CN115047391A - Batch product sampling online calibration method and system - Google Patents
Batch product sampling online calibration method and system Download PDFInfo
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- CN115047391A CN115047391A CN202210368234.6A CN202210368234A CN115047391A CN 115047391 A CN115047391 A CN 115047391A CN 202210368234 A CN202210368234 A CN 202210368234A CN 115047391 A CN115047391 A CN 115047391A
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- 238000012886 linear function Methods 0.000 claims abstract description 12
- 238000004590 computer program Methods 0.000 claims description 14
- 238000004364 calculation method Methods 0.000 claims description 5
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R35/00—Testing or calibrating of apparatus covered by the other groups of this subclass
- G01R35/005—Calibrating; Standards or reference devices, e.g. voltage or resistance standards, "golden" references
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention discloses a batch product sampling online calibration method and a batch product sampling online calibration system, wherein the method comprises the following steps: collecting the check collection values and the check actual values of the samples at all check points, and storing the check collection values and the check actual values into a buffer area; establishing a calibration model based on a linear function, and obtaining a coefficient of the calibration model based on a calibration acquisition value and a calibration actual value; and integrating the calibration model into the product, and calibrating the real-time acquisition value based on the calibration model to obtain a real-time actual value. The invention is applied to the technical field of software sampling automatic calibration, can automatically calibrate by integrating an intelligent calibration model in a product and matching with an external tool, improves the calibration efficiency, reduces the calibration working time, improves the calibration accuracy, and effectively saves the labor cost and the cost of a calibration circuit.
Description
Technical Field
The invention relates to the technical field of software sampling automatic calibration, in particular to a batch product sampling online calibration method and system.
Background
In the existing industrial products, especially in power supply products, physical variables such as input/output voltage, current and the like need to be sampled, displayed and protected, and the sampling precision has high requirements, so that the acquired value needs to be calibrated to be closer to an actual value. In the conventional calibration method, a correction circuit needs to be added on a power supply, deviation data is recorded in an external mode, deviation compensation values are manually converted, and each sampling value is compensated. The method needs to sample and check each product, which is time-consuming and labor-consuming and has low efficiency.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the batch product sampling online calibration method and the batch product sampling online calibration system, which can be used for automatically calibrating by integrating an intelligent calibration model in a product and matching with an external tool, thereby effectively saving the labor cost and the cost of a calibration circuit.
In order to achieve the above object, the present invention provides an online calibration method for sampling a batch product, comprising the following steps:
step 1, collecting check collection values and check actual values of a sample at all check points, and storing the check collection values and the check actual values into a buffer area;
step 2, establishing a calibration model based on a linear function, and obtaining a coefficient of the calibration model based on a calibration acquisition value and a calibration actual value;
and step 3, integrating the calibration model into the product, and calibrating the real-time acquisition value based on the calibration model to obtain a real-time actual value.
In one embodiment, in step 1, the process of acquiring the checkpoint includes:
taking N check points in the whole measuring range X of the parameter to be calibrated as follows: (1/N) · X, (2/N) · X, X.
In one embodiment, in step 2, a calibration model based on a linear function is established, and a coefficient of the calibration model is obtained based on the calibration acquired value and the calibration actual value, specifically:
establishing a calibration model based on a linear function, which comprises the following steps:
Y＝AX+B
wherein Y is an actual value, X is an acquired value, and A, B is a coefficient of the calibration model;
sequentially substituting the calibration collected values and the calibration actual values into the calibration model in different combination modes in a permutation and combination mode to obtain an initial coefficient A data set and an initial coefficient B data set;
and respectively carrying out summation and averaging on the initial coefficient A data group and the initial coefficient B data group to obtain the coefficients A, B of the calibration model.
In one embodiment, before the sum-average of the initial coefficient a data set and the initial coefficient B data set, the maximum value and the minimum value in the initial coefficient a data set and the initial coefficient B data set are respectively removed.
In order to achieve the above object, the present invention further provides an online calibration system for sampling a batch product, comprising:
the external tool is connected with the product through a serial port and used for setting a checking actual value of the product;
the acquisition module is used for acquiring corresponding check acquisition values of the product during each actual value inspection;
the coefficient calculation module is used for obtaining the coefficient of the calibration model according to the calibration acquisition value and the calibration actual value;
and the calibration module is used for storing the coefficient of the calibration model into the product and calibrating the real-time acquisition value based on the calibration model to obtain a real-time actual value.
