High-precision displacement correction method and device for wooden box steel belt transmission

Abstract

The present invention relates to the field of displacement correction technology, and discloses a method and device for high-precision displacement correction of a wooden box steel belt drive. The method comprises: installing and calibrating a high-precision grating ruler to create a first calibration curve and a first compensation model; acquiring steel belt displacement data within a high dynamic range to obtain raw displacement data; performing wavelet noise reduction and multi-scale trend analysis to obtain noise-reduced displacement data and displacement trend information; performing adaptive correction control to generate correction control instructions; precisely executing the correction control instructions through linear motor drive and air-floating guide rails to obtain actual correction actions and corresponding execution results; and performing digital twin simulation and multi-objective optimization based on the actual correction actions and execution results to obtain a second calibration curve and a second compensation model. The present invention achieves high-precision displacement correction of the wooden box steel belt drive system, improving the overall performance and reliability of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of displacement correction, and in particular to a high-precision displacement correction method and device for a wooden box steel belt drive. Background Art

[0002] Wooden crate steel belt drive systems are widely used in industries such as packaging and logistics, where their displacement accuracy directly impacts product quality and production efficiency. However, due to mechanical errors, environmental factors, and system nonlinearity, traditional displacement control methods struggle to meet these high-precision requirements. Especially under high-speed and long-duration operating conditions, the steel belts are prone to drift, vibration, and creep, leading to cumulative displacement errors.

[0003] Existing displacement correction technologies primarily rely on simple PID control and fixed parameter compensation, which cannot effectively cope with system changes in complex dynamic environments. Furthermore, traditional displacement measurement methods have limited accuracy, making it difficult to provide reliable data support for high-precision correction. Furthermore, system model uncertainty and parameter drift also pose challenges to precise control. Summary of the Invention

[0004] The present invention provides a high-precision displacement correction method and device for a wooden box steel belt transmission, which are used to achieve high-precision displacement correction of a wooden box steel belt transmission system and improve the overall performance and reliability of the system.

[0005] In a first aspect, the present invention provides a high-precision displacement correction method for a wooden box steel belt transmission, the high-precision displacement correction method for a wooden box steel belt transmission comprising:

[0006] Install and calibrate the high-precision grating ruler, and create the first calibration curve and the first compensation model;

[0007] According to the first calibration curve and the first compensation model, the steel strip displacement data is collected in a high dynamic range to obtain original displacement data;

[0008] Performing wavelet noise reduction and multi-scale trend analysis on the original displacement data to obtain noise-reduced displacement data and displacement trend information;

[0009] Performing adaptive deviation correction control based on the noise reduction displacement data and the displacement trend information to generate a deviation correction control instruction;

[0010] The correction control instruction is accurately executed by a linear motor drive and an air-floating guide rail to obtain an actual correction action and a corresponding execution result;

[0011] According to the actual correction action and the execution result, digital twin simulation and multi-objective optimization are performed on the first calibration curve and the first compensation model to obtain a second calibration curve and a second compensation model.

[0012] In a second aspect, the present invention provides a high-precision displacement correction device for a wooden box steel belt drive, the high-precision displacement correction device for a wooden box steel belt drive comprising:

[0013] An installation module is used to install and calibrate the high-precision grating ruler and create a first calibration curve and a first compensation model;

[0014] an acquisition module, configured to acquire the steel strip displacement data in a high dynamic range according to the first calibration curve and the first compensation model to obtain original displacement data;

[0015] An analysis module, configured to perform wavelet noise reduction and multi-scale trend analysis on the original displacement data to obtain noise-reduced displacement data and displacement trend information;

[0016] a control module, configured to perform adaptive deviation correction control based on the noise reduction displacement data and the displacement trend information, and generate a deviation correction control instruction;

[0017] An execution module, configured to drive the linear motor and the air-floating guide rail to accurately execute the deviation correction control instruction, thereby obtaining an actual deviation correction action and a corresponding execution result;

[0018] An optimization module is used to perform digital twin simulation and multi-objective optimization on the first calibration curve and the first compensation model according to the actual correction action and the execution result to obtain a second calibration curve and a second compensation model.

[0019] The third aspect of the present invention provides a high-precision displacement correction device for wooden box steel belt transmission, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory to enable the high-precision displacement correction device for wooden box steel belt transmission to execute the above-mentioned high-precision displacement correction method for wooden box steel belt transmission.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when run on a computer, enable the computer to execute the above-mentioned high-precision displacement correction method for wooden box steel belt transmission.

[0021] In the technical solution provided by the present invention, the application and precise calibration of high-precision grating rulers improve the basic accuracy of displacement measurement, and high dynamic range data acquisition and multi-level signal processing technology effectively improve the quality of original displacement data and reduce the influence of measurement noise and interference. Wavelet noise reduction and multi-scale trend analysis methods can effectively separate noise and effective information in displacement data, thereby improving the accuracy of subsequent control. The adaptive correction control strategy combines fuzzy control, PID control and reinforcement learning to achieve rapid response and precise adjustment to complex dynamic environments. The precise actuators of linear motor drive and air-floating guide rails improve the execution accuracy of the correction action and reduce the influence of mechanical errors. Digital twin simulation and multi-objective optimization methods realize dynamic optimization of calibration curves and compensation models, thereby improving the long-term stability and adaptability of the system. Taking into account a variety of influencing factors such as temperature and speed, a more comprehensive and accurate compensation model is constructed, which effectively improves the overall effect of correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A schematic flow chart of a high-precision displacement correction method for a wooden box steel belt drive provided in an embodiment of the present application;

[0024] Figure 2 This is a schematic block diagram of the structure of the high-precision displacement correction device for wooden box steel belt transmission provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0026] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may change based on actual circumstances.

[0027] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0028] It should be further understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0029] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0030] See also Figure 1 , Figure 1 The flowchart of the high-precision displacement correction method for the wooden box steel belt transmission provided in the embodiment of the present application is as follows: Figure 1 As shown, the high-precision displacement correction method for wooden box steel belt transmission provided in the embodiment of the present application includes steps S100 to S600.

[0031] Step S100: Install and calibrate the high-precision grating ruler, and create a first calibration curve and a first compensation model;

[0032] It is understandable that the execution subject of the present invention can be a high-precision displacement correction device for a wooden box steel belt drive, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0033] Specifically, the grating scale is calibrated using a precision level and an inclinometer to obtain initial installation error data. This initial installation error data is used to identify horizontal or angular deviations of the grating scale during installation. Based on this initial installation error data, the high-precision grating scale is adjusted to ensure that the grating scale is in the calibrated installation position. After the grating scale is installed and adjusted, a laser interferometer is used to perform multi-point measurements to obtain corresponding data between the grating scale readings and the interferometer readings, reflecting the relationship between the grating scale readings at different positions and the precise measurement results of the laser interferometer. The corresponding data of the grating scale readings and the interferometer readings are fitted using the least squares method to obtain the initial calibration curve equation. This equation is a mathematical model between the grating scale readings and the actual position, and preliminarily reflects any systematic errors that may exist in the grating scale measurement process. Based on the initial calibration curve equation, a nonlinear error analysis is performed on the grating scale to obtain a nonlinear error distribution function that describes the distribution of nonlinear errors within the grating scale measurement range. Based on this function, the initial calibration curve equation is corrected to generate a more accurate first calibration curve. The first calibration curve is subjected to a Fourier series expansion to extract the periodic error compensation term during the grating scale measurement process. Periodic errors are typically related to the mechanical properties of the grating scale, and the Fourier series expansion can decompose these errors into several harmonic components, each corresponding to a specific physical phenomenon. The extracted harmonic components are used to construct a temperature compensation model. In actual operation, temperature has a significant impact on the measurement accuracy of the grating scale. Based on the periodic error compensation term and temperature data obtained from the temperature sensor, a temperature compensation model is constructed to obtain a temperature compensation function. The temperature compensation function and the periodic error compensation term are combined to construct a first compensation model. This compensation model is used to correct the reading deviation of the grating scale at different temperatures and different measurement positions, thereby improving measurement accuracy. This compensation model can be expressed as C(x, T) = x + Σ[Ai*sin(2πix / P+φi)] + k(T-T0), where C(x, T) is the actual position value after compensation, x is the original reading of the scale, Ai is the amplitude of the i-th harmonic in the Fourier series, P is the fundamental period of the scale, φi is the phase of the i-th harmonic, k is the temperature compensation coefficient, T is the current ambient temperature, and T0 is the reference temperature. This model can effectively correct the original reading of the scale, eliminate the effects of periodic errors and temperature changes, and achieve high-precision displacement measurement.

