Automatic calibration method and device for electromagnetic linear displacement sensor

Abstract

The invention relates to the technical field of linear displacement sensors, in particular to an electromagnetic linear displacement sensor automatic calibration method and device, and the method comprises the steps: correcting a first displacement signal based on a first environment parameter collected in real time, and obtaining a first correction signal; correcting the second displacement signal based on a second environment parameter collected in real time to obtain a second correction signal; and performing data comparison according to the timestamp, generating a comparison curve by taking the second correction signal as a reference, calculating a correction value, and calibrating the measured sensor. According to the invention, environmental parameters are collected in real time and a correction algorithm is introduced, so that the influence of environmental changes on sensor output is effectively eliminated; through dynamic comparison and fitting based on a standard sensor, the calibration accuracy is improved; automatic calibration of the electromagnetic linear displacement sensor is realized, manual intervention is reduced, and efficiency and applicability are improved.

Description

Technical Field

[0001] This invention relates to the field of linear displacement sensor technology, and specifically to an automatic calibration method and apparatus for an electromagnetic linear displacement sensor. Background Technology

[0002] Electromagnetic linear displacement sensors are precision measuring devices commonly used in engineering surveying, industrial automation, and scientific experiments. They achieve real-time monitoring of target displacement by detecting changes in the displacement of a conductor. These sensors offer advantages such as simple structure, fast response speed, and high resolution, making them widely used in structural health monitoring, robotic arm control, and precision machining equipment. However, due to the complex operating environment of these sensors, their output signals are easily affected by various factors such as ambient temperature, humidity, air pressure, and material properties, leading to systematic biases and random errors in the measurement results.

[0003] In existing technologies, the calibration of electromagnetic linear displacement sensors typically relies on manual operation or the use of standard displacement platforms under fixed environmental conditions. These methods have the following drawbacks: First, manual calibration is inefficient and prone to introducing human error; second, calibration methods under fixed environmental conditions cannot reflect the dynamic changes in the actual application environment, making it difficult to guarantee the sensor's output accuracy under different conditions; third, existing methods mostly employ static comparisons during calibration, lacking a dynamic comparison and correction mechanism for signal changes over time. Summary of the Invention

[0004] (I) Purpose of the Invention

[0005] The purpose of this invention is to provide an automatic calibration method and device for electromagnetic linear displacement sensors that effectively eliminates the influence of environmental changes on sensor output by real-time acquisition of environmental parameters and the introduction of correction algorithms; improves calibration accuracy by dynamic comparison and fitting based on standard sensors; and realizes automated calibration of electromagnetic linear displacement sensors, reducing manual intervention and improving efficiency and applicability.

[0006] (II) Technical Solution

[0007] To address the above problems, this invention provides an automatic calibration method for an electromagnetic linear displacement sensor, comprising:

[0008] Acquire several first displacement signals detected by the sensor under test according to a preset displacement amount and several second displacement signals detected by the standard sensor according to a preset displacement amount;

[0009] The first displacement signal is corrected based on the first environmental parameters acquired in real time to obtain a first corrected signal;

[0010] The second displacement signal is corrected based on the second environmental parameters acquired in real time to obtain a second corrected signal;

[0011] Several first correction signals and several second correction signals are compared according to timestamps, and a comparison curve is generated based on the second correction signals.

[0012] Calculate the correction value based on the comparison curve;

[0013] The sensor under test is calibrated using the correction value.

[0014] In another aspect of the present invention, preferably, the first displacement signal is corrected based on a first environmental parameter acquired in real time to obtain a first corrected signal, including:

[0015] The first environmental parameters are preprocessed to obtain the preprocessed first environmental parameters.

[0016] The preprocessed first environmental parameters are input into a preset environmental correction algorithm to obtain the first environmental correction coefficient;

[0017] The first displacement signal is corrected using the first environmental correction coefficient to obtain the first corrected signal.

[0018] In another aspect of the present invention, preferably, the first environmental parameter includes: ambient temperature, ambient humidity, air pressure, and material surface temperature;

[0019] The first environmental parameters are preprocessed to obtain preprocessed first environmental parameters, including:

[0020] IIR low-pass filtering is used for ambient temperature and humidity;

[0021] The air pressure was filtered using a moving average.

