Miniature piezoelectric driving type detection contact integrated laser module and contact type calibration method

Abstract

The invention discloses a miniature piezoelectric driving type detection contact integrated laser module and a contact type calibration method, particularly relates to the technical field of precision measurement, is used for solving the problem of dynamic time-varying interference of high-voltage driving pulses on micro-force signals, and is an interference coupling path synchronous acquisition and dynamic resolving technology based on a distributed electromagnetic probe. Online identification of transient interference energy distribution characteristics is realized; a multi-dimensional parameter fusion mechanism of an interference-response dynamic model quantifies cross-domain characteristics of interference energy, system response and signal noise into a comprehensive suppression coefficient, and reverse compensation waveform injection and adaptive filter dynamic adjustment are performed, so that full life cycle offset of common-mode noise is realized, and damage of residual interference to closed-loop control is avoided; through cross-domain collaborative analysis of laser-mechanical data, a self-healing iteration mechanism of contact type calibration parameters is established, progressive pollution of dynamic interference to a calibration reference is eradicated, and long-term consistency of detection precision under complex working conditions is ensured.

Description

Technical Field

[0001] The present invention relates to the field of precision measurement technology. More specifically, the present invention relates to a micro piezoelectric drive type detection contact integrated laser module and a contact calibration method. Background Art

[0002] In a micro piezoelectric drive type detection contact integrated laser module and a contact calibration system, a piezoelectric driver drives a contact to achieve micro displacement through high-frequency high-voltage pulses, a laser module synchronously measures the displacement amount, and contact calibration relies on a micro-Newton level contact force feedback to correct system errors. However, when the high-voltage drive unit and the micro-force sensing unit share a grounding loop, transient induced charges invade the micro-force signal link through hidden coupling paths such as distributed capacitance and parasitic inductance, forming wide-spectrum common-mode noise. Such noise is dynamically time-varying with the evolution of the system working state, environmental conditions, and physical characteristics, resulting in distortion of the contact force feedback signal and failure of laser-mechanical data fusion.

[0003] Traditional suppression techniques rely on hardware shielding and static filtering means. Their design is based on a fixed parameter model and cannot cope with dynamic disturbance mechanisms such as gradual changes in material dielectric properties, parasitic parameter offsets induced by mechanical deformation, spectrum mismatch caused by working condition adaptability, and multi-physical field coupling effects. Such interference gradually contaminates the calibration reference, and residual noise accumulates phase lag in closed-loop control, triggering hidden instability of the system and irreversible attenuation of detection accuracy. Due to the lack of real-time perception and dynamic suppression capabilities for the interference transmission path, the prior art is difficult to maintain the consistency of multi-modal data, resulting in the loss of credibility of the contact calibration reference. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a micro piezoelectric drive type detection contact integrated laser module and a contact calibration method. Based on the synchronous acquisition and dynamic calculation technology of the interference coupling path of distributed electromagnetic probes, it realizes the online identification of the transient interference energy distribution characteristics; the multi-dimensional parameter fusion mechanism of the interference-response dynamic model quantifies the cross-domain characteristics of interference energy, system response, and signal noise into a comprehensive suppression coefficient, and performs reverse compensation waveform injection and adaptive filter dynamic adjustment to achieve the full-life cycle cancellation of common-mode noise and avoid the damage of residual interference to closed-loop control; the cross-domain collaborative analysis of laser-mechanical data establishes a self-healing iterative mechanism for contact calibration parameters, eradicates the gradual contamination of the calibration reference by dynamic interference, and ensures the long-term consistency of detection accuracy under complex working conditions, so as to solve the problems proposed in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions: S1: Real-time collect the time-domain differential characteristics of driving pulses, the impedance fluctuation amount of the grounding loop, and environmental electromagnetic spectrum data, and jointly analyze and extract the coupling path parameters of transient interference; S2: Convolve the time-domain differential characteristics of the driving pulse with the frequency-domain impedance characteristics of the grounding loop to generate an equivalent transient current density distribution, and determine the dominant frequency band and spatial weight of the interference through power spectral density analysis and coupling path parameter weighting; S3: Quantify the time-varying influence of the interference on the contact force transmission according to the energy distribution of the dominant frequency band of the interference and the dynamic fluctuation characteristics of the contact position movement, and combine the noise fluctuation characteristics of the micro-force signal to generate a comprehensive suppression coefficient through multi-dimensional parameter correlation; S4: Judge whether to perform compensation processing according to the comprehensive suppression coefficient, and adjust the passband attenuation slope and notch frequency of the filter when necessary, and synchronously inject a reverse compensation waveform to cancel the common-mode oscillation; S5: Correlate the contact position displacement measured by laser with the filtered micro-force signal, analyze the distortion of the force-displacement mapping, and iteratively update the calibration parameters until the dynamic consistency converges.

[0006] In a preferred embodiment, step S1 includes the following: Deploy an electromagnetic coupling probe array at the high-voltage side of the piezoelectric drive unit, the low-voltage side of the micro-force sensing unit, and the key nodes of the grounding loop; collect the time-domain differential characteristics of the driving pulse to quantify the transient spike characteristics of the high-voltage pulse, collect the impedance fluctuation of the grounding loop to reflect the dynamic response of the grounding loop to the interference current, and collect the ambient electromagnetic spectrum data to identify the external interference frequency distribution.

[0007] In a preferred embodiment, step S1 further includes the following: Analyze the correlation between the time-domain differential characteristics of the driving pulse and the noise fluctuation of the micro-force sensing unit, and combine the impedance fluctuation of the grounding loop and the frequency characteristics of the ambient electromagnetic spectrum data to construct a coupling path transfer function to characterize the transient interference transfer characteristics and coupling strength, and thus obtain the coupling path parameters.

[0008] In a preferred embodiment, step S2 includes the following: Convert the time-domain differential characteristics of the driving pulse into a frequency-domain form through a time-domain to frequency-domain transformation, and convolve it with the frequency-domain impedance characteristics of the grounding loop to generate an equivalent transient current density distribution, where the convolution operation is specifically the multiplication and accumulation of the frequency-domain form of the time-domain differential characteristics of the driving pulse and the frequency-domain impedance characteristics of the grounding loop in the frequency domain; by analyzing the spectral characteristics of the equivalent transient current density distribution, calculate the power spectral density, specifically by squaring and smoothing the equivalent transient current density distribution to determine the dominant frequency band of the interference, that is, the frequency range where the energy is concentrated; use the coupling path parameters and the spectral characteristics of the equivalent transient current density distribution for weighting processing to calculate the spatial weight function, specifically by multiplying the coupling path parameters and the spectral characteristics of the equivalent transient current density distribution and normalizing them to characterize the spatial distribution intensity of the interference inside the system, that is, quantify the relative intensity of the interference at different positions.

