Calibration and error correction method and system based on nonlinear error correction

Abstract

The invention discloses a non-linear error correction-based calibration and error correction method and system, and relates to the technical field of measurement system optimization, and the method comprises the steps: carrying out the precise measurement of a standard object, calculating the error between a measurement value and a real value, and describing the relation between the error and the measurement value through a non-linear model; performing error correction on each obtained measurement value by applying a nonlinear model; model parameters are updated in real time according to the newest measurement data so as to adjust the measurement result of each time. By analyzing the measurement data from machine vision and combining polynomial modeling and linear regression technologies, nonlinear errors in a measurement system can be effectively identified and corrected and automatically adjusted, so that long-term accuracy and stability are ensured. In addition, according to the technology, the influence of environmental factors is considered at the same time, the error correction process is further optimized, the system can intelligently adapt to different measurement conditions and environmental changes, and therefore high precision and reliability in application are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of measurement system optimization, and in particular to a calibration and error correction method and system based on nonlinear error correction. Background Art

[0002] In the field of high-precision measurement, it is crucial to obtain accurate and reliable measurement data. Especially in machine vision systems, the accuracy of the data directly affects the effect of subsequent processing and application. Traditional calibration methods often cannot adapt to complex and changing environments and measurement conditions, resulting in inaccurate measurement results, and errors cannot be adjusted in time, resulting in reduced precision and accuracy of the final measurement results, affecting the subsequent processing and use of the data. Summary of the invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by the present invention is: how to adjust the calculation error in real time to improve the measurement precision and accuracy.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a calibration and error correction method based on nonlinear error correction, comprising: accurately measuring a standard object, calculating the error between the measured value and the true value, and using a nonlinear model to describe the relationship between the error and the measured value; applying the nonlinear model to each obtained measured value for error correction; and adjusting the result of each measurement by updating the model parameters in real time according to the latest measurement data.

[0006] As a preferred solution of the calibration and error correction method based on nonlinear error correction described in the present invention, the nonlinear model is expressed as:

[0007]

[0008] Among them, c0 is the constant term, representing the basic error, c1 is the linear term coefficient, indicating the degree to which the error changes linearly with the measured value, c2 is the quadratic term coefficient, reflecting the quadratic change characteristics of the error, and c3 is the cubic term coefficient, which is used to capture the cubic change trend of the error. measured Indicates the measured value.

[0009] As a preferred solution of the calibration and error correction method based on nonlinear error correction described in the present invention, the error correction is expressed as:

[0010]

[0011] Among them, (c0, c1, c2, c3) are coefficients adjusted based on real-time environmental parameters, L new is the new measurement value.

[0012] As a preferred solution of the calibration and error correction method based on nonlinear error correction described in the present invention, the real-time update includes:

[0013] Collect the raw data L for each measurement measured And the corrected data L corrected , record the environmental data related to each measurement, and use the data of the most recent N measurements to build a sliding window. The window size is adjusted according to the actual situation of the system;

[0014] For each sliding window, calculate the corrected length L corrected and the actual length L actual The error between the two is calculated by linear regression analysis to update the coefficients of the polynomial model. After each measurement, the sliding window is updated with new data and the model parameters are recalculated. The updated parameters are used for error correction in the next measurement.

[0015] As a preferred solution of the calibration and error correction method based on nonlinear error correction described in the present invention, the sliding window construction includes continuously using the latest N measurement data in the error correction process. When the model parameters are updated and new measurement data enters the window, the earliest data point exits the window and only the data within the sliding window is used, that is, at each new measurement, the sliding window contains the current measurement value and the most recent N-1 measurement values.

[0016] As a preferred solution of the calibration and error correction method based on nonlinear error correction described in the present invention, the error calculation of the sliding window is expressed as: Error = L actual -L corrected ;

[0017] The linear regression is expressed as: Error = α + β1 × T + β2 × H + ∈;

[0018] Among them, α is the intercept, β1 and β2 are coefficients related to temperature and humidity, ∈ is the error term, T is temperature, and H is humidity.

