Data processing device and operating method thereof

Abstract

A battery diagnosis device according to one embodiment disclosed in the present document may comprise: an acquisition unit for acquiring time-series voltage data of a plurality of battery cells; a calculation unit for calculating the voltage deviation between the average voltage of the plurality of battery cells and the voltage of each of the plurality of battery cells on the basis of the time-series voltage data; a generation unit for generating, on the basis of the voltage deviations of the plurality of battery cells, entropy-voltage data indicating a voltage deviation for an entropy value; and a diagnosis unit for diagnosing an anomaly of the plurality of battery cells on the basis of the entropy-voltage data.

Description

Data processing device and its operating method

[0001] Cross-citation with related applications

[0002] This invention claims the benefit of priority to Korean Patent Application No. 10-2023-0117958, filed September 5, 2023, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to a data processing device and a method of operating the same.

[0005] Recently, research and development on secondary batteries has been actively conducted. Here, secondary batteries are rechargeable and include both conventional Ni / Cd and Ni / MH batteries, as well as recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of having a much higher energy density than conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight form, making them suitable for use as power sources for mobile devices. Recently, their use has expanded to include power sources for electric vehicles, attracting attention as a next-generation energy storage medium.

[0006] When processing these secondary battery process data, outlier filtering techniques have been used to remove outliers by exploiting the characteristics of the median. For example, process data are listed in order of size, and data falling outside the range of the lower inner fence and the upper inner fence of a box plot using quartiles are considered outliers and removed.

[0007] However, the existing outlier filtering technique has a problem in that the minimum value inside the lower boundary and the maximum value inside the upper boundary of the box plot are fixed, and normal data may also be removed because it does not reflect the process capability of the battery process equipment.

[0008] Embodiments disclosed in this document can provide a data processing device and an operating method thereof that can determine the range of outlier removal by taking into account the process capability of a battery process facility.

[0009] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the descriptions below.

[0010] A data processing device according to an embodiment disclosed in the present document includes a communication circuit for acquiring process data related to a battery process, and a processor for filtering the process data, wherein the processor may be configured to generate a reference distribution profile following a standard normal distribution based on the process data, calculate a process capability parameter related to a process capability of a process facility involved in the battery process based on the process data, generate a box plot for the battery process based on the reference distribution profile and the process capability parameter, and remove data out of the process data that falls outside a critical section corresponding to the box plot.

[0011] In a data processing device according to an embodiment disclosed in the present document, the box plot includes quartiles, an interquartile range, a minimum limit line, and a maximum limit line, and the processor can set the quartiles and the interquartile range based on the reference distribution profile, and set the minimum limit line and the maximum limit line based on at least some of the quartiles, the interquartile range, and the process capability parameter.

[0012] In a data processing device according to an embodiment disclosed in this document, the critical section may be a section greater than or equal to the minimum limit line and less than or equal to the maximum limit line.

[0013] In a data processing device according to an embodiment disclosed in this document, the processor can calculate a scale value based on the process capability parameter, and set the minimum limit line and the maximum limit line based on the following mathematical expressions 1 and 2.

[0014] [Mathematical Formula 1]

[0015] Minimum cutoff line = Q1-k·IQR

[0016] [Equation 2]

[0017] Minimum cutoff = Q3 + k · IQR

[0018] (In mathematical expressions 1 and 2, Q1 is the first quartile among the quartiles, Q3 is the third quartile among the quartiles, k is the scale value, and IQR is the interquartile range.)

[0019] In a data processing device according to an embodiment disclosed in this document, the processor can calculate the scale value based on the following mathematical expression 3.

[0020] [Equation 3]

[0021] k=-0.5+(n / 1.35)

[0022] (In mathematical expression 3, k is the scale value and n is the process capability parameter.)

[0023] In a data processing device according to an embodiment disclosed in this document, the processor can calculate a difference value between a maximum value and a minimum value among numerical values ​​of a specified process factor based on the process data, calculate a standard deviation value of the numerical values ​​of the process factor based on the process data, and calculate the process capability parameter based on the difference value and the standard deviation value.

[0024] In a data processing device according to an embodiment disclosed in this document, the processor can calculate the process capability parameter based on the following mathematical expression 4.

