High-precision measurement system and calibration method

Abstract

To provide a high-precision measurement system and a calibration method.SOLUTION: A high-precision measurement system includes: a data collection circuit for acquiring a plurality of pieces of first output data corresponding to a plurality of pieces of first setting data; a machine learning circuit which is used for creating a machine learning model on the basis of the plurality of pieces of first setting data, the plurality of pieces of first output data, and a plurality of first calibration parameters between the plurality of pieces of first setting data and the plurality of pieces of first output data, and which is used for generating a second calibration parameter corresponding to second setting data on the basis of the machine learning model; and an output circuit for calibrating second output data corresponding to second input data to generate calibrated output data on the basis of the second calibration parameter.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a high-precision measurement system and a calibration method, and particularly to a high-precision measurement system and a calibration method calibrated by a machine learning model.

Background Art

[0002] Before shipping, an electronic device or an electronic component needs to be electrically measured using an electronic measurement system. That is, the electronic measurement system provides the electrical energy required for the electronic device or the electronic component, and verifies whether the function of the electronic device or the electronic component is normal based on the feedback signal of the electronic device or the electronic component.

[0003] However, due to factors such as the hardware circuit, environmental temperature, and environmental humidity, there is an error between the actual output electrical energy of the electronic measurement system and the set output electrical energy. Therefore, it is necessary to calibrate the actual output electrical energy of the electronic measurement system by measuring the feedback signal so that the actual output electrical energy of the electronic measurement system matches the set output electrical energy.

[0004] As a conventional calibration form, when there is a linear relationship between the actual output electrical energy and the set output electrical energy, the actual output electrical energy can be calibrated using the point-slope method. However, in reality, since the actual output electrical energy and the set output electrical energy are not in a linear relationship, the optimal calibration cannot be achieved by using the point-slope method.

Summary of the Invention

Means for Solving the Problems

[0005] The content of the invention aims to provide a simplified summary of the present disclosure so that readers can basically understand the present disclosure. The content of this invention is not a complete summary of the present disclosure, nor is it intended to identify the important / key elements of the embodiments of the present invention or limit the scope of the present invention.

[0006] One technical aspect of the content of the present invention is a data acquisition circuit for acquiring a plurality of first output data corresponding to a plurality of first setting data, a machine learning circuit coupled to the data acquisition circuit and used to create a machine learning model based on the plurality of first setting data, the plurality of first output data, and a plurality of first calibration parameters between the plurality of first setting data and the plurality of first output data, and used to generate second calibration parameters corresponding to second setting data based on the machine learning model, and an output circuit coupled to the machine learning circuit for calibrating the second output data corresponding to the second setting data based on the second calibration parameters to generate calibrated output data. A high-precision measurement system is disclosed.

[0007] Another technical aspect of the content of the present invention relates to a calibration method applied to a high-precision measurement system, including the steps of acquiring a plurality of first setting data, a plurality of first output data, and a plurality of first calibration parameters between the plurality of first setting data and the plurality of first output data; creating a machine learning model based on the plurality of first setting data, the plurality of first output data, and the plurality of first calibration parameters; generating second calibration parameters corresponding to second setting data based on the machine learning model; and calibrating the second output data corresponding to the second setting data based on the second calibration parameters to generate calibrated output data.

Brief Description of the Drawings

[0008] To make the above and other objects, features, advantages, and embodiments of the present invention clearer and easier to understand, the description of the accompanying drawings is as follows.

Figure 1

Figure 2

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Figure 4

Mode for Carrying Out the Invention

[0009] To make the description of the present disclosure more detailed and complete, exemplary descriptions of embodiments and specific examples of the present invention are provided below, but this is not the only form for implementing or using the specific examples of the present invention. The embodiments include the features of a plurality of specific examples, and the method steps and their order for constructing and operating these specific examples. However, it is also possible to use other specific examples to achieve the same or equivalent functions and process orders.

