Measurement calibration method and system suitable for digital multimeter
By constructing a one-way drift index and error model and setting the compensation coefficient dynamically, the problem of insufficient calibration accuracy caused by aging of digital multimeter and multi-factor coupling is solved, and a high-precision calibration effect is achieved.
Patent Information
- Application Number
- CN202510779642.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-12
- Publication Date
- 2025-07-11
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing digital multimeter calibration method fails to fully consider the multimeter's own aging factors and the coupling of multiple influencing factors, resulting in insufficient or excessive calibration accuracy, affecting the accuracy of the measurement results.
By constructing a one-way drift index, error main factor coefficient, independent influence error and coupling error model, dynamically set the compensation coefficient to perform high-precision calibration of the digital multimeter.
The calibration accuracy and applicability of the digital multimeter are improved, and the compensation coefficient can be dynamically adjusted to adapt to the aging of the multimeter and changes in environmental factors to ensure the accuracy of the measurement results.
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Figure CN120294655A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of multimeter calibration, and specifically relates to a measurement calibration method and system applicable to digital multimeters. Background Art
[0002] As a basic electronic measurement tool, digital multimeters are widely used in industrial production, scientific research experiments, electronic manufacturing, energy maintenance and other fields, and are key devices to ensure the reliability of equipment operation, product quality and the accuracy of scientific research data. However, under complex working conditions, affected by environmental interference, device aging and other factors, the multimeter will generate measurement errors, which may lead to misjudgment or failure, directly affecting production safety and the credibility of scientific research conclusions. Therefore, it is crucial to measure and calibrate digital multimeters. Through calibration, systematic errors can be corrected, device performance indicators can be verified, and the traceability and consistency of measurement results can be ensured.
[0003] Digital multimeters already integrate an automatic calibration function, which can set appropriate compensation coefficients for multimeter calibration according to changes in the environmental factors around the digital multimeter. However, existing automatic calibrations mostly set fixed static compensation coefficients based on independent environmental factor interferences, and do not fully consider the dynamic error effects brought by the aging factors of the digital multimeter itself and the coupling of multiple influencing factors. As a result, the matching degree of the set compensation coefficients is poor, and problems such as under-calibration or over-calibration are likely to occur, affecting the calibration accuracy. Summary of the Invention
[0004] In order to solve the technical problem of insufficient calibration accuracy of digital multimeters, this application provides a measurement calibration method and system applicable to digital multimeters. The specific technical solutions adopted are as follows: In the first aspect, this application proposes a measurement calibration method applicable to digital multimeters, and the method includes the following steps: Collect influencing factors and their corresponding influencing factor sequences, as well as current errors and error sequences through sensors. The influencing factors include voltage, temperature and electromagnetism; Perform linear fitting on the voltage sequence, calculate the first-order difference sequence of the voltage sequence, and count the difference in the number of positive and negative values therein; obtain the unidirectional drift index of the voltage sequence based on the slope of the fitting line and the difference in the number of positive and negative values; For any influencing factor sequence and error sequence, use the causality test algorithm to obtain the causality value; and obtain the lag order; extract subsequences of the influencing factor sequence and the error sequence based on the lag order; perform linear regression on the two subsequences to obtain the slope of the regression line; obtain the main error factor coefficient of the influencing factor through the slope of the regression line and the causality value; use the difference between the main error factor coefficient of any one influencing factor and the main error factor coefficients of the remaining influencing factors as the influencing difference of the influencing factor; judge the influence on the error according to the unidirectional drift index of the voltage sequence and the influencing difference of each influencing factor; and calculate the independent influence error based on the slope of the regression line and the main error factor coefficient. Use the error sequence as the dependent variable and any two influencing factor sequences as the independent variables to construct a regression model; and obtain the regression coefficient of the interaction term; combine all the regression coefficients with the main error factor coefficients of the influencing factors to obtain the total coupling error. Calculate the compensation coefficient according to the independent influence error and the total coupling error; adjust the current based on the compensation coefficient, so as to calibrate the digital multimeter.
