CN116046049B - Self-calibration sensor and calibration method and system - Google Patents
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Abstract
The application discloses a self-calibration sensor, a calibration method and a calibration system. According to the method and the device, the aging characteristic of the target sensor is predicted through simulation, the aging characteristic of the target sensor and the calibration coefficient are stored in the storage unit of the target sensor, meanwhile, the dynamic parameter of the running time of the current sensor is recorded and updated in use engineering, the target sensor is calibrated according to the calibration coefficient, the aging characteristic relation and the time parameter, the influence due to the aging physical characteristic can be removed according to the running time of the sensor during calibration, the related deviation is compensated, and the sensor still has accurate and stable output under the long-time working condition through the calibration scheme, so that the accuracy and the practicability of the sensor calibration are greatly improved.
Description
Technical Field
The application relates to the technical field of digital sensor chips, in particular to a self-calibration sensor, a calibration method and a calibration system.
Background
The sensor chip needs to be calibrated firstly in application to obtain data meeting the precision requirement, the analog sensor chip needs to be calibrated in an application system, and calibration parameters are stored in the application system, so that the production efficiency of the application system is low. Digital sensor chips generally require pre-calibration prior to shipment of the chip.
In the prior art, a relevant static calibration coefficient of a digital sensor is obtained through pre-calibration, the static calibration coefficient is stored in a sensor storage unit, and compensation calculation is performed according to the static calibration coefficient in the actual use process. However, the calibration coefficient of the sensor reflects the inherent deviation of the sensor individual from the theoretical design value, and the calibration coefficient is to compensate the response inherent deviation of the sensor individual, so that the output data is stabilized within the quality acceptance range, and the deviation is derived from the sensor structure, the processing error, the inherent physicochemical characteristics of the raw materials and the like. Therefore, as the service time of the sensor goes on, the static calibration coefficient in the prior art only reflects the possible inherent deviation of the sensor during calibration, and cannot accurately reflect the current inherent deviation of the sensor, which is one of reasons that the accuracy and precision of the sensor are poorer and worse as the service time is longer. Therefore, there is a need for a self-calibrating sensor and a calibration method thereof to improve the accuracy and precision of the sensor.
Disclosure of Invention
Based on the above, aiming at the existing problems and defects, the application provides a self-calibration sensor, a calibration method and a calibration system, so that the target sensor can still store more accurate and stable output after long-time working, and the accuracy and precision of the sensor are improved.
In view of this, the first aspect of the present application provides a method for automatically calibrating a sensor, characterized in that it comprises:
acquiring a sample sensor of a target sensor, and performing an aging test on the sample sensor;
pre-calibrating the target sensor and the aged sample sensor;
acquiring a calibration coefficient and an aging correction function of a target sensor;
storing the calibration coefficient and the aging correction function into a storage unit of the target sensor;
recording the current running time parameter of the target sensor;
and automatically calibrating the target sensor according to the running time parameter, the calibration coefficient and the aging correction function.
Further, the pre-calibration test includes: selecting a preset number of sample sensors; grouping the sample sensors; performing different-degree aging tests on the grouped sample sensors, and simulating aging conditions of the sensors in different time periods; and placing the target sensor and the aged sample sensor into a test bench for pre-calibration test.
Further, the test tables comprise a plurality of test tables, and each test table is arranged to work under different stable environment parameters Ti; the target sensor and each group of aging sample sensors enter each test bench to be tested to obtain the measurement parameters of the target sensorV i Measurement parameters of each aging sample sensorU kij The method comprises the steps of carrying out a first treatment on the surface of the According to the corresponding measurement parameters of the target sensorV i Measurement parameters of each aging sample sensorU kij Obtaining the aging correction function of the target sensor;
wherein,,V i representing the measured parameters obtained by the test of the target sensor at the ith test bench,U kij represent the firstjPost group agingkThe sample sensor is at the firstiThe test bench tests the obtained measurement parameters.
