Automatic calibration method for displacement sensor of wooden box and related equipment

Abstract

The invention relates to the technical field of sensors, and provides an automatic calibration method and related equipment for a displacement sensor of a wooden case, and the method comprises the steps: collecting a movement track of the wooden case in a conveying process through the displacement sensor, and extracting a standard displacement track segment and a real-time movement track segment based on the movement track; and constructing a track pairing data set and carrying out segment-by-segment difference calculation to obtain updated track control parameters so as to control the laser interferometer to collect displacement reference data of the wooden box and then drive the calibration execution mechanism to adjust the displacement sensor. By controlling displacement reference data collected by a laser interferometer and combining accurate adjustment of a calibration execution mechanism, manual intervention is reduced, stable operation in different working environments is achieved, and the problems that in practical application, a calibration method of external equipment usually needs to be operated by special personnel, calibration is tedious, and the calibration cost is high are solved. And the adaptability to the external environment is poor.

Description

Technical Field

[0001] The present application relates to the technical field of sensors, and in particular to an automatic calibration method for a displacement sensor used in a wooden box and related equipment. Background Art

[0002] With the continuous development of industrial automation, crate conveyor lines have gained widespread application in modern logistics and packaging industries. Crate production and transportation require precise position control to ensure efficient and accurate packaging. To this end, displacement sensors are widely used in crate conveyor lines to monitor crate motion, position, and control speed. However, as conveyor systems operate, sensor accuracy is affected by various factors, such as mechanical wear and ambient temperature fluctuations.

[0003] Among related technologies, automatic calibration of displacement sensors typically relies on external standard equipment (such as a laser interferometer or a known position calibration tool). This equipment collects the difference between the sensor's output signal and the known standard data, and performs corrections based on this difference. This allows for real-time monitoring and adjustment of errors, maintaining sensor accuracy and ensuring the position control and stability of the wooden boxes during transportation.

[0004] Regarding the above technical solution, although the existing technology can improve the accuracy of displacement sensors to a certain extent by using external standard equipment for sensor calibration, in actual applications, the calibration method of external equipment usually requires specialized personnel to operate, which makes it difficult to achieve large-scale automated applications, and has poor adaptability to the external environment. It cannot effectively deal with sensor errors under dynamically changing working conditions. Summary of the Invention

[0005] In order to improve the problems in actual applications where the calibration method of external equipment usually requires specialized personnel to operate, is cumbersome to calibrate, and has poor adaptability to the external environment, the present application provides an automatic calibration method for a displacement sensor for a wooden box and related equipment.

[0006] The present invention provides an automatic calibration method for a displacement sensor of a wooden box, which is applied to a wooden box conveyor line. A calibration actuator is provided on the wooden box conveyor line, and a displacement sensor is provided on the calibration actuator. The method is characterized in that the calibration method comprises: collecting the motion trajectory of the wooden box during the conveying process by using the displacement sensor, extracting standard displacement trajectory segments and real-time motion trajectory segments based on the motion trajectory, and constructing a trajectory pairing data set by using the standard displacement trajectory segments and the real-time motion trajectory segments; performing segment-by-segment difference calculation on the trajectory pairing data set to obtain an error characteristic parameter set, and generating a three-dimensional calibration offset vector and a dynamic correction factor according to the error characteristic parameter set; generating a sensor correction parameter group by using the three-dimensional calibration offset vector and the dynamic correction factor, dynamically updating the trajectory control parameters in a preset laser interferometer calibration module based on the sensor correction parameter group, and controlling the laser interferometer to collect displacement reference data of the wooden box based on the updated trajectory control parameters; and driving the calibration actuator to adjust the displacement sensor based on the displacement reference data.

[0007] As a preferred solution, the calibration actuator includes a linear motor and an air-floating guide rail, the linear motor is used to drive the air-floating guide rail to slide, and the displacement sensor is installed on the air-floating guide rail.

[0008] As a preferred solution, the steps of collecting the motion trajectory of the wooden box during transportation by the displacement sensor, extracting standard displacement trajectory segments and real-time motion trajectory segments based on the motion trajectory, and constructing a trajectory pairing data set using the standard displacement trajectory segments and the real-time motion trajectory segments include: controlling the wooden box to pass through the conveyor line at a constant speed, while starting the displacement sensor to synchronously collect the displacement information of the wooden box at each time point, performing preliminary interpolation fitting on the displacement information to generate a speed curve, jointly analyzing the high-frequency fluctuation characteristics in the speed curve and the displacement fluctuation amplitude within the time period, and identifying the interference segment and the remaining segment; filtering the interference segment using a wavelet threshold filtering algorithm The method comprises the following steps: processing the first trajectory segment to obtain a first trajectory segment, performing second-order curve fitting on the remaining segment to obtain a second trajectory segment, splicing the first trajectory segment and the second trajectory segment in chronological order and smoothing the boundary continuity to obtain a fitted motion trajectory; extracting a standard displacement trajectory segment and a real-time motion trajectory segment based on the fitted motion trajectory; aligning the key feature points of the standard displacement trajectory segment and the real-time motion trajectory segment, and constructing a trajectory pairing dataset based on the aligned standard displacement trajectory segment and the real-time motion trajectory segment; wherein the key feature points refer to the velocity extreme points and acceleration inflection points with synchronized timestamps in the standard displacement trajectory segment and the real-time motion trajectory segment.

[0009] As a preferred embodiment, the steps of performing segment-by-segment difference calculation on the trajectory pairing data set to obtain an error characteristic parameter set, and generating a three-dimensional calibration offset vector and a dynamic correction factor based on the error characteristic parameter set include: performing segment-by-segment difference calculation on the standard displacement trajectory segments and the real-time motion trajectory segments in the trajectory pairing data set to generate an error sequence, segmenting the error sequence by time period through a sliding window, and calculating the average error, peak error, and error change rate in each segment to generate an error characteristic parameter set; identifying the error source based on the error change rate in the error characteristic parameter set and the response time of the displacement sensor, extracting the corresponding time position interval based on the error source, then calculating the cumulative error mean based on the time position interval, and generating a preliminary calibration offset using the cumulative error mean; and weightedly integrating the error characteristic parameter set and the preliminary calibration offset to generate a comprehensive calibration correction, and generating a three-dimensional calibration offset vector and a dynamic correction factor using the comprehensive calibration correction.

[0010] As a preferred embodiment, the steps of weighting and integrating the error characteristic parameter set with the preliminary calibration offset to generate a comprehensive calibration correction, and using the comprehensive calibration correction to generate a three-dimensional calibration offset vector and a dynamic correction factor include: weighting the average error, peak error, and error change rate in the error characteristic parameter set with the numerical value of the preliminary calibration offset to generate a preliminary calibration correction, numerically integrating the preliminary calibration correction with the error characteristic parameter set using a weighted least squares method to obtain a comprehensive calibration correction; generating a three-dimensional calibration offset vector based on the comprehensive calibration correction, and fusing the three-dimensional calibration offset vector with the dynamic response characteristics of the displacement sensor to generate a dynamic correction factor; wherein the dynamic response characteristics refer to the response capability of the displacement sensor at different frequencies.

[0011] As a preferred solution, the steps of generating a sensor correction parameter group using the three-dimensional calibration offset vector and the dynamic correction factor, dynamically updating the trajectory control parameters in the preset laser interferometer calibration module based on the sensor correction parameter group, and controlling the laser interferometer to collect the displacement reference data of the wooden box based on the updated trajectory control parameters include: performing coordinate transformation on the three-dimensional calibration offset vector to obtain a correction offset matrix, weightedly combining the correction offset matrix and the dynamic correction factor to obtain comprehensive correction data; filtering the comprehensive correction data to obtain a smooth correction result, generating a sensor correction parameter group based on the smooth correction result, and inputting the sensor correction parameter group into the laser interferometer calibration module; dynamically updating the trajectory control parameters preset in the laser interferometer calibration module using the sensor correction parameter group to obtain updated trajectory control parameters, and controlling the laser interferometer to collect the displacement reference data of the wooden box based on the updated trajectory control parameters.