[0034] Step S200: performing high dynamic range acquisition on the steel strip displacement data according to the first calibration curve and the first compensation model to obtain original displacement data;

[0035] Specifically, the scale signal is pre-amplified to produce an amplified analog signal. This amplified signal has a higher signal-to-noise ratio and can more effectively capture displacement changes. The amplified analog signal is bandpass filtered to eliminate high- and low-frequency noise, retaining only the effective frequency band related to displacement. Dynamic gain adjustment is performed on the filtered signal using a programmable gain amplifier to ensure that the signal's dynamic range matches the input range of the ADC (analog-to-digital converter), preventing ADC distortion or sampling inaccuracies caused by excessively high or low signal amplitudes. After dynamic gain adjustment, the signal adapted to the ADC input range undergoes analog-to-digital conversion, converting the analog signal into digitized displacement data. Nonlinear error correction is performed on the digitized displacement data based on a first calibration curve to correct for measurement errors caused by the scale at different positions or under different conditions, ensuring the data more accurately reflects actual displacement. Periodic error compensation is then applied to the corrected displacement data based on the periodic error compensation term in the first compensation model. Periodic error is often associated with periodic vibrations in the mechanical structure or system. Compensating for these errors improves measurement accuracy and reliability. Combining the data from the temperature sensor and the temperature compensation function in the first compensation model, the compensated displacement data is temperature compensated to reduce or eliminate the influence of temperature on the displacement measurement, thereby obtaining more accurate temperature-compensated displacement data. The temperature-compensated displacement data is subjected to moving average filtering to smooth the data and remove occasional high-frequency noise, thereby obtaining preliminary filtered displacement data. Based on the Kalman filter algorithm, the displacement data after preliminary filtering is subjected to noise suppression. Kalman filtering is an adaptive filtering algorithm applied to dynamic systems that can provide optimal estimates under a variety of noise conditions. After Kalman filtering, the random noise in the displacement data is effectively suppressed, resulting in smoother and more reliable noise-suppressed displacement data. Data integrity checks and outlier detection are performed on the noise-suppressed displacement data to identify and eliminate potential erroneous data, ultimately obtaining high-quality original displacement data.

[0036] Step S300: performing wavelet noise reduction and multi-scale trend analysis on the original displacement data to obtain noise-reduced displacement data and displacement trend information;

[0037] Specifically, wavelet decomposition is performed on the original displacement data, breaking the displacement signal into multiple wavelet coefficients of different scales, which reflect the characteristic information of the signal at different frequencies. Soft thresholding is then performed on the multiple layers of wavelet coefficients to remove noise from the signal. By setting an appropriate threshold and performing soft thresholding on the wavelet coefficients, the influence of noise is effectively reduced, resulting in denoised wavelet coefficients. Wavelet reconstruction is then performed on the denoised wavelet coefficients, recombining them to recover displacement data close to the original signal, thus obtaining preliminarily denoised displacement data. The preliminarily denoised displacement data is then decomposed to obtain multiple singular value components. Singular value components are different components in the signal that reveal the inherent structure and trends of the data. These multiple singular value components are selected based on the cumulative contribution ratio criterion. The cumulative contribution ratio criterion helps determine which components contribute most to the overall trend of the signal. After selecting these target trend components, reconstruction is performed to obtain long-term trend data, which reflects the main direction of change and long-term trend in the signal. Long-term trend data is the low-frequency component of the signal. Simultaneously, modal decomposition is performed on the initially denoised displacement data to obtain multiple intrinsic mode functions (IMFs), which reflect the signal's oscillatory components at different time scales. By analyzing their energy ratios and frequency characteristics, IMF combinations representing short-term fluctuations are screened. Short-term fluctuations are high-frequency components in the signal, reflecting rapid changes and localized oscillations in the data. These IMF combinations representing short-term fluctuations are reconstructed to generate short-term fluctuation data. By weightedly combining the long-term trend data with the short-term fluctuation data, denoised displacement data and complete displacement trend information are obtained. Denoised displacement data provides more accurate displacement measurements, while displacement trend information reveals both long-term trends and short-term fluctuation characteristics in the signal.

[0038] Step S400: performing adaptive deviation correction control based on the noise reduction displacement data and displacement trend information, and generating a deviation correction control instruction;

[0039] Specifically, the noise-reduced displacement data and displacement trend information are fuzzified, converting continuous displacement data and trend information into fuzzy input variables. Fuzzy variables can effectively handle uncertainty and nonlinear characteristics in the system. A preset fuzzy rule base is used to infer the fuzzy input variables. Fuzzy output variables are generated based on the logical relationships and empirical rules in the rule base, preliminarily reflecting the control adjustment direction and magnitude required by the system. The fuzzy output variables are defuzzified using the center of gravity method, converting the fuzzy output into specific numerical values ​​to obtain initial PID parameters. These initial PID parameters, including proportional, integral, and differential coefficients, serve as preliminary control parameters and are applied to the PID controller to perform preliminary PID control calculations on the noise-reduced displacement data, obtaining the initial control variable. Feedforward compensation is then applied to the initial control variable in combination with displacement trend information. Feedforward compensation uses displacement trend information to predict the future state of the system, thereby preemptively correcting potential errors and obtaining the compensated control variable. Reinforcement learning is then performed on the compensated control variable and the current system state. Reinforcement learning continuously adjusts and improves the control action based on the current system state. Through interactive learning with the environment, the control strategy is gradually optimized, resulting in the optimized control action. Based on the optimized control action, the initial PID parameters are adjusted online to obtain adaptive PID parameters. Adaptive PID parameters can adaptively adjust the controller's response characteristics based on changes in real-time operating conditions, ensuring that the system maintains good control performance under different operating conditions. Based on the adaptive PID parameters, a secondary PID control calculation is performed on the noise-reduced displacement data to obtain the optimized control variable. The optimized control variable is saturated and limited to ensure that the control variable does not exceed the system's physical limitations or safety range during actual execution, resulting in a limited control variable. This limited control variable is ultimately converted into a correction control instruction, specifically including motor drive current and direction information. The motor drive current determines the strength of the correction action, while the direction information indicates the direction of the correction.

