Method, device and system for controlling a microscale mems ultrasonic sensor

Abstract

The application relates to a control method, device and system of a micro-scale MEMS ultrasonic sensor, the method comprising the following steps: acquiring a sweep detection signal of the MEMS ultrasonic sensor, and identifying a real-time mechanical resonance frequency of the MEMS ultrasonic sensor according to the sweep detection signal; constructing a driving waveform according to a preset quality factor, the real-time mechanical resonance frequency and an adjusted bias voltage, obtaining a driving voltage waveform, and driving the MEMS ultrasonic sensor to send an ultrasonic pulse to a target test object according to the driving voltage waveform; and performing signal conditioning on the ultrasonic echo signal according to the real-time mechanical resonance frequency, the adjusted direct current bias voltage and the driving voltage waveform, and obtaining an optimized echo signal. The application can dynamically track the diaphragm state drift caused by environmental changes, so as to ensure that the sensor always works at an optimal response point.

Description

Technical Field

[0001] This invention relates to the technical field of ultrasonic sensors, and in particular to a control method, device, and system for a micron-scale MEMS ultrasonic sensor. Background Technology

[0002] Micrometer-scale MEMS ultrasonic sensors are widely used in precision detection, typically employing open-loop control with fixed parameters. However, the sensor diaphragm is sensitive to environmental changes such as temperature and pressure, which can easily cause the mechanical resonant frequency to drift. When the driving frequency mismatches with the drifted resonant frequency, the sensor's transmission efficiency and receiving sensitivity decrease significantly, and the signal-to-noise ratio of the echo signal deteriorates. Furthermore, existing signal conditioning methods, which use static filtering and fixed gain compensation, cannot dynamically match the signal according to actual operating conditions, further affecting the quality of the echo signal reconstruction. Summary of the Invention

[0003] The main objective of this invention is to provide a control method, device, and system for a micron-level MEMS ultrasonic sensor, which can dynamically track the diaphragm state drift caused by environmental changes, and solve the problem of mismatch between the driving frequency and the resonant frequency caused by temperature and pressure fluctuations, thereby ensuring that the sensor always works at the optimal response point.

[0004] To achieve the above objectives, the present invention provides a control method for a micrometer-scale MEMS ultrasonic sensor, comprising: The swept frequency detection signal of the MEMS ultrasonic sensor is acquired, and the real-time mechanical resonant frequency of the MEMS ultrasonic sensor is identified based on the swept frequency detection signal. The bias DC adjustment of the MEMS ultrasonic sensor is performed based on the real-time mechanical resonant frequency to obtain the adjustment bias voltage. A driving waveform is constructed based on a preset quality factor, the real-time mechanical resonant frequency, and the adjusted bias voltage to obtain a driving voltage waveform. The MEMS ultrasonic sensor is then driven to send ultrasonic pulses to the target test object based on the driving voltage waveform. The ultrasonic echo signal generated by the ultrasonic pulse is received, and the ultrasonic echo signal is conditioned according to the real-time mechanical resonant frequency, the adjusted DC bias voltage, and the driving voltage waveform to obtain an optimized echo signal.

[0005] Further, the step of acquiring the swept-frequency detection signal of the MEMS ultrasonic sensor and identifying the real-time mechanical resonant frequency of the MEMS ultrasonic sensor based on the swept-frequency detection signal includes: A preset electrical signal is applied to the piezoelectric drive end of the MEMS ultrasonic sensor, and the piezoelectric drive end is responded to and its output is acquired to obtain the swept frequency detection signal; The frequency response of the swept frequency detection signal is analyzed to obtain the response amplitude. The response amplitude is then correlated with a preset frequency point to obtain a frequency amplitude response sequence. The response amplitude of each frequency point in the frequency amplitude response sequence is traversed, and the current response amplitude is compared with the adjacent response amplitudes of two adjacent frequency points. If the current response amplitude is greater than both adjacent response amplitudes, the frequency point corresponding to the current response amplitude is marked as a candidate peak frequency point. The peak frequency point with the largest response amplitude is selected from the candidate peak frequency points, and the frequency of the peak frequency point is read to obtain the real-time mechanical resonant frequency.

[0006] Further, the step of adjusting the bias DC of the MEMS ultrasonic sensor based on the real-time mechanical resonant frequency to obtain the adjusted bias voltage includes: The corresponding initial bias voltage value is extracted from the preset voltage frequency mapping table based on the real-time mechanical resonant frequency. The initial bias voltage value is applied to the MEMS ultrasonic sensor, and the background noise signal of the MEMS ultrasonic sensor is acquired. The background noise power of the background noise signal is calculated. The background noise power is compared with a preset noise threshold. If the background noise power is higher than the noise threshold, the initial bias voltage value is adjusted step by step according to a preset step size. After each adjustment, the background noise signal is re-acquired and the background noise power is recalculated until the background noise power is lower than the noise threshold. When the background noise power is lower than the noise threshold, the corresponding initial bias voltage value is set as the adjusted bias voltage.

[0007] Further, the step of constructing the driving voltage waveform based on the preset quality factor, the real-time mechanical resonant frequency, and the adjusted bias voltage includes: Extract the frequency values ​​from the real-time mechanical resonant frequency, and construct a carrier sequence based on the frequency values ​​to obtain an initial carrier sequence; The preset quality factor is extracted based on the real-time mechanical resonant frequency to obtain a quality factor value. The corresponding pulse envelope curve is matched from the preset envelope shape library based on the quality factor value, and an envelope modulation sequence aligned with the initial carrier sequence is generated based on the pulse envelope curve. The carrier amplitude at each time point in the initial carrier sequence is multiplied point by point with the envelope amplitude at the corresponding time point in the envelope modulation sequence to obtain the modulation waveform sequence; The voltage amplitude is extracted from the adjusted bias voltage as an amplitude scaling reference, and the amplitude of each waveform in the modulation waveform sequence is proportionally scaled according to the amplitude scaling reference to obtain the driving voltage waveform.

[0008] Further, the step of driving the MEMS ultrasonic sensor to send ultrasonic pulses to the target test object according to the driving voltage waveform includes: Extract the voltage amplitude sequence and the corresponding sampling time point sequence from the driving voltage waveform; Based on the sampling time point sequence, each voltage amplitude in the voltage amplitude sequence is instantaneously converted to obtain the instantaneous driving voltage value; The instantaneous driving voltage value is sequentially applied to the MEMS ultrasonic sensor according to the sampling time sequence, and the MEMS ultrasonic sensor is driven to emit ultrasonic pulses toward the target test object.

[0009] Further, the step of receiving the ultrasonic echo signal generated by the ultrasonic pulse, and performing signal conditioning on the ultrasonic echo signal according to the real-time mechanical resonant frequency, the adjusted DC bias voltage, and the driving voltage waveform to obtain an optimized echo signal includes: The original echo electrical signal generated by the ultrasonic pulse reflected by the target test object is acquired by the MEMS ultrasonic sensor. The voltage amplitude is extracted from the adjusted DC bias voltage as a fixed gain coefficient, and the original echo signal is linearly optimized based on the fixed gain coefficient to obtain an amplitude-optimized echo signal. The amplitude-optimized echo signal is bandpass filtered according to the preset filtering bandwidth conditions and the real-time mechanical resonant frequency to obtain the filtered echo signal; The driving voltage waveform is combined with a preset dielectric attenuation coefficient to construct a gain compensation coefficient. The filtered echo signal is then dynamically amplified based on the gain compensation coefficient to obtain the optimized echo signal.

