CN109141587B - Product quality guarantee method and device for flow sensor, terminal and medium - Google Patents
Product quality guarantee method and device for flow sensor, terminal and medium Download PDFInfo
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Abstract
The invention discloses a method and a device for guaranteeing product quality of a flow sensor, a terminal and a medium. The product quality guarantee method comprises the following steps: two flow sensors with the same caliber are arranged on the same pipeline; synchronously acquiring instantaneous flow of the two flow sensors in real time, and comparing the instantaneous flow with the instantaneous flow of the two flow sensors; when the instantaneous flow of the two flow sensors has deviation, the instantaneous flow of the two flow sensors is respectively compared with the respective originally accumulated instantaneous flow, and the originally accumulated instantaneous flow is an instantaneous flow average value in a period of time; if the deviation between the instantaneous flow of a certain flow sensor and the instantaneous flow accumulated originally exceeds a preset value, the flow sensor is judged to be abnormal. The invention can ensure that the fault of the instrument is judged by data analysis before the product has a problem, thereby realizing advance prevention.
Description
Technical Field
The invention relates to a product quality guarantee method in the field of big data application, in particular to a product quality guarantee method of a flow sensor and a device, a computer terminal and a computer readable storage medium thereof.
Background
In practical application of the existing flow sensor, the principle of post-control is adopted to maintain or replace the existing flow sensor when the product quality is in problem. However, the method cannot ensure that the product judges the fault of the instrument through data analysis before the product has problems, so that advance prevention is realized.
Disclosure of Invention
The invention aims to provide a product quality guarantee method and device of a flow sensor, a computer terminal and a computer readable storage medium, which can ensure that the fault of an instrument is judged through data analysis before a product has a problem, and the prior prevention is realized.
The invention is realized by adopting the following technical scheme: a product quality guarantee method of a flow sensor comprises the following steps:
step S41, installing two flow sensors with the same caliber on the same pipeline;
step S42, synchronously acquiring instantaneous flow of two flow sensors in real time, and comparing the instantaneous flow with the instantaneous flow;
step S43, when the instantaneous flow of the two flow sensors has deviation, the instantaneous flow of the two flow sensors is respectively compared with the respective originally accumulated instantaneous flow, and the originally accumulated instantaneous flow is the average value of the instantaneous flow in a period of time;
in step S44, if the deviation between the instantaneous flow rate of a certain flow sensor and the instantaneous flow rate accumulated originally exceeds a preset value, it is determined that the certain flow sensor is abnormal.
As a further improvement of the above scheme, if the deviation between the instantaneous flow rate of a certain flow sensor and the originally accumulated instantaneous flow rate is zero, it is determined that the flow sensor is accurate in measurement.
As a further improvement of the above scheme, the product quality assurance method further comprises the steps of:
step S21, calibrating coefficients of the application flow sensor of different use sites;
step S22, detecting the instantaneous flow of each applied flow sensor in different process range changes to obtain multiple data sets W4Each group of data set W4Comprises the following steps: z21，Z22，……，Z2uWherein u represents the number of process ranges;
step S23, when the instantaneous flow is detected by using the on-site application flow sensor, the detected instantaneous flow k is matched with Z in the corresponding flow range2uComparing;
if the instantaneous flow k and Z in the corresponding process range2uIf so, the process ends, otherwise, the process goes to step S24: performing software compensation on the instantaneous flow k to ensure that the instantaneous flow k and Z in the corresponding process range2uThe same is true.
Further, calibration using coefficients of the flow sensor is measured and calibrated using an instrument with a higher accuracy than the accuracy of the flow sensor.
The invention also provides a product quality guarantee device of the flow sensor, which comprises:
the two instantaneous flow acquisition units are used for respectively and synchronously acquiring instantaneous flow for two flow sensors with the same caliber which are arranged on the same pipeline in real time;
the comparison unit I is used for comparing instantaneous flow of two flow sensors with the same caliber;
the comparison unit II is used for comparing the instantaneous flow of the two flow sensors with respective originally accumulated instantaneous flow when the instantaneous flow of the two flow sensors has deviation, wherein the originally accumulated instantaneous flow is an instantaneous flow average value in a period of time;
the conclusion judging unit is used for judging that the flow sensor is abnormal if the deviation between the instantaneous flow of the certain flow sensor and the instantaneous flow originally accumulated by the certain flow sensor exceeds a preset value; and if the deviation between the instantaneous flow of a certain flow sensor and the originally accumulated instantaneous flow is zero, judging that the flow sensor is accurate in measurement.
The invention also provides a computer terminal which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the steps of the product quality guarantee method of the flow sensor.
The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the product quality assurance method of the flow sensor described above.
The product quality guarantee method of the flow sensor can judge and adopt normal sensor data in time, repair or replace abnormal sensors in time, reduce the risk of production shutdown caused by equipment failure and monitor the accuracy of the sensor data constantly.
Drawings
Fig. 1 is a schematic flowchart of a method for improving the measurement accuracy of a flow sensor in embodiment 1.
Fig. 2 is a schematic block diagram of the apparatus for improving the measurement accuracy of the flow sensor according to embodiment 2.
Fig. 3 is a flowchart illustrating an accuracy ensuring method of the flow sensor according to embodiment 7.
Fig. 4 is a flowchart illustrating a method for guaranteeing accuracy of another flow sensor according to embodiment 8.
Fig. 5 is a schematic block diagram of an accuracy ensuring apparatus of a flow sensor according to embodiment 9.
Fig. 6 is a flowchart illustrating a product quality assurance method of the flow sensor according to embodiment 16.
Fig. 7 is a schematic block configuration diagram of a product quality assurance device of a flow sensor according to embodiment 17.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
The embodiment discloses a method for improving the measurement accuracy of a flow sensor based on repetitive data of the flow sensor. A flow sensor has a plurality of flow points, and the repeatability of the flow points refers to the repeatability of a group of data obtained by measuring the same flow point by the flow sensor for multiple times. The flow sensor has certain errors in a certain flow range, and the detection by using a contrast method in practical application can generate accumulated errors. The accuracy error of the flow sensor must not exceed 3 times the repeatability error in general. Based on the precision of the data repeated at the same flow point and the difference in different measuring range ranges, the method for improving the measurement accuracy of the flow sensor of the embodiment uses the repeated error to detect the repeated of different points in a certain flow range of the flow sensor, so as to determine the accuracy in the flow range.
