Data processing system and data processing method thereof

Abstract

A data processing system according to an embodiment of the present disclosure may comprise: a data acquisition module for acquiring battery data; a data analysis module for analyzing the battery data by using multiple analysis engines which are based on different analysis algorithms and are driven independently of each other; and a driving module for selectively driving each of the multiple analysis engines on the basis of multiple driving conditions of each of the multiple analysis engines.

Description

Data processing system and its data processing method

[0001] This application claims the benefit of priority based on Korean Patent Application No. 10-2024-0132750 filed on September 30, 2024, and all contents disclosed in the document of said Korean Patent Application are incorporated herein as part of this specification.

[0002] The embodiments disclosed in this document relate to a data processing system and a data processing method thereof.

[0003] Recently, active research and development on secondary batteries has been underway. Here, secondary batteries refer to rechargeable batteries, encompassing conventional Ni / Cd and Ni / MH batteries as well as the more recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of significantly higher energy density compared to conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight manner, making them suitable for use as power sources for mobile devices. Recently, their scope of application has expanded to include electric vehicles, drawing attention as a next-generation energy storage medium.

[0004] As batteries are used as the power source for electric vehicles, the management and analysis of data from these installed batteries are becoming increasingly important. In particular, with technological advancements, the types of battery data are becoming more sophisticated and diverse. Therefore, efficient data processing is required for the analysis and management of battery data.

[0005] In systems that process battery data, a battery analysis engine that monitors battery status and analyzes performance plays an important role.

[0006] Referring to FIG. 1, the battery analysis engine (110) included in the conventional data processing system (100) is configured as a monolithic structure in which a plurality of battery analysis algorithms that analyze input data and generate result data are integrated. The battery analysis engine (110) of this monolithic structure has a problem in that it lacks flexibility and scalability, making it difficult to respond to various situations. For example, if a problem occurs in a specific algorithm among the plurality of battery analysis algorithms included in the battery analysis engine (110), it leads to a problem with the battery analysis engine (110), and thus the problem can extend to all battery analysis algorithms.

[0007] Accordingly, a system is required that separates multiple battery analysis algorithms included in a conventional battery analysis engine (110) into independent services and can flexibly operate data analysis algorithms depending on the situation.

[0008] According to one embodiment of the present disclosure, a data processing system and a data processing method thereof can be provided in a form separated into multiple engines so that an analysis algorithm can be operated independently.

[0009] According to one embodiment of the present disclosure, a data processing system and a data processing method thereof can be provided, which diversify the analysis cycle of each analysis algorithm according to the data fluctuation cycle to minimize unnecessary analysis and select an analysis algorithm according to the situation.

[0010] The technical problems to be solved by the embodiments of the present disclosure are not limited to the technical problems described above, and other technical problems can be inferred from the following embodiments.

[0011] A data processing system according to one embodiment of the present disclosure may include a data acquisition module for acquiring battery data, a data analysis module for analyzing the battery data using a plurality of analysis engines that are based on different analysis algorithms and are driven independently of each other, and a driving module for selectively driving each of the plurality of analysis engines based on a plurality of driving conditions for each of the plurality of analysis engines.

[0012] In a data processing system according to one embodiment of the present disclosure, the plurality of analysis engines are configured as a microservices architecture (MSA) and can be distinguished based on the type of battery data used for analysis and the type of analysis result data.

[0013] A data processing system according to one embodiment of the present disclosure may further include a plurality of data pipelines that transmit battery data from the data acquisition module to the driving module based on different transmission cycles.

[0014] In a data processing system according to one embodiment of the present disclosure, each of the plurality of data pipelines has a plurality of transmission cycles that correspond one-to-one with each of the plurality of analysis engines, and the driving module can drive the analysis engine to analyze the transmitted battery data when battery data is transmitted from the data pipeline for each driving cycle corresponding to the analysis engine.

[0015] In a data processing system according to one embodiment of the present disclosure, the driving module drives a first analysis engine among the plurality of analysis engines every first driving cycle, identifies whether a first analysis result data is generated at a specified time point by the first analysis engine every specified cycle having a time length shorter than the first driving cycle, and if it is identified that the first analysis result data is not generated at the specified time point, the first analysis engine can be driven.