In particular, according to the measured parameters of each aged sample sensorU kij Corresponding measurement parameters of the target sensorV i Obtaining the target sensor aging correction function, including:
obtaining measured parameters of each aged sample sensorU kij Corresponding measurement parameters relative to the target sensorV i Is a relative change trend curve of (2);
inputting the relative change trend curve into a preset curve classification model to conduct classification prediction of the trend change curve so as to obtain a predicted curve type;
based on measured parameters of each aged sample sensorU kij Corresponding measurement parameters relative to the target sensorV i Fitting to obtain the aging correction function of the target sensor.
Further, the method further comprises the steps of:
calculating aging timetjMeasuring the relative change value of the parameter:，mfor each set of number of aged sample sensors,nthe number of test stations;
constructing each coordinate pointt j, ▽t j ) Drawing a relative change trend curve according to each coordinate point, inputting the relative change trend curve into a preset curve classification model to conduct classification prediction of the trend change curve, obtaining a predicted curve type, selecting a corresponding function fitting according to the predicted curve type, and obtaining an aging correction function。
Further, the method comprises the steps of,n=3, the method comprising:
calculating calibration coefficients of a target sensora、b、c：
；
The calibration coefficient is seta、b、cAging correction functionA storage unit that stores the data to the target sensor;
recording the current running time parameter of the target sensort；
Automatically calibrating the target sensor according to the runtime parameters, calibration coefficients and aging correction functions:；
wherein,,T 1 、T 2 、T 3 respectively set stable test values for the three test tables,V measuring For the current measured voltage value of the target sensor,Toutput values for the target parameters after calibration of the target sensor,fand measuring a conversion mapping relation between the voltage value and the target parameter for the sensor.
Further, n=2, the method comprising:
calculating a target sensor calibration coefficient:
the calibration coefficient is setl、eAging correction functionA storage unit that stores the data to the target sensor;
recording the current running time parameter of the target sensort；
Automatically calibrating the target sensor according to the runtime parameters, calibration coefficients and aging correction functions:；
wherein,,T 1 、T 2 respectively set stable test values for the two test tables,V measuring For the current measured voltage value of the target sensor,Toutput values for the target parameters after calibration of the target sensor,fand measuring a conversion mapping relation between the voltage value and the target parameter for the sensor.
A second aspect of the present application provides a self-calibrating sensor comprising one or more processing units, a memory unit storing runtime parameters, calibration coefficients, an aging correction function, such that the sensor implements the method of any of the above.
A third aspect of the present application provides a sensor self-calibration processing system comprising a plurality of test stations and a self-calibrating sensor such that the system performs the steps of the method as described in any of the preceding claims.
A fourth aspect of the present application provides a computer storage medium storing a computer program which, when executed by a processor, causes the processor to perform a method as claimed in any one of the preceding claims.
According to the scheme, the aging characteristic of the target sensor is predicted through simulation, the aging characteristic of the target sensor and the calibration coefficient are stored in the storage unit of the target sensor, meanwhile, the dynamic parameter of the running time of the current sensor is recorded and updated, the target sensor is calibrated according to the calibration coefficient, the aging characteristic relation and the time parameter, the influence caused by the aging physical characteristic can be removed according to the running time of the sensor during calibration, the related deviation is compensated, and the sensor still has accurate and stable output under the long-time working condition through the calibration scheme, so that the accuracy and the practicability of the sensor calibration are greatly improved. Furthermore, the scheme of the application comprises that a plurality of test tables are respectively arranged in different stable measuring parameter environments, the environment parameters of each test table do not need to be adjusted and time is consumed to stabilize the environment parameters, the requirements on test conditions are reduced, and the test time is shortened.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Wherein:
FIG. 1 is a block diagram of an automatic calibration method of a sensor in one embodiment;
FIG. 2 is a test flow diagram of an automatic calibration method of a sensor in one embodiment;
FIG. 3 is a block diagram of a self-calibrating digital sensor in one embodiment.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
It is noted that the terms "comprising," "including," and "having," and any variations thereof, in the description and claims of the present application and in the foregoing figures, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus. In the claims, specification, and drawings of this application, relational terms such as "first" and "second," and the like are used solely to distinguish one entity/operation/object from another entity/operation/object without necessarily requiring or implying any actual such relationship or order between such entities/operations/objects.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases 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. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In one embodiment, as shown in fig. 1, the present application provides a method for automatic calibration of a sensor, the method comprising:
s1, acquiring a sample sensor of a target sensor, and performing aging test on the sample sensor.