The flow sensor to be mentioned in this embodiment is divided into two parts, one part is located in a production site or a laboratory, and is defined as a calibration flow sensor herein, and the measurement error of such a flow sensor is close to zero, and various coefficients are stable, so that in the method for improving the measurement accuracy of the flow sensor to be described next, the flow sensor located in the production site or the laboratory needs to be collected substantially only once when collecting data. The other part is flow sensors at different sites of use, each site of use having at least one flow sensor installed, defined herein as an application flow sensor. The environment of the use site is different from that of the production site or laboratory, and the conditions are far from ideal conditions. Therefore, there is uncertainty error in the measurement using the flow sensor, and even if the same flow sensor is used, the measurement for the same object at different times will produce different measurement values; measuring with different application flow sensors for the same object also results in different measured values. Therefore, how to calibrate the measurement accuracy of these flow sensors at different sites of use is critical in practical applications.
Referring to fig. 1, the method for improving the measurement accuracy of the flow sensor of the present embodiment includes the following steps.
Step S11, defining the flow sensor in the production field or laboratory as the calibration flow sensor, collecting the average repeatability data of each flow point of the calibration flow sensor, thereby obtaining the data set W1Comprises the following steps: x is the number of1，x2，……，xnWhere n represents the number of flow points of the flow sensor, xnThe average repeatability data for the calibrated flow sensor at the nth flow point is shown.
After at least three times of repeated data acquisition is carried out on each flow point, the average repeated data of the corresponding flow point can be obtained by averaging.
Step S12, defining each flow sensor of the use site as an application flow sensor, wherein the calibration flow sensor and the application flow sensor have the same model; data for the repeatability of each flow point of each application flow sensor at different sites of use are collected at the same time, thereby obtaining a data set W2Comprises the following steps: { (y)11，y12，……，y1n)，(y21，y22，……，y2n)，……，(ym1，ym2，……，ymn) Where m denotes the number of applied flow sensors, ymnRepresenting repeatability data for the mth application flow sensor at the nth flow point.
Step S13, repeating step S12 at different time points, thereby obtaining multiple sets of data sets W2。
Step S14, counting each group for the same flow point of the same application flow sensorAccording to the group W2Repeatability data and data set W of corresponding flow points1Comparing the corresponding average repeatability data to obtain a plurality of data groups W3Each group of data set W3Comprises the following steps: { (Δ)11＝y11-x1，Δ12＝y12-x2，……，Δ1n＝y1n-xn)，(Δ21＝y11-x1，Δ22＝y12-x2，……，Δ2n＝y1n-xn)，……，(Δn1＝y11-x1，Δn2＝y12-x2，……，Δnn＝y1n-xn)}。
Step S15, averaging all differences (i.e., averaging multiple sets of data W3Taking an average value), an average value delta is obtained.
Step S16, judging each group of data group W3Is equal to the average value delta.
If each group of data set W3Is equal to the average value delta, then the process ends. Otherwise (i.e. if each set of data sets W3The difference in (d) is not equal to the average value Δ), step S17 is performed: the coefficients of the respective application flow sensors are adjusted until the respective difference is equal to the difference mean value delta.
The coefficient of the corresponding application flow sensor can be adjusted by hardware, such as directly adjusting the coefficient of the corresponding application flow sensor. Software adjustment can also be adopted to perform software compensation on the measured value of the corresponding application flow sensor, for example, positive compensation is performed on the condition that the corresponding difference value is smaller than the difference average value delta, and negative compensation is performed on the condition that the corresponding difference value is larger than the difference average value delta. This can be used continuously to correct for subsequent repeated data measurements using the flow sensor.
The method for improving the measurement accuracy of the flow sensor of the embodiment is characterized in that firstly, the average repeatability data of the calibrated flow sensor is assumed to be accurate, the average repeatability data of the calibrated flow sensor is taken as a reference standard, then, the average value of the repeatability data of a large number of application flow sensors is taken as the reference standard through big data application, the actually existing error of the calibrated flow sensor is calibrated in a phase-changing mode, and meanwhile, the application flow sensor deviating from the average value is adjusted, so that the accuracy of the application flow sensor is improved.
Example 2
The present embodiment provides a device for improving the measurement accuracy of a flow sensor, and the measurement accuracy device of the present embodiment adopts the method for improving the measurement accuracy of a flow sensor of embodiment 1.
Referring to fig. 2, the measurement accuracy device includes a first repetitive data acquisition unit 1, a second repetitive data acquisition unit 2, a difference comparison unit 3, an average value acquisition unit 4, a difference judgment unit 5, and an adjustment unit 6.
Defining the flow sensor in the production field or laboratory as a calibration flow sensor, and acquiring the average repeatability data of each flow point of the calibration flow sensor by a first repeatability data acquisition unit 1 to obtain a data set W1Comprises the following steps: x is the number of1，x2，……，xn. Where n represents the number of flow points of the flow sensor, xnThe average repeatability data for the calibrated flow sensor at the nth flow point is shown. After at least three times of repeated data acquisition is carried out on each flow point, the average repeated data of the corresponding flow point can be obtained by averaging.
The flow sensor of each use site is defined as an application flow sensor, and the calibration flow sensor and the application flow sensor have the same model. The second repetitive data acquisition unit 2 is used for acquiring repetitive data of each flow point of each application flow sensor in different use sites at the same time, thereby obtaining a data set W2Comprises the following steps: { (y)11，y12，……，y1n)，(y21，y22，……，y2n)，……，(ym1，ym2，……，ymn)}. Where m denotes the number of applied flow sensors, ymnRepresenting repeatability data for the mth application flow sensor at the nth flow point.
Repeatedly acquiring the repeated data of each flow point of each application flow sensor in different use sites for multiple times by the repeated data acquisition unit 2 at different time points, thereby obtaining multiple groups of data sets W2。
The difference comparison unit 3 is used for comparing the repeatability data of the corresponding flow point with the data group W aiming at the same flow point of the same application flow sensor1Comparing the corresponding average repeatability data to obtain a plurality of data groups W3Each group of data set W3Comprises the following steps: { (Δ)11＝y11-x1，Δ12＝y12-x2，……，Δ1n＝y1n-xn)，(Δ21＝y11-x1，Δ22＝y12-x2，……，Δ2n＝y1n-xn)，……，(Δn1＝y11-x1，Δn2＝y12-x2，……，Δnn＝y1n-xn)}。
The average value obtaining unit 4 is configured to average all the difference values to obtain an average value Δ.
