavitation intensity is calculated.

[0009] A further improvement of the present invention is: Preferably, the cavitation noise signal is obtained by collecting a voltage signal of a transducer and preprocessing it, and the preprocessing device is a bandpass filter.

[0010] Preferably, the state equation after dimension expansion is: (3) in, is the state quantity at time k-1, is the process noise at the k-1th moment, is the jth group of state transfer equations at time k-1, is the process noise of the jth group at time k-1.

[0011] Preferably, the measurement equation after dimension expansion is: (4) in, is the measured value at time k, is the k-time measurement matrix, is the state quantity at time k, is the measurement noise at time k, is the measurement matrix of the jth group at time k, is the state quantity of the jth group at time k, is the measurement noise of the jth group at time k.

[0012] Preferably, the process of tracking and estimating the expanded state equation and the expanded measurement equation through the CKF frequency tracking algorithm is as follows: decomposing the error covariance matrix at time k-1, and selecting 2n volume points with equal weights for nonlinear transformation to obtain state prediction values ​​and one-step prediction error covariance matrices. After nonlinear transformation of the state prediction values ​​and the one-step prediction error covariance matrix, the measurement prediction values ​​and covariance matrix at time k are obtained. Furthermore, the filter gain at time k is obtained and the state estimation value is calculated to obtain the filtered signal at time k.

[0013] Preferably, the state prediction value and one-step prediction error covariance matrix is: (9) in, is the state prediction value at time k, n is the state dimension, is the volume point after the i-th volume point at time k-1 is transferred through the state equation, is the one-step forecast error covariance matrix at time k, for The transposed matrix of for The transposed matrix of .

[0014] Preferably, the measurement prediction value and covariance matrix at time k are: (10) in, is the measured predicted value at time k, n is the state dimension, yes The transposed matrix of is the innovation covariance at time k, for The transposed matrix of is the cross-correlation covariance at time k.

[0015] Preferably, the filtering gain at time k and the calculated state estimate are: (11) in, is the filter gain at time k, is the estimated value of the state at time k, Update the covariance matrix for time k, is the cross-correlation covariance at time k, is the innovation covariance at time k, for The inverse matrix of is the state prediction value at time k, is the filter gain The transposed matrix of is the measured value, is the measured predicted value at time k, is the one-step forecast error covariance matrix.

[0016] Preferably, the process of obtaining the steady-state cavitation intensity based on the real-time filtered signal is: performing Fourier transform on the real-time filtered signal to obtain a steady-state cavitation noise spectrum diagram, normalizing the amplitude of the steady-state cavitation noise signal in the spectrum in the steady-state cavitation noise spectrum diagram, and then calculating the steady-state cavitation intensity.

[0017] A steady-state ultrasonic cavitation intensity online self-sensing monitoring system, comprising: An acquisition module is used to acquire cavitation noise signals and obtain cavitation noise signal spectrum; The model optimization module is used to obtain the corresponding frequency component of the steady-state cavitation noise signal based on the cavitation noise signal spectrum; based on the measurement equation of the frequency component of the steady-state cavitation noise signal, the frequency tracking model of the cavitation noise signal is expanded to obtain the state equation and measurement equation of the frequency tracking model after the expansion; A filtering module is used to track and estimate the expanded state equation and the expanded measurement equation through the CKF frequency tracking algorithm to obtain a real-time filtering signal; The determination module is used to calculate the steady-state cavitation intensity based on the real-time filtered signal.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The invention discloses an online self-sensing monitoring method for steady-state ultrasonic cavitation intensity. The method first extracts a steady-state cavitation noise signal from the spectrum of the cavitation noise signal; based on the frequency component of the steady-state cavitation noise signal, a frequency tracking model is expanded to obtain a state equation and a measurement equation after the expansion, and real-time tracking of multi-frequency steady-state cavitation noise signals is realized; based on CKF, the steady-state cavitation noise signal is estimated in real time to realize online filtering of the steady-state cavitation noise signal; finally, the steady-state cavitation intensity is calculated according to the real-time filtered signal. The method realizes online monitoring of the steady-state ultrasonic cavitation intensity without adding a sensor, and can be used for online self-sensing monitoring of the steady-state cavitation intensity of an ultrasonic cavitation system to accurately describe the steady-state ultrasonic cavitation intensity. The method saves costs and realizes online monitoring of the ultrasonic cavitation field without disturbance; online tracking of multi-frequency steady-state cavitation noise signals is realized, which provides a way of thinking for analyzing ultrasonic cavitation activities and studying ultrasonic cavitation mechanisms. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of the steps of an online self-sensing monitoring method for steady-state ultrasonic cavitation intensity of the present invention; Figure 2 A cavitation noise spectrum diagram of an online self-sensing monitoring method for steady-state ultrasonic cavitation intensity of the present invention; Figure 3 This is a diagram of the ultrasonic cavitation device; Figure 4 This is a time-frequency tracking result diagram of a steady-state cavitation noise signal; Figure 5 This is the time-frequency tracking result diagram of the steady-state cavitation noise signal; Figure 6 This is the three-time-frequency tracking result diagram of the steady-state cavitation noise signal; Figure 7 This is the four-time-frequency tracking result diagram of the steady-state cavitation noise signal; Figure 8 Figure 5 is the time-frequency tracking result of steady-state cavitation noise signal. DETAILED DESCRIPTION