[0012] As a preferred embodiment, the step of driving the calibration actuator to adjust the displacement sensor based on the displacement reference data includes: inputting the displacement reference data into a preset control system, the control system extracting an error distribution curve based on the displacement reference data, and analyzing the deviation direction of the displacement sensor based on the error distribution curve; generating an adjustment instruction set according to the deviation direction, and controlling the calibration actuator to slide according to the adjustment instruction set to drive the displacement sensor to adjust.

[0013] The present application also provides an automatic calibration system for displacement sensors of wooden boxes, which is applied to wooden box conveyor lines. A calibration actuator is provided on the wooden box conveyor line, and a displacement sensor is provided on the calibration actuator. The calibration system includes: an acquisition module, which is used to acquire the motion trajectory of the wooden box during the conveying process through the displacement sensor, extract standard displacement trajectory segments and real-time motion trajectory segments based on the motion trajectory, and construct a trajectory pairing data set using the standard displacement trajectory segments and the real-time motion trajectory segments; a calculation module, which is used to perform segment-by-segment difference calculation on the trajectory pairing data set to obtain an error characteristic parameter set, and generate a three-dimensional calibration offset vector and a dynamic correction factor based on the error characteristic parameter set; an update module, which is used to generate a sensor correction parameter group using the three-dimensional calibration offset vector and the dynamic correction factor, dynamically update the trajectory control parameters in a preset laser interferometer calibration module based on the sensor correction parameter group, and control the laser interferometer to acquire displacement reference data of the wooden box based on the updated trajectory control parameters; and an adjustment module, which is used to drive the calibration actuator to adjust the displacement sensor based on the displacement reference data.

[0014] The present application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements any one of the above-mentioned methods for automatically calibrating a displacement sensor for a wooden box.

[0015] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor is enabled to execute the automatic calibration method for a displacement sensor for a wooden box as described in any one of the above.

[0016] Compared with the existing technology, the present application has the following beneficial effects: fast calibration and strong adaptability. By collecting the motion trajectory of the wooden box in real time and comparing it with the standard trajectory, the displacement sensor can be accurately calibrated; by calculating the difference between each segment, the error characteristics of the sensor can be identified, and the displacement error of the sensor can be further calibrated by generating a three-dimensional calibration offset vector and a dynamic correction factor. By controlling the displacement reference data collected by the laser interferometer and combining it with the precise adjustment of the calibration actuator, the adjustment accuracy of the displacement sensor reaches the sub-micron level, reducing manual intervention and being able to operate stably in different working environments, significantly improving the efficiency and accuracy of calibration, and improving the problem that in actual applications, the calibration method of external equipment usually requires specialized personnel to operate, is cumbersome to calibrate, and has poor adaptability to the external environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] The structures, proportions, sizes, etc. depicted in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not intended to limit the conditions under which the present invention can be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportional relationships, or adjustments in size should still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and objectives that can be achieved by the present invention.

[0019] Figure 1 1 is a flow chart of an automatic calibration method for a displacement sensor of a wooden box provided by an embodiment of the present invention; Figure 2 This is a schematic block diagram of the structure of an automatic calibration system for a displacement sensor of a wooden box provided by an embodiment of the present invention; Figure 3It is a schematic block diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0020] Description of reference numerals: 10. Automatic calibration system for displacement sensors used in wooden boxes; 11. Acquisition module; 12. Calculation module; 13. Update module; 14. Adjustment module; 20. Electronic equipment; 21. Memory; 22. Processor. DETAILED DESCRIPTION

[0021] 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.

[0022] 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 vary depending on the actual situation.

[0023] 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.

[0024] 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.

[0025] The technical solution of the present invention will be further described below with reference to the accompanying drawings and through specific implementation methods.

[0026] Example 1: like Figure 1 As shown, the present application provides an automatic calibration method for a displacement sensor for a wooden box, which is applied to a wooden box conveyor line. A calibration actuator is provided on the wooden box conveyor line, and a displacement sensor is provided on the calibration actuator. The calibration actuator includes a linear motor and an air-floating guide rail. The linear motor is used to drive the air-floating guide rail to slide, and the displacement sensor is installed on the air-floating guide rail.

[0027] The calibration method includes steps S100 to S400.

[0028] Step S100: The motion trajectory of the wooden box during transportation is collected by a displacement sensor, standard displacement trajectory segments and real-time motion trajectory segments are extracted based on the motion trajectory, and a trajectory pairing dataset is constructed using the standard displacement trajectory segments and the real-time motion trajectory segments.

[0029] In this step, displacement sensors synchronously collect displacement data of the crates on the crate conveyor line during their movement. Specifically, the displacement sensors obtain the crates' motion trajectories by recording their displacement information at every point in time during the conveyance process in real time. Next, based on the collected motion trajectories, a specific algorithm is used to extract standard displacement trajectory segments and real-time motion trajectory segments. Standard displacement trajectory segments are typically reference trajectories obtained through previous calibration or external standard equipment (such as a laser interferometer), while real-time motion trajectory segments are trajectory data acquired in real time by the displacement sensors. Ultimately, these standard displacement trajectory segments and real-time motion trajectory segments are combined to form a trajectory pairing dataset through time alignment and data synchronization techniques.

[0030] For example, in this embodiment, the displacement sensor collects displacement information at regular intervals as a wooden box moves along a conveyor line at a constant speed. The standard displacement trajectory segment is a reference trajectory obtained through high-precision measurement using a laser interferometer, while the real-time motion trajectory segment is the trajectory data recorded in real time by the displacement sensor during the actual movement of the wooden box. These two data are time-matched to generate a paired trajectory dataset for subsequent error analysis.

[0031] Step S200 : performing segment-by-segment difference calculation on the trajectory pairing data set to obtain an error characteristic parameter set, and generating a three-dimensional calibration offset vector and a dynamic correction factor based on the error characteristic parameter set.

[0032] In this step, segment-by-segment difference calculations are performed using the trajectory pairing dataset to obtain the difference between the standard displacement trajectory segment and the real-time motion trajectory segment at each moment. These differences reflect the errors generated by the displacement sensor during the measurement process. Specifically, by analyzing the difference data, various error characteristics, such as the average error value, peak error value, and error rate of change, are calculated to generate a set of error characteristic parameters. Next, based on this set of error characteristic parameters, a specific algorithm (such as weighted least squares) is used to generate a three-dimensional calibration offset vector and a dynamic correction factor. The calibration offset vector represents the offset of the sensor's spatial position, while the dynamic correction factor is used to compensate for errors caused by changes in dynamic response.

[0033] For example, suppose that on a certain trajectory, the difference between the standard displacement trajectory segment and the real-time trajectory segment is 0.5 mm, and the frequency of error fluctuation is high. This will generate a large error change rate. On the other hand, the error change in the stable segment is small, and the corresponding error characteristic parameter set will show a low change rate. Ultimately, based on this characteristic data, the 3D calibration offset vector and dynamic correction factor can be effectively generated.

[0034] Step S300: Generate a sensor correction parameter group using the three-dimensional calibration offset vector and the dynamic correction factor, dynamically update the trajectory control parameters in the preset laser interferometer calibration module based on the sensor correction parameter group, and control the laser interferometer to collect the displacement reference data of the wooden box based on the updated trajectory control parameters.

[0035] In this step, a sensor correction parameter set is generated based on the three-dimensional calibration offset vector and the dynamic correction factor. This correction parameter set is used to calibrate the displacement sensor. Specifically, the correction parameter set is input into the laser interferometer calibration module, which dynamically updates the trajectory control parameters in the calibration module based on the input correction parameters. These updated trajectory control parameters reflect the real-time calibrated sensor position and motion trajectory, ensuring more accurate displacement data of the wooden box acquired by the laser interferometer.