[0009] In a preferred embodiment, step S3 includes the following: Quantify the time-varying influence of interference on contact force transmission by using the interference dominant frequency band, spatial weight, and the dynamic fluctuation characteristics of the contact displacement. Specifically, calculate the correlation between the distribution of interference energy within the interference dominant frequency band and the sensitivity of the contact displacement to frequency changes to generate an interference influence factor; combine the micro-force signal noise fluctuation characteristics, and calculate the correlation between the interference influence factor and the micro-force signal noise over time to generate a correlation coefficient.

[0010] In a preferred embodiment, step S3 further includes the following: Set the critical condition for interference suppression as the correlation coefficient exceeding a preset threshold. Specifically, when the correlation coefficient is greater than the preset threshold, it is determined that the influence of interference on the system is significant; generate a comprehensive suppression coefficient by comprehensively calculating the interference influence factor, the correlation coefficient, the total interference energy within the interference dominant frequency band, and the maximum value of the spatial weight. Specifically, divide the product of the interference influence factor and the correlation coefficient by the total interference energy within the interference dominant frequency band, and then divide by the maximum value of the spatial weight.

[0011] In a preferred embodiment, step S4 includes the following: Compare the comprehensive suppression coefficient with the preset threshold. When the comprehensive suppression coefficient is less than or equal to the preset threshold, it is determined that the interference influence is within an acceptable range, and no compensation processing is performed; when the comprehensive suppression coefficient is greater than the preset threshold, it is determined that the interference influence is significant, and compensation processing is performed.

[0012] In a preferred embodiment, step S4 further includes the following: The compensation processing includes adjusting the passband attenuation slope of the filter and generating a reverse compensation waveform. Among them, the adjustment of the passband attenuation slope of the filter is based on the degree to which the comprehensive suppression coefficient exceeds the preset threshold. The increment of the passband attenuation slope is proportional to the part by which the comprehensive suppression coefficient exceeds the preset threshold. At the same time, set the notch frequency as the center frequency of the interference dominant frequency band.

[0013] In a preferred embodiment, step S4 further includes the following: Generate a reverse compensation waveform and inject it into the signal link of the micro-force sensing unit. Calculate the estimated value of the interference signal through the equivalent transient current density distribution and transient interference coupling path parameters, take its inverse phase as the reverse compensation waveform, and perform spatial weighting according to the spatial weight to ensure the accuracy of the reverse compensation waveform.

[0014] In a preferred embodiment, step S5 further includes the following: Obtain the real-time measured contact displacement data from the laser module, combine it with the filtered micro-force signal, construct a force-displacement mapping model to characterize the corresponding relationship between the contact force and the contact displacement under ideal conditions, and represent the actually measured filtered micro-force signal as the superposition of the ideal mapping function value and the distortion error. Determine the root mean square value of the distortion error by calculating the difference between the filtered micro-force signal and the ideal mapping function. Use the iterative optimization method to adjust the calibration parameters, including the displacement correction factor and the gain correction factor, to minimize the root mean square value of the distortion error. Continuously monitor the root mean square value of the distortion error until it converges to the preset precision threshold to confirm that the calibration parameters reach dynamic consistency. Finally, achieve the dynamic calibration of the force-displacement mapping by correlating the contact displacement data with the filtered micro-force signal.

[0015] Technical effects and advantages of the integrated laser module and contact calibration method for the micro piezoelectric drive type detection contact of the present invention: The present invention significantly improves the precision stability and long-term reliability of the micro piezoelectric detection system by constructing a real-time perception-suppression-calibration closed-loop system for dynamic interference. First, based on the synchronous acquisition and dynamic resolution technology of the interference coupling path of the distributed electromagnetic probe, the online identification of the transient interference energy distribution characteristics is realized, breaking through the limitation of the traditional fixed parameter filtering for suppressing time-varying interference. Second, through the multi-dimensional parameter fusion mechanism of the interference-response dynamic model, the cross-domain characteristics of the interference energy, system response, and signal noise are quantified as a comprehensive suppression coefficient, solving the mismatch problem of the traditional linear compensation model in multi-physical field coupling scenarios such as material aging and mechanical deformation. Third, combined with the injection of the reverse compensation waveform and the dynamic adjustment of the adaptive filter, the full-life cycle cancellation of the common mode noise is realized, avoiding the hidden damage of the residual interference to the closed-loop control. Finally, through the cross-domain collaborative analysis of the laser-mechanical data, a self-healing iterative mechanism for the contact calibration parameters is established, eradicating the progressive pollution of the calibration reference by the dynamic interference and ensuring the long-term consistency of the detection accuracy under complex working conditions. Brief Description of the Drawings

[0016] Figure 1 It is a schematic flow chart of the integrated laser module and contact calibration method for the micro piezoelectric drive type detection contact of the present invention.

[0017] Figure 2 It is a schematic flow chart of the iterative update of the calibration parameters of the integrated laser module and contact calibration method for the micro piezoelectric drive type detection contact of the present invention. Detailed Description of the Invention

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment 1: Figure 1 A micro piezoelectric drive type detection contact integrated laser module and a contact calibration method of the present invention are given, including: S1: Real-time collect the time-domain differential characteristics of the drive pulse, the impedance fluctuation of the grounding loop, and the environmental electromagnetic spectrum data, and jointly analyze and extract the coupling path parameters of the transient interference.

[0020] S2: Perform a convolution operation on the time-domain differential characteristics of the drive pulse and the frequency-domain impedance characteristics of the grounding loop to generate an equivalent transient current density distribution, and determine the dominant frequency band and spatial weight of the interference through power spectral density analysis and coupling path parameter weighting.

[0021] S3: Quantify the time-varying influence of the interference on the contact force transmission according to the energy distribution of the dominant frequency band of the interference and the dynamic fluctuation characteristics of the contact displacement, and generate a comprehensive suppression coefficient through multi-dimensional parameter correlation in combination with the noise fluctuation characteristics of the micro-force signal.

[0022] S4: Judge whether to perform compensation processing according to the comprehensive suppression coefficient, and adjust the passband attenuation slope and notch frequency of the filter when necessary, and synchronously inject a reverse compensation waveform to cancel the common-mode oscillation.

