Acceleration data processing method based on piezoelectric effect

Abstract

The invention relates to the technical field of performance characteristic analysis, in particular to an acceleration data processing method based on a piezoelectric effect. The method comprises the following steps: acquiring signal data of a piezoelectric material; performing signal adjustment according to the piezoelectric material signal data to obtain signal adjustment data; constructing a charge acceleration model according to the signal adjustment data to obtain a charge acceleration model; and performing analysis according to the charge acceleration model to obtain piezoelectric material characteristic data so as to perform piezoelectric material detection auxiliary operation. Through clear signal adjustment steps, efficient model construction and accurate feature extraction, higher signal quality, detection efficiency and analysis reliability are provided.

Description

Technical Field

[0001] The present invention relates to the technical field of performance characteristic analysis, and in particular to an acceleration data processing method based on piezoelectric effect. Background Art

[0002] In sensors based on the piezoelectric effect and their applications, the acceleration data processing technology of piezoelectric materials has long received widespread attention. Traditional acceleration signal processing methods are mainly based on direct time domain or frequency domain analysis, assuming that the signal is stable and the noise is small. However, in practical applications, the acceleration signals detected by piezoelectric sensors often have complex dynamic characteristics, including significant non-stationarity, superposition of multiple frequency components, and high-frequency noise interference. These characteristics make it difficult for traditional simple analysis methods to fully and accurately describe signal characteristics and reflect material performance changes. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes an acceleration data processing method based on piezoelectric effect to solve at least one of the above technical problems.

[0004] The present application provides an acceleration data processing method based on piezoelectric effect, comprising the following steps: Acquire piezoelectric material signal data; Perform signal adjustment according to the piezoelectric material signal data to obtain signal adjustment data; Constructing a charge acceleration model according to the signal adjustment data to obtain a charge acceleration model; By analyzing the charge acceleration model, characteristic data of piezoelectric materials are obtained to assist in the detection of piezoelectric materials.

[0005] In the present invention, through the preliminary signal adjustment step, the noise and interference in the signal can be effectively removed to obtain cleaner and more reliable signal adjustment data. The charge acceleration model is constructed using the adjusted signal data to more accurately describe the response behavior of the piezoelectric material, thereby improving the accuracy of the analysis of the acceleration characteristics. By establishing a charge acceleration model, key parameters related to the characteristics of the piezoelectric material can be extracted from the original signal to reflect the health status, working stability or potential signs of failure of the material, providing strong support for detection and diagnosis. The characteristic data of piezoelectric materials are directly used for auxiliary detection operations. During the production, use or maintenance process, the characteristic data obtained by analysis can be used to timely discover problem points, predict potential risks or optimize material use conditions.

[0006] Preferably, the obtaining of piezoelectric material signal data comprises: The signal acquisition device is controlled to acquire signals from the piezoelectric sensor in an accelerated state at a preset sampling frequency to obtain piezoelectric material signal data.

[0007] In the present invention, the output of the piezoelectric sensor is directly collected under the accelerated state, which can accurately capture the real response of the material under dynamic conditions. After adopting a fixed sampling frequency, the obtained signal data is easier to match with subsequent processing steps (such as signal adjustment and feature extraction), ensuring data compatibility and consistency between different links.

[0008] Preferably, performing signal adjustment according to the piezoelectric material signal data to obtain signal adjustment data includes: Amplify the signal according to the piezoelectric material signal data to obtain signal amplification data; Signal shaping is performed according to the signal amplification data to obtain signal adjustment data.

[0009] In the present invention, by amplifying the original piezoelectric material signal, the amplitude of the signal can be significantly increased, so that weak signals become easier to detect and analyze, and the signal resolution ability is enhanced. Shaping the amplified signal can eliminate distortion or noise in the signal waveform, making the signal smoother and more standardized, thereby improving the signal quality. After signal amplification and shaping, the adjusted signal is closer to the ideal waveform, providing high-quality input data for modeling, feature extraction and analysis.

[0010] Preferably, the step of constructing a charge acceleration model according to the signal adjustment data to obtain the charge acceleration model includes: Perform signal calibration according to the signal adjustment data to obtain signal calibration data; The charge acceleration model is constructed according to the signal calibration data to obtain the charge acceleration model.

[0011] The present invention eliminates the deviation and error in the data through calibration, ensures that the data input into the model is more accurate and real, significantly improves the calculation accuracy of the charge acceleration model, and reduces the model deviation caused by uncalibrated signals. Modeling based on the calibration signal can reflect the real physical process, making the model more reliable and stable.

[0012] Preferably, the piezoelectric material characteristic data includes first piezoelectric material characteristic data and second piezoelectric material characteristic data, and the analysis based on the charge acceleration model to obtain the piezoelectric material characteristic data for performing auxiliary operations for piezoelectric material detection includes: Extracting first data features according to the charge acceleration model to obtain first piezoelectric material feature data; A second data feature extraction is performed according to the charge acceleration model to obtain second piezoelectric material feature data, wherein the first data feature extraction and the second data feature extraction are different data feature extraction methods.

[0013] The data features extracted by various methods in the present invention can complement each other and help accurately identify the state, health or potential faults of the material. For example, the first feature reflects the overall dynamic characteristics of the material, and the second feature reveals the detailed behavior of the material under specific conditions. Combined analysis can greatly improve the accuracy of the test results. Different feature extraction methods can perform multi-angle verification on the same data source, making the analysis more robust. Even if a feature is affected by noise, other features can still provide a reliable reference, thereby increasing the stability and reliability of the results.

[0014] Preferably, the extracting the first data feature according to the charge acceleration model to obtain the first piezoelectric material feature data includes: Performing a root mean square calculation according to the charge acceleration model to obtain first signal strength data, and performing a peak amplitude calculation according to the charge acceleration model to obtain second signal strength data; Performing energy consumption calculation according to the first signal strength data and the second signal strength data to obtain energy consumption data; According to the charge acceleration model, the non-stationary-stationary interval is divided to obtain non-stationary signal data and stationary signal data respectively; Calculating the instantaneous amplitude according to the non-stationary signal data to obtain first fluctuation amplitude data; Calculate the standard deviation based on the stable signal data to obtain the second fluctuation amplitude data; Characteristic data of the first piezoelectric material is obtained by performing characteristic generation according to the energy consumption data, the first fluctuation amplitude data and the second fluctuation amplitude data.

[0015] The present invention can provide signal strength data based on RMS calculation and peak value calculation. By dividing non-stationary and stationary signals, the instantaneous changes and long-term stability of complex signals can be analyzed separately. The non-stationary part reflects the instantaneous response of the material under dynamic conditions, while the stationary part shows its long-term stable behavior. The energy consumption data provides performance indicators of the material under different working conditions, and can quantify the energy loss of the material due to vibration, loading and other factors in practical applications. The fluctuation amplitude of non-stationary and stationary signals is calculated using the instantaneous amplitude and standard deviation, respectively, which can capture the subtle dynamic changes and long-term fluctuation patterns in the signal.

[0016] Preferably, the non-stationary-stationary interval division is performed according to the charge acceleration model to obtain non-stationary signal data and stationary signal data respectively, including: The peak-valley area is divided according to the charge acceleration model to obtain the peak-valley area data; Perform periodic parameter fitting according to the peak-valley area data to obtain periodic parameter fitting data; The charge acceleration model is divided into similar parameter intervals according to the periodic parameter fitting data to obtain non-stationary signal data and stationary signal data.

[0017] In the present invention, the non-stationary signal reflects the complex changes of the piezoelectric material during dynamic loading or vibration, while the stationary signal represents the behavior of the material under relatively stable conditions. By dividing the peak and valley regions and fitting the periodic parameters, the periodicity and instantaneous change characteristics of the signal can be described more accurately. The present invention can realize signal processing in special environments, such as separating non-stationary signals in a strong vibration environment and capturing dynamic characteristics; or analyzing the long-term performance of materials in a stable state.

[0018] Preferably, the instantaneous amplitude calculation is performed according to the non-stationary signal data to obtain the first fluctuation amplitude data, including: According to the periodic parameter fitting data, the neighboring extreme value is extracted to obtain the neighboring extreme value data; The amplitude of the non-stationary signal data is calculated according to the adjacent extreme value data to obtain the first fluctuation amplitude data.

[0019] In the present invention, by extracting adjacent extreme points from the periodic parameter fitting data, the local change amplitude of the signal can be accurately located to avoid masking the instantaneous characteristics due to the overall trend or changes in the long time window. Non-stationary signals contain rapid changes or irregular fluctuations, and traditional average values ​​or long time window statistical methods will ignore these details. By extracting adjacent extreme values ​​and calculating the amplitude based on these extreme values, short-term mutations or drastic fluctuations in non-stationary signals can be detected more sensitively. Focusing on extreme points when calculating the amplitude reduces dependence on global trends, can eliminate interference from some low-frequency or stable backgrounds, and thus improves the resolution of the results.