[0022] The surface temperature of the material is periodically calibrated using a blackbody furnace.

[0023] In another aspect of the present invention, preferably, the preset environmental correction algorithm includes an environmental temperature error algorithm, a humidity error algorithm, an air pressure error algorithm, and a material error algorithm;

[0024] The preprocessed first environmental parameters are input into a preset environmental correction algorithm to obtain the first environmental correction coefficient, including:

[0025] The preprocessed ambient temperature is corrected based on an ambient temperature error algorithm to obtain a temperature correction coefficient.

[0026] The humidity correction coefficient is obtained by correcting the preprocessed ambient humidity based on the humidity error algorithm.

[0027] The pre-processed air pressure is corrected based on the air pressure error algorithm to obtain the air pressure correction coefficient;

[0028] The surface temperature of the pre-processed material is corrected based on a material error algorithm to obtain a material correction coefficient.

[0029] The first environmental correction coefficient is obtained by weighting and fusing the temperature correction coefficient, humidity correction coefficient, air pressure correction coefficient and material correction coefficient.

[0030] In another aspect of the present invention, preferably, a comparison is performed between a plurality of first correction signals and a plurality of second correction signals according to timestamps, and a comparison curve is generated based on the second correction signals, including:

[0031] The first and second correction signals are synchronized based on the timestamp to obtain the synchronized first and second correction signals.

[0032] Using the second correction signal as the x-axis and the first correction signal as the y-axis, a comparison curve is generated.

[0033] In another aspect of the present invention, preferably, the step of calculating the correction value based on the comparison curve includes:

[0034] Divide the comparison curve into several intervals to determine the modification interval;

[0035] The deviation between the tested sensor and the standard sensor within the modification interval is subjected to polynomial fitting to obtain the fitting result;

[0036] The fitting result is a correction value.

[0037] In another aspect of the present invention, preferably, the comparison curve is divided into several intervals to determine the modification interval, including:

[0038] Calculate the average deviation between the tested sensor and the standard sensor within each interval;

[0039] If the average deviation exceeds the preset deviation threshold, the corresponding interval is determined as the modification interval.

[0040] In another aspect of the present invention, preferably, the method further includes: verifying the calibration results;

[0041] The verification includes:

[0042] Based on the calibration results and the second correction signal, plot the corrected comparison curve;

[0043] If the fitting degree of the corrected comparison curve meets the preset fitting degree threshold, then the calibration is successful.

[0044] If the fitting degree of the corrected comparison curve does not meet the preset fitting degree threshold, then recalibrate.

[0045] In another aspect of the present invention, preferably, acquiring a plurality of first displacement signals detected by the sensor under test according to a preset displacement amount and a plurality of second displacement signals detected by the standard sensor according to a preset displacement amount includes:

[0046] Control the sensor under test to move according to a preset displacement amount, and acquire several first displacement signals detected;

[0047] The standard sensor is controlled to move according to a preset displacement amount, and several second displacement signals are detected.

[0048] In another aspect, preferably, an automatic calibration device for an electromagnetic linear displacement sensor includes:

[0049] First acquisition module: acquires several first displacement signals detected by the sensor under test according to a preset displacement amount;

[0050] Second acquisition module: Acquires several second displacement signals detected by the standard sensor according to a preset displacement amount;

[0051] First correction module: Corrects the first displacement signal based on the first environmental parameters acquired in real time to obtain a first corrected signal;

[0052] The second correction module corrects the second displacement signal based on the real-time acquired second environmental parameters to obtain a second corrected signal;

[0053] The comparison module compares several first correction signals with several second correction signals according to their timestamps, and generates a comparison curve based on the second correction signals.

[0054] Calculation module: Calculates correction values ​​based on the comparison curve;

[0055] Calibration module: Uses the correction value to calibrate the sensor under test.