[0019] As a preferred solution of the calibration and error correction method based on nonlinear error correction described in the present invention, the final measurement correction formula combines the polynomial model and the linear regression model to comprehensively correct the measurement value, and the formula is expressed as follows:

[0020]

[0021] In the second aspect, another object of the present invention is to provide a calibration and error correction system based on nonlinear error correction, comprising: a scanning acquisition module, a data processing module and an update feedback module; the scanning acquisition module is used to accurately measure and collect data on objects of standard size; the data processing module is used to calculate the error between the measured length and the actual length, and to correct it; the update feedback module is used to update the model parameters in real time to adjust the results of each measurement.

[0022] In a third aspect, a computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the calibration and error correction method based on nonlinear error correction as described above when executing the computer program.

[0023] In a fourth aspect, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the calibration and error correction method based on nonlinear error correction as described above.

[0024] Beneficial effects of the invention: The calibration and error correction method and system based on nonlinear error correction provided by the invention can effectively identify and correct the nonlinear errors in the measurement system and automatically adjust them by analyzing the measurement data from machine vision, and ensure long-term accuracy and stability. In addition, the technology of the invention also considers the influence of environmental factors to further optimize the error correction process, so that the system can intelligently adapt to different measurement conditions and environmental changes, thereby ensuring high accuracy and reliability in applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0026] Figure 1 An overall flow chart of a calibration and error correction method based on nonlinear error correction provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0028] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0029] Example 1

[0030] Reference Figure 1 , is an embodiment of the present invention, and provides a calibration and error correction method based on nonlinear error correction, comprising:

[0031] S1: Accurately measure the standard object, calculate the error between the measured value and the true value, and use a nonlinear model to describe the relationship between the error and the measured value;

[0032] Furthermore, the standard object is precisely measured and the error between the measured value and the true value is calculated. The error pattern is analyzed, especially the nonlinear characteristics, such as the situation where the error changes with the size of the measured value. The error pattern is manifested in the error changing with the measured value. For example, the error may increase when the measured value is large. In order to identify this pattern, it is necessary to analyze the relationship between the measurement error and the measured value to find out whether there is a significant nonlinear characteristic, and select a suitable error correction model based on this.

[0033] Data collection: First, you need to record the measurement data and the corresponding errors over a period of time. Usually, we collect this data by taking multiple measurements of a standard object (i.e. an object whose true length is known). By calculating the difference between the measured value and the actual value, we can get the error of each measurement.

[0034] Error characteristic identification: Analyze the trend of error as measured values ​​change. For nonlinear errors, it is usually found that the error value is not constant (for example, due to the influence of temperature, humidity or time), but changes as the measured value increases or decreases. A relationship graph between the measured value and the error value can be plotted to visualize the trend of error as measured values ​​change.

[0035] In data collection, not only the side length of the standard cube is measured, but also its angle and position information to fully capture the environmental and equipment factors that may affect the measurement accuracy. Comprehensive error calculations, including direct length error and the trend of error with angle and position, can identify potential nonlinear error sources.

[0036] S2: Apply a nonlinear model to each measurement value obtained for error correction;

[0037] Taking this a step further, using a cubic polynomial:

[0038]

[0039] Parameter determination: The parameter determination process uses advanced least squares methods to ensure the accuracy and generalization ability of model fitting.

[0040] Among them: c0 is the constant term, representing the basic error; c1 is the linear term coefficient, indicating the degree to which the error changes linearly with the measured value; c2 is the quadratic term coefficient, reflecting the quadratic change characteristics of the error; c3 is the cubic term coefficient, used to capture the cubic change trend of the error.

[0041] Furthermore, a nonlinear model is applied to each measurement value obtained by the measurement system for error correction. The model correction value is combined with the real-time environmental parameters (such as temperature and humidity) to dynamically adjust the correction formula. The values ​​of (c0, c1, c2, c3) are adjusted according to the current environmental conditions.