[0020] Preferably, the extracting the second data feature according to the charge acceleration model to obtain the second piezoelectric material feature data includes: Frequency domain feature extraction is performed according to the charge acceleration model to obtain frequency domain feature data; Sample entropy is calculated according to the frequency domain feature data to obtain frequency domain sample entropy data; Perform permutation entropy calculation based on frequency domain feature data to obtain frequency domain permutation entropy data; Perform kernel density estimation based on frequency domain sample entropy data and frequency domain permutation entropy data to obtain entropy kernel density data; A probability cloud map is constructed according to the entropy kernel density data to obtain characteristic data of the second piezoelectric material.

[0021] In the present invention, by calculating the sample entropy and permutation entropy of the frequency domain feature data, combined with kernel density estimation, the density and distribution characteristics of these entropy values ​​at different frequency distributions can be quantified. This precise probability density estimation makes the analysis no longer dependent on a single feature value, but based on comprehensive distribution information, thereby improving the reliability of the analysis. By constructing a probability cloud map, the distribution of multidimensional entropy features is presented, making the division of different feature states (such as normal and abnormal) more intuitive and clear.

[0022] Preferably, the performing sample entropy calculation according to the frequency domain feature data to obtain frequency domain sample entropy data includes: Extract the main frequency component according to the frequency domain feature data to obtain the main frequency component data; Generate scale factors according to the main frequency component data to obtain scale factor data; Perform frequency domain division on the frequency domain feature data according to the scale factor data to obtain frequency domain feature division data; The data is divided according to the frequency domain characteristics to calculate the sample entropy and obtain the frequency domain sample entropy data.

[0023] In the present invention, before calculating the sample entropy, the main frequency component of the frequency domain data is extracted to remove the information of irrelevant frequency bands. The scale factor generated by the main frequency data makes the calculation of sample entropy more accurate, ensuring that the dynamic characteristics of different frequency bands can be captured in a finer granularity. By generating different scale factors, this method enables the sample entropy to be calculated in multiple frequency ranges and time scales. The frequency domain feature data is divided according to the generated scale factors, so that the characteristics of different frequency ranges appear independently, reducing the influence of frequency aliasing on the entropy value calculation. Sample entropy calculation is performed on the basis of scale division, and sample entropy calculation in multiple dimensions is realized, thereby providing more accurate data support.

[0024] The beneficial effects of the present invention are: in the process of signal adjustment, measurement noise and environmental interference are effectively eliminated, and the generated signal adjustment data is more accurate and consistent. The constructed charge acceleration model is based on physical principles, can capture the characteristics of piezoelectric materials under dynamic acceleration conditions, and deeply explore the dynamic response behavior of the material, so that the extracted piezoelectric material characteristic data can characterize the performance of the material in multiple dimensions. The characteristic data generated by model analysis can be used to track material performance changes over a long period of time, provide a scientific basis for maintenance decisions, fault prediction and life assessment, and provide higher signal quality, detection efficiency and analysis reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting implementations made with reference to the following drawings: Figure 1A flowchart showing a method for processing acceleration data based on piezoelectric effect according to an embodiment of the present invention is provided; Figure 2 A flowchart showing the steps of a first piezoelectric material feature extraction method according to an embodiment is shown; Figure 3 A flowchart of a method for dividing a non-stationary-stationary interval according to an embodiment is shown; Figure 4 A flow chart of the steps of a second piezoelectric material feature extraction method according to an embodiment is shown. DETAILED DESCRIPTION

[0026] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0027] In addition, the drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0028] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0029] See also Figures 1 to 4 The present application provides an acceleration data processing method based on piezoelectric effect, comprising the following steps: S1, obtaining piezoelectric material signal data; In one embodiment, signal data from a piezoelectric material is obtained by a piezoelectric sensor (e.g., a piezoelectric accelerometer). Piezoelectric materials generate electric charges when subjected to mechanical stress or vibration, and the signal data is expressed as a voltage or current signal proportional to the acceleration. In order to ensure the quality of the signal, it is converted into a digital signal by a data acquisition system (e.g., an oscilloscope or a data acquisition card).

[0030] S2, performing signal adjustment according to the piezoelectric material signal data to obtain signal adjustment data; In one embodiment, after the original signal data is acquired, signal adjustment is performed. The original signal may be affected by noise, baseline drift or other interference. A low-pass filter is selected and a cutoff frequency is set to remove noise components above the cutoff frequency; or a band-pass filter is used to retain the main signal components within the expected frequency band (such as 5-100Hz), or baseline drift can be removed by removing DC from the signal.

[0031] S3, constructing a charge acceleration model according to the signal adjustment data to obtain a charge acceleration model; In one embodiment, a charge acceleration model is constructed based on the signal adjustment data. According to the piezoelectric effect, the output charge of the piezoelectric material is proportional to the applied force or acceleration. The stress-charge relationship of the piezoelectric material is: ,in is the charge output by the piezoelectric material, is a constant related to material properties, is the applied acceleration. Or, According to the piezoelectric effect formula, the output charge With acceleration The relationship is expressed by integration: .

[0032] S4. Analyze according to the charge acceleration model to obtain characteristic data of piezoelectric materials to perform auxiliary operations for piezoelectric material detection.

[0033] In the present invention as well as , represents the charge output by the piezoelectric material, the unit is coulomb C. is the piezoelectric coefficient, which indicates the amount of charge generated per unit acceleration, and its unit is , which can also be expressed as (where is the gravitational acceleration constant). as well as , represents the applied acceleration, in units of or ( ). This formula is directly derived from the basic theory of the piezoelectric effect and has been verified by a large number of experiments. For example, the charge output measured in a static pressure experiment is proportional to the applied force (or acceleration), and the proportionality factor is the known piezoelectric constant of the material. This formula describes the cumulative result of the transient response. In a dynamic loading experiment, a time-varying acceleration is applied. , the total charge output obtained by integration is consistent with the charge accumulation result measured experimentally. The accuracy of this integral relationship has been repeatedly proved in material mechanics and electrical coupling experiments. For example, a piezoelectric accelerometer is generally composed of a shell and a spring, a mass block, a piezoelectric element and a fixed base installed in the shell. A mass block is placed on the piezoelectric sheet, and then the mass block is preloaded with a hard spring, and then the entire assembly is installed in a metal shell of a base. When the sensor senses vibration, due to the considerable stiffness of the spring and the relatively small mass of the mass block, it can be considered that the inertia of the mass block is very small, and it senses the same vibration as the sensor base and is acted upon by an inertial force in the opposite direction of the acceleration. In this way, the mass block has a force proportional to the acceleration acting on the piezoelectric sheet. Through the piezoelectric effect of the piezoelectric sheet, a voltage that changes with the vibration acceleration will be generated on the surface of the piezoelectric sheet. When the vibration frequency is much lower than the natural frequency of the sensor, the voltage output by the sensor is proportional to the force, that is, proportional to the acceleration felt by the sensor.

[0034] In one embodiment, the charge acceleration model is used to perform signal analysis and extract characteristic data of the piezoelectric material. The charge acceleration model establishes the relationship between acceleration and induced charge signal. The functional relationship between the actual measured acceleration signal can be converted into the corresponding charge time series. By analyzing the charge acceleration model, different acceleration ranges, frequency responses and other characteristics can be identified. For example, the resonant frequency of the piezoelectric material in different vibration modes can be extracted by analyzing the frequency spectrum of the acceleration. Calculate the spectrum of the signal: ; in, is the spectrum of the signal, is the length of the time window, is the base of the natural exponent, is an imaginary unit, is the constant term of pi, is the frequency variable of the signal, is the time parameter. Identify significant peaks in the spectrum.

[0035] Since the piezoelectric response signal has non-stationary and local modulation characteristics, the Hilbert transform can effectively extract its instantaneous energy changes and improve the response detection capability of dynamic processes. Perform Hilbert transform to obtain its analytical signal, ,in To analyze the signal, is an imaginary unit, for The Hilbert transform of represents the 90° phase shifted portion of the signal. Calculate the instantaneous amplitude : , and obtain the instantaneous amplitude varying with time , to represent the dynamic intensity change of the acceleration signal. The extracted spectral features (such as resonant frequency) and instantaneous amplitude changes can be used to identify the response performance of the material, determine whether there is performance degradation, or evaluate its dynamic behavior at a specific frequency, and are used in the identification and early warning module of the piezoelectric material detection auxiliary system.