[0005] In the above solution, the present application constructs a unidirectional drift index, which can reflect the unidirectional drift trend of the multimeter due to device aging, and thus provides a basis for judging the aging degree for subsequent calibration; then constructs the main error factor coefficient and the independent influence error, which can reflect the associated influence and independent influence degree between the measurement error and the influencing factors, and further evaluate whether the measurement error is affected by independent factors or multi-factor coupling, providing a basis for constructing different compensation coefficients in the future; by constructing the coupling error, it can reflect the non-linear error amplification / weakening effect generated under the synergistic action of multiple environmental factors, thus providing more accurate correction parameters for dynamic compensation; the present application aims at the problem that the prior art does not fully consider the aging factors of the multimeter itself and the coupling influence of multiple influencing factors, resulting in poor calibration effect. By constructing the independent influence error and the coupling error, it can dynamically set the dynamic compensation coefficient according to the aging state of the multimeter itself and the changes in environmental factors, and thus can improve the applicability and accuracy of the calibration method, and achieve high-precision calibration of the digital multimeter.
[0006] In one embodiment, the unidirectional drift index is positively correlated with the positive and negative quantity difference and the absolute value of the slope; the positive and negative quantity difference is the absolute value of the difference between the number of elements with positive values and the number of elements with negative values in the first-order difference sequence.
[0007] In one embodiment, the causality test algorithm is the Granger causality test algorithm, where the influencing factor sequence is used as the cause and the error sequence is used as the effect.
[0008] In one embodiment, the method for extracting subsequences of the influencing factor sequence and the error sequence based on the lag order is: Extract the first N - z elements from the influencing factor sequence to construct an influencing factor subsequence, and extract all elements after the z - th element from the error sequence to construct an error subsequence; where N is the number of elements in the influencing factor sequence.
[0009] In one embodiment, the main error factor coefficients are positively correlated with the causal value and the absolute value of the slope of the regression line respectively.
[0010] In one embodiment, the method for determining the influence on the error according to the unidirectional drift index of the voltage sequence and the influence difference of each influencing factor; and calculating the error independently affected based on the slope of the regression line and the main error factor coefficient is as follows: , is the main error factor coefficient of the voltage data, represents the main error factor coefficient of the error except for the o - th influencing factor being voltage, represents the second slope between the voltage sequence and the error sequence, represents the second slope between the influencing factor sequence corresponding to the o - th influencing factor except for voltage and the error sequence, represents the influence difference of voltage, represents the influence difference of the o - th influencing factor, represents the independent threshold, represents the sign function, represents the normalization function, represents the independently - influenced error; the second slope is the slope of the regression line.
[0011] In one embodiment, the method for obtaining the total coupling error by combining all regression coefficients with the main error factor coefficients of the influencing factors is as follows: Calculate the mean of the main error factor coefficients of any two influencing factor sequences, and take the product of the mean and the regression coefficient as the coupling error of the two influencing factor sequences; calculate the coupling errors of any two influencing factor sequences, and record the sum of all coupling errors as the total coupling error.
[0012] In one embodiment, the method for calculating the compensation coefficient according to the independently - influenced error and the total coupling error is as follows: , represents the independently - influenced error, represents the second slope between the influencing factor sequence corresponding to the j - th influencing factor and the error sequence, is the main error factor coefficient of the - th influencing factor, represents the number of influencing factors, represents the total coupling error, represents the compensation coefficient.
[0013] In one embodiment, the method for adjusting current based on a compensation coefficient is as follows: , represents the compensation coefficient, represents the tanh normalization function, represents the weight modification factor, represents the current data before calibration, represents the adjusted current data.
[0014] In a second aspect, an embodiment of the present application further provides a measurement calibration system applicable to a digital multimeter, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the measurement calibration method applicable to a digital multimeter described in any one of the above are implemented.
[0015] The beneficial effects of the present application are as follows: By constructing a unidirectional drift index, the present application can reflect the unidirectional drift trend of the multimeter due to device aging, and thus provide a basis for determining the aging degree for subsequent calibration; then, by constructing an error main factor coefficient and an independent influence error, it can reflect the associated influence and independent influence degree between the measurement error and the influencing factors, and further evaluate whether the measurement error is affected by independent factors or multi-factor coupling, providing a basis for constructing different compensation coefficients in the future; by constructing a coupling error, it can reflect the non-linear error amplification / weakening effect generated under the synergistic action of multiple environmental factors, thus providing more accurate correction parameters for dynamic compensation; aiming at the problem that the prior art does not fully consider the aging factors of the multimeter itself and the coupling influence of multiple influencing factors, resulting in poor calibration effect, the present application constructs an independent influence error and a coupling error, so as to dynamically set dynamic compensation coefficients according to the aging state of the multimeter itself and the changes in environmental factors, and thus can improve the applicability and accuracy of the calibration method, and achieve high-precision calibration of the digital multimeter. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.