[0016] In a data processing system according to one embodiment of the present disclosure, the first analysis engine may be based on a RUL algorithm for analyzing the RUL (Remaining Useful Lifecycle) of a battery or a SOH algorithm for analyzing the SOH (State of Health) of a battery.

[0017] A data processing system according to one embodiment of the present disclosure may further include a database for storing data, and a data pipeline for processing analysis result data generated by the data analysis module and storing it in the database.

[0018] In a data processing system according to one embodiment of the present disclosure, the data pipeline may include an extract process, a transform process, and a load process.

[0019] A data processing method performed by a data processing system according to one embodiment of the present disclosure may include an operation of acquiring battery data, an operation of selectively driving each of a plurality of analysis engines based on a plurality of driving conditions of each of a plurality of analysis engines that are driven independently of each other and based on different analysis algorithms, and an operation of analyzing the battery data using the driven analysis engine among the plurality of analysis engines.

[0020] In a data processing method performed by a data processing system according to one embodiment of the present disclosure, the plurality of analysis engines are configured as a microservices architecture (MSA) and can be distinguished based on the type of battery data used for analysis and the type of analysis result data.

[0021] In a data processing method performed by a data processing system according to one embodiment of the present disclosure, the operation of selectively driving each of the plurality of analysis engines may include the operation of driving the analysis engine to analyze the battery data for each driving cycle corresponding to each analysis engine.

[0022] In a data processing method performed by a data processing system according to one embodiment of the present disclosure, the operation of selectively driving each of the plurality of analysis engines may include: driving a first analysis engine among the plurality of analysis engines at a first driving cycle; identifying whether a first analysis result data has been generated at a designated time by the first analysis engine at a designated cycle having a time length shorter than the first driving cycle; and driving the first analysis engine if it is identified that the first analysis result data has not been generated at the designated time.

[0023] In a data processing method performed by a data processing system according to one embodiment of the present disclosure, the first analysis engine may be based on a RUL algorithm for analyzing the RUL (Remaining Useful Lifecycle) of a battery or a SOH algorithm for analyzing the SOH (State of Health) of a battery.

[0024] A data processing method performed by a data processing system according to one embodiment of the present disclosure may further include an operation of processing analysis result data generated by the driven analysis engine among the plurality of analysis engines and storing it in a database.

[0025] In a data processing method performed by a data processing system according to one embodiment of the present disclosure, the operation of processing the analysis result data and storing it in the database may include the operation of extracting the analysis result data, the operation of converting the extracted analysis result data, and the operation of storing the converted analysis result data in the database.

[0026] According to the embodiments disclosed in this document, the flexibility and scalability of the system can be ensured by configuring a structure in which multiple analysis engines included in the data analysis module operate independently.

[0027] According to the embodiments disclosed in this document, unnecessary analysis can be minimized by diversifying the analysis cycle of each analysis algorithm according to the data fluctuation cycle and selecting an analysis algorithm according to the situation.

[0028] The effects of the invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description in the claims.

[0029] Figure 1 is a diagram showing a conventional data processing system.

[0030] FIG. 2 is a block diagram of a data processing system according to one embodiment of the present disclosure.

[0031] FIG. 3 is a drawing for explaining a data processing method of a data processing system according to one embodiment of the present disclosure.

[0032] FIG. 4 is an operation flowchart of a data processing system according to one embodiment of the present disclosure.

[0033] FIG. 5 is an operation flowchart of a data processing system according to one embodiment of the present disclosure.

[0034] In describing the embodiments, technical details that are well known in the technical field to which this disclosure belongs and are not directly related to this disclosure are omitted. This is intended to convey the essence of this disclosure more clearly without obscuring it by omitting unnecessary explanations.

[0035] For the same reason, some components in the attached drawings have been exaggerated, omitted, or schematically depicted. Additionally, the size of each component does not entirely reflect its actual dimensions. Identical or corresponding components in each drawing have been assigned the same reference numbers.

[0036] The advantages and features of the present disclosure and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below but may be implemented in various different forms. The embodiments provided are merely to ensure that the disclosure is complete and to fully inform those skilled in the art of the scope of the invention, and the present disclosure is defined only by the scope of the claims. Throughout the specification, the same reference numerals refer to the same components.