The target sensor is the target sensor to be calibrated, and the sensor can be various digital sensors such as a digital infrared sensor, pressure, humidity and temperature. In the scheme, in order to calibrate the target sensor more accurately, the same batch of sensors with the same internal structure, processing technology, inherent physical and chemical characteristics of raw materials and the like are selected as the sample sensor, and the sample sensor is subjected to aging test.
Specifically, a preset number of sample sensors are selected, and the sample sensors are grouped. The number of the sensors included in each group is the same as the number of the sensors included in each group, such as 5 sample sensors included in each group Nj, and the arrangement of a plurality of sample sensors included in each group is beneficial to improving the precision and accuracy of subsequent calculation.
And carrying out different-degree aging tests on the grouped sample sensors, and simulating the aging conditions of the sensors in different time periods. Specifically, different aging tests are carried out on different groups of grouped sample sensors, the aging tests experienced by the same group of sample sensors are the same, and the different groups of sample sensors are placed under different working conditions to carry out the aging tests.
In the scheme, sample aging tests are carried out on different groups of samples under different degrees, different working conditions and different aging environments, physical aging characteristic curves of a plurality of key characteristic indexes in target materials in the sample sensor are established, vectors of the key characteristics are established by measuring the key characteristics of the target materials of the sample sensor, comprehensive comparison is carried out on the vectors and the physical aging characteristic curves of the key characteristic indexes in the target materials, and N groups after the aging tests are respectively determined j Corresponding aging time t of sample sensor j Thus by different groups N j Aging test of the sample simulates different time periods t j Aging condition of the sample sensor under operation.
S2, performing pre-calibration test on the target sensor and the aged sample sensor.
Specifically, the application contains many testboards, and every testboard includes that a plurality of testboards can carry out the parameter test to a plurality of sensors, can carry out wired or wireless communication connection through one or more main control unit between a plurality of testboards simultaneously to the measurement parameter transmission of each sensor carries out analysis processing for main control unit. In another embodiment, the test stations are arranged independently of each other,each test bench can be independently provided with a control device, and after analysis and calculation of the respective measurement parameters, the corresponding parameters are directly written and updated into the storage unit of the target sensor through the communication interface. In the scheme, the measuring parameters T with different stabilities are respectively arranged between the test tables i The test work is performed below, and the temperature sensor is exemplified in the following embodiments.
Each test temperature measuring table is respectively stabilized at a temperature T i The test is carried out in the environment, the temperature measuring table does not need to change temperature, the temperature changing stabilizing time is not needed, the target sensor and each sample after aging are sensed to carry out the running water type test, when the first target sensor to be tested and the sample sensor after aging enter the No. 2 temperature measuring table for testing, the second target sensor to be tested and the corresponding sample can enter the No. 1 temperature measuring table for testing. The parallel test processing greatly improves the test efficiency, and does not need to spend time waiting for each test bench to be stabilized to other temperatures and then to test, thereby saving the test time.
As shown in fig. 2, the test procedure in one embodiment of the present application includes:
step S21: the target sensor and the aging sample sensor are sequentially put into each test bench to be tested to obtain the measurement parameters of the target sensorV i Measurement parameters of various sample sensors after agingU kij . Wherein,,V i indicating that the target sensor is at the firstiThe test bench tests the obtained measurement parameters,U kij indicating the j th group after agingkThe sample sensor is at the firstiThe test bench tests the obtained measurement parameters, and the method adopts a mode of measuring the voltage value parameters of the sensor for testing.
Step S22: based on measured parameters of each aged sample sensorU kij Corresponding measurement parameters of the target sensorV i And obtaining the aging correction function of the target sensor.