[0010] Further, the amplitude-optimized echo signal is bandpass filtered according to the preset filtering bandwidth conditions and the real-time mechanical resonant frequency to obtain a filtered echo signal, including: The center frequency value is extracted from the real-time mechanical resonant frequency, and the upper passband frequency and lower passband frequency corresponding to the center frequency value are extracted from the filter bandwidth condition. The amplitude-optimized echo signal is subjected to frequency domain transformation to obtain a frequency component distribution set; Iterate through each component frequency in the frequency component distribution set. If the component frequency is lower than the lower passband frequency or higher than the upper passband frequency, then set the amplitude of the component frequency to zero. If the component frequency is between the lower passband frequency and the upper passband frequency, then the amplitude of the component frequency remains unchanged; The processed component frequencies are sequentially integrated and inversely transformed in the frequency domain to obtain the filtered echo signal.

[0011] Furthermore, it also includes: Extract the peak echo amplitude and echo arrival time from the optimized echo signal; The peak echo amplitude is compared with a preset effective echo amplitude threshold. If the peak echo amplitude is less than the effective echo amplitude threshold, an insufficient energy indicator is generated. The time offset is calculated by comparing the arrival time of the echo with the preset theoretical echo time. If the time offset exceeds the preset allowable offset range, a drift flag is generated. Based on the insufficient energy indicator and the drift indicator, a matching target correction record is identified from a preset correction mapping table, and the corresponding control parameter correction amount is extracted from the target correction record; Based on the control parameter correction amount, the real-time mechanical resonant frequency, the adjusted bias voltage, and the driving voltage waveform of the current cycle are optimized and superimposed to obtain the corrected real-time mechanical resonant frequency, the adjusted bias voltage, and the driving voltage waveform.

[0012] The present invention also provides a control device for a micrometer-scale MEMS ultrasonic sensor, and a control method for the micrometer-scale MEMS ultrasonic sensor described in any one of the above claims, comprising: The acquisition module is used to acquire the frequency sweep detection signal of the MEMS ultrasonic sensor and identify the real-time mechanical resonant frequency of the MEMS ultrasonic sensor based on the frequency sweep detection signal. Analysis module, the analysis module is used to perform bias DC adjustment on the MEMS ultrasonic sensor according to the real-time mechanical resonant frequency to obtain the adjustment bias voltage; The association module is used to construct a driving waveform based on a preset quality factor, the real-time mechanical resonant frequency and the adjusted bias voltage, to obtain a driving voltage waveform, and to drive the MEMS ultrasonic sensor to send ultrasonic pulses to the target test object based on the driving voltage waveform. The processing module is used to receive the ultrasonic echo signal generated by the ultrasonic pulse, and to perform signal conditioning on the ultrasonic echo signal according to the real-time mechanical resonant frequency, the adjusted DC bias voltage, and the driving voltage waveform to obtain an optimized echo signal.

[0013] The present invention also provides a control system for a micrometer-scale MEMS ultrasonic sensor, comprising: Memory, used to store programs; A processor is configured to execute the program to implement the various steps of the control method for a micrometer-scale MEMS ultrasonic sensor as described in any one of claims 1-8.

[0014] The present invention provides a control method, device, and system for a micrometer-scale MEMS ultrasonic sensor, which has the following beneficial effects: By acquiring swept-frequency detection signals and identifying the mechanical resonant frequency in real time, and dynamically tracking diaphragm state drift caused by environmental changes, the mismatch between the driving frequency and the resonant frequency is solved, ensuring that the sensor always operates at the optimal response point, thus improving transmission efficiency and receiving sensitivity. By adjusting the bias DC according to the real-time resonant frequency and iteratively optimizing the bias voltage based on the background noise power, the background noise level is reduced, improving the signal-to-noise ratio of the echo signal and overcoming the signal quality degradation caused by static bias. By combining a preset quality factor, real-time resonant frequency, and adjusted bias voltage to construct the driving waveform, a pre-distortion driving waveform is generated, enabling the transmitted pulse to accurately match the diaphragm's dynamic response, suppressing harmonic distortion, and improving the directivity and energy utilization of the ultrasonic pulse. By performing bandpass filtering and dynamic gain compensation on the echo signal based on the real-time resonant frequency, adjusted bias voltage, and driving waveform, adaptive tracking of the filter center frequency and real-time matching of the attenuation compensation curve are achieved, solving the problem that static filtering and fixed gain cannot adapt to varying operating conditions, significantly improving the echo signal restoration quality and detection accuracy. This invention enables micron-scale MEMS ultrasonic sensors to possess closed-loop adaptive capabilities in complex environments, thereby enhancing the stability and reliability of the system. Attached Figure Description

[0015] Figure 1 This is a flowchart of a control method for a micrometer-scale MEMS ultrasonic sensor provided by the present invention; Figure 2 This is a structural diagram of a control device for a micrometer-scale MEMS ultrasonic sensor provided for this invention; Figure 3 This is a control system structure diagram of a micron-level MEMS ultrasonic sensor provided for this invention.

[0016] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0019] Reference Figure 1 As shown, the present invention provides a control method for a micrometer-scale MEMS ultrasonic sensor, comprising: Step S1: Acquire the swept frequency detection signal of the MEMS ultrasonic sensor, and identify the real-time mechanical resonant frequency of the MEMS ultrasonic sensor based on the swept frequency detection signal; Specifically, a frequency-sweeping probe signal covering the sensor's expected resonant range is generated by a signal generation unit. This signal can be a continuous linear frequency-modulated wave or a discrete stepped frequency sequence. The frequency-sweeping probe signal is applied to the MEMS ultrasonic sensor, which generates mechanical vibration due to the inverse piezoelectric effect or electrostatic driving force. When the excitation frequency approaches the inherent mechanical resonant frequency of the MEMS ultrasonic sensor, the vibration amplitude increases significantly, resulting in a detectable impedance change or peak amplitude in the output signal at the sensor's electrical ports. During the frequency sweep, the sensor's response signal is simultaneously acquired, for example, by measuring the current flowing through the sensor, the voltage across its terminals, or monitoring the amplitude of the electrical signal at its output, obtaining a set of frequency and response amplitude (or phase) correspondence data. Processing this data, such as finding the frequency corresponding to the amplitude maximum or detecting the phase abrupt change position, allows for the accurate determination of the real-time mechanical resonant frequency. This identification process typically includes smoothing the response curve, peak searching, or curve fitting to eliminate noise interference and improve the accuracy of frequency extraction.