[0042] Correction formula:

[0043]

[0044] Where (c0, c1, c2, c3) are coefficients adjusted based on real-time environmental parameters, and the corrected values ​​are used as the final measurement results. Every month, the system automatically extracts new data points from all collected data and uses these data to update the coefficients of the polynomial model. This is done through an automated least squares regression analysis. The system updates the coefficient set for the corresponding environmental conditions in the database based on the newly calculated coefficients. This will achieve a complete, closed-loop real-time adaptive algorithm to adapt to environmental changes and slight drifts of the measuring equipment. The core idea is to use a real-time feedback correction mechanism combined with a sliding window method to dynamically update the model parameters.

[0045] S3: Update the model parameters in real time according to the latest measurement data to adjust the results of each measurement.

[0046] 1. Data collection: Collect the raw data L for each measurement measured And the corrected data L corrected . Record environmental data related to each measurement, such as temperature T and humidity H.

[0047] 2. Sliding window method: Use the data of the most recent N measurements to build a sliding window.

[0048] Sliding window refers to the continuous use of the latest N measurement data during the error correction process. It can help the algorithm update in real time to adapt to changes in measurement conditions. The size of the sliding window N can be adjusted, and an appropriate value is usually selected based on the system characteristics. The sliding window technology uses the latest N measurement data to update the model parameters at each measurement. This ensures that the system always uses the latest data for error correction, which helps to cope with measurement error fluctuations caused by changes in the environment and device status.

[0049] The size of the window is usually determined by the application requirements and the characteristics of the measurement system. A smaller window can make the system more sensitive to short-term changes, but it is easily affected by a single outlier. A larger window is smoother, but may cause the system to respond lags. Generally, in practical applications, the appropriate N value is selected through experiments and debugging.

[0050] Specific operations:

[0051] At each new measurement, the sliding window contains the current measurement value and the most recent N-1 measurements.

[0052] As new measurements enter the window, the oldest data points exit the window (a “sliding” process).

[0053] When updating the model parameters, only the data in the sliding window is used. In this way, the model parameters will be dynamically adjusted according to the data in the window to reflect the latest system status.

[0054] Main function: To track changes in system status in real time. For example, if environmental conditions (such as temperature or humidity) affect measurement accuracy, the sliding window can help the model quickly adapt to these changes, making error correction more accurate and timely. At the same time, this method reduces the accumulation of data volume, allowing the system to be dynamically updated and adjusted without storing all historical data.

[0055] The window size N can be adjusted according to the actual situation of the system, for example, set to the most recent 100 measurements.

[0056] 3. Error calculation and feedback:

[0057] For each sliding window, calculate the corrected length L corrected and the actual length L actual The error between .

[0058] Error calculation formula: Error = L actual -L corrected

[0059] 4. Dynamic adjustment of model parameters:

[0060] Use linear regression analysis to update the coefficients of the polynomial model.

[0061] Regression model: [Error = α + β1 × T + β2 × H + ∈]

[0062] Among them, α is the intercept, β1 and β2 are coefficients related to temperature and humidity, and ∈ is the error term.

[0063] Use the data in the sliding window to estimate α, β1, and β2.

[0064] 5. Real-time updates:

[0065] After each measurement, the sliding window is updated with new data and the model parameters are recalculated. The updated parameters are used for error correction in the next measurement.

[0066] The final measurement correction formula combines the polynomial model and the linear regression model to fully correct the measurement value. The comprehensive formula is:

[0067]

[0068] This combined formula first applies a polynomial model to correct the nonlinear error of the measured value, and then uses a linear regression model to adjust the error caused by temperature and humidity. This ensures that the measurement results take into account the influence of both the measured value and the environmental conditions, thereby achieving more accurate error correction.