[0017] Figure 1 is a flowchart of a measurement calibration method applicable to a digital multimeter provided by an embodiment of the present application; Figure 2Flowchart of a measurement calibration system applicable to a digital multimeter provided by an embodiment of the present application. Detailed implementation manners
[0018] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following describes in detail the specific implementation manners, structures, features, and effects of the measurement calibration method and system applicable to a digital multimeter proposed according to the present application in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.
[0020] Embodiment of the measurement calibration method and system applicable to a digital multimeter: The following specifically describes the specific solutions of the measurement calibration method and system applicable to a digital multimeter provided by the present application in combination with the accompanying drawings.
[0021] Please refer to Figure 1 , which shows a flowchart of a measurement calibration method applicable to a digital multimeter provided by an embodiment of the present application. The method includes the following steps: Step S001, collect influencing factors and their corresponding influencing factor sequences, as well as current error and error sequence through a sensor.
[0022] In the data acquisition and preprocessing module, measure the current value output by the power supply through a multimeter, and take the difference between it and the standard current value as the current error; the standard current value is the current constantly output by the power supply. Then collect influencing factors through different sensors; sort all the data collected for each influencing factor according to time sequence to obtain the influencing factor sequence; sort the current error according to time sequence to obtain the error sequence. In order to eliminate the dimensionality influence between sequences, all sequences are normalized.
[0023] In this embodiment, the collected influencing factors include voltage, temperature, and electromagnetic data, the collection frequency is 60 Hz, the collection duration is 10 minutes; the normalization method is the maximum-minimum normalization method.
[0024] So far, the influencing factors and their corresponding influencing factor sequences, as well as the current error and error sequence have been obtained.
[0025] Step S002, obtain the unidirectional drift index of the voltage sequence based on the positive and negative quantity differences of the voltage sequence difference and the slope of the fitting line.
[0026] When measuring electrical parameters with a digital multimeter, not only will changes in environmental factors cause measurement errors, but also with the long-term use of the digital multimeter, internal components will inevitably age, resulting in a decline in the performance of electronic components and a drift in the reference voltage. This will also cause measurement errors in the digital multimeter. Moreover, the aging effect may be intertwined with the influence of environmental factors, forming a more complex coupling relationship. Therefore, it is necessary to further analyze the measurement errors of the digital multimeter to perform dynamic adaptive calibration and improve the measurement accuracy of the digital multimeter.
[0027] Therefore, first, an aging assessment module is used to analyze whether the digital multimeter has experienced component aging. Due to the optimization of the circuit design, packaging technology, and material selection of the reference voltage source in the digital multimeter, the output voltage of the reference voltage source is less affected by environmental factors. Under normal conditions, the output of the internal reference voltage source of the digital multimeter should be a stable DC voltage close to an ideal straight line. If aging occurs, then over time, the internal reference voltage will have a one-way drift, resulting in the characteristic of a continuous increase or decrease in the output voltage.
[0028] The voltage sequence is linearly fitted using a linear fitting algorithm, and the absolute value of the slope of the fitted straight line is obtained. The larger the absolute value of the slope, the greater the drift degree of the voltage sequence, and thus the greater the possible aging degree of the digital multimeter can be reflected. The linear fitting algorithm includes the least squares method, regression fitting, and RANSAN algorithm. In this embodiment, the least squares method is used.
[0029] The first-order difference sequence of the voltage sequence is obtained, and the absolute difference between the number of positive elements and the number of negative elements in the first-order difference sequence is calculated, which is denoted as the first difference of the voltage sequence. The first difference can reflect the one-way drift degree of the voltage sequence. The larger the value, the greater the difference between the number of positive and negative elements in the first-order difference sequence of the voltage sequence, and the greater the possibility of continuous increase or decrease of the voltage, thus indicating the greater the possible aging degree of the multimeter.
[0030] Therefore, the one-way drift index of the voltage sequence is calculated based on the first difference and the absolute value of the slope of the voltage sequence.