[0037] At this time, it will be understood that each block of the process flow diagrams and combinations of the flow diagrams can be executed by computer program instructions. Since these computer program instructions can be loaded into the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, the instructions executed through the processor of the computer or other programmable data processing equipment create means for performing the functions described in the flow diagram block(s). Since these computer program instructions can also be stored in computer-available or computer-readable memory that can be directed toward the computer or other programmable data processing equipment to implement functions in a specific way, the instructions stored in such computer-available or computer-readable memory can also produce a manufactured item containing means of instruction for performing the functions described in the flow diagram block(s). Since computer program instructions can also be loaded onto a computer or other programmable data processing equipment, the instructions that execute the computer or other programmable data processing equipment by creating a process that is executed by a computer through a series of operation steps performed on the computer or other programmable data processing equipment can also provide steps for executing the functions described in the flow diagram block(s).

[0038] Additionally, each block may represent a module, segment, or part of code containing one or more executable instructions for executing a specific logical function(s). It should also be noted that in some alternative execution examples, the functions mentioned in the blocks may occur out of order. For instance, two blocks described in succession may actually be executed substantially simultaneously, or the blocks may be executed in reverse order according to their corresponding functions.

[0039] In this embodiment, the term "part" refers to a software or hardware component, such as an FPGA or ASIC, and the "part" performs certain roles. However, the meaning of "part" is not limited to software or hardware. The "part" may be configured to reside in an addressable storage medium or configured to operate one or more processors. Thus, for example, the "part" includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and "parts" may be combined into a smaller number of components and "parts" or further separated into additional components and "parts." Furthermore, the components and "parts" may be implemented to operate one or more CPUs within a device or secure multimedia card.

[0040] The expression “at least one of a, b, and c” described throughout the specification may include ‘a alone’, ‘b alone’, ‘c alone’, ‘a and b’, ‘a and c’, ‘b and c’, or ‘a, b, and c all’.

[0041] The "terminal" mentioned below may be implemented as a computer or portable terminal capable of connecting to a server or other terminal via a network. Here, the computer includes, for example, a notebook, desktop, or laptop equipped with a web browser, and the portable terminal is a wireless communication device that ensures portability and mobility, and may include all types of handheld-based wireless communication devices such as IMT (International Mobile Telecommunication), CDMA (Code Division Multiple Access), W-CDMA (W-Code Division Multiple Access), LTE (Long Term Evolution), communication-based terminals, smartphones, tablet PCs, etc.

[0042] Embodiments of the present disclosure are described below with reference to the attached drawings so that those skilled in the art can easily implement them. However, the present disclosure may be embodied in various different forms and is not limited to the embodiments described herein.

[0043] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0044] FIG. 2 is a block diagram of a data processing system (200) according to one embodiment of the present disclosure.

[0045] Referring to FIG. 2, the data processing system (200) may include a data acquisition module (210), a data analysis module (220), a driving module (230), and a database (240). According to an embodiment, the data processing system (200) 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.

[0046] According to one embodiment, the data acquisition module (210) can acquire battery data related to the state of the battery (e.g., voltage, current, temperature, etc.). Here, the battery may be a battery cell, battery module, or battery pack placed in an electric vehicle.

[0047] For example, the data acquisition module (210) may be implemented as a communication circuit that establishes a wired communication channel and / or a wireless communication channel between the data processing system (200) and an external electronic device (e.g., a vehicle), and transmits and receives data to and from the external electronic device through the established communication channel. In this case, the data acquisition module (210) may receive battery data from the external electronic device.

[0048] Here, communication, that is, the transmission and reception of data, can be performed via wired or wireless means. To this end, the data acquisition module (210) may include a wired communication module that connects to the internet, etc., via a LAN (Local Area Network), a mobile communication module that connects to a mobile communication network via a mobile communication base station to transmit and receive data, a short-range communication module that uses a communication method of the WLAN (Wireless Local Area Network) family such as Wi-Fi, a communication method of the WPAN (Wireless Personal Area Network) family such as Bluetooth or Zigbee, a satellite communication module that uses a GNSS (Global Navigation Satellite System) such as GPS (Global Positioning System), or a combination thereof.

[0049] According to one embodiment, the data analysis module (220) may include a plurality of analysis engines. The data analysis module (220) may analyze battery data using the plurality of analysis engines.