[0040] Step S500: The linear motor drives and the air-floating guide rails accurately execute the deviation correction control instruction to obtain the actual deviation correction action and the corresponding execution result;

[0041] Specifically, the correction control command is converted into a current signal. A dedicated current signal conversion device generates a drive current signal for the linear motor. These drive current signals are the core parameters for controlling the linear motor. Based on these drive current signals, closed-loop current control is performed on the linear motor to precisely control its actual output force. This closed-loop current control ensures that the linear motor can output stable and accurate force according to the desired command. During the drive current control process, Hall sensors detect the linear motor's position in real time, generating motor position feedback signals. Analysis of these feedback signals enables real-time monitoring of motor position changes and, based on these signals, position closed-loop control. This closed-loop position control process continuously corrects the motor's position error, ensuring that the linear motor performs displacement correction operations along the preset trajectory, resulting in precise position control. Simultaneously, the air pressure of the air-bearing guide rail is adaptively adjusted to ensure optimal air bearing conditions. The air pressure of the air-bearing guide rail directly affects its floating performance. This adaptive adjustment ensures smooth operation under varying load conditions. A capacitive displacement sensor samples the floating clearance of the air-bearing guide rail at high frequency, generating floating clearance data that reflects the minute fluctuations during operation. This data is then used to dynamically balance the guide rail. Dynamic balancing control effectively reduces vibration and drift during guide rail motion, ensuring stable guide motion. To achieve optimal correction, the motion of the linear motor and the air-bearing guide rail is collaboratively optimized to form a comprehensive execution strategy. This strategy comprehensively considers the linear motor's force output and position control, as well as the floating characteristics of the air-bearing guide rail, ensuring their coordinated operation for optimal correction. Based on this comprehensive execution strategy, the correction mechanism is precisely driven to execute the correction operation and obtain the actual correction action. After the actual correction action is completed, the correction results are measured using a high-precision optical scale to obtain accurate execution results. This process verifies the accuracy and effectiveness of the correction operation, and based on the execution results, makes necessary adjustments and optimizations to subsequent correction operations, ensuring that the entire system maintains high-precision correction control under various operating conditions.

[0042] Step S600: Based on the actual correction action and the execution result, digital twin simulation and multi-objective optimization are performed on the first calibration curve and the first compensation model to obtain a second calibration curve and a second compensation model.

[0043] Specifically, data preprocessing is performed on the actual correction actions and execution results to eliminate noise and normalize data of different scales to obtain a standardized input dataset. Based on this standardized dataset, a virtual system simulation environment is constructed, which realistically simulates the behavior of the physical system using digital twin technology. Within this virtual system simulation environment, parameter sensitivity analysis is performed on the first calibration curve to identify key factors affecting system performance. Based on these key influencing factors, polynomial fit optimization is performed on the first calibration curve to generate a more accurate second calibration curve. Simultaneously, the first compensation model is structurally decomposed into multiple sub-models, including a periodic error compensation sub-model and a temperature compensation sub-model. Each sub-model addresses different error factors involved in the compensation process. Parameter optimization is performed on these multiple sub-models using a genetic algorithm. The genetic algorithm simulates the process of natural selection, progressively optimizing sub-model parameters through iterative selection and crossover mutation, resulting in an optimized sub-model parameter set. Based on this optimized parameter set, the model is globally optimized to find a global optimal solution, ensuring that the compensation model achieves optimal performance under all operating conditions. Based on the global optimal solution, a multi-objective optimization problem is constructed. The objective function not only considers displacement accuracy but also incorporates model complexity into the optimization objectives, thereby obtaining a Pareto-optimal solution set. Each solution in the Pareto-optimal solution set represents a trade-off between these two objectives, reflecting the highest achievable accuracy within a certain complexity. To select the optimal solution from the Pareto-optimal solution set, the TOPSIS decision-making method is used to evaluate and select it. The TOPSIS method performs a weighted calculation on multiple evaluation indicators to select the solution that is closest to the ideal solution, thereby obtaining the second compensation model. The second compensation model is expressed as: C(x, T, v) = x + Σ[Aisin(2πix / P + φi)] + k(T - T0) + f(v), where C(x, T, v) represents the actual position value after compensation, x is the original reading of the scale, Ai is the amplitude of the i-th harmonic, P is the fundamental period of the scale, φi is the phase of the i-th harmonic, k is the temperature compensation coefficient, T is the current temperature, T0 is the reference temperature, and f(v) is the speed-related compensation function, which takes the form of f(v) = αv + βv^2 + γsign(v), where α, β, and γ are the speed compensation coefficients, and sign(v) is the speed direction function. This comprehensive model not only takes into account temperature and periodic errors, but also introduces the influence of speed, enabling the compensation model to more comprehensively cope with various complex working conditions and improve the overall accuracy and response speed of the correction system.

[0044] In the embodiment of the present invention, the application and precise calibration of high-precision grating rulers improve the basic accuracy of displacement measurement, and high dynamic range data acquisition and multi-level signal processing technology effectively improve the quality of original displacement data and reduce the influence of measurement noise and interference. Wavelet noise reduction and multi-scale trend analysis methods can effectively separate noise and effective information in displacement data, thereby improving the accuracy of subsequent control. The adaptive correction control strategy combines fuzzy control, PID control and reinforcement learning to achieve rapid response and precise adjustment to complex dynamic environments. The precise actuators of linear motor drive and air-floating guide rails improve the execution accuracy of the correction action and reduce the influence of mechanical errors. Digital twin simulation and multi-objective optimization methods realize dynamic optimization of calibration curves and compensation models, thereby improving the long-term stability and adaptability of the system. Taking into account a variety of influencing factors such as temperature and speed, a more comprehensive and accurate compensation model is constructed, which effectively improves the overall effect of correction.

[0045] In a specific embodiment, the process of executing step S100 may specifically include the following steps:

[0046] Calibrate the high-precision grating ruler with a precision level and inclinometer to obtain initial installation error data, and adjust the high-precision grating ruler based on the initial installation error data to obtain the calibrated grating ruler installation position;

[0047] Based on the laser interferometer, multi-point measurement is performed on the calibrated grating scale to obtain the corresponding data of the grating scale reading and the interferometer reading. The corresponding data of the grating scale reading and the interferometer reading are fitted by the least squares method to obtain the initial calibration curve equation;

[0048] According to the initial calibration curve equation, a nonlinear error analysis is performed on the grating ruler to obtain a nonlinear error distribution function, and based on the nonlinear error distribution function, the initial calibration curve equation is corrected to obtain a first calibration curve;

[0049] Performing a Fourier series expansion on the first calibration curve to obtain a periodic error compensation term, and constructing a temperature compensation model based on the periodic error compensation term and temperature sensor data to obtain a temperature compensation function;

[0050] Based on the temperature compensation function and the periodic error compensation term, the first compensation model is constructed, where the first compensation model is: C(x, T)=x+Σ[Ai*sin(2πix / P+φi)]+k(T-T0), where C(x, T) is the compensated position value, x is the original reading of the grating scale, Ai is the i-th harmonic amplitude, P is the basic period of the grating scale, φi is the i-th harmonic phase, k is the temperature compensation coefficient, T is the current temperature, and T0 is the reference temperature.