[0056] (III) Beneficial Effects

[0057] The above-described technical solution of the present invention has the following beneficial technical effects:

[0058] This invention dynamically corrects the sensor output signal by real-time acquisition of environmental parameters, fundamentally reducing the impact of environmental changes on measurement accuracy. By synchronizing the corrected signals of the tested sensor and the standard sensor with timestamps and generating a comparison curve based on a fitting method, the calibration process no longer relies on static or manual comparison, but achieves real-time, dynamic, and high-precision comparison, improving calibration consistency and reliability. Utilizing a combination of interval deviation analysis and a polynomial correction model, errors across the entire measurement range are modeled and compensated, enabling both global correction and local optimization for deviation exceeding limits, significantly improving the sensor's accuracy and stability across the entire range. This invention enables automated, high-precision calibration of electromagnetic linear displacement sensors, reducing manual intervention and improving calibration efficiency, making it particularly suitable for long-term operation and batch applications. Attached Figure Description

[0059] Figure 1 This is an overall flowchart of one embodiment of the present invention;

[0060] Figure 2 This is a schematic diagram of the overall structure of one embodiment of the present invention;

[0061] Figure label:

[0062] 1: First placement platform, 2: Second placement platform. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0064] Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0065] In the description of this invention, it should be noted that the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0066] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0067] The invention will now be described in more detail with reference to the accompanying drawings. In the various drawings, the same elements are indicated by similar reference numerals. For clarity, the various parts in the drawings are not drawn to scale.

[0068] Example 1

[0069] An automatic calibration method for an electromagnetic linear displacement sensor. Figure 1 An overall flowchart of one embodiment of the present invention is shown, as follows: Figure 1 As shown, before calibration, the sensor under test and the standard sensor are respectively placed on the moving stage. The two sensors can be controlled to move to preset positions, including:

[0070] Acquire several first displacement signals detected by the sensor under test according to a preset displacement amount. The first displacement signals reflect the response characteristics of the sensor under test under the corresponding displacement state.

[0071] Acquire several second displacement signals detected by a standard sensor according to a preset displacement amount. These second displacement signals serve as comparative reference data to reflect the true displacement amount.

[0072] The acquisition of several first displacement signals detected by the sensor under test according to a preset displacement amount includes:

[0073] Control the sensor under test to move according to a preset displacement amount, and acquire several first displacement signals detected;

[0074] The acquisition of several second displacement signals detected by the standard sensor according to a preset displacement amount includes:

[0075] The standard sensor is controlled to move according to a preset displacement amount, and several second displacement signals are detected.

[0076] The first displacement signal is corrected based on the first environmental parameters acquired in real time to obtain a first corrected signal. Simultaneously with acquiring the aforementioned sensor signals, environmental parameters related to the sensor's operating state are acquired in real time. These environmental parameters include, but are not limited to, temperature, humidity, electromagnetic interference intensity, and power supply voltage stability. Since electromagnetic linear displacement sensors may experience zero-point drift, sensitivity changes, or output fluctuations under different environmental conditions, it is necessary to use the acquired environmental parameters to correct the original signal.

[0077] The second displacement signal is corrected based on the real-time acquired second environmental parameters to obtain a second corrected signal; specifically, the first environmental parameters are input into a preset correction model to compensate the first displacement signal to obtain several first corrected signals; the second environmental parameters are input into the corresponding correction model to compensate the second displacement signal to obtain several second corrected signals.

[0078] Furthermore, in this embodiment, the first environmental parameters include: ambient temperature, ambient humidity, air pressure, and material surface temperature. The first displacement signal is corrected based on the real-time collected first environmental parameters to obtain a first corrected signal, including:

[0079] The first environmental parameters are preprocessed to obtain the preprocessed first environmental parameters.

[0080] The preprocessed first environmental parameters are input into a preset environmental correction algorithm to obtain the first environmental correction coefficient;

[0081] The first displacement signal is corrected using the first environmental correction coefficient to obtain the first corrected signal.

[0082] The first environmental parameters are preprocessed to obtain preprocessed first environmental parameters, including:

[0083] IIR low-pass filtering is used for ambient temperature and humidity. During actual data acquisition, ambient temperature and humidity typically experience high-frequency random fluctuations and transient interference, which may originate from sensor thermal noise, acquisition circuit jitter, or ambient air disturbances. Directly using the raw signals for correction can easily cause rapid changes in the correction coefficients, leading to overcompensation or jitter in the measured displacement signal. Therefore, this embodiment employs an Infinite Impulse Response (IIR) low-pass filtering method for preprocessing ambient temperature and humidity. Low-pass filtering effectively suppresses high-frequency components above the preset cutoff frequency, retaining the slowly changing trend values ​​over time, thus obtaining smoother temperature and humidity data that more closely approximates the actual changes.