[0050] According to one embodiment, a plurality of analysis engines may be based on different analysis algorithms and may operate independently of each other. For example, the plurality of analysis engines may operate independently of each other and may be configured as a microservices architecture (MSA) in which mutual dependencies are minimized. MSA refers to an architecture that enables the modification and combination of applications by dividing a large application into multiple small applications, and each analysis engine may operate as a single application.

[0051] According to one embodiment, each of the plurality of analysis engines may include different analysis algorithms for generating analysis results. For example, the analysis algorithms may include various types of algorithms, such as a RUL (Remaining Useful Lifecycle) algorithm for analyzing the battery's RUL, a SOH (State of Health) algorithm for analyzing the battery's SOH, a statistical algorithm for performing statistical analysis on battery data, and a failure prediction algorithm for predicting battery failure. However, the types of analysis algorithms described above are merely examples and are not limited thereto.

[0052] According to one embodiment, a plurality of analysis engines may be distinguished from one another based on the type of battery data used for analysis and the type of analysis result data. Each of the plurality of analysis engines may have different types of battery data used for analysis, types of analysis result data, and analysis cycles depending on the analysis algorithm included. For example, the SOH algorithm may use battery data such as battery voltage, battery current, and battery SOC, and may calculate battery SOH data as an analysis result.

[0053] According to one embodiment, the driving module (230) can drive a plurality of analysis engines included in the data analysis module (220). The driving module (230) can selectively drive each of the plurality of analysis engines based on the driving conditions of each of the plurality of analysis engines. For example, the driving module (230) can drive each of the plurality of analysis engines according to the driving cycle of each analysis engine. In addition, the driving module (230) can selectively combine and drive the analysis engines according to the purpose of battery data analysis. For example, different analysis algorithms and analysis engines may be combined depending on whether the battery data analysis is real-time analysis or batch analysis.

[0054] According to one embodiment, the database (240) can serve as a storage facility for storing data. The data processing system (200) can process the analysis result data generated by the data analysis module (220) and store it in the database (240).

[0055] FIG. 3 is a drawing for explaining a data processing method of a data processing system (200) according to one embodiment of the present disclosure.

[0056] Referring to FIG. 3, the data processing system (200) may include a plurality of data pipelines (310, 320, 330). According to one embodiment, the plurality of data pipelines (310, 320, 330) may include at least one of an extract process for extracting data, a transform process for transforming the extracted data, and a load process for storing the transformed data.

[0057] According to one embodiment, among a plurality of data pipelines (310, 320), the first to Nth data pipelines (310, 320, provided that N is a natural number greater than or equal to 2) can perform the role of transmitting battery data from a data acquisition module (210) to a driving module (230). The first to Nth data pipelines (310, 320) can each transmit battery data from the data acquisition module (210) to the driving module (230) based on different transmission cycles.

[0058] According to one embodiment, the delivery cycle of each of the first to Nth data pipelines (310, 320) may correspond one-to-one with the driving cycle of each of the first to Nth analysis engines (221, 222) included in the data analysis module (220). For example, the delivery cycle of the first data pipeline (310) may be the same as the driving cycle of the first analysis engine (221). Accordingly, the driving module (230) may drive the analysis engine (221 or 222) to analyze the delivered battery data when battery data is delivered from the data pipeline (310 or 320) for each driving cycle corresponding to each analysis engine (221 or 222).

[0059] For example, the first data pipeline (310) can transmit battery data acquired and accumulated by the data acquisition module (210) to the driving module (230) every first transmission cycle (e.g., 2 weeks or 4 weeks) so that battery data can be transmitted to the first analysis engine (221) requiring batch analysis in accordance with the analysis cycle. The driving module (230) can drive the first analysis engine (221) to analyze the battery data transmitted by the first data pipeline (310) every first driving cycle having the same time length as the first transmission cycle.

[0060] As another example, the Nth data pipeline (320) can transmit battery data acquired by the data acquisition module (210) to the driving module (230) every Nth transmission cycle (e.g., 1 minute) so that battery data can be transmitted in real time to the Nth analysis engine (222) that requires real-time operation. The driving module (230) can drive the Nth analysis engine (222) to analyze the battery data transmitted by the Nth data pipeline (320) every Nth driving cycle having the same time length as the Nth transmission cycle. In one embodiment, the Nth data pipeline (320) can start transmitting battery data to the driving module (230) when the terminal of the vehicle that collected the battery data (e.g., OBD (on-board diagnostics)) enters a sleep state.