[0020] Step S2: Adjust the bias DC of the MEMS ultrasonic sensor according to the real-time mechanical resonant frequency to obtain the adjustment bias voltage; Specifically, the DC bias voltage of the MEMS ultrasonic sensor is adjusted based on the real-time mechanical resonant frequency. The mechanical stiffness of the MEMS ultrasonic sensor is related to the applied DC bias voltage; changing the bias voltage leads to a change in equivalent stiffness, which in turn causes a slight shift in the resonant frequency. To ensure the sensor operates optimally at the real-time resonant frequency, the bias voltage is adjusted to a value that matches the current resonant frequency. A set of correlations between bias voltage and resonant frequency is pre-calibrated experimentally. The identified real-time resonant frequency is used as a lookup condition to find the corresponding ideal bias voltage value in a pre-defined correlation table or curve. This ideal value should place the sensor's operating point in the region of highest electromechanical conversion efficiency, such as the peak of transmission sensitivity or the optimal signal-to-noise ratio of the receiver. The bias voltage is adjusted to this calculated value using an adjustable DC power supply and continuously applied to the sensor. After adjustment, a frequency sweep signal is applied again to verify whether the resonant frequency has stabilized within the expected range; if there is a deviation, iterative fine-tuning is performed. The final adjusted bias voltage ensures that the sensor operates in the most efficient manner during subsequent transmission and reception.

[0021] Step S3: Construct a driving waveform based on the preset quality factor, the real-time mechanical resonant frequency, and the adjusted bias voltage to obtain a driving voltage waveform. Drive the MEMS ultrasonic sensor to send ultrasonic pulses to the target test object based on the driving voltage waveform. The preset quality factor is a dimensionless parameter that characterizes the energy loss characteristics of the mechanical vibration system of a MEMS ultrasonic sensor.

[0022] Specifically, three input parameters are acquired: a preset quality factor (stored in a parameter table), a real-time mechanical resonant frequency, and an adjusted bias voltage. Based on the quality factor, the rise time and decay time constant of the sensor's mechanical vibration are calculated, thereby determining the envelope shape of the driving voltage waveform. The carrier frequency of the driving waveform is set to the real-time mechanical resonant frequency to ensure that the excitation signal matches the sensor's mechanical resonance condition, thus achieving the most efficient electroacoustic conversion. The amplitude of the driving waveform needs to be determined in conjunction with the adjusted bias voltage. The magnitude of the bias voltage directly affects the sensor's sensitivity and maximum tolerable driving voltage; therefore, an appropriate excitation amplitude must be selected while ensuring that it does not exceed the sensor's linear range. After combining the above parameters, a digital driving waveform sequence is generated, which contains the voltage value at each sampling moment. This digital sequence is converted into an analog voltage signal by a digital-to-analog converter, then boosted to the required level by a power amplifier, and finally applied to the driving electrode of the MEMS ultrasonic sensor. Under the excitation of this driving voltage waveform, the sensor generates mechanical vibration and radiates focused ultrasonic pulses towards the medium containing the target test object.

[0023] Step S4: Receive the ultrasonic echo signal generated by the ultrasonic pulse, and perform signal conditioning on the ultrasonic echo signal according to the real-time mechanical resonant frequency, the adjusted DC bias voltage, and the driving voltage waveform to obtain an optimized echo signal.

[0024] Specifically, immediately after the ultrasonic pulse is emitted, the system enters receiving mode. The MEMS ultrasonic sensor converts the received acoustic energy into a weak electrical signal. This signal is first amplified by a low-noise preamplifier to increase the signal amplitude and suppress noise introduced by subsequent circuits. The amplified signal undergoes bandpass filtering based on the real-time mechanical resonant frequency. The center frequency of the filter is aligned with the resonant frequency, and the bandwidth is set according to the quality factor. Simultaneously, the bias point of the receiving circuit is optimized by adjusting the bias voltage to maintain high sensitivity and linearity of the sensor during the front-end amplification stage. Information from the driving voltage waveform is used to design a matched filter or correlator, performing correlation operations between the received signal and the known transmitted waveform to further improve the signal-to-noise ratio and accurately determine the echo arrival time. In some embodiments, phase compensation is also performed on the echo signal based on the phase information of the driving waveform to eliminate phase shift caused by propagation distance. After amplification, filtering, correlation processing, and phase compensation, the signal is then adjusted by an automatic gain control circuit to a dynamic range suitable for analog-to-digital conversion, ultimately outputting an optimized echo signal.

[0025] This invention provides a control method for a micrometer-scale MEMS ultrasonic sensor. By acquiring a swept-frequency detection signal and identifying the mechanical resonant frequency in real time, it dynamically tracks the diaphragm state drift caused by environmental changes, solving the problem of mismatch between the driving frequency and the resonant frequency. This ensures the sensor always operates at the optimal response point, improving transmission efficiency and receiving sensitivity. By adjusting the bias DC according to the real-time resonant frequency and iteratively optimizing the bias voltage based on the background noise power, the background noise level is reduced, improving the signal-to-noise ratio of the echo signal and overcoming the defect of signal quality degradation caused by static bias. By combining a preset quality factor, real-time resonant frequency, and adjusted bias voltage to construct the driving waveform, a pre-distortion driving waveform is generated, enabling the transmitted pulse to accurately match the dynamic response of the diaphragm, suppressing harmonic distortion, and improving the directivity and energy utilization of the ultrasonic pulse. By performing bandpass filtering and dynamic gain compensation on the echo signal based on the real-time resonant frequency, adjusted bias voltage, and driving waveform, adaptive tracking of the filter center frequency and real-time matching of the attenuation compensation curve are achieved. This solves the problem that static filtering and fixed gain cannot adapt to changing operating conditions, significantly improving the echo signal reconstruction quality and detection accuracy. This invention enables micron-scale MEMS ultrasonic sensors to possess closed-loop adaptive capabilities in complex environments, thereby enhancing the stability and reliability of the system.

[0026] In one embodiment, acquiring the swept-frequency detection signal of the MEMS ultrasonic sensor and identifying the real-time mechanical resonant frequency of the MEMS ultrasonic sensor based on the swept-frequency detection signal includes: A preset electrical signal is applied to the piezoelectric drive end of the MEMS ultrasonic sensor, and the piezoelectric drive end is responded to and its output is acquired to obtain the swept frequency detection signal; The frequency response of the swept frequency detection signal is analyzed to obtain the response amplitude. The response amplitude is then correlated with a preset frequency point to obtain a frequency amplitude response sequence. Specifically, complete response waveform data and corresponding frequency marker information are extracted from the frequency sweep detection signal. Different analysis methods are used depending on the scanning method of the preset electrical signal. If the preset electrical signal is a continuous linear frequency sweep, the acquired time-domain response waveform needs to be segmented, dividing the waveform into continuous segments corresponding to frequency changes along the time axis. Each segment represents a narrow frequency range of response. Bandpass filtering detection is performed on each segment to extract the response amplitude at the center frequency of that segment. If the preset electrical signal is a stepped frequency sweep, the stable segment of the response signal within each frequency dwell time period is directly extracted, and the average or root mean square value of the amplitude of the sampling points within that time period is calculated as the response amplitude at that frequency point. When extracting the response amplitude, a digital filter is typically used to preprocess the original signal to eliminate environmental noise and power supply interference. For each preset frequency point, a corresponding response amplitude is calculated, and then this frequency value and its response amplitude are associated and paired to form a frequency-amplitude correspondence. These correspondences are arranged in ascending order of frequency to form a frequency amplitude response sequence. Each data point in this sequence consists of a frequency value and an amplitude value, clearly depicting the amplitude-frequency characteristics of the sensor across the entire frequency sweep range. The quality of this sequence directly affects the accuracy of subsequent resonant frequency identification; therefore, it may be necessary to interpolate the amplitude data to improve frequency resolution or perform smoothing filtering to eliminate abnormal fluctuations caused by random noise. The frequency amplitude response sequence, as the output of this step, provides a direct processing object for subsequent peak detection.

