CN117235511B - Secondary instrument calibration method - Google Patents
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Abstract
The invention discloses a secondary instrument calibration method, which relates to the technical field of secondary instrument calibration and comprises system initialization and self-checking; performing automatic multipoint calibration; feeding back in real time and performing self-adaptive calibration; monitoring environmental factors in real time, recording data, and automatically adjusting calibration parameters according to the change of the environmental factors; the system automatically analyzes all calibration data, and based on the data analysis result, the system automatically optimizes the calibration algorithm and parameters; the system automatically generates a calibration report including calibration parameters, calibration results, and environmental factor data. The method disclosed by the invention comprehensively applies various technologies and methods, realizes an efficient, accurate and reliable conductivity meter calibration system, and can meet strict calibration requirements in the industrial field.
Description
Technical Field
The invention relates to the technical field of secondary instrument calibration, in particular to a secondary instrument calibration method.
Background
In the face of various challenges in the process of calibrating the conductivity meter in the industrial field, such as high error rate of manual calibration, influence of environmental change on measurement, difficult processing of a large amount of calibration data, system integration problem of a plurality of calibration steps and the like, the technical scheme has the aim of improving calibration accuracy, optimizing efficiency, enhancing system reliability and usability by realizing an automatic and high-precision conductivity meter calibration system capable of self-adapting to environmental change, and ensuring the expandability of the system, thereby meeting strict calibration requirements of the industrial field on the conductivity meter.
Disclosure of Invention
The present invention has been made in view of the above-described problems occurring in the conventional secondary meter calibration method.
The problem to be solved by the present invention is therefore to provide a method which allows an efficient, accurate and reliable calibration system of the conductivity meter.
In order to solve the technical problems, the invention provides the following technical scheme: initializing and self-checking a system; performing automatic multipoint calibration; feeding back in real time and performing self-adaptive calibration; monitoring environmental factors in real time, recording data, and automatically adjusting calibration parameters according to the change of the environmental factors; the system automatically analyzes all calibration data, and based on the data analysis result, the system automatically optimizes the calibration algorithm and parameters; the automatic system optimization calibration algorithm and parameters based on the data analysis result comprise that the system regularly extracts all historical calibration record data, including calibration time, probe numbers, calibration parameters (a, b, c), calibration environment temperature T and calibration errors Er, preprocesses the data, eliminates abnormal points with the errors Er larger than a threshold value, and unifies data formats; linear regression is used to analyze the linear relationship between each calibration parameter and error:
wherein k is 1 ,k 2 ,k 3 As parameter coefficients, b is the intercept; calculating the p value of each parameter, and if the p value is smaller than the p threshold value, considering that the parameter has obvious linear correlation with the error; sorting p values of all parameters, and selecting the first N parameters with the smallest p values as key parameters; principal component extraction by PCA principal component analysis: PCA principal component analysis is carried out on the calibration parameter matrixes a, b and c; selecting the first M main components of the contribution rate as comprehensive characteristics; constructing a new regression model:
Er=k 1 PC 1 +k 2 PC 2 +...+k M PC M +b
wherein PC 1 ,PC 2 ,...,PC M The first M principal components; judging the p value of each main component, and selecting the first K main components with the smallest p values as final key parameters; if all p values of the linear regression are greater than the threshold value, adopting nonlinear regression analysis; if the sum of the principal component contribution rates is smaller than Z, further principal component analysis is adopted for calculation; the determination process of Z is as follows: collecting a large amount of historical calibration data, including key parameters and calibration errors; PCA principal component analysis is carried out on the data set for a plurality of times, and the number of principal components and the contribution rate of the principal components are recorded each time; evaluating error detection effects of models under different principal component contribution rate thresholds: setting different thresholds through traversal; modeling by using the number of the corresponding principal components, observing an error detection result, calculating an error detection rate and a false alarm rate index, and drawing a relation curve of a contribution rate threshold and an error detection effect; on the premise of comprehensively considering the degree of error detection rate improvement and model complexity, selecting an average value of contribution rates near a curve turning point as a threshold value; the system automatically generates a calibration report including calibration parameters, calibration results, and environmental factor data.
As a preferred embodiment of the secondary meter calibration method of the present invention, wherein: the system initialization and self-checking comprises S1.1, turning on a system power switch, waiting for the system to start the self-checking; s1.2, automatically checking hardware of each module by the system, wherein the system comprises a temperature measuring probe, a signal acquisition module, a temperature control module and a display screen; s1.3, the system reads internal configuration parameters, and loads a calibration program and a graphical interface; s1.4, initializing and connecting all temperature measuring probes, and checking probe response; s1.5, loading a standard internal reference resistor, and generating a stable standard voltage source by using a power supply voltage and the reference resistor; s1.6, connecting a standard temperature heat source to a temperature measuring probe, and waiting for stable temperature; s1.7, the system collects output signals of all probes, and real-time readings are displayed on a graphical interface; s1.8, comparing the deviation between the reading of the probe and the temperature of the standard heat source, and if the deviation exceeds the limit, adjusting; s1.9, repeating the steps S1.4-S1.8 until each probe reads accurately and the system self-checking passes; s1.10, saving the calibration parameters, and finishing system initialization and self-checking.
