Inclinometer temperature compensation method, medium and system

Abstract

The present invention discloses a temperature compensation method, medium and system for an inclinometer, the method comprising the steps of: S1, obtaining an angle data array of the inclinometer at every n°C interval and preprocessing; S2, adaptively determining a polynomial temperature compensation model according to the preprocessed angle data array; S3, compensating the inclinometer temperature based on the polynomial temperature compensation model. The present invention can effectively improve the temperature compensation accuracy and calibration efficiency of the inclinometer.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of inclinometer temperature compensation, and in particular to an inclinometer temperature compensation method, medium and system. Background Art

[0002] High-precision inclinometers play an important role in many fields, such as aerospace, automotive industry, robotics, civil engineering, marine exploration, and precision instrument calibration. These devices usually rely on accelerometers or other types of sensors to measure the tilt angle. In many applications, such as structural health monitoring and precise navigation, the measurement accuracy of the inclinometer is crucial. However, the performance of these sensors is often affected by changes in ambient temperature. Sensor elements (such as accelerometers) exhibit different electrical characteristics at different temperatures, such as zero drift and sensitivity changes, resulting in measurement errors. Temperature compensation can significantly improve the measurement accuracy of the inclinometer under different environmental conditions and ensure the reliability of the data. Therefore, the development of an effective temperature compensation method is crucial to improve the measurement accuracy of the inclinometer.

[0003] When developing temperature compensation algorithms, a variety of mathematical models are used to describe and predict the relationship between sensor output and temperature. Commonly used mathematical models include:

[0004] 1) Linear model: Simple linear regression can be used to describe the linear relationship between sensor output and temperature in the form of y = mx + b, where m is the slope and b is the intercept.

[0005] 2) Polynomial model: When the data shows a nonlinear trend, a polynomial model can be used to fit it, such as the quadratic polynomial y=ax 2 +bx+c.

[0006] 3) Exponential and logarithmic models: For exponentially changing data, the exponential model y=ae can be used bx Or the logarithmic model y=a+blog(x).

[0007] 4) Machine learning models: including decision trees, support vector machines (SVMs), random forests, neural networks, etc. These models can handle complex nonlinear relationships.

[0008] 5) Adaptive filters: such as Kalman filters, can be used to estimate the state of dynamic systems and are suitable for real-time updating of temperature compensation parameters.

[0009] 6) Least Squares Method: This is a commonly used parameter estimation method that can be used to determine the model parameters so that the sum of squared errors between the model predictions and the actual observed values ​​is minimized.

[0010] At present, there are many studies on improving the accuracy of inclinometers based on accelerometers. For example, the invention patent CN111076748A proposes a method and system for compensating horizontal tilt errors based on MEMS accelerometers. Although a functional relationship between the accelerometer measurement error and temperature is established, the twelve-position static tumbling scheme used is relatively cumbersome and requires collecting data from the twelve positions of the inclinometer on the turntable. The operation time is too long and there are too many steps, so it is not suitable for rapid tilt calibration occasions.

[0011] For example, in the invention patent "A high-precision inclinometer temperature compensation method" (publication number: CN118225127A), the The iterative optimization algorithm optimizes the BP neural network model. The training set includes the inclination value, temperature and corresponding measurement value of the inclinometer. Standardization method preprocessing. The number of input nodes, output nodes, and hidden layer nodes of the BP neural network model are set. MSE mean square error is used as the loss function and ReLU function is used as the activation function. The limitations of this method are as follows:

[0012] 1) Data dependence: The performance of neural networks depends heavily on the quality and quantity of training data. If the training data is insufficient or unrepresentative, the compensation effect of the model may be affected.

[0013] 2) Model complexity: Building and training neural network models may require high computing resources and expertise, which may be a challenge for users with limited resources.

[0014] 3) Overfitting risk: If the model is too complex or the training data is limited, the neural network may overfit, resulting in reduced generalization ability in practical applications.

[0015] For example, in the invention patent "Inclinometer zero point and sensitivity temperature compensation device and compensation method thereof" (publication number: CN1673677A), the temperature compensation method used for the inclinometer is: fix the inclinometer to be compensated on the inclinometer fixing frame. Adjust the reference plane of the inclinometer fixing frame to make it parallel to the horizontal plane. Record the zero point and full-scale sensitivity output of the inclinometer sensor at different temperatures. Use the linear interpolation method to calculate the zero point and sensitivity of the inclinometer at the working temperature to achieve temperature compensation. The limitations of this method include:

[0016] 1) High dependence on equipment: This compensation method relies on the accuracy and stability of equipment such as motors, controllers, and high and low temperature test chambers. Any equipment failure may affect the compensation effect.