[0084] A moving average filter is used for air pressure. While air pressure changes have a certain degree of continuity, during actual sensor sampling, it may be affected by instantaneous pulse interference or fluctuations in measurement accuracy, leading to abnormal data points. To reduce such errors, this embodiment uses a moving average filter to process the air pressure data. Specifically, a fixed-length time window is selected, and the air pressure sampling values ​​within the window are weighted and averaged or arithmetic-averaged to smooth short-term fluctuations, enabling the obtained air pressure data to more accurately reflect the changing trend of real environmental pressure.

[0085] The material surface temperature is periodically calibrated using a blackbody furnace. The material surface temperature directly affects the characteristics of the electromagnetic linear displacement sensor, potentially altering coil resistance or the permeability of magnetic components. In practical testing, the acquisition of material surface temperature is easily affected by factors such as sensor accuracy, installation location, and radiation interference. To ensure the accuracy of the measurement results, this embodiment uses a blackbody furnace as a reference temperature source for periodic calibration of the material surface temperature sensor. Specifically, the sensor is periodically compared with the blackbody furnace standard temperature source to correct systematic deviations in the temperature sensor output, thereby ensuring the long-term stability and accuracy of the material surface temperature data.

[0086] The preset environmental correction algorithm includes an environmental temperature error algorithm, a humidity error algorithm, an air pressure error algorithm, and a material error algorithm;

[0087] The preprocessed first environmental parameters are input into a preset environmental correction algorithm to obtain the first environmental correction parameters, including:

[0088] An ambient temperature error algorithm is used to correct the preprocessed ambient temperature, resulting in a temperature correction coefficient. This preprocessed ambient temperature is then input into the algorithm. The algorithm, employing methods such as linear drift models, nonlinear temperature dependence functions, or lookup table mappings, calculates the impact of temperature on the sensor output. The resulting temperature correction coefficient compensates for zero-point shifts or sensitivity changes caused by temperature variations, making the sensor output closer to the true displacement.

[0089] A humidity error algorithm is used to correct the pre-processed ambient humidity, resulting in a humidity correction coefficient. The pre-processed ambient humidity is then input into the humidity error algorithm. Humidity can affect the sensor's insulation performance, resistance value, and the stability of the electromagnetic coil, thus causing signal deviation. The humidity error algorithm calculates the humidity correction coefficient by establishing a humidity-output deviation table. The humidity correction coefficient can offset the interference of humidity changes on the displacement signal, ensuring signal reliability.

[0090] The pre-processed air pressure is corrected using a pressure error algorithm to obtain a pressure correction coefficient; the pre-processed air pressure is then input into the pressure error algorithm. Changes in air pressure can affect the sensor's response characteristics through minute deformations of the mechanical structure or variations in air density. The pressure error algorithm obtains the pressure correction coefficient based on a pressure-displacement deviation table, which is used to correct deviations in the sensor's output signal under different air pressure conditions.

[0091] The material error algorithm corrects the pre-processed material surface temperature to obtain a material correction coefficient; the pre-processed material surface temperature is then input into the material error algorithm. Material surface temperature affects the thermal coupling between the sensor and the measured object, as well as the performance of magnetic components, thus introducing systematic errors into the sensor output. The material error algorithm compensates for the influence of material temperature on the signal by establishing a material temperature-sensor output deviation table to obtain the material correction coefficient.

[0092] The temperature correction factor, humidity correction factor, air pressure correction factor, and material correction factor are weighted and fused to obtain the first environmental correction factor. The weighting coefficients can be obtained based on experience or a machine learning model. For example, the four correction factors can be weighted and fused with weights of 0.4, 0.2, 0.2, and 0.2 to obtain the first environmental correction factor. The second environmental parameter is the same as the first environmental parameter. The second displacement signal is corrected based on the real-time acquired second environmental parameter. The process of obtaining the second corrected signal is the same as the process of obtaining the first corrected signal.