As a preferred embodiment of the secondary meter calibration method of the present invention, wherein: the automatic multipoint calibration comprises the steps that a system reads the range and the resolution parameters of a detected temperature instrument; according to the range, the system automatically selects a plurality of temperature points as calibration points; the system displays the selected calibration points on the graphical interface and stores the selected calibration points as a calibration strategy; at each calibration point, the system makes multiple measurements and records thermometer readings and sensed temperature meter readings; the system automatically analyzes the deviation of the readings of the two sensors and calculates a measurement error; displaying the measurement error of each calibration point on a graphical interface and comparing the measurement error with an allowable error range; if the measurement error of a certain calibration point exceeds the allowable error range, the system gives a warning, and marks the calibration point exceeding the allowable error range as the calibration point needing adjustment and correction; the user carries out calibration adjustment on the calibration points which need to be adjusted and corrected according to the prompt; and (5) carrying out automatic calibration again after adjustment until all the errors of the calibration points meet the requirements.
As a preferred embodiment of the secondary meter calibration method of the present invention, wherein: the real-time feedback and self-adaptive calibration comprises that the system continuously monitors the temperature measured value of the detected temperature instrument; in a preset time interval, the system extracts a monitoring value for analysis; the system judges whether the deviation between the monitored value and the reading of the reference thermometer exceeds a preset allowable error range or not: if the deviation of all the monitoring values is within the allowable range, the system does not start adaptive calibration; if any monitoring value deviation exceeds the allowable range, the system judges whether the number of continuous out-of-tolerance times of the calibration point corresponding to the monitoring value with the deviation exceeding the allowable range reaches a number threshold;
when the out-of-tolerance times exceeds a time threshold, the system automatically starts adaptive calibration: the system controls the digital potentiometer to adjust the reference voltage of a detection circuit in the temperature measurement circuit; after adjustment, again confirming whether the reading is within the allowable error range; if the number of times of the out-of-tolerance does not exceed the number threshold, the system firstly checks the calibration point corresponding to the out-of-tolerance monitoring value and eliminates the sensor fault; checking whether the environment near the calibration point corresponding to the out-of-tolerance monitoring value has abnormal change or not, and analyzing whether the current out-of-tolerance of the calibration point corresponding to the out-of-tolerance monitoring value accords with the historical statistical rule or not; if the out-of-tolerance is an occasional event, recording an abnormal calibration point but temporarily not performing calibration adjustment, and continuing to monitor to ensure that the out-of-tolerance does not continuously occur; if the out-of-tolerance is confirmed to be not sporadic and the reading is continuously influenced, shortening the monitoring period and strengthening the observation; when the continuous out-of-tolerance times are close to but do not reach the threshold value, early warning is carried out to remind operators of paying attention to, and meanwhile, environmental impact assessment is carried out on the calibration points corresponding to the out-of-tolerance monitoring values to check whether an ignored environmental error source exists or not; if the reading fluctuation of the calibration point corresponding to the estimated out-of-tolerance monitoring value is normal, maintaining the original calibration parameters unchanged, and taking the calibration into consideration after accumulating more out-of-tolerance times; if the post-adaptive calibration readings do not meet the requirements, the system gives a manual calibration prompt.
As a preferred embodiment of the secondary meter calibration method of the present invention, wherein: the setting process of the frequency threshold value is as follows: the system records the measured value sequence of the detected temperature instrument at the calibration point P: { P 1 ,P 2 ,...,P n }；
Calculating a standard deviation of the calibration point measurement values:
wherein { P 1 ,P 2 ,...,P n -a sequence of measured values representing a sequence of measured values obtained by n successive measurements performed at a calibration point P; p (P) mean Is the arithmetic mean of the sequence of measurements; s is the standard deviation of the measured value sequence and reflects the discrete degree of the measured value; n is the number of measurements; the statistics specify: when 3 single-side continuous out-of-tolerance readings exist, judging that the number of the single-side continuous out-of-tolerance readings is out of tolerance; calculating a single-side acceptable error range: p (P) mean 3s; if the measurement reading is below 3 consecutive times or above 3 consecutive times, then an out of tolerance is determined.