The difference value judging unit 5 is used for judging each group of data group W3Is equal to the average value delta. If each group of data set W3Is equal to the average value delta, then the process ends. The adjusting unit 6 is used for setting each group of data group W3If the difference in (d) is not equal to the average value delta, the coefficients of the respective application flow sensors are adjusted until the respective difference is equal to the difference average value delta.
When adjusting the coefficient of the corresponding application flow sensor, the adjusting unit 6 may adopt hardware adjustment, such as directly adjusting the coefficient of the corresponding application flow sensor; software adjustment can also be adopted to perform software compensation on the measured value of the corresponding application flow sensor, for example, positive compensation is performed on the condition that the corresponding difference value is smaller than the difference average value delta, and negative compensation is performed on the condition that the corresponding difference value is larger than the difference average value delta.
The measurement accuracy device of the embodiment has the same advantageous effects as the method of improving the measurement accuracy of the flow sensor of embodiment 1.
Example 3
The present embodiments provide a computer terminal comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor, when executing the program, implements the steps of the method of improving the measurement accuracy of a flow sensor of embodiment 1.
When the method for improving the measurement accuracy of the flow sensor in embodiment 1 is applied, the method can be applied in a software form, for example, a program designed to run independently is installed on a computer terminal, and the computer terminal can be a computer, a smart phone, or the like. Or it can be designed as embedded running program and installed on computer terminal, such as single-chip computer.
Example 4
The present embodiment provides a computer-readable storage medium having a computer program stored thereon. The program, when executed by a processor, implements the steps of the method of improving the measurement accuracy of a flow sensor of embodiment 1.
When the method for improving the measurement accuracy of the flow sensor in embodiment 1 is applied, the method may be applied in the form of software, such as a program designed to be independently run by a computer-readable storage medium, where the computer-readable storage medium may be a usb disk, and the usb disk is designed as a usb shield, and the usb disk is designed to be a program for starting the whole method through external triggering.
Example 5
This embodiment provides a flow sensor that is adjusted or corrected by the method of embodiment 1 for improving the measurement accuracy of the flow sensor, thereby improving the measurement accuracy.
Example 6
The embodiment provides a big data acquisition and processing system of thing allies oneself with, big data acquisition and processing system of thing allies oneself with carries out data acquisition to various electrical apparatus devices of user, then does various processings that need to the data of gathering back, and the data after will handling show, or upload to the network, or collect to the server and supply the customer to look over and look over.
The flow sensor in a production field or a laboratory is defined as a calibration flow sensor, the flow sensor in each use field is defined as an application flow sensor, and the calibration flow sensor and the application flow sensor have the same model.
The big data acquisition and processing system of the Internet of things firstly receives the average repeatability data of each flow point of the calibration flow sensor, thereby obtaining a data set W1Comprises the following steps: x is the number of1，x2，……，xnWhere n represents the number of flow points of the flow sensor, xnThe average repeatability data for the calibrated flow sensor at the nth flow point is shown. After at least three times of repeated data acquisition is carried out on each flow point, the average repeated data of the corresponding flow point can be obtained by averaging.
Then, the Internet of things big data receiving and processing system collects the repetitive data of each flow point of each application flow sensor of different use sites at the same time, thereby obtaining a data set W2Comprises the following steps: { (y)11，y12，……，y1n)，(y21，y22，……，y2n)，……，(ym1，ym2，……，ymn) Where m denotes the number of applied flow sensors, ymnRepresenting repeatability data for the mth application flow sensor at the nth flow point.
Then, the internet of things big data acquisition and processing system repeatedly receives the repetitive data of each flow point of each application flow sensor of different use sites at different time points, thereby obtaining a plurality of groups of data sets W2。
Then, the big data acquisition and processing system of the Internet of things aims at the same flow point of the same application flow sensor and combines the repetitive data of the corresponding flow point with the data set W1Comparing the corresponding average repeatability data to obtain a plurality of data groups W3Each group of data set W3Comprises the following steps: { (Δ)11＝y11-x1，Δ12＝y12-x2，……，Δ1n＝y1n-xn)，(Δ21＝y11-x1，Δ22＝y12-x2，……，Δ2n＝y1n-xn)，……，(Δn1＝y11-x1，Δn2＝y12-x2，……，Δnn＝y1n-xn)}。
Then, the big data acquisition and processing system of the Internet of things averages all the difference values to obtain an average value delta, and then judges each group of data groups W3If the difference in (a) is equal to the mean value Δ, if each set of data W is of the same type3Is equal to the mean value delta, ends if each group of data sets W3If the difference in (d) is not equal to the average value delta, the coefficients of the respective application flow sensors are adjusted until the respective difference is equal to the difference average value delta.
The coefficient of the corresponding application flow sensor can be adjusted by hardware, such as directly adjusting the coefficient of the corresponding application flow sensor. The measured value of the corresponding application flow sensor can be subjected to software compensation by adopting software adjustment through the large data acquisition and processing system of the internet of things, if the corresponding difference value is smaller than the average difference value delta, positive compensation is carried out, and if the corresponding difference value is larger than the average difference value delta, negative compensation is carried out.
Example 7
The accuracy of the detection data of the flow sensor varies with the environment in the field. Generally, the change is ignored, only the environmental change from a laboratory (namely, a production environment) to a use site is considered, and even the flowmeter with low precision requirement does not need to consider the environmental change from the laboratory to the use site.
The embodiment provides an accuracy guarantee method of a flow sensor for environmental change from a laboratory to a use site and for the flow sensor with constant instantaneous flow. Referring to fig. 3, the method for guaranteeing the accuracy of the flow sensor of the present embodiment includes the following steps.
In step S21, the coefficients of the flow sensor are calibrated for different applications. The coefficient calibration using the flow sensor generally uses an instrument with higher precision than that using the flow sensor to measure and calibrate, and the national standard stipulates that an instrument with precision three times higher than that of the sensor to be measured is used.