[0023] S5: Correlate the contact displacement measured by the laser and the filtered micro-force signal, analyze the distortion of the force-displacement mapping, and iteratively update the calibration parameters until the dynamic consistency converges.

[0024] In the field of precision measurement technology, the integrated laser module and contact calibration technology of micro piezoelectric drive type detection contacts is a key detection means, which is widely used in semiconductor manufacturing, micro electro mechanical system (MEMS) processing and other scenarios. This technology generates tiny displacements through piezoelectric drivers, precisely measures the displacement amounts using laser modules, and relies on contact probes to detect the contact force feedback at the micro Newton level to achieve high-precision calibration and data fusion. However, since the piezoelectric drive unit requires high-voltage drive, while the micro force sensing unit processes weak signals, and the two share a grounding loop, the high-voltage drive pulses are coupled to the micro force signal link through paths such as distributed capacitance and parasitic inductance, generating broadband common-mode noise. This noise varies dynamically with the drive state, ambient electromagnetic field, and system physical characteristics, seriously interfering with the accuracy of the contact force feedback, and thus affecting the fusion reliability of the displacement data measured by the laser and the contact force data. Traditional hardware shielding or static filtering methods are difficult to adapt to this time-varying interference, resulting in the distortion of the calibration reference and the decline of the detection accuracy. The present invention proposes an innovative interference suppression and calibration method. Step S1, as the basis of the entire process, aims to extract the coupling path parameters of transient interference through real-time monitoring and analysis, providing accurate interference characteristic data for subsequent steps.

[0025] The purpose of step S1 is to deploy an electromagnetic coupling probe array at the high-voltage side of the piezoelectric drive unit, the low-voltage side of the micro force sensing unit, and key nodes of the grounding loop, to collect the time-domain differential characteristics of the drive pulse, the impedance fluctuation of the grounding loop, and the ambient electromagnetic spectrum data in real time, and to extract the coupling path parameters of transient interference through joint analysis. The following is the detailed expansion of the specific technical logic: Step S1 includes the following: To ensure the measurement accuracy and reliability of the integrated laser module and contact calibration system of micro piezoelectric drive type detection contacts, it is necessary to collect three parameters: the time-domain differential characteristics of the drive pulse, the impedance fluctuation of the grounding loop, and the ambient electromagnetic spectrum data. The necessity stems from the following analysis: First, as the internal high-voltage excitation source of the system, the time-domain differential characteristics of the drive pulse (i.e., the voltage change rate) directly reflect the intensity and frequency distribution of transient spikes. By collecting this parameter, the transient characteristics of interference can be accurately characterized, providing a basis for the analysis of the interference path. Second, as the main coupling channel for interference current, the impedance fluctuation of the grounding loop over time will significantly affect the interference transfer efficiency. Monitoring this parameter can reveal the dynamic characteristics of interference, thus laying a foundation for optimizing the suppression measures. Finally, the impact of the external electromagnetic environment on high-precision measurement cannot be ignored. Collecting the ambient electromagnetic spectrum data can identify the frequency distribution of external interference and its differences from internal interference, improving the pertinence of interference suppression. In summary, the comprehensive collection of these three types of parameters can systematically describe the source, transfer path, and external influence of interference, providing a scientific basis for achieving efficient interference suppression and high-precision measurement.

[0026] S1-1, Deployment of the electromagnetic coupling probe array: Install an electromagnetic coupling probe array at the high-voltage side of the piezoelectric drive unit, the low-voltage side of the micro-force sensing unit, and the key nodes of the grounding loop to monitor the internal signals and states of the system in real time.

[0027] By arranging the probe array at these three key positions, namely the high-voltage drive side, the low-voltage sensing side, and the grounding loop, capture the characteristics of the drive signal, the noise fluctuations of the sensing signal, and the dynamic changes of the grounding loop respectively, so as to realize multi-point monitoring of interference signals.

[0028] The high-voltage drive side is the main generation area of interference. By monitoring its signal, the origin characteristics of interference can be directly grasped; the low-voltage sensing side is the receiving area affected by interference. Monitoring its noise fluctuations can evaluate the actual impact degree of interference on the sensing signal; the grounding loop is an important path for interference transmission. Monitoring its state helps to reveal the propagation mechanism of interference inside the system. This multi-point deployment strategy can comprehensively capture the complete process of interference from generation to transmission and then to influence, providing comprehensive and reliable basic information for subsequent data collection and analysis.

[0029] S1-2, Data acquisition and feature calculation: Utilize the deployed electromagnetic coupling probe array to collect the time-domain differential characteristics of the drive pulse, the impedance fluctuation of the grounding loop, and the environmental electromagnetic spectrum data in real time, and perform feature calculation on these data. Specifically: The time-domain differential characteristics of the drive pulse quantify its transient spike characteristics by analyzing the change rate of the drive pulse voltage over time; The impedance fluctuation of the grounding loop reflects the dynamic response of the grounding loop to the interference current by measuring the changes in the voltage and current of the grounding loop over time and calculating their ratio; The environmental electromagnetic spectrum data identify the characteristics of external interference by recording the external electromagnetic field intensity and analyzing its frequency distribution.

[0030] The time-domain differential characteristics of the drive pulse are used to characterize the rapidly changing part of the high-voltage pulse, the impedance fluctuation of the grounding loop is used to describe the dynamic impact of the interference current in the grounding loop, and the environmental electromagnetic spectrum data are used to identify the frequency range of external interference. The rapid change of the drive pulse is the core factor for interference generation, the dynamic response of the grounding loop reveals the path characteristics of interference transmission, and the external electromagnetic spectrum data helps to distinguish the sources of internal and external interference.

[0031] S1-3, Joint analysis to extract coupling path parameters: Perform time-domain and frequency-domain correlation analysis on the collected time-domain differential characteristics of the drive pulse, the impedance fluctuation of the grounding loop, and the environmental electromagnetic spectrum data to extract the coupling path parameters of transient interference. The specific processing process includes: First, by comparing the similarity between the time-domain differential characteristics of the driving pulse and the noise fluctuation of the micro-force sensing unit, calculate the correlation degree between the two, and evaluate the direct influence of the driving pulse on the micro-force sensing signal; Secondly, convert the impedance fluctuation of the grounding loop and the environmental electromagnetic spectrum data into frequency distribution characteristics to identify the main frequency range of the interference signal; Finally, based on the above analysis results, construct the interference transfer relationship from the driving pulse to the micro-force sensing signal, and describe the coupling strength and frequency distribution characteristics of the interference.