[0061] According to one embodiment, the driving module (230) identifies in real time whether analysis result data is generated by a specific analysis engine (221 or 222) and can drive the analysis engine (221 or 222) once based on the identification result. For example, the driving module (230) can identify whether the first analysis result data was generated at a designated time point by the first analysis engine (221) at designated intervals (e.g., 1 minute) having a time length shorter than the first driving cycle (e.g., 2 weeks or 4 weeks) of the first analysis engine (221). Here, the first analysis engine (221) is based on the RUL algorithm or the SOH algorithm, and the designated time point may mean the driving time point closest to the current time point among a plurality of driving times points according to the first driving cycle. That is, the driving module (230) can identify whether the first analysis result data was generated at the time point immediately prior to the driving time of the first analysis engine (221). The driving module (230) can drive the first analysis engine (221) once if it is identified that the first analysis result data has not been generated at a designated time. At this time, the driving module (230) can drive the first analysis engine (221) by transmitting the battery data transmitted by the first data pipeline (310) back to the first analysis engine (221).

[0062] According to one embodiment, the N+1 data pipeline (330) can process analysis result data generated by the data analysis module (220) and store it in the database (240). The N+1 data pipeline (330) can extract analysis result data generated by each of the plurality of analysis engines (221, 222), convert the extracted analysis result data, and store the converted analysis result data in the database (240).

[0063] FIG. 4 is a flowchart of the operation of a data processing system according to one embodiment of the present disclosure. Since the operation method of FIG. 4 can be performed by the data processing system (200) of FIG. 2, descriptions that overlap with the above-mentioned content may be omitted and may be explained using the components of FIG. 2.

[0064] The embodiment illustrated in FIG. 4 is merely one example, and the sequence of operations according to various embodiments of the present disclosure may differ from that illustrated in FIG. 4, and some operations illustrated in FIG. 4 may be omitted, the order of operations may be changed, or operations may be merged.

[0065] Referring to FIG. 4, in operation 410, the data processing system (200) can obtain battery data related to the state of the battery (e.g., voltage, current, temperature, etc.).

[0066] In operation 420, the data processing system (200) can selectively drive each of the multiple analysis engines based on multiple driving conditions of each of the multiple analysis engines. Here, the multiple analysis engines may be composed of MSAs that are based on different analysis algorithms and are driven independently of each other.

[0067] According to one embodiment, the data processing system (200) can drive each of a plurality of analysis engines according to the driving cycle of each analysis engine. In addition, the data processing system (200) can selectively combine and drive the analysis engines according to the purpose of battery data analysis.

[0068] In operation 430, the data processing system (200) can analyze the battery data obtained in operation 410 using the analysis engine operated in operation 420.

[0069] FIG. 5 is a flowchart of the operation of a data processing system according to one embodiment of the present disclosure. Since the operation method of FIG. 5 can be performed by the data processing system (200) of FIG. 2, descriptions that overlap with the above-mentioned content may be omitted and may be explained using the components of FIG. 2.

[0070] The embodiment illustrated in FIG. 5 is merely one example, and the sequence of operations according to various embodiments of the present disclosure may differ from that illustrated in FIG. 5, and some operations illustrated in FIG. 5 may be omitted, the order of operations may be changed, or operations may be merged.

[0071] In operation 510, the data processing system (200) can drive a first analysis engine among a plurality of analysis engines at each first driving cycle. Here, the first analysis engine may be based on the RUL algorithm or the SOH algorithm.

[0072] In operation 520, the data processing system (200) can identify whether the first analysis result data was generated at a specified point in time by the first analysis engine at each specified period. Here, the specified point in time may mean the driving point closest to the current point in time among a plurality of driving points in time according to the first driving cycle.

[0073] If, in operation 520, it is identified that the first analysis result data was not generated at the time specified by the first analysis engine ('NO'), in operation 530, the data processing system (200) can drive the first analysis engine.