[0027] The response amplitude of each frequency point in the frequency amplitude response sequence is traversed, and the current response amplitude is compared with the adjacent response amplitudes of two adjacent frequency points. If the current response amplitude is greater than both adjacent response amplitudes, the frequency point corresponding to the current response amplitude is marked as a candidate peak frequency point. The peak frequency point with the largest response amplitude is selected from the candidate peak frequency points, and the frequency of the peak frequency point is read to obtain the real-time mechanical resonant frequency.

[0028] Specifically, all candidate peak frequency points are integrated to obtain a candidate peak list. The response amplitude of the first candidate peak frequency point in the list is taken as the current maximum value, and its corresponding frequency value is taken as the current optimal frequency. Then, each remaining candidate peak frequency point in the candidate peak list is traversed. For each visited candidate point, its response amplitude is extracted and compared with the currently saved maximum amplitude. If the response amplitude of the visited point is greater than the current maximum amplitude, the current maximum amplitude is updated to the response amplitude of that point, and the current optimal frequency is updated to the frequency value of that point. If the response amplitude of the visited point is less than or equal to the current maximum amplitude, the current maximum value and optimal frequency remain unchanged, and the traversal continues to the next candidate point. After traversing all candidate points in the candidate peak list, the currently saved optimal frequency is the frequency corresponding to the peak frequency point with the largest response amplitude in the entire frequency sweep range. If there are multiple peaks with similar amplitudes in the frequency sweep range, quality factor or phase information should also be considered for auxiliary judgment, but this step uses the largest amplitude as the core screening principle. After the selection process is complete, the precise frequency value of the optimal frequency point is read from the data storage unit. This value is then formatted to meet the data accuracy and unit requirements of subsequent control procedures. Finally, this frequency value is output as the real-time mechanical resonant frequency of the sensor within the current control cycle.

[0029] The method provided in this embodiment actively acquires the sensor's raw response data over a wide frequency range by applying a preset electrical signal to the piezoelectric drive end and simultaneously acquiring the response output, providing a complete source of raw signals for subsequent resonant frequency identification. By performing frequency response analysis on the swept-frequency detection signal and correlating the response amplitude with frequency points, the time-domain signal can be transformed into a feature sequence characterizing the sensor's amplitude-frequency characteristics, providing a structured data foundation for peak detection. By traversing the sequence, comparing adjacent amplitudes, and marking candidate peak points, random noise interference and single-point abnormal fluctuations can be effectively eliminated, ensuring that the detected peaks have local significance and physical meaning. By selecting the frequency point with the largest response amplitude from the candidate peaks, the true resonant frequency of the sensor with the strongest mechanical vibration in the current environment can be accurately located, avoiding misjudgments caused by multi-peak or multi-mode coupling.

[0030] In one embodiment, the step of adjusting the bias DC of the MEMS ultrasonic sensor based on the real-time mechanical resonant frequency to obtain the adjusted bias voltage includes: The corresponding initial bias voltage value is extracted from the preset voltage frequency mapping table based on the real-time mechanical resonant frequency. The initial bias voltage value is applied to the MEMS ultrasonic sensor, and the background noise signal of the MEMS ultrasonic sensor is acquired. The background noise power of the background noise signal is calculated. Specifically, an initial bias voltage is applied to the bias electrode of the MEMS ultrasonic sensor. This application is achieved using an adjustable voltage source and a voltage buffer. After the bias voltage is established, the MEMS ultrasonic sensor enters a standby state, during which its output continuously generates a weak electrical signal containing background noise. To acquire this background noise signal, the sensor is ensured to be in a silent environment, i.e., without ultrasonic pulse emission, external acoustic wave excitation, and mechanical vibration interference around the MEMS ultrasonic sensor. The signal acquisition circuit is activated, which includes a low-noise preamplifier and an analog-to-digital converter, continuously digitizing the MEMS ultrasonic sensor output at a preset sampling rate and resolution. The acquisition duration is preset to multiple noise cycles, for example, tens to hundreds of milliseconds. During acquisition, any intentional excitation signal is avoided from being applied to the sensor. After acquisition, a discrete digital noise sequence is obtained. To calculate the background noise power, the amplitude of all sampling points in the digital noise sequence is squared to obtain the instantaneous power at each point. Then, all instantaneous powers are summed and divided by the total number of sampling points to obtain the average power value. This average power value is the background noise power.

[0031] The background noise power is compared with a preset noise threshold. If the background noise power is higher than the noise threshold, the initial bias voltage value is adjusted step by step according to a preset step size. After each adjustment, the background noise signal is re-acquired and the background noise power is recalculated until the background noise power is lower than the noise threshold. The preset step size refers to the magnitude of change used each time the bias voltage is adjusted. This step size can be a fixed value or it can adaptively change according to the adjustment process. The size of the step size determines the precision and convergence speed of the bias voltage search. Successive adjustment refers to repeatedly executing the adjustment-acquisition-calculation cycle based on the comparison results. Each adjustment changes the bias voltage value based on the previous one until the termination condition is met.

[0032] When the background noise power is lower than the noise threshold, the corresponding initial bias voltage value is set as the adjusted bias voltage.

[0033] The method provided in this embodiment extracts the initial bias voltage from a preset mapping table based on the real-time mechanical resonant frequency, providing the sensor with a reasonable bias starting point that matches the current resonant state. This avoids large-scale blind searching and improves the targeting and efficiency of bias adjustment. By acquiring the background noise signal in the absence of ultrasonic emission and calculating its power, the noise floor level of the sensor under the current bias can be objectively quantified, providing a real feedback basis for bias optimization. By comparing the background noise power with a preset threshold and iteratively adjusting the bias according to the step size, the noise power can be actively suppressed below the threshold, effectively overcoming the problem of noise degradation caused by environmental changes or device aging under a fixed bias, and significantly improving the signal-to-noise ratio of the sensor.