Step S22, detecting the instantaneous flow of each applied flow sensor in different process range changes to obtain multiple data sets W4Each group of data set W4Comprises the following steps: z21，Z22，……，Z2uWhere u represents the number of process ranges.
Step S23, when the instantaneous flow is detected by using the on-site application flow sensor, the detected instantaneous flow k is matched with Z in the corresponding flow range2uAnd (6) comparison.
If the instantaneous flow k and Z in the corresponding process range2uIf so, the process ends, otherwise, the process goes to step S24: performing software compensation on the instantaneous flow k to ensure that the instantaneous flow k and Z in the corresponding process range2uThe same is true.
In the embodiment, the coefficients of the application flow sensors in different using sites are calibrated in advance, and then the instantaneous flow of the calibrated application flow sensor in different measuring range ranges is taken as a standard value and is taken as a reference for subsequent detection, so that if an error occurs in the subsequent detection, the correction can be performed by comparing the standard value with the instantaneous flow of the calibrated application flow sensor.
Example 8
The embodiment provides an accuracy guarantee method of a flow sensor. The change of the laboratory environment influences the calibration of the flow sensor, the change of the field use environment also influences the application of the flow sensor, the probability of the change of the laboratory environment is low, namely the environment is relatively stable, but the change does not represent that no environmental change exists in the laboratory, and if the change of the laboratory environment is ignored, the accuracy of the application of the flow sensor is reduced. However, in real-world applications, changes in laboratory environments are often ignored, and on the other hand, when the environments change from a production site to a use site, measurement errors are also generated by the application of the flow sensor. Therefore, it is necessary to correct the amount of accuracy change of the flow sensor in real time during the use of the flow sensor. The present embodiment is different from embodiment 7 in that the environment of the use site of the present embodiment is not constant, and the accuracy of the flow sensor is guaranteed under such conditions.
Referring to fig. 4, the method for guaranteeing the accuracy of the flow sensor of the present embodiment includes the following steps.
Step S31, detecting the instantaneous flow k of the calibrated flow sensor in the production field or laboratory0。
Step S32, detecting the instantaneous flow of the application flow sensor at different use sites under the condition of keeping the original factory coefficient of the application flow sensor unchanged to obtain a data set w5Comprises the following steps: k is a radical of11，k12，……，k1m。
In step S33, the coefficients of the flow sensor are calibrated for different applications. The coefficient calibration using the flow sensor generally uses an instrument with higher precision than that using the flow sensor to measure and calibrate, and the national standard stipulates that an instrument with precision three times higher than that of the sensor to be measured is used. Step S34, detecting the instantaneous flow of the application flow sensor in different use sites to obtain multiple data groups W when different periods and different flow ranges change6Each group of data set W6Comprises the following steps: k is a radical of21，k22，……，k2m。
Step S35, each group of data W6And a data group W5Comparing to obtain multiple data sets W7Each group of data set W7Comprises the following steps: k is a radical of21-k11，k22-k12，……，k2m-k1m。
Step S36, for multiple data groups W6The average value Δ' is taken.
Step S37, when the instantaneous flow is detected by using the on-site application flow sensor, the detected instantaneous flow k and the instantaneous flow k of the calibration flow sensor are detected0And comparing, and judging whether the comparison result is equal to the average value delta'.
If the comparison result is equal to the average value Δ ', then the process ends, otherwise (i.e., if the comparison result is not equal to the average value Δ'), proceeding to step S38: software compensation is performed on the instantaneous flow k until the comparison result is equal to the average value delta'.
The accuracy of the flow sensor on the platform (namely in a production field or a laboratory) is judged according to a large amount of data rules, timely judgment is made, and the accuracy of the measured data is automatically restored. The influence of the change of the laboratory environment and the field use environment of the product on the flow sensor is solved, and the real-time correction of the accuracy change in the use process is realized.
Example 9
The present embodiment provides an accuracy guaranteeing apparatus for a flow sensor, and the accuracy guaranteeing apparatus for a flow sensor of the present embodiment adopts the accuracy guaranteeing method for a flow sensor of embodiment 7.
Referring to fig. 5, the accuracy assurance device includes a first detection unit 11, a second detection unit 12, a calibration unit 13, a third detection unit 14, an instantaneous flow rate comparison unit 15, an averaging unit 17, an average judgment unit 18, and a compensation unit 19.
The flow sensor in a production field or a laboratory is defined as a calibration flow sensor, the flow sensor in each use field is defined as an application flow sensor, and the calibration flow sensor and the application flow sensor have the same model.
The detection unit I11 is used for detecting the instantaneous flow k of a calibrated flow sensor in a production field or a laboratory0. The second detection unit 12 is used for detecting the instantaneous flow of the application flow sensor at different use sites under the condition of keeping the original factory coefficient of the application flow sensor unchanged to obtain a data set w5Comprises the following steps: k is a radical of11，k12，……，k1m. The calibration unit 13 is used to calibrate the coefficients of the application flow sensor at different sites of use.
The third detection unit 14 is used for detecting the instantaneous flow of the application flow sensor in different use sites when different periods and different process ranges change to obtain a plurality of groups of data groups W6Each group of data set W6Comprises the following steps: k is a radical of21，k22，……，k2m。
The instantaneous flow rate comparison unit 15 is used for comparing each group of data W6And a data group W5Comparing to obtain multiple data sets W7Each group of data set W7Comprises the following steps: k is a radical of21-k11，k22-k12，……，k2m-k1m. The averaging unit 17 is used for averaging multiple data groups W7The average value Δ' is taken. The average value judging unit 18 is used for detecting the instantaneous flow k and calibrating the instantaneous flow k of the flow sensor when the instantaneous flow is detected by using the on-site application flow sensor0And comparing, and judging whether the comparison result is equal to the average value delta'. If the comparison result is equal to the average value Δ ', the process is ended, otherwise (i.e. if the comparison result is not equal to the average value Δ '), the compensation unit 19 is configured to perform software compensation on the instantaneous flow k until the comparison result is equal to the average value Δ '.
The measurement accuracy device of the embodiment has the same advantageous effects as the method of improving the measurement accuracy of the flow sensor of embodiment 1.
Example 10
The present embodiments provide a computer terminal comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor, when executing the program, implements the steps of the accuracy assurance method of the flow sensor of embodiment 7 or embodiment 8.