[0069] Example 2

[0070] According to an embodiment of the present invention, a calibration and error correction system based on nonlinear error correction is provided, comprising:

[0071] Scanning and acquisition module, data processing module and update feedback module;

[0072] The scanning acquisition module is used to accurately measure and collect data on objects of standard size;

[0073] The data processing module is used to calculate the error between the measured length and the actual length and make corrections;

[0074] The update feedback module is used to update the model parameters in real time to adjust the results of each measurement.

[0075] Example 3

[0076] An embodiment of the present invention is different from the first two embodiments in that:

[0077] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0078] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0079] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0080] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0081] Example 4

[0082] As an embodiment of the present invention, a calibration and error correction method based on nonlinear error correction is provided. In order to verify the beneficial effects of the present invention, actual experiments are performed to verify the effects.

[0083] Measure a series of standard length objects. We measured a batch of standard cubes with lengths of 10cm, 15cm, and 20cm. Calculate the error between each measurement and the true value. The measurements are 10.05cm, 15.07cm, and 20.10cm.

[0084] We determined to use a cubic polynomial model to describe the relationship between the error and the measured value and perform nonlinear model fitting:

[0085]

[0086] The least square method was used to determine the model parameters (c0, c1, c2, c3), and we obtained c0 = 0.05 cm, c1 = -0.01, c2 = 0.0005, c3 = -0.00001.

[0087] Apply the model to the measured value to perform error correction. For a measured value L new =25cm, the correction value is calculated as:

[0088] L corrected =L new -(0.05-0.01×25+0.0005×25 2 -0.00001×25 3 )

[0089] Consider the effects of temperature and humidity and adjust the coefficients. If the temperature increases by 5°C, the coefficients are adjusted to c′0 = 0.055 cm, c′1 = -0.009, c′2 = 0.0006, c′3 = -0.000011. Similarly, if the humidity increases by 10%, the coefficients may be adjusted to c″0 = 0.053 cm, c″1 = -0.010, c″2 = 0.00055, c″3 = -0.000012.

[0090] Dynamic correction application: In actual measurement, the coefficients of the correction formula are dynamically adjusted according to the current temperature and humidity readings. If the current temperature is 5°C higher than the standard temperature and the humidity is 10% higher than the standard humidity, the adjusted coefficients (c″0, c″1, c″2, c″3) are used for correction. If the current temperature is 25°C and the humidity is 60%, a specific set of coefficients is selected, such as c0=0.06cm, c1=-0.008, c2=0.0006, c3=-0.000011

[0091] For the new measured value L new =25cm, the corrected value will be: L corrected =25cm-(0.053-0.010×25+0.00055×25 2 -0.000012×25 3 )

[0092] Assume that the current sliding window contains 100 measurement data, the temperature range is from 20℃ to 25℃, and the humidity range is from 40% to 60%. Through linear regression analysis, we may get the following model parameters: α=0.02cm, β1=-0.001cm / ℃, β2=0.0005cm / %.

[0093] When the next measurement is made, for example, the measured value is L measured =30cm, current temperature (T=23℃), humidity (H=50%), we can adjust the correction formula according to these parameters:

[0094] Calculate the error adjustment value: Adjustment = α + β1 × T + β2 × H = 0.02-0.001 × 23 + 0.0005 × 50

[0095] Apply correction: L corrected =L measured -Adjustment.

[0096] This method enables us to accurately adjust and correct the measurement results based on real-time data of measured values, temperature and humidity, thus ensuring a high degree of accuracy and reliability of the measurement results.

[0097] 1. Nonlinear error correction: Using a cubic polynomial model to model the error can capture the nonlinear error caused by the measurement value. This method allows the system to dynamically correct the error based on the change trend of the measurement value itself.

[0098] 2. Environmental factor correction: Considering the potential impact of temperature and humidity on measurement accuracy, a linear regression model is applied to further correct environmental errors. This model ensures the system's correction accuracy under different environmental conditions by updating the temperature and humidity coefficients in real time.