[0010] Unless otherwise defined herein, the scientific and technical terms used herein have the same meaning as those understood and commonly used by those skilled in the art. Also, when not conflicting with the context, the singular nouns used herein include the plural forms of these nouns, and the plural nouns used also include the singular forms of these nouns.

[0011] Also, regarding the "coupling" used herein, it may refer to two or more elements being in direct physical or electrical contact with each other, or in indirect physical or electrical contact with each other, or it may refer to two or more elements operating or functioning with each other.

[0012] FIG. 1 is a schematic diagram of a high-precision measurement system 10 according to some embodiments of the present invention. As shown in the figure, the high-precision measurement system 10 includes a data collection circuit 110, a machine learning circuit 130, and an output circuit 150. In terms of the connection relationship, the machine learning circuit 130 is coupled to the data collection circuit 110, and the output circuit 150 is coupled to the machine learning circuit 130. Hereinafter, with reference to FIG. 2, the operation method of the high-precision measurement system 10 will be described.

[0013] It should be noted that the embodiments of the present invention are not limited to the structure and operation shown in FIG. 1. This is merely an example for illustratively explaining one of the implementation forms of the present invention in order to facilitate the understanding of the technology of the present invention. The patent scope of the present invention shall be based on the scope of the invention patent application. Modifications and changes made by those skilled in the art to the embodiments of the present invention without departing from the spirit of the present invention are still included within the scope of the invention patent application of the present invention.

[0014] FIG. 2 is a flowchart of a calibration method 200 according to some embodiments of the present invention. To facilitate the understanding of the calibration method 200, FIG. 1 is also referred to, and the detailed process of the calibration method 200 will be described later.

[0015] In step S210, a plurality of first setting data, a plurality of first output data, and a plurality of first calibration parameters between the plurality of first setting data and the plurality of first output data are acquired. In some embodiments, step S210 is executed by the machine learning circuit 130 in FIG. 1.

[0016] Refer to FIG. 1. In one embodiment, the output circuit 150 of the high-precision measurement system 10 generates an output voltage VT based on the first set of data and outputs it to the device under measurement 900. For example, in one embodiment, the user operates an operation interface (not shown) of the high-precision measurement system 10 to set the first set of data to 10 volts, expecting the output circuit 150 of the high-precision measurement system 10 to output an output voltage VT of 10 volts to the device under measurement 900. However, the actual output voltage VT output by the actual output circuit 150 may be different from the set data of 10 volts due to factors such as internal circuits or environmental temperature and humidity, that is, the actual output voltage may not be 10 volts. The output voltage VT is output data generated and output corresponding to the set data.

[0017] For example, in one embodiment, the output voltage VT generated and output by the output circuit 150 based on the set data of 10 volts is 9.7 volts. The output voltage VT generated and output by the output circuit 150 based on the set data of 20 volts is 20.3 volts. The data collection circuit 110 collects the above set data and the corresponding output data (output voltage VT).

[0018] In some embodiments, after the high-precision measurement system 10 generates and outputs a plurality of output voltages VT with different voltage values based on a plurality of different first set of data, the voltage values of the output voltages VT obtained by the data collection circuit 110 are obtained by an external circuit (not shown) and then fed back to the data collection circuit 110 via the external circuit.

[0019] Subsequently, after the machine learning circuit 130 obtains the plurality of different first set of data and the corresponding plurality of first output data, it calculates a plurality of first calibration parameters between the set data of the plurality of different voltage values and the corresponding plurality of first output data using the point-slope method calibration form. In some embodiments, the plurality of first calibration parameters include a plurality of gain values and a plurality of offset values.

[0020] In some embodiments, after calibrating the calibration parameters, the generated calibrated output data is the same as the set data. That is, when the set data is 10 volts, the calibrated output data is also 10 volts.

[0021] In some embodiments, the calculation formula for the point-slope method calibration is the following formula (1).