When the accuracy guaranteeing method of the flow sensor in embodiment 7 or embodiment 8 is applied, the accuracy guaranteeing method may be applied in a form of software, for example, a program designed to run independently is installed on a computer terminal, and the computer terminal may be a computer, a smart phone, or the like. Or it can be designed as embedded running program and installed on computer terminal, such as single-chip computer.
Example 11
The present embodiment provides a computer-readable storage medium having a computer program stored thereon. The program, when executed by a processor, implements the steps of the accuracy assurance method of the flow sensor of embodiment 7 or embodiment 8.
The accuracy guaranteeing method of the flow sensor according to embodiment 7 or embodiment 8 may be implemented in software, for example, a program configured to be executed independently by a computer readable storage medium, where the computer readable storage medium may be a usb flash drive, and the usb flash drive is configured as a usb shield, and the usb flash drive is configured to be a program configured to start the whole method by external triggering.
Example 12
This embodiment provides a flow sensor that employs the accuracy securing method of the flow sensor of embodiment 7 or embodiment 8 to correct an instantaneous flow rate detected using the flow sensor, thereby securing the accuracy of the flow sensor.
Example 13
This embodiment provides a flow sensor, which not only uses the accuracy guaranteeing method of the flow sensor of embodiment 7 or embodiment 8 to correct the instantaneous flow detected by the flow sensor, thereby guaranteeing the accuracy of the flow sensor, but also uses the method of embodiment 1 to improve the measurement accuracy of the flow sensor to adjust or correct, thereby improving the measurement accuracy.
Example 14
The embodiment provides a big data acquisition and processing system of thing allies oneself with, big data acquisition and processing system of thing allies oneself with carries out data acquisition to various electrical apparatus devices of user, then does various processings that need to the data of gathering back, and the data after will handling show, or upload to the network, or collect to the server and supply the customer to look over and look over.
The flow sensor in a production field or a laboratory is defined as a calibration flow sensor, the flow sensor in each use field is defined as an application flow sensor, and the calibration flow sensor and the application flow sensor have the same model.
The big data acquisition and processing system of the Internet of things firstly receives the instantaneous flow k of a calibration flow sensor in a production field or a laboratory0(ii) a Then, under the condition of keeping the original factory coefficient of the application flow sensor unchanged, receiving the instantaneous flow of the application flow sensor at different use sites to obtain a data set w5Comprises the following steps: k is a radical of11，k12，……，k1m。
The big data acquisition and processing system of the internet of things is used for calibrating the coefficients of the application flow sensors on different use sites, and after the coefficients of the application flow sensors are calibrated, the big data acquisition and processing system of the internet of things also receives the instantaneous flow of the application flow sensors on different use sites in different periods and different flow ranges to obtain a plurality of groups of data groups W6Each group of data set W6Comprises the following steps: k is a radical of21，k22，……，k2m(ii) a Then each group of data W6And a data group W5Comparing to obtain multiple data sets W7Each group of data set W7Comprises the following steps: k is a radical of21-k11，k22-k12，……，k2m-k1m(ii) a Then, for multiple data groups W7The average value Δ' is taken.
When the on-site application flow sensor is used for detecting the instantaneous flow, the internet of things big data acquisition and processing system receives the detected instantaneous flow k and the instantaneous flow k of the calibration flow sensor0And comparing, and judging whether the comparison result is equal to the average value delta'. And if the comparison result is equal to the average value delta ', ending the process, otherwise (namely if the comparison result is not equal to the average value delta '), performing software compensation on the instantaneous flow k by the internet of things big data acquisition and processing system until the comparison result is equal to the average value delta '.
The big data acquisition and processing system of the internet of things judges the accuracy of the flow sensor on the platform (namely in a production field or a laboratory) according to a large number of data rules, makes timely judgment and automatically restores the accuracy of the measured data. The influence of the change of the laboratory environment and the field use environment of the product on the flow sensor is solved, and the real-time correction of the accuracy change in the use process is realized.
Example 15
In this embodiment, on the basis of embodiment 14, the internet of things big data acquisition and processing system further receives average repeatability data of each flow point of the calibrated flow sensor, thereby obtaining a data set W1Comprises the following steps: x is the number of1，x2，……，xnWhere n represents the number of flow points of the flow sensor, xnThe average repeatability data for the calibrated flow sensor at the nth flow point is shown. After at least three times of repeated data acquisition is carried out on each flow point, the average repeated data of the corresponding flow point can be obtained by averaging.
Then, the Internet of things big data receiving and processing system collects the repetitive data of each flow point of each application flow sensor of different use sites at the same time, thereby obtaining a data set W2Comprises the following steps: { (y)11，y12，……，y1n)，(y21，y22，……，y2n)，……，(ym1，ym2，……，ymn) Where m denotes the number of applied flow sensors, ymnRepresenting repeatability data for the mth application flow sensor at the nth flow point.
Then, the internet of things big data acquisition and processing system repeatedly receives the repetitive data of each flow point of each application flow sensor of different use sites at different time points, thereby obtaining a plurality of groups of data sets W2。
Then, the big data acquisition and processing system of the Internet of things aims at the same flow point of the same application flow sensor and combines the repetitive data of the corresponding flow point with the data set W1Comparing the corresponding average repeatability data to obtain a plurality of data groups W3Each group of data set W3Comprises the following steps: { (Δ)11＝y11-x1，Δ12＝y12-x2，……，Δ1n＝y1n-xn)，(Δ21＝y11-x1，Δ22＝y12-x2，……，Δ2n＝y1n-xn)，……，(Δn1＝y11-x1，Δn2＝y12-x2，……，Δnn＝y1n-xn)}。
Then, the big data acquisition and processing system of the Internet of things averages all the difference values to obtain an average value delta, and then judgesEach group of data set W3If the difference in (a) is equal to the mean value Δ, if each set of data W is of the same type3Is equal to the mean value delta, ends if each group of data sets W3If the difference in (d) is not equal to the average value delta, the coefficients of the respective application flow sensors are adjusted until the respective difference is equal to the difference average value delta.
The coefficient of the corresponding application flow sensor can be adjusted by hardware, such as directly adjusting the coefficient of the corresponding application flow sensor. The measured value of the corresponding application flow sensor can be subjected to software compensation by adopting software adjustment through the large data acquisition and processing system of the internet of things, if the corresponding difference value is smaller than the average difference value delta, positive compensation is carried out, and if the corresponding difference value is larger than the average difference value delta, negative compensation is carried out.