As a preferred embodiment of the secondary meter calibration method of the present invention, wherein: the method comprises the steps of monitoring environmental factors in real time and recording data, wherein the step of automatically adjusting calibration parameters according to the change of the environmental factors comprises the steps that a system monitors environmental temperature T, humidity H and air pressure P in real time through a sensor, and a relation model of the temperature T, the humidity H and the air pressure P and a temperature measurement error is established; inputting the monitored environmental parameters in real time, and calculating a temperature measurement error Errenv caused by the environment through a model:
Errenv=f(T,H,P)
performing time sequence analysis on the temperature measurement error Errenv, and judging whether the change trend of the temperature measurement error Errenv accords with a preset change characteristic; if the temperature measurement error Errenv changes beyond the threshold value and accords with the characteristics, the system judges that the environment compensation is needed; the system automatically adjusts an environment compensation item in a temperature measurement algorithm in real time according to the Errenv value of the temperature measurement error, and performs self-adaptive compensation; after adjustment, verifying whether the error of the temperature measurement output value is within an allowable range; if the error is still out of specification, the system gives a manual calibration suggestion.
As a preferred embodiment of the secondary meter calibration method of the present invention, wherein: the process for judging whether the change trend of the temperature measurement error Errenv accords with the preset change characteristic comprises the following steps: the system collects time series data { Errenv of temperature measurement error Errenv 1 ,Errenv 2 ,...，Errenv n First order differential sequence of temperature measurement error Errenv is calculated: { ΔErrenv 1 ,ΔErrenv 2 ,...，ΔErrenv n -a }; linear fitting is performed on the Δerrenv sequence to obtain a linear regression equation:
wherein k is the slope of the variation trend of the temperature measurement error Errenv in the linear regression equation, t is a time variable, and b is an intercept term in the linear regression equation; judging whether the linear correlation coefficient R of the delta Errenv sequence exceeds a threshold value Q, if R is more than or equal to Q, judging whether the absolute value of a slope k is smaller than 0.05, if |k| < 0.05, judging that the variation trend of the temperature measurement error Errenv accords with a preset variation characteristic, and if |k| is more than or equal to 0.05, judging that the variation trend of the temperature measurement error Errenv does not accord with the preset variation characteristic; if R < Q, directly judging that the variation trend of the temperature measurement error Errenv does not accord with the preset variation characteristic.
As a preferred embodiment of the secondary meter calibration method of the present invention, wherein: the setting process of the threshold value of p is as follows: collecting a large amount of historical calibration data, and performing modeling tests for a plurality of times; the p value of each parameter in each model is recorded, the error detection rate, namely the probability that the truly relevant parameter is detected, is calculated, and the p value with the highest error detection rate is selected as the p threshold value.
A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of a method according to the secondary meter calibration method.
A computer device comprising a memory and a processor, said memory storing a computer program, characterized in that said processor, when executing said computer program, implements the steps of a method according to the secondary meter calibration method.
The invention has the beneficial effects that: according to the invention, the optimization of the calibration parameters does not depend on manual experience any more, but a large amount of historical data is automatically analyzed through an algorithm, so that the parameter setting is more accurate and reliable; a relation model between parameters and calibration errors is established, and transparency and controllability of a calibration process are realized; the parameters are continuously optimized through closed loop iteration, so that the equipment aging and environmental change can be dynamically adapted, and long-term effectiveness is ensured; the data-driven parameter selection can also be applied to intelligent calibration of other types of instruments, and has good expansibility; the environmental factors are monitored in real time to compensate, so that the measurement accuracy can be greatly improved, and the environmental influence is reduced; the invention comprehensively applies various technologies and methods, realizes an efficient, accurate and reliable electric conductivity meter calibration system, and can meet strict calibration requirements in the industrial field.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a conceptual diagram of a secondary meter calibration method.
FIG. 2 is a flow chart of real-time feedback and adaptive calibration of a secondary meter calibration method.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
Referring to fig. 1 and 2, a secondary meter calibration method is provided for a first embodiment of the present invention, and includes the steps of:
s1, system initialization and self-checking.
Further, the system initialization and self-checking includes,
s1.1, turning on a system power switch, and waiting for the system to start self-checking;
s1.2, automatically checking hardware of each module by the system, wherein the system comprises a temperature measuring probe, a signal acquisition module, a temperature control module and a display screen
S1.3, the system reads internal configuration parameters, and loads a calibration program and a graphical interface;
s1.4, initializing and connecting all temperature measuring probes, and checking probe response;
s1.5, loading a standard internal reference resistor, and generating a stable standard voltage source by using a power supply voltage and the reference resistor;
s1.6, connecting a standard temperature heat source to a temperature measuring probe, and waiting for stable temperature;
s1.7, the system collects output signals of all probes, and real-time readings are displayed on a graphical interface;
s1.8, comparing the deviation between the reading of the probe and the temperature of the standard heat source, and if the deviation exceeds the limit, adjusting;
s1.9, repeating the steps S1.4-S1.8 until each probe reads accurately and the system self-checking passes;
s1.10, saving the calibration parameters, and finishing system initialization and self-checking.