[0051] Specifically, the high-precision grating scale is calibrated with a precision level and an inclinometer. By using a precision level and an inclinometer, the grating scale is preliminarily calibrated to detect the angular error and horizontal error of the grating scale during the installation process. These errors may be caused by slight tilt or distortion of the grating scale during installation, affecting the measurement accuracy. A level is used to detect the horizontal inclination of the grating scale, while an inclinometer is used to measure the angular deviation between the grating scale and the ideal installation position. The initial installation error data is obtained through measurement. The grating scale is adjusted according to the initial installation error data to achieve the ideal calibration position. The adjustment process may involve fine-tuning the grating scale support structure to ensure that the errors of the grating scale in the horizontal and angular directions are minimized to achieve the accuracy standards required for measurement. The calibrated grating scale is measured at multiple points using a laser interferometer to ensure that it has consistent accuracy throughout the entire measurement range. The laser interferometer is a high-precision measurement tool that provides higher measurement accuracy than the grating scale. By selecting multiple measurement points within the scale's measurement range, the scale readings and interferometer readings are measured. Corresponding data between the scale readings and the laser interferometer readings is obtained, reflecting the actual measurement error of the scale at different positions. The scale readings and interferometer readings are then fitted using the least squares method. The least squares method is a mathematical technique that minimizes the squared error of the measured data to obtain a best-fit curve, which serves as the equation for the initial calibration curve. This equation represents the relationship between the scale reading and the actual position, mathematically describing the error characteristics of the scale in an uncompensated state. Nonlinear error analysis is performed on the initial calibration curve equation to obtain a nonlinear error distribution function. The nonlinear error distribution function describes the nonlinear deviations of the scale at different measurement points. By analyzing these deviations, the initial calibration curve equation is modified to obtain a more accurate first calibration curve. For example, suppose the initial calibration curve equation for the scale is y=ax+b, where y is the scale reading, x is the actual position, and a and b are fitting parameters. Nonlinear error analysis reveals that this equation has systematic deviations in certain sections, resulting in inaccurate measurement results. Based on the nonlinear error distribution function, the equation is modified, for example by adding a nonlinear term: y = ax + b + c * sin (dx), where c and d are correction parameters obtained through nonlinear error analysis. This modified equation becomes the first calibration curve, which more closely approximates the actual measurement conditions of the scale. To compensate for errors caused by ambient temperature fluctuations during scale operation, the first calibration curve is subjected to a Fourier series expansion to obtain a periodic error compensation term. The Fourier series expansion decomposes the periodic error in the calibration curve into multiple harmonic components, expressed as Σ[Ai * sin (2πix / P + φi)], where Ai is the amplitude of the i-th harmonic, P is the scale's fundamental period, and φi is the phase of the i-th harmonic. These harmonic components reflect the errors in the scale caused by the mechanical periodicity.Consider the impact of temperature on the measurement accuracy of the grating ruler. Real-time temperature data is obtained through a temperature sensor, and a temperature compensation model is constructed based on this data to obtain a temperature compensation function. The temperature compensation function can be formalized as k(T-T0), where k is the temperature compensation coefficient, T is the current temperature, and T0 is the reference temperature. This function is used to correct the measurement error of the grating ruler at different temperatures. Combining the temperature compensation function and the periodic error compensation term, a first compensation model is constructed. The mathematical expression of this model is C(x, T)=x+Σ[Ai*sin(2πix / P+φi)]+k(T-T0), where C(x, T) represents the actual position value after compensation and x is the original reading of the grating ruler. This model takes into account the linear and nonlinear errors of the grating ruler and introduces compensation for periodic error and temperature error, thereby improving measurement accuracy.

[0052] In a specific embodiment, the process of executing step S200 may specifically include the following steps:

[0053] Pre-amplifying the grating scale signal to obtain an amplified analog signal, and performing band-pass filtering on the amplified analog signal to obtain a filtered signal;

[0054] Based on the programmable gain amplifier, the filtered signal is dynamically adjusted in gain to obtain a signal that is adapted to the ADC input range, and the signal that is adapted to the ADC input range is converted into analog to digital to obtain digital displacement data;

[0055] Performing nonlinear error correction on the digitized displacement data according to the first calibration curve to obtain corrected displacement data, and performing periodic error compensation on the corrected displacement data based on the periodic error compensation term in the first compensation model to obtain compensated displacement data;

[0056] performing temperature compensation on the compensated displacement data according to the temperature sensor data and the temperature compensation function in the first compensation model to obtain temperature compensated displacement data, and performing moving average filtering on the temperature compensated displacement data to obtain preliminary filtered displacement data;

[0057] Based on the Kalman filter algorithm, noise suppression is performed on the displacement data after preliminary filtering to obtain noise-suppressed displacement data, and data integrity check and outlier detection are performed on the noise-suppressed displacement data to obtain the original displacement data.

[0058] Specifically, the scale signal is preamplified to amplify the weak signal output by the scale, generating a higher-amplitude analog signal and effectively improving the signal-to-noise ratio. The amplified analog signal is then bandpass filtered. The bandpass filter allows signals within a specific frequency range to pass while filtering out frequency components outside this range. By selecting appropriate bandpass filter parameters, the effective components of the scale signal are retained while removing unwanted noise, resulting in a filtered signal. Dynamic gain adjustment of the signal is performed using a programmable gain amplifier (PGA). A PGA is an amplifier that dynamically adjusts gain based on the input signal's characteristics, ensuring that the signal amplitude is within the input range of the analog-to-digital converter (ADC) before entering the ADC. Signal amplitudes that are too low can result in loss of signal detail, while amplitudes that are too high can lead to signal saturation. The PGA's dynamic gain adjustment optimizes the signal amplitude to the ADC's optimal input range, ensuring high-precision signal conversion. After dynamic gain adjustment, the signal is fed into the ADC for analog-to-digital conversion. This conversion process converts the analog signal into a digital signal, generating digitized displacement data. Nonlinear error correction is performed on the digitized displacement data based on a first calibration curve. The first calibration curve is usually obtained through error analysis and fitting of the grating scale, which describes the systematic error of the grating scale during the measurement process. By applying this calibration curve, the nonlinear error in the digitized displacement data is corrected to obtain corrected data that is closer to the actual displacement. For example, if the calibration curve is in the form of:

[0059] ;

[0060] in, is the actual location, is the original reading of the grating ruler, 、 、 is the parameter of the calibration curve. According to this relationship, the digitized data is corrected to eliminate the influence of nonlinear errors. Based on the periodic error compensation term in the first compensation model, the corrected displacement data is corrected. The periodic error compensation term is usually in the form of a Fourier series. Through this compensation term, the error caused by periodic factors during the measurement of the grating scale is effectively eliminated to obtain the compensated displacement data. According to the data of the temperature sensor and the temperature compensation function in the first compensation model, the compensated displacement data is temperature compensated. The temperature compensation function is usually expressed as a temperature compensation coefficient and temperature difference The product of . Through this function, the displacement data is adjusted to eliminate the deviation caused by temperature changes, and the temperature-compensated displacement data is obtained. In order to smooth the data and reduce the influence of random noise, the temperature-compensated displacement data is subjected to moving average filtering. The moving average filter smoothes short-term fluctuations and emphasizes long-term trends by calculating the average value of a set of data. For example, using a length of The window of smoothing the data is used, and the value of each data point is the value before and after The average value of each data point is calculated, effectively reducing high-frequency noise in the measured data and producing preliminary filtered displacement data. A Kalman filter algorithm is then used to suppress noise in this filtered displacement data. Kalman filtering is a recursive filter that suppresses noise by estimating the system state and is suitable for processing noise in dynamic systems. Kalman filtering estimates the true system state and produces noise-suppressed displacement data. Data integrity checks and outlier detection are performed on this noise-suppressed displacement data to ensure data accuracy throughout the entire processing chain. Possible outlier data points can be identified and removed to produce reliable raw displacement data.