[0017] 2) Many environmental restrictions: The temperature range and control accuracy of the high and low temperature test chamber limit the application environment of the compensation method and may not cover the temperature range under all actual use conditions.

[0018] 3) High initial investment: Although the manufacturing cost is relatively low, a certain initial investment is still required to purchase and install the required equipment and software. Summary of the invention

[0019] In view of the technical problems existing in the prior art, the present invention provides an inclinometer temperature compensation method, medium and system for improving the temperature compensation accuracy and calibration efficiency of the inclinometer.

[0020] In order to solve the above technical problems, the technical solution proposed by the present invention is:

[0021] A temperature compensation method for an inclinometer comprises the following steps:

[0022] S1, obtain the angle data array of the inclinometer at every n° interval and perform preprocessing;

[0023] S2, adaptively determining a polynomial temperature compensation model according to the preprocessed angle data array;

[0024] S3. Compensate the inclinometer temperature based on a polynomial temperature compensation model.

[0025] Preferably, in step S1, preprocessing the angle data array specifically includes the following steps:

[0026] S101, calculating the first quartile Q1 and the third quartile Q3;

[0027] S102, calculate the interquartile range IQR = Q3-Q1;

[0028] S103, determining a threshold of an outlier according to the interquartile range;

[0029] S104, identifying and eliminating outliers according to an outlier threshold;

[0030] S105, update the array and calculate the average value as the final angle data.

[0031] Preferably, the specific steps of step S101 are:

[0032] S1011, angle data array {x1, x2, ..., x n}, specifically sort the array {x1,x2,…,x n}Sort from small to large;

[0033] S1012, calculate Q1: transform the angle data array {x1, x2,…, x n} is divided into four equal parts, that is, the number at the 25% position is defined as the first quartile Q1; if the number of data points n is an odd number, Q1 takes the number in the middle position; if n is an even number, Q1 takes the average of the two middle numbers;

[0034] S1013. Calculate Q3: define the number at the 75% position as the third quartile Q3. The point selection method is the same as that for calculating Q1.

[0035] Preferably, in S103, the threshold of the outlier is determined according to the interquartile range as follows:

[0036] Calculate the lower bound: Set the lower bound to Q1-1.5×IQR;

[0037] Calculate the upper bound: Set the upper bound to Q3 + 1.5 × IQR.

[0038] Preferably, step S2 specifically includes:

[0039] S201, setting a k-order polynomial function model, where the value of k ranges from 1 to 5;

[0040] S202, calculating the coefficients of the polynomial functions of order 1 to 5 respectively by the least square method;

[0041] S203, obtaining the likelihood function of the 1st to 5th order polynomial function according to the coefficients of the 1st to 5th order polynomial function, and calculating the maximum likelihood estimation value L of each order;

[0042] S204, calculating the Bayesian Information Criterion BIC according to the number of polynomial parameters m, the number of temperature acceleration data n and the maximum likelihood estimate L;

[0043] S204, comparing the BIC values ​​of the Bayesian Information Criterion of each order, and selecting the order with the smallest BIC value as the optimal order;

[0044] S205. Obtain a polynomial temperature compensation model according to the optimal order and polynomial function coefficients.

[0045] Preferably, the specific process of step S202 is:

[0046] S2021. Constructing a polynomial model:

[0047] f(T)=a k T k +a k-1 T k-1 +…+a1T+a0

[0048] S2022. Calculate the value of the polynomial: Use the least squares method to fit the data, that is, minimize

[0049] S2023. Solve the coefficients: Construct the matrix X, and we have:

[0050]

[0051] in For the kth power of the mth data point, use the normal equation Solving for the coefficient vector

[0052] Preferably, the specific process of step S203 is:

[0053] S2031, calculate likelihood value

[0054]

[0055] S2032. Calculate log-likelihood value

[0056]

[0057] σ 2 is the error term; m is the number of data.

[0058] Preferably, in step S3, the Bayesian Information Criterion BIC is calculated:

[0059]

[0060] The present invention also discloses a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described above are executed.

[0061] The present invention further discloses a temperature compensation system for an inclinometer, comprising a memory and a processor connected to each other, wherein a computer program is stored in the memory, and when the computer program is run by the processor, the steps of the method described above are executed.