[0020] In the following, the terms "first", "second", "third", and "fourth" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "first", "second", "third", and "fourth" may explicitly or implicitly include one or more of the features.

[0021] The co-shooting method provided in the embodiment of the present application can be applied to terminal devices such as mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptop computers, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA), etc. The embodiment of the present application does not impose any restrictions on the specific type of the terminal device.

[0022] It should be noted that the terms "first", "second", etc. in the specification and drawings of the present invention are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] The first aspect of the present invention discloses a method for online self-sensing monitoring of steady-state ultrasonic cavitation intensity, which specifically comprises the following steps: S1, collecting cavitation noise signals and obtaining cavitation noise signal spectrum; S2, based on the spectrum of the cavitation noise signal, obtain the corresponding frequency component of the steady-state cavitation noise signal; based on the frequency component of the steady-state cavitation noise signal, expand the frequency tracking model of the cavitation noise signal to obtain the state equation and measurement equation of the frequency tracking model after the expansion; S3, using the CKF frequency tracking algorithm, the expanded state equation and the expanded measurement equation are tracked and estimated to obtain a real-time filtered signal; S4, based on the real-time filtered signal, the steady-state cavitation intensity is obtained.

[0024] In some embodiments of the present invention, the cavitation noise signal is obtained by preprocessing a voltage signal collected by a transducer, and the preprocessing is performed through a bandpass filter.

[0025] Specifically, the transducer voltage signal is collected to obtain a steady-state cavitation noise signal. The influence of the transient cavitation noise signal and the driving signal is filtered out through a bandpass filter to improve the signal-to-noise ratio of the steady-state cavitation noise signal and ensure the accuracy and reliability of the original signal before tracking.

[0026] Based on the cavitation noise signal, the cavitation noise signal spectrum is obtained as follows Figure 2 As shown in the figure, it can be seen that the cavitation noise signal is composed of the driving frequency ,harmonic (in is an integer greater than 1), subharmonic (where m is an integer greater than 1), superharmonic The signal of the driving frequency and the harmonic signal are considered to be generated by direct drive, the subharmonic and superharmonic are considered to be the result of steady-state cavitation, and the broadband noise is mainly generated during transient cavitation. The steady-state cavitation noise signal is obtained by filtering out the transient cavitation noise signal and the driving signal.

[0027] The present invention extracts characteristic frequency signals related to steady-state cavitation noise signals from cavitation noise signals for frequency tracking model dimension expansion, so that subsequent model dimension expansion is an extended equation obtained based on the steady-state cavitation noise signals.

[0028] In some embodiments of the present invention, in S2, based on the cavitation noise signal spectrum, subharmonics and superharmonics related to the steady-state cavitation noise signal are obtained; and based on the steady-state cavitation noise signal, the frequency tracking model is expanded to obtain the expanded state equation and measurement equation; this process is to expand the frequency tracking model to achieve simultaneous tracking and estimation of multi-frequency steady-state cavitation noise signals, and the specific process is: S21, the frequency tracking model is shown in the following formula (1): (1) in, for Moment The group state quantity is the steady-state cavitation noise signal containing frequency components. is the state transfer equation, is the process noise, which obeys Gaussian distribution, that is, ,in is the process noise variance at time k-1. is a scalar quantity, Moment The group state quantity corresponds to the measured value. The measurement matrix , is the measurement noise, which also obeys the Gaussian distribution, that is, ,in, is the measurement noise variance at time k.