[0025] [Equation 4]

[0026] n=T / (2·s)

[0027] (In mathematical expression 4, n is the process capability parameter, T is the difference value, and s is the standard deviation value.)

[0028] A data processing method according to an embodiment disclosed in the present document may include an operation of acquiring process data related to a battery process, an operation of generating a reference distribution profile following a standard normal distribution based on the process data, an operation of calculating a process capability parameter related to a process capability of a process facility involved in the battery process based on the process data, an operation of generating a box plot for the battery process based on the reference distribution profile and the process capability parameter, and an operation of removing data out of a critical section corresponding to the box plot among the process data.

[0029] In a data processing method according to an embodiment disclosed in the present document, the box plot may include a quartile, an interquartile range, a minimum limit line, and a maximum limit line, and the operation of generating the box plot may include an operation of setting the quartiles and the interquartile range based on the reference distribution profile, and an operation of setting the minimum limit line and the maximum limit line based on at least some of the quartiles, the interquartile range, and the process capability parameter.

[0030] In a data processing method according to an embodiment disclosed in this document, the critical section may be a section greater than or equal to the minimum limit line and less than or equal to the maximum limit line.

[0031] A data processing method according to an embodiment disclosed in this document further includes an operation of calculating a scale value based on the process capability parameter, and the operation of setting the minimum limit line and the maximum limit line may be based on the above mathematical expressions 1 and 2.

[0032] In a data processing method according to an embodiment disclosed in this document, the operation of calculating the scale value may be based on the above mathematical expression 3.

[0033] In a data processing method according to an embodiment disclosed in this document, the operation of calculating the process capability parameter may include an operation of calculating a difference value between a maximum value and a minimum value among numerical values ​​of a specified process factor based on the process data, an operation of calculating a standard deviation value of the numerical values ​​of the process factor based on the process data, and an operation of calculating the process capability parameter based on the difference value and the standard deviation value.

[0034] In a data processing method according to an embodiment disclosed in this document, the operation of calculating the process capability parameter may be based on the above mathematical expression 4.

[0035] According to the embodiments disclosed in this document, existing conservative outlier filtering techniques can be improved by determining the scope of outlier removal by considering the process capability of the battery process equipment.

[0036] In addition, various effects may be provided, either directly or indirectly, through this document.

[0037] Figure 1 is a schematic diagram illustrating a reference distribution profile and a typical box plot generated based on process data.

[0038] Figure 2 is a block diagram of a data processing device according to one embodiment.

[0039] FIG. 3 is a diagram illustrating an example of a data processing device generating a box plot according to one embodiment.

[0040] Figure 4 is a flowchart of the operation of a data processing device according to one embodiment.

[0041] Hereinafter, various embodiments of the present invention will be described with reference to the attached drawings. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that the present invention encompasses various modifications, equivalents, and / or alternatives of the embodiments.

[0042] The various embodiments and terminology used in this document are not intended to limit the technical features described in this document to specific embodiments, but should be understood to encompass various modifications, equivalents, or alternatives of the embodiments. In connection with the description of the drawings, similar reference numerals may be used to refer to similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise.

[0043] In this document, the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can each include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first", "second", "first", "second", "A", "B", "(a)", or "(b)" may be used merely to distinguish the corresponding element from other corresponding elements, and do not limit the corresponding elements in any other respect (e.g., importance or order) unless specifically stated otherwise.

[0044] In this document, whenever a component (e.g., a first component) is referred to as being “connected,” “coupled,” or “connected,” with or without the terms “functionally” or “communicatively,” or “coupled” or “connected,” it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or via a third component.

[0045] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0046] Figure 1 is a schematic diagram illustrating a reference distribution profile and a typical box plot generated based on process data.

[0047] Referring to Figure 1, a reference distribution profile generated based on process data related to a battery process and a general box plot generated based on the reference distribution profile can be confirmed.

[0048] Here, process data may refer to data related to process parameters (e.g., temperature, output, etc.) obtained from battery process equipment and / or inspection data (e.g., dimensional data, thickness data, etc.) obtained from an automatic inspection device installed in the battery process equipment. The reference distribution profile may be a profile representing a continuous probability distribution of process data for a specified process parameter. Additionally, the reference distribution profile may be a profile following a standard normal distribution.

