Data processing system and data processing method thereof

Abstract

A data processing system according to an embodiment of the present disclosure can include a data acquisition module for acquiring battery data, a data analysis module for analyzing the battery data by using a plurality of analysis engines based on different analysis algorithms and 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 of each of the plurality of analysis engines.

Description

Technical Field

[0001] This application claims the benefit of Korean Patent Application No. 10-2024-0132750, filed with the Korean Intellectual Property Office on September 30, 2024, the disclosure of which is incorporated herein by reference in its entirety.

[0002] The exemplary implementations disclosed in this document relate to data processing systems and methods for processing data. Background Technology

[0003] In recent years, active research and development have been carried out in the field of rechargeable batteries. Here, rechargeable batteries refer to batteries capable of being charged and discharged, including conventional Ni / Cd batteries, Ni / MH batteries, and more recently, lithium-ion batteries. Lithium-ion batteries, among other rechargeable batteries, have the advantage of higher energy density than Ni / Cd and Ni / MH batteries. Furthermore, because lithium-ion batteries can be manufactured in a compact and lightweight form, they are used as power sources for mobile devices, and in recent years, their application has expanded to electric vehicles, gaining attention as a next-generation energy storage device.

[0004] As batteries are used as the power source for electric vehicles, the management and analysis of battery data in these vehicles has become increasingly important. In particular, with technological advancements, the types of battery data have become more complex and diverse. Therefore, efficient data processing is required to analyze and manage battery data.

[0005] For systems that process battery data, the battery analysis engine plays a crucial role in monitoring battery status and analyzing performance.

[0006] refer to Figure 1 The battery analysis engine 110 included in the existing data processing system 100 is configured as a monolithic structure, integrating multiple battery analysis algorithms that analyze input data and generate result data. In this monolithic structure, the battery analysis engine 110 lacks flexibility and scalability, making it difficult to respond to various situations. For example, when a fault occurs in a specific algorithm among the multiple battery analysis algorithms included in the battery analysis engine 110, this can cause the battery analysis engine 110 to malfunction, potentially allowing the fault to propagate throughout the entire battery analysis algorithm.

[0007] Therefore, there is a need for a system that can separate multiple battery analysis algorithms included in the existing battery analysis engine 110 into independent services and flexibly and context-dependently run the data analysis algorithms. Summary of the Invention

[0008] Technical issues

[0009] According to exemplary embodiments of the present disclosure, a data processing system and a method thereof may be provided, the data processing system employing the form of dividing the analysis algorithm into several engines to be driven independently.

[0010] According to exemplary embodiments of this disclosure, a data processing system and a method thereof can be provided that can diversify the analysis cycle of each analysis algorithm according to the data change cycle and select the analysis algorithm according to the context to minimize unnecessary analysis.

[0011] The technical tasks to be achieved by the exemplary embodiments of this disclosure are not limited to those described above, and other technical tasks can be derived from the exemplary embodiments described below.

[0012] Technical solution

[0013] According to an example embodiment of this disclosure, a data processing system may include: a data acquisition module that acquires battery data; a data analysis module that analyzes the battery data using multiple analysis engines, the multiple analysis engines being based on different analysis algorithms and driven independently; and a driving module that selectively drives each of the multiple analysis engines based on multiple driving conditions of each of the multiple analysis engines.

[0014] According to an example implementation of this disclosure, in a data processing system, multiple analytics engines can be configured as a microservices architecture (MSA) and divided based on the type of battery data used for analysis and the type of analysis result data.

[0015] According to an exemplary embodiment of this disclosure, the data processing system may further include multiple data pipelines that transmit battery data from the data acquisition module to the drive module based on different transmission cycles.

[0016] According to an example implementation of this disclosure, in a data processing system, multiple transmission cycles of each of a plurality of data pipelines can correspond one-to-one with multiple drive cycles of each of a plurality of analysis engines, and when battery data is transmitted from the data pipeline for each drive cycle corresponding to each analysis engine, the drive module can drive the analysis engine to analyze the transmitted battery data.

[0017] According to an example implementation of this disclosure, in a data processing system, a driving module can drive a first analysis engine among multiple analysis engines for each first driving cycle, identify whether the first analysis engine generates first analysis result data at a predetermined time point for each predetermined cycle having a time length shorter than the first driving cycle, and drive the first analysis engine when it is identified that no first analysis result data is generated at the predetermined time point.