[0061] In a specific embodiment, the process of executing step S300 may specifically include the following steps:

[0062] Perform wavelet decomposition on the original displacement data to obtain multi-layer wavelet coefficients, and perform soft threshold processing on the multi-layer wavelet coefficients to obtain the denoised wavelet coefficients;

[0063] Performing wavelet reconstruction on the denoised wavelet coefficients to obtain initial denoised displacement data, and decomposing the initial denoised displacement data to obtain multiple singular value components;

[0064] According to the cumulative contribution rate criterion, multiple singular value components are selected to obtain the target trend component, and the target trend component is reconstructed to obtain long-term trend data;

[0065] Perform modal decomposition on the initially denoised displacement data to obtain multiple intrinsic mode functions (IMFs), and screen these multiple IMFs based on energy ratio and frequency characteristics to obtain an IMF combination representing short-term fluctuations.

[0066] The IMF combination representing short-term fluctuations is reconstructed to obtain short-term fluctuation data, and the long-term trend data and short-term fluctuation data are weightedly combined to obtain noise-reduced displacement data and displacement trend information.

[0067] Specifically, the original data is subjected to wavelet decomposition. Wavelet decomposition is a signal processing tool that can decompose complex signals into wavelet coefficients of different scales. These coefficients represent the characteristic information of the signal in different frequency ranges. By performing multi-level wavelet decomposition on the displacement data, a series of wavelet coefficients are obtained. The wavelet coefficients of each layer correspond to the performance of the signal at different scales, revealing the multi-scale characteristics of the signal. Soft threshold processing is performed on the multi-level wavelet coefficients. Soft threshold processing is a commonly used noise reduction method that suppresses those wavelet coefficients with small amplitudes that may represent noise by applying a threshold to the wavelet coefficients. Assume that a certain wavelet coefficient is , the threshold value is set to , then the processed wavelet coefficients It can be expressed as:

[0068] ;

[0069] By using this formula, the noise is reduced and the main features of the signal are retained to obtain the denoised wavelet coefficients. The denoised wavelet coefficients are subjected to wavelet reconstruction, and the decomposed multi-layer wavelet coefficients are recombined into a complete signal, that is, the displacement data after preliminary denoising. In order to extract the main trend information in the signal, the displacement data after preliminary denoising is decomposed and the singular value decomposition is used to decompose the signal into several singular value components, each of which represents a specific pattern in the signal. By using the cumulative contribution rate criterion, the singular value components that contribute the most to the signal are selected as the target trend components. Assume that the data matrix Perform SVD decomposition and the decomposition form is:

[0070] ;

[0071] in, and is an orthogonal matrix, is a diagonal matrix containing the singular values ​​of the signal. According to the cumulative contribution rate criterion, The component corresponding to the singular value that contributes most to the signal is selected and reconstructed to obtain the main trend portion of the signal, namely the long-term trend data. After selecting the target trend component, reconstruction is performed to obtain the long-term trend data. The long-term trend data reflects the low-frequency components in the signal and represents the overall trend and long-term variation of the signal. Simultaneously, the displacement data after preliminary noise reduction undergoes modal decomposition, using the commonly used tool empirical mode decomposition (EMD). EMD decomposes the signal into several intrinsic mode functions (IMFs), each representing a natural vibration mode in the signal. IMF combinations typically contain short-term fluctuations in the signal, which may be caused by environmental changes or random fluctuations within the system. To extract the short-term fluctuation information of the signal, IMFs are screened based on their energy ratio and frequency characteristics, and representative IMF combinations are selected. The selected IMF combinations accurately reflect the short-term fluctuation characteristics of the signal. These short-term fluctuation data often reflect the rapidly changing components of the signal and complement the long-term trend data. The long-term trend data and short-term fluctuation data are weighted and combined to balance the long-term trend and short-term fluctuations in the signal. This allows the final result to reflect the overall trend of the signal while not ignoring subtle changes, thus obtaining the final noise-reduced displacement data and displacement trend information.

[0072] In a specific embodiment, the process of executing step S400 may specifically include the following steps:

[0073] Perform fuzzy processing on the noise reduction displacement data and displacement trend information to obtain fuzzy input variables, and then perform reasoning on the fuzzy input variables according to the preset fuzzy rule base to obtain fuzzy output variables;

[0074] The fuzzy output variables are defuzzified using the centroid method to obtain the initial PID parameters. Based on the initial PID parameters, PID control calculation is performed on the noise reduction displacement data to obtain the initial control quantity.

[0075] According to the displacement trend information, the initial control amount is feedforward compensated to obtain the compensated control amount;

[0076] Reinforce learning is performed on the compensated control quantity and the current system state to obtain the optimized control action. Based on the optimized control action, the initial PID parameters are adjusted online to obtain the adaptive PID parameters.

[0077] Based on the adaptive PID parameters, secondary PID control calculation is performed on the noise reduction displacement data to obtain the optimized control quantity;

[0078] The optimized control quantity is saturated and limited to obtain the limited control quantity, and the limited control quantity is converted into a correction control instruction, which includes motor drive current and direction information.

[0079] Specifically, the noise reduction displacement data and displacement trend information are fuzzified, and the input data is converted into fuzzy input variables. The uncertainty and nonlinear characteristics of the input data are handled by using fuzzy sets, such as "high", "low" or "medium". Assume that the input noise reduction displacement data is , after fuzzification processing, the fuzzy input variables can be obtained . After getting the fuzzy input variables Finally, the fuzzy input variables are inferred using the preset fuzzy rule base to generate fuzzy output variables. The fuzzy rule base consists of a series of "if...then..." rules that reflect the expert experience or preset logic in the control system. By substituting the fuzzy input variables into these rules, the fuzzy output variables are obtained. , represents the fuzzy expression of the control action that the system should take. Convert it into an actual executable control signal and perform defuzzification. For example, the centroid method is used to calculate the centroid of the fuzzy output variable to determine an accurate control signal value. Assume that the membership function corresponding to the fuzzy output variable is , then the precise control signal calculated by the center of gravity method is It can be expressed as:

[0080] ;

[0081] The initial PID controller parameters are obtained through this formula. The PID controller consists of three parts: proportional (P), integral (I) and differential (D). These parameters determine the control performance of the system. Perform PID control calculations and use the initial PID parameters to obtain the initial control quantity , directly reflects the control action that the system should perform based on the current displacement state. Combined with the displacement trend information, the initial control amount is feedforward compensated. Feedforward compensation is to adjust the control amount in advance according to the expected changes of the system to reduce lag and error. Through feedforward compensation, the initial control amount is corrected to obtain the compensated control amount. The control quantity after compensation can better adapt to the dynamic changes of the system and improve the control accuracy. Optimize. Reinforcement learning is a feedback-based adaptive optimization method that continuously adjusts the control strategy through interaction with the environment to obtain the best control effect. In this process, the system will perform tentative actions based on the current state and the compensated control amount, and adjust the control strategy by evaluating the results, and finally obtain the optimized control action. .

[0082] In a specific embodiment, the process of executing step S500 may specifically include the following steps:

[0083] The correction control command is converted into a current signal to obtain the driving current signal of the linear motor. Based on the driving current signal, the linear motor is controlled in a current closed loop to obtain the actual output force.