[0036] For example, in this embodiment, the sensor correction parameter set includes spatial displacement offsets in three directions (X, Y, and Z) and an offset value for the rotation angle. Using these correction parameters, the laser interferometer recalculates the actual displacement of the crate, ensuring high-precision displacement data and enabling real-time data acquisition based on the updated trajectory control parameters.

[0037] Step S400: driving a calibration actuator to adjust the displacement sensor based on the displacement reference data.

[0038] In this step, the calibration actuator precisely adjusts the displacement sensor based on the displacement reference data collected by the laser interferometer. Specifically, the displacement reference data is compared and analyzed with the current state of the displacement sensor to calculate the direction and amount of sensor deviation. Based on the calculated deviation, the calibration actuator (including the linear motor and air bearing guide) makes fine adjustments using a precision control system, ensuring submicron position adjustment of the displacement sensor.

[0039] For example, by comparing the precise data from the laser interferometer with the real-time sensor measurements, the direction and distance the sensor needs to be adjusted are calculated. A linear motor then drives the air-bearing guide rail to make corresponding fine adjustments, ensuring the displacement sensor's adjustment accuracy reaches submicron levels, thereby improving the accuracy of the entire wooden box conveyor line.

[0040] In this embodiment, the motion trajectory of the wooden box during transportation is collected by a displacement sensor to obtain the motion data of the wooden box. Based on the motion trajectory, the system extracts the standard displacement trajectory segment and the real-time motion trajectory segment, and uses these two trajectory segments to construct a trajectory pairing data set. Next, the trajectory pairing data set is subjected to segment-by-segment difference calculation to generate a set of error characteristic parameters, and then a three-dimensional calibration offset vector and a dynamic correction factor are generated based on these error characteristic parameter sets. Then, a sensor correction parameter group is generated using the three-dimensional calibration offset vector and the dynamic correction factor, and the trajectory control parameters in the laser interferometer calibration module are dynamically updated based on the correction parameter group. Finally, based on the updated trajectory control parameters, the laser interferometer is controlled to collect the displacement reference data of the wooden box, and the calibration actuator is driven according to these displacement reference data to perform sub-micron adjustments to the displacement sensor, thereby ensuring the high precision and stability of the sensor.

[0041] Through the automatic calibration method of the displacement sensor, more accurate sensor calibration can be achieved. This method can not only dynamically compare the motion trajectory collected in real time with the standard trajectory to generate high-precision calibration data, but also effectively compensate for the errors generated by the sensor during operation through the combination of the three-dimensional calibration offset vector and the dynamic correction factor. Compared with the traditional calibration method, the present invention has significant advantages in automation and real-time adjustment, reduces dependence on manual intervention, improves calibration efficiency and accuracy, and improves the problem that in actual applications, the calibration method of external equipment usually requires special personnel to operate, has cumbersome calibration, and has poor adaptability to the external environment. In addition, the installation position of the displacement sensor can be accurately adjusted according to the displacement reference data to ensure that the displacement sensor can maintain high sub-micron precision in different working environments, further improving the automation level and reliability of the wooden box conveyor line.

[0042] Example 2: In step S100, the wooden box is controlled to pass through the conveyor line at a constant speed, and the displacement sensor is started to synchronously collect the displacement information of the wooden box at each time point. The displacement information is preliminarily interpolated and fitted to generate a continuous displacement curve, and its first-order derivative is further calculated to generate a speed curve. The high-frequency fluctuation characteristics in the speed curve are jointly analyzed with the displacement fluctuation amplitude within the time period to identify the interference segment and the remaining segment.

[0043] The displacement information of the wooden box is collected in real time by the displacement sensor. The displacement sensor records the displacement data of the wooden box during transportation at a constant sampling frequency and marks the data with a timestamp to form a time series. In the collected data, there is a non-uniform distribution or insufficient sampling points. Therefore, an interpolation algorithm is first used to fit the displacement information to generate a continuous displacement curve. Specifically, linear interpolation, spline interpolation or other existing algorithms are used to fill the non-uniform points to make the displacement curve smoother and more uniform. After the continuous displacement curve is generated, its first-order derivative is further calculated to form a velocity curve. The velocity curve is formed by calculating the rate at which the displacement changes over time. The generated velocity curve and displacement fluctuation data are combined, and a specific algorithm is used to jointly analyze the high-frequency fluctuation characteristics and the displacement fluctuation amplitude within the time period to identify interference segments (such as noise or abnormal data) and distinguish the remaining segments (such as stable data with physical rationality).

[0044] For example, the displacement information of wooden boxes on the conveyor line is collected by displacement sensors, and the sampling frequency is set to 1 time per second. Assume that in a series of sampling points, the displacement value x0=10 mm is recorded at time point t0=0 seconds, the displacement value x1=20 mm is recorded at time point t1=1 second, and the displacement value x2=40 mm is recorded at time point t2=3 seconds. Since the time point intervals are uneven, spline interpolation can be used to fill in the displacement value at t=2 seconds. For example, the interpolation result is x2=30 mm, thereby generating a uniformly distributed continuous displacement curve. After generating the continuous displacement curve, the speed curve is then calculated using the formula The velocity changes between time points are obtained. If a segment of the velocity and displacement curves exhibits significant high-frequency fluctuations (e.g., a frequency exceeding 10 Hz) and the standard deviation of the data within that time period is high, that segment is identified as an interference segment, while segments with lower fluctuations and smaller standard deviations are identified as residual segments.

[0045] The interference segment is filtered using the wavelet threshold filtering algorithm to obtain the first trajectory segment, and the remaining segment is fitted with a second-order curve to maintain the overall curve continuity and physical rationality to obtain the second trajectory segment.

[0046] The interference segment is denoised by processing the high-frequency noise of the interference segment using a wavelet threshold filtering algorithm. The wavelet threshold filtering algorithm belongs to the existing technology and is suitable for removing high-frequency noise in the signal while retaining the main features of the original signal. Specifically, a wavelet function suitable for the texture characteristics of the data (such as Haar wavelet, Daubechies wavelet, etc.) is selected to perform wavelet decomposition on the interference segment data, decompose the signal into a low-frequency part and a high-frequency part, and then remove the high-frequency noise part by setting a threshold, and finally reconstruct the signal to obtain a smooth first trajectory segment. For the data of the remaining segments, a second-order curve fitting process is used to maintain the continuity and physical rationality of the data. The second-order curve fitting is performed through a polynomial function It means that by fitting and eliminating the unstable fluctuations in the remaining segments, a smooth trajectory that can reflect the physical laws is output, and the second trajectory segment is generated.

[0047] For example, in the interference segment, assuming that there is high-frequency noise with a frequency greater than 10 Hz in the speed curve of the time period [0 seconds, 5 seconds], Haar wavelet can be used to remove it, setting the threshold T=0.5, filtering out the noise part and reconstructing the signal, resulting in a clean and noise-free first trajectory segment. For the remaining segments, assuming that the speed curve data of the time period [6 seconds, 10 seconds] has no obvious noise but has slight fluctuations, a second-order curve fitting can be used, where the second-order polynomial fitting result is y=0.2x 2 −0.8x+5, so that the second trajectory segment after fitting shows continuity and conforms to the laws of physics.

[0048] The first trajectory segment and the second trajectory segment are spliced ​​in time sequence and the boundary continuity is smoothed to obtain the fitted motion trajectory.

[0049] The discontinuity between the trajectory segments is eliminated by splicing the first and second trajectory segments in timeline order and smoothing the boundary at the splicing point. Specifically, a spline interpolation algorithm is used to smooth the trajectory at the splicing boundary, ensuring that the fitted trajectory has no abrupt changes at the splicing point, ensuring the continuity of the displacement curve and the physical rationality of the trajectory.

[0050] For example, the first trajectory segment covers the time range [0 seconds, 5 seconds], and the second trajectory segment covers the time range [6 seconds, 10 seconds]. There is a 0.1 mm difference in displacement value at the splicing point [5 seconds, 6 seconds]. A smooth transition is achieved through the spline interpolation algorithm. After interpolation, the displacement curve shows continuous changes and meets the requirement of no sudden acceleration change in terms of physical rationality.