[0036] In the present invention Represents the induced charge signal, in coulomb C, Represents the spectrum of the signal, and the unit is same, because the integral reflects the signal at frequency The amplitude under is in coulombs C, Indicates the time window length in seconds. represents the base of natural logarithms, dimensionless, is an imaginary unit, dimensionless, is the constant term of pi, dimensionless, The frequency of the signal, in Hertz (Hz) or the reciprocal of a second , is the time parameter, the unit is second s. By converting to the frequency domain, the amplitude and phase distribution of the signal at different frequencies can be obtained. This spectrum analysis method can reveal the response characteristics of piezoelectric materials to specific acceleration frequencies; directly find the resonant frequency of the material from the significant peak position in the spectrum, and characterize the important parameters of the inherent performance of the material. This formula is based on the theoretical basis of Fourier transform, which is a widely used signal analysis tool. By integrating the time domain signal Convert to the frequency domain and extract the amplitude and phase of the frequency component. This is a classic method of signal processing. In physical terms, it can be understood as the contribution of the signal in different resonance modes. In the present invention for The Hilbert transform is still part of the induced charge signal, and its unit is the same as The same is Coulomb C, To analyze the signal, and its Hilbert transform Composition, units and The same is Coulomb C, is the instantaneous amplitude, defined as , whose dimension is the same as The same is Coulomb C. The process of generating an analytical signal by Hilbert transform is to expand the original signal to the complex domain. The real part of the analytical signal is the original signal. , the imaginary part is the orthogonal component of the signal By analyzing the signal and further calculating the instantaneous amplitude, we can effectively describe the energy changes of the signal at different time points. , we can intuitively see the trend of signal strength over time, especially when the signal amplitude fluctuates significantly, the change of instantaneous amplitude can reflect the dynamic response characteristics. Non-stationary signals usually contain local amplitude modulation components, which are clearly visible in the instantaneous amplitude. Compared with traditional spectrum analysis, instantaneous amplitude can more accurately capture short-term bursts or local changes in time-varying signals. For piezoelectric materials, the change of instantaneous amplitude can be used to determine whether its response performance is stable. If the instantaneous amplitude decays rapidly at a specific frequency or vibration mode, it indicates that the material performance has declined; if the amplitude remains stable over time, it means that the dynamic performance of the material under the current conditions is good. The role of the Hilbert transform is to expand the real signal into a complex signal and provide an orthogonal component. This expansion is mathematically complete and can be used to calculate the instantaneous amplitude and instantaneous phase. The result can well characterize the time-varying characteristics in non-stationary signals. The parameters in the formula have the same dimensions, and all addition, subtraction, product and integration operations have no dimension conflicts and can be directly added.

[0037] Preferably, the obtaining of piezoelectric material signal data comprises: The signal acquisition device is controlled to acquire signals from the piezoelectric sensor in an accelerated state at a preset sampling frequency to obtain piezoelectric material signal data.

[0038] In one embodiment, a signal acquisition device is configured. The signal acquisition device includes an analog-to-digital converter (ADC) and an acquisition control unit. During the configuration stage, the control system sets the sampling frequency and sampling time. When selecting the sampling frequency, adjust it to at least twice the highest frequency of the signal. The piezoelectric sensor is installed on the object to be tested (such as a machine part, a structural surface, etc.), and ensures that it is tightly coupled with the applied acceleration or vibration state. The control device starts sampling according to the change in acceleration or a specific trigger condition (for example, a set vibration threshold). After the trigger, the system starts to collect signal data at a set frequency. The sampling device obtains the charge signal output by the sensor at a set time interval. The collected piezoelectric signal data is stored through a data storage device (such as a hard disk, a solid-state drive, etc.). During the storage process, the signal is saved in a database in a discrete digital form or directly exported as a file.

[0039] Preferably, performing signal adjustment according to the piezoelectric material signal data to obtain signal adjustment data includes: Amplify the signal according to the piezoelectric material signal data to obtain signal amplification data; In one embodiment, the main purpose of signal amplification is to increase the amplitude of the original signal to ensure the detectability of the signal during the processing. Since the signal output by the piezoelectric sensor is usually weak, direct use will affect the analysis effect due to noise or too small signal amplitude. The amplification factor is selected according to the actual amplitude and expected range of the signal. If the amplitude of the signal is too small, a larger amplification factor (such as 5-10 times) can be selected; if the signal amplitude is large, a moderate amplification factor (such as 1.1-3 times) is selected to avoid excessive amplification and signal distortion. In order to prevent saturation or distortion caused by excessive amplification, an automatic gain control (AGC) mechanism can be set or a limiting device can be introduced to dynamically adjust the amplified signal.

[0040] Signal shaping is performed according to the signal amplification data to obtain signal adjustment data.

[0041] In one embodiment, the purpose of signal shaping is to process the amplified signal so that it meets the analysis requirements. Since the piezoelectric signal contains high-frequency noise or low-frequency drift, a filter (such as an RC filter) is used to process the signal. The stress-charge relationship of the piezoelectric material is: ,in is the charge output by the piezoelectric material, is a constant related to material properties, is the applied acceleration. Or, According to the piezoelectric effect formula, the output charge With acceleration The relationship is expressed by integration: .

[0042] Preferably, the step of constructing a charge acceleration model according to the signal adjustment data to obtain the charge acceleration model includes: Perform signal calibration according to the signal adjustment data to obtain signal calibration data; In one embodiment, a vibration table, an exciter, or a physical environment with known acceleration is used to generate a vibration signal of a certain amplitude and frequency through these acceleration sources. Under the action of the standard acceleration source, the output signal of the piezoelectric sensor is recorded. Through experimental data, the relationship between the standard acceleration signal and the piezoelectric signal is established and expressed as a linear relationship. The relationship between acceleration and signal is: , is the output signal of the piezoelectric sensor, is the calibration constant of the sensor, which indicates the proportional relationship between the signal and acceleration. is the standard acceleration signal. The above relationship is estimated using the least squares fitting method to calculate the calibration constant, which represents the electrical signal response ratio under unit acceleration input and is used for signal inversion. The value and linear relationship are part of the signal calibration data and are used to convert the raw signal data into standard acceleration values.

[0043] In the present invention Represents the output signal of the piezoelectric sensor. Its unit depends on the form of the signal. For example, if the signal is voltage, the unit is volt V; if the signal is charge, the unit is coulomb C, is the calibration constant of the sensor. Its dimension is the output signal unit divided by the acceleration unit. For example, if is the voltage V, and is the acceleration ,but The dimension of .if is Coulomb C, and is the acceleration ,but The dimension of . Both physical experiments and theories show that the charge output of piezoelectric materials is usually proportional to the applied force, which in turn is proportional to the acceleration, so it is reasonable for the output signal to have a linear relationship with the standard acceleration. Within the normal operating range of the sensor (not reaching saturation or nonlinear failure state), the use of a linear model is not only reasonable, but also simplifies calibration and processing. The least squares method is one of the most commonly used methods for parameter estimation. Its core idea is to minimize the sum of squares of the deviations between the actual measured data and the fitting model, so as to obtain the optimal fitting parameters. For the linear model , the least squares method can quickly and stably calculate the proportional coefficient , and this estimate has the minimum variance when the noise is random white noise, so it is an ideal choice in the calibration process. The calibration constant directly reflects the sensitivity of the sensor and its unit is the signal output value (such as voltage, charge) divided by the acceleration (such as V / g or C / g). By accurately determining , which can ensure the repeatability and accuracy of the measurement system and provide a solid quantitative basis for experimental analysis. The calibration constant can be used to convert any raw output signal into a standard acceleration value, which is convenient for data comparison and result verification. The calibration constant can also be used to evaluate the long-term stability and consistency of the sensor. Significant changes indicate drift or degradation in sensor performance, which in turn guides maintenance or recalibration.

[0044] The charge acceleration model is constructed according to the signal calibration data to obtain the charge acceleration model.

[0045] In one embodiment, after signal calibration, a charge acceleration model is constructed to establish the relationship between acceleration and charge. Due to the characteristics of piezoelectric materials, the charge generated by acceleration is proportional to the acceleration. Constructing a linear relationship model: , is the induced charge per unit time, is the sensitivity coefficient of the piezoelectric material, is the acceleration data, is the zero drift term or initial offset. The model derives the relationship between acceleration and charge based on the calibrated signal.