Specifically, step S221 obtains the measurement parameters of each aging sample sensorU kij Measuring parameters relative to the target sensorV i Trend of relative change of (2) and baseFitting a relative trend curve to the trend:
step S222: calculating aging time of aging sample sensort j Post-measurement parameter relative change value:wherein, the method comprises the steps of, wherein,mfor each set of number of aged sample sensors,nthe number of test stations; and constructing the relative coordinate points of each aging time-change valuet j, ▽t j ) And drawing and fitting a relative change trend curve according to each coordinate point.
Step S223: and inputting the relative change trend curve into a preset curve classification model to perform classification prediction of the trend change curve so as to obtain the type of the aging change trend prediction curve.
Specifically, the change trend curve and the aging time-change value relative characteristic data are input into a preset curve classification model to conduct curve classification prediction, and the aging change trend prediction curve type is obtained. Specifically, the change trend curve and the aging time-change value relative characteristic data are input into a preset curve classification model to conduct curve classification prediction, and data obtained through the classification prediction are used as classification prediction vectors; searching a vector element with the maximum value from the classified prediction vector, and taking the searched vector element as a hit vector element; and taking the category corresponding to the hit vector element as the aging change trend prediction curve type. The curve classification model is a model trained based on a Support Vector Machine (SVM). Meanwhile, determining whether sectional classification prediction is needed according to the fitted curve shape, and respectively classifying and predicting the sectional curves and data to obtain and combine the aging change trend prediction curve types of different sections.
Step S224: and obtaining an aging change trend function according to the curve type. After the ageing change trend prediction curve type is obtained, directly calling a corresponding type function to simulate the ageing change trend function so as to obtainThe target sensor aging correction function: selecting a corresponding function according to the type of the prediction curve, and carrying out fitting on corresponding aging sample measurement data to obtain an aging correction function. In the embodiment, the curve classification model is adopted for classification prediction, and the prediction by the network model is beneficial to improving the accuracy of the prediction.
According to the method and the device, the aging relative change difference of the target sensor is simulated and corrected by measuring the relative change trend of the aging sample sensor relative to the target sensor, so that the physical characteristic difference of the target sensor caused by time aging is eliminated and corrected, the calibration accuracy of the target sensor is improved, the aging change trend function is obtained by fitting out the relative change trend curve and inputting the relative change trend curve into a preset model, and the accuracy of type matching and calibration is improved.
S3, acquiring a calibration coefficient and an aging correction function of the target sensor, and storing the calibration coefficient and the aging correction function into a storage unit of the target sensor.
In the scheme, each test machine station sequentially stores the measurement data of the target sensor and the sample sensor into a storage unit of the target sensor, the last test machine station reads out the measurement digital signals from the target sensor, the last test machine station calculates all calibration parameters and aging correction functions, then clears other data of the storage unit of the target sensor, and stores the calibration parameters and the aging correction functions back to the target sensor. The storage unit can be provided with two or more storage areas so as to respectively store the measurement parameters of the target sensor and the measurement parameters of the sample sensor in the measurement process, and respectively store the measurement parameters and the measurement parameters of the sample sensor in different storage areas of the storage unit of the target sensor after calculating the calibration coefficient and the aging correction function, so that the subsequent reading and distinguishing use are convenient.
S5, recording the current running time parameter of the target sensort；
In the scheme of the application, the current running time parameter can be recorded in real timeThe method can also adopt a mode of recording and updating in a preset time period. The memory unit of the target sensor further comprises a dynamic parameter storage area for recording and updating the current running time parameter of the target sensortNamely, the storage unit of the target sensor stores not only the calibration parameters and the aging correction function, but also the dynamic parameters, and when the sensor is calibrated, the dynamic parameters and the static parameters are extracted from the storage unit for calibration, so that the accuracy of the sensor output is improved.
And S6, automatically calibrating the target sensor according to the running time parameter, the calibration coefficient and the aging correction function.