[0084] Based on the Hall effect sensor, the position of the linear motor is detected in real time to obtain the motor position feedback signal. Based on the motor position feedback signal, the linear motor is controlled in a closed loop to obtain accurate position control results.

[0085] The air pressure of the air-floating guide rail is adaptively adjusted to obtain the optimal air-floating state, and the floating gap of the air-floating guide rail is sampled at high frequency based on a capacitive displacement sensor to obtain the floating gap data;

[0086] Based on the floating gap data, the air-floating guide rail is dynamically balanced to obtain a stable guide rail motion state. The motion of the linear motor and the air-floating guide rail is collaboratively optimized to obtain a comprehensive execution strategy.

[0087] According to the comprehensive execution strategy, the correction mechanism is precisely driven to obtain the actual correction action, and based on the high-precision grating ruler, the displacement of the actual correction action is measured to obtain the execution result.

[0088] Specifically, the correction control instruction is converted into a current signal to obtain the driving current signal of the linear motor. The correction control instruction is usually given in the form of a voltage signal or a digital signal, but the linear motor requires a current signal to drive. These instructions are converted into a driving current signal through a current amplifier or controller. This signal is directly used to control the action of the linear motor. Assume that the input correction control instruction is , the driving current signal obtained after conversion is ,but Is a The linear motor is controlled in a closed-loop manner according to the drive current signal to ensure that the force output by the motor is consistent with the desired control command. By measuring the current output of the motor and comparing it with the set drive current signal, the output is adjusted in time so that the actual output force of the motor reaches the desired effect. Assuming the target output force is , the actual output force of the motor The formula can be Calculate, where is the force constant of the motor. Through closed-loop control, real-time adjustment ,make sure near , achieving precise force control. At the same time, the position of the linear motor is precisely controlled. Through real-time position detection based on the Hall sensor, the position change of the motor is measured and a position feedback signal is generated. The feedback signal is compared with the preset target position, and the motor drive signal is adjusted through position closed-loop control so that the motor position can accurately follow the target trajectory. Assuming the target position is , the actual location is , the purpose of position closed-loop control is to reduce the position error To minimize. At the same time, in order to ensure the stable operation of the linear motor with high precision, the state of the air-floating guide rail is optimized and controlled. The air-floating guide rail reduces friction through air pressure suspension to ensure the smooth movement of the linear motor. The air pressure of the air-floating guide rail is adaptively adjusted to obtain the optimal air-floating state, so that the floating gap of the guide rail is kept within the ideal range. The output pressure of the air source is adjusted by the control system to ensure the stability of the floating gap. The accurate measurement of the floating gap depends on the capacitive displacement sensor. The capacitive displacement sensor can sample the floating gap data of the guide rail at high frequency. Real-time feedback is provided. The current state of the air-bearing guide is determined based on the floating gap data, and dynamic balancing control is implemented to ensure the guide remains stable and free of deviation throughout the entire motion process. The motion of the linear motor and the air-bearing guide is collaboratively optimized. A comprehensive execution strategy is generated by comprehensively considering the motor's force output, position control, and the floating state of the air-bearing guide. This comprehensive execution strategy ensures that the linear motor and the air-bearing guide work together to achieve optimal operation. For example, if the floating gap increases, the system may adjust the motor's output force or the guide's air pressure to compensate for this change, ensuring overall system stability. Based on the comprehensive execution strategy, the correction mechanism is precisely driven to perform the actual correction. This process requires not only controlling the motor's force output and position, but also real-time adjustment of the air-bearing guide's state to ensure a smooth and accurate correction process. The actual correction action after execution is a physical response to the original correction command, reflecting the actual adjustment effect of the system. To verify the accuracy of the actual correction action, the displacement after correction is measured using a high-precision optical scale. High-precision linear scales provide submicron displacement measurement accuracy. The scale provides feedback on displacement data, which is then used to measure the system's execution results. These results are compared with pre-set targets to determine the accuracy and effectiveness of the corrective action. If deviations are detected, the control strategy is further adjusted based on this feedback to optimize subsequent corrective actions.

[0089] In a specific embodiment, the process of executing step S600 may specifically include the following steps:

[0090] Perform data preprocessing on actual correction actions and execution results to obtain standardized input data sets, and build a system virtual simulation environment based on the standardized input data sets;

[0091] Based on the system virtual simulation environment, a parameter sensitivity analysis is performed on the first calibration curve to obtain key influencing factors, and based on the key influencing factors, a polynomial fitting optimization is performed on the first calibration curve to obtain a second calibration curve;

[0092] Structural decomposition is performed on the first compensation model to obtain a plurality of sub-models, including a period error compensation sub-model and a temperature compensation sub-model;

[0093] Based on the genetic algorithm, multiple sub-models are optimized to obtain the optimized sub-model parameter set, and then global optimization is performed based on the optimized sub-model parameter set to obtain the global optimal solution;

[0094] Based on the global optimal solution, a multi-objective optimization problem is constructed to obtain the Pareto optimal solution set, where the objective function includes displacement accuracy and model complexity;

[0095] Based on the TOPSIS decision-making method, the Pareto optimal solution set is evaluated and selected to obtain the second compensation model, where the second compensation model is: C(x, T, v)=x+Σ[Ai*sin(2πix / P+φi)]+k(T-T0)+f(v), where C(x, T, v) is the compensated position value, x is the original reading of the grating scale, Ai is the i-th harmonic amplitude, P is the basic period of the grating scale, φi is the i-th harmonic phase, k is the temperature compensation coefficient, T is the current temperature, T0 is the reference temperature, f(v) is the speed-related compensation function, v is the current speed, and f(v)=α*v+β*v 2 +γ*sign(v), where α, β, and γ are velocity compensation coefficients, and sign(v) is the velocity direction function.

[0096] Specifically, data preprocessing is performed on the actual correction actions and execution results to obtain a standardized input data set. In data preprocessing, the data is normalized or standardized to eliminate the influence between different dimensions so that each variable can be compared on the same scale. Based on the standardized input data set, a system virtual simulation environment is constructed to test and optimize the control strategy in a simulation environment. The virtual environment predicts the response of the system under different conditions by simulating physical behavior in reality. For example, in this environment, the impact of temperature changes and speed changes on system performance is simulated to provide a reliable testing platform for subsequent parameter optimization. In the virtual simulation environment, a parameter sensitivity analysis is performed on the first calibration curve to identify those parameters that have the greatest impact on the calibration curve, that is, the key influencing factors. These factors may include temperature, pressure, speed, etc. By analyzing the impact of changes in these factors on the calibration accuracy of the system, the parameters that need to be optimized are determined. Assume that the calibration curve is expressed in the form of ,in 、 、 For the parameters to be optimized, a sensitivity analysis is performed to determine which parameter is most sensitive to changes in the calibration curve. Based on the key influencing factors, a polynomial fit is performed on the first calibration curve to obtain a more accurate second calibration curve. By introducing higher-order terms, the polynomial fit can better describe the nonlinear behavior of the system. The optimized second calibration curve may be as follows:

[0097] ;