[0032] For example, the process of jointly analyzing and extracting the coupling path parameters can be expressed as follows: The time-domain differential characteristics of the driving pulse , the real-time impedance of the grounding loop and the spectrum of the environmental electromagnetic field are subjected to time-domain and frequency-domain correlation analysis to extract the coupling path parameters of the transient interference, denoted as the transfer function .

[0033] Specific method: Through time-domain correlation analysis, calculate and 's correlation coefficient to determine the direct influence of the transient change of the driving pulse on the noise of the micro-force signal.

[0034] Through frequency-domain analysis, convert to frequency-domain characteristics through Fourier transform , and combine with to identify the frequency range of the interference.

[0035] Based on the above data, construct the coupling path transfer function: ; Among them, represents the interference transfer characteristics from the driving pulse to the micro-force signal, including the coupling strength and frequency distribution.

[0036] : The frequency-domain representation of the noise fluctuation on the low-voltage side of the micro-force sensing unit at frequency describes the specific manifestation form of the interference in the micro-force sensing signal. It characterizes how the interference affects the micro-force sensing signal, that is, the actual fluctuation situation of the external noise or interference in the sensor output. It is part of the "result" in the interference coupling path and reflects the final influence degree of the interference.

[0037] : The time-domain differential characteristics of the driving pulse at frequency The frequency-domain representation at [location] reflects the characteristics of the transient changes (such as rapidly rising or falling edges) of the high-voltage drive pulse at different frequencies. It is used to quantify the intensity of the interference source and its frequency distribution. It reveals how the rapidly changing part of the high-voltage pulse becomes the main source of interference, helping to analyze the origin and characteristics of the interference.

[0038] : The impedance characteristic of the ground loop at frequency represents the ability of the ground loop to impede current as a function of frequency. It describes the ability of the ground loop to suppress or transmit interference current. The smaller the impedance, the easier it is for the interference current to propagate through the ground loop to the micro-force sensing unit; conversely, the larger the impedance, the more difficult it is for interference to be transmitted. It is a key "channel" parameter in the interference coupling path.

[0039] Coupling path transfer function is constructed to quantify the interference transfer characteristics of the high-voltage drive pulse coupled to the micro-force sensing signal through the ground loop. Specifically, this formula comprehensively characterizes the coupling process of interference through two parts: describes the direct relationship between the transient change of the drive pulse (through its differential characteristics) and the noise in the micro-force sensing signal, reflecting how the rapid change of the drive pulse affects the noise level; while introduces the reciprocal of the ground loop impedance, indicating that the smaller the impedance, the easier it is for the interference current to be transmitted, thus exacerbating the interference effect. Through the product of these two parts, comprehensively considers the intensity of the interference source, the characteristics of the transmission path, and the manifestation of interference in the sensing signal, aiming to clearly characterize the coupling path characteristics of interference from the source to the target. involves the differential characteristics of the drive pulse through Fourier transform.

[0040] The correlation analysis of the drive pulse and the micro-force sensing signal can directly quantify the impact of the interference source, the frequency distribution characteristic analysis can reveal the frequency range of the interference and support the design of targeted suppression measures, and the construction of the interference transfer relationship can systematically describe the propagation path of the interference.

[0041] In step S1, an electromagnetic coupling probe array is deployed at the high-voltage side of the piezoelectric drive unit, the low-voltage side of the micro-force sensing unit, and the key nodes of the ground loop to collect the time-domain differential characteristics of the drive pulse, the impedance fluctuation of the ground loop, and the environmental electromagnetic spectrum data in real time, and extract the coupling path parameters of the transient interference through the joint analysis of the time domain and the frequency domain. This multi-dimensional monitoring and data analysis method comprehensively captures the generation, transmission, and impact process of the interference, providing accurate data support for subsequent interference suppression and system optimization.

[0042] Step S1 collects the time-domain differential characteristics of the driving pulse, the fluctuation of the grounding loop impedance, and the environmental electromagnetic spectrum data in real time by deploying an electromagnetic coupling probe array, and extracts the coupling path parameters of the transient interference, laying a data foundation for subsequent analysis. However, only the coupling path parameters are not sufficient to comprehensively characterize the frequency-domain distribution and spatial propagation characteristics of the interference. In step S2, these data need to be further processed to generate the equivalent transient current density distribution, determine the dominant frequency band and spatial weight of the interference, and provide key characteristic information for interference suppression and calibration optimization.

[0043] Step S2 includes the following: S2-1, Data preparation: At the beginning of step S2, first obtain the time-domain differential characteristics of the driving pulse and the frequency-domain impedance characteristics of the grounding loop from step S1. The time-domain differential characteristics of the driving pulse refer to the instantaneous change rate of the high-voltage driving pulse with time, which is used to characterize the key time characteristics of interference generation; the frequency-domain impedance characteristics of the grounding loop refer to the impedance response of the grounding loop at different frequencies, which is used to reflect the transfer efficiency of the interference current.

[0044] Through Fourier transform, the time-domain differential characteristics of the driving pulse are transformed from the time domain to the frequency domain to the corresponding frequency-domain form for unified analysis and calculation with the frequency-domain impedance characteristics of the grounding loop.

[0045] S2-2, Convolution operation: After completing the data preparation, the frequency-domain form of the time-domain differential characteristics of the driving pulse and the frequency-domain impedance characteristics of the grounding loop are convolved in the frequency domain to generate the equivalent transient current density distribution.

[0046] The specific calculation logic of the convolution operation is to multiply the value of the frequency-domain form of the time-domain differential characteristics of the driving pulse at each frequency point by the value of the frequency-domain impedance characteristics of the grounding loop at the corresponding frequency point, and accumulate the product results at all frequency points to obtain a comprehensive frequency-domain distribution result.

[0047] Through this operation method, the interaction between the driving pulse and the grounding loop impedance is simulated, and the density distribution characteristics of the interference current at different frequencies are reflected. This operation method can comprehensively consider the instantaneous change of the driving pulse and the impedance response of the grounding loop to generate an index that accurately characterizes the spectral characteristics of the interference current.