[0062] Compared with the prior art, the advantages of the present invention are:

[0063] The present invention places the inclinometer in a high and low temperature test box, establishes a functional relationship between the inclination error and the temperature, obtains a temperature compensation formula, realizes the calibration of the temperature compensation of the inclinometer, and effectively improves the temperature compensation accuracy of the inclinometer; the present invention remotely controls the inclinometer to start the temperature compensation mode through a cloud platform, and the inclinometer automatically enters the temperature compensation process after receiving the wireless network signal, thereby avoiding manual operation and effectively improving the calibration efficiency of the inclinometer; the present invention can calibrate the temperature compensation of the inclinometer on a large scale, and the inclinometer only needs to be horizontally stacked in the high and low temperature box, thereby effectively improving the batch calibration efficiency of the inclinometer. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 The present invention is a flowchart of a high-precision inclinometer temperature compensation method in an embodiment.

[0065] Figure 2 The present invention is a flow chart of the adaptive determination of the polynomial temperature compensation formula.

[0066] Figure 3 This is a flow chart of the angle data output by the inclinometer after temperature compensation in the present invention.

[0067] Figure 4 1 and 1 , which are diagrams for verifying the temperature compensation effect of an embodiment of the present invention; wherein (a) is an x-axis angle compensation diagram; and (b) is a y-axis angle compensation diagram.

[0068] Figure 5 This is a layout diagram of a temperature compensation data acquisition device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0070] like Figure 1 As shown, the inclinometer temperature compensation method of the embodiment of the present invention comprises the steps of:

[0071] S1, obtain the angle data array of the inclinometer at every n° interval and perform preprocessing;

[0072] S2, adaptively determining a polynomial temperature compensation model according to the preprocessed angle data array;

[0073] S3. Compensate the inclinometer temperature based on a polynomial temperature compensation model.

[0074] Specifically, in step S1, for the angle data array {x1, x2, ..., x n}, use the quartile method to remove abnormal values ​​in the array and calculate the mean, and store the mean result in the storage chip of the inclinometer. The specific steps are as follows:

[0075] S101, calculating the first quartile Q1 and the third quartile Q3;

[0076] S1011. Sort the array: First, sort the array {x1, x2, …, x n}Sort from small to large;

[0077] S1012, calculate Q1: divide the data into four equal parts, that is, define the number at the 25% position as the first quartile Q1; if the number of data points n is an odd number, Q1 takes the number in the middle position; if n is an even number, Q1 takes the average of the two middle numbers;

[0078] S1013. Calculate Q3: define the number at the 75% position as the third quartile Q3. The point selection method is the same as that for calculating Q1.

[0079] S102. Calculate the interquartile range IQR. The interquartile range IQR is the difference between Q3 and Q1, that is:

[0080] IQR=Q3-Q1

[0081] S103. Determine the threshold of the outlier value according to the interquartile range IQR, specifically:

[0082] Lower bound: Q1-1.5×IQR; Upper bound: Q3+1.5×IQR;

[0083] S104, identify and remove outliers according to the upper and lower bounds to obtain a new array, specifically:

[0084] S1041, traverse the array: check each data point in the array;

[0085] S1042. Comparison value: For each data point, if it is less than the lower bound or greater than the upper bound, it is considered an outlier;

[0086] S1043. Eliminate outliers: remove these outliers from the array.

[0087] S105, obtaining the mean of the new array as the final abundance data, specifically:

[0088] S1051, updating the array: using the array after removing the outliers as a new array;

[0089] S1052, obtaining the average value of the new array: taking the average value of the new array as the angle data collection result of the temperature point, and the inclinometer saves the temperature and angle data of the point.

[0090] like Figure 2 As shown in the figure, the temperature data stored in the inclinometer ranges from 25 to 65°C, and the temperature and acceleration arrays are {(T1, A1), (T2, A2), …, (T m ,A m )}, where T i Indicates temperature, A i represents acceleration; the specific steps corresponding to step S2 in adaptively determining the polynomial temperature compensation model are as follows:

[0091] S201, setting a polynomial model, specifically:

[0092] f(x)=a k x k +a k-1 xk-1 +…+a1x+a0

[0093] Among them, {a0,a1,…,a k} is the polynomial coefficient, k is the polynomial order, and k ranges from 1 to 5.