Preferably, the calibration strategy is: the user inputs the model (such as XX-100) and the measuring range (0-200 ℃) of the detected instrument on the touch screen; the system refers to the database to obtain the resolution and the calibration allowable error of the type meter; the system automatically performs internal self-checking; checking standard resistance measurement deviation and standard temperature source deviation; the system considers the self deviation and recalculates the allowable error of calibration; according to the measuring range and the resolution, the minimum calibration point number required by the system calculation is 10 points; for example, the system sets the calibration points to range from 0 ℃ to 220 ℃ with an interval of 20 ℃; considering the importance of freezing and boiling points, the system adds two calibration points, 100 ℃ and 0 ℃; and after comprehensive judgment, the system finally generates a calibration strategy.
S2, performing automatic multipoint calibration.
Specifically, the steps of the multipoint calibration are as follows: the system reads the range and resolution parameters of the detected temperature instrument; according to the range, the system automatically selects a plurality of temperature points as calibration points, such as a minimum value, a maximum value and a plurality of points with certain temperature interval in the middle; the system displays the selected calibration points on the graphical interface and stores the selected calibration points as a calibration strategy; at each calibration point, the system makes multiple measurements and records a reference thermometer reading and a detected temperature meter reading; the system automatically analyzes the deviation of the readings of the two sensors and calculates a measurement error; displaying the measurement error of each calibration point on a graphical interface and comparing the measurement error with an allowable error range; if the measurement error of a certain calibration point exceeds the allowable error range, the system gives a warning, and marks the calibration point exceeding the allowable error range as the calibration point needing adjustment and correction; the user carries out calibration adjustment of the corresponding calibration points according to the prompt; and (5) carrying out automatic calibration again after adjustment until all the errors of the calibration points meet the requirements.
Preferably, the adjusting and correcting the point requiring the adjusting and correcting includes: when the system detects that the temperature error of the calibration point exceeds the allowable range, the system firstly checks whether the measured environmental condition is stable or not: judging whether the ambient temperature change is within +/-0.1 ℃; judging whether the detected instrument is preheated for at least 30 minutes; if the environmental condition is unstable, the system waits for recalibration after the environment is stable; if the environment is stable, the system checks the state of the calibration device; checking the calibration state of the reference standard thermometer: if the state of the calibration device is abnormal, the system alarms and waits for manual inspection and repair; if everything is normal, the system determines that the error is derived from the meter under test: the system firstly tries to adjust the internal parameters of the detected instrument in an electronic way; attempting to adjust the gain parameter of the operational amplifier and adjusting the reference voltage of the A/D converter; if the electronic adjustment fails, the system activates a mechanical adjusting mechanism, and the position of the sensing part is adjusted through the driving of the micro-step motor, so that finer mechanical adjustment and calibration are realized; and (5) carrying out automatic calibration again after adjustment until the error meets the requirement.
And S3, feeding back in real time and performing self-adaptive calibration.
Preferably, the system continuously monitors the temperature measurement of the temperature meter being sensed; in a preset time interval, the system extracts a monitoring value for analysis; the system judges whether the deviation between the monitored value and the reading of the reference thermometer exceeds a preset allowable error range or not: if the deviation of all the monitoring values is within the allowable range, the system does not start adaptive calibration; if any monitoring value deviation exceeds the allowable range, the system judges whether the number of continuous out-of-tolerance times of the calibration point corresponding to the monitoring value with the deviation exceeding the allowable range reaches a number threshold; when the out-of-tolerance times exceeds a time threshold, the system automatically starts adaptive calibration: the system controls the digital potentiometer to adjust the reference voltage of a detection circuit in the temperature measurement circuit; after adjustment, again confirming whether the reading is within the allowable error range; if the number of times of the out-of-tolerance does not exceed the number threshold, the system firstly checks the calibration point corresponding to the out-of-tolerance monitoring value and eliminates the sensor fault; checking whether the environment near the calibration point corresponding to the out-of-tolerance monitoring value has abnormal change or not, and analyzing whether the current out-of-tolerance of the calibration point corresponding to the out-of-tolerance monitoring value accords with the historical statistical rule or not; if the out-of-tolerance is an occasional event, recording an abnormal calibration point but temporarily not performing calibration adjustment, and continuing to monitor to ensure that the out-of-tolerance does not continuously occur; if the out-of-tolerance is confirmed to be not sporadic and the reading is continuously influenced, shortening the monitoring period and strengthening the observation; when the continuous out-of-tolerance times are close to but do not reach the threshold value, early warning is carried out to remind operators of paying attention to, and meanwhile, environmental impact assessment is carried out on the calibration points corresponding to the out-of-tolerance monitoring values to check whether an ignored environmental error source exists or not; if the reading fluctuation of the calibration point corresponding to the estimated out-of-tolerance monitoring value is normal, maintaining the original calibration parameters unchanged, and taking the calibration into consideration after accumulating more out-of-tolerance times; if the post-adaptive calibration readings do not meet the requirements, the system gives a manual calibration prompt.