[0034] In one embodiment, the step of constructing a drive voltage waveform based on a preset quality factor, the real-time mechanical resonant frequency, and the adjusted bias voltage includes: Extract the frequency values ​​from the real-time mechanical resonant frequency, and construct a carrier sequence based on the frequency values ​​to obtain an initial carrier sequence; The preset quality factor is extracted based on the real-time mechanical resonant frequency to obtain a quality factor value. The corresponding pulse envelope curve is matched from the preset envelope shape library based on the quality factor value, and an envelope modulation sequence aligned with the initial carrier sequence is generated based on the pulse envelope curve. Specifically, a preset quality factor parameter is read, and the quality factor value is extracted from it—the digital quantity directly used for matching and calculation. Based on the magnitude of the quality factor value, a matching search is performed in a preset envelope shape library. This library stores multiple pre-generated envelope curves, each corresponding to a quality factor interval. For example, a low quality factor corresponds to a gently rising and falling envelope shape, while a high quality factor corresponds to a shape with rapid oscillation and rapid decay. The matching rule can be based on numerical interval division; that is, determining which interval the quality factor value falls into, and then selecting the envelope curve corresponding to that interval. The selected envelope curve is usually stored in the library as discrete points, with each point corresponding to an amplitude coefficient at a normalized time position. Based on the length of the initial carrier sequence (i.e., the number of sampling points), the selected envelope curve is resampled or interpolated to generate an envelope modulation sequence with the exact same length as the initial carrier sequence. Specifically, the original time axis of the envelope curve is mapped to an interval from zero to the pulse duration, and the corresponding envelope amplitude is calculated at each sampling point using linear interpolation or nearest-neighbor interpolation. Each value in the envelope modulation sequence represents a modulation coefficient at that moment, typically ranging from 0 to 1, used to control the envelope shape of the carrier amplitude. This sequence is strictly aligned with the initial carrier sequence on the time axis; that is, the first sampling point corresponds to the start time of the pulse, and the last sampling point corresponds to the end time of the pulse. The generated envelope modulation sequence is temporarily stored in a waveform buffer to provide modulation coefficients for subsequent point-by-point multiplication operations.

[0035] The carrier amplitude at each time point in the initial carrier sequence is multiplied point by point with the envelope amplitude at the corresponding time point in the envelope modulation sequence to obtain the modulation waveform sequence; The voltage amplitude is extracted from the adjusted bias voltage as an amplitude scaling reference, and the amplitude of each waveform in the modulation waveform sequence is proportionally scaled according to the amplitude scaling reference to obtain the driving voltage waveform.

[0036] The method provided in this embodiment, by extracting the real-time mechanical resonant frequency to construct the carrier sequence, can generate a fundamental oscillation waveform that precisely matches the current mechanical resonant state of the sensor, ensuring that the driving signal frequency always acts on the sensor's optimal response point, effectively improving the electroacoustic conversion efficiency. By matching the corresponding pulse envelope curve from the envelope shape library based on the quality factor value and generating an aligned envelope modulation sequence, the optimal envelope shape can be dynamically selected according to the sensor's energy loss characteristics, suppressing spectral leakage caused by sudden pulse start-up and cutoff, and making the transmitted energy more concentrated. By multiplying the initial carrier sequence and the envelope modulation sequence point by point, the carrier amplitude is precisely modulated by the envelope curve, so that the driving waveform has both an accurate carrier frequency and an optimized envelope shape, avoiding the time-frequency domain mismatch problem that may be caused by independently generated waveforms. By extracting the voltage amplitude from the adjusted bias voltage as a scaling reference to proportionally scale the modulated waveform, the final amplitude of the driving waveform is coordinated with the sensor's bias state, avoiding nonlinear distortion or transmission efficiency reduction caused by mismatch between the driving intensity and the static operating point.

[0037] In one embodiment, driving the MEMS ultrasonic sensor to send ultrasonic pulses to the target test object according to the driving voltage waveform includes: Extract the voltage amplitude sequence and the corresponding sampling time point sequence from the driving voltage waveform; Based on the sampling time point sequence, each voltage amplitude in the voltage amplitude sequence is instantaneously converted to obtain the instantaneous driving voltage value; Specifically, the output timing is determined based on the interval between the first and second time points in the sampling time sequence. The first voltage amplitude is read from the voltage amplitude sequence and sent to the voltage generation unit. This unit generates a corresponding analog voltage based on the input voltage value, which is the instantaneous drive voltage value at the first sampling moment. After this voltage value stabilizes, the system waits for the appropriate time length based on the time difference between the first and second time points in the sampling time sequence. After the wait is complete, the second voltage amplitude is read from the voltage amplitude sequence and sent to the voltage generation unit again, updating the output voltage to the instantaneous drive voltage value at the second sampling moment. This process is repeated, converting each voltage amplitude in the voltage amplitude sequence into an instantaneous drive voltage value at the corresponding sampling moment and outputting it. Throughout the output process, the voltage generation unit continues to operate, ensuring that the output voltage remains constant between two adjacent sampling moments until the next sampling moment arrives and it is updated to a new voltage value. Through this point-by-point output method, a continuous voltage change curve that is completely consistent with the original drive voltage waveform is reconstructed on the time axis. The output instantaneous drive voltage sequence is directly fed to the drive end of the MEMS ultrasonic sensor, providing a precise electrical signal input for the sensor's mechanical excitation.

[0038] The instantaneous driving voltage value is sequentially applied to the MEMS ultrasonic sensor according to the sampling time sequence, and the MEMS ultrasonic sensor is driven to emit ultrasonic pulses toward the target test object.

[0039] The method provided in this embodiment extracts the voltage amplitude sequence and sampling time point sequence from the driving voltage waveform, decomposing the complete driving waveform into precisely corresponding amplitude commands and time references in the time domain, providing accurate data for subsequent voltage output. Based on the sampling time point sequence, each amplitude in the voltage amplitude sequence is output instantaneously, ensuring that each voltage value is established at a preset precise time. This ensures that the output analog driving waveform and the constructed digital waveform are strictly consistent on the time axis, avoiding waveform distortion caused by timing deviations. The instantaneous driving voltage values ​​are sequentially applied to the sensor according to the sampling time point sequence, so that the voltage change trajectory received by the sensor's driving end completely reproduces the original driving waveform, driving the internal microstructure of the sensor to vibrate according to the expected pattern, thereby emitting ultrasonic pulses with pure waveform and concentrated energy towards the target test object.

[0040] In one embodiment, receiving the ultrasonic echo signal generated by the ultrasonic pulse and performing signal conditioning on the ultrasonic echo signal based on the real-time mechanical resonant frequency, the adjusted DC bias voltage, and the driving voltage waveform to obtain an optimized echo signal includes: The original echo electrical signal generated by the ultrasonic pulse reflected by the target test object is acquired by the MEMS ultrasonic sensor. The voltage amplitude is extracted from the adjusted DC bias voltage as a fixed gain coefficient, and the original echo signal is linearly optimized based on the fixed gain coefficient to obtain an amplitude-optimized echo signal. Specifically, the voltage amplitude is extracted from the adjusted DC bias voltage parameters. A fixed gain coefficient is obtained by using the bias voltage value as a gain factor or by comparing the bias voltage with a preset reference voltage. The original echo signal is amplified based on the fixed gain coefficient. The fixed gain coefficient is applied to the amplifier's gain control terminal in the form of a control voltage or digital code, ensuring that the amplifier amplifies the amplitude of the input signal by the same factor for each instantaneous value. If the original echo signal has been digitized, amplitude scaling in the digital domain is achieved by multiplying the value of each sampling point by the fixed gain coefficient. The amplified signal amplitude is boosted to the optimal input range for subsequent processing circuits, for example, making the signal peak value close to the full-scale input voltage of the analog-to-digital converter. The amplitude-optimized echo signal is the output of this step, and its amplitude is related to the adjusted bias voltage, reflecting the direct impact of the bias voltage on the sensor's receiving sensitivity.