[0051] Based on the fitted motion trajectory, representative trajectory segments within a single acceleration-deceleration cycle are extracted, and a stably reproducible standard displacement trajectory segment and the original sampling time point of the sensor are constructed. The corresponding time series point set is extracted from the fitted motion trajectory to generate a real-time motion trajectory segment.

[0052] By analyzing the fitted trajectory, a representative acceleration-deceleration cycle is extracted for the typical acceleration-deceleration process of a wooden box on a conveyor line. This cycle encompasses the complete acceleration and deceleration process of the box from rest to uniform motion and then back to rest. This cycle serves as a representative trajectory segment. Specifically, by analyzing the velocity curve of the fitted trajectory, intervals with clear starting and ending points of velocity change are identified. For example, within the velocity curve, the entire cycle from zero velocity to maximum uniform velocity and back to zero is detected to determine the time range of the acceleration-deceleration movement. Displacement data is then extracted within this time range to generate a representative trajectory segment with a timestamp. This segment is then repeatedly sampled to construct a stably reproducible standard displacement trajectory segment. Simultaneously, the original sampling time points of the sensor acquisition are extracted from the fitted trajectory and used to generate corresponding real-time trajectory segments, thus achieving trajectory synchronization.

[0053] For example, in the acceleration-deceleration motion of a wooden box, the velocity curve marks a motion cycle. During the time period [t0, t1], the wooden box accelerates from an initial rest (velocity = 0) to a maximum velocity (e.g., 10 mm / s), and gradually decelerates back to a rest (velocity = 0) during the time period [t2, t3]. Assume that the displacement trajectory in the time period [t0, t3] is from the starting position (x0 = 0) to the ending position (x3 = 100 mm). Based on the period range [t0, t3] detected by the velocity curve, the displacement data of this segment is extracted to form a representative trajectory segment with a timestamp, for example: At time t0 = 0 seconds, displacement x0 = 0 mm; At time t1 = 2 seconds, displacement x1 = 40 mm; At time t2 = 4 seconds, displacement x2 = 80 mm; At time t3 = 6 seconds, the displacement x3 = 100 mm.

[0054] When constructing a standard displacement trajectory segment, the trajectory is repeatedly sampled and stored as a stable and reproducible reference data. Simultaneously, based on the original sampling time points of the fitted motion trajectory (e.g., once per second), a real-time motion trajectory segment is generated and technically synchronized with the representative trajectory segment.

[0055] The key feature points of the standard displacement trajectory segment and the real-time motion trajectory segment are aligned, and a trajectory pairing dataset is constructed based on the aligned standard displacement trajectory segment and the real-time motion trajectory segment; among them, the key feature points refer to the velocity extreme points and acceleration inflection points with synchronized timestamps in the standard displacement trajectory segment and the real-time motion trajectory segment.

[0056] By matching and aligning the key feature points of the standard displacement trajectory segment and the real-time motion trajectory segment, the two trajectory segments are ensured to be synchronized in terms of time axis and physical characteristics. The definition of key feature points includes velocity extreme points and acceleration inflection points. Therefore, it is first necessary to identify these feature points based on the velocity curve and acceleration curve respectively. Specifically, for the two trajectory segments (standard displacement trajectory segment and real-time motion trajectory segment), the first-order derivative of the velocity curve (velocity change rate) and the second-order derivative of the acceleration curve (acceleration change rate) are calculated respectively. Then, the time points when the velocity reaches the maximum or minimum value (velocity extreme points) and the time points when the acceleration changes from positive to negative or from negative to positive (acceleration inflection points) are extracted. By synchronizing the timestamps of the two trajectories, the standard trajectory points and real-time trajectory points with the same timestamps are matched to construct a trajectory pairing dataset.

[0057] For example, assume that the standard displacement trajectory segment has the following key feature points at time [0 seconds, 6 seconds]: Speed ​​extreme point: at time t1=2 seconds, the speed reaches the maximum value v max =10 mm / s; Acceleration inflection point: At time t2 = 4 seconds, the acceleration changes from positive to negative, and the value changes from a max =5 mm / s² converted to a min =−5 mm / s².

[0058] At the same time, the key feature points recorded in the real-time motion trajectory segment within the corresponding time are: Velocity extreme point: time point t1 = 2 seconds, recorded as v′ = 9.8 mm / s after velocity error adjustment; Acceleration inflection point: at time t2 = 4 seconds, after adjusting for acceleration error, recorded as a′ = −4.9 mm / s².

[0059] By matching these key feature points with the same timestamp (e.g., t1=2 seconds and t2=4 seconds) point by point, the system is able to construct a trajectory pairing dataset, thereby ensuring synchronization and precise alignment between the standard displacement trajectory segments and the real-time motion trajectory segments.

[0060] In step S200, the difference between the standard displacement trajectory segment and the real-time motion trajectory segment in the trajectory pairing dataset is calculated segment by segment to generate an error sequence. The error sequence is segmented by sliding window according to time period, and the average error, peak error and error change rate in each segment are calculated to generate a set of error feature parameters.

[0061] By performing difference calculation on each pair of track points with synchronized timestamps in the standard displacement track segment and the real-time motion track segment, the instantaneous error between the two is obtained to form an error sequence. Specifically, the error calculation adopts the Euclidean distance or component difference method, and at each time point , calculate the standard displacement point With real-time displacement point The difference Then, the entire error sequence is segmented into sliding windows with a fixed window length w, for example, 0.5 seconds as a sliding window, to generate several error segments. In each sliding window, the average error (i.e., the average error of all the errors in the sliding window) is calculated. The average value of the sliding window), the peak error (the maximum ), the error change rate (i.e., the absolute value of the average error difference between two adjacent sliding windows divided by the time difference), and finally generate a set of error characteristic parameters to characterize the error distribution state of the sensor in each time segment.

[0062] For example, suppose the trajectory pairing dataset contains 100 sets of trajectory points from t=0s to t=10s, and the displacement data is collected every 0.1 seconds. The error in each set of data is By formula The system sets the sliding window length to 1 second and the window sliding interval to 0.5 seconds, resulting in 19 sliding window segments. For each sliding window segment, for example, the sliding window [1.0s, 2.0s], the average error value (e.g., 0.25 mm), the maximum error value (e.g., 0.38 mm), and the error change rate of the preceding and following sliding windows (e.g., from 0.15 mm to 0.25 mm, with a rate of change of 0.1 mm / s) are calculated for each sliding window segment. These statistical values ​​are used as the error characteristic parameters for that sliding window.

[0063] The error source is identified based on the error change rate in the error characteristic parameter set and the response time of the displacement sensor, and the corresponding time position interval is extracted according to the error source. The cumulative error mean is then calculated based on the time position interval, and the preliminary calibration offset is generated using the cumulative error mean.

[0064] By correlating the error change rate of each time window in the error characteristic parameter set with the known displacement sensor response time, the potential source of the error is identified. Specifically, the normal response time range of the sensor is preset (for example, the response delay should not exceed 50ms). If the error change rate within a certain time window exceeds the change threshold corresponding to the response time, the system will determine that this time period is the interval where response lag or drift occurs. Combined with the persistence and growth trend of the error change rate and the stability of the error peak, it can be further determined whether the error is caused by response lag, sensor drift, or installation offset. After identification, the system extracts the corresponding time position interval and accumulates the mean of all error values ​​in this interval as a stable representation of the error in this segment. This is used to generate a preliminary calibration offset, reflecting the direction and magnitude of the offset correction required for the sensor.

[0065] For example, between seconds 5 and 7, the average error increased from 0.3 mm to 0.8 mm. This rate of change was significantly higher than the sensor's known maximum change threshold (0.1 mm / s) and persisted for more than two seconds. The system therefore determined this error segment to be a typical error caused by "installation offset." The system then extracted the corresponding time interval and calculated the cumulative error average to be 0.72 mm. This value is the initial calibration offset, indicating that the sensor needs to be translated approximately 0.72 mm in the corresponding direction for initial correction.