[0046] Preferably, the piezoelectric material characteristic data includes first piezoelectric material characteristic data and second piezoelectric material characteristic data, and the analysis based on the charge acceleration model to obtain the piezoelectric material characteristic data for performing auxiliary operations for piezoelectric material detection includes: Extracting first data features according to the charge acceleration model to obtain first piezoelectric material feature data; In one embodiment, the first data feature extraction is signal time feature extraction. By finding the maximum value of the signal, the peak value of the signal is obtained, which indicates the maximum amplitude of the piezoelectric material response. The mean of the signal is calculated to indicate the average response value of the signal. The variance of the signal is calculated to indicate the degree of fluctuation of the signal. The duration when the signal is greater than a certain threshold is calculated to describe the pulse characteristics of the signal and obtain the signal duration. By calculating these features, the first piezoelectric material characteristic data is obtained.

[0047] A second data feature extraction is performed according to the charge acceleration model to obtain second piezoelectric material feature data, wherein the first data feature extraction and the second data feature extraction are different data feature extraction methods.

[0048] In one embodiment, the second data feature extraction is signal spectrum feature extraction. Perform a fast Fourier transform (FFT) on the charge signal to obtain a frequency domain signal. Find the frequency point with the largest amplitude in the spectrum as the main frequency, that is, the natural frequency or the strongest response frequency of the piezoelectric material to calculate the frequency bandwidth of the signal, that is, the frequency range with an amplitude greater than a certain threshold starting from the main frequency. The bandwidth can be defined by analyzing the area where the spectrum amplitude is greater than a certain threshold. Calculate the power density of the spectrum to obtain the energy distribution of the signal in each frequency band. If the signal contains harmonics, the amplitude of each harmonic can be analyzed to obtain the contribution of high-order harmonics to the overall signal as part of the second feature. Based on the aforementioned frequency domain analysis, the second piezoelectric material characteristic data is obtained.

[0049] Preferably, the extracting the first data feature according to the charge acceleration model to obtain the first piezoelectric material feature data includes: S41, performing a root mean square calculation according to a charge acceleration model to obtain first signal strength data, and performing a peak amplitude calculation according to the charge acceleration model to obtain second signal strength data; In one embodiment, the RMS calculation is: ; in, is the first signal strength data, is the number of sampling points of the signal, is the time point sequence item, For time point, is the value of the charge acceleration model at the corresponding time point.

[0050] Determine the time interval [tN, tM] for signal analysis, where tN and tM are the start and end time points, respectively. Use the charge acceleration model to obtain the acceleration signal data at each time point. The peak amplitude is the maximum absolute value of the signal in the time interval, that is, find the maximum value of the acceleration signal in the interval. The peak amplitude represents the maximum response amplitude of the signal, which reflects the ultimate response capability of the piezoelectric material. Use the charge signal to find the maximum value of the signal in a given interval.

[0051] In the present invention is the root mean square value, which indicates the energy intensity of the signal. Its dimension depends on the physical unit of the signal itself. If is the charge, measured in coulombs, then The dimension of is also Coulomb, is the number of sampling points of the signal, dimensionless, is the order term of the time point, dimensionless, is the time point in seconds, is the charge signal at time point The instantaneous value of is in coulombs C. It is the value of the same physical quantity at different time points, and its square The dimension of ,and is dimensionless, so The dimension is still After taking the square root, The dimension of ,and The dimensions of the parameters in this formula are consistent and can be directly added and operated. Indicates the overall energy level of the signal and is a comprehensive indicator of signal strength. , which can clearly quantify the average amplitude of the signal within a time interval, which is very important for evaluating the continuous response capability of piezoelectric materials under certain working conditions. The RMS value has little effect on occasional extreme points and can reflect the overall characteristics of the signal rather than the response at a single moment, making it an important reference for judging the stability of material performance. The peak amplitude reflects the maximum response level of the signal within the selected time interval. The calculation of the peak amplitude provides the ultimate response capability of the material, helps identify possible overload conditions or maximum bearing capacity, and provides a direct basis for the safety assessment and performance debugging of the material. If the peak amplitude exceeds the expected range, it may indicate that the material may have experienced an abnormal event or is approaching its operating limit. It is a standard calculation method widely used in signal analysis. By squaring, averaging and square root, it can effectively capture the overall energy level of the signal, and at the same time has a consistent measurement standard for symmetrical signals with positive and negative values. RMS has little effect on high-frequency noise in a short period of time, which helps to stably reflect the average strength of the signal. Peak amplitude is a physically clear quantity. It only depends on the extreme points of the signal, does not involve complex mathematical operations, and has direct engineering significance. When evaluating the extreme performance of piezoelectric materials, the maximum value provides a clear reference indicator and is an important basis for judging the dynamic behavior of the material. The combination of RMS value and peak amplitude can simultaneously obtain the average response level and the extreme response level of the signal. These two characteristics together constitute a comprehensive analysis of the signal strength. RMS focuses more on the overall characteristics, and peak amplitude focuses more on extreme situations. The two complement each other and provide multi-angle support for evaluating material performance.

[0052] S42, calculating energy consumption according to the first signal strength data and the second signal strength data to obtain energy consumption data; In one embodiment, the signal has an approximately periodic oscillation characteristic. By performing square integration of the signal strength and its sinusoidally modulated value, the energy consumption per unit time can be reflected, and the overall energy dissipation level can be approximately estimated. The energy consumption is calculated as follows: ; in For energy consumption data, is the order of the data points, is the number of data points, From the initial time to the time The corresponding first signal strength data, For the frequency factor, is the angular frequency, representing the oscillation frequency of the signal, is the specific time point of signal sampling ( time points), is the initial phase of the first signal strength data, used to characterize the phase shift of the signal waveform, From the initial time to the time The corresponding second signal strength data, For the Time parameter items, is the time parameter, is the initial phase of the second signal strength data, similar to the phase offset of the first signal, affecting the waveform of signal synthesis. is the time step, which represents the time interval between data points.

[0053] In the present invention is the energy consumption data, expressed in energy units (such as joules J), is the data point order term, dimensionless, is the number of data points, dimensionless, From the initial time to the time The corresponding first signal strength data, the unit depends on the specific signal type. If the signal represents voltage, the unit is volt V; if it represents charge, the unit is coulomb C, For the frequency factor, dimensionless (representing the scale factor of a certain frequency), is the angular frequency, in radians per second rad / s, representing the oscillation frequency of the signal, is the specific time point of signal sampling ( time point), in seconds s, is the initial phase of the first signal strength data, used to characterize the phase shift of the signal waveform, in radians, From the initial time to the time The corresponding second signal strength data, the unit depends on the specific signal type. If the signal represents voltage, the unit is volt V; if it represents charge, the unit is coulomb C, For the Time parameter items, is the time parameter, The initial phase of the second signal strength data is similar to the phase offset of the first signal, which affects the waveform of the signal synthesis. The unit is radian rad. is the time step, which indicates the time interval between data points, in seconds; The dimension and The square dimension is (like or )same, The dimension of the square of is . Sum all the points and multiply by , which is equivalent to approximate integration, so the dimension of the result is or .like and is the voltage (V), then The dimension is watt-second , that is, joule J. If and is the charge (Coulomb C), then the charge is used to derive the voltage, such as V=IZ, V is the voltage, I is the current, through and Converted, Z is the complex impedance, which is the preset value. Each term in the formula is consistent in dimension (energy unit), can be directly added and summed, and has physical rationality. The formula approximates the total energy consumption of the signal per unit time by the method of square integration, providing a means of quantitatively analyzing the signal dissipation level. Different signal strengths , and frequency factor The combination reflects the energy distribution of the signal in different oscillation modes and can describe the dynamic behavior of piezoelectric materials under multi-frequency excitation. It can help identify changes in material properties under different excitation conditions, such as whether the energy consumption in high-frequency oscillation mode is significantly higher than that in low-frequency mode. , or frequency factor ), the response law of materials under different vibration modes can be studied, so as to optimize the use conditions and performance evaluation. By integrating the square of the signal, it can be regarded as the calculation process of energy density, which conforms to the common method of energy calculation in physics. The square operation is used to eliminate the offset effect of the positive and negative values ​​of the signal, so as to more accurately reflect the overall strength of the signal, while the integral reflects the cumulative effect of the signal strength over time. The sinusoidal modulation and phase shift in the formula reflect the periodicity and modulation characteristics of the signal, which affect the instantaneous energy change of the signal and reasonably incorporate the dynamic characteristics into the energy calculation.