[0094] S202, respectively calculating the coefficients of the 1st to 5th order polynomial functions by the least square method; specifically, for the kth order polynomial, performing the following operations:

[0095] S2021. Constructing a polynomial model:

[0096] f(T)=a k T k +a k-1 T k-1 +…+a1T+a0

[0097] S2022. Calculate the value of the polynomial:

[0098] Use the least squares method to fit the data, that is, minimize

[0099] S2023. Solve the coefficients: Construct the matrix X, and we have:

[0100]

[0101] in For the kth power of the mth data point, use the normal equation Solving for the coefficient vector

[0102] S203, deriving the likelihood function of the 1st to 5th order polynomial function according to the coefficients of the 1st to 5th order polynomial function, and calculating the maximum likelihood estimation value L of each order, specifically:

[0103] S2031, calculate likelihood value

[0104]

[0105] S2032. Calculate log-likelihood value

[0106]

[0107] S204, calculating the Bayesian Information Criterion (BIC) according to the number of polynomial parameters m, the number of temperature acceleration data n and the maximum likelihood estimate L;

[0108]

[0109] Where k is the number of model parameters (including k coefficients and the error term σ 2 ), m is the number of data.

[0110] S205. Select the optimal model

[0111] For polynomial models of order 1 to 5, their BIC values ​​were compared, and the model with the smallest BIC value was selected considering model complexity and model accuracy.

[0112] S206. Output the temperature compensation formula result.

[0113] Output the polynomial order and coefficients of the optimal model to obtain the optimal temperature compensation formula.

[0114] In specific application, the specific application method steps of the high-precision inclinometer temperature compensation method of the present invention are as follows:

[0115] 1) If Figure 5 As shown, the inclinometers are placed in batches in a high and low temperature box;

[0116] 2) Level the inclinometer so that the z-axis of its acceleration sensor is perpendicular to the horizontal plane of the high and low temperature chamber;

[0117] 3) Set the temperature rise (or fall) range of the high and low temperature box to 25~65℃;

[0118] 4) The temperature of the high and low temperature box is stabilized for 2 hours every time the temperature changes by 5℃;

[0119] 5) The temperature compensation mode is turned on by remotely controlling the inclinometer through the cloud platform, and the inclinometer enters the automatic temperature compensation process according to the temperature compensation method as described above.

[0120] like Figure 1 As shown, every time the inclinometer rises (or falls) by 5°C and the temperature reaches stability, it will continuously collect multiple sets of ambient temperature and angle data. After using the quartile method to eliminate outliers in the multiple sets of data and taking the average, the inclinometer will save the processed data in the storage chip of the internal main control board; during the data collection, processing and storage process, the inclinometer will determine whether the stored temperature data reaches the range of 25 to 65°C. When the temperature range is reached, the inclinometer enters the calculation compensation formula process, adaptively determines the optimal order and coefficient of the polynomial, completes the temperature compensation formula, and realizes temperature compensation of the inclinometer output angle.

[0121] like Figure 3 As shown in the figure, the specific steps of the inclinometer output angle data after temperature compensation are as follows:

[0122] Step 1: Start: Start the temperature compensation process of the inclinometer;

[0123] Step 2: Continuously collect temperature and angle values: Continuously collect multiple sets of temperature and angle data through the inclinometer. The temperature and angle arrays are {(T1, A1), (T2, A2), …, (T m ,A m )};

[0124] Step 3: Use the quartile method to eliminate outliers in temperature and angle data: Use the quartile method to process the collected temperature and angle data to identify and eliminate outliers;

[0125] Step 4: Calculate the average value of temperature and angle: Calculate the average value of temperature and angle data after removing outliers, and record them as T aver and A aver ;

[0126] Step 5: Temperature average value T aver Substitute the calculated average temperature T into the temperature compensation formula: aver Applied to the temperature compensation formula, the compensation angle value A is calculated c ;

[0127] Step 6: Output the temperature compensation angle value: According to the calculated angle compensation value A c , adjust the original angle average A aver , output the angle value A after temperature compensation correction = A aver -A c .

[0128] The present invention places the inclinometer in a high and low temperature test box, establishes a functional relationship between the inclination error and the temperature, obtains a temperature compensation formula, realizes the calibration of the temperature compensation of the inclinometer, and effectively improves the temperature compensation accuracy of the inclinometer; the present invention remotely controls the inclinometer to start the temperature compensation mode through a cloud platform, and the inclinometer automatically enters the temperature compensation process after receiving the wireless network signal, thereby avoiding manual operation and effectively improving the calibration efficiency of the inclinometer; the present invention can calibrate the temperature compensation of the inclinometer on a large scale, and the inclinometer only needs to be horizontally stacked in the high and low temperature box, thereby effectively improving the batch calibration efficiency of the inclinometer.