[0066] The error characteristic parameter set and the preliminary calibration offset are weighted and integrated to generate a comprehensive calibration correction, which is then used to generate a three-dimensional calibration offset vector and a dynamic correction factor.

[0067] By integrating the error statistical indicators (average error, peak error, error change rate) in the error feature parameter set with the value of the preliminary calibration offset, a weighted fusion algorithm is used to generate a comprehensive calibration correction. Specifically, according to the sensitivity and credibility of each error feature in error identification, the corresponding weight factor is set. 、 、 、 , calculate the comprehensive calibration correction according to the following relationship : ; in, represents the average error; represents the peak error; represents the error change rate; Indicates the preliminary calibration offset.

[0068] Integrated calibration correction This value represents the overall strength of the sensor's offset correction. This value is mapped to three-dimensional space and, combined with the sensor's installation posture and measurement direction, generates a three-dimensional calibration offset vector (representing the correction amount on the X, Y, and Z axes). This is then combined with the sensor's dynamic response characteristics (such as frequency response curve and hysteresis time constant) to generate a dynamic correction factor for subsequent dynamic adjustments.

[0069] For example, in one set of analysis results, it was calculated that: Average error =0.42 mm; Peak error =0.65 mm; Error change rate =0.08 mm / s; Preliminary offset =0.55 mm.

[0070] Set the weight coefficient to =0.3, =0.2, =0.2, =0.3, the comprehensive calibration correction is: C = 0.3*0.42+0.2*0.65+0.2*0.08+0.3*0.55=0.437 mm. After mapping this comprehensive correction to the actual installation orientation of the sensor, the three-dimensional calibration offset vector is (0.3, 0.2, −0.1) mm. At the same time, combined with the frequency response characteristics of the sensor (for example, a cutoff frequency of 25 Hz and a lag time of 40 ms), a dynamic correction factor Df = 0.87 is generated to correct the response delay error in the sensor output.

[0071] The steps of weighting and integrating the error characteristic parameter set and the preliminary calibration offset to generate a comprehensive calibration correction, and using the comprehensive calibration correction to generate a three-dimensional calibration offset vector and a dynamic correction factor specifically include: The average error, peak error and error change rate in the error characteristic parameter set are weighted with the numerical value of the preliminary calibration offset to generate a preliminary calibration correction. The preliminary calibration correction and the error characteristic parameter set are numerically integrated using the weighted least squares method to obtain a comprehensive calibration correction.

[0072] A preliminary calibration correction is generated by weighting the average error, peak error, error rate of change, and preliminary calibration offset values ​​within the error characteristic parameter set. This information is then comprehensively analyzed using a weighting model. Specifically, the error characteristic parameters are first assigned different weights based on their source and impact. For example, the average error represents an important indicator of overall deviation behavior and is given a higher weight; the peak error reflects the maximum single-point error and is given a medium weight; and the error rate of change represents the error's temporal trend and is given a lower weight. This is then combined with the preliminary calibration offset value (representing the cumulative average error) to generate a preliminary calibration correction from both a single-point and overall perspective. The numerical integration process uses a weighted least squares method to minimize data fluctuations in the error calculation, effectively and fully accounting for the weighting effects of each parameter and the inter-data relationship, thereby avoiding excessive deviations or data imbalance. Finally, a comprehensive calibration correction is output, which serves as the core foundation for subsequent generation of the 3D calibration offset vector.

[0073] For example, assume the average error is assessed to be 0.4 mm, the peak error is 0.7 mm, the error rate of change is 0.1 mm / s, and the initial calibration offset is 0.5 mm. The weights are set to 50% for the average error, 30% for the peak error, and 20% for the error rate of change. Through weighted processing, the impact of these error characteristics is comprehensively integrated, and weighted least squares is used to reduce the imbalance between numerical bias and weights. Ultimately, a comprehensive calibration correction is generated, such as an output result of 0.53 mm. This comprehensive calibration correction serves as a static parameter for sensor calibration in the current control cycle and is directly applied to the subsequent calibration offset generation step.

[0074] A three-dimensional calibration offset vector is generated based on the integrated calibration correction. This three-dimensional calibration offset vector is used to describe the spatial displacement and angular offset of the sensor. The three-dimensional calibration offset vector is then integrated with the dynamic response characteristics of the displacement sensor to generate a dynamic correction factor. The dynamic correction factor is used to compensate for errors caused by sensor response lag or environmental changes. The dynamic response characteristics refer to the response capability of the displacement sensor at different frequencies.

[0075] By generating a three-dimensional calibration offset vector based on the integrated calibration corrections, the sensor's spatial displacement offset (including translation in the X, Y, and Z directions) and angular offset (typically expressed as sensor rotation, such as yaw and pitch) are described using a three-dimensional reference coordinate system. Specifically, the integrated calibration corrections are mapped to the sensor's actual mounting position and within the three-dimensional operating frame, assigning corrections to specific coordinate axes (e.g., 0.45 mm offset in the X axis, 0.30 mm offset in the Y axis, and -0.10 mm drift in the Z axis). Furthermore, the sensor's angular offset can be calculated by incorporating the adjustment direction of the mounting bracket, for example, generating a pitch correction value of 0.2 degrees. The generated three-dimensional calibration offset vector also reflects the integrated correction offset between the sensor's current actual position and its ideal position. The three-dimensional calibration offset vector is then combined with the sensor's dynamic response characteristics to generate a dynamic correction factor. The dynamic response characteristics encompass the sensor's applicable range under various environmental conditions, such as operating frequency, response time lag, temperature drift, and vibration sensitivity. The dynamic correction factor compensates for these dynamic response characteristics, allowing the calibration process to account for dynamic sensor changes and adjust in real time. For example, when the response time delay is large, the dynamic correction factor generates a larger compensation value for correcting the output of the displacement sensor, ensuring that the final data can reflect the actual state of the sensor.

[0076] For example, assuming a comprehensive calibration correction of 0.53 mm, this correction is directly distributed to an X-axis offset of 0.40 mm, a Y-axis offset of 0.30 mm, and a Z-axis offset of -0.20 mm based on the sensor's position frame. The sensor's dynamic response characteristics are also considered, including an operating frequency range of 10 Hz to 100 Hz, a vibration sensitivity of 0.05 mm / MHz, and a temperature drift of 0.03 mm / °C. Based on these dynamic characteristics, the Y-axis offset (0.30 mm) in the three-dimensional calibration offset vector is multiplied by the frequency response sensitivity to generate a dynamic correction factor, such as 0.85, which is used to adjust the sensor's output data in real time. Finally, these generated corrections and the dynamic factors are applied together in the next control cycle to ensure that the sensor can effectively compensate for errors and maintain high accuracy.

[0077] In step S300 , coordinate transformation is performed on the three-dimensional calibration offset vector to obtain a correction offset matrix, and the correction offset matrix is ​​weightedly combined with a dynamic correction factor to obtain comprehensive correction data.

[0078] The three-dimensional calibration offset vector is converted into a correction offset matrix for sensor adjustment by performing coordinate transformation. Specifically, the three-dimensional calibration offset vector contains the sensor's offsets in three axes of space (e.g., translational offsets along the X, Y, and Z axes) as well as angular offsets (e.g., yaw, pitch, and roll). First, based on the actual sensor installation framework, the three-dimensional offset is mapped to the corresponding physical coordinate system. A correction offset matrix is ​​generated by establishing a coordinate relationship or referencing a reference coordinate transformation formula. The correction offset matrix is ​​then weighted and combined with the dynamic correction factor to comprehensively reflect the combined effects of the dynamic response characteristics and the three-dimensional offset characteristics on the correction amount. The dynamic correction factor is primarily used to compensate for errors caused by sensor response lag, external interference, and environmental changes. By weighted combination with the correction offset matrix, a comprehensive correction data suitable for the current working cycle is formed.