[0041] The amplitude-optimized echo signal is bandpass filtered according to the preset filtering bandwidth conditions and the real-time mechanical resonant frequency to obtain the filtered echo signal; The driving voltage waveform is combined with a preset dielectric attenuation coefficient to construct a gain compensation coefficient. The filtered echo signal is then dynamically amplified based on the gain compensation coefficient to obtain the optimized echo signal.

[0042] Specifically, the center frequency, duration, and energy distribution characteristics of the transmitted pulse are extracted from the driving voltage waveform. Based on these characteristics and the medium attenuation coefficient, the attenuation rate of the sound wave propagating in the medium, i.e., the attenuation per unit distance, is calculated. Since the arrival time of the echo signal is proportional to the propagation distance of the sound wave, the total propagation distance experienced by the sound wave at that moment can be deduced from the echo arrival time, thus calculating the total attenuation accumulated from transmission to reception. The total attenuation is converted into a time-varying gain compensation coefficient, which gradually increases on the time axis from the pulse transmission moment with the increase of the echo arrival time, and the rate of increase is related to the medium attenuation rate and the speed of sound. The filtered echo signal is input to a variable gain amplifier, and the gain compensation coefficient is synchronously applied to the gain control terminal of the amplifier in the form of a control signal, so that the amplifier has different gain values ​​in different time intervals. The gain is small in the early stage of signal arrival and gradually increases with time. Through this dynamic amplification, the amplitude of echo signals from different distances is compensated to a similar level. If the filtered echo signal has been digitized, a multiplier can be used to multiply the amplitude of each sampling point by the gain compensation coefficient corresponding to that sampling time, thereby achieving dynamic amplification in the digital domain. After dynamic amplitude amplification, the amplitude difference caused by medium attenuation in the output optimized echo signal is effectively eliminated.

[0043] The method provided in this embodiment acquires the original echo electrical signal using a MEMS ultrasonic sensor, completely preserving the target's physical characteristic information. The amplitude is extracted from the adjusted bias voltage and used as a fixed gain coefficient for linear amplification, enhancing the weak signal to a suitable processing amplitude, laying the foundation for subsequent optimization. Bandpass filtering is performed based on the real-time resonant frequency and preset bandwidth, allowing the filter's center frequency to adaptively track the sensor's operating state, effectively suppressing out-of-band noise and significantly improving the signal-to-noise ratio. A gain compensation curve is constructed by combining the driving voltage waveform characteristics and the dielectric attenuation coefficient, and dynamic amplification is performed to compensate for amplitude differences in echoes at different distances, eliminating signal unevenness caused by propagation attenuation. The final output optimized echo signal has low noise and balanced amplitude, providing high-quality data for target recognition and feature extraction.

[0044] In one embodiment, the amplitude-optimized echo signal is bandpass filtered according to a preset filtering bandwidth condition and the real-time mechanical resonant frequency to obtain a filtered echo signal, including: The center frequency value is extracted from the real-time mechanical resonant frequency, and the upper passband frequency and lower passband frequency corresponding to the center frequency value are extracted from the filter bandwidth condition. The amplitude-optimized echo signal is subjected to frequency domain transformation to obtain a frequency component distribution set; Specifically, the amplitude-optimized echo signal is initially stored in a data buffer as discrete sampling points, each containing a sampling time and a corresponding signal amplitude. Before frequency domain transformation, the sampled data is preprocessed, including removing DC components and applying window functions to reduce spectral leakage. DC component removal is achieved by calculating the average of all sampling points and subtracting this average from each sampling point. Applying window functions involves multiplying the entire sampling sequence point-by-point with a pre-stored sequence of window functions, such as Hanning or Hamming windows, to smooth boundary effects caused by signal truncation. After preprocessing, a frequency domain transformation is performed on the windowed sampling sequence, decomposing the time-domain signal into a series of sinusoidal components of different frequencies. The result of the frequency domain transformation generates two sequences: a frequency sequence and an amplitude sequence. The frequency sequence contains multiple discrete frequency points ranging from zero to half the sampling frequency, with the interval between adjacent frequency points determined by the sampling duration. The amplitude sequence contains the amplitude corresponding to each frequency point, reflecting the energy intensity of that frequency component in the original signal. The frequency sequence and amplitude sequence together constitute the frequency component distribution set, which is usually stored in array form. Each array element corresponds to a frequency point, and each element contains two data points: frequency value and amplitude value. The frequency component distribution set fully reveals the spectral characteristics of the amplitude-optimized echo signal.

[0045] Iterate through each component frequency in the frequency component distribution set. If the component frequency is lower than the lower passband frequency or higher than the upper passband frequency, then set the amplitude of the component frequency to zero. If the component frequency is between the lower passband frequency and the upper passband frequency, then the amplitude of the component frequency remains unchanged; The processed component frequencies are sequentially integrated and inversely transformed in the frequency domain to obtain the filtered echo signal.

[0046] The method provided in this embodiment extracts the center frequency value from the real-time mechanical resonant frequency and the upper and lower limits of the passband from the filter bandwidth conditions, enabling the passband range of the bandpass filter to be dynamically adjusted according to the actual working state of the sensor, ensuring that the filtering operation is always targeted at the currently effective frequency range. A frequency component distribution set is obtained by performing a frequency domain transformation on the amplitude-optimized echo signal, converting the time-domain signal into a frequency-domain representation, providing a directly operable data foundation for precise frequency-selective processing. By traversing each component frequency in the frequency component distribution set, the amplitude of frequency components below the lower passband limit or above the upper passband limit is set to zero, while the amplitude of frequency components within the passband remains unchanged, achieving precise suppression of noise outside the passband and complete preservation of useful signals within the passband. The processed frequency components are integrated and subjected to an inverse frequency domain transformation to obtain the filtered echo signal, ensuring that the reconstructed time-domain signal contains only the effective frequency components within the passband.

[0047] In one embodiment, it also includes: Extract the peak echo amplitude and echo arrival time from the optimized echo signal; The peak echo amplitude is compared with a preset effective echo amplitude threshold. If the peak echo amplitude is less than the effective echo amplitude threshold, an insufficient energy indicator is generated. The time offset is calculated by comparing the arrival time of the echo with the preset theoretical echo time. If the time offset exceeds the preset allowable offset range, a drift flag is generated. Based on the insufficient energy indicator and the drift indicator, a matching target correction record is identified from a preset correction mapping table, and the corresponding control parameter correction amount is extracted from the target correction record; Based on the control parameter correction amount, the real-time mechanical resonant frequency, the adjusted bias voltage, and the driving voltage waveform of the current cycle are optimized and superimposed to obtain the corrected real-time mechanical resonant frequency, the adjusted bias voltage, and the driving voltage waveform.