[0048] S2-3, Generate the equivalent transient current density distribution: The equivalent transient current density distribution is a direct result of the convolution operation and represents the distribution characteristics of the interference current in the frequency domain. By analyzing the spectral characteristics of the equivalent transient current density distribution, the concentrated regions of the interference energy on the frequency axis are identified. The calculation logic is to perform a spectral scan on the equivalent transient current density distribution, determine the interference current density values corresponding to each frequency point, and record its distribution trend.

[0049] The frequency regions where the interference energy is concentrated usually have a more significant impact on the operation of the system and need to be identified and processed preferentially. By generating the equivalent transient current density distribution, specific frequency distribution information can be provided for the design of interference suppression measures, ensuring that subsequent processing can specifically reduce the interference impact.

[0050] S2-4, Determine the dominant frequency band of the interference: To determine the dominant frequency band of the interference, first calculate the power spectral density of the equivalent transient current density distribution. The calculation logic of the power spectral density is to square the values of the equivalent transient current density distribution at each frequency point and then smooth these squared values to obtain the distribution curve of the interference energy varying with frequency.

[0051] Analyze the power spectral density curve, find the peak frequency of the energy distribution, and take the peak frequency as the center to determine a frequency range such that the integral value of the interference energy within this range accounts for a significant proportion of the total energy, for example, more than 80% of the total energy. The reason is that the dominant frequency band of the interference is the frequency region where the energy is most concentrated, and suppressing this region can significantly reduce the overall impact of the interference on the system. By accurately determining the dominant frequency band of the interference, an accurate frequency target can be provided for subsequent filter design or compensation waveform generation, thereby improving the accuracy and efficiency of interference suppression.

[0052] S2-5, Determine the spatial weight of the interference: The spatial weight of the interference is used to characterize the relative intensity distribution of the interference at different positions within the system.

[0053] Using the coupling path parameters extracted in step S1 and combining with the equivalent transient current density distribution, calculate the spatial weight function. The calculation logic is to perform a weighted processing on the spectral characteristics of the equivalent transient current density distribution and the coupling path parameters. Specifically: multiply the coupling path parameter value at each position by the equivalent transient current density value at the corresponding frequency and normalize the result to obtain the spatial weight function.

[0054] The reason is that the distribution of the interference in space is not uniform, and the interference impact on some positions may be stronger. Quantifying this spatial difference helps to optimize the suppression measures. The spatial weight function can provide guidance for the spatial layout of the interference suppression measures, making the suppression measures more concentrated and effective in the regions with greater interference impact, thereby improving the overall anti-interference performance of the system.

[0055] Step S2 generates an equivalent transient current density distribution by performing a convolution operation on the time-domain differential characteristics of the drive pulse and the frequency-domain impedance characteristics of the grounding loop, and determines the dominant frequency band and spatial weight of the interference based on power spectral density analysis and coupling path parameter weighting.

[0056] Based on this data, Step S2 generated an equivalent transient current density distribution through convolution operation and determined the dominant frequency band and spatial weight of the interference. These results provide a quantitative characterization of the spectral characteristics and spatial distribution of the interference, laying a foundation for subsequent analysis. However, the above data only reflects the physical properties of the interference and has not been directly associated with the specific impact of the interference on contact force transmission and the noise characteristics of the micro-force signal. Step S3 aims to utilize the dominant frequency band energy distribution output by Step S2 and the dynamic fluctuation characteristics of the contact displacement to quantify the time-varying impact of the interference on contact force transmission, and combine the noise fluctuation characteristics of the micro-force signal to define the critical conditions for interference suppression, and finally generate a comprehensive suppression coefficient to provide a basis for the interference suppression decision in Step S4.

[0057] Step S3 includes the following: S3-1, Quantify the time-varying impact of the interference on contact force transmission: Utilize the dominant frequency band and spatial weight of the interference determined in Step S2, as well as the dynamic fluctuation characteristics of the contact displacement, to quantify the time-varying impact of the interference on contact force transmission. It is achieved by calculating the correlation between the distribution of interference energy within the dominant frequency band of the interference and the sensitivity of the contact displacement to frequency changes, generating a time-varying impact factor to reflect the dynamic impact degree of the interference on contact force transmission.

[0058] The calculation logic is to divide the sum of interference energy within the dominant frequency band of the interference by the fluctuation amplitude of the contact displacement within the same frequency band, and then multiply by a preset normalization coefficient to ensure that the result has dynamic characteristics in time. The reason is that the impact of the interference on contact force transmission is not fixed but fluctuates with time and frequency. Especially when the contact displacement changes rapidly, the impact of the interference may increase significantly. This quantification method can track the change of the impact of the interference on contact force transmission in real time.

[0059] For example, the method of quantifying the time-varying impact of the interference on contact force transmission can be as follows: Obtain the dominant frequency band of the interference from Step S2 , in hertz (Hz), and the spatial weight , dimensionless, representing the spatial distribution characteristics of the interference; at the same time, obtain the dynamic fluctuation characteristics of the contact displacement, denoted as , in meters (m), representing the change of the contact displacement over time. Define the interference impact factor , dimensionless, used to quantify the time-varying impact of interference energy on contact force transmission, and the calculation formula is: ; : The power spectral density of the equivalent transient current density distribution generated in step S2, with the unit of per hertz , indicating the distribution of interference energy in the frequency domain.

[0060] : The partial derivative of the contact displacement with respect to frequency, with the unit of meters per hertz (m / Hz), characterizing the sensitivity of the contact displacement to the change in interference frequency.

[0061] The larger the value of

[0062] S3-2. Combine the noise fluctuation characteristics of the micro-force signal: After quantifying the time-varying impact of interference on contact force transmission, combine the noise fluctuation characteristics of the micro-force signal obtained in step S1 to analyze the correlation between the interference impact and the micro-force signal noise. This is done by calculating the correlation between the interference impact factor and the micro-force signal noise over time to generate a correlation coefficient for evaluating the actual impact degree of interference on the micro-force signal. The calculation logic is to normalize the fluctuation values of the interference impact factor and the micro-force signal noise within a selected time interval, and then calculate the similarity of their fluctuation trends through integration to obtain a numerical correlation coefficient. The reason is that the noise fluctuation characteristics of the micro-force signal directly affect the accuracy of contact force feedback. By quantifying its correlation with the interference impact factor, the specific effect of interference on system performance can be clarified. This correlation analysis provides a quantitative basis for judging the necessity of interference suppression, ensuring the pertinence and effectiveness of subsequent suppression measures and avoiding unnecessary resource waste.