[0093] Several first correction signals and several second correction signals are compared according to their timestamps. A comparison curve is generated using the second correction signals as a reference. To ensure accuracy, the first and second correction signals are matched according to their timestamps, forming one-to-one signal pairs. A comparison curve is established between the two signals using the second correction signal as a reference. This comparison curve visually reflects the response difference between the tested sensor and the standard sensor under the same measurement conditions.

[0094] Furthermore, in this embodiment, several first correction signals and several second correction signals are compared according to timestamps, and a comparison curve is generated based on the second correction signals, including:

[0095] The first and second corrected signals are synchronized based on timestamps to obtain synchronized first and second corrected signals; time synchronization processing is then performed on several first and second corrected signals. Since the sampling times of the measured sensor and the standard sensor may have slight deviations or inconsistent sampling frequencies during actual data acquisition, direct comparison can easily lead to errors or data misalignment. Therefore, this embodiment aligns the first and second corrected signals based on the timestamp of each sampling point. Time synchronization processing can include methods such as interpolation, nearest neighbor matching, or spline fitting to precisely correspond the two sets of signals on the time axis, obtaining synchronized first and second corrected signals.

[0096] A comparison curve is generated using the second correction signal as the x-axis and the first correction signal as the y-axis. The comparison curve is then plotted on a two-dimensional plane using the synchronized second correction signal as the x-axis coordinate and the synchronized first correction signal as the y-axis coordinate. This comparison curve visually reflects the output relationship between the tested sensor and the standard sensor under the same displacement conditions, including linear deviation, nonlinear deviation, and potential environmental coupling errors. By analyzing the shape and characteristics of the comparison curve, the response characteristics of the tested sensor in different displacement ranges can be determined, providing a data basis for calculating the correction values.

[0097] Based on the comparison curve, the correction value is calculated, including:

[0098] The comparison curve is divided into several intervals to determine the modification intervals, including:

[0099] Calculate the average deviation between the tested sensor and the standard sensor within each interval; divide the comparison curve into several intervals and determine the intervals requiring correction. Specifically, the curve can be divided into several continuous intervals based on the displacement range or the number of sampling points. For each interval, calculate the average deviation between the output of the tested sensor and the output of the standard sensor. The average deviation can be obtained by the arithmetic mean of the differences between all sampling points within the interval, reflecting the overall deviation level within that interval.

[0100] If the average deviation exceeds a preset deviation threshold, the corresponding interval is determined as a modification interval. The calculated average deviation is compared with the preset deviation threshold. When the average deviation exceeds the preset deviation threshold, the interval is identified as a modification interval, i.e., the interval that needs precise correction. By dividing the intervals and selecting the modification intervals, targeted compensation can be made for intervals where the sensor has significant deviations, improving correction efficiency and calibration accuracy, while avoiding unnecessary corrections to intervals that are already close to the standard.

[0101] The deviations between the tested sensor and the standard sensor within the specified modification interval are fitted using a polynomial to obtain the fitting result. After determining the modification interval, a polynomial fitting is performed on the deviations between the tested sensor output and the standard sensor output within each modification interval to obtain the fitting result. The polynomial fitting can use first-order, second-order, or higher-order polynomials, with the appropriate fitting order selected based on the nonlinear characteristics of the interval deviations. The fitting process aims to describe the continuous relationship between the tested sensor output and the standard sensor output, thereby accurately capturing the systematic error characteristics of the sensor within that interval.

[0102] The fitting result is a correction value.

[0103] The sensor under test is calibrated using the correction value.

[0104] Furthermore, in this embodiment, the method further includes: verifying the calibration results;

[0105] The verification includes:

[0106] Based on the calibration results and the second correction signal, plot the corrected comparison curve;

[0107] If the fitting degree of the corrected comparison curve meets the preset fitting degree threshold, then the calibration is successful.

[0108] If the fitting degree of the corrected comparison curve does not meet the preset fitting degree threshold, then recalibrate.