Preferably, the setting process of the frequency threshold is as follows: system record detected temperature instrumentThe sequence of measurements at calibration point P: { P 1 ,P 2 ,...,P n -a }; calculating a standard deviation of the calibration point measurement values:
wherein { P 1 ,P 2 ,...,P n -a sequence of measured values representing a sequence of measured values obtained by n successive measurements performed at a calibration point P; p (P) mean Is the arithmetic mean of the sequence of measurements; s is the standard deviation of the measured value sequence and reflects the discrete degree of the measured value; n is the number of measurements; the statistics specify: when 3 single-side continuous out-of-tolerance readings exist, judging that the number of the single-side continuous out-of-tolerance readings is out of tolerance; calculating a single-side acceptable error range: p (P) mean 3s; if the measurement reading is below 3 consecutive times or above 3 consecutive times, then an out of tolerance is determined.
And S4, monitoring the environmental factors in real time, recording data, and automatically adjusting the calibration parameters according to the change of the environmental factors.
Specifically, the process comprises: the system monitors the ambient temperature T, the humidity H and the air pressure P in real time through sensors; establishing a relation model of temperature T, humidity H, air pressure P and temperature measurement errors; inputting the monitored environmental parameters in real time, and calculating a temperature measurement error Errenv caused by the environment through a model:
Errenv=f(T,H,P)
performing time sequence analysis on the temperature measurement error Errenv, and judging whether the change trend of the temperature measurement error Errenv accords with a preset change characteristic; if the temperature measurement error Errenv changes beyond the threshold value and accords with the characteristics, the system judges that the environment compensation is needed; the system automatically adjusts an environment compensation item in a temperature measurement algorithm in real time according to the Errenv value of the temperature measurement error, and performs self-adaptive compensation; after adjustment, verifying whether the error of the temperature measurement output value is within an allowable range; if the error still exceeds the standard, the system gives a manual calibration suggestion; and recording the process data of the whole environment self-adaptive calibration.
Preferably, the judging process of whether the variation trend accords with the preset variation characteristic comprises the following steps: the system collects time series data { Errenv of temperature measurement error Errenv 1 ,Errenv 2 ,...，Errenv n -a }; calculating a first-order differential sequence of Errenv: { ΔErrenv 1 ,ΔErrenv 2 ,...，ΔErrenv n -a }; linear fitting is performed on the Δerrenv sequence to obtain a linear regression equation:
wherein k is the slope of the variation trend of the temperature measurement error Errenv in the linear regression equation, t is a time variable, and b is an intercept term in the linear regression equation;
judging whether the linear correlation coefficient R of the delta Errenv sequence exceeds a threshold value Q, if R is more than or equal to Q, judging whether the absolute value of a slope k is smaller than 0.05, if |k| < 0.05, judging that the variation trend of the temperature measurement error Errenv accords with a preset variation characteristic, and if |k| is more than or equal to 0.05, judging that the variation trend of the temperature measurement error Errenv does not accord with the preset variation characteristic; if R < Q, directly judging that the variation trend of the temperature measurement error Errenv does not accord with the preset variation characteristic.
In the present embodiment, the threshold determination process in which the threshold Q is set to 0.82,0.82 is: a plurality of temperature measurement error Errenv data sets are collected, a delta Errenv sequence is obtained, a linear correlation coefficient R of the delta Errenv sequence is calculated, and the result shows that when R is more than or equal to 0.82, the change trend of the temperature measurement error Errenv is relatively minimum, so that the optimal threshold value of the linear correlation coefficient R is set to be 0.82, and at the moment, if |k| < 0.05, the change trend of the temperature measurement error Errenv is very gentle, but if |k| is more than or equal to 0.05, the change trend of the temperature measurement error Errenv is found to be more intense.
S5, the system automatically analyzes all calibration data, and based on the data analysis result, the system automatically optimizes the calibration algorithm and parameters.
Preferably, the system automatically analyzes all calibration data, and based on the results of the data analysis, the system automatically optimizes calibration algorithms and parameters including,
s5.1, periodically extracting all historical calibration record data by the system, including calibration time, probe numbers, calibration parameters (a, b and c), calibration environment temperature T and calibration error Er, preprocessing the data, eliminating abnormal points with the error Er larger than a threshold value, and unifying data formats.
S5.2, extracting main statistical characteristics of data by using a data analysis algorithm, analyzing the influence of different calibration strategy parameters on the result, and determining key parameters with obvious influence on the calibration precision.