[0029] The state transfer equation is shown in formula (2): (2) S22, expanding the frequency tracking model through the cavitation noise signal spectrum diagram to obtain the expanded state equation and measurement equation.

[0030] from Figure 2 It can be observed that the steady-state cavitation noise signal contains multiple signals of different frequencies, so it is necessary to expand the dimension of the model to realize the tracking and estimation of multi-frequency steady-state cavitation noise signals. If the steady-state cavitation noise signal contains j groups of state quantities, the state quantity at time k can be expressed as , the process noise is expressed as Assuming that each group of state quantities is unrelated, the state equation after dimensional expansion can be expressed as: (3) in, is the state quantity at time k-1, is the process noise at the k-1th moment, is the jth group of state transfer equations at time k-1, is the process noise of the jth group at time k-1.

[0031] Assuming there are j groups of state quantities, the measured value can be expressed as , the measurement matrix is ​​expressed as , measurement noise , the measurement equation after dimension expansion can be expressed as: (4) in, is the measurement matrix of the jth group at time k, is the state quantity of the jth group at time k, is the measurement noise of the jth group at time k.

[0032] S3, based on the CKF frequency tracking algorithm, the steady-state cavitation noise signal is tracked and estimated to achieve online filtering of the steady-state cavitation noise signal. The steps of the CKF frequency tracking algorithm are as follows: S31, time update The state error covariance matrix at time k-1 is Perform singular value decomposition (SVD) decomposition: (5) in, is a diagonal matrix, , is a singular value, and Corresponding to the left singular vector and the right singular vector respectively, for The transpose of .

[0033] Calculate the volume point, based on the volume point calculation after the state equation is transferred: (6) in, , n is the state dimension, ,in for n dimensional unit vector, where the i The element is 1, is the estimated value at time k-1, is the i-th volume point at time k-1, is the i-th singular value, It is the volume point after the i-th volume point at time k-1 is transferred through the state equation.

[0034] Furthermore, the state prediction value at time k and the one-step prediction error covariance matrix are estimated: (7) in, is the state prediction value at time k, for The transposed matrix of for The transposed matrix of is the one-step forecast error covariance matrix at time k.

[0035] S32, measurement update The one-step forecast error covariance matrix Perform SVD decomposition: (8) in, is a diagonal matrix, , is a singular value, and They correspond to the left and right singular vectors after the prediction error covariance decomposition, respectively. for The transposed matrix of .

[0036] Calculate the volume point and the volume point after the measurement equation is transferred: (9) in, is the i-th singular value, is the i-th volume point, It is the volume point after propagation of the measurement equation.

[0037] Estimate the measurement prediction value and information covariance matrix and cross-correlation covariance matrix at time k: (10) in, is the measured predicted value at time k, yes The transposed matrix of is the innovation covariance at time k, for The transposed matrix of is the cross-correlation covariance at time k.

[0038] Calculate the filter gain at time k, the state estimate at time k, and the updated covariance matrix. The corresponding formula is: (11) in, is the filter gain at time k, for The inverse matrix of is the estimated value of the state at time k, is the covariance matrix updated at time k, for The transposed matrix of .

[0039] S4, based on the CKF steady-state cavitation noise signal frequency tracking result, that is, the real-time filtering signal, the steady-state cavitation noise spectrum is obtained through fast Fourier transform, and the steady-state cavitation intensity is calculated according to the frequency domain amplitude. The steady-state cavitation intensity calculation process is: S41, the steady-state cavitation noise signal is transformed by Fast Fourier Transformation (FFT) to obtain the steady-state cavitation noise spectrum. The fast Fourier transform formula is shown in (12): (12) in, is the complex number of the frequency domain signal, N is the signal length, is the time domain signal, is the corresponding frequency component, k is the time.

[0040] S42, normalize the amplitude of the steady-state cavitation noise signal in the spectrum and calculate the steady-state cavitation intensity. The calculation formula is: (13) in, For the frequency The amplitude at is the driving frequency of the ultrasonic cavitation system, and and the driving frequency Related superharmonics and subharmonics.