[0109] Perform a fitting analysis on the corrected comparison curves and calculate the goodness of fit. The goodness of fit can be expressed using the coefficient of determination R0. 2 Evaluation metrics such as root mean square error (RMSE) or maximum absolute error quantify the closeness between the output of the calibrated sensor and the output of the standard sensor. A higher goodness of fit indicates that the calibrated sensor output is closer to the standard sensor output and the error is smaller. The calibration results are judged based on a preset goodness-of-fit threshold. If the goodness of fit of the corrected comparison curve meets the preset threshold, for example, R0... 2 If the accuracy is ≥0.99 or RMSE≤0.05mm, the calibration is considered successful, and the sensor under test has met the expected accuracy requirements and can be used for subsequent measurements and applications. If the goodness of fit of the corrected comparison curve does not reach the preset threshold, the calibration is considered to have failed to meet the design requirements. In this case, the method will automatically enter the recalibration process, including re-acquiring the output of the sensor under test and environmental parameters, regenerating the comparison curve, recalculating the correction value and applying the calibration, until the calibration result meets the goodness of fit requirements.

[0110] This invention dynamically corrects the sensor output signal by real-time acquisition of environmental parameters, fundamentally reducing the impact of environmental changes on measurement accuracy. By synchronizing the corrected signals of the tested sensor and the standard sensor with timestamps and generating a comparison curve based on a fitting method, the calibration process no longer relies on static or manual comparison, but achieves real-time, dynamic, and high-precision comparison, improving calibration consistency and reliability. Utilizing a combination of interval deviation analysis and a polynomial correction model, errors across the entire measurement range are modeled and compensated, enabling both global correction and local optimization for deviation exceeding limits, significantly improving the sensor's accuracy and stability across the entire range. This invention enables automated, high-precision calibration of electromagnetic linear displacement sensors, reducing manual intervention and improving calibration efficiency, making it particularly suitable for long-term operation and batch applications.

[0111] Example 2

[0112] An automatic calibration device for an electromagnetic linear displacement sensor includes:

[0113] First acquisition module: acquires several first displacement signals detected by the sensor under test according to a preset displacement amount;

[0114] Second acquisition module: Acquires several second displacement signals detected by the standard sensor according to a preset displacement amount;

[0115] First correction module: Corrects the first displacement signal based on the first environmental parameters acquired in real time to obtain a first corrected signal;

[0116] The second correction module corrects the second displacement signal based on the real-time acquired second environmental parameters to obtain a second corrected signal;

[0117] The comparison module compares several first correction signals with several second correction signals according to their timestamps, and generates a comparison curve based on the second correction signals.

[0118] Calculation module: Calculates correction values ​​based on the comparison curve;

[0119] Calibration module: Uses the correction value to calibrate the sensor under test.

[0120] Figure 2 A schematic diagram of the overall structure of an embodiment of the present invention is shown, as follows. Figure 2 As shown, in this embodiment, the device includes a first placement platform 1 for placing the sensor under test and a second placement platform 2 for a standard sensor. The first placement platform 1 is provided with a first moving mechanism that can move the sensor under test, and the second placement platform 2 is provided with a second moving mechanism that can move the standard sensor.

[0121] It should be understood that the specific embodiments described above are merely illustrative or explanatory of the principles of the invention and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of the invention should be included within the protection scope of the invention. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

[0122] The present invention has been described above with reference to embodiments thereof. However, these embodiments are merely illustrative and not intended to limit the scope of the invention. The scope of the invention is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of the invention, and all such substitutions and modifications should fall within the scope of the invention.

[0123] Although embodiments of the present invention have been described in detail, it should be understood that various changes, substitutions, and modifications can be made to the embodiments of the present invention without departing from the spirit and scope of the invention.

[0124] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. An automatic calibration method for an electromagnetic linear displacement sensor, characterized in that, include: Acquire several first displacement signals detected by the sensor under test according to a preset displacement amount and several second displacement signals detected by the standard sensor according to a preset displacement amount; The first displacement signal is corrected based on the first environmental parameters acquired in real time to obtain a first corrected signal; The second displacement signal is corrected based on the second environmental parameters acquired in real time to obtain a second corrected signal; Several first correction signals and several second correction signals are compared according to timestamps, and a comparison curve is generated based on the second correction signals. Calculate the correction value based on the comparison curve; The sensor under test is calibrated using the correction value.

2. The automatic calibration method for an electromagnetic linear displacement sensor according to claim 1, characterized in that, The first displacement signal is corrected based on the first environmental parameters acquired in real time to obtain a first corrected signal, including: The first environmental parameters are preprocessed to obtain the preprocessed first environmental parameters. The preprocessed first environmental parameters are input into a preset environmental correction algorithm to obtain the first environmental correction coefficient; The first displacement signal is corrected using the first environmental correction coefficient to obtain the first corrected signal.