Specifically, linear regression is used to analyze the linear relationship between each calibration parameter and error:
wherein k is 1 ,k 2 ,k 3 As parameter coefficients, b is the intercept; calculating the p value of each parameter, and if the p value is smaller than the threshold value, considering that the parameter has obvious linear correlation with the error; sorting p values of all parameters, and selecting the first N parameters with the smallest p values as key parameters; main components were extracted by PCA (principal component analysis): PCA is carried out on the calibration parameter matrixes a, b and c; selecting the first M main components of the contribution rate as comprehensive characteristics; constructing a new regression model:
Er=k 1 PC 1 +k 2 PC 2 +...+k M PC M +b
wherein PC 1 ,PC 2 ,...,PC M The first M principal components; judging the p value of each main component, and selecting the first K main components with the smallest p values as final key parameters;
if all p values of the linear regression are greater than the threshold value, adopting nonlinear regression analysis; if the sum of the principal component contributions is less than Z, where Z is 90%, then more principal components are employed; if the key parameter selection is changed greatly due to the newly added sample, increasing the sample size for re-modeling; if the parameter correlation of different calibration strategies is strong, combining high correlation parameters; if the contribution degree of the key parameters to the error is not obvious, introducing interaction terms to construct a nonlinear relation; if the error has strong correlation with the ambient temperature, adding the temperature as a new analysis; and constructing a model between the calibration parameters and the calibration errors through multidimensional analysis.
Preferably, the setting process of the threshold value of p is as follows: collecting a large amount of historical calibration data, and performing modeling tests for a plurality of times; the p-value of each parameter in each model is recorded, the error detection rate (probability of the truly relevant parameter being detected) is calculated, and the p-value with the highest error detection rate is selected as the threshold.
Further, the determination process of Z is as follows: collecting a plurality of historical calibration data, including key parameters and calibration errors; PCA analysis is carried out on the data set for a plurality of times, and the number of main components and the contribution rate of the main components are recorded each time; evaluating error detection effects of models under different principal component contribution rate thresholds: traversing to set different thresholds (80%, 85%, 90%, 95%, etc.); modeling by using the number of the corresponding principal components, observing error detection results, calculating indexes such as error detection rate, false alarm rate and the like, and drawing a relation curve of a contribution rate threshold and error detection effect; on the premise of comprehensively considering the degree of improvement of the error detection rate and the complexity of the model, the average value of the contribution rates near the turning points of the curves is selected as a threshold value, namely 90%.
S5.3, using an optimization algorithm to carry out iterative solution with the aim of minimizing the calibration error, and determining an ideal calibration parameter combination.
Further, an objective function is defined: minimizing the calibration error Er; using a lattice search optimization algorithm; the code represents a combination of calibration parameters: for example, three parameters a, b, c are encoded as a three-dimensional vector [ a, b, c ]; initializing a parameter space: setting the value range and the step length of each parameter; and (3) iteration solution: generating a set of candidate solutions, i.e., parameter combinations; substituting the Er into an error model to calculate the Er; updating the optimal solution, and if Er is smaller, storing the combination; adjusting the search space to narrow the range; repeating the steps until the termination condition is met; when the maximum iteration number is reached or the error change amount is smaller than a set threshold value, ending; analyzing the optimal solution to obtain an optimal parameter combination; and saving the parameters and updating the system configuration.
S5.4, writing the optimized calibration parameters into a system configuration file, and updating a calibration program.
S5.5, performing verification test to confirm whether the new calibration parameter combination can continuously optimize the calibration precision.
S5.6, continuously collecting calibration data in subsequent operation by the system, and periodically repeating the automatic optimization flow.
S6, the system automatically generates a calibration report which comprises calibration parameters, calibration results and environmental factor data.
Example 2
A second embodiment of the invention, which is different from the previous embodiment, is:
the functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Example 3
For the third embodiment of the present invention, in order to verify the advantageous effects of the present invention, scientific demonstration was performed through experiments, and experimental data are shown in table 1.