[0099] 3. Real-time dynamic adjustment: The sliding window technology is introduced to update the coefficients based on the most recent \(N\) data, so that the polynomial and environmental correction models can quickly adapt to changes in measurement conditions. This technology not only improves the response speed of the measurement system, but also ensures reliability in various changing environments.

[0100] 4. Combined correction formula: By combining the polynomial model and the environmental correction model, a more comprehensive error correction method is provided. The final correction formula is:

[0101]

[0102] This formula ensures that the measurements are doubly corrected, resulting in highly accurate, environmentally adaptive error correction.

[0103] In summary, the calibration and error correction method based on nonlinear error correction proposed in the present invention combines polynomial modeling and linear regression technology, which can effectively identify and correct nonlinear errors in the measurement system while taking into account the influence of environmental factors. This method not only improves the measurement accuracy, but also has strong real-time and adaptive capabilities, providing strong support for high-precision measurement applications.

[0104] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A calibration and error correction method based on nonlinear error correction, characterized in that: include: Accurately measure the standard object, calculate the error between the measured value and the true value, and use a nonlinear model to describe the relationship between the error and the measured value; Apply a nonlinear model to each measurement value obtained for error correction; The model parameters are updated in real time based on the latest measurement data to adjust the results of each measurement.

2. The calibration and error correction method based on nonlinear error correction according to claim 1, characterized in that: The nonlinear model is expressed as: Among them, c0 is the constant term, representing the basic error, c1 is the linear term coefficient, indicating the degree to which the error changes linearly with the measured value, c2 is the quadratic term coefficient, reflecting the quadratic change characteristics of the error, and c3 is the cubic term coefficient, which is used to capture the cubic change trend of the error. measured Indicates the measured value.

3. The calibration and error correction method based on nonlinear error correction as claimed in claim 2, characterized in that: The error correction is expressed as: Among them, (c0, c1, c2, c3) are coefficients adjusted based on real-time environmental parameters, L new is the new measurement value.

4. The calibration and error correction method based on nonlinear error correction as claimed in claim 3, characterized in that: The real-time update includes: Collect the raw data L for each measurement measured And the corrected data L corrected , record the environmental data related to each measurement, and use the data of the most recent N measurements to build a sliding window. The window size is adjusted according to the actual situation of the system; For each sliding window, calculate the corrected length L corrected and the actual length L actual The error between the two is calculated by linear regression analysis to update the coefficients of the polynomial model. After each measurement, the sliding window is updated with new data and the model parameters are recalculated. The updated parameters are used for error correction in the next measurement.

5. The calibration and error correction method based on nonlinear error correction as claimed in claim 4, characterized in that: The sliding window construction includes continuously using the latest N measurement data during the error correction process. When new measurement data enters the window during the update of model parameters, the earliest data point exits the window and only the data within the sliding window is used. That is, at each new measurement, the sliding window contains the current measurement value and the most recent N-1 measurement values.

6. The calibration and error correction method based on nonlinear error correction according to claim 5, characterized in that: The error calculation of the sliding window is expressed as: Error = L actual -L corrected ; The linear regression is expressed as: Error = α + β1 × T + β2 × H + ∈; Among them, α is the intercept, β1 and β2 are coefficients related to temperature and humidity, ∈ is the error term, T is temperature, and H is humidity.

7. The calibration and error correction method based on nonlinear error correction according to claim 6, characterized in that: The final measurement correction formula combines the polynomial model and the linear regression model to fully correct the measurement value. The formula is expressed as:

8. A system using the calibration and error correction method based on nonlinear error correction as claimed in any one of claims 1 to 7, characterized in that: include: Scanning and acquisition module, data processing module and update feedback module; The scanning acquisition module is used to accurately measure and collect data on objects of standard size; The data processing module is used to calculate the error between the measured length and the actual length and make corrections; The update feedback module is used to update the model parameters in real time to adjust the result of each measurement.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the calibration and error correction method based on nonlinear error correction according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the calibration and error correction method based on nonlinear error correction according to any one of claims 1 to 7 are implemented.

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