[0079] For example, the 3D calibration offset vector determines the sensor's current spatial offset: 0.35 mm on the X-axis, 0.25 mm on the Y-axis, and -0.15 mm on the Z-axis. The angular offsets increase the yaw angle by 0.3 degrees and decrease the pitch angle by 0.25 degrees. A dynamic correction factor of 0.85 is estimated for the sensor's operating environment to compensate for measurement drift caused by environmental vibration. When the correction matrix is ​​weighted and combined with the dynamic correction factor, for example, giving the dynamic correction factor a higher weight, the X-axis correction offset of the 3D offset is amplified to 0.40 mm to offset errors introduced by lag and vibration during real-time sensor data acquisition. This ultimately generates comprehensive correction data for use in the next calibration step.

[0080] The comprehensive correction data is filtered to obtain a smooth correction result. A sensor correction parameter group is generated based on the smooth correction result and used as the calibration control quantity of the current control cycle. The sensor correction parameter group is input into the laser interferometer calibration module.

[0081] The calibration data is smoothed by effectively filtering the integrated correction data to remove any high-frequency noise or short-term abnormal data values. Specifically, the filtering process employs existing signal processing methods, such as low-pass filtering, mean filtering, or median filtering, to smooth the data while preserving the original characteristics of the dynamic correction factors. The resulting smoothed correction result represents a more stable and accurately calibrated data set, serving as the basis for the sensor correction parameter set. This sensor correction parameter set contains a correction matrix (such as position and angle correction values) and the combined weights of the dynamic correction factors, describing the sensor adjustments and compensation parameters for the current cycle. Finally, the correction parameter set is used as the calibration control variable for the current control cycle and is input into the laser interferometer calibration module to update the calibration module's trajectory control parameters.

[0082] For example, the integrated correction data records the sensor calibration offset matrix, where the correction offsets are: 0.40 mm for the X-axis, 0.30 mm for the Y-axis, and -0.20 mm for the Z-axis. The angular error corrections are: 0.35 degrees for yaw, 0.25 degrees for pitch, and 0.10 degrees for roll. Based on this, a mean filter is applied to remove high-frequency noise, optimizing the calibration offsets in the smoothed correction result to: 0.38 mm for the X-axis, 0.28 mm for the Y-axis, and -0.18 mm for the Z-axis. The angular error corrections are optimized to 0.33 degrees for yaw, 0.23 degrees for pitch, and 0.08 degrees for roll. This generates a sensor correction parameter set for input into the calibration module to further update the calibration control parameters.

[0083] The trajectory control parameters preset in the laser interferometer calibration module are dynamically updated using the sensor correction parameter group to obtain updated trajectory control parameters. Based on the updated trajectory control parameters, the laser interferometer is controlled to collect the displacement reference data of the wooden box.

[0084] By inputting the sensor correction parameter group into the laser interferometer calibration module, the calibration module updates its internal preset trajectory control parameters based on the input parameters to match the changes in the sensor calibration status in real time. Specifically, the trajectory control parameters include the scanning path, scanning speed, and spatial coordinate points of the measurement benchmark of the laser interferometer. The sensor correction parameter group directly affects the way the calibration module calculates these parameters, such as adjusting the laser reflection point on the scanning path to match the displacement data calibrated in the correction offset matrix; or changing the scanning speed to better match the real-time compensation of the error data with the sensor dynamic response time. The updated trajectory control parameters can ensure that the laser interferometer can more accurately collect the displacement benchmark data of the wooden box, and match these benchmark data with the sensor output data in real time to further calibrate the working status of the sensor.

[0085] For example, the X-axis correction value of the calibration offset matrix in the sensor correction parameter group is 0.38 mm. The dynamic correction factor recommends increasing the laser interferometer scanning speed from 10 mm / s to 15 mm / s to compensate for the sensor's response lag. The calibration module resets the trajectory control parameters based on this update and adjusts the coordinate points of the laser scanning path. For example, it updates the reference points in the scanning path from (50 mm, 100 mm, 150 mm) to (50.38 mm, 100.28 mm, 149.82 mm) and increases the scanning speed to the preset dynamic compensation amplitude, making the collection of reference data more accurate. Ultimately, the displacement reference data of the wooden box is collected in real time and provides accurate input for the next cycle of sensor calibration.

[0086] In step S400 , the displacement reference data is input into a preset control system, and the control system extracts an error distribution curve based on the displacement reference data, and analyzes the deviation direction of the displacement sensor based on the error distribution curve.

[0087] By inputting the displacement reference data collected by the laser interferometer into a preset control system, the system performs error analysis based on the received data and generates an error distribution curve that reflects the current abnormality of the sensor. Specifically, the control system first compares the displacement reference data with the real-time measurement output data of the sensor point by point, calculates the error magnitude, and generates an error curve graph based on time or sampling points. The error distribution curve focuses on showing the trend of error changes over time, spatial coordinates (such as X, Y, Z directions), or specific physical properties (such as angular offset). By analyzing the displacement reference data and the error distribution curve, the system can identify the direction of deviation, such as a positive cumulative error in the X direction, or a negative offset trend in the angle, indicating the specific calibration requirements of the displacement sensor in the three-dimensional coordinate system.

[0088] For example, consider inputting baseline displacement data for a wooden box on a conveyor line into a control system. Assuming a sampling frequency of once per second and a time window of 10 seconds, the baseline displacement data for the X-axis is (10 mm, 20 mm, 30 mm, etc.), while the actual displacement data measured by the sensor is (9.7 mm, 19.5 mm, 29.3 mm, etc.). The control system calculates the error value for each sampling point and generates an error distribution curve. For example, the X-axis error values ​​are (-0.3 mm, -0.5 mm, -0.7 mm, etc.), gradually increasing over time, forming a clear negative trend. Meanwhile, the Y- and Z-axis error distributions are relatively stable, with no significant change. The control system can also detect angular deviations, such as a gradual positive shift of 0.1 degrees in the yaw angle while maintaining no significant change in the pitch angle. Using these results, the system accurately identifies that the sensor's X-axis deviation is negative, while the angular deviation is positive.

[0089] An adjustment instruction set is generated according to the deviation direction, and the calibration actuator is controlled to slide according to the adjustment instruction set, so as to drive the displacement sensor to be adjusted.

[0090] Based on the sensor deviation direction and specific error values ​​extracted from the error distribution curve, an adjustment instruction set for calibrating the actuator's operation is generated. Specifically, the control system calculates the required calibration amplitude based on the sensor deviation direction (e.g., negative X-axis deviation, positive yaw angle deviation). The control system first converts the deviation value into a control instruction for the calibration actuator. For example, the linear motor slides a specific distance in a certain direction, and the air-bearing guideway performs a micro-movement to compensate for the offset. Furthermore, when generating the adjustment instructions, the control system comprehensively considers the actuator's accuracy range and incorporates a smoothing adjustment algorithm (e.g., linear deceleration or acceleration control) to ensure smoothness and high precision during the calibration process. The adjustment instructions are transmitted to the calibration actuator via a preset interface. The calibration actuator performs the corresponding sliding operation according to the instructions, adjusting the position and angle of the displacement sensor, gradually approaching the ideal reference position, until the error falls within the preset threshold.

[0091] For example, if the negative deviation in the X-axis direction is -0.7 mm, as analyzed in the previous steps, the control system generates an adjustment command: "Slide the linear motor 0.7 mm in the positive X-axis direction." This command is transmitted to the linear motor drive module in the calibration actuator. Simultaneously, if the angular deviation indicates a positive yaw offset of 0.1 degrees, the corresponding adjustment command is: "Control the air-bearing guideway rotation module to correct the yaw angle by 0.1 degrees." The control system further applies a linear acceleration algorithm to optimize the adjustment process. For example, during the initial sliding phase, the adjustment is performed at a low speed, followed by steady acceleration to a preset sliding speed of 0.5 mm / s. As the target position is approached, the control system gradually decelerates to zero to prevent overshoot due to inertia. Throughout the entire operation, the control system monitors the actuator's position feedback in real time and adjusts the calibration command accordingly until the displacement sensor reaches the ideal position for the X-axis offset and the angular adjustment is complete. Finally, the calibration process concludes, and the sensor is successfully positioned, ensuring that its measurement state is consistent with the ideal reference state.