[0018] According to an example embodiment of this disclosure, in a data processing system, the first analysis engine may be based on a RUL algorithm that analyzes the remaining useful life (RUL) of the battery or a SOH algorithm that analyzes the state of health (SOH) of the battery.

[0019] According to an example embodiment of this disclosure, the data processing system 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 the analysis result data in the database.

[0020] According to an exemplary embodiment of this disclosure, in a data processing system, a data pipeline may include an extraction process, a transformation process, and a storage process.

[0021] According to an example embodiment of this disclosure, a data processing method performed by a data processing system may include: acquiring battery data; selectively driving each of a plurality of analysis engines based on a plurality of driving conditions for each of the plurality of analysis engines, the plurality of analysis engines being driven independently based on different analysis algorithms; and analyzing the battery data by using the driven analysis engine among the plurality of analysis engines.

[0022] According to an example implementation of this disclosure, for a data processing method performed by a data processing system, multiple analysis engines can be configured as MSA and divided according to the type of battery data used for analysis and the type of analysis result data.

[0023] According to an example embodiment of this disclosure, for a method of data processing performed by a data processing system, the step of selectively driving each of a plurality of analysis engines may include driving the analysis engine for each drive cycle corresponding to each analysis engine to analyze battery data.

[0024] According to an example embodiment of this disclosure, for a method of data processing performed by a data processing system, the step of selectively driving each of a plurality of analysis engines may include: driving the first analysis engine among the plurality of analysis engines for each first driving cycle; identifying whether the first analysis engine generates first analysis result data at a predetermined time point for each predetermined cycle having a time length shorter than the first driving cycle; and driving the first analysis engine when it is identified that no first analysis result data is generated at the predetermined time point.

[0025] According to an example embodiment of this disclosure, for a data processing method performed by a data processing system, the first analysis engine may be based on a RUL algorithm for analyzing the RUL of a battery or a SOH algorithm for analyzing the SOH of a battery.

[0026] According to an example embodiment of this disclosure, the data processing method performed by the data processing system may further include processing analysis result data generated by a driven analysis engine among a plurality of analysis engines, and storing the analysis result data in a database.

[0027] According to an example embodiment of this disclosure, for a data processing method performed by a data processing system, the steps of processing analysis result data and storing the analysis result data in a database may include extracting the analysis result data, transforming the extracted analysis result data, and storing the transformed analysis result data in the database.

[0028] Beneficial effects

[0029] According to the example implementations disclosed in this document, the flexibility and scalability of the system can be ensured by a structure configured such that multiple analysis engines, including those in the data analysis module, are driven independently.

[0030] According to the example implementation in this document, unnecessary analysis can be minimized by diversifying the analysis cycle of each analysis algorithm based on the data change cycle and selecting the analysis algorithm according to the context.

[0031] The effects of the present invention are not limited to those described above, and those skilled in the art can clearly understand other effects not explicitly described within the scope of the claims. Attached Figure Description

[0032] Figure 1 This is a diagram illustrating an existing data processing system.

[0033] Figure 2 This is a block diagram of a data processing system according to an exemplary embodiment of the present disclosure.

[0034] Figure 3 This is a diagram used to describe a method of data processing of a data processing system according to an exemplary embodiment of the present disclosure.

[0035] Figure 4 This is an operation flowchart of a data processing system according to an exemplary embodiment of the present disclosure.

[0036] Figure 5 This is an operation flowchart of a data processing system according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0037] For the purpose of describing exemplary embodiments, descriptions of technical content that is well-known in the art to which this disclosure pertains and is not directly related to this disclosure will be omitted. This is to convey the subject matter of this disclosure more clearly without obscuring it by omitting unnecessary descriptions.

[0038] For the same reason, some components are shown enlarged, omitted, or schematically in the accompanying drawings. Furthermore, the size of each component does not perfectly reflect its actual size. Identical or corresponding components in the various drawings are given the same reference numerals.

[0039] The advantages and features of this disclosure, as well as methods of implementing them, will become apparent from the following exemplary embodiments, which will be described in more detail with reference to the accompanying drawings. However, it should be noted that this disclosure is not limited to the following exemplary embodiments and can be implemented in various forms. Therefore, the exemplary embodiments are provided only to disclose this disclosure and to inform those skilled in the art of its scope, which is defined only by the scope of the claims. Throughout the specification, the same reference numerals or designations denote the same components.