Example 16
The embodiment provides a product quality guarantee method of a flow sensor. In practical application of the existing flow sensor, the principle of post-control is adopted to maintain or replace the existing flow sensor when the product quality is in problem. This embodiment can guarantee the product before the problem appears, through data analysis, judges the trouble of instrument, accomplishes prevention in advance.
Referring to fig. 6, the method for guaranteeing product quality of a flow sensor of the present embodiment includes the following steps.
And step S41, installing two flow sensors with the same caliber on the same pipeline.
And step S42, acquiring instantaneous flow of the two flow sensors synchronously in real time, and comparing the instantaneous flow with each other.
Step S43, when the instantaneous flow rates of the two flow sensors are deviated, the instantaneous flow rates of the two flow sensors are respectively compared with respective originally accumulated instantaneous flow rates, and the originally accumulated instantaneous flow rates are the average values of the instantaneous flow rates over a period of time.
In step S44, if the deviation between the instantaneous flow rate of a certain flow sensor and the instantaneous flow rate accumulated originally exceeds a preset value, it is determined that the certain flow sensor is abnormal. Therefore, this flow sensor needs to be repaired and replaced in time. And the abnormal sensors can be repaired or replaced in time, so that the risk of production shutdown caused by equipment failure can be reduced.
In step S45, if the deviation between the instantaneous flow rate of a certain flow sensor and the originally accumulated instantaneous flow rate is zero, it is determined that the flow sensor is accurate.
According to the product quality guarantee method of the flow sensor, the two flow sensors are compared in time during operation, and when data deviation occurs, the data deviation is compared with respective original accumulated data in time, so that the risk of production shutdown caused by equipment failure can be reduced, and the data accuracy of the flow sensors is monitored constantly.
Example 17
The present embodiment provides a product quality assurance device for a flow sensor, and the product quality assurance device for a flow sensor of the present embodiment adopts the product quality assurance method for a flow sensor of embodiment 15.
Referring to fig. 7, the product quality assurance device of the flow sensor includes a first comparison unit 32, a second comparison unit 33, two instantaneous flow acquisition units 31, and a conclusion judgment unit 34.
The two instantaneous flow acquisition units 31 are used for synchronously acquiring instantaneous flow in real time for two flow sensors with the same caliber which are installed on the same pipeline respectively.
And the first comparison unit 32 is used for comparing the instantaneous flow of two flow sensors with the same caliber.
The second comparing unit 33 is configured to compare the instantaneous flows of the two flow sensors with respective originally accumulated instantaneous flows when the instantaneous flows of the two flow sensors have a deviation, where the originally accumulated instantaneous flows are average instantaneous flows over a period of time.
The conclusion judging unit 34 is used for judging that a certain flow sensor is abnormal if the deviation between the instantaneous flow of the certain flow sensor and the instantaneous flow accumulated originally exceeds a preset value; and if the deviation between the instantaneous flow of a certain flow sensor and the originally accumulated instantaneous flow is zero, judging that the flow sensor is accurate in measurement. When the flow sensor is judged to be abnormal, the flow sensor needs to be repaired and replaced in time. Therefore, the abnormal sensors can be repaired or replaced in time, and the risk of production shutdown caused by equipment failure can be reduced.
Example 18
The present embodiments provide a computer terminal comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor, when executing the program, implements the steps of the product quality assurance method of a flow sensor of embodiment 16.
The product quality assurance method for a flow sensor according to embodiment 16 may be applied in the form of software, for example, a program designed to run independently is installed on a computer terminal, and the computer terminal may be a computer, a smart phone, or the like. Or it can be designed as embedded running program and installed on computer terminal, such as single-chip computer.
Example 19
The present embodiment provides a computer-readable storage medium having a computer program stored thereon. The program, when executed by a processor, implements the steps of the product quality assurance method of the flow sensor of embodiment 16.
The product quality assurance method of the flow sensor according to embodiment 16 may be implemented in the form of software, for example, a program configured to be independently run on a computer-readable storage medium, which may be a usb flash disk configured as a usb shield, where the usb flash disk is configured to be a program for starting the whole method by external triggering.
Example 20
This embodiment provides a flow sensor that uses the product quality assurance method of the flow sensor of embodiment 16 to ensure the product quality of the flow sensor during use, thereby reducing the risk of production downtime due to equipment failure.
Example 21
This embodiment provides a flow sensor that not only adopts the product quality assurance method of the flow sensor of embodiment 16 to ensure the product quality of the flow sensor during use, thereby reducing the risk of production downtime due to equipment failure, but also adopts the accuracy assurance method of the flow sensor of embodiment 7 or embodiment 8 to correct the instantaneous flow rate detected by the flow sensor, thereby ensuring the accuracy of the flow sensor.
Example 22
The present embodiment provides a flow sensor, in which, in a first aspect, the product quality assurance method of the flow sensor in embodiment 16 is used to ensure the product quality of the flow sensor during use, so as to reduce the risk of production shutdown caused by equipment failure; the second aspect further employs the accuracy assurance method of the flow sensor of embodiment 7 or embodiment 8 to correct the instantaneous flow rate detected by the flow sensor, thereby assuring the accuracy of the flow sensor; the third aspect is also adjusted or corrected by the method for improving the measurement accuracy of the flow sensor of embodiment 1, thereby improving the measurement accuracy.
Example 23
The embodiment provides a big data acquisition and processing system of thing allies oneself with, big data acquisition and processing system of thing allies oneself with carries out data acquisition to various electrical apparatus devices of user, then does various processings that need to the data of gathering back, and the data after will handling show, or upload to the network, or collect to the server and supply the customer to look over and look over.
The flow sensor in a production field or a laboratory is defined as a calibration flow sensor, the flow sensor in each use field is defined as an application flow sensor, and the calibration flow sensor and the application flow sensor have the same model.