[0092] In this embodiment, the dynamic calibration process of the sensor is ensured by collecting displacement information from the wooden box's motion trajectory in real time and applying a specific algorithm to it in combination with standard displacement trajectory segments. First, based on the collected displacement data, a continuous displacement curve is generated through interpolation, and a velocity curve is generated by combining it with first-order derivative calculations. The high-frequency fluctuation characteristics and displacement fluctuation amplitude in the velocity curve are jointly analyzed, effectively dividing the trajectory into an interference segment and a residual segment. In the interference segment, a wavelet threshold filtering algorithm is used to process high-frequency noise, resulting in a smooth first trajectory segment. In the residual segment, a second-order curve fitting algorithm is used to generate a physically consistent second trajectory segment. The final fitted motion trajectory is then obtained through time sequence splicing and boundary continuity processing. Subsequently, representative trajectory segments within a single acceleration-deceleration cycle are extracted by analyzing the fitted motion trajectory, and a stably reproducible standard displacement trajectory segment is constructed. This segment is then combined with the sensor's original sampling time points to generate a real-time motion trajectory segment.

[0093] By aligning key feature points (such as velocity extremes and acceleration inflection points) between standard displacement trajectory segments and real-time motion trajectory segments, a trajectory pairing dataset is established, laying the foundation for difference calculation and error analysis. During the error analysis process, a sliding window segmentation approach is used to calculate the average error, peak error, and error rate of change, generating a set of error characteristic parameters. The response time is then combined to identify the error source and time interval. During the calibration phase, preliminary calibration corrections are calculated through weighted processing, and a weighted least squares method is used to generate a comprehensive calibration correction, effectively generating a three-dimensional calibration offset vector and dynamic correction factor. After dynamic correction, the system combines the comprehensive correction data to complete real-time sensor adjustments. Finally, a calibration actuator is used to perform submicron adjustments on the sensor to ensure that the spatial position and angle of the displacement sensor are optimally calibrated, enabling high-precision automated calibration of the wooden box conveyor line inspection system.

[0094] Example 3: like Figure 2 As shown, the present application also provides an automatic calibration system 10 for displacement sensors of wooden boxes, which is applied to a wooden box conveyor line. A calibration actuator is provided on the wooden box conveyor line, and a displacement sensor is provided on the calibration actuator. The calibration system includes an acquisition module 11, a calculation module 12, an update module 13 and an adjustment module 14.

[0095] The acquisition module 11 is mainly used to collect the motion trajectory of the wooden box during transportation through a displacement sensor, extract standard displacement trajectory segments and real-time motion trajectory segments based on the motion trajectory, and use the standard displacement trajectory segments and real-time motion trajectory segments to construct a trajectory pairing dataset.

[0096] The calculation module 12 is mainly used to perform segment-by-segment difference calculation on the trajectory pairing data set to obtain an error characteristic parameter set, and generate a three-dimensional calibration offset vector and a dynamic correction factor based on the error characteristic parameter set.

[0097] The update module 13 is mainly used to generate a sensor correction parameter group using the three-dimensional calibration offset vector and the dynamic correction factor, dynamically update the trajectory control parameters in the preset laser interferometer calibration module based on the sensor correction parameter group, and control the laser interferometer to collect the displacement reference data of the wooden box based on the updated trajectory control parameters.

[0098] The adjustment module 14 is mainly used to drive the calibration actuator to adjust the displacement sensor based on the displacement reference data.

[0099] In this embodiment, by providing an acquisition module 11, a calculation module 12, an update module 13, and an adjustment module 14, combined with systematic functional allocation, a fully automated calibration of the displacement sensors in the wooden box conveyor line is achieved. The acquisition module 11 uses the displacement sensor to collect the motion trajectory of the wooden box in real time and constructs a trajectory pairing dataset based on existing standard displacement trajectory segments and real-time motion trajectory segments, effectively ensuring the accuracy of data acquisition and the synchronization of trajectory comparison. The calculation module 12 obtains a set of error characteristic parameters by performing segment-by-segment difference calculations on the trajectory pairing dataset and uses these parameters to generate a three-dimensional calibration offset vector and dynamic correction factor, improving the accuracy of error identification and offset compensation. The update module 13 uses the generated three-dimensional calibration offset vector and dynamic correction factor to dynamically update the trajectory control parameters in the laser interferometer calibration module, allowing the benchmark measurement data to match the sensor calibration status in real time, realizing the calibration module's adaptive response to environmental and sensor changes. The adjustment module 14 drives the calibration actuator based on the updated displacement benchmark data to complete submicron-level adjustment of the sensor. Through modular design, the system simplifies the automatic calibration process of displacement sensors into a system-integrated operation mode, greatly improving the efficiency and accuracy of calibration, while reducing the difficulty of manual operation and enhancing its application adaptability in complex wooden box conveyor lines.

[0100] It should be noted that, those skilled in the art will clearly understand that, for the sake of convenience and brevity of description, the specific working processes of the above-described system and each module can refer to the corresponding processes in the aforementioned embodiment 1 and will not be repeated here.

[0101] Example 4: like Figure 3 As shown, the present application also provides an electronic device 20, including a memory 21 and a processor 22, wherein the memory 21 stores a computer program that can be run on the processor 22, and when the processor 22 executes the computer program, the automatic calibration method of the displacement sensor for the wooden box in Example 1 is implemented.

[0102] In this embodiment, by storing a computer program that implements the automatic calibration method for displacement sensors for wooden boxes in Example 1 in an electronic device 20, combined with the memory 21 and processor 22 of the electronic device 20, an efficient automatic calibration function is achieved. When the processor 22 runs the computer program in the memory 21, it can fully execute the automatic calibration process of the displacement sensor described in Example 1, from collecting the motion trajectory of the wooden box to extracting the standard and real-time trajectories to construct a trajectory pairing data set, and then to generating a three-dimensional calibration offset vector and dynamic correction factor based on the error characteristics, and finally completing the sub-micron adjustment of the sensor based on the displacement reference data. Through the execution of the program, the electronic device 20 realizes a series of calibration tasks such as data acquisition, error calculation, real-time update and precision adjustment, effectively integrating the complex algorithms and control operations required for displacement sensor calibration, ensuring the automation and real-time performance of the calibration process of the wooden box conveyor line, and reducing the impact of hardware limitations on the calibration function. In addition, the design of the electronic device 20 allows the calibration system to be easily upgraded and expanded, and adapted to different types of displacement sensors and conveyor line environments.

[0103] Example 5: The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor is caused to execute the method for automatically calibrating a displacement sensor for a wooden box according to embodiment 1.

[0104] In this embodiment, by providing a computer-readable storage medium containing a computer program, the processor can execute the automatic calibration method for the displacement sensor for the wooden box described in Example 1 when running the computer program. The computer program has been independently designed and optimized, and includes the complete calibration method steps of Example 1, including collecting the motion trajectory of the wooden box, matching the standard trajectory with the real-time trajectory, building a trajectory pairing data set, calculating error characteristic parameters, generating a three-dimensional calibration offset vector and a dynamic correction factor, updating the laser interferometer trajectory control parameters, and finally completing the sub-micron adjustment of the sensor. The application of the computer-readable storage medium provides a flexible and portable calibration function that can be adapted to a variety of computing platforms and processor architectures, ensuring the efficient execution of the calibration method in different hardware environments. At the same time, the design of the storage medium also enables the calibration method to maintain modularity and independence, without relying on other external systems, significantly improving the convenience and intelligence of the displacement sensor calibration.