[0098] in 、 、 、 The optimized parameters better adapt to the system's actual operating conditions. The first compensation model undergoes structural decomposition. Compensation models typically contain multiple submodels to address different types of errors, such as periodic error and temperature error. This structural decomposition process decomposes the first compensation model into several submodels, such as a periodic error compensation submodel and a temperature compensation submodel. The periodic error compensation submodel may be based on Fourier series, while the temperature compensation submodel may be based on temperature sensitivity analysis. To optimize the submodel parameters, a genetic algorithm is employed. A genetic algorithm is an optimization method based on natural selection, mimicking the biological evolution process to gradually optimize the model's parameter set. The genetic algorithm randomly generates a set of parameter values ​​from an initial population and then, through operations such as crossover, mutation, and selection, gradually evolves the optimal parameter combination. The optimized submodel parameter set effectively reduces system error, thereby improving the accuracy of the compensation model. Once the optimized submodel parameter set is obtained, global optimization is performed to find the global optimal solution for the entire system. The goal of global optimization is to find a parameter combination that performs well under all operating conditions. The global optimal solution comprehensively considers the performance of all submodels, ensuring optimal compensation across a wide range of conditions. Based on the global optimal solution, a multi-objective optimization problem is constructed. In this problem, the objective function includes two main objectives: displacement accuracy and model complexity. Displacement accuracy requires that the model can provide accurate displacement compensation under various conditions, while model complexity requires that the model be as simple as possible to reduce the amount of calculation and system burden. By optimizing these two objectives, a Pareto optimal solution set is obtained, which contains a set of solutions that perform best under different trade-offs. In order to select the optimal solution from the Pareto optimal solution set, the TOPSIS decision method is adopted. TOPSIS is a ranking method based on ideal solutions, which ranks each solution by calculating the distance from the ideal solution and the negative ideal solution. The final selected solution is the solution closest to the ideal solution, and this solution will be used as the final form of the second compensation model. The final second compensation model can be expressed as:

[0099] ;

[0100] in, is the position value after compensation, is the original reading of the grating ruler, For the The amplitude of the subharmonics, is the basic period of the grating ruler, For the The phase of the subharmonics, is the temperature compensation coefficient, is the current temperature, is the reference temperature, is the speed-related compensation function, expressed as sign ,in is the speed compensation coefficient, is a function of velocity direction.

[0101] See also Figure 2 , Figure 2 This is a schematic block diagram of the structure of the high-precision displacement correction device 200 for wooden box steel belt transmission provided in the embodiment of the present application, as shown in FIG. Figure 2 As shown, the wooden box steel belt transmission high-precision displacement correction device 200 includes:

[0102] An installation module 210 is used to install and calibrate the high-precision grating ruler and create a first calibration curve and a first compensation model;

[0103] An acquisition module 220 is configured to acquire the steel strip displacement data in a high dynamic range according to the first calibration curve and the first compensation model to obtain raw displacement data;

[0104] Analysis module 230, for performing wavelet noise reduction and multi-scale trend analysis on the original displacement data to obtain noise-reduced displacement data and displacement trend information;

[0105] A control module 240 is configured to perform adaptive deviation correction control based on the noise reduction displacement data and the displacement trend information, and generate deviation correction control instructions;

[0106] The execution module 250 is used to accurately execute the correction control instruction by the linear motor drive and the air-floating guide rail to obtain the actual correction action and the corresponding execution result;

[0107] The optimization module 260 is used to perform digital twin simulation and multi-objective optimization on the first calibration curve and the first compensation model according to the actual correction action and execution results to obtain the second calibration curve and the second compensation model.

[0108] Through the collaborative efforts of the various components mentioned above, the application and precise calibration of high-precision grating scales improve the basic accuracy of displacement measurement. High-dynamic range data acquisition and multi-level signal processing technologies effectively enhance the quality of raw displacement data and reduce the impact of measurement noise and interference. Wavelet noise reduction and multi-scale trend analysis methods can effectively separate noise and valid information in displacement data, improving the accuracy of subsequent control. The adaptive correction control strategy combines fuzzy control, PID control, and reinforcement learning to achieve rapid response and precise adjustment to complex dynamic environments. The precise actuators of linear motor drives and air-floating guides improve the execution accuracy of correction actions and reduce the impact of mechanical errors. Digital twin simulation and multi-objective optimization methods enable dynamic optimization of calibration curves and compensation models, improving the long-term stability and adaptability of the system. By comprehensively considering multiple influencing factors such as temperature and speed, a more comprehensive and accurate compensation model is constructed, effectively improving the overall correction effect.

[0109] The present application also provides a high-precision displacement correction device for wooden box steel belt transmission, which includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor executes the steps of the high-precision displacement correction method for wooden box steel belt transmission in the above-mentioned embodiments.

[0110] The present application also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, and when the instructions are run on a computer, the computer executes the steps of the high-precision displacement correction method for wooden box steel belt transmission.

[0111] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0112] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0113] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A high-precision displacement correction method for wooden box steel belt transmission, characterized in that: include: Install and calibrate the high-precision grating ruler, and create the first calibration curve and the first compensation model; According to the first calibration curve and the first compensation model, the steel strip displacement data is collected in a high dynamic range to obtain original displacement data; Performing wavelet noise reduction and multi-scale trend analysis on the original displacement data to obtain noise-reduced displacement data and displacement trend information; Performing adaptive deviation correction control based on the noise reduction displacement data and the displacement trend information to generate a deviation correction control instruction; The correction control instruction is accurately executed by a linear motor drive and an air-floating guide rail to obtain an actual correction action and a corresponding execution result; According to the actual deviation-correcting action and the execution result, the first calibration curve and the first compensation model are subjected to digital twin simulation and multi-objective optimization to obtain a second calibration curve and a second compensation model; specifically comprising: performing data preprocessing on the actual deviation-correcting action and the execution result to obtain a standardized input data set, and constructing a system virtual simulation environment based on the standardized input data set; based on the system virtual simulation environment, performing parameter sensitivity analysis on the first calibration curve to obtain key influencing factors, and based on the key influencing factors, performing polynomial fitting optimization on the first calibration curve to obtain a second calibration curve; performing structural decomposition on the first compensation model to obtain multiple sub-models, including a periodic error compensation sub-model and a temperature compensation sub-model; performing parameter optimization on the multiple sub-models based on a genetic algorithm to obtain an optimized sub-model parameter set, and according to the The optimized sub-model parameter set is globally optimized to obtain a global optimal solution; based on the global optimal solution, a multi-objective optimization problem is constructed to obtain a Pareto optimal solution set, wherein the objective function includes displacement accuracy and model complexity; based on the TOPSIS decision method, the Pareto optimal solution set is evaluated and selected to obtain a second compensation model, wherein the second compensation model is: C(x, T, v)=x+Σ[Ai*sin(2πix / P+φi)]+k(T-T0)+f(v), wherein C(x, T, v) is the compensated position value, x is the original reading of the grating scale, Ai is the i-th harmonic amplitude, P is the basic period of the grating scale, φi is the i-th harmonic phase, k is the temperature compensation coefficient, T is the current temperature, T0 is the reference temperature, f(v) is the speed-related compensation function, v is the current speed, and f(v)=α*v+β*v 2 +γ*sign(v), where α, β, and γ are velocity compensation coefficients, and sign(v) is the velocity direction function.