In one embodiment, a selection is maden=3, i.e. three test stations are selected, which are set at stable environmental parameters respectivelyT 1 、T 2 、T 3 The method comprises the following steps of obtaining a calibration coefficient and performing calibration processing on a target sensor:
calculating calibration coefficients of a target sensora、b、c：；
The calibration coefficient is seta、b、cAging correction functionA storage unit that stores the data to the target sensor;
recording the current running time parameter of the target sensort；
Automatically calibrating the target sensor according to the runtime parameters, calibration coefficients and aging correction functions:；
wherein,,T 1 、T 2 、T 3 the stable environment values set by the three test tables are respectively, and the stable environment temperatures set by the three temperature measuring tables are used for the temperature sensor;V measuring For the current measured voltage value of the target sensor,Toutput values for the target parameters after calibration of the target sensor,fand measuring a conversion mapping relation between the voltage value and the target parameter for the target sensor. According to the sensor, the external target parameters (such as a temperature value for a temperature sensor, a photosensitive value for a photosensitive sensor, an infrared parameter for an infrared sensor and the like) are sensed, then the sensed values are converted into voltage values through a preset circuit, the voltage values are obtained through measurement and converted into digital signals, the voltage digital signal values are obtained and reversely converted into corresponding target parameters (such as the temperature values, the photosensitive values and the infrared parameters) through corresponding conversion mapping relations, and the purpose of sensing target measurement parameters is achieved. In general, different sensors have different conversion mapping relationships between target parameters and measured voltage values, some sensors are mapped by adopting a mapping table, some sensors are mapped by adopting a function mapping manner, and the mapping manners can be implemented in the scheme of the application, and the scheme of the application is not particularly limited.
It can be understood that the calibration is performed by using a nonlinear fitting calibration method, and in this embodiment, 3 test stations are used for testing, and of course, those skilled in the art can understand that more test stations can be used for testing data to perform higher-order calibration under the concept of the application.
In another embodiment, the scheme of the application is selectedn=2, i.e. two test stations are selected, which are respectively set at stable environmental parametersT 1 、T 2 The method comprises the following steps of obtaining a calibration coefficient and performing calibration processing on a target sensor:
calculating a target sensor calibration coefficient:
the calibration coefficient is setl、eAging correction functionA storage unit that stores the data to the target sensor;
recording the current running time parameter of the target sensort；
Automatically calibrating the target sensor according to the runtime parameters, calibration coefficients and aging correction functions:；
wherein,,T 1 、T 2 the stable environment values set for the two test tables are respectively,V measuring For the current measured voltage value of the target sensor,Toutput values for the target parameters after calibration of the target sensor,fand measuring a conversion mapping relation between the voltage value and the target parameter for the sensor.
According to the embodiment of the application, the aging characteristic of the target sensor is predicted through simulation, the aging characteristic of the target sensor and the calibration coefficient are stored into the storage unit of the target sensor, meanwhile, the dynamic parameter of the running time of the current sensor is recorded and updated, the target sensor is calibrated according to the calibration coefficient, the aging characteristic relation and the time parameter, the influence of the aging physical characteristic can be removed according to the running time of the sensor during calibration, the deviation caused by running aging is compensated in time, and the sensor still has accurate and stable output under the long-time working condition through the calibration scheme, so that the accuracy and the practicability of the sensor calibration are greatly improved.
In one embodiment, as shown in fig. 3, the present application provides a self-calibrating digital sensor that includes one or more processing units, a memory unit. In particular, the digital sensor chip integrates the MEMS sensor chip and the memory chip, and more preferably, the digital sensor chip integrates the MEMS sensitive chip, the signal processing chip or the MCU and the memory chip, wherein the PGA, the ADC, the calibration module, the digital communication module, the memory module and the logic control module can be partially or completely integrated in the MCU. The storage unit stores the running time parameter, the calibration coefficient, the aging correction function program and the corresponding calibration parameters, so that the sensor realizes the automatic calibration method.
In one embodiment, the present application provides a sensor self-calibration processing system that includes a plurality of test stations and self-calibration sensors such that the system implements the steps of the auto-calibration method described above.