[0040] Here, it should be understood that each block and combination of flowchart illustrations can be executed by computer program instructions. Since these computer program instructions can be mounted on the processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, those instructions, which execute via the processor of the computer or other programmable data processing apparatus, can create means for performing the functions described in the flowchart blocks. These computer program instructions can be stored in a computer-usable or computer-readable storage medium that can be directed to the computer or other programmable data processing apparatus to perform its functions in a particular manner; thus, the computer program instructions stored in the computer-usable or computer-readable storage medium can produce an article of art containing instruction means for performing the functions described in the flowchart blocks. The computer program instructions can also be mounted on a computer or other programmable data processing apparatus such that a series of operational steps can be performed on the computer or other programmable data processing apparatus to create a computer-implemented process for creating computer or other programmable data. The instructions for performing the processing apparatus can also provide steps for performing the functions described in the flowchart blocks.

[0041] Additionally, each box may represent a portion of a module, segment, or code that includes one or more executable instructions for performing a specified logical function. It should also be noted that in some alternative implementations, the functions mentioned in the boxes may occur out of order. For example, depending on the corresponding function, two boxes shown consecutively may actually execute substantially simultaneously, or these boxes may sometimes execute in reverse order.

[0042] Furthermore, the term "unit" refers to software or hardware components such as FPGAs or ASICs. A "unit" performs certain roles. However, a "unit" is not limited to software or hardware. A "unit" can be configured to reside in addressable storage media or to reboot one or more processors. Thus, for example, a "unit" includes components such as software components, object-oriented software components, class components, and task components, and includes procedures, functions, properties, processes, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within components and "units" can be combined into a smaller number of components and "units," or can be further divided into additional components and "units." Furthermore, components and "units" can be implemented as reboot devices or one or more CPUs within a secure multimedia card.

[0043] The expression “at least one of A, B and C” described in this specification may indicate the following meanings, including: only A; only B; only C; both A and B; both A and C; both B and C; or all three of A, B and C.

[0044] In this disclosure, a "terminal" can be implemented as a computer or portable terminal capable of accessing a server or another terminal via a network. Here, a computer can include, for example, a laptop, desktop computer, and laptop computer equipped with a web browser. A portable terminal can be a wireless communication device that ensures portability and mobility, and can include any type of handheld wireless communication device, such as a tablet PC, a smartphone, or a communication-based terminal such as International Mobile Telecommunications (IMT), Code Division Multiple Access (CDMA), W-CDMA, and Long Term Evolution (LTE).

[0045] In the following description, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings, enabling those skilled in the art to readily implement the present disclosure. However, the present disclosure may be implemented in many different forms and is not limited to the embodiments described herein.

[0046] In the following, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0047] Figure 2 This is a block diagram of a data processing system 200 according to an exemplary embodiment of the present disclosure.

[0048] refer to Figure 2 The data processing system 200 may include a data acquisition module 210, a data analysis module 220, a driver module 230, and a database 240. According to an example implementation, Figure 2 The data processing system 200 shown may also include, in addition to Figure 2At least one component other than the components shown (e.g., a display, input device, or output device).

[0049] According to an example implementation, the data acquisition module 210 can acquire battery data related to the state of the battery (e.g., voltage, current, and temperature). Here, the battery can be a battery cell, battery module, or battery pack disposed in an electric vehicle.

[0050] For example, the data acquisition module 210 can establish 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 can be implemented as a communication circuit that sends data to and receives data from the external electronic device through the established channel. In this case, the data acquisition module 210 can receive battery data from the external electronic device.

[0051] Here, communication for sending and receiving data can be performed in a wired or wireless manner. For this purpose, the data acquisition module 210 may include a wired communication module that accesses the Internet via a local area network (LAN), a mobile communication module that sends and receives data via a mobile communication network through a mobile communication base station, a short-range communication module that uses communication methods such as wireless local area networks (WLANs) (e.g., Wi-Fi) or wireless personal area networks (WPANs) (e.g., Bluetooth or Zigbee), a satellite communication module that uses a global navigation satellite system (GNSS) (e.g., Global Positioning System (GPS)), or a combination thereof.