[0054] S43, performing non-stationary-stationary interval division according to the charge acceleration model, and obtaining non-stationary signal data and stationary signal data respectively; In one embodiment, the signal interval is divided by analyzing the statistical characteristics of the signal, especially the change of the mean and variance. In the range of t1 and t2, the part where the signal has a significant change (the change of the mean and variance is not within the threshold range) is regarded as a non-stationary interval, and the part where the change is stable (the change of the mean and variance is within the threshold range) is regarded as a stable interval.

[0055] S44, performing instantaneous amplitude calculation according to the non-stationary signal data to obtain first fluctuation amplitude data; In one embodiment, for non-stationary signal data , and its instantaneous amplitude is obtained by calculation. Instantaneous amplitude for: , is the time parameter. The instantaneous amplitude is obtained by changing the instantaneous frequency of the signal.

[0056] In the present invention is the instantaneous amplitude, which is defined as the square root of the sum of the squares of the signal and its derivative, so the unit is the same as The units are consistent, For a non-stationary signal, the dimension depends on the physical unit of the signal. If If it is a charge signal, the unit is coulomb C; if it is a voltage signal, the unit is volt V, is the time parameter, s, is the time constant or time scale, in seconds. Therefore, after taking the square root, the units of the instantaneous amplitude are still the same as The same, maintaining dimension consistency, in the physical sense, the operation of the formula is valid, and the parameters can be added directly. It provides information about the signal's strength at any point in time. For non-stationary signals, their strength may change over time, and the instantaneous amplitude can capture these dynamic changes, allowing us to have a more intuitive understanding of the local energy or fluctuations of the signal. and its time derivative Combined, it not only reflects the strength of the signal itself, but also takes into account the rate of signal change. For example, even if the signal value is small at a certain moment, if the signal change rate is very high, the instantaneous amplitude will still show a larger value. This characteristic enables the instantaneous amplitude to simultaneously characterize the static strength and dynamic strength of the signal. For non-stationary signals, traditional root mean square (RMS) values ​​or power spectrum analysis may not accurately describe the changes in the signal at different time points. The instantaneous amplitude formula directly provides the intensity change at each time point, especially for signals containing modulation, burst components or short-term anomalies, the instantaneous amplitude can reveal its time-varying characteristics.

[0057] S45, calculating the standard deviation according to the stable signal data to obtain second fluctuation amplitude data; In one embodiment, for stationary signal data , standard deviation calculation (to measure the fluctuation range): ; in is the second fluctuation range data, is the number of time parameters, is the time parameter sequence term, For the time parameters, For the The sampling points of the stationary signal data corresponding to the time parameter, is the average value of the stationary signal data.

[0058] In the standard deviation formula of the present invention, The dimension of After summing all the points, the dimension of the result is still , then divided by After normalization, the dimension of the result is still Finally, take the square root and the dimension of the standard deviation becomes , which is consistent with the unit of the original signal. Therefore, the dimensions in the formula are self-consistent, and all addition, subtraction, multiplication and division operations are valid. The standard deviation is a key indicator to measure the overall fluctuation degree of the signal. A larger standard deviation means that the signal has a larger range of fluctuations, reflecting the discrete degree of the amplitude distribution of the stationary signal. Through calculation, the stability and consistency of the signal in a given time interval can be quickly determined. As an intuitive statistic, the standard deviation can be used to compare the fluctuation amplitudes of different signal data sets, providing a basis for feature extraction and classification. In material testing or performance evaluation, the trend of standard deviation changes may indicate changes in material response characteristics or changes in environmental conditions. The standard deviation formula is a standard discrete degree measurement method in statistics and is widely used in the field of signal processing. Under the assumption of a stationary signal, the fluctuation amplitude of the signal will not change dramatically over time, so the calculation result of the standard deviation can stably reflect its fluctuation range. The standard deviation provides quantitative information about the signal fluctuation, which is more intuitive than just observing the signal waveform. The standard deviation makes it easier to distinguish the stability of different types of signals. For example, if the standard deviation of the signal under a certain working condition increases significantly, it may indicate abnormal fluctuations or deterioration of equipment performance. In material performance testing, the standard deviation can be used to verify whether the test conditions are consistent or to determine the difference in the response amplitude of the material under different stimuli.

[0059] S46. Generate characteristics according to the energy consumption data, the first fluctuation amplitude data and the second fluctuation amplitude data to obtain first piezoelectric material characteristic data.

[0060] In one embodiment, the above three types of features can be spliced ​​according to preset weights to form a feature vector with a length of N, in which the energy feature, the first wave feature, and the second wave feature account for the proportions of α, β, and γ respectively (α+β+γ=1), which is used to characterize the comprehensive response characteristics of the first piezoelectric material.

[0061] In one embodiment, based on the energy consumption data, the first fluctuation amplitude data and the second fluctuation amplitude data, statistical characteristics (such as mean, standard deviation, maximum value, etc.) are extracted or compression characteristics are generated through a specific algorithm (such as principal component analysis PCA) to form first piezoelectric material characteristic data.

[0062] Preferably, the non-stationary-stationary interval division is performed according to the charge acceleration model to obtain non-stationary signal data and stationary signal data respectively, including: S431, dividing the peak-valley area according to the charge acceleration model to obtain peak-valley area data; In one embodiment, the extreme value points (i.e., local maximum and minimum values) of the signal are found by performing first-order difference on the signal. Based on the above detection results, the time period between two consecutive extreme value points is used as the basis for division to divide the peak area and valley area of ​​the signal. The peak area refers to the time interval when the signal value reaches the local maximum, and the valley area refers to the time interval when the signal value reaches the local minimum.

[0063] S432, performing periodic parameter fitting according to the peak-valley region data to obtain periodic parameter fitting data; In one embodiment, the peak-valley data and the corresponding signal interval are represented as periodic fluctuations, and a periodic function corresponding to each peak-valley data and the corresponding signal interval is given, such as: ; in is the periodic parameter fitting data, is the amplitude, is the constant term of pi, is the frequency, is the time parameter, For phase.

[0064] A least squares fit is performed on the signal to find the periodic parameters that best fit the signal. The fitting process minimizes the fitting error using an optimization algorithm such as gradient descent or Newton's method: , where the parameters are consistent with the previous formula parameters, is the number of sampling points. The above objective function is iteratively solved through numerical optimization algorithms (such as gradient descent method or Newton method) to obtain the optimal three parameter values , used to represent the periodic structure within the current peak-to-valley interval.

[0065] In the present invention The dimension of Decide, is dimensionless, so The dimension and Each term of the objective function is ,and After summing, the result dimension is still the square of the original signal (for example or ). The dimensions in the entire objective function optimization process are self-consistent, and the dimensions involved can be directly added, subtracted and optimized. Through least squares fitting, the periodic characteristics of the signal can be accurately extracted, including amplitude, frequency and phase. This method is suitable for signals with regular fluctuations (such as signals with obvious peak and valley patterns), and can obtain the optimal periodic parameters that describe the signal behavior within a period of time. The obtained periodic parameters provide intuitive quantitative indicators for signal characteristic analysis. In material testing and dynamic monitoring, these parameters can be used to compare signal characteristics in different time periods and under different working conditions, and then determine whether the material has changed, aged or behaved abnormally.

[0066] S433. Divide the charge acceleration model into similar parameter intervals according to the periodic parameter fitting data to obtain non-stationary signal data and stationary signal data.

[0067] In one embodiment, a certain similarity threshold is set based on the frequency and amplitude obtained by periodic fitting. When the frequency and amplitude of the signal are relatively consistent, the signal is considered to belong to a stable interval; when the frequency or amplitude changes significantly, the signal is considered to belong to a non-stationary interval. The division is performed by calculating the amplitude change rate and the frequency change rate within the signal segment. If the amplitude change rate and the frequency change rate within the signal segment exceed a predetermined threshold, it is defined as a non-stationary interval, otherwise it is a stable interval. According to the above interval division rules, the signal is divided into non-stationary signal data and stable signal data. Non-stationary signal data refers to time intervals with large changes in frequency and amplitude, and stable signal data refers to time intervals with smaller changes.

[0068] Preferably, the instantaneous amplitude calculation is performed according to the non-stationary signal data to obtain the first fluctuation amplitude data, including: According to the periodic parameter fitting data, the neighboring extreme value is extracted to obtain the neighboring extreme value data; In one embodiment, the periodic characteristic parameters of the signal, such as amplitude, frequency, and phase, are obtained by a periodic fitting model. For non-stationary signal data, local extreme points are obtained by solving its first-order derivative. Local extreme values ​​occur when the first-order derivative changes from positive to negative or from negative to positive. The peak value (local maximum value) changes from positive to negative at a time point, and the value corresponding to the time point is a local maximum value. The valley value (local minimum value) changes from negative to positive at a time point, and the value corresponding to the time point is a local minimum value. Through these conditions, the extreme points in the signal, that is, the adjacent extreme value data, can be extracted. Local extreme values ​​(peak values ​​and valley values) are adjacent extreme value data. Specifically, adjacent extreme value data refers to continuous local maximum and local minimum pairs in the signal, which can be obtained by the above calculation.