[0049] Box plots can include quartiles (Q1, Q2, Q3), interquartile range (IQR), minimum within lower bound (Q1-1.5*IQR), and maximum within upper bound (Q3+1.5*IQR).

[0050] Box plots can be generated based on the median of the process data. That is, the second quantile (Q2) can be set based on the median of the process data.

[0051] Quartiles (Q1, Q2, Q3) are used to divide the standard distribution profile into four parts, and can mean the variance values ​​of 1 / 4, 1 / 2, and 3 / 4 places, respectively. Specifically, in the standard distribution profile, data equal to 1 / 4 of the total process data may be distributed in the range below the first quantile (Q1), the range from the first quantile (Q1) to the second quantile (Q2), the range from the second quantile (Q2) to the third quantile (Q3), and the range above the third quantile (Q3).

[0052] For example, when quartiles (Q1, Q2, Q3) are expressed as a probability distribution function F(x), they can be expressed as F(Q1) = 0.25, F(Q2) = 0.5, F(Q3) = 0.75. Accordingly, the first quartile (Q1) can be -0.6745σ, the second quartile (Q2) can be 0, and the third quartile (Q3) can be 0.6745σ (where σ is the standard deviation of the process data).

[0053] The interquartile range (IQR) can be defined as the range between the first quartile (Q1) and the third quartile (Q3). Accordingly, the IQR can be 1.349σ.

[0054] According to the general definition of a box plot, the inner minimum of the lower boundary can be defined as "the first quantile (Q1) - (1.5 * interquartile range (IQR))". Accordingly, the inner minimum of the lower boundary can be -2.698σ. Furthermore, the inner maximum of the upper boundary can be defined as "the third quantile (Q3) + (1.5 * interquartile range (IQR))". Accordingly, the inner maximum of the upper boundary can be 2.698σ.

[0055] According to conventional outlier removal methods, any data falling outside the range between the minimum value within the lower boundary and the maximum value within the upper boundary of a typical box plot in the reference distribution profile of process data is considered an outlier and removed. However, this method has the disadvantage of not reflecting the process capability of the battery processing equipment, which can lead to the removal of normal data as well.

[0056] Accordingly, when removing outliers from process data, there is a need for a technology that can flexibly set the range of outlier removal by considering the process capability of the process equipment.

[0057] Hereinafter, a data processing device according to an embodiment of the present disclosure will be described with reference to FIGS. 2 and 3.

[0058] Fig. 2 is a block diagram of a data processing device according to one embodiment. Fig. 3 is a diagram illustrating an example of a data processing device generating a box plot according to one embodiment.

[0059] Referring to FIG. 2, the data processing device (100) may include a communication circuit (110), a memory (120), and a processor (130). Depending on the embodiment, the data processing device (100) illustrated in FIG. 2 may further include at least one component (e.g., a display, an input device, or an output device) other than the components illustrated in FIG. 2.

[0060] According to one embodiment, the communication circuit (110) can establish a communication channel between the data processing device (100) and an external electronic device (e.g., a battery process facility) and transmit and receive data with the external electronic device through the established communication channel. According to one embodiment, the communication channel can be established based on a wired network and / or a wireless network. In one embodiment, the wired network can be based on a local area network (LAN) communication or a power line communication. In one embodiment, the wireless network can be based on a short-range communication network (e.g., Bluetooth, wireless fidelity (WiFi), or infrared data association (IrDA)) or a long-range communication network (cellular network, 4G network, 5G network).

[0061] According to one embodiment, the communication circuit (110) may acquire process data related to a battery process. Here, the process data may refer to data related to process factors (e.g., temperature, output, etc.) acquired from a battery process facility and / or inspection data (e.g., dimensional data, thickness data, etc.) acquired from an automatic inspection device installed in the battery process facility.

[0062] According to one embodiment, the memory (120) may include volatile memory and / or non-volatile memory.