The internet of things big data acquisition and processing system respectively receives real-time instantaneous flows of two flow sensors with the same caliber, which are arranged on the same pipeline, and compares the instantaneous flows of the two flow sensors with the same caliber. When the big data acquisition and processing system of the internet of things analyzes that the instantaneous flows of the two flow sensors have deviation (namely when the instantaneous flows of the two flow sensors are different), the big data acquisition and processing system of the internet of things compares the instantaneous flows of the two flow sensors with respective originally accumulated instantaneous flows respectively, and the originally accumulated instantaneous flows are the average value of the instantaneous flows in a period of time.
The big data acquisition and processing system of the Internet of things judges whether the deviation between the instantaneous flow of a certain flow sensor and the instantaneous flow originally accumulated by the certain flow sensor exceeds a preset value or not, and then sends out a warning to prompt the flow sensor to be abnormal; the big data acquisition processing system of thing allies oneself with judges if the instantaneous flow of a certain flow sensor and the instantaneous flow deviation of its original accumulation are zero, and then thinks that this flow sensor measures accurately. This flow sensor may not issue any warning when it is considered to be accurate. When the flow sensor is judged to be abnormal, the flow sensor needs to be repaired and replaced in time. Therefore, the abnormal sensors can be repaired or replaced in time, and the risk of production shutdown caused by equipment failure can be reduced.
Example 24
In this embodiment, on the basis of embodiment 23, the internet of things big data acquisition and processing system further receives the instantaneous flow k of the calibrated flow sensor in the production field or the laboratory0(ii) a Then, under the condition of keeping the original factory coefficient of the application flow sensor unchanged, receiving the instantaneous flow of the application flow sensor at different use sites to obtain a data set w5Comprises the following steps: k is a radical of11，k12，……，k1m(ii) a The coefficients of the flow sensor are then calibrated for different applications at the site of use.
After the coefficient of the application flow sensor is calibrated, when the coefficient of the application flow sensor is changed in different periods and different flow ranges, the big data acquisition and processing system of the internet of things also receives the instantaneous flow of the application flow sensor in different use fields to obtain a plurality of groups of data groups W6Each group of data set W6Comprises the following steps: k is a radical of21，k22，……，k2m(ii) a Then each group of data W6And a data group W5Comparing to obtain multiple data sets W7Each group of data set W7Comprises the following steps: k is a radical of21-k11，k22-k12，……，k2m-k1m(ii) a Then, for multiple data groups W7The average value Δ' is taken.
When the on-site application flow sensor is used for detecting the instantaneous flow, the internet of things big data acquisition and processing system receives the detected instantaneous flow k and the instantaneous flow k of the calibration flow sensor0And comparing, and judging whether the comparison result is equal to the average value delta'. And if the comparison result is equal to the average value delta ', ending the process, otherwise (namely if the comparison result is not equal to the average value delta '), performing software compensation on the instantaneous flow k by the internet of things big data acquisition and processing system until the comparison result is equal to the average value delta '.
The big data acquisition and processing system of the internet of things judges the accuracy of the flow sensor on the platform (namely in a production field or a laboratory) according to a large number of data rules, makes timely judgment and automatically restores the accuracy of the measured data. The influence of the change of the laboratory environment and the field use environment of the product on the flow sensor is solved, and the real-time correction of the accuracy change in the use process is realized.
Example 25
In this embodiment, on the basis of embodiment 24, the internet of things big data acquisition and processing system further receives average repeatability data of each flow point of the calibrated flow sensor, thereby obtaining a data set W1Comprises the following steps: x is the number of1，x2，……，xnWhere n represents the number of flow points of the flow sensor, xnThe average repeatability data for the calibrated flow sensor at the nth flow point is shown. After at least three times of repeated data acquisition is carried out on each flow point, the average repeated data of the corresponding flow point can be obtained by averaging.
Then, the Internet of things big data receiving and processing system collects the repetitive data of each flow point of each application flow sensor of different use sites at the same time, thereby obtaining a data set W2Comprises the following steps: { (y)11，y12，……，y1n)，(y21，y22，……，y2n)，……，(ym1，ym2，……，ymn) Where m denotes the number of applied flow sensors, ymnRepresenting repeatability data for the mth application flow sensor at the nth flow point.
Then, the internet of things big data acquisition and processing system repeatedly receives the repetitive data of each flow point of each application flow sensor of different use sites at different time points, thereby obtaining a plurality of groups of data sets W2。
Then, the big data acquisition and processing system of the Internet of things aims at the same flow point of the same application flow sensor and combines the repetitive data of the corresponding flow point with the data set W1Comparing the corresponding average repeatability data to obtain a plurality of data groups W3Each group of data set W3Comprises the following steps: { (Δ)11＝y11-x1，Δ12＝y12-x2，……，Δ1n＝y1n-xn)，(Δ21＝y11-x1，Δ22＝y12-x2，……，Δ2n＝y1n-xn)，……，(Δn1＝y11-x1，Δn2＝y12-x2，……，Δnn＝y1n-xn)}。
Then, the big data acquisition and processing system of the Internet of things averages all the difference values to obtain an average value delta, and then judges each group of data groups W3If the difference in (a) is equal to the mean value Δ, if each set of data W is of the same type3Is equal to the mean value delta, ends if each group of data sets W3If the difference in (d) is not equal to the average value delta, the coefficients of the respective application flow sensors are adjusted until the respective difference is equal to the difference average value delta.