[0069] The amplitude of the non-stationary signal data is calculated according to the adjacent extreme value data to obtain the first fluctuation amplitude data.

[0070] In one embodiment, for a non-stationary signal , the amplitude refers to the maximum amplitude of the signal between local extreme values. The amplitude between local extreme values ​​can be expressed by the difference between the maximum and minimum values: each local extreme value (peak and valley) of the signal, and then calculate the amplitude between these extreme values. The amplitude between each pair of extreme values ​​is regarded as the instantaneous amplitude of the signal. For each pair of adjacent extreme points, the instantaneous amplitude can be expressed as the difference between each pair of extreme values, indicating the speed of change of the signal in this time interval. In this way, the instantaneous amplitude of the signal at each moment can be obtained. By calculating the amplitude between all adjacent extreme values, the first fluctuation amplitude data of the signal in this interval is obtained. This data can reflect the instantaneous change intensity of the non-stationary signal and describe the dynamic volatility of the signal.

[0071] Preferably, the extracting the second data feature according to the charge acceleration model to obtain the second piezoelectric material feature data includes: S47, extracting frequency domain features according to the charge acceleration model to obtain frequency domain feature data; In one embodiment, the charge acceleration model is converted into a frequency domain signal by fast Fourier transform (FFT). From the frequency domain signal, multiple frequency domain features are extracted, such as power spectral density (PSD), to express the power distribution of the signal at different frequencies. It is obtained by calculating the square of the amplitude; the main frequency of the signal is obtained by identifying the peak frequency in the power spectrum; the frequency band energy of the signal (such as the energy of the low frequency band, the middle frequency band and the high frequency band) is calculated by integrating the power spectrum, and the frequency band boundary of the integration is set according to the frequency range of the specific signal. The obtained frequency domain features include data such as power spectral density, main frequency and frequency band energy.

[0072] S48, performing sample entropy calculation according to the frequency domain feature data to obtain frequency domain sample entropy data; In one embodiment, given a time series corresponding to a frequency domain feature data, an embedding dimension is selected and a tolerance (set to 0.2 times the standard deviation of the sequence). Calculate all lengths as subsequence of (target subsequence) and The similarity of (the rest of the subsequences) is defined as the distance between the two subsequences .if , then the two subsequences are considered similar. The formula for sample entropy is: ; in, is an embedding dimension with similarity The number of subsequence logarithms of is an embedding dimension with similarity The number of subsequence pairs. In the present invention, the distance is calculated hour, and The units of the subsequences A and B are consistent, and they can be directly compared logically. The subsequence logarithms A and B are dimensionless integers, and A / B in the formula is a dimensionless ratio. The logarithm of the ratio A / B is dimensionless, and the sample entropy value is also dimensionless. Therefore, the dimensions of all parameters in the formula are consistent, and the operations involved (such as addition, subtraction, multiplication, division, and logarithm) are not contradictory in terms of dimensions. Sample entropy measures the complexity and uncertainty of a time series by counting the similarities of subsequences in the sequence. A higher sample entropy value indicates that the sequence is more complex and has strong uncertainty; a lower sample entropy value indicates that the sequence is relatively more regular and easier to predict. Sample entropy is highly sensitive to the implicit pattern changes and randomness in the signal. Even if the signal does not have significant changes in frequency components, sample entropy can capture subtle differences in complexity. The calculation method of sample entropy can cope well with non-stationary signals because it does not rely on a fixed periodicity assumption, but directly measures complexity based on the similarity between subsequences.

[0073] S49, performing permutation entropy calculation according to the frequency domain feature data to obtain frequency domain permutation entropy data; In one embodiment, the input is a frequency domain feature data sequence within a certain frequency domain subinterval , the data can be a power spectrum extracted from the spectrum, a main frequency energy sequence, or an intensity sequence of a characteristic frequency band. Select an embedding dimension (such as 3, 4, 5) and a time delay (such as 1, 2), the sequence Constructed into an embedded vector set, thus generating -dimensional time series embedding vector . Each vector Sort by the size of its elements to get its arrangement mode , and then calculate the probability distribution of the permutation pattern. The calculation formula for permutation entropy is: ; in Arrange entropy data in the frequency domain, For the arrangement mode, For arrangement mode The probability of , which indicates the frequency of occurrence of the arrangement pattern in the sequence.

[0074] The operations in the present invention are all performed in a dimensionless probability space, the dimensions in the formula are consistent, and all parameters can directly participate in the operation. Permutation entropy measures the complexity and randomness of the sequence through the probability distribution of the permutation pattern. If the probability distribution of all permutation patterns in the sequence is close to uniform, the permutation entropy value is high, indicating that the sequence is more complex or closer to random. If the probability of some patterns appearing is much higher than that of other patterns, the permutation entropy value is low, indicating that the sequence is more regular or structured. Permutation entropy is suitable for analyzing the time series characteristics of frequency domain data and can capture the time dynamic laws of spectrum energy distribution. For example, by calculating the permutation entropy of the main frequency energy sequence, it is possible to quantify whether there is significant periodic modulation or random fluctuation in the signal. Permutation entropy can help discover the dynamic behavior differences in a specific frequency band and reveal the potential patterns and changing trends of the sequence. Permutation entropy does not need to assume the stationarity and linear characteristics of the signal and is suitable for analyzing non-stationary time series with frequency domain characteristics. For example, when the signal has modulation characteristics or short-term mutation characteristics, permutation entropy can provide a reliable complexity quantification indicator.

[0075] S410, performing kernel density estimation based on the frequency domain sample entropy data and the frequency domain permutation entropy data to obtain entropy kernel density data; In one embodiment, the input data is a set of two-dimensional feature points ,in Indicates The frequency domain sample entropy value of samples, Indicates The frequency domain permutation entropy value of samples, Represents the number of samples. The Gaussian kernel function is used and is defined as follows: , is the Gaussian kernel function value, which is the standardized distance between the sample point and the target point The corresponding kernel function output value is used as the weighting factor for probability density estimation. is the constant term of pi, is the base of natural logarithms, is the standardized distance variable, which represents the difference between the target point and the sample point and the bandwidth parameter The kernel function is used to make a smooth estimate of the probability density of each sample point. Set the bandwidth parameter of the kernel density estimate , which is used to control the extension range of the kernel function. The larger the bandwidth, the stronger the smoothing; the smaller the bandwidth, the more sensitive the density estimation. is between 0.05 and 0.2. For any target point , whose estimated joint probability density , can be calculated by the following formula: ; in is the probability density (entropy kernel density data) calculated by kernel density estimation, is the total number of sample points in the data set, is the bandwidth parameter in kernel density estimation, which controls the width of the kernel function. is the index of the sample point, is the total number of sample points, is the Gaussian kernel function, is the target point in the frequency domain sample entropy data / frequency domain arrangement entropy data, The sample points in the frequency domain sample entropy data / frequency domain arrangement entropy data.

[0076] All parameters in the formula of the present invention are dimensionless values, so operations (including subtraction, division, calculation of kernel functions and final weighted average) can be performed directly without dimension conflicts. Kernel density estimation can generate a smooth probability density distribution in a two-dimensional entropy data space (joint distribution of frequency domain sample entropy and frequency domain permutation entropy). Indicated in The joint probability density at can reflect the distribution density of entropy data near this point. Bandwidth parameter Controls the degree of smoothing, smaller Can accurately capture the distribution of details, while the larger A smoother global distribution is generated. Kernel density estimation can clearly describe the distribution pattern of frequency domain entropy feature data through a smoothed probability density function. For example, the main pattern or typical value in the entropy data can be identified by estimating the density peak; the distribution range and discreteness of the data can be found by observing the extended area of ​​the density. Entropy kernel density data provides a description of a probability space, which is helpful for subsequent pattern recognition, classification, and anomaly detection. For example, if the density in a certain area is very low, it indicates that the entropy data in the area is abnormal or rare, which can be used for early warning or fault detection.

[0077] S411. Construct a probability cloud map based on the entropy kernel density data to obtain characteristic data of the second piezoelectric material.