[0063] According to one embodiment, the memory (120) may store data used by at least one component (e.g., processor (130)) of the data processing device (100). For example, the data may include software (or instructions related thereto), input data, or output data. In one embodiment, the instructions, when executed by the processor (130), may cause the data processing device (100) to perform operations defined by the instructions.

[0064] According to one embodiment, the processor (130) may include a central processing unit, an application processor, a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor.

[0065] According to one embodiment, the processor (130) may execute software stored in the memory (120) to control at least one other component (e.g., hardware or software component) of the data processing device (100) connected to the processor (130) and perform various data processing or operations.

[0066] According to one embodiment, the processor (130) can filter process data obtained by the communication circuit (110).

[0067] According to one embodiment, the processor (130) may generate a reference distribution profile following a standard normal distribution based on process data. Here, the reference distribution profile may be a profile representing a continuous probability distribution of process data for a specified process factor.

[0068] According to one embodiment, the processor (130) can calculate process capability parameters related to process capability of process equipment involved in the battery process based on process data.

[0069] According to one embodiment, the processor (130) may calculate a difference value between the maximum and minimum values ​​of the specified process factors based on process data. For example, if the process data represents a temperature detected by a battery process facility, the processor (130) may calculate a difference value between the maximum and minimum temperature values ​​among the detected temperature values.

[0070] According to one embodiment, the processor (130) may calculate a standard deviation value of the numerical values ​​of a specified process factor based on process data. For example, if the process data is data indicating a temperature detected by a battery process equipment, the processor (130) may calculate a standard deviation value of the detected temperature values.

[0071] According to one embodiment, the processor (130) can calculate the process capability parameter based on the following mathematical expression 1.

[0072] [Mathematical Formula 1]

[0073] n=T / (2·s)

[0074] In the above mathematical expression 1, n is the process capability parameter, T is the difference value between the maximum and minimum values ​​among the numerical values ​​of the specified process factor, and s is the standard deviation value of the numerical values ​​of the specified process factor.

[0075] In one embodiment, the processor (130) may generate a box plot for a battery process based on a reference distribution profile and process capability parameters. Here, the box plot may include quartiles, an interquartile range, a minimum limit line, and a maximum limit line.

[0076] According to one embodiment, the processor (130) may set the quartiles and the interquartile range based on the reference distribution profile. The processor (130) may set the variate values ​​at positions that divide the data of the reference distribution profile into four equal parts as quartiles. For example, the processor (130) may set the median of the reference distribution profile as the second quartile, the variate value at the position where the probability distribution function F(x) becomes 0.25 as the first quartile, and the variate value at the position where F(x) becomes 0.75 as the third quartile. The processor (130) may set the range between the first quartile and the third quartile as the interquartile range.

[0077] According to one embodiment, the processor (130) can set the minimum limit line and the maximum limit line based on at least some of the quartiles, the quartile range, and the process capability parameters.

[0078] According to one embodiment, the processor (130) can set the minimum limit line and the maximum limit line based on the following mathematical expressions 2 and 3.

[0079] [Equation 2]

[0080] Minimum cutoff line = Q1-k·IQR

[0081] [Equation 3]

[0082] Minimum cutoff = Q3 + k · IQR

[0083] In the above mathematical expressions 2 and 3, Q1 is the first quartile among the quartiles, Q3 is the third quartile among the quartiles, k is the scale value, and IQR is the interquartile range. Here, the scale value is related to the process capability parameter, and the method of calculating the scale value can be described below.

[0084] According to one embodiment, the processor (130) may calculate a scale value based on a process capability parameter. For example, the processor (130) may calculate the scale value based on the following mathematical expression 4.

[0085] [Equation 4]

[0086] k=-0.5+(n / 1.35)

[0087] In the above mathematical expression 4, k is the scale value, and n is the process capability parameter.

[0088] According to one embodiment, the processor (130) may remove data that falls outside a critical range corresponding to the box plot among the process data. The critical range may mean, for example, a range greater than or equal to the minimum limit line and less than or equal to the maximum limit line.

[0089] In this way, the data processing device (100) can flexibly set the range of outlier removal by reflecting the process capability of the process equipment by newly setting the minimum limit line and maximum limit line based on the process capability parameters of the battery process equipment.