[0052] According to an example implementation, the data analysis module 220 may include multiple analysis engines. The data analysis module 220 can analyze battery data by using multiple analysis engines.

[0053] According to the example implementation, multiple analytics engines can be based on different analytics algorithms and driven independently. For example, multiple analytics engines can be configured in a microservices architecture (MSA) to be driven independently and minimize interdependencies. MSA refers to an architecture that implements a single large application and divides it into several smaller applications to allow for application modification and composition, and each analytics engine can be driven by a single application.

[0054] According to the example implementation, each of the multiple analysis engines may include different analysis algorithms for generating analysis results. For example, analysis algorithms may include various types of algorithms, such as RUL algorithms for analyzing the remaining useful life (RUL) of a battery, SOH algorithms for analyzing the state of health (SOH) of a battery, statistical algorithms for performing statistical analysis, and fault prediction algorithms for predicting battery failures. However, the algorithm types described are merely examples, and the algorithm types are not limited thereto.

[0055] According to the example implementation, multiple analysis engines can be divided based on the type of battery data used for analysis and the type of analysis result data. Depending on the analysis algorithm included in each of the multiple analysis engines, the type of battery data used for analysis, the type of analysis result data, and the analysis cycle can differ. For example, the SOH algorithm can use battery data such as battery voltage, battery current, and battery SOC, and calculate the battery's SOH data as the analysis result.

[0056] According to the example implementation, the driving module 230 can drive multiple analysis engines included in the data analysis module 220. The driving module 230 can selectively drive each of the multiple analysis engines based on the driving conditions of the multiple analysis engines. For example, the driving module 230 can drive each of the multiple analysis engines according to the driving cycle of each analysis engine. Furthermore, the driving module 230 can selectively combine and drive analysis engines according to the purpose of battery data analysis. For example, different analysis algorithms and analysis engines can be combined depending on whether the battery data analysis is real-time or batch analysis.

[0057] According to the example implementation, database 240 can function as a storage device for storing data. Data processing system 200 can process the analysis result data generated by data analysis module 220 and store the analysis result data in database 240.

[0058] Figure 3 This is a diagram used to describe a data processing method of a data processing system 200 according to an exemplary embodiment of the present disclosure.

[0059] refer to Figure 3 The data processing system 200 may include a plurality of data pipelines 310, 320, and 330. According to an example embodiment, the plurality of data pipelines 310, 320, and 330 may include at least one of an extraction process for extracting data, a transformation process for transforming the extracted data, and a storage process for storing the transformed data.

[0060] According to the example implementation, the first data pipe 310 to the Nth data pipe 320 (here, N is a natural number equal to or greater than 2) of the plurality of data pipes 310 and 320 can perform the role of transmitting battery data from the data acquisition module 210 to the drive module 230. The first data pipe 310 to the Nth data pipe 320 can transmit battery data from the data acquisition module 210 to the drive module 230 based on each of different transmission cycles.

[0061] According to an example implementation, the transmission cycle of each of the first data pipes 310 to the Nth data pipes 320 can correspond one-to-one with multiple drive cycles of each of the first analysis engines 221 to the Nth analysis engines 222. For example, the transmission cycle of the first data pipe 310 can be the same as the drive cycle of the first analysis engine 221. Therefore, when battery data is transmitted from the data pipes 310 or 320 for each drive cycle corresponding to each analysis engine 221 or 222, the drive module 230 can drive the analysis engines 221 or 222 to analyze the transmitted battery data.

[0062] For example, the first data pipeline 310 can transmit the battery data acquired and accumulated by the data acquisition module 210 to the drive module 230 for each first transmission cycle (e.g., two weeks or four weeks), so that the battery data is transmitted to the first analysis engine 221, which needs to perform batch analysis according to the analysis cycle. The drive module 230 can drive the first analysis engine 221 for each first drive cycle having the same time length as the first transmission cycle to analyze the battery data transmitted by the first data pipeline 310.

[0063] In another example, the Nth data pipeline 320 can transmit battery data acquired by the data acquisition module 210 to the drive module 230 for each Nth transmission cycle (e.g., one minute), so that the battery data is transmitted in real time to the Nth analysis engine 222, which requires real-time driving. The drive module 230 can drive the Nth analysis engine 222 for each Nth drive cycle having the same duration as the Nth transmission cycle to analyze the battery data transmitted by the Nth data pipeline 320. In the example implementation, the Nth data pipeline 320 can begin transmitting battery data to the drive module 230 when the vehicle's battery data collection terminal (e.g., on-board diagnostics (OBD)) enters a sleep state.