[0022] Formula (1): Y = AX + B.

[0023] In the above formula (1), Y is the calibrated output data, X is the first output data, A is the gain value, and B is the offset value. The output data can be calibrated to the calibrated output data by the point-slope method calibration.

[0024] The above formula (1) is only for illustrative purposes. In some other embodiments, the first calibration parameter may include a plurality of calibration formulas, a plurality of gain values, and a plurality of offset values.

[0025] In step S230, a machine learning model is created based on a plurality of first set data, a plurality of first output data, and a plurality of first calibration parameters. In some embodiments, step S230 is executed by the machine learning circuit 130 in FIG. 1.

[0026] In some embodiments, the machine learning model includes a look-up table stored in a memory (not shown).

[0027] In one embodiment, the first calibration parameters (including the gain value and the offset value) and the calibrated output data calculated by the machine learning circuit 130 based on a plurality of different first set data and the corresponding first output data are shown in the following look-up table 1.

[0028] Look-up table 1 TIFF2025102679000002.tif51157

[0029] The first output data in the lookup table 1 is a plurality of first setting data acquired by the data acquisition circuit 110 of the high-precision measurement system 10 and the first output data corresponding to the first setting data. As shown in the lookup table 1, after the gain value and the offset value are calibrated based on the above formula (1), by calibrating the first output data to the calibrated output data, the calibrated output data becomes the same as the first setting data.

[0030] In some embodiments, since the error between the first setting data and the first output data is non-linear, the first calibration parameters corresponding to different first setting data and first output data are also different.

[0031] In another embodiment, the lookup table 1 is the first setting data, the first output data, and the first calibration parameters corresponding thereto acquired when the output current of the high-precision measurement system 10 is a first current value (for example, 5 amperes). In another embodiment, the machine learning circuit 130 can create another lookup table based on the first setting data, the first output data, and the first calibration parameters corresponding thereto acquired when the output current of the high-precision measurement system 10 is a second current value (for example, 10 amperes). That is, the setting data and the corresponding output data may be voltage values / current values / power values, etc. The lookup table 1 is described by taking only voltage values as an example.

[0032] In some embodiments, when creating a machine learning model, the machine learning circuit 130 further generates a lookup table based on a plurality of environmental data acquired by the data acquisition circuit 110, and the first calibration parameters (including gain values and offset values) calculated based on the plurality of different first setting data and the first output data corresponding thereto and the environmental data, and is used to create a machine learning model.

[0033] For example, assume that the above lookup table 1 is created at an environmental temperature of 27°C and an environmental humidity of 70%. At another environmental temperature and environmental humidity, the machine learning circuit 130 calculates calibration parameters based on the first set data obtained at yet another environmental temperature and environmental humidity and the corresponding first output data, and creates another lookup table.

[0034] In this way, by creating a plurality of lookup tables, the machine learning circuit 130 creates a machine learning model. Also, the lookup table 1 as described above is a lookup table for the output voltage VT. In some other embodiments, the lookup table created by the machine learning circuit 130 may be a lookup table created based on the set data and output data of the output current of the high-precision measurement system 10, or may be based on the set data and output data of the output current and output voltage of the high-precision measurement system 10 at the same time, or may be a lookup table created based on the set data and output data of the input current of the high-precision measurement system 10, or may be a lookup table created based on the set data and output data of the input voltage of the high-precision measurement system 10, or may be a lookup table created based on the set data and output data of the input current and input voltage of the high-precision measurement system 10. The embodiments of the present invention are not limited to the above.

[0035] In step S250, a second calibration parameter corresponding to the second set data is generated based on the machine learning model. In some embodiments, step S250 is executed by the machine learning circuit 130 in FIG. 1.

[0036] In some embodiments, in step S250, after creating the machine learning model, the machine learning circuit 130 inputs the second set data into the machine learning model and obtains the second calibration parameter corresponding to the second set data. In some embodiments, the second set data is data set by the user via an operation interface (not shown) and input into the high-precision measurement system 10.