2. The high-precision displacement correction method for wooden box steel belt transmission according to claim 1 is characterized in that: The step of installing and calibrating the high-precision grating ruler and creating a first calibration curve and a first compensation model includes: Calibrate the high-precision grating ruler with a precision level and an inclinometer to obtain initial installation error data, and adjust the high-precision grating ruler according to the initial installation error data to obtain a calibrated grating ruler installation position; Based on the laser interferometer, multi-point measurement is performed on the calibrated grating ruler to obtain corresponding data of the grating ruler reading and the interferometer reading, and the corresponding data of the grating ruler reading and the interferometer reading are fitted by the least squares method to obtain the initial calibration curve equation; According to the initial calibration curve equation, a nonlinear error analysis is performed on the grating ruler to obtain a nonlinear error distribution function, and based on the nonlinear error distribution function, the initial calibration curve equation is corrected to obtain a first calibration curve; Performing a Fourier series expansion on the first calibration curve to obtain a periodic error compensation term, and constructing a temperature compensation model based on the periodic error compensation term and temperature sensor data to obtain a temperature compensation function; Based on the temperature compensation function and the periodic error compensation term, a first compensation model is constructed, wherein the first compensation model is: C(x, T)=x+Σ[Ai*sin(2πix / P+φi)]+k(T-T0), wherein C(x, T) is the compensated position value, x is the original reading of the grating scale, Ai is the i-th harmonic amplitude, P is the grating scale basic period, φi is the i-th harmonic phase, k is the temperature compensation coefficient, T is the current temperature, and T0 is the reference temperature.

3. The high-precision displacement correction method for wooden box steel belt transmission according to claim 1 is characterized in that: The step of performing high dynamic range acquisition on the steel strip displacement data according to the first calibration curve and the first compensation model to obtain original displacement data includes: Pre-amplifying the grating ruler signal to obtain an amplified analog signal, and performing band-pass filtering on the amplified analog signal to obtain a filtered signal; Based on a programmable gain amplifier, dynamically adjust the gain of the filtered signal to obtain a signal adapted to the ADC input range, and perform analog-to-digital conversion on the signal adapted to the ADC input range to obtain digitized displacement data; performing nonlinear error correction on the digitized displacement data according to the first calibration curve to obtain corrected displacement data, and performing periodic error compensation on the corrected displacement data based on the periodic error compensation term in the first compensation model to obtain compensated displacement data; performing temperature compensation on the compensated displacement data according to the temperature sensor data and the temperature compensation function in the first compensation model to obtain temperature-compensated displacement data, and performing moving average filtering on the temperature-compensated displacement data to obtain preliminarily filtered displacement data; Based on the Kalman filter algorithm, noise suppression is performed on the displacement data after the preliminary filtering to obtain noise-suppressed displacement data, and data integrity check and abnormal value detection are performed on the noise-suppressed displacement data to obtain original displacement data.

4. The high-precision displacement correction method for wooden box steel belt transmission according to claim 1 is characterized in that: The performing of wavelet denoising and multi-scale trend analysis on the original displacement data to obtain denoised displacement data and displacement trend information includes: Performing wavelet decomposition on the original displacement data to obtain multi-layer wavelet coefficients, and performing soft threshold processing on the multi-layer wavelet coefficients to obtain denoised wavelet coefficients; Performing wavelet reconstruction on the denoised wavelet coefficients to obtain preliminary denoised displacement data, and decomposing the preliminary denoised displacement data to obtain a plurality of singular value components; Selecting the plurality of singular value components according to a cumulative contribution rate criterion to obtain a target trend component, and reconstructing the target trend component to obtain long-term trend data; Performing modal decomposition on the displacement data subjected to the preliminary noise reduction to obtain a plurality of intrinsic mode functions, and screening the plurality of intrinsic mode functions according to energy proportion and frequency characteristics to obtain an IMF combination representing short-term fluctuations; The IMF combination representing the short-term fluctuation is reconstructed to obtain short-term fluctuation data, and the long-term trend data and the short-term fluctuation data are weightedly combined to obtain noise-reduced displacement data and displacement trend information.

5. The high-precision displacement correction method for wooden box steel belt transmission according to claim 1 is characterized in that: The adaptive deviation correction control is performed based on the noise reduction displacement data and the displacement trend information to generate a deviation correction control instruction, including: Performing fuzzification processing on the noise reduction displacement data and the displacement trend information to obtain fuzzy input variables, and performing reasoning on the fuzzy input variables according to a preset fuzzy rule base to obtain fuzzy output variables; Defuzzifying the fuzzy output variable using a centroid method to obtain initial PID parameters, and performing PID control calculation on the noise reduction displacement data based on the initial PID parameters to obtain an initial control variable; performing feedforward compensation on the initial control amount according to the displacement trend information to obtain a compensated control amount; Performing reinforcement learning on the compensated control quantity and the current system state to obtain an optimized control action, and adjusting the initial PID parameters online according to the optimized control action to obtain adaptive PID parameters; Based on the adaptive PID parameters, a secondary PID control calculation is performed on the noise reduction displacement data to obtain an optimized control quantity; The optimized control amount is saturated and limited to obtain a limited control amount, and the limited control amount is converted into a deviation correction control instruction, wherein the deviation correction control instruction includes motor drive current and direction information.

6. The high-precision displacement correction method for wooden box steel belt transmission according to claim 5 is characterized in that: The linear motor drive and the air-floating guide rail are used to accurately execute the correction control instruction to obtain the actual correction action and the corresponding execution result, including: Converting the correction control instruction into a current signal to obtain a driving current signal of the linear motor, and performing current closed-loop control on the linear motor according to the driving current signal to obtain an actual output force; Based on the Hall effect sensor, the position of the linear motor is detected in real time to obtain a motor position feedback signal, and the linear motor is controlled in a closed loop according to the motor position feedback signal to obtain an accurate position control result; Adaptively adjust the air pressure of the air-floating guide rail to obtain an optimal air-floating state, and perform high-frequency sampling of the floating gap of the air-floating guide rail based on a capacitive displacement sensor to obtain floating gap data; According to the floating gap data, the air-floating guide rail is dynamically balanced to obtain a stable guide rail motion state, and the motion of the linear motor and the air-floating guide rail is collaboratively optimized to obtain a comprehensive execution strategy; According to the comprehensive execution strategy, the correction mechanism is precisely driven to obtain the actual correction action, and based on the high-precision grating ruler, the displacement of the actual correction action is measured to obtain the execution result.

7. A high-precision displacement correction device for wooden box steel belt transmission, characterized in that: A method for performing high-precision displacement correction of a wooden box steel belt drive according to any one of claims 1 to 6, comprising: An installation module is used to install and calibrate the high-precision grating ruler and create a first calibration curve and a first compensation model; an acquisition module, configured to acquire the steel strip displacement data in a high dynamic range according to the first calibration curve and the first compensation model to obtain original displacement data; An analysis module, configured to perform wavelet noise reduction and multi-scale trend analysis on the original displacement data to obtain noise-reduced displacement data and displacement trend information; a control module, configured to perform adaptive deviation correction control based on the noise reduction displacement data and the displacement trend information, and generate a deviation correction control instruction; An execution module, configured to drive the linear motor and the air-floating guide rail to accurately execute the deviation correction control instruction, thereby obtaining an actual deviation correction action and a corresponding execution result; An optimization module is used to perform digital twin simulation and multi-objective optimization on the first calibration curve and the first compensation model according to the actual correction action and the execution result to obtain a second calibration curve and a second compensation model.

8. A high-precision displacement correction device for wooden box steel belt transmission, characterized in that: The wooden box steel belt transmission high-precision displacement correction device includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the wooden box steel belt transmission high-precision displacement correction device to execute the wooden box steel belt transmission high-precision displacement correction method according to any one of claims 1 to 6.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the high-precision displacement correction method for wooden box steel belt transmission according to any one of claims 1 to 6 is implemented.

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