[0048] Specifically, each correction component is extracted from the control parameter correction amount, and it is identified whether it includes a first correction value for the real-time mechanical resonant frequency, a second correction value for adjusting the bias voltage, and a third correction value for the drive voltage waveform. If the correction amount includes the first correction value for the real-time mechanical resonant frequency, the real-time mechanical resonant frequency value of the current cycle is read, and the first correction value is superimposed on the current frequency value to obtain the corrected real-time mechanical resonant frequency. If the correction amount is in incremental form, the correction value is added to the current frequency value; if the correction amount is in proportional form, the current frequency value is multiplied by the correction coefficient.

[0049] If the correction includes a second correction value for the adjusted bias voltage, then the adjusted bias voltage value of the current cycle is read, and the second correction value is added to the current bias voltage value to obtain the corrected adjusted bias voltage.

[0050] If the correction includes a third correction value for the drive voltage waveform, the drive voltage waveform sequence of the current cycle is read, and the waveform is adjusted overall according to the correction value type. If the third correction is an amplitude scaling factor, the amplitude of each sampling point in the waveform sequence is multiplied by this factor; if the third correction is a phase adjustment, the waveform sequence is compensated accordingly for phase. During the superposition operation, it must be ensured that the corrected parameter values ​​do not exceed the sensor's allowable operating range. If the superposition result exceeds the allowable range, it is limited to the boundary values. After all corrections are completed, the corrected real-time mechanical resonant frequency, the corrected adjusted bias voltage, and the corrected drive voltage waveform are formally established as the control parameters for the next control cycle, replacing the original parameters for the ultrasonic pulse transmission and echo reception cycle of the next cycle.

[0051] The method provided in this embodiment achieves a quantitative evaluation of the current control cycle's performance by extracting the peak echo amplitude and echo arrival time from the optimized echo signal, providing a reliable feedback basis for subsequent parameter correction. Based on the insufficient energy and drift indicators, matching correction records are identified from a preset correction mapping table, and the control parameter correction amount is extracted, enabling intelligent identification of various operating conditions and selection of targeted correction schemes. Based on the control parameter correction amount, the real-time mechanical resonant frequency, adjusted bias voltage, and drive voltage waveforms are optimized and superimposed, allowing the sensor to dynamically adjust core control parameters according to the actual echo quality, forming a complete closed-loop self-optimization mechanism.

[0052] Reference Figure 2 As shown, the present invention also provides a control device for a micrometer-scale MEMS ultrasonic sensor, applicable to the control method for the micrometer-scale MEMS ultrasonic sensor described in any one of the above claims, comprising: The acquisition module is used to acquire the frequency sweep detection signal of the MEMS ultrasonic sensor and identify the real-time mechanical resonant frequency of the MEMS ultrasonic sensor based on the frequency sweep detection signal. Analysis module, the analysis module is used to perform bias DC adjustment on the MEMS ultrasonic sensor according to the real-time mechanical resonant frequency to obtain the adjustment bias voltage; The association module is used to construct a driving waveform based on a preset quality factor, the real-time mechanical resonant frequency and the adjusted bias voltage, to obtain a driving voltage waveform, and to drive the MEMS ultrasonic sensor to send ultrasonic pulses to the target test object based on the driving voltage waveform. The processing module is used to receive the ultrasonic echo signal generated by the ultrasonic pulse, and to perform signal conditioning on the ultrasonic echo signal according to the real-time mechanical resonant frequency, the adjusted DC bias voltage, and the driving voltage waveform to obtain an optimized echo signal.

[0053] This invention provides a control device for a micrometer-scale MEMS ultrasonic sensor. By acquiring a swept-frequency detection signal and identifying the mechanical resonant frequency in real time, it dynamically tracks the diaphragm state drift caused by environmental changes, solving the problem of mismatch between the driving frequency and the resonant frequency. This ensures the sensor always operates at the optimal response point, improving transmission efficiency and receiving sensitivity. By adjusting the bias DC according to the real-time resonant frequency and iteratively optimizing the bias voltage based on the background noise power, the background noise level is reduced, improving the signal-to-noise ratio of the echo signal and overcoming the signal quality degradation caused by static bias. By combining a preset quality factor, the real-time resonant frequency, and the adjusted bias voltage to construct the driving waveform, a pre-distortion driving waveform is generated, enabling the transmitted pulse to accurately match the diaphragm's dynamic response, suppressing harmonic distortion, and improving the directivity and energy utilization of the ultrasonic pulse. By performing bandpass filtering and dynamic gain compensation on the echo signal based on the real-time resonant frequency, adjusted bias voltage, and driving waveform, adaptive tracking of the filter center frequency and real-time matching of the attenuation compensation curve are achieved. This solves the problem that static filtering and fixed gain cannot adapt to varying operating conditions, significantly improving the echo signal reconstruction quality and detection accuracy. This invention enables micron-scale MEMS ultrasonic sensors to possess closed-loop adaptive capabilities in complex environments, thereby enhancing the stability and reliability of the system.

[0054] Reference Figure 3 The present invention also provides a control system for a micrometer-scale MEMS ultrasonic sensor, comprising: Memory, used to store programs; A processor is used to execute the program to implement the various steps of the control method for a micrometer-scale MEMS ultrasonic sensor described in any of the above-mentioned embodiments.

[0055] In this embodiment, the processor and memory can be connected via a bus or other means. The memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive. The processor may be a general-purpose processor, such as a central processing unit, digital signal processor, application-specific integrated circuit, or one or more integrated circuits configured to implement embodiments of the present invention.

[0056] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the system and each module described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0057] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A control method for a micrometer-scale MEMS ultrasonic sensor, characterized in that, include: The swept frequency detection signal of the MEMS ultrasonic sensor is acquired, and the real-time mechanical resonant frequency of the MEMS ultrasonic sensor is identified based on the swept frequency detection signal. The bias DC adjustment of the MEMS ultrasonic sensor is performed based on the real-time mechanical resonant frequency to obtain the adjustment bias voltage. A driving waveform is constructed based on a preset quality factor, the real-time mechanical resonant frequency, and the adjusted bias voltage to obtain a driving voltage waveform. The MEMS ultrasonic sensor is then driven to send ultrasonic pulses to the target test object based on the driving voltage waveform. The ultrasonic echo signal generated by the ultrasonic pulse is received, and the ultrasonic echo signal is conditioned according to the real-time mechanical resonant frequency, the adjusted DC bias voltage, and the driving voltage waveform to obtain an optimized echo signal.

2. The control method for a micrometer-scale MEMS ultrasonic sensor according to claim 1, characterized in that, The step of acquiring the swept-frequency detection signal of the MEMS ultrasonic sensor and identifying the real-time mechanical resonant frequency of the MEMS ultrasonic sensor based on the swept-frequency detection signal includes: A preset electrical signal is applied to the piezoelectric drive end of the MEMS ultrasonic sensor, and the piezoelectric drive end is responded to and its output is acquired to obtain the swept frequency detection signal; The frequency response of the swept frequency detection signal is analyzed to obtain the response amplitude. The response amplitude is then correlated with a preset frequency point to obtain a frequency amplitude response sequence. The response amplitude of each frequency point in the frequency amplitude response sequence is traversed, and the current response amplitude is compared with the adjacent response amplitudes of two adjacent frequency points. If the current response amplitude is greater than both adjacent response amplitudes, the frequency point corresponding to the current response amplitude is marked as a candidate peak frequency point. The peak frequency point with the largest response amplitude is selected from the candidate peak frequency points, and the frequency of the peak frequency point is read to obtain the real-time mechanical resonant frequency.