[0037] In some embodiments, the machine learning circuit 130 obtains at least two of the first calibration parameters from the look-up table of the machine learning model based on the second set data, and obtains the second calibration parameter by an interpolation method.

[0038] In one embodiment, the machine learning circuit 130 obtains at least two pieces of first set data that are closest to the second set data from the look-up table of the machine learning model based on the second set data, and obtains the second calibration parameter in the form of an interpolation method based on the first calibration parameters corresponding to the at least two pieces of first set data.

[0039] For example, refer to Look-up Table 1 together. In step S250, when the second set data is 15 volts, the machine learning circuit 130 obtains that the first set data closest to the second set data is 10 volts and 20 volts. Subsequently, the machine learning circuit 130 knows that the gain value corresponding to the second set data of 15 volts in the form of an interpolation method is TIFF2025102679000003.tif1011, and the offset value corresponding to the second set data of 15 volts is TIFF2025102679000004.tif911.

[0040] Furthermore, for example, refer to FIG. 3 together. FIG. 3 is a schematic diagram of a calibration method according to some embodiments of the present invention. G(I1, V2) in FIG. 3 is the gain value when the current value in the first set data is I1 and the voltage value is V2, G(I2, V2) is the gain value when the current value in the first set data is I2 and the voltage value is V2, G(I1, V1) is the gain value when the current value in the first set data is I1 and the voltage value is V1, and G(I2, V1) is the gain value when the current value in the first set data is I2 and the voltage value is V1.

[0041] The gain values G(I1, V2), G(I2, V2), G(I1, V1), and G(I2, V1) are gain values obtained by the machine learning circuit 130 based on a look-up table in the machine learning model, and the voltage values and current values in the gain values G(I1, V2), G(I2, V2), G(I1, V1), and G(I2, V1), and the voltage value V* and current value I* in the gain value G(I*, V*) to be calculated are at least the two closest ones in the look-up table. That is, the current values I1 and I2 are the current values closest to the current value I* in the look-up table, and the voltage values V1 and V2 are the voltage values closest to the voltage value V* in the look-up table.

[0042] Subsequently, the machine learning circuit 130 determines the gain value of the second setting data (i.e., the current value I* and the voltage value V*) based on the gain values G(I1, V2), G(I2, V2), G(I1, V1), and G(I2, V1) and the following formulas (2) to (5). Obtain TIFF2025102679000005.tif716.

[0043] Formula (2): TIFF2025102679000006.tif840

[0044] Formula (3): TIFF2025102679000007.tif1083

[0045] Formula (4): TIFF2025102679000008.tif982

[0046] Formula (5): TIFF2025102679000009.tif966

[0047] In step S270, the second output data corresponding to the second setting data is calibrated based on the second calibration parameter to generate calibrated output data. In some embodiments, step S270 is executed by the output circuit 150 in FIG. 1.

[0048] In some embodiments, after the machine learning circuit 130 obtains the second calibration parameter corresponding to the second set of data, the output circuit 150 calibrates the second output data based on the second calibration parameter to generate calibrated output data.

[0049] In some embodiments, the machine learning circuit 130 calibrates the second output data using the point-slope formula (e.g., the above formula (1)).

[0050] For example, if the gain value corresponding to the second set of data of 15 volts obtained by the machine learning circuit 130 in step S250 is TIFF2025102679000010.tif911 and the offset value corresponding to the second set of data of 15 volts is TIFF2025102679000011.tif911, the machine learning circuit 130 calculates the calibrated output data based on formula (1) as follows. TIFF2025102679000012.tif1147

[0051] Y2 is the calibrated output data, and X2 is the second output data generated by the output circuit 150 corresponding to the second set of data of 15 volts without calibration. TIFF2025102679000013.tif911 is the gain value, and TIFF2025102679000014.tif910 is the offset value. By point-slope calibration, the second output data can be calibrated so that the calibrated output data is the same as the set data.