[0090] Fig. 4 is a flowchart illustrating the operation of a data processing device according to an embodiment. Fig. 4 may be an explanation of the operation of the data processing device (100) of Fig. 1, and may be explained using the configuration of Fig. 1.

[0091] The embodiment illustrated in FIG. 4 is only one embodiment, and the order of steps according to various embodiments of the present invention may be different from that illustrated in FIG. 4, and some of the steps illustrated in FIG. 4 may be omitted, the order between steps may be changed, or steps may be merged.

[0092] Referring to FIG. 4, in operation 405, the data processing device (100) may obtain process data related to a battery process. Here, the process data may refer to data related to process factors (e.g., temperature, output, etc.) obtained from a battery process facility and / or inspection data (e.g., dimensional data, thickness data, etc.) obtained from an automatic inspection device installed in the battery process facility.

[0093] In operation 410, the data processing device (100) may generate a reference distribution profile following a standard normal distribution based on the process data acquired in operation 405. Here, the reference distribution profile may be a profile representing a continuous probability distribution of process data for a specified process factor.

[0094] In operation 415, the data processing device (100) can calculate process capability parameters related to process capability of process equipment involved in the battery process based on the process data acquired in operation 405.

[0095] According to one embodiment, the data processing device (100) may calculate a difference value between the maximum and minimum values ​​of the numerical values ​​of the specified process factors based on the process data. For example, if the process data is data indicating a temperature detected by a battery process facility, the data processing device (100) may calculate a difference value between the maximum and minimum temperature values ​​among the detected temperature values.

[0096] According to one embodiment, the data processing device (100) can calculate a standard deviation value of the numerical values ​​of a specified process factor based on process data. For example, if the process data is data indicating a temperature detected by a battery process facility, the data processing device (100) can calculate a standard deviation value of the detected temperature values.

[0097] According to one embodiment, the data processing device (100) can calculate the process capability parameter based on the above mathematical expression 1.

[0098] In operation 420, the data processing device (100) may generate a box plot for the battery process based on the reference distribution profile generated in operation 410 and the process capability parameters calculated in operation 415. Here, the box plot may include quartiles, interquartile ranges, minimum limits, and maximum limits.

[0099] According to one embodiment, the data processing device (100) can set the quartiles and the interquartile range based on the reference distribution profile. The data processing device (100) can set the variate values ​​at positions that divide the data of the reference distribution profile into four equal parts as quartiles. For example, the data processing device (100) can set the median of the reference distribution profile as the second quartile, the variate value at the position where the probability distribution function F(x) becomes 0.25 as the first quartile, and the variate value at the position where F(x) becomes 0.75 as the third quartile. The data processing device (100) can set the range between the first quartile and the third quartile as the interquartile range.

[0100] According to one embodiment, the data processing device (100) can set the minimum limit line and the maximum limit line based on at least some of the quartiles, the quartile range, and the process capability parameter.

[0101] According to one embodiment, the data processing device (100) can set the minimum limit line and the maximum limit line based on the above mathematical expressions 2 and 3.

[0102] According to one embodiment, the data processing device (100) can calculate a scale value based on a process capability parameter. For example, the data processing device (100) can calculate the scale value based on the mathematical expression 4 above.

[0103] In operation 425, the data processing device (100) may remove data that falls outside a critical range corresponding to the box plot among the process data. The critical range may mean, for example, a range that is greater than or equal to the minimum limit line and less than or equal to the maximum limit line.

[0104] The terms "include," "comprise," or "have" used herein, unless otherwise specifically stated, imply that the corresponding component may be included, and therefore should be interpreted to include other components rather than to exclude other components. All terms, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document pertain, unless otherwise defined. Commonly used terms, such as terms defined in a dictionary, should be interpreted to be consistent with the contextual meaning of the relevant technology, and shall not be interpreted in an idealized or overly formal sense, unless explicitly defined in this document.

Claims

1. A communication circuit for acquiring process data related to the battery process; and A processor for filtering the above process data is included, The above processor, Based on the above process data, a reference distribution profile following a standard normal distribution is generated, Based on the above process data, process capability parameters related to the process capability of the process equipment involved in the battery process are calculated, Based on the above reference distribution profile and the process capability parameters, a box plot for the battery process is generated, A data processing device configured to remove data that falls outside a critical range corresponding to the box plot among the above process data.