[0031] The one-way drift index is positively correlated with the first difference and the absolute value of the slope respectively.
[0032] It should be noted that positive correlation means that when one variable increases, the other variable also increases, and the change directions of the two variables are the same. When one variable changes from large to small or from small to large, the other variable also changes from large to small or from small to large. The specific relationship is determined by the actual application, and this application does not make special restrictions.
[0033] Preferably, in this embodiment, the expression of the unidirectional drift index is as follows: . In the formula, is the unidirectional drift index of the voltage sequence; is the absolute value of the slope of the fitting line of the voltage sequence; is the first difference of the voltage sequence; is the normalization function.
[0034] The unidirectional drift index can reflect whether there is a unidirectional drift phenomenon in the voltage sequence. The larger the value, the greater the unidirectional drift of the output power supply of the reference voltage source of the digital multimeter, and thus the greater the degree of aging of the digital multimeter. Furthermore, it can characterize that during normal measurement and use, the measurement error caused by device aging may be greater.
[0035] So far, the unidirectional drift index of each voltage sequence has been obtained.
[0036] Step S003: Analyze the causal value of the influence factor sequence and the error sequence, and calculate the slope of the regression line of the corresponding subsequence; obtain the main error cause coefficient based on the slope and the causal value; obtain the influence difference according to the main error cause coefficient of the influence factor, and calculate the independent influence error by combining the unidirectional drift index and the slope of the regression line.
[0037] Since both aging factors and environmental factors can cause measurement errors. Therefore, after analyzing the self-aging degree of the digital multimeter, the correlation between the measurement error of the digital multimeter and the changes of various environmental factors can be analyzed, so as to evaluate whether the measurement error is affected by environmental factors, caused by aging factors, or caused by coupled effects. If the error is caused by only a single influencing factor, calibration can be directly carried out according to the influence degree; if the measurement error is affected by the coupled effects of multiple influencing factors, the coupling relationship needs to be further analyzed for dynamic calibration to avoid over-calibration.
[0038] Therefore, the influence factor evaluation module analyzes whether the measurement error is affected by a single influencing factor.
[0039] Considering that the measurement error of the digital multimeter has a time lag compared with the changes of various influencing factors, this application uses a causality test algorithm to analyze the influence of the influence factor sequence on the measurement error, so as to ignore the time lag effect and accurately evaluate whether aging is the cause of the measurement error. The causality test algorithm used in this embodiment is the Granger causality test algorithm.
[0040] For any sequence of influencing factors, the sequence of influencing factors and the error sequence are used as the inputs of the causality test algorithm. The sequence of influencing factors is regarded as the cause, and the error sequence is regarded as the effect. The F statistic output by the causality test algorithm is denoted as the causality value F of the sequence of influencing factors with respect to the error sequence. The optimal lag order z is obtained through the VAR model during the calculation process.
[0041] The causality value can reflect whether there is a strong causal relationship between the sequence of influencing factors and the error sequence. The larger the causality value, the more significant the impact of the change in the sequence of influencing factors on the error sequence. That is, the greater the voltage, the greater the error. Therefore, the larger the value, the more important the aging phenomenon can be inferred as the cause of measurement error.
[0042] Furthermore, based on the obtained lag order z, the influencing factor data and the error can be aligned to further analyze whether the impact of the change in the influencing factors on the measurement error is significant. Extract the first N - z elements from the sequence of influencing factors to construct an influencing factor subsequence, and extract all elements after the z - th element (excluding the z - th element) from the error sequence to construct an error subsequence; N is the number of elements in the sequence of influencing factors.
[0043] Perform linear regression fitting with the influencing factor subsequence as the independent variable and the error subsequence as the dependent variable, and denote the slope of the fitted straight line as the second slope between the influencing factor subsequence and the error sequence. The second slope can directly quantify the average response degree of the error corresponding to the change in the unit influencing factor data; the larger the value, the greater the change in the error caused by the same - magnitude fluctuation of the influencing factor data, that is, the measurement error is more sensitive to the influencing factor data, thus reflecting that the aging phenomenon has a greater impact on the error.
[0044] Calculate the error main - cause coefficient of the influencing factor based on the causality value of the sequence of influencing factors with respect to the error sequence and the second slope between the influencing factor subsequence and the error sequence.
[0045] The error main - cause coefficient is positively correlated with the causality value and the absolute value of the second slope respectively.