[0052] In some embodiments, the output circuit 150 calibrates the second output data in PWM (pulse width modulation) form based on the second calibration parameter to generate calibrated output data. Various forms of calibrating the second output data are all within the embodiments of the present invention, and the embodiments of the present invention are not limited to PWM.

[0053] It should be noted that the present invention is not limited to the process shown in FIG. 2. This is merely for exemplarily explaining one of the implementation forms of the present invention to facilitate understanding of the technology of the present invention. The patent scope of the present invention shall be based on the scope of the invention patent application. Modifications and changes made by those skilled in the art to the embodiments of the present invention without departing from the spirit of the present invention are still included within the scope of the invention patent application of the present invention.

[0054] Refer also to FIG. 4. FIG. 4 is a schematic diagram of a machine learning circuit 130 according to some embodiments of the present invention. As shown in FIG. 4, the machine learning circuit 130 includes an analog-to-digital conversion circuit 131, a look-up table processing circuit 135, and a calibration circuit 137. The look-up table processing circuit 135 is coupled to the analog-to-digital conversion circuit 131, and the calibration circuit 137 is coupled to the look-up table processing circuit 135.

[0055] In some embodiments, the analog-to-digital conversion circuit 131 is used to convert the output data acquired by the data collection circuit 110 from analog data to digital data. The look-up table processing circuit 135 is used to input the output data converted into digital data into a machine learning model to obtain calibration parameters. In some embodiments, the look-up table processing circuit 135 performs interpolation calculation by the moving average method to obtain calibration parameters.

[0056] The calibration circuit 137 is used to generate a PWM control signal based on the calibration parameters and transfer the PWM control signal to the output circuit 150 shown in FIG. 1 so that the output circuit 150 generates a calibrated output signal based on the PWM control signal.

[0057] In some embodiments, the high-precision measurement system 10 further includes a memory (not shown) for storing a machine learning model and a look-up table for access by the machine learning circuit 130.

[0058] As can be seen from the embodiments of the present invention, applying the present invention has the following advantages. The high-precision measurement system and calibration method shown in the embodiments of the present invention calculate calibration parameters according to changes in different environmental variables and circuit variables in the form of a machine learning model, and calibrate the output data to the calibrated output data, so that the data actually output by the high-precision measurement system becomes the same as the set data. In addition, the embodiments of the present invention can continuously learn, update, and correct calibration parameters in the form of a machine learning model. The above embodiments can quickly obtain calibration parameters and effectively reduce calibration errors.

[0059] In some embodiments, a machine learning circuit 130 as described above may be integrated into an electronic device or an electronic system. In some embodiments, the server may be implemented as a cloud server including a central processing unit. In some embodiments, the server may be a single processor or an assembly of multiple microprocessors, but is not limited thereto, and includes a memory and input / output circuits.

[0060] In some embodiments, the machine learning circuit 130 may be a central processor unit (CPU), a microcontroller unit (MCU), a digital signal processor (DSP), a field programmable gate array (FPGA), a server, or other arithmetic circuits, processing circuits, or elements having functions such as data access, data calculation, data storage, data transmission and reception, or the like.

[0061] In some embodiments, the machine learning circuit 130 includes a processor and input / output circuits. The processor may be a circuit or element having functions such as data access, data calculation, data storage, or the like. The input / output circuit may be a circuit or element having functions such as data transmission and reception or the like.

[0062] In some embodiments, the data collection circuit 110 may be a current detection circuit, a voltage detection circuit, a temperature detection circuit, a humidity detection circuit, or other circuits or elements having data detection, data transmission / reception, or similar functions. In some embodiments, the data collection circuit 110 obtains input data and environmental data via an external current detection circuit, voltage detection circuit, temperature detection circuit, humidity detection circuit, or other circuits or elements having similar functions.