3. The control method for the micrometer-scale MEMS ultrasonic sensor according to claim 1, characterized in that, The step of adjusting the bias DC of the MEMS ultrasonic sensor based on the real-time mechanical resonant frequency to obtain the adjusted bias voltage includes: The corresponding initial bias voltage value is extracted from the preset voltage frequency mapping table based on the real-time mechanical resonant frequency. The initial bias voltage value is applied to the MEMS ultrasonic sensor, and the background noise signal of the MEMS ultrasonic sensor is acquired. The background noise power of the background noise signal is calculated. The background noise power is compared with a preset noise threshold. If the background noise power is higher than the noise threshold, the initial bias voltage value is adjusted step by step according to a preset step size. After each adjustment, the background noise signal is re-acquired and the background noise power is recalculated until the background noise power is lower than the noise threshold. When the background noise power is lower than the noise threshold, the corresponding initial bias voltage value is set as the adjusted bias voltage.

4. The control method for a micrometer-scale MEMS ultrasonic sensor according to claim 1, characterized in that, The step of constructing a driving voltage waveform based on a preset quality factor, the real-time mechanical resonant frequency, and the adjusted bias voltage includes: Extract the frequency values ​​from the real-time mechanical resonant frequency, and construct a carrier sequence based on the frequency values ​​to obtain an initial carrier sequence; The preset quality factor is extracted based on the real-time mechanical resonant frequency to obtain a quality factor value. The corresponding pulse envelope curve is matched from the preset envelope shape library based on the quality factor value, and an envelope modulation sequence aligned with the initial carrier sequence is generated based on the pulse envelope curve. The carrier amplitude at each time point in the initial carrier sequence is multiplied point by point with the envelope amplitude at the corresponding time point in the envelope modulation sequence to obtain the modulation waveform sequence; The voltage amplitude is extracted from the adjusted bias voltage as an amplitude scaling reference, and the amplitude of each waveform in the modulation waveform sequence is proportionally scaled according to the amplitude scaling reference to obtain the driving voltage waveform.

5. The control method for a micrometer-scale MEMS ultrasonic sensor according to claim 4, characterized in that, The step of driving the MEMS ultrasonic sensor to send ultrasonic pulses to the target test object according to the driving voltage waveform includes: Extract the voltage amplitude sequence and the corresponding sampling time point sequence from the driving voltage waveform; Based on the sampling time point sequence, each voltage amplitude in the voltage amplitude sequence is instantaneously converted to obtain the instantaneous driving voltage value; The instantaneous driving voltage value is sequentially applied to the MEMS ultrasonic sensor according to the sampling time sequence, and the MEMS ultrasonic sensor is driven to emit ultrasonic pulses toward the target test object.

6. The control method for a micrometer-scale MEMS ultrasonic sensor according to claim 1, characterized in that, The process of receiving the ultrasonic echo signal generated by the ultrasonic pulse and conditioning the ultrasonic echo signal according to the real-time mechanical resonant frequency, the adjusted DC bias voltage, and the driving voltage waveform to obtain an optimized echo signal includes: The original echo electrical signal generated by the ultrasonic pulse reflected by the target test object is acquired by the MEMS ultrasonic sensor. The voltage amplitude is extracted from the adjusted DC bias voltage as a fixed gain coefficient, and the original echo signal is linearly optimized based on the fixed gain coefficient to obtain an amplitude-optimized echo signal. The amplitude-optimized echo signal is bandpass filtered according to the preset filtering bandwidth conditions and the real-time mechanical resonant frequency to obtain the filtered echo signal; The driving voltage waveform is combined with a preset dielectric attenuation coefficient to construct a gain compensation coefficient. The filtered echo signal is then dynamically amplified based on the gain compensation coefficient to obtain the optimized echo signal.

7. The control method for a micrometer-scale MEMS ultrasonic sensor according to claim 6, characterized in that, The amplitude-optimized echo signal is bandpass filtered according to the preset filtering bandwidth conditions and the real-time mechanical resonant frequency to obtain the filtered echo signal, including: The center frequency value is extracted from the real-time mechanical resonant frequency, and the upper passband frequency and lower passband frequency corresponding to the center frequency value are extracted from the filter bandwidth condition. The amplitude-optimized echo signal is subjected to frequency domain transformation to obtain a frequency component distribution set; Iterate through each component frequency in the frequency component distribution set. If the component frequency is lower than the lower passband frequency or higher than the upper passband frequency, then set the amplitude of the component frequency to zero. If the component frequency is between the lower passband frequency and the upper passband frequency, then the amplitude of the component frequency remains unchanged; The processed component frequencies are sequentially integrated and inversely transformed in the frequency domain to obtain the filtered echo signal.

8. The control method for a micrometer-scale MEMS ultrasonic sensor according to claim 1, characterized in that, Also includes: Extract the peak echo amplitude and echo arrival time from the optimized echo signal; The peak echo amplitude is compared with a preset effective echo amplitude threshold. If the peak echo amplitude is less than the effective echo amplitude threshold, an insufficient energy indicator is generated. The time offset is calculated by comparing the arrival time of the echo with the preset theoretical echo time. If the time offset exceeds the preset allowable offset range, a drift flag is generated. Based on the insufficient energy indicator and the drift indicator, a matching target correction record is identified from a preset correction mapping table, and the corresponding control parameter correction amount is extracted from the target correction record; Based on the control parameter correction amount, the real-time mechanical resonant frequency, the adjusted bias voltage, and the driving voltage waveform of the current cycle are optimized and superimposed to obtain the corrected real-time mechanical resonant frequency, the adjusted bias voltage, and the driving voltage waveform.

9. A control device for a micrometer-scale MEMS ultrasonic sensor, characterized in that, A control method for the micrometer-scale MEMS ultrasonic sensor according to any one of claims 1-8, comprising: The acquisition module is used to acquire the frequency sweep detection signal of the MEMS ultrasonic sensor and identify the real-time mechanical resonant frequency of the MEMS ultrasonic sensor based on the frequency sweep detection signal. Analysis module, the analysis module is used to perform bias DC adjustment on the MEMS ultrasonic sensor according to the real-time mechanical resonant frequency to obtain the adjustment bias voltage; The association module is used to construct a driving waveform based on a preset quality factor, the real-time mechanical resonant frequency and the adjusted bias voltage, to obtain a driving voltage waveform, and to drive the MEMS ultrasonic sensor to send ultrasonic pulses to the target test object based on the driving voltage waveform. The processing module is used to receive the ultrasonic echo signal generated by the ultrasonic pulse, and to perform signal conditioning on the ultrasonic echo signal according to the real-time mechanical resonant frequency, the adjusted DC bias voltage, and the driving voltage waveform to obtain an optimized echo signal.

10. A control system for a micrometer-scale MEMS ultrasonic sensor, characterized in that, include: Memory, used to store programs; A processor is configured to execute the program to implement the various steps of the control method for a micrometer-scale MEMS ultrasonic sensor as described in any one of claims 1-8.

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