[0046] Preferably, in this embodiment, the expression of the error main - cause coefficient is: , is the error main - cause coefficient of the th influencing factor; is the causality value of the sequence of influencing factors corresponding to the th influencing factor with respect to the error sequence; is the second slope between the sequence of influencing factors corresponding to the th influencing factor and the error sequence.
[0047] The error main - cause coefficient It can comprehensively reflect the causal influence intensity and sensitivity to errors. The larger the value, the greater the possibility that the influencing factor is an important cause leading to measurement errors.
[0048] For each influencing factor, calculate its main error factor coefficient. For each influencing factor, subtract its main error factor coefficient from the main error factor coefficients of all the others. If the value is larger, it indicates that the influence of this influencing factor is greater compared to the other influencing factors. Then it means that the measurement error is more likely to be mainly affected by this influencing factor.
[0049] Take the difference between the main error factor coefficient and the main error factor coefficients of all the others as the influence difference of this influencing factor.
[0050] Preferably, the expression of the influence difference is: , is the main error factor coefficient of the th influencing factor, is the main error factor coefficient of the th influencing factor, is the th influencing factor's influence difference, represents the number of influencing factors.
[0051] Furthermore, analyze the possibility that the measurement error is caused by independent influencing factors, and construct the independent influence error of each influencing factor. Since voltage reflects the aging condition of the digital multimeter to a certain extent, independent aging factor influence and independent environmental factor influence are thus formed. The expression of the independent influence error is: , is the main error factor coefficient of the voltage data, represents the main error factor coefficient of the th influencing factor except for voltage, represents the second slope between the voltage sequence and the error sequence, represents the second slope between the influencing factor sequence corresponding to the th influencing factor except for voltage and the error sequence, represents the influence difference of voltage, represents the influence difference of the th influencing factor, represents the sign function, represents the normalization function,
[0052] Among them, if the second slope in the sign function is positive, it indicates that the change in the influencing factor causes the measured value to be greater than the standard value. At this time, the sign function is negative; the subsequent calibration is to reduce the measured data. If the second slope is negative, it indicates that the voltage change causes the measured value to be less than the standard value, and the measured data is increased during the subsequent calibration.
[0053] In this embodiment, the value of the independent threshold is 0.95.
[0054] Among them, and respectively reflect the independent aging factor and the independent environmental factor. If this value is large, it indicates that it is caused by a certain independent influencing factor. At this time, only the error corresponding to this influencing factor needs to be calibrated. If a coupling error occurs, it is necessary to further calibrate by combining the coupling error between the influencing factors to avoid over-calibration or insufficient calibration intensity. At this time, the independent influence error W is set to 0 for subsequent coupling error calibration.
[0055] So far, the independent influence error has been obtained.
[0056] Step S004, construct a regression model of the error sequence and the influencing factor sequence, and obtain the total coupling error based on the regression coefficient and the main error factor coefficient.
[0057] Considering that in different measurement environments, there is a coupling effect in the changes between multiple influencing factors. For example, an increase in temperature will cause an increase in the thermal noise of electronic components, reduce the anti-electromagnetic interference ability, and cause an increase in electromagnetic interference. Therefore, if the current measurement error is not affected by a single independent factor, it is necessary to further consider the influence degree of the coupling factor on the measurement error to realize the dynamic adjustment of the subsequent calibration parameters.
[0058] For any two influencing factors, analyze the non-linear superposition effect on the error when the two influencing factors act together.
[0059] Multiply any two influencing factor sequences point by point to construct an interaction sequence. The interaction sequence can characterize their synergistic effect. Among them, the elements of the interaction sequence are interaction terms, and the non-linear characteristics of the interaction terms can reflect the amplification or weakening effect between temperature and electromagnetism. For example, an increase in temperature exacerbates electromagnetic interference.
[0060] Taking the error sequence as the dependent variable, any two influencing factor sequences WD, DC and their interaction sequence as independent variables, construct a multiple linear regression model: . In the formula, a and b are the regression coefficients of the two influencing factor sequences, c is the regression coefficient of the interaction sequence, and d is the intercept. The methods for solving the regression coefficients include the least squares method, ridge regression, stepwise regression analysis, etc. In this embodiment, the least squares method is used to solve.