[0064] According to an example implementation, the drive module 230 can identify in real time whether a specific analysis engine 221 or 222 generates analysis result data, and drive the analysis engine 221 or 222 once based on the identified result. For example, the drive module 230 can identify whether the first analysis engine 221 generates first analysis result data at a predetermined time point for each predetermined period (e.g., one minute) having a time length shorter than the first drive cycle (e.g., two weeks or four weeks) of the first analysis engine 221. Here, the first analysis engine 221 can be based on the RUL algorithm or the SOH algorithm, and the predetermined time point can refer to the drive time point closest to the current time point among multiple drive time points according to the first drive cycle. That is, the drive module 230 can identify whether the first analysis result data is generated at a time point only before the drive of the first analysis engine 221. When it is identified that the first analysis result data is not generated at the predetermined time point, the drive module 230 can drive the first analysis engine 221 once. Here, the drive module 230 can transmit battery data transmitted by the first data pipeline 310 to the first analysis engine 221 and drive the first analysis engine 221.

[0065] According to the example implementation, the N+1th data pipeline 330 can process the analysis result data generated by the data analysis module 220 and store the analysis result data in the database 240. The N+1th data pipeline 330 can extract the analysis result data generated by each of the multiple analysis engines 221 and 222, transform the extracted analysis result data, and store the transformed analysis result data in the database 240.

[0066] Figure 4 This is an operation flowchart of a data processing system according to an exemplary embodiment of the present disclosure. Because... Figure 4 The operation method can be provided by Figure 2 The data processing system 200 executes the data, therefore redundant descriptions may be omitted, and it may be possible to use... Figure 2 The components are used to describe the operation methods.

[0067] Figure 4 The illustrated embodiments are merely examples. The order of operations according to the various illustrated embodiments of this disclosure may differ. Figure 4 The operation sequence shown can be omitted. Figure 4 Some of the operations shown can be changed in order or combined.

[0068] refer to Figure 4 In operation 410, the data processing system 200 can acquire battery data related to the state of the battery (e.g., voltage, current, and temperature).

[0069] 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 can be configured as MSAs based on different analysis algorithms and driven independently.

[0070] According to an example implementation, the data processing system 200 can drive each of a plurality of analysis engines according to the driving cycle of each analysis engine. Furthermore, the data processing system 200 can selectively combine and drive the analysis engines according to the purpose of battery data analysis.

[0071] In operation 430, the data processing system 200 can analyze the battery data acquired in operation 410 by using the analysis engine driven in operation 420.

[0072] Figure 5 This is an operation flowchart of a data processing system according to an exemplary embodiment of the present disclosure. Because... Figure 5 The operation method can be provided by Figure 2 The data processing system 200 executes the data, therefore redundant descriptions may be omitted, and it may be possible to use... Figure 2 The components are used to describe the operation methods.

[0073] Figure 5 The illustrated implementation is merely an example. The order of operations may differ according to various illustrated implementations of this disclosure. Figure 5 The order shown can be omitted. Figure 5 Some of the operations shown can be changed in order or combined.

[0074] In operation 510, the data processing system 200 can drive the first analysis engine among multiple analysis engines for each first drive cycle. Here, the first analysis engine can be based on the RUL algorithm or the SOH algorithm.

[0075] In operation 520, for each predetermined period, the data processing system 200 can identify whether the first analysis engine has generated the first analysis result data at a predetermined time point. Here, the predetermined time point can refer to the driving time point closest to the current time point among multiple driving time points of the first driving period.

[0076] In operation 520, when it is identified that the first analysis engine did not generate the first analysis result data at the predetermined time point ("No"), in operation 530, the data processing system 200 can drive the first analysis engine.