[0074] Various embodiments of the present disclosure may be represented by functional block configurations and various processing steps. These functional blocks may be implemented by various numbers of hardware and / or software configurations that execute specific functions. For example, the embodiments may employ integrated circuit configurations such as memory, processing, logic, look-up tables, etc., which can execute various functions by the control of one or more microprocessors or other control devices. Similar to how components may be implemented as software programming or software elements, the embodiments may be implemented in programming or scripting languages ​​such as C, C++, Java, assembler, etc., including various algorithms implemented as combinations of data structures, processes, routines, or other programming configurations. Functional aspects may be implemented as algorithms executed on one or more processors. Additionally, the embodiments may employ prior art for electronic configuration, signal processing, and / or data processing. Terms such as “mechanism,” “element,” “means,” and “configuration” may be used broadly and are not limited to mechanical and physical configurations. The above terms may include the meaning of a series of software processes (routines) in conjunction with processors, etc.

[0075] The aforementioned embodiments are merely examples, and other embodiments may be implemented within the scope of the claims set forth below.

Claims

1. In a data processing system, Data acquisition module for acquiring battery data; A data analysis module that analyzes the battery data using a plurality of analysis engines based on different analysis algorithms and operating independently of each other; and A data processing system comprising a driving module that selectively drives each of the plurality of analysis engines based on a plurality of driving conditions for each of the plurality of analysis engines.

2. In Paragraph 1, The above-mentioned plurality of analysis engines are configured as a microservices architecture (MSA) and are a data processing system distinguished based on the type of battery data used for analysis and the type of analysis result data.

3. In Paragraph 1, A data processing system further comprising a plurality of data pipelines that transmit battery data from the data acquisition module to the driving module based on different transmission cycles.

4. In Paragraph 3, Each of the plurality of data pipelines above corresponds one-to-one with each of the plurality of analysis engines above, and The above-described driving module is a data processing system that drives an analysis engine to analyze the transmitted battery data when battery data is transmitted from a data pipeline for each driving cycle corresponding to each analysis engine.

5. In Paragraph 1, The above-mentioned drive module is, Among the plurality of analysis engines above, the first analysis engine is driven at each first driving cycle, and Identifying whether first analysis result data was generated at a point specified by the first analysis engine at every specified period having a time length shorter than the first driving period, and A data processing system that drives the first analysis engine when it is identified that the first analysis result data was not generated at the specified time.

6. In Paragraph 5, The above-mentioned first analysis engine is a data processing system based on a RUL algorithm for analyzing the RUL (Remaining Useful Lifecycle) of a battery or a SOH algorithm for analyzing the SOH (State of Health) of a battery.

7. In Paragraph 1, A database that stores data; and A data processing system further comprising a data pipeline that processes analysis result data generated by the above data analysis module and stores it in the above database.

8. In Paragraph 7, The above data pipeline is a data processing system comprising an extract process, a transform process, and a load process.

9. In a data processing method performed by a data processing system, Operation of acquiring battery data; An operation of selectively driving each of the plurality of analysis engines based on a plurality of driving conditions of each of the plurality of analysis engines, each of which is based on a different analysis algorithm and driven independently of each other; and A data processing method comprising the operation of analyzing battery data using an analysis engine driven among the plurality of analysis engines.

10. In Paragraph 9, A data processing method in which the above plurality of analysis engines are configured as a microservices architecture (MSA) and distinguished based on the type of battery data used for analysis and the type of analysis result data.

11. In Paragraph 9, A data processing method wherein the operation of selectively driving each of the plurality of analysis engines includes the operation of driving the analysis engine to analyze the battery data for each driving cycle corresponding to each analysis engine.

12. In Paragraph 9, The operation of selectively driving each of the above plurality of analysis engines is, The operation of driving the first analysis engine among the plurality of analysis engines at each first driving cycle, An operation to identify whether first analysis result data has been generated at a point specified by the first analysis engine at each specified period having a time length shorter than the first driving period, and A data processing method comprising the operation of driving the first analysis engine when it is identified that the first analysis result data was not generated at the specified time.

13. In Paragraph 12, The above-mentioned first analysis engine is a data processing method based on a RUL algorithm for analyzing the RUL (Remaining Useful Lifecycle) of a battery or a SOH algorithm for analyzing the SOH (State of Health) of a battery.

14. In Paragraph 9, A data processing method further comprising the operation of processing analysis result data generated by the driven analysis engine among the plurality of analysis engines and storing it in a database.

15. In Paragraph 14, The operation of processing the above analysis result data and storing it in the above database is, The operation of extracting the above analysis result data, The operation of converting the above-mentioned extracted analysis result data, and A data processing method comprising the operation of storing the above-described converted analysis result data in the above database.

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