[0105] The structures, proportions, sizes, etc. depicted in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not intended to limit the conditions under which the present invention can be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportional relationships, or adjustments in size should still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and objectives that can be achieved by the present invention.

[0106] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. 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 invention.

Claims

1. A method for automatically calibrating a displacement sensor for a wooden box, applied to a wooden box conveyor line, wherein a calibration actuator is provided on the wooden box conveyor line, and a displacement sensor is provided on the calibration actuator, characterized in that: The calibration method comprises: The displacement sensor is used to collect the motion trajectory of the wooden box during transportation, and a standard displacement trajectory segment and a real-time motion trajectory segment are extracted based on the motion trajectory. The standard displacement trajectory segment and the real-time motion trajectory segment are used to construct a trajectory pairing dataset; performing segment-by-segment difference calculation on the trajectory pairing data set to obtain an error characteristic parameter set, and generating a three-dimensional calibration offset vector and a dynamic correction factor based on the error characteristic parameter set; Generating a sensor correction parameter group using the three-dimensional calibration offset vector and the dynamic correction factor, dynamically updating trajectory control parameters in a preset laser interferometer calibration module based on the sensor correction parameter group, and controlling the laser interferometer to collect displacement reference data of the wooden box based on the updated trajectory control parameters; The calibration actuator is driven to adjust the displacement sensor based on the displacement reference data.

2. The automatic calibration method for a displacement sensor for a wooden box according to claim 1, characterized in that: The calibration actuator includes a linear motor and an air-floating guide rail. The linear motor is used to drive the air-floating guide rail to slide. The displacement sensor is installed on the air-floating guide rail.

3. The automatic calibration method for a displacement sensor for a wooden box according to claim 1, characterized in that: The step of collecting the motion trajectory of the wooden box during transportation by the displacement sensor, extracting standard displacement trajectory segments and real-time motion trajectory segments based on the motion trajectory, and constructing a trajectory pairing dataset using the standard displacement trajectory segments and the real-time motion trajectory segments includes: The wooden boxes are controlled to pass through the conveyor line at a constant speed, and the displacement sensors are activated to synchronously collect displacement information of the wooden boxes at various time points. A preliminary interpolation fitting is performed on the displacement information to generate a speed curve. The high-frequency fluctuation characteristics in the speed curve are jointly analyzed with the displacement fluctuation amplitude within a time period to identify the interference segment and the remaining segment. Filtering the interference segment using a wavelet threshold filtering algorithm to obtain a first trajectory segment, performing second-order curve fitting on the remaining segment to obtain a second trajectory segment, splicing the first trajectory segment and the second trajectory segment in chronological order and smoothing their boundaries to obtain a fitted motion trajectory; extracting a standard displacement trajectory segment and a real-time motion trajectory segment based on the fitted motion trajectory; The key feature points of the standard displacement trajectory segment and the real-time motion trajectory segment are aligned, and a trajectory pairing dataset is constructed based on the aligned standard displacement trajectory segment and the real-time motion trajectory segment; wherein the key feature points refer to the velocity extreme points and acceleration inflection points with synchronized timestamps in the standard displacement trajectory segment and the real-time motion trajectory segment.

4. The automatic calibration method for a displacement sensor for a wooden box according to claim 1, characterized in that: The step of performing segment-by-segment difference calculation on the trajectory pairing data set to obtain an error characteristic parameter set, and generating a three-dimensional calibration offset vector and a dynamic correction factor based on the error characteristic parameter set includes: Performing segment-by-segment difference calculation on the standard displacement trajectory segments and the real-time motion trajectory segments in the trajectory pairing dataset to generate an error sequence, segmenting the error sequence by time period using a sliding window, and calculating the average error, peak error, and error change rate within each segment to generate a set of error feature parameters; Identifying an error source based on an error change rate in the error characteristic parameter set and a response time of the displacement sensor, extracting a corresponding time position interval based on the error source, calculating a cumulative error mean based on the time position interval, and generating a preliminary calibration offset using the cumulative error mean; The error characteristic parameter set and the preliminary calibration offset are weighted and integrated to generate a comprehensive calibration correction, and the comprehensive calibration correction is used to generate a three-dimensional calibration offset vector and a dynamic correction factor.

5. The automatic calibration method for a displacement sensor for a wooden box according to claim 4, characterized in that: The step of weighting and integrating the error characteristic parameter set and the preliminary calibration offset to generate a comprehensive calibration correction, and using the comprehensive calibration correction to generate a three-dimensional calibration offset vector and a dynamic correction factor includes: performing weighted processing on the average error, peak error, and error change rate in the error characteristic parameter set and the value of the preliminary calibration offset to generate a preliminary calibration correction, and numerically integrating the preliminary calibration correction and the error characteristic parameter set using a weighted least squares method to obtain a comprehensive calibration correction; A three-dimensional calibration offset vector is generated based on the comprehensive calibration correction amount, and the three-dimensional calibration offset vector is fused with the dynamic response characteristics of the displacement sensor to generate a dynamic correction factor; wherein the dynamic response characteristics refer to the response capability of the displacement sensor at different frequencies.

6. The automatic calibration method for a displacement sensor for a wooden box according to claim 1, characterized in that: The steps of generating a sensor correction parameter group using the three-dimensional calibration offset vector and the dynamic correction factor, dynamically updating trajectory control parameters in a preset laser interferometer calibration module based on the sensor correction parameter group, and controlling the laser interferometer to collect displacement reference data of the wooden box based on the updated trajectory control parameters include: Performing coordinate transformation on the three-dimensional calibration offset vector to obtain a correction offset matrix, and performing weighted combination of the correction offset matrix and the dynamic correction factor to obtain comprehensive correction data; Performing filtering on the comprehensive correction data to obtain a smooth correction result, generating a sensor correction parameter group based on the smooth correction result, and inputting the sensor correction parameter group into a laser interferometer calibration module; The sensor correction parameter group is used to dynamically update the trajectory control parameters preset in the laser interferometer calibration module to obtain updated trajectory control parameters, and the laser interferometer is controlled based on the updated trajectory control parameters to collect the displacement reference data of the wooden box.

7. The automatic calibration method for a displacement sensor for a wooden box according to claim 1, characterized in that: The step of driving the calibration actuator to adjust the displacement sensor based on the displacement reference data includes: Inputting the displacement reference data into a preset control system, the control system extracting an error distribution curve based on the displacement reference data, and analyzing the deviation direction of the displacement sensor based on the error distribution curve; An adjustment instruction set is generated according to the deviation direction, and the calibration actuator is controlled to slide according to the adjustment instruction set, so as to drive the displacement sensor to adjust.

8. An automatic calibration system for displacement sensors of wooden boxes, applied to wooden box conveyor lines, wherein a calibration actuator is provided on the wooden box conveyor line, and a displacement sensor is provided on the calibration actuator, characterized in that: The calibration system comprises: an acquisition module, configured to acquire the motion trajectory of the wooden box during transportation using the displacement sensor, extract standard displacement trajectory segments and real-time motion trajectory segments based on the motion trajectory, and construct a trajectory pairing dataset using the standard displacement trajectory segments and the real-time motion trajectory segments; a calculation module, configured to perform segment-by-segment difference calculation on the trajectory pairing data set to obtain a set of error characteristic parameters, and generate a three-dimensional calibration offset vector and a dynamic correction factor based on the set of error characteristic parameters; an updating module, configured to generate a sensor correction parameter group using the three-dimensional calibration offset vector and the dynamic correction factor, dynamically update trajectory control parameters in a preset laser interferometer calibration module based on the sensor correction parameter group, and control the laser interferometer to collect displacement reference data of the wooden box based on the updated trajectory control parameters; An adjustment module is used to drive the calibration actuator to adjust the displacement sensor based on the displacement reference data.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the method for automatically calibrating a displacement sensor for a wooden box according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the processor is caused to execute the automatic calibration method for a displacement sensor used for a wooden box according to any one of claims 1 to 7.

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