In one embodiment, the present application provides a computer storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the auto-calibration method described above.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage units, databases, or other media used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile 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 (Synchl)ink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (9)
1. A method of automatically calibrating a sensor, the method comprising:
acquiring a sample sensor of a target sensor, and performing an aging test on the sample sensor;
pre-calibrating the target sensor and the aged sample sensor;
acquiring a calibration coefficient and an aging correction function of a target sensor, and storing the calibration coefficient and the aging correction function into a storage unit of the target sensor;
recording the current running time parameter of the target sensor;
automatically calibrating the target sensor according to the running time parameter, the calibration coefficient and the aging correction function;
the method further comprises the steps of:
calculating aging timetjMeasuring the relative change value of the parameter:
；
wherein,,V i indicating that the target sensor is at the firstiThe test bench tests the obtained measurement parameters,U kij represent the firstjPost group agingkThe sample sensor is at the firstiThe test bench tests the obtained measurement parameters,mfor each set of number of aged sample sensors,nthe number of test stations;
constructing each coordinate pointt j, ▽t j ) Drawing a relative change trend curve according to each coordinate point, inputting the relative change trend curve into a preset curve classification model to conduct classification prediction of the trend change curve, obtaining a predicted curve type, selecting a corresponding function fitting according to the predicted curve type, and obtaining an aging correction function。
2. The method according to claim 1, wherein the method further comprises:
selecting a preset number of sample sensors;
grouping the sample sensors;
performing different-degree aging tests on the grouped sample sensors, and simulating aging conditions of the sensors in different time periods;
and placing the target sensor and the aged sample sensor into a test bench for pre-calibration test.
3. The method of claim 2, wherein the test stations comprise a plurality of test stations, each test station being disposed at a different stable environmental parameterT i Working downwards;
the target sensor and each group of aging sample sensors enter each test bench to be tested to obtain the measurement parameters of the target sensorV i Measurement parameters of each aging sample sensorU kij ；
According to the corresponding measurement parameters of the target sensorV i Measurement parameters of each aging sample sensorU kij And obtaining the aging correction function of the target sensor.
4. A method according to claim 3, wherein, in accordance with the target sensor corresponding measurement parameterV i Measurement parameters of each aging sample sensorU kij Obtaining the target sensor aging correction function, including:
obtaining measured parameters of each aged sample sensorU kij Corresponding measurement parameters relative to the target sensorV i Is a relative change trend curve of (2);
inputting the relative change trend curve into a preset curve classification model to conduct classification prediction of the trend change curve so as to obtain a predicted curve type;
based on measured parameters of each aged sample sensorU kij Corresponding measurement parameters relative to the target sensorV i Fitting to obtain the aging correction function of the target sensor.
5. The method of claim 1, wherein the step of determining the position of the substrate comprises,n=3, the method comprising:
calculating calibration coefficients of a target sensora、b、c：
；
The calibration coefficient is seta、b、cAging correction functionA storage unit that stores the data to the target sensor;
recording the current running time parameter of the target sensort；
Automatically calibrating the target sensor according to the runtime parameters, calibration coefficients and aging correction functions:；
wherein,,T 1 、T 2 、T 3 stable environment values respectively set for the three test tables,V measuring For the current measured voltage value of the target sensor,Toutput values for the target parameters after calibration of the target sensor,fmeasuring voltage for target sensorConversion mapping relation between the value and the target parameter.
6. The method according to claim 1, wherein n = 2, the method comprising:
calculating calibration coefficients of a target sensorl、e：
；
The calibration coefficient is setl、eAging correction functionA storage unit that stores the data to the target sensor;
recording the current running time parameter of the target sensort；
Automatically calibrating the target sensor according to the runtime parameters, calibration coefficients and aging correction functions:
；
wherein,,T 1 、T 2 the stable environment values set for the two test tables are respectively,V measuring For the current measured voltage value of the target sensor,Toutput values for the target parameters after calibration of the target sensor,fand measuring a conversion mapping relation between the voltage value and the target parameter for the target sensor.
7. A self-calibrating sensor comprising one or more processing units, a memory unit storing runtime parameters, calibration coefficients, aging correction functions, such that the self-calibrating sensor implements the method of any of claims 1-6.
8. A sensor self-calibration processing system comprising a plurality of test stations and a self-calibration sensor such that the system performs the steps of the method of any of claims 1-6.
9. A computer storage medium storing a computer program which, when executed by a processor, causes the processor to perform the method of any one of claims 1-6.
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