To achieve the above object, the present invention further provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements part or all of the steps of the above method when executing the computer program.
To achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a computer program, which when executed by a processor, performs some or all of the steps of the method described above.
Compared with the prior art, the batch product sampling online calibration method and system provided by the invention have the following beneficial technical effects:
1. the error influence caused by manual calculation and calibration is effectively reduced, and the manual investment is reduced;
2. the checking efficiency is improved, and the checking working time is reduced;
3. the accuracy of the verification is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a flow chart of a batch product sampling online calibration method according to an embodiment of the present invention;
fig. 2 is an internal structural diagram of a computer device in an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that all directional indicators (such as upper, lower, left, right, front and rear … …) in the embodiment of the present invention are only used to explain the relative position relationship between the components, the movement situation, etc. in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indicator is changed accordingly.
In addition, the descriptions related to "first", "second", etc. in the present invention are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "connected," "secured," and the like are to be construed broadly, and for example, "secured" may be a fixed connection, a removable connection, or an integral part; the connection can be mechanical connection, electrical connection, physical connection or wireless communication connection; they may be directly connected or indirectly connected through intervening media, or they may be interconnected within two elements or in a relationship where two elements interact with each other unless otherwise specifically limited. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In addition, the technical solutions in the embodiments of the present invention may be combined with each other, but it must be based on the realization of the technical solutions by those skilled in the art, and when the technical solutions are contradictory to each other or cannot be realized, such a combination of the technical solutions should not be considered to exist, and is not within the protection scope of the present invention.
The embodiment discloses an online calibration method for batch product sampling, and with reference to fig. 1, the method specifically includes the following steps:
step 1, collecting calibration collection values and calibration actual values of a sample at all calibration points, and storing the calibration collection values and the calibration actual values into a buffer area, wherein the acquisition process of the calibration points is as follows:
taking N check points in the whole measuring range X of the parameter to be calibrated as follows: (1/N). X, (2/N). X, · X;
step 2, establishing a calibration model based on a linear function, and obtaining coefficients of the calibration model based on the calibration acquisition value and the calibration actual value, wherein the coefficients specifically comprise:
establishing a calibration model based on a linear function, which comprises the following steps:
Y＝AX+B
in the formula, Y is an actual value, X is an acquired value, and A, B is a coefficient of a calibration model;
sequentially substituting the calibration collected values and the calibration actual values into the calibration model in different combination modes in a permutation and combination mode to obtain an initial coefficient A data set and an initial coefficient B data set;
after the maximum value and the minimum value in the initial coefficient A data group and the initial coefficient B data group are respectively removed, summing and averaging are respectively carried out on the initial coefficient A data group and the initial coefficient B data group, and then the coefficient A, B of the calibration model is obtained;
the batch product sampling online calibration method in the embodiment is realized by a batch product sampling online calibration system, and the system comprises an external tool, an acquisition module, a coefficient calculation module and a calibration module. Specifically, the external tool is connected with the product through a serial port and used for setting a checking actual value of the product, the acquisition module is used for acquiring a checking acquisition value corresponding to the product when each checking actual value is detected, the coefficient calculation module is used for obtaining a coefficient of the calibration model according to the checking acquisition value and the checking actual value, and the calibration module is used for storing the coefficient of the calibration model into the product and calibrating the real-time acquisition value based on the calibration model to obtain the real-time actual value.
The batch product sampling online calibration method and system in the embodiment can be applied to the acquisition and calibration of power supply current and voltage, and have good sampling linearity of voltage and current, so that the calibration can be performed only by referring to the relation of a linear function. The method and system in this embodiment will be further described below by taking the current collection calibration as an example.
Firstly, setting a current calibration current value through an external tool, and sending the set calibration current value to a power supply product to be checked through serial ports through tool software;
secondly, after the power product to be checked receives the current calibration current value, the current ADC sampling value and the received actual current value are recorded and stored in a buffer area. In this way, 5 different current check points are taken. The value taking mode is that the whole range value is divided by 5, for example, if the maximum quantity of the whole current is X, the check points are respectively taken as 0.2X, 0.4X, 0.6X, 0.8X and 1.0X;
then, the data of 5 points are saved and modeled by a linear function, where X is the ADC sampling value of each check point and Y is the actual current value. Data for every 5 points, two sets of data are arbitrarily combined, and 10 sets of data for a and B can be calculated.