2. In claim 1, The above box plot includes quartiles, interquartile range, minimum limit line, and maximum limit line, The above processor, Setting the quartiles and the interquartile range based on the above reference distribution profile, A data processing device that sets the minimum limit line and the maximum limit line based on at least some of the quartiles, the quartile range, and the process capability parameter.

3. In claim 2, A data processing device wherein the above critical section is a section greater than or equal to the minimum limit line and less than or equal to the maximum limit line.

4. In claim 2, The above processor, Calculate the scale value based on the above process capability parameters, A data processing device that sets the minimum limit line and the maximum limit line based on the following mathematical expressions 1 and 2. [Mathematical Formula 1] Minimum cutoff line = Q1-k·IQR [Equation 2] Minimum cutoff = Q3 + k · IQR (In mathematical expressions 1 and 2, Q1 is the first quartile among the quartiles, Q3 is the third quartile among the quartiles, k is the scale value, and IQR is the interquartile range.) 5. In claim 4, The above processor is a data processing device that calculates the scale value based on the following mathematical expression 3. [Equation 3] k=-0.5+(n / 1.35) (In mathematical expression 3, k is the scale value and n is the process capability parameter.) 6. In claim 1, The above processor, Based on the above process data, the difference value between the maximum and minimum values ​​among the numerical values ​​of the specified process factors is calculated, Based on the above process data, the standard deviation value of the above numerical value of the above process factor is calculated, A data processing device that calculates the process capability parameter based on the difference value and the standard deviation value.

7. In claim 6, The above processor is a data processing device that calculates the process capability parameter based on the following mathematical expression 4. [Equation 4] n=T / (2·s) (In mathematical expression 4, n is the process capability parameter, T is the difference value, and s is the standard deviation value.) 8. Action to acquire process data related to the battery process; An operation of generating a reference distribution profile following a standard normal distribution based on the above process data; An operation of calculating process capability parameters related to the process capability of process equipment involved in the battery process based on the above process data; An operation of generating a box plot for the battery process based on the above reference distribution profile and the process capability parameters; and A data processing method, comprising an operation of removing data that falls outside a critical range corresponding to the box plot among the above process data.

9. In claim 8, The above box plot includes quartiles, interquartile range, minimum limit line, and maximum limit line, The action of generating the above box plot is: An operation of setting the quartiles and the interquartile range based on the above reference distribution profile, and A data processing method comprising an operation of setting the minimum limit line and the maximum limit line based on at least some of the quartiles, the quartile range, and the process capability parameter.

10. In claim 9, A data processing method wherein the above critical section is a section greater than or equal to the minimum limit line and less than or equal to the maximum limit line.

11. In claim 9, Further comprising an operation of calculating a scale value based on the above process capability parameter, A data processing method in which the operation of setting the above minimum limit line and the above maximum limit line is based on the following mathematical expressions 1 and 2. [Mathematical Formula 1] Minimum cutoff line = Q1-k·IQR [Equation 2] Minimum cutoff = Q3 + k · IQR (In mathematical expressions 1 and 2, Q1 is the first quartile among the quartiles, Q3 is the third quartile among the quartiles, k is the scale value, and IQR is the interquartile range.) 12. In claim 11, A data processing method in which the operation of calculating the above scale value is based on the following mathematical expression 3. [Equation 3] k=-0.5+(n / 1.35) (In mathematical expression 3, k is the scale value and n is the process capability parameter.) 13. In claim 8, The operation of calculating the above process capability parameters is: An operation of calculating the difference between the maximum and minimum values ​​of the specified process factors based on the above process data, An operation of calculating a standard deviation value of the numerical value of the process factor based on the above process data, and A data processing method comprising an operation of calculating the process capability parameter based on the difference value and the standard deviation value.

14. In claim 13, A data processing method for calculating the above process capability parameters based on the following mathematical expression 4. [Equation 4] n=T / (2·s) (In mathematical expression 4, n is the process capability parameter, T is the difference value, and s is the standard deviation value.)

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