[0041] In the method disclosed in the present invention, first, the transducer voltage signal is collected, and the collected signal is preprocessed to obtain a steady-state cavitation noise signal containing an interference signal; secondly, the frequency tracking model is expanded to realize real-time tracking of multi-frequency steady-state cavitation noise signals; furthermore, the steady-state cavitation noise signal is estimated in real time based on CKF to realize online filtering of the steady-state cavitation noise signal; finally, the tracking result is subjected to fast Fourier transform to obtain the spectrum of the steady-state cavitation noise signal, and the steady-state cavitation intensity is calculated according to the frequency domain amplitude of the steady-state cavitation noise signal. The present invention realizes online tracking and estimation of multi-frequency steady-state cavitation noise signals by expanding the model, extracts multi-frequency signals related to the steady-state cavitation intensity, and improves the integrity of the steady-state ultrasonic cavitation noise signal and the accuracy of steady-state ultrasonic cavitation intensity monitoring. The use of the CKF frequency tracking algorithm is a prerequisite for realizing online tracking of steady-state cavitation noise signals, and is a key step in online monitoring of steady-state cavitation intensity self-sensing technology. In addition, the CKF algorithm can suppress interference from other signals and improve the real-time and accuracy of steady-state cavitation intensity monitoring.

[0042] The second aspect of the present invention discloses an online self-sensing monitoring system for steady-state ultrasonic cavitation intensity, comprising: An acquisition module is used to acquire cavitation noise signals and obtain cavitation noise signal spectrum; The model optimization module is used to obtain the corresponding frequency component of the steady-state cavitation noise signal based on the cavitation noise signal spectrum; based on the measurement equation of the frequency component of the steady-state cavitation noise signal, the frequency tracking model of the cavitation noise signal is expanded to obtain the state equation and measurement equation of the frequency tracking model after the expansion; A filtering module is used to track and estimate the expanded state equation and the expanded measurement equation through the CKF frequency tracking algorithm to obtain a real-time filtering signal; The determination module is used to calculate the steady-state cavitation intensity based on the real-time filtered signal.

[0043] The following is further described in conjunction with specific embodiments.

[0044] Example 1 The overall experimental equipment Figure 3 As shown in the figure, the frequency tuning and tracking of the ultrasonic power supply ensures that the transducer always works in a resonant state. When the liquid generates ultrasonic cavitation, the transducer voltage signal is collected. According to the cavitation noise spectrum Figure 2 It can be seen that the steady-state cavitation noise signal contains five different frequency components, and five sets of measurement values ​​are obtained by preprocessing the voltage signal through a bandpass filter.

[0045] The frequency tracking model is expanded. According to the spectrum of the cavitation noise signal, it can be known that the steady-state cavitation noise signal contains five frequency signals related to steady-state cavitation activity. Then the state quantity can be expressed as , the process noise is expressed as , the corresponding quantity measurement can be expressed as , measurement matrix , measurement noise , the dimension-expanding model is expressed as: (14) The CKF frequency tracking algorithm is used to realize the online tracking and estimation of the steady-state cavitation noise signal. The time-frequency tracking results of the steady-state cavitation noise signal are as follows: Figures 4 to 8 As shown in the figure, it can be observed that the CKF algorithm realizes the online filtering of multi-frequency steady-state cavitation noise signals, which improves the accuracy and reliability of steady-state cavitation noise signals.

[0046] Calculate the mean square error (MSE) of each signal tracking frequency separately. The MSE calculation formula is: , where N is the signal length, is the true frequency, is the frequency estimation value. The MSE calculation results are shown in Table 1. From the results, it can be seen that the error between the estimated value and the actual value of the CKF frequency tracking algorithm is small, and it can maintain high accuracy in different steady-state cavitation noise signals.

[0047] Table 1 MSE statistical results

[0048] Based on the frequency tracking results of CKF multi-frequency steady-state cavitation noise signal, the steady-state cavitation noise spectrum after online filtering is obtained by fast Fourier transform, and the steady-state cavitation intensity is calculated according to formula (15).

[0049] The steady-state cavitation intensity and the sound intensity measured by the hydrophone were calculated at three different powers, and the statistical results are shown in Table 1. From Table 1, it can be seen that the steady-state cavitation intensity ( ) are 1.7, 2.4 and 2.8, which have the same indication as the sound pressure values ​​of 14mV, 28mV and 33mV measured by the hydrophone.

[0050] Table 2 Results and statistics

[0051] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for online self-sensing monitoring of steady-state ultrasonic cavitation intensity, characterized in that: The following steps are involved: Collecting cavitation noise signals and obtaining cavitation noise signal spectrum; Based on the cavitation noise signal spectrum, the corresponding frequency components of the steady-state cavitation noise signal are obtained; Based on the frequency component of the steady-state cavitation noise signal, the frequency tracking model of the cavitation noise signal is expanded to obtain the state equation and measurement equation of the frequency tracking model after the expansion; Through the CKF frequency tracking algorithm, the expanded state equation and the expanded measurement equation are tracked and estimated to obtain the real-time filtering signal; Based on the real-time filtered signal, the steady-state cavitation intensity is calculated.