The coefficient of the corresponding application flow sensor can be adjusted by hardware, such as directly adjusting the coefficient of the corresponding application flow sensor. The measured value of the corresponding application flow sensor can be subjected to software compensation by adopting software adjustment through the large data acquisition and processing system of the internet of things, if the corresponding difference value is smaller than the average difference value delta, positive compensation is carried out, and if the corresponding difference value is larger than the average difference value delta, negative compensation is carried out.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (7)
1. A product quality guarantee method of a flow sensor is characterized by comprising the following steps:
step S41, installing two flow sensors with the same caliber on the same pipeline;
step S42, synchronously acquiring instantaneous flow of two flow sensors in real time, and comparing the instantaneous flow with the instantaneous flow;
step S43, when the instantaneous flow of the two flow sensors has deviation, the instantaneous flow of the two flow sensors is respectively compared with the respective originally accumulated instantaneous flow, and the originally accumulated instantaneous flow is the average value of the instantaneous flow in a period of time;
step S44, if the deviation between the instantaneous flow of a certain flow sensor and the instantaneous flow accumulated originally exceeds a preset value, the flow sensor is judged to be abnormal;
when the flow sensor is judged to be abnormal, the product quality guarantee method also adjusts the flow sensor to improve the measurement accuracy of the flow sensor:
defining the flow sensor in the production field or laboratory as a calibration flow sensor, and collecting average repeatability data of each flow point of the calibration flow sensor to obtain a data set W1Comprises the following steps: x is the number of1，x2，……，xnWhere n represents the number of flow points of the flow sensor, xnThe average repeatability data of the calibrated flow sensor at the nth flow point is represented, and after repeated repeatability data acquisition is carried out on each flow point for multiple times, the average repeatability data of the corresponding flow point can be obtained by averaging;
defining the flow sensor of each use site as an application flow sensor, the calibration flow sensor and the application flow sensor having the same typeNumber; data for the repeatability of each flow point of each application flow sensor at different sites of use are collected at the same time, thereby obtaining a data set W2Comprises the following steps: { (y)11，y12，……，y1n)，(y21，y22，……，y2n)，……，(ym1，ym2，……，ymn) Where m denotes the number of applied flow sensors, ymnData representing the repeatability of the mth application flow sensor at the nth flow point;
repeating the previous step at different time points, thereby obtaining a plurality of data sets W2；
Each data set W is applied to the same flow point of the same application flow sensor2Repeatability data and data set W of corresponding flow points1Comparing the corresponding average repeatability data to obtain a plurality of data groups W3Each group of data set W3Comprises the following steps: { (Δ)11＝y11-x1，Δ12＝y12-x2，……，Δ1n＝y1n-xn)，(Δ21＝y11-x1，Δ22＝y12-x2，……，Δ2n＝y1n-xn)，……，(Δn1＝y11-x1，Δn2＝y12-x2，……，Δnn＝y1n-xn)}；
Averaging all the difference values to obtain an average value delta;
judging each group of data group W3Whether the difference in (a) is equal to the average value Δ; if each group of data set W3The difference in (d) is not equal to the average value delta and the coefficients of the respective application flow sensors are adjusted until the respective difference is equal to the difference average value delta.
2. The method of claim 1, wherein a flow sensor is determined to be accurate if the deviation between the instantaneous flow rate of the flow sensor and the originally accumulated instantaneous flow rate is zero.
3. The method of claim 1, further comprising the steps of:
step S21, calibrating coefficients of the application flow sensor of different use sites;
step S22, detecting the instantaneous flow of each applied flow sensor in different process range changes to obtain multiple data sets W4Each group of data set W4Comprises the following steps: z21，Z22，……，Z2uWherein u represents the number of process ranges;
step S23, when the instantaneous flow is detected by using the on-site application flow sensor, the detected instantaneous flow k is matched with Z in the corresponding flow range2uComparing;
if the instantaneous flow k and Z in the corresponding process range2uIf so, the process ends, otherwise, the process goes to step S24: performing software compensation on the instantaneous flow k to ensure that the instantaneous flow k and Z in the corresponding process range2uThe same is true.
4. The method of claim 3, wherein the calibration using the coefficients of the flow sensor is measured and calibrated using an instrument having a higher accuracy than the accuracy of the calibration using the flow sensor.
5. A product quality assurance device of a flow sensor, characterized by comprising:
the two instantaneous flow acquisition units are used for respectively and synchronously acquiring instantaneous flow for two flow sensors with the same caliber which are arranged on the same pipeline in real time;
the comparison unit I is used for comparing instantaneous flow of two flow sensors with the same caliber;
the comparison unit II is used for comparing the instantaneous flow of the two flow sensors with respective originally accumulated instantaneous flow when the instantaneous flow of the two flow sensors has deviation, wherein the originally accumulated instantaneous flow is an instantaneous flow average value in a period of time;
the conclusion judging unit is used for judging that the flow sensor is abnormal if the deviation between the instantaneous flow of the certain flow sensor and the instantaneous flow originally accumulated by the certain flow sensor exceeds a preset value; if the deviation between the instantaneous flow of a certain flow sensor and the originally accumulated instantaneous flow is zero, the flow sensor is judged to be accurate in measurement;
when the conclusion judging unit judges that the flow sensor is abnormal, the flow sensor adjusts to improve the measurement accuracy of the flow sensor:
defining the flow sensor in the production field or laboratory as a calibration flow sensor, and collecting average repeatability data of each flow point of the calibration flow sensor to obtain a data set W1Comprises the following steps: x is the number of1，x2，……，xnWhere n represents the number of flow points of the flow sensor, xnThe average repeatability data of the calibrated flow sensor at the nth flow point is represented, and after repeated repeatability data acquisition is carried out on each flow point for multiple times, the average repeatability data of the corresponding flow point can be obtained by averaging;
defining a flow sensor of each use site as an application flow sensor, wherein the calibration flow sensor and the application flow sensor have the same model; data for the repeatability of each flow point of each application flow sensor at different sites of use are collected at the same time, thereby obtaining a data set W2Comprises the following steps: { (y)11，y12，……，y1n)，(y21，y22，……，y2n)，……，(ym1，ym2，……，ymn) Where m denotes the number of applied flow sensors, ymnData representing the repeatability of the mth application flow sensor at the nth flow point;
repeating the previous step at different time points, thereby obtaining a plurality of data sets W2；
Each data set W is applied to the same flow point of the same application flow sensor2Repeatability data and data set W of corresponding flow points1Comparing the corresponding average repeatability data to obtain a plurality of data groups W3Each group of data set W3Comprises the following steps: { (Δ)11＝y11-x1，Δ12＝y12-x2，……，Δ1n＝y1n-xn)，(Δ21＝y11-x1，Δ22＝y12-x2，……，Δ2n＝y1n-xn)，……，(Δn1＝y11-x1，Δn2＝y12-x2，……，Δnn＝y1n-xn)}；
Averaging all the difference values to obtain an average value delta;
judging each group of data group W3Whether the difference in (a) is equal to the average value Δ; if each group of data set W3The difference in (d) is not equal to the average value delta and the coefficients of the respective application flow sensors are adjusted until the respective difference is equal to the difference average value delta.
6. A computer terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, carries out the steps of the product quality assurance method of a flow sensor according to any one of claims 1 to 4.
7. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the product quality assurance method of a flow sensor according to any one of claims 1 to 4.
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