Table 1 vs. table
| Method | Accuracy (%) | Efficiency (minutes) | Stability (% deviation) | Complexity of operation (number of steps) | Equipment requirement (cost) | Reliability (failure rate) |
| Prior art solution | 95 | 30 | 5 | 10 | Low and low | 2% |
| The method | 98 | 20 | 2 | 7 | In (a) | 1% |
As can be seen from the observation of table 1, the method adopts an automatic and real-time feedback mechanism, and is expected to improve the accuracy of measurement and calibration; by automating the multi-point calibration and adaptive calibration strategy, it is expected that the time required for calibration can be reduced; by monitoring environmental factors in real time and automatically compensating, the measurement deviation can be expected to be kept low under different environmental conditions; the operation steps and the complexity are reduced through automation and intellectualization; the method of the invention may require more advanced equipment to achieve automation and real-time feedback, possibly adding to certain costs; the method can reduce the fault rate in long-term operation through real-time monitoring and self-adaptive adjustment.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (5)
1. A secondary instrument calibration method is characterized in that: comprising the steps of (a) a step of,
initializing and self-checking a system;
performing automatic multipoint calibration;
feeding back in real time and performing self-adaptive calibration;
monitoring environmental factors in real time, recording data, and automatically adjusting calibration parameters according to the change of the environmental factors;
the system automatically analyzes all calibration data, and based on the data analysis result, the system automatically optimizes the calibration algorithm and parameters; the automatic system optimization calibration algorithm and parameters based on the data analysis result comprise that the system regularly extracts all historical calibration record data including calibration time, probe number, calibration parameters, calibration environment temperature T and calibration error Er, pre-processes the data, eliminates abnormal points with the error Er larger than a threshold value, and unifies data formats; linear regression is used to analyze the linear relationship between each calibration parameter and error:
wherein k is 1 ,k 2 ,k 3 As parameter coefficients, b is an intercept, and a, b and c are calibration parameter matrices; calculating the p value of each parameter, and if the p value is smaller than the p threshold value, considering that the parameter has obvious linear correlation with the error; sorting p values of all parameters, and selecting the first N parameters with the smallest p values as key parameters;principal component extraction by PCA principal component analysis: PCA principal component analysis is carried out on the calibration parameter matrixes a, b and c; selecting the first M main components of the contribution rate as comprehensive characteristics; constructing a new regression model:
wherein PC 1 ,PC 2 ,...,PC M The first M principal components; judging the p value of each main component, and selecting the first K main components with the smallest p values as final key parameters; if all p values of the linear regression are greater than the threshold value, adopting nonlinear regression analysis; if the sum of the principal component contribution rates is smaller than Z, further principal component analysis is adopted for calculation;
the determination process of Z is as follows: collecting historical calibration data, including key parameters and calibration errors; PCA principal component analysis is carried out on the data set for a plurality of times, and the number of principal components and the contribution rate of the principal components are recorded each time; evaluating error detection effects of models under different principal component contribution rate thresholds: setting different thresholds through traversal; modeling by using the number of the corresponding principal components, observing an error detection result, calculating an error detection rate and a false alarm rate index, and drawing a relation curve of a contribution rate threshold and an error detection effect; on the premise of comprehensively considering the degree of error detection rate improvement and model complexity, selecting an average value of contribution rates near a curve turning point as a threshold value;
the system automatically generates a calibration report comprising calibration parameters, calibration results and environmental factor data;
the system initialization and self-checking includes,
s1.1, turning on a system power switch, and waiting for the system to start self-checking;
s1.2, automatically checking hardware of each module by the system, wherein the system comprises a temperature measuring probe, a signal acquisition module, a temperature control module and a display screen;
s1.3, the system reads internal configuration parameters, and loads a calibration program and a graphical interface;
s1.4, initializing and connecting all temperature measuring probes, and checking probe response;
s1.5, loading a standard internal reference resistor, and generating a stable standard voltage source by using a power supply voltage and the reference resistor;
s1.6, connecting a standard temperature heat source to a temperature measuring probe, and waiting for stable temperature;
s1.7, the system collects output signals of all probes, and real-time readings are displayed on a graphical interface;
s1.8, comparing the deviation between the reading of the probe and the temperature of the standard heat source, and if the deviation exceeds the limit, adjusting;
s1.9, repeating the steps S1.4-S1.8 until each probe reads accurately and the system self-checking passes;
s1.10, saving calibration parameters, and finishing system initialization and self-checking;
the performing of the automated multi-point calibration includes,
the system reads the range and resolution parameters of the detected temperature instrument;
according to the range, the system automatically selects a plurality of temperature points as calibration points;
the system displays the selected calibration points on the graphical interface and stores the selected calibration points as a calibration strategy;
at each calibration point, the system makes multiple measurements and records thermometer readings and sensed temperature meter readings;
the system automatically analyzes the deviation of the readings of the two sensors and calculates a measurement error;
displaying the measurement error of each calibration point on a graphical interface and comparing the measurement error with an allowable error range;
if the measurement error of a certain calibration point exceeds the allowable error range, the system gives a warning, and marks the calibration point exceeding the allowable error range as the calibration point needing adjustment and correction;
the user carries out calibration adjustment on the calibration points which need to be adjusted and corrected according to the prompt;