3. The automatic calibration method for an electromagnetic linear displacement sensor according to claim 2, characterized in that, The first environmental parameters include: ambient temperature, ambient humidity, air pressure, and material surface temperature; The first environmental parameters are preprocessed to obtain preprocessed first environmental parameters, including: IIR low-pass filtering is used for ambient temperature and humidity; The air pressure was filtered using a moving average. The surface temperature of the material is periodically calibrated using a blackbody furnace.

4. The automatic calibration method for an electromagnetic linear displacement sensor according to claim 3, characterized in that, The preset environmental correction algorithm includes an environmental temperature error algorithm, a humidity error algorithm, an air pressure error algorithm, and a material error algorithm; The preprocessed first environmental parameters are input into a preset environmental correction algorithm to obtain the first environmental correction coefficient, including: The preprocessed ambient temperature is corrected based on an ambient temperature error algorithm to obtain a temperature correction coefficient. The humidity correction coefficient is obtained by correcting the preprocessed ambient humidity based on the humidity error algorithm. The pre-processed air pressure is corrected based on the air pressure error algorithm to obtain the air pressure correction coefficient; The surface temperature of the pre-processed material is corrected based on a material error algorithm to obtain a material correction coefficient. The first environmental correction coefficient is obtained by weighting and fusing the temperature correction coefficient, humidity correction coefficient, air pressure correction coefficient and material correction coefficient.

5. The automatic calibration method for an electromagnetic linear displacement sensor according to claim 4, characterized in that, Several first correction signals and several second correction signals are compared according to timestamps. A comparison curve is generated based on the second correction signals, including: The first and second correction signals are synchronized based on the timestamp to obtain the synchronized first and second correction signals. Using the second correction signal as the x-axis and the first correction signal as the y-axis, a comparison curve is generated.

6. The automatic calibration method for an electromagnetic linear displacement sensor according to claim 5, characterized in that, The step of calculating the correction value based on the comparison curve includes: Divide the comparison curve into several intervals to determine the modification interval; The deviation between the tested sensor and the standard sensor within the modification interval is subjected to polynomial fitting to obtain the fitting result; The fitting result is a correction value.

7. The automatic calibration method for an electromagnetic linear displacement sensor according to claim 6, characterized in that, The comparison curve is divided into several intervals to determine the modification intervals, including: Calculate the average deviation between the tested sensor and the standard sensor within each interval; If the average deviation exceeds the preset deviation threshold, the corresponding interval is determined as the modification interval.

8. The automatic calibration method for an electromagnetic linear displacement sensor according to claim 7, characterized in that, The method further includes: verifying the calibration results; The verification includes: Based on the calibration results and the second correction signal, plot the corrected comparison curve; If the fitting degree of the corrected comparison curve meets the preset fitting degree threshold, then the calibration is successful. If the fitting degree of the corrected comparison curve does not meet the preset fitting degree threshold, then recalibrate.

9. The automatic calibration method for an electromagnetic linear displacement sensor according to claim 1, characterized in that, The acquisition of several first displacement signals detected by the sensor under test according to a preset displacement amount and several second displacement signals detected by the standard sensor according to a preset displacement amount includes: Control the sensor under test to move according to a preset displacement amount, and acquire several first displacement signals detected; The standard sensor is controlled to move according to a preset displacement amount, and several second displacement signals are detected.

10. An automatic calibration device for an electromagnetic linear displacement sensor, characterized in that, include: First acquisition module: acquires several first displacement signals detected by the sensor under test according to a preset displacement amount; Second acquisition module: Acquires several second displacement signals detected by the standard sensor according to a preset displacement amount; First correction module: Corrects the first displacement signal based on the first environmental parameters acquired in real time to obtain a first corrected signal; The second correction module corrects the second displacement signal based on the real-time acquired second environmental parameters to obtain a second corrected signal; The comparison module compares several first correction signals with several second correction signals according to their timestamps, and generates a comparison curve based on the second correction signals. Calculation module: Calculates correction values ​​based on the comparison curve; Calibration module: Uses the correction value to calibrate the sensor under test.

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