2. The method for online self-sensing monitoring of steady-state ultrasonic cavitation intensity according to claim 1, characterized in that: The cavitation noise signal is obtained by collecting the voltage signal of the transducer and preprocessing it, and the preprocessing device is a bandpass filter.

3. The method for online self-sensing monitoring of steady-state ultrasonic cavitation intensity according to claim 1, characterized in that: The state equation after the dimension expansion is: (3) in, is the state quantity at time k-1, is the process noise at the k-1th moment, is the jth group of state transfer equations at time k-1, is the process noise of the jth group at time k-1.

4. The method for online self-sensing monitoring of steady-state ultrasonic cavitation intensity according to claim 1, characterized in that: The measurement equation after dimension expansion is: (4) in, is the measured value at time k, is the k-time measurement matrix, is the state quantity at time k, is the measurement noise at time k, is the measurement matrix of the jth group at time k, is the state quantity of the jth group at time k, is the measurement noise of the jth group at time k.

5. The method for online self-sensing monitoring of steady-state ultrasonic cavitation intensity according to claim 1, characterized in that: The process of tracking and estimating the expanded state equation and the expanded measurement equation through the CKF frequency tracking algorithm is as follows: decomposing the error covariance matrix at time k-1, and selecting 2n volume points with equal weights for nonlinear transformation to obtain state prediction values ​​and one-step prediction error covariance matrices. After nonlinear transformation of the state prediction values ​​and the one-step prediction error covariance matrix, the measurement prediction values ​​and covariance matrix at time k are obtained. Furthermore, the filter gain at time k is obtained and the state estimation value is calculated to obtain the filtered signal at time k.

6. The method for online self-sensing monitoring of steady-state ultrasonic cavitation intensity according to claim 1, characterized in that: The state prediction value and one-step prediction error covariance matrix is: (9) in, is the state prediction value at time k, n is the state dimension, is the volume point after the i-th volume point at time k-1 is transferred through the state equation, is the one-step forecast error covariance matrix at time k, for The transposed matrix of for The transposed matrix of .

7. The method for online self-sensing monitoring of steady-state ultrasonic cavitation intensity according to claim 1, characterized in that: The measured prediction value and covariance matrix at time k are: (10) in, is the measured predicted value at time k, n is the state dimension, yes The transposed matrix of is the innovation covariance at time k, for The transposed matrix of is the cross-correlation covariance at time k.

8. The method for online self-sensing monitoring of steady-state ultrasonic cavitation intensity according to claim 1, characterized in that: The filter gain at time k is calculated and the state estimate is: (11) in, is the filter gain at time k, is the estimated value of the state at time k, Update the covariance matrix for time k, is the cross-correlation covariance at time k, is the innovation covariance at time k, for The inverse matrix of is the state prediction value at time k, is the filter gain The transposed matrix of is the measured value, is the measured predicted value at time k, is the one-step forecast error covariance matrix.

9. The method for online self-sensing monitoring of steady-state ultrasonic cavitation intensity according to claim 1, characterized in that: The process of obtaining the steady-state cavitation intensity based on the real-time filtered signal is as follows: performing Fourier transform on the real-time filtered signal to obtain a steady-state cavitation noise spectrum diagram, normalizing the amplitude of the steady-state cavitation noise signal in the spectrum in the steady-state cavitation noise spectrum diagram, and then calculating and obtaining the steady-state cavitation intensity.

10. A steady-state ultrasonic cavitation intensity online self-sensing monitoring system, characterized in that: include: An acquisition module is used to acquire cavitation noise signals and obtain cavitation noise signal spectrum; The model optimization module is used to obtain the corresponding frequency component of the steady-state cavitation noise signal based on the cavitation noise signal spectrum; based on the measurement equation of the frequency component of the steady-state cavitation noise signal, the frequency tracking model of the cavitation noise signal is expanded to obtain the state equation and measurement equation of the frequency tracking model after the expansion; A filtering module is used to track and estimate the expanded state equation and the expanded measurement equation through the CKF frequency tracking algorithm to obtain a real-time filtering signal; The determination module is used to calculate the steady-state cavitation intensity based on the real-time filtered signal.

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