[0063] For example, the way to obtain the noise fluctuation characteristics of the micro-force signal can be as follows: Obtain the noise fluctuation characteristics of the micro-force signal from step S1, denoted as , with the unit of volts (V), representing the noise component in the micro-force sensing signal.

[0064] Calculate the correlation between the interference impact factor and the micro-force signal noise , and define the correlation coefficient , dimensionless, and the calculation formula is: ; The average value within the time interval , dimensionless.

[0065] The average value within the time interval , in volts (V).

[0066] and : The endpoints of the time interval for analysis, in seconds (s), determined by the actual measurement range.

[0067] The value range of [[value]] is [-1, 1]. The closer the value is to 1, the stronger the positive correlation between the interference effect and the micro-force signal noise.

[0068] S3-3. Define the critical condition for interference suppression: After analyzing the correlation between the interference influence factor and the micro-force signal noise, define the critical condition for interference suppression. Numerically compare the correlation coefficient with a preset threshold. If the correlation coefficient is greater than the preset threshold, trigger the interference suppression process; otherwise, maintain the current state of the system. By setting the critical condition, unnecessary processing can be avoided when the interference influence is small, thus saving system resources. At the same time, measures can be taken in a timely manner when the interference influence is large to ensure the stable operation of the system.

[0069] S3-4. Generate the comprehensive suppression coefficient: After defining the critical condition for interference suppression, generate the comprehensive suppression coefficient through the dimensionless correlation of multi-dimensional parameters. It comprehensively calculates the interference influence factor, the correlation coefficient, the total interference energy within the dominant interference frequency band, and the maximum value of the spatial weight to generate a dimensionless coefficient, which is used to reflect the comprehensive influence degree of interference on the contact force transmission and the micro-force signal. The calculation logic can be as follows: Generate the comprehensive suppression coefficient through the dimensionless correlation of multi-dimensional parameters , dimensionless, and the calculation formula is: ; : The total interference energy within the dominant frequency band, in [[unit]] , indicating the overall energy intensity of the interference.

[0070] : The spatial weight The maximum value of [[spatial_weight]], dimensionless, used to normalize the influence of the spatial distribution.

[0071] The larger the value of [[comprehensive_suppression_coefficient]], the more serious the comprehensive influence of interference on the contact force transmission and the micro-force signal, providing a decision basis for step S4.

[0072] Step S3 comprehensively characterizes the comprehensive impact of interference on system performance by quantifying the time-varying impact of interference on contact force transmission, analyzing the correlation between interference impact factors and micro-force signal noise, defining the critical conditions for interference suppression, and generating a comprehensive suppression coefficient.

[0073] Steps S1 to S3 gradually extract the coupling path parameters of transient interference, the dominant frequency band and spatial weight of interference, and generate a comprehensive suppression coefficient through the deployment of an electromagnetic coupling probe array, convolution operation, and multi-dimensional parameter correlation, providing a data basis for quantifying the time-varying impact of interference on contact force transmission. However, specific interference suppression measures cannot be directly formulated relying solely on the comprehensive suppression coefficient. Step S4 needs to judge the necessity of interference suppression based on this coefficient and cancel the common-mode oscillation by adjusting the filter parameters and injecting a reverse compensation waveform to ensure the stability and calibration accuracy of the micro-force signal.

[0074] Step S4 includes the following: S4-1, judge whether the comprehensive suppression coefficient meets the preset conditions: Using the comprehensive suppression coefficient generated in step S3, judge whether the impact of interference on the system reaches the level that needs to be compensated. Numerically compare the comprehensive suppression coefficient with the preset threshold: if the comprehensive suppression coefficient is less than or equal to the preset threshold, it is determined that the interference impact is within an acceptable range and no compensation treatment is required; if the comprehensive suppression coefficient is greater than the preset threshold, it is determined that the interference impact is significant and compensation treatment needs to be performed. The reason is that the comprehensive suppression coefficient comprehensively characterizes the impact degree of interference on contact force transmission and micro-force signal, and the preset threshold is a reference value preset according to the accuracy requirements and application scenarios of the system, used to distinguish whether interference needs to be intervened.

[0075] S4-2, adjust the passband attenuation slope and notch frequency of the filter: After confirming that compensation treatment is required, adjust the passband attenuation slope and notch frequency of the filter according to the specific value of the comprehensive suppression coefficient. The calculation logic is divided into two parts: first, calculate the increment of the passband attenuation slope according to the degree to which the comprehensive suppression coefficient exceeds the preset threshold, and add it to the initial passband attenuation slope to obtain the adjusted passband attenuation slope; second, set the notch frequency to the center frequency of the interference dominant frequency band to concentrate on suppressing the frequency point where the interference energy is most concentrated. Adjusting the passband attenuation slope can control the attenuation speed of the filter's frequency response. The larger the comprehensive suppression coefficient, the larger the adjusted passband attenuation slope, thus stronger suppressing interference; setting the notch frequency directly targets the dominant frequency band of interference to ensure that the filter has the maximum suppression effect at the key frequency point.

[0076] S4-3, synchronously inject a reverse compensation waveform to cancel the common-mode oscillation: While adjusting the filter parameters, a reverse compensation waveform is generated and injected to counteract the influence of common-mode oscillation on the micro-force signal. This is achieved by estimating the waveform of the interference signal and generating an inverted waveform to inject into the signal link of the micro-force sensing unit to neutralize the interference. The calculation logic includes the following steps: First, using the equivalent transient current density distribution in step S2 and the transient interference coupling path parameters in step S1, calculate the estimated value of the interference signal; then, take the inversion of this estimated value as the reverse compensation waveform and perform spatial weighting according to the spatial weight in step S2 to ensure the spatial accuracy of the reverse compensation waveform.

[0077] Step S4 determines whether compensation processing is required by comparing the comprehensive suppression coefficient with a preset threshold, and when necessary, adjusts the passband attenuation slope and notch frequency of the filter according to the specific value of the comprehensive suppression coefficient, while injecting the reverse compensation waveform to counteract the common-mode oscillation. This adaptive interference suppression mechanism combines real-time monitoring and dynamic adjustment of interference to ensure the stability and calibration accuracy of the micro-force signal. By adaptively adjusting the filter parameters and injecting the reverse compensation waveform, the cancellation of common-mode oscillation is achieved, ensuring the stability and calibration accuracy of the micro-force signal.