And finally, after the maximum value and the minimum value of the data of the 10 groups A and B are removed, carrying out average summation to obtain a final current sampling check formula value, and automatically storing the formula value in the product. When the product current value needs to be calculated next time, the normal current value can be calculated only by sampling the real-time ADC value and calling the calibrated linear function formula.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 2. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize a parameter selection method for low-temperature lossless alternating current self-heating of the battery. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 2 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the method in the above embodiments when the processor executes the computer program.
In an embodiment, a computer storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, is adapted to carry out the steps of the method of the above embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (7)
1. An online calibration method for sampling of batch products is characterized by comprising the following steps:
step 1, collecting check collection values and check actual values of a sample at all check points, and storing the check collection values and the check actual values into a buffer area;
step 2, establishing a calibration model based on a linear function, and obtaining a coefficient of the calibration model based on a calibration acquisition value and a calibration actual value;
and step 3, integrating the calibration model into the product, and calibrating the real-time acquisition value based on the calibration model to obtain a real-time actual value.
2. The batch product sampling online calibration method according to claim 1, wherein in step 1, the check point obtaining process is as follows:
taking N check points in the whole measuring range X of the parameter to be calibrated as follows: (1/N). X, (2/N). X,. cndot.X.
3. The batch product sampling online calibration method according to claim 2, wherein in step 2, a calibration model based on a linear function is established, and coefficients of the calibration model are obtained based on the calibration acquired value and the calibration actual value, specifically:
establishing a calibration model based on a linear function, which comprises the following steps:
Y＝AX+B
wherein Y is an actual value, X is an acquired value, and A, B is a coefficient of the calibration model;
sequentially substituting the calibration collected values and the calibration actual values into the calibration model in different combination modes in a permutation and combination mode to obtain an initial coefficient A data set and an initial coefficient B data set;
and respectively carrying out summation and averaging on the initial coefficient A data group and the initial coefficient B data group to obtain the coefficients A, B of the calibration model.
4. The batch product sampling online calibration method according to claim 3, wherein before the initial coefficient A data set and the initial coefficient B data set are subjected to sum-averaging, the maximum value and the minimum value in the initial coefficient A data set and the initial coefficient B data set are respectively removed.
5. An online calibration system for sampling of a batch product, comprising:
the external tool is connected with the product through a serial port and used for setting a checking actual value of the product;
the acquisition module is used for acquiring corresponding check acquisition values of the product during each actual value inspection;
the coefficient calculation module is used for obtaining the coefficient of the calibration model according to the calibration acquisition value and the calibration actual value;
and the calibration module is used for storing the coefficient of the calibration model into the product and calibrating the real-time acquisition value based on the calibration model to obtain a real-time actual value.
6. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor when executing the computer program implements some or all of the steps of the method of any of claims 1 to 4.
7. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out part or all of the steps of the method according to any one of claims 1 to 4.
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| RU2660026C1 (en) * | 2017-08-14 | 2018-07-04 | Сергей Александрович Полищук | Method and device for measurement data receiver calibration |
| CN111431530A (en) * | 2020-03-13 | 2020-07-17 | 苏州浪潮智能科技有限公司 | Method and device for calibrating DA device |
| CN112510973A (en) * | 2020-12-29 | 2021-03-16 | 大禹电气科技股份有限公司 | Voltage or current calibration method of frequency converter, frequency converter and system |
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- 2022-04-08 CN CN202210368234.6A patent/CN115047391A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080033772A1 (en) * | 2006-07-18 | 2008-02-07 | Fujitsu Limited | Information processing method, information processing apparatus and program |
| CN106092375A (en) * | 2016-08-24 | 2016-11-09 | 南京飞利宁航空科技发展有限公司 | The method of calibration of airborne equipment surface temperature sensor and tester |
| RU2660026C1 (en) * | 2017-08-14 | 2018-07-04 | Сергей Александрович Полищук | Method and device for measurement data receiver calibration |
| CN111431530A (en) * | 2020-03-13 | 2020-07-17 | 苏州浪潮智能科技有限公司 | Method and device for calibrating DA device |
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