[0061] If the regression coefficient c obtained by solving is greater than 0, it indicates that the synergistic effect between temperature and electromagnetic field will amplify the measurement error; if c < 0, it indicates that the synergistic effect will weaken the measurement error; if c ≈ 0, it means that there is no significant coupling effect between temperature and electromagnetic field. That is to say, the larger the absolute value of c, the more necessary it is to introduce an interaction term compensation during the calibration of the digital multimeter, otherwise it is likely to cause under-calibration or over-calibration phenomena.
[0062] Calculate the mean value of the error main factor coefficients of any two influencing factor sequences, and take the product of the mean value and the regression coefficient as the coupling error of the two influencing factor sequences. Calculate the coupling errors of any two influencing factor sequences, and record the sum of all coupling errors as the total coupling error sum. sum can reflect the cumulative error generated by all influencing factors due to the coupling effect. It should be noted that sum may be negative.
[0063] Thus, the total coupling error is obtained.
[0064] Step S005, calculate the compensation coefficient according to the independent influence error and the total coupling error; thereby perform measurement calibration on the digital multimeter.
[0065] The calibration module is used to distinguish and analyze the independent influence errors of various influencing factors, and then set the compensation coefficient R. The expression of the compensation coefficient is: , represents the independent image error, represents the second slope between the influencing factor sequence corresponding to the jth influencing factor and the error sequence, is the error main factor coefficient of the th influencing factor, represents the number of influencing factors, represents the total coupling error, represents the compensation coefficient.
[0066] Among them, if W is not 0, it means that the current measurement error is more likely to be caused by independent factors. At this time, only the error corresponding to this influencing factor needs to be used for calibration. If W is 0, it means that the measurement error is affected by coupling, so it is necessary to combine the coupling error for further calibration.
[0067] Collect the current data before calibration, and adjust the current data before calibration based on the compensation coefficient to calibrate the digital multimeter.
[0068] The expression for adjusting the current data is: , represents the compensation coefficient, represents the tanh normalization function, represents the weight modification factor, represents the current data before calibration, represents the adjusted current data. The empirical range of the weight modification factor is (2, 4), and the value is 2 in this embodiment. The weight modification factor is set to balance the magnitude of the compensation coefficient, ensuring that the compensated data is neither too large nor too small, thereby improving the calibration accuracy.
[0069] Based on this, the compensation coefficient is calculated based on the data before the current moment, so as to calibrate the current data at the current moment, and then complete the measurement calibration of the digital multimeter.
[0070] So far, the measurement calibration of the digital multimeter has been completed.
[0071] Please refer to Figure 2 , which shows the flow chart of the measurement calibration system applicable to the digital multimeter provided by an embodiment of the present application. The system includes the following modules: a data acquisition and preprocessing module, an aging evaluation module, an influencing factor evaluation module, and a calibration module.
[0072] Data acquisition and preprocessing module: Use the high-precision ADC circuit built in the digital multimeter to collect the output voltage value data of the internal reference voltage source of the multimeter. Then, take a constant current output by a certain power supply as the standard current, and use the digital multimeter to measure the magnitude of the current output by the power supply in real time. Record the difference between the current value measured by the digital multimeter at the same moment and the standard current value output by the power supply as the current measurement error of the multimeter at this moment. Then, collect the temperature data of the digital multimeter during the measurement process through a temperature sensor; monitor the electromagnetic field intensity of the digital multimeter during the measurement process through an electromagnetic sensor.
[0073] Aging evaluation module, based on the voltage sequence to quantify the unidirectional drift effect, and analyze the aging degree of the multimeter through it.
[0074] Influencing factor evaluation module: Quantify the influence of different influencing factors on the measurement error, and detect whether the measurement error is caused by the interference of independent influencing factors.
[0075] Calibration module: Construct the compensation coefficient and calibrate the measurement result.
[0076] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limiting them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included within the protection scope of the present application.
[0077] Each embodiment in this specification is described in a progressive manner. For the same or similar parts between the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments.