[0063] In some embodiments, the high-precision measurement system 10 further includes a display circuit (not shown) for displaying setting data, output data, calibrated output data, and the like.

[0064] In some embodiments, the output circuit 150 may be a PWM drive circuit, or other circuits or elements having current output / voltage output / power output, or other circuits or elements having the same or similar functions.

[0065] Specific embodiments of the present invention are disclosed in the above embodiments, but are not intended to limit the present invention. Those skilled in the art can make various changes and modifications without departing from the principles and spirit of the present invention. Therefore, the protection scope of the present invention should be based on that defined by the appended patent application scope.

Description of Reference Numerals

[0066] 10: High-precision measurement system 110: Data collection circuit 130: Machine learning circuit 150: Output circuit VT: Output voltage 900: Device under measurement 200: Calibration method S210, S230, S250, S270: Steps G(I1, V2), G(I2, V2), G(I1, V1), G(I2, V1): Gain values G(I*, V*), G1, G2: Gain values I1, I2, I3, I4, I*: Current values V1, V2, V3, V4, V*: Voltage values 131: Analog-to-digital conversion circuit 135: Look-up table processing circuit 137: Calibration circuit

Claims

1. A data collection circuit for obtaining a plurality of first output data corresponding to a plurality of first setting data, coupled to the data collection circuit, used to create a machine learning model based on the plurality of first setting data, the plurality of first output data, and a plurality of first calibration parameters between the plurality of first setting data and the plurality of first output data, and used to generate a second calibration parameter corresponding to the second setting data based on the machine learning model A machine learning circuit; An output circuit coupled to the machine learning circuit for calibrating the second output data corresponding to the second setting data based on the second calibration parameter to generate calibrated output data; A high-precision measurement system comprising.

2. The high-precision measurement system according to claim 1, wherein the plurality of first calibration parameters include a plurality of gain values and a plurality of offset values.

3. The high-precision measurement system according to claim 1, wherein the machine learning model further includes a look-up table created based on the plurality of first setting data, the plurality of first output data, and the plurality of first calibration parameters.

4. The machine learning circuit further obtains at least two of the plurality of first calibration parameters from the look-up table based on the second setting data, and is used to obtain the second calibration parameter by an interpolation method. The high-precision measurement system according to claim 3.

5. The data collection circuit is further used to obtain a plurality of environmental data, and the machine learning circuit is further used to create the machine learning model based on the plurality of first setting data, the plurality of first output data, the plurality of first calibration parameters, and the plurality of environmental data. The high-precision measurement system according to claim 1.

6. A calibration method applied to a high-precision measurement system, comprising: Obtaining a plurality of first setting data, a plurality of first output data, and a plurality of first calibration parameters between the plurality of first setting data and the plurality of first output data; Creating a machine learning model based on the plurality of first setting data, the plurality of first output data, and the plurality of first calibration parameters; Generating a second calibration parameter corresponding to the second setting data based on the machine learning model; A step of calibrating second output data corresponding to the second setting data based on the second calibration parameter to generate calibrated output data; A calibration method including the above. **Claim 7** The calibration method according to claim 6, wherein the plurality of first calibration parameters include a plurality of gain values and a plurality of offset values. **Claim 8** The calibration method according to claim 6, further including a step of creating a look-up table of the machine learning model based on the plurality of first setting data, the plurality of first output data, and the plurality of first calibration parameters. **Claim 9** The calibration method according to claim 8, further including a step of obtaining at least two of the plurality of first calibration parameters from the look-up table based on the second setting data, and obtaining the second calibration parameter by an interpolation method. **Claim 10** The calibration method according to claim 6, further including a step of obtaining a plurality of environment data and creating the machine learning model based on the plurality of first setting data, the plurality of first output data, the plurality of first calibration parameters, and the plurality of environment data.

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