[0129] Experimental verification: In the range of ambient temperature from 25℃ to 65℃, the position of the tilt monitor remains unchanged. Before temperature compensation, the measured x-axis and y-axis angle values ​​gradually decrease as the ambient temperature increases. The change in ambient temperature produces a large measurement error. The x-axis angle value shifts from 0.1° to -0.8°, and the y-axis angle value shifts from -0.1° to -2.5°. Through binomial fitting and temperature compensation, the temperature dependence of the angle value measured by the tilt monitor is greatly reduced. The measurement errors of the compensated angle values ​​Ax and Ay are both less than ±0.1°, such as Figure 4 shown.

[0130] The present invention also discloses a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are executed. The present invention further discloses an inclinometer temperature compensation system, including a memory and a processor connected to each other, on which a computer program is stored, and when the computer program is executed by the processor, the steps of the above method are executed. The medium and system of the present invention correspond to the above method and also have the advantages described in the above method.

[0131] The present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiment when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable storage media include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. The memory is used to store computer programs and / or modules, and the processor implements various functions by running or executing computer programs and / or modules stored in the memory, and calling data stored in the memory. The memory may include high-speed random access memory and may also include non-volatile memory, such as a hard disk, an internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0132] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A temperature compensation method for an inclinometer, characterized in that: Includes steps: S1, obtain the angle data array of the inclinometer at every n° interval and perform preprocessing; S2, adaptively determining a polynomial temperature compensation model according to the preprocessed angle data array; S3. Compensate the inclinometer temperature based on a polynomial temperature compensation model.

2. The inclinometer temperature compensation method according to claim 1, characterized in that: In step S1, preprocessing the angle data array specifically includes the following steps: S101, calculating the first quartile Q1 and the third quartile Q3; S102, calculate the interquartile range IQR = Q3-Q1; S103, determining a threshold of an outlier according to the interquartile range; S104, identifying and eliminating outliers according to an outlier threshold; S105, update the array and calculate the average value as the final angle data.

3. The inclinometer temperature compensation method according to claim 2, characterized in that: The specific steps of step S101 are: S1011, angle data array {x1, x2, ..., x n }, specifically sort the array {x1,x2,…,x n }Sort from small to large; S1012, calculate Q1: transform the angle data array {x1, x2,…, x n } is divided into four equal parts, that is, the number at the 25% position is defined as the first quartile Q1; if the number of data points n is an odd number, Q1 takes the number in the middle position; if n is an even number, Q1 takes the average of the two middle numbers; S1013. Calculate Q3: define the number at the 75% position as the third quartile Q3. The point selection method is the same as that for calculating Q1.

4. The inclinometer temperature compensation method according to claim 2 or 3, characterized in that: In S103, the threshold of the outlier is determined according to the interquartile range: Calculate the lower bound: Set the lower bound to Q1-1.5×IQR; Calculate the upper bound: Set the upper bound to Q3 + 1.5 × IQR.

5. The inclinometer temperature compensation method according to claim 1, 2 or 3, characterized in that: Step S2 is specifically as follows: S201, setting a k-order polynomial function model, where the value of k ranges from 1 to 5; S202, calculating the coefficients of the polynomial functions of order 1 to 5 respectively by the least square method; S203, obtaining the likelihood function of the 1st to 5th order polynomial function according to the coefficients of the 1st to 5th order polynomial function, and calculating the maximum likelihood estimation value L of each order; S204, calculating the Bayesian Information Criterion BIC according to the number of polynomial parameters m, the number of temperature acceleration data n and the maximum likelihood estimate L; S204, comparing the BIC values ​​of the Bayesian Information Criterion of each order, and selecting the order with the smallest BIC value as the optimal order; S205. Obtain a polynomial temperature compensation model according to the optimal order and polynomial function coefficients.

6. The inclinometer temperature compensation method according to claim 5, characterized in that: The specific process of step S202 is: S2021. Constructing a polynomial model: f(T)=a k T k +a k-1 T k-1 +…+a1T+a0 S2022. Calculate the value of the polynomial: Use the least squares method to fit the data, that is, minimize S2023. Solve the coefficients: Construct the matrix X, and we have: in For the kth power of the mth data point, use the normal equation Solving for the coefficient vector 7. The inclinometer temperature compensation method according to claim 6, characterized in that: The specific process of step S203 is: S2031, calculate likelihood value S2032. Calculate log-likelihood value σ 2 is the error term; m is the number of data.

8. The inclinometer temperature compensation method according to claim 7, characterized in that: In step S3, the Bayesian Information Criterion BIC is calculated:

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the computer program performs the steps of the method according to any one of claims 1 to 8.

10. An inclinometer temperature compensation system, comprising a memory and a processor connected to each other, wherein a computer program is stored in the memory, characterized in that: When the computer program is executed by a processor, the computer program performs the steps of the method according to any one of claims 1 to 8.

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