Claims
1. A measurement calibration method applicable to a digital multimeter, characterized in that, The method comprises the following steps: Collect influencing factors and their corresponding influencing factor sequences, as well as current error and error sequences through sensors. The influencing factors include voltage, temperature, and electromagnetism; Perform linear fitting on the voltage sequence, calculate the first-order difference sequence of the voltage sequence, and count the difference in the number of positive and negative values therein; obtain the unidirectional drift index of the voltage sequence based on the slope of the fitted line and the difference in the number of positive and negative values; For any influencing factor sequence and error sequence, use the causality test algorithm to obtain the causality value; and obtain the lag order; extract subsequences of the influencing factor sequence and error sequence based on the lag order; perform linear regression on the two subsequences to obtain the slope of the regression line; obtain the main error coefficient of the influencing factor through the slope of the regression line and the causality value; use the difference between the main error coefficient of any one influencing factor and the main error coefficients of the other influencing factors as the influencing difference of the influencing factor; judge the influence on the error according to the unidirectional drift index of the voltage sequence and the influencing difference of each influencing factor; and calculate the independent influencing error based on the slope of the regression line and the main error coefficient; Use the error sequence as the dependent variable and any two influencing factor sequences as independent variables to construct a regression model; and obtain the regression coefficient of the interaction term; combine all the regression coefficients with the main error coefficients of the influencing factors to obtain the total coupling error; Calculate the compensation coefficient according to the independent influencing error and the total coupling error; adjust the current based on the compensation coefficient, thereby performing measurement calibration on the digital multimeter.
2. The measurement calibration method applicable to a digital multimeter according to claim 1, characterized in that The unidirectional drift index is positively correlated with the difference in the number of positive and negative values and the absolute value of the slope; the difference in the number of positive and negative values is the absolute value of the difference between the number of elements with positive values and the number of elements with negative values in the first-order difference sequence.
3. A measurement calibration method applicable to a digital multimeter according to claim 1, characterized in that, The causality test algorithm is the Granger causality test algorithm, where the influencing factor sequence is used as the cause and the error sequence is used as the effect.
4. A measurement calibration method applicable to a digital multimeter according to claim 1, characterized in that, The method for extracting subsequences of the influencing factor sequence and error sequence based on the lag order is as follows: Extract the first N - z elements from the influencing factor sequence and construct an influencing factor subsequence, and extract all elements after the z-th element from the error sequence and construct an error subsequence; N is the number of elements in the influencing factor sequence.
5. A measurement calibration method applicable to a digital multimeter according to claim 1, characterized in that, The main error coefficient is positively correlated with the causality value and the absolute value of the slope of the regression line.
6. A measurement and calibration method applicable to a digital multimeter according to claim 1, characterized in that, The method for judging the influence on the error according to the unidirectional drift index of the voltage sequence and the influencing difference of each influencing factor; and calculating the independent influencing error based on the slope of the regression line and the main error coefficient is as follows: , is the main error factor coefficient of voltage data, represents the main error factor coefficient excluding the o-th influencing factor of voltage, represents the second slope between the voltage sequence and the error sequence, represents the second slope between the influencing factor sequence corresponding to the o-th influencing factor except voltage and the error sequence, represents the influence difference of voltage, represents the influence difference of the o-th influencing factor, represents the independent threshold, represents the sign function, represents the normalization function, represents the independent image error; the second slope is the slope of the regression line.
7. A measurement calibration method applicable to a digital multimeter according to claim 1, characterized in that The method for combining all the regression coefficients with the main error coefficients of the influencing factors to obtain the total coupling error is as follows: Calculate the mean of the main error coefficients of any two influencing factor sequences, and use the product of the mean and the regression coefficient as the coupling error of the two influencing factor sequences; calculate the coupling errors of any two influencing factor sequences, and record the cumulative sum of all the coupling errors as the total coupling error.
8. A measurement and calibration method applicable to a digital multimeter according to claim 1, characterized in that, The method for calculating the compensation coefficient according to the independent influencing error and the total coupling error is as follows: , represents the independent image error, represents the second slope between the influence factor sequence corresponding to the jth influence factor and the error sequence, is the error main factor coefficient of the th influence factor, represents the number of influence factors, represents the total coupling error, represents the compensation coefficient.
9. A measurement calibration method applicable to a digital multimeter according to claim 1, characterized in that, The method for adjusting the current based on the compensation coefficient is as follows: , represents a compensation coefficient, represents the tanh normalization function, represents the weight modification factor, represents the current data before calibration, represents the adjusted current data.
10. A measurement calibration system applicable to a digital multimeter, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a measurement calibration method applicable to a digital multimeter as described in any one of claims 1-9.