[0078] Step S4 has effectively suppressed the interference of the high-voltage drive pulse on the micro-force signal, providing a stable filtered micro-force signal and the contact displacement measured by the laser. However, due to the influence of system errors and residual interference, there may be a force-displacement mapping distortion between the displacement measured by the laser and the filtered micro-force signal, resulting in inaccurate calibration parameters. Step S5 aims to analyze the distortion characteristics and iteratively optimize the calibration parameters by correlating these two sets of data to achieve dynamic consistency convergence and ensure the high precision of contact calibration.

[0079] Step S5 includes the following: S5-1, Obtain the contact displacement measured by the laser and the filtered micro-force signal: Obtain the real-time measured contact displacement data from the laser module, where the contact displacement data represents the position change of the contact over time; at the same time, obtain the filtered micro-force signal after interference suppression processing from step S4, and the filtered micro-force signal represents the stable value of the contact force feedback. By synchronously collecting these two sets of data, it lays the foundation for subsequent force-displacement mapping analysis. The calculation logic is to record the output data of the laser module and the processing result of step S4 as time series respectively to ensure the time alignment of the data.

[0080] The reason is that the contact displacement and the filtered micro-force signal are the core input parameters in the calibration process, and the accuracy of both directly affects the reliability of the system measurement accuracy. Real-time acquisition of these data can ensure the timeliness and data consistency of the analysis process, providing reliable original information for subsequent distortion analysis and calibration parameter optimization.

[0081] S5-2, Construct the force-displacement mapping model: After obtaining the contact displacement data and the filtered micro-force signal, construct a force-displacement mapping model to describe the corresponding relationship between the contact force and the contact displacement under ideal conditions. Based on the functional relationship determined by physical characteristics, map the contact displacement to the contact force, while considering the possible distortion errors in actual measurements. The calculation logic is to first assume an ideal mapping function representing the theoretical relationship between the contact displacement and the contact force, and then represent the actually measured filtered micro-force signal as the superposition result of the ideal mapping function value and the distortion error. The reason is that this model can separate the deviation between the ideal state and the actual measurement, providing a theoretical basis for analyzing the distortion error. By constructing the force-displacement mapping model, the gap between the actual measurement and the ideal state can be quantified, providing a clear direction and basis for the optimization of calibration parameters.

[0082] S5-3, Analyze the distortion of the force-displacement mapping: After constructing the force-displacement mapping model, analyze the distortion error between the actually measured filtered micro-force signal and the ideal mapping function. Quantify the magnitude of the distortion error by calculating the average deviation in time between the actual data and the ideal model. The calculation logic is to calculate the difference between the actually measured filtered micro-force signal and the ideal mapping function, square these differences, sum them up over the selected time period, take the average value, and then take the square root of the average value to obtain the root mean square value as the measure of the distortion error. The reason is that the root mean square value can comprehensively reflect the overall level of the distortion error over the entire time period and is an effective indicator for evaluating the accuracy of the force-displacement mapping.

[0083] As Figure 2 shown, S5-4, Iteratively update the calibration parameters: After analyzing the distortion error, update the calibration parameters through an iterative optimization method to reduce the degree of the distortion error. Introduce a displacement correction factor and a gain correction factor, and by adjusting these factors, minimize the root mean square value of the distortion error. The calculation logic is to construct an optimization objective function, where the displacement correction factor and the gain correction factor are variables to be adjusted, and the root mean square value of the distortion error is the objective value to be minimized. Through the optimization algorithm, gradually adjust the displacement correction factor and the gain correction factor until the root mean square value of the distortion error reaches the preset accuracy threshold.

[0084] The displacement correction factor corrects the measurement error of the contact displacement data, and the gain correction factor adjusts the amplitude error of the filtered micro-force signal. The combination of the two can effectively eliminate the main error sources in the system. This iterative optimization method can automatically complete the adjustment process of the calibration parameters, ensuring a high degree of consistency in the force-displacement mapping, while improving the accuracy and efficiency of calibration.

[0085] S5-5, Determine the convergence of dynamic consistency: During the process of iteratively updating the calibration parameters, continuously monitor the root mean square value of the distortion error until it converges to a preset precision threshold. By comparing the root mean square value of the distortion error with the preset precision threshold, determine whether the calibration parameters meet the requirements of dynamic consistency. The calculation logic is to calculate the root mean square value of the distortion error after each iteration. When its value is less than or equal to the preset precision threshold, stop the iterative process and consider that the calibration parameters have been optimized to a satisfactory state.

[0086] The preset precision threshold is set according to the actual precision requirements of the system, which can ensure that the calibration results meet the requirements of the application scenario. The advantage is that this judgment mechanism can automatically terminate the optimization process, avoid waste of resources caused by excessive iteration, and at the same time ensure the accuracy and reliability of the calibration parameters, providing guarantee for the stable operation of the system.

[0087] Step S5 constructs a force-displacement mapping model by correlating the contact displacement data measured by the laser module with the filtered micro-force signal output in step S4, analyzes the distortion error between the actual measurement and the ideal model, and iteratively optimizes the calibration parameters to make the root mean square value of the distortion error converge to the preset precision threshold, ultimately achieving the calibration goal of dynamic consistency.

[0088] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0089] It should be noted that the system of the present invention can be deployed on the device itself to achieve embedded applications, or can also run on a PC or other terminals with a user interface, so as to meet various hardware environments and usage requirements.

[0090] Only some exemplary embodiments of the present invention are described above by way of illustration. Undoubtedly, for those of ordinary skill in the art, without departing from the spirit and scope of the present invention, the described embodiments can be modified in various different ways. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

[0091] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0092] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A micro piezoelectric driven detection contact integrated laser module and a contact calibration method, characterized in that: Includes steps: S1: Real-time acquisition of the time-domain differential characteristics of the driving pulse, the impedance fluctuation of the ground loop and the environmental electromagnetic spectrum data, and joint analysis to extract the coupling path parameters of transient interference; S2: Perform convolution operation on the time-domain differential characteristics of the driving pulse and the frequency-domain impedance characteristics of the ground loop to generate an equivalent transient current density distribution, and determine the dominant frequency band and spatial weight of the interference through power spectrum density analysis and coupling path parameter weighting; S3: According to the energy distribution of the dominant frequency band of the interference and the dynamic fluctuation characteristics of the contact displacement, the time-varying influence of the interference on the contact force transmission is quantified, and the comprehensive suppression coefficient is generated through multi-dimensional parameter correlation in combination with the noise fluctuation characteristics of the micro-force signal; S4: Determine whether to perform compensation processing based on the comprehensive suppression coefficient, and adjust the filter passband attenuation slope and notch frequency when necessary, and simultaneously inject a reverse compensation waveform to offset common mode oscillation; S5: Correlate the laser-measured contact displacement with the filtered micro-force signal, analyze the force-displacement mapping distortion, and iteratively update the calibration parameters until the dynamic consistency converges.