[0077] The exemplary embodiments of this disclosure can be represented by functional blocks and various processing steps. These functional blocks can be implemented by various numbers of hardware and / or software configurations that perform specific functions. For example, the exemplary embodiments of this disclosure can employ integrated circuit configurations such as memory, processors, logic circuits, and lookup tables, which can perform various functions by controlling one or more microprocessors or other control devices. Similar to components that can be executed by software programming or software components, the exemplary embodiments of this disclosure can be implemented by programming or scripting languages ​​such as C, C++, Java, and assembly languages, including various algorithms implemented by combinations of data structures, procedures, routines, or other programming configurations. Functional aspects can be implemented by algorithms executed by one or more processors. In addition, the exemplary embodiments of this disclosure can employ related techniques for electronic environment setup, signal processing, and / or data processing. The terms “mechanism,” “component,” “device,” and “configuration” can be used broadly and are not limited to mechanical and physical elements. These terms can include the meaning of a series of software routines associated with a processor.

[0078] The above-described exemplary embodiments are merely examples, and other exemplary embodiments may be implemented within the scope of the appended claims.

Claims

1. A data processing system, the data processing system comprising: The data acquisition module acquires battery data; A data analysis module that analyzes the battery data using multiple analysis engines, each based on a different analysis algorithm and driven independently. as well as 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. The data processing system according to claim 1, wherein, The multiple analytics engines are configured as a microservices architecture (MSA) and are divided based on the type of battery data used for analysis and the type of analysis result data.

3. The data processing system according to claim 1, further comprising a plurality of data pipelines configured to transmit the battery data from the data acquisition module to the drive module based on different transmission cycles.

4. The data processing system according to claim 3, wherein, Each of the multiple data pipelines has multiple transmission cycles that correspond one-to-one with the multiple drive cycles of each of the multiple analysis engines, and Specifically, when the battery data is transmitted from the data pipeline for each drive cycle corresponding to each analysis engine, the drive module drives the analysis module to analyze the transmitted battery data.

5. The data processing system according to claim 1, wherein, The driving module drives the first analysis engine among the plurality of analysis engines for each first driving cycle, identifies whether the first analysis engine generates first analysis result data at a predetermined time point for each predetermined cycle with a time length shorter than the first driving cycle, and drives the first analysis engine when it is identified that the first analysis result data is not generated at the predetermined time point.

6. The data processing system according to claim 5, wherein, The first analysis engine is based on either the RUL algorithm, which analyzes the remaining lifespan (RUL) of the battery, or the SOH algorithm, which analyzes the state of health (SOH) of the battery.

7. The data processing system according to claim 1, further comprising: A database, which stores data; as well as A data pipeline processes the analysis result data generated by the data analysis module to store the analysis result data in the database.

8. The data processing system according to claim 7, wherein, The data pipeline includes an extraction process, a transformation process, and a storage process.

9. A method for data processing performed by a data processing system, the data processing method comprising the following steps: Obtain battery data; Each of the multiple analysis engines is selectively driven based on multiple driving conditions of each of the multiple analysis engines, which are based on different analysis algorithms and are driven independently. as well as The battery data is analyzed by using the driven analysis engine among the plurality of analysis engines.

10. The data processing method according to claim 9, wherein, The multiple analytics engines are configured as a microservices architecture (MSA) and are divided based on the type of battery data used for analysis and the type of analysis result data.

11. The data processing method according to claim 9, wherein, The step of selectively driving each of the plurality of analysis engines includes the following steps: driving the analysis engine for each driving cycle corresponding to each analysis engine to analyze the battery data.

12. The data processing method according to claim 9, wherein, The step of selectively driving each of the plurality of analytics engines includes the following steps: For each first driving cycle, drive the first analysis engine among the plurality of analysis engines; For each predetermined period having a duration shorter than the first driving cycle, identify whether the first analysis engine generates first analysis result data at a predetermined time point; and When it is detected that the first analysis result data has not been generated at the predetermined time point, the first analysis engine is activated.

13. The data processing method according to claim 12, wherein, The first analysis engine is based on either the RUL algorithm, which analyzes the remaining lifespan (RUL) of the battery, or the SOH algorithm, which analyzes the state of health (SOH) of the battery.

14. The data processing method according to claim 9, further comprising the following steps: The analysis result data generated by the driven analysis engine among the plurality of analysis engines is processed to store the analysis result data in a database.

15. The data processing method according to claim 14, wherein, The steps of processing the analysis result data to store the analysis result data in the database include the following steps: Extract the analysis results data; Transform the extracted analysis results data; and The transformed analysis results are stored in the database.

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