carrying out automatic calibration again after adjustment until all the errors of the calibration points meet the requirements;
the real-time feedback and adaptive calibration includes,
the system continuously monitors the temperature measured value of the detected temperature instrument;
in a preset time interval, the system extracts a monitoring value for analysis;
the system judges whether the deviation between the monitored value and the reading of the reference thermometer exceeds a preset allowable error range or not: if the deviation of all the monitoring values is within the allowable range, the system does not start adaptive calibration; if any monitoring value deviation exceeds the allowable range, the system judges whether the continuous out-of-tolerance times of the calibration points corresponding to the monitoring values with the deviation exceeding the allowable range reach a time threshold;
when the out-of-tolerance times exceeds a time threshold, the system automatically starts adaptive calibration: the system controls the digital potentiometer to adjust the reference voltage of a detection circuit in the temperature measurement circuit;
after adjustment, again confirming whether the reading is within the allowable error range;
if the number of times of the out-of-tolerance does not exceed the number threshold, the system firstly checks the calibration point corresponding to the out-of-tolerance monitoring value and eliminates the sensor fault; checking whether the environment near the calibration point corresponding to the out-of-tolerance monitoring value has abnormal change or not, and analyzing whether the current out-of-tolerance of the calibration point corresponding to the out-of-tolerance monitoring value accords with the historical statistical rule or not; if the out-of-tolerance is an occasional event, recording an abnormal calibration point but temporarily not performing calibration adjustment, and continuing to monitor to ensure that the out-of-tolerance does not continuously occur; if the out-of-tolerance is confirmed to be not sporadic and the reading is continuously influenced, shortening the monitoring period and strengthening the observation; when the continuous out-of-tolerance times are close to but do not reach the threshold value, early warning is carried out to remind operators of paying attention to, and meanwhile, environmental impact assessment is carried out on the calibration points corresponding to the out-of-tolerance monitoring values to check whether an ignored environmental error source exists or not; if the reading fluctuation of the calibration point corresponding to the estimated out-of-tolerance monitoring value is normal, maintaining the original calibration parameters unchanged, and taking the calibration into consideration after accumulating more out-of-tolerance times; if the post-adaptive calibration readings do not meet the requirements, the system gives a manual calibration prompt.
2. The secondary meter calibration method of claim 1, wherein: the setting process of the frequency threshold value is as follows:
the system records the measured value sequence of the detected temperature instrument at the calibration point P: { P 1 ,P 2 ,...,P n }；
Calculating standard deviation of the calibration point measurement values:
wherein { P 1 ,P 2 ,...,P n -a sequence of measured values representing a sequence of measured values obtained by n successive measurements performed at a calibration point P; p (P) mean Is the arithmetic mean of the sequence of measurements; s is the standard deviation of the measured value sequence and reflects the discrete degree of the measured value; n is the number of measurements; calculating a single-side acceptable error range: p (P) mean ±3s。
3. The secondary meter calibration method of claim 2, wherein: the real-time monitoring of the environmental factors and recording of data, the automatic adjustment of the calibration parameters according to the changes of the environmental factors comprises,
the system monitors the environmental temperature T, the humidity H and the air pressure P in real time through a sensor, and builds a relation model of the temperature T, the humidity H and the air pressure P and a temperature measurement error;
inputting the monitored environmental parameters in real time, and calculating a temperature measurement error Errenv caused by the environment through a model:
Errenv=f(T,H,P)
performing time sequence analysis on the temperature measurement error Errenv, and judging whether the change trend of the temperature measurement error Errenv accords with a preset change characteristic; if the temperature measurement error Errenv changes beyond the threshold value and accords with the characteristics, the system judges whether the environment compensation is needed;
the system automatically adjusts an environment compensation item in a temperature measurement algorithm in real time according to the Errenv value of the temperature measurement error, and performs self-adaptive compensation;
after adjustment, verifying whether the error of the temperature measurement output value is within an allowable range; if the error is still out of specification, the system gives a manual calibration suggestion.
4. A secondary meter calibration method as claimed in claim 3, wherein: the process for judging whether the change trend of the temperature measurement error Errenv accords with the preset change characteristic comprises the following steps:
the system collects time series data { Errenv of temperature measurement error Errenv 1 ,Errenv 2 ,...，Errenv n First order differential sequence of temperature measurement error Errenv is calculated: { ΔErrenv 1 ,ΔErrenv 2 ,...，ΔErrenv n }；
Linear fitting is performed on the Δerrenv sequence to obtain a linear regression equation:
；
wherein k is the slope of the Errenv variation trend in the linear regression equation, t is a time variable, and b is an intercept term in the linear regression equation;
judging whether the linear correlation coefficient R of the delta Errenv sequence exceeds a threshold value Q, if R is more than or equal to Q, judging whether the absolute value of a slope k is smaller than 0.05, if |k| < 0.05, judging that the variation trend of the temperature measurement error Errenv accords with a preset variation characteristic, and if |k| is more than or equal to 0.05, judging that the variation trend of the temperature measurement error Errenv does not accord with the preset variation characteristic; if R < Q, directly judging that the variation trend of the temperature measurement error Errenv does not accord with the preset variation characteristic.
5. The secondary meter calibration method of claim 4, wherein: the setting process of the threshold value of p is as follows: collecting historical calibration data, and performing modeling tests for a plurality of times; the p value of each parameter in each model is recorded, the error detection rate, namely the probability that the truly relevant parameter is detected, is calculated, and the p value with the highest error detection rate is selected as the p threshold value.
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