2. The micro piezoelectric driven detection contact integrated laser module and contact calibration method according to claim 1, characterized in that: Step S1 includes the following contents: An electromagnetic coupling probe array is deployed on the high-voltage side of the piezoelectric drive unit, the low-voltage side of the micro-force sensing unit, and the key nodes of the ground loop. The time-domain differential characteristics of the drive pulse are collected to quantify the transient peak characteristics of the high-voltage pulse, the ground loop impedance fluctuation is collected to reflect the dynamic response of the ground loop to the interference current, and the environmental electromagnetic spectrum data is collected to identify the frequency distribution of external interference.

3. The micro piezoelectric driven detection contact integrated laser module and contact calibration method according to claim 2, characterized in that: Step S1 also includes the following contents: The correlation between the time-domain differential characteristics of the driving pulse and the noise fluctuation of the micro-force sensing unit is analyzed. The coupling path transfer function is constructed by combining the ground loop impedance fluctuation quantity and the frequency characteristics of the environmental electromagnetic spectrum data to characterize the transient interference transfer characteristics and coupling strength, so as to obtain the coupling path parameters.

4. The micro piezoelectric driven detection contact integrated laser module and contact calibration method according to claim 3, characterized in that: Step S2 includes the following contents: The time-domain differential characteristics of the driving pulse are converted into a frequency-domain form through a transformation from the time domain to the frequency domain, and a convolution operation is performed with the frequency-domain impedance characteristics of the ground loop to generate an equivalent transient current density distribution, wherein the convolution operation is specifically the multiplication and accumulation of the frequency-domain form of the time-domain differential characteristics of the driving pulse and the frequency-domain impedance characteristics of the ground loop in the frequency domain; the power spectrum density is calculated by analyzing the frequency spectrum characteristics of the equivalent transient current density distribution, specifically by squaring and smoothing the equivalent transient current density distribution to determine the dominant frequency band of the interference, that is, the frequency range where the energy is concentrated; the coupling path parameters and the frequency spectrum characteristics of the equivalent transient current density distribution are weighted to calculate the spatial weight function, specifically by multiplying the coupling path parameters with the frequency spectrum characteristics of the equivalent transient current density distribution and normalizing them to characterize the spatial distribution intensity of the interference inside the system, that is, to quantify the relative intensity of the interference at different positions.

5. The micro piezoelectric driven detection contact integrated laser module and contact calibration method according to claim 4, characterized in that: Step S3 includes the following contents: The interference dominant frequency band and spatial weight, as well as the dynamic fluctuation characteristics of contact displacement, are used to quantify the time-varying effect of interference on contact force transmission. Specifically, the interference influence factor is generated by calculating the correlation between the distribution of interference energy in the interference dominant frequency band and the sensitivity of contact displacement to frequency changes. Combined with the fluctuation characteristics of micro-force signal noise, the temporal correlation between the interference influencing factor and the micro-force signal noise is calculated to generate the correlation coefficient.

6. The micro piezoelectric driven detection contact integrated laser module and contact calibration method according to claim 5, characterized in that: Step S3 also includes the following: The critical condition for interference suppression is set as the correlation coefficient exceeding the preset threshold. Specifically, when the correlation coefficient is greater than the preset threshold, it is determined that the interference has a significant impact on the system. The comprehensive suppression coefficient is generated by comprehensively calculating the interference influence factor, the correlation coefficient, the total interference energy in the interference-dominated frequency band, and the maximum value of the spatial weight. Specifically, the product of the interference influence factor and the correlation coefficient is divided by the total interference energy in the interference-dominated frequency band, and then divided by the maximum value of the spatial weight.

7. The micro piezoelectric driven detection contact integrated laser module and contact calibration method according to claim 6, characterized in that: Step S4 includes the following contents: The comprehensive suppression coefficient is compared with a preset threshold value. When the comprehensive suppression coefficient is less than or equal to the preset threshold value, it is determined that the interference effect is within an acceptable range and no compensation processing is performed; When the comprehensive suppression coefficient is greater than a preset threshold, it is determined that the interference effect is significant and compensation processing is performed.

8. The micro piezoelectric driven detection contact integrated laser module and contact calibration method according to claim 7, characterized in that: Step S4 also includes the following contents: The compensation processing includes adjusting the passband attenuation slope of the filter and generating a reverse compensation waveform, wherein the passband attenuation slope of the filter is adjusted according to the degree to which the comprehensive suppression coefficient exceeds the preset threshold, and the increment of the passband attenuation slope is proportional to the part of the comprehensive suppression coefficient that exceeds the preset threshold, and the notch frequency is set to the center frequency of the interference-dominant frequency band.

9. The micro piezoelectric driven detection contact integrated laser module and contact calibration method according to claim 8, characterized in that: Step S4 also includes the following contents: A reverse compensation waveform is generated and injected into the signal chain of the micro-force sensing unit. The estimated value of the interference signal is calculated through the equivalent transient current density distribution and the transient interference coupling path parameters. Its inverse phase is taken as the reverse compensation waveform, and spatial weighting is performed according to the spatial weight to ensure the accuracy of the reverse compensation waveform.

10. The micro piezoelectric driven detection contact integrated laser module and contact calibration method according to claim 9, characterized in that: Step S5 also includes the following contents: The real-time measured contact displacement data is obtained from the laser module. Combined with the filtered micro-force signal, a force-displacement mapping model is constructed to characterize the corresponding relationship between the contact force and the contact displacement under the ideal state. The actually measured filtered micro-force signal is represented as the superposition of the ideal mapping function value and the distortion error. The root mean square value of the distortion error is determined by calculating the difference between the filtered micro-force signal and the ideal mapping function. The calibration parameters, including the displacement correction factor and the gain correction factor, are adjusted using an iterative optimization method to minimize the root mean square value of the distortion error. The root mean square value of the distortion error is continuously monitored until it converges to a preset accuracy threshold to confirm that the calibration parameters have achieved dynamic consistency. Finally, the dynamic calibration of the force-displacement mapping is achieved by associating the contact displacement data with the filtered micro-force signal.

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