[0078] In one embodiment, the probability cloud map is a three-dimensional map constructed based on the entropy kernel density data, which reflects the probability distribution of the data. The probability cloud map maps the entropy kernel density data to a three-dimensional space and uses color depth to represent the probability density. When constructing the probability cloud map, each point of the entropy kernel density data (sample entropy data, permutation entropy data) is used as a coordinate point in the three-dimensional space, and the probability cloud map is formed by adjusting the size, transparency and color of the point. For example, the entropy kernel density data used is composed of sample entropy data and permutation entropy data, and each data point represents the density value of the signal complexity at a certain scale or frequency band. The data comes from the entropy calculation and kernel density estimation processing of the frequency domain feature sub-interval. Each point in the entropy kernel density data is mapped to a three-dimensional coordinate system, where the X-axis represents the sample entropy value SE; the Y-axis represents the permutation entropy value PE; and the Z-axis represents the kernel density estimate corresponding to the point. , which is the probability density value of the joint distribution of entropy. When constructing the probability cloud map, color depth, transparency and point size are introduced as visual mapping dimensions to assist in expressing the density. The higher the density, the darker the color; the lower the density, the more transparent the point; the point size is associated with the local gradient or density to highlight the significant area. All points constitute a three-dimensional probability density map, which is a probability cloud map. This map can be used to visually identify the characteristic density concentration areas of piezoelectric materials under different response states, and assist in anomaly identification, feature clustering or material state attribution.

[0079] Preferably, the performing sample entropy calculation according to the frequency domain feature data to obtain frequency domain sample entropy data includes: Extract the main frequency component according to the frequency domain feature data to obtain the main frequency component data; In one embodiment, a frequency domain signal processed by FFT is obtained to represent the distribution of the signal at different frequencies. For each signal, its power spectral density (PSD) is extracted to reflect the energy distribution of the signal. The main frequency component refers to the frequency component with the most concentrated energy in the signal. The main frequency component is extracted by the following steps: the power spectrum of the frequency domain signal is calculated to obtain the power value at each frequency. The frequency with the maximum value in the power spectrum is found to represent the main frequency component of the signal, that is, the part with the highest energy in the frequency component.

[0080] Generate scale factors according to the main frequency component data to obtain scale factor data; In one embodiment, according to the main frequency component , generate the scale factor. The scale factor reflects the periodic characteristics of the signal, and sets a scale factor related to the main frequency component For example, to set the scale factor to the inverse of the main frequency: , scale factor Used for detail decomposition of signals, such as using wavelet transform to decompose signals into components of different scales. The scale factor can be used to divide the frequency domain signal into different scales. Larger scale factors correspond to lower frequency components, and smaller scale factors correspond to higher frequency components. When selecting the scale factor, set the value of the scale factor according to the logarithmic interval. The high-frequency part of the signal changes faster, while the low-frequency part changes slower. The logarithmic interval ensures that sufficient scale resolution can be obtained within a wide frequency band. , For the The scale factor, is the minimum scale factor, is the scale factor, is the scale factor order item, with values ​​of 1, 2...N, is the value item of the corresponding scale, and N is the total number of scale levels. The obtained scale factor data is used as the basis for signal frequency domain division to control signal characteristics at different scales.

[0081] In the present invention, since the reciprocal of the main frequency component defines the basic unit of the scale factor, and the other parameters are dimensionless integers, all parameters have the same dimension and can perform reasonable multiplication and division operations. Through the scale factor, the signal spectrum can be divided into different scale levels, and low-frequency components (representing long-term trends) and high-frequency components (representing short-term changes) can be extracted respectively. Larger scale factors correspond to lower frequency components, reflecting the slow changes or long-term trends of the signal. Smaller scale factors correspond to higher frequency components, capturing the rapid changes and detailed features of the signal. When selecting the scale factor, logarithmic intervals are used to ensure sufficient scale resolution over the entire spectrum. The high-frequency part requires finer resolution because these components change faster; the low-frequency part changes slower, and a wider interval can be used to maintain the balance of resolution. The generated scale factor data can be used as an input parameter for signal decomposition. For example, in wavelet transform, the scale factor determines the degree of stretching and compression of the wavelet basis function, thereby affecting the decomposition effect and time resolution of the spectrum.

[0082] In one embodiment, further, based on the frequency domain main frequency energy distribution, the main frequency energy point cloud data is constructed; based on the continuous homology theory, a persistence graph is constructed for the main frequency energy point cloud, and the birth time and death time of its topological characteristics are calculated; based on the life cycle of all topological structures in the persistence graph, the topological life value is calculated. ,in is the topological lifetime value, is the birth time of the topological feature, is the death time of the topological feature, is the power weight parameter, set , is the birth-death time pair of the topological structure; the scale factor is generated according to the topological lifetime value, specifically: ,in is the maximum scale factor, is a constant normalization factor used to control the smoothness of the scale when the lifetime is small to avoid division by zero or too small a scale; In the present invention is the birth time of the topological feature, is the death time of the topological feature, in time (such as seconds), Decided Dimension of By calculating and The dimensions of all items are consistent, and the denominator and numerator are the same dimension, so the calculation The dimensions of the formula are correct, and the formula can perform reasonable addition, subtraction, multiplication and division operations. The formula is based on the theory of continuous homology, using the point cloud data of the main frequency energy distribution to analyze the birth and death of topological features and quantify the topological life value. . Represents the total life cycle weight of the topological structure in the data. The higher the Indicates that there are more significant topological features or more persistent topological structures in the data. , can effectively extract the topological characteristics in the data and provide quantitative basis at the topological level for signal analysis and data understanding. Generate Scale Factor , which can dynamically adjust the scale resolution in signal processing. When smaller, through Smoothing factors ensure that the scale factor is not too small, thus preventing analysis instability in extreme cases. As it increases, the scale factor gradually approaches , providing greater resolution for identifying more detailed signal features. , analysis can be performed at different signal frequency bands or resolution levels to facilitate the discovery of long-term trends and local anomalies. Large scale factors correspond to lower frequencies, which are suitable for observing long-term changes; small scale factors correspond to higher frequencies, which are suitable for capturing details and rapid changes.

[0083] Specifically, by modeling the topological structure of the main frequency component of the signal in the frequency domain, calculating its structural stability (topological lifetime), and based on this, generating a scale factor that can be adapted to complexity metrics (such as sample entropy, multi-scale entropy, etc.), it has good noise resistance and frequency response consistency. Perform frequency domain transformation on the target signal, such as using fast Fourier transform (FFT), power spectral density estimation, wavelet transform and other spectrum analysis methods to obtain the frequency-energy distribution diagram. Extract the main frequency interval or main frequency component from it and construct the main frequency energy point cloud data ,in For the frequency points, is the signal energy value or spectral density value of the corresponding frequency point. The point cloud data is used as the input basis for topological analysis. Based on the continuous homology theory in topological data analysis, the frequency point cloud is The Vietoris–Rips complex method is used to construct the filter complex and extract the 0-dimensional (connected components) and 1-dimensional (hole) features in the topological structure. All topological structures are continuously calculated to obtain their birth time. Time of death , thus generating a persistence graph , where each pair Represents the life cycle of a topological structure. The life cycle of all topological structures in the persistence graph is nonlinearly weighted summed to obtain the topological life value , and its calculation formula is: ,in is the topological lifetime value, which represents the weighted sum of the life cycle lengths of all topological structures (such as connected components, holes, etc.), and is used to reflect the stability of the spectrum structure. The larger the value, the more significant and stable the topological structure in the main frequency point cloud. For the The birth time of a topological feature indicates the scale or filtering threshold at which the topological structure first appears in the continuous homology structure. The unit is related to the point cloud filtering radius or energy threshold. For the The death time of a topological feature, is the birth time of the topological feature, is the death time of the topological feature, It is a continuous graph data set, representing the set of all topological structure life cycles, and is the result of continuous homology analysis. is the power weight parameter, set to 2, which controls the weight distribution degree of different life cycle lengths in the topological life value. In this embodiment, =2, used to enhance the emphasis on long-lived topological structures and the suppression of short-lived structures, is the birth-death time pair of the topological structure. According to the topological lifetime value , the scale factor is generated using the following formula : ,in is the maximum scale factor set (such as the maximum sliding window length, the maximum sample entropy scale), The value is between 30-50. is a constant normalization factor used to control the scale factor generated when the lifetime is small. It can be used to control the window length, subsequence extraction granularity or multi-scale analysis level in subsequent complexity analysis. For example, when calculating the frequency domain sample entropy, it can be used as the basis for scale division to achieve self-adjustment of adaptability to different frequency bands.

[0084] The present invention can capture the stability and density of the main frequency component by topological modeling of the main frequency energy distribution of the signal, and adjust the scale factor accordingly, so that the scale factor changes with the signal spectrum structure, thereby improving the adaptability of the complexity measurement method to non-stationary signals. Compared with the fixed or manually set scale parameters in the existing methods, this method realizes a dynamic scale control mechanism driven by the frequency domain, has strong noise resistance, suppresses the influence of abnormal frequency points on scale selection, and uses the topological life value calculated by continuous coherence to effectively distinguish structural main frequency characteristics from high-frequency noise or short-lived disturbances. The power weight p=2 is used in the life accumulation, which can amplify the importance of the real main frequency structure and weaken the influence of random frequency points, thereby improving the robustness and stability of scale selection. The present invention introduces a topological life → scale factor mapping mechanism, which is equivalent to introducing a mathematical means for structural induction and evaluation of frequency response, so that the scale selection is no longer based solely on the main frequency value, but on the persistence information of the spectrum topological structure, effectively solving the problem of the lack of theoretical basis for the frequency-scale matching relationship in the prior art.

[0085] Perform frequency domain division on the frequency domain feature data according to the scale factor data to obtain frequency domain feature division data; In one embodiment, the main frequency component is extracted from the frequency domain signal , which corresponds to the maximum energy point or local peak point in the signal energy spectrum. Set the basic width parameter of the frequency band , which is a fixed constant (e.g. 5 Hz, 10 Hz) or adaptively determined based on the actual spectrum bandwidth. , the frequency domain signal Divide into several frequency bands. The division standard is to distinguish according to the frequency range. Set a frequency band width , then according to the scale factor The boundary of each frequency band can be determined by the following formula: ; ; ; in, Divide the data into frequency domain features, The frequency domain signal is divided into multiple sub-intervals according to the divided frequency bands, and each sub-interval contains the signal components in a specific frequency band. The obtained sub-intervals of the frequency domain signal are the frequency domain feature division data, and each sub-interval represents the frequency domain feature of the signal in a certain frequency band.

[0086] In the present invention is the window factor, 100HZ or half of the maximum frequency. is the scale factor, unit 1 / HZ, the dimensions of each item in the formula are consistent, and all parameter addition, subtraction and multiplication operations can be performed reasonably, so these operations are physically and mathematically reasonable operations. and scale factor , each frequency band can be divided symmetrically according to the position of the main frequency component. The formula gives clear boundaries (starting frequency and ending frequency), so that the frequency domain signal can be divided into multiple frequency band sub-intervals, each sub-interval contains the signal components in a specific frequency band. The sub-intervals obtained after division can be used to analyze the energy distribution, main frequency component changes or modulation characteristics of the signal in different frequency bands. Each sub-interval, as a representative of the frequency domain characteristics, helps to study the characteristic differences of the signal in each frequency band, which is helpful for pattern recognition, feature extraction and fault diagnosis. Use scale factors The range of frequency band division can be adjusted dynamically: larger Corresponding to a wider frequency band, suitable for capturing the overall characteristics of the signal; smaller Corresponding to a narrower frequency band, it is suitable for analyzing detailed changes in signals.

[0087] The data is divided according to the frequency domain characteristics to calculate the sample entropy and obtain the frequency domain sample entropy data.

[0088] In one embodiment, sample entropy is an indicator used to measure signal complexity. Sample entropy is calculated for each frequency domain subinterval. The sample entropy calculation process is as follows: The embedding dimension is set according to the pre-parameters. and tolerance When calculating sample entropy, set the embedding dimension and tolerance The tolerance is the standard deviation of the signal. A ratio of, for example For each frequency domain subinterval, the construction length is Calculate the similarity between each pair of subsequences, using a distance metric such as the maximum difference distance. Repeat the above process to obtain an embedding dimension of The similarity logarithm when ; the calculation formula of sample entropy is: ; in is the frequency domain sample entropy data, The embedding dimension is And the similarity is less than The number of subsequence logarithms of The embedding dimension is And the similarity is less than The number of subsequences of .

[0089] Therefore, from any point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the attached application documents rather than the above description, and it is intended that all changes falling within the meaning and scope of equivalent elements of the application documents are included in the present invention.

[0090] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for processing acceleration data based on piezoelectric effect, characterized in that: The following steps are involved: Acquire piezoelectric material signal data; Perform signal adjustment according to the piezoelectric material signal data to obtain signal adjustment data; Constructing a charge acceleration model according to the signal adjustment data to obtain a charge acceleration model; By analyzing the charge acceleration model, characteristic data of piezoelectric materials are obtained to assist in the detection of piezoelectric materials.

2. The method according to claim 1, characterized in that The obtaining of piezoelectric material signal data comprises: The signal acquisition device is controlled to acquire signals from the piezoelectric sensor in an accelerated state at a preset sampling frequency to obtain piezoelectric material signal data.

3. The method according to claim 1, characterized in that The step of performing signal adjustment according to the piezoelectric material signal data to obtain signal adjustment data includes: Amplify the signal according to the piezoelectric material signal data to obtain signal amplification data; Signal shaping is performed according to the signal amplification data to obtain signal adjustment data.

4. The method according to claim 1, characterized in that: The charge acceleration model is constructed according to the signal adjustment data to obtain the charge acceleration model, including: Perform signal calibration according to the signal adjustment data to obtain signal calibration data; The charge acceleration model is constructed according to the signal calibration data to obtain the charge acceleration model.

5. The method according to claim 1, characterized in that The piezoelectric material characteristic data includes first piezoelectric material characteristic data and second piezoelectric material characteristic data. The piezoelectric material characteristic data is obtained by analyzing according to the charge acceleration model to perform auxiliary operations for piezoelectric material detection, including: Extracting first data features according to the charge acceleration model to obtain first piezoelectric material feature data; A second data feature extraction is performed according to the charge acceleration model to obtain second piezoelectric material feature data, wherein the first data feature extraction and the second data feature extraction are different data feature extraction methods.

6. The method according to claim 5, characterized in that The first data feature extraction is performed according to the charge acceleration model to obtain the first piezoelectric material feature data, including: Performing a root mean square calculation according to the charge acceleration model to obtain first signal strength data, and performing a peak amplitude calculation according to the charge acceleration model to obtain second signal strength data; Performing energy consumption calculation according to the first signal strength data and the second signal strength data to obtain energy consumption data; According to the charge acceleration model, the non-stationary-stationary interval is divided to obtain non-stationary signal data and stationary signal data respectively; Calculating the instantaneous amplitude according to the non-stationary signal data to obtain first fluctuation amplitude data; Calculate the standard deviation based on the stable signal data to obtain the second fluctuation amplitude data; Characteristic data of the first piezoelectric material is obtained by performing characteristic generation according to the energy consumption data, the first fluctuation amplitude data and the second fluctuation amplitude data.

7. The method according to claim 6, characterized in that The non-stationary-stationary interval division is performed according to the charge acceleration model to obtain non-stationary signal data and stationary signal data respectively, including: The peak-valley area is divided according to the charge acceleration model to obtain the peak-valley area data; Perform periodic parameter fitting according to the peak-valley area data to obtain periodic parameter fitting data; The charge acceleration model is divided into similar parameter intervals according to the periodic parameter fitting data to obtain non-stationary signal data and stationary signal data.

8. The method according to claim 7, characterized in that The instantaneous amplitude calculation is performed according to the non-stationary signal data to obtain the first fluctuation amplitude data, including: According to the periodic parameter fitting data, the neighboring extreme value is extracted to obtain the neighboring extreme value data; The amplitude of the non-stationary signal data is calculated according to the adjacent extreme value data to obtain the first fluctuation amplitude data.

9. The method according to claim 5, characterized in that The step of extracting the second data feature according to the charge acceleration model to obtain the second piezoelectric material feature data includes: Frequency domain feature extraction is performed according to the charge acceleration model to obtain frequency domain feature data; Sample entropy is calculated according to the frequency domain feature data to obtain frequency domain sample entropy data; Perform permutation entropy calculation based on frequency domain feature data to obtain frequency domain permutation entropy data; Perform kernel density estimation based on frequency domain sample entropy data and frequency domain permutation entropy data to obtain entropy kernel density data; A probability cloud map is constructed according to the entropy kernel density data to obtain characteristic data of the second piezoelectric material.

10. The method according to claim 9, characterized in that The step of calculating sample entropy based on the frequency domain feature data to obtain frequency domain sample entropy data includes: Extract the main frequency component according to the frequency domain feature data to obtain the main frequency component data; Generate scale factors according to the main frequency component data to obtain scale factor data; Perform frequency domain division on the frequency domain feature data according to the scale factor data to obtain frequency domain feature division data; The data is divided according to the frequency domain characteristics to calculate the sample entropy and obtain the frequency domain sample entropy data.

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