Electronic device, recording medium, and method for processing data of vehicle

Abstract

An electronic device for processing data of a vehicle is provided. The electronic device may be configured to: acquire a plurality of pieces of vehicle data for a plurality of vehicles; extract data related to a vehicle battery from among the plurality of pieces of vehicle data; classify and store the data related to the vehicle battery on the basis of at least one of a vehicle type, vehicle identification information, and a data acquisition date; and convert the stored data related to the vehicle battery into data to be analyzed in the form corresponding to a set analysis purpose.

Description

Electronic device, recording medium and data processing method of its vehicle

[0001] The present disclosure relates to an electronic device, a recording medium, and a method for processing data of a vehicle thereof, and more specifically, to a technology that provides a method for processing vehicle data to enable easier analysis of a vehicle battery of an electric vehicle.

[0002] This application claims the benefit of priority based on Korean Patent Application No. 2024-0128129 dated September 23, 2024, and all contents disclosed in the document of said Korean Patent Application are incorporated herein as part of this specification.

[0003] The adoption rate of electric vehicles (EVs) is rapidly increasing. Consequently, there is a growing need to acquire and analyze data on batteries, a core component of EVs, for their efficient management and operation. Meanwhile, data acquired from EVs has recently been centralized on servers for management and sharing. Aside from the advantages of centralized management and sharing, this approach can result in a mixture of various data acquired from the vehicles, including data related to vehicle batteries. Therefore, prior to analyzing battery data in this context, there is a growing need for a method to extract only data related to vehicle batteries and process it to facilitate analysis.

[0004] An embodiment of the present disclosure is proposed to solve the above-described problem and provides an electronic device, a recording medium, and a method for processing data of the vehicle thereof.

[0005] The technical problem to be solved by this embodiment is not limited to the problem described above, and other technical problems can be inferred from the following embodiments.

[0006] An electronic device according to one embodiment includes a transceiver; a processor; and one or more memories for storing one or more instructions. The one or more instructions may be configured such that, when executed, the processor acquires a plurality of vehicle data for a plurality of vehicles, extracts data regarding a vehicle battery among the plurality of vehicle data, classifies and stores the data regarding the vehicle battery based on at least one of a vehicle type, vehicle identification information, and a data acquisition date, and converts the stored data regarding the vehicle battery into data for analysis in a form corresponding to a set analysis purpose.

[0007] According to one embodiment, each of the plurality of vehicle data may include at least one of information regarding the time at which each of the plurality of vehicle data is acquired by an electronic device, information regarding the vehicle type, identification information of the vehicle, information regarding the corresponding data type, and information regarding the data value.

[0008] According to one embodiment, each of the plurality of vehicle data may further include information regarding the order in which each of the plurality of vehicle data is acquired by an electronic device.

[0009] According to one embodiment, one or more instructions may be configured such that, at the time of execution, a processor identifies a data type corresponding to each of a plurality of vehicle data, and identifies at least one vehicle data corresponding to at least one type related to a vehicle battery among the plurality of vehicle data as data related to a vehicle battery.

[0010] According to one embodiment, one or more instructions may be configured such that, at execution, a processor inputs a plurality of vehicle data into an artificial intelligence-based model and obtains data regarding a vehicle battery output from the artificial intelligence-based model.

[0011] According to one embodiment, the artificial intelligence-based model may be a machine learning model constructed by modeling the correlation between the input data and the output data, wherein the data values ​​obtained for each of a plurality of vehicles and the data type notation forms corresponding to each of the obtained data values ​​are used as input data for training, and the data types corresponding to the data obtained for each of the plurality of vehicles are used as output data for training.

[0012] According to one embodiment, one or more instructions may be configured such that, at execution, a processor classifies data regarding vehicle batteries by data acquisition date, classifies each of the data regarding vehicle batteries classified by date by vehicle type, and classifies each of the data regarding vehicle batteries classified by date and vehicle type by vehicle identification information.

[0013] According to one embodiment, one or more instructions may be configured such that, at the time of execution, a processor performs merging between at least one set of data regarding a vehicle battery that has the same data acquisition time among the stored data regarding a vehicle battery, and, based on a set analysis purpose, adds information regarding at least one additional data value and information regarding the data type of each of the at least one additional data for each of the merged data regarding the vehicle battery.

[0014] According to one embodiment, information regarding additional data values ​​and information regarding the data type of each of at least one additional data can be set according to the set analysis purpose.

[0015] According to one embodiment, the analysis purpose may include at least one of a data analysis purpose by vehicle type, a data analysis purpose by vehicle, and a data analysis purpose by data acquisition date.

[0016] A method for processing vehicle data performed by an electronic device according to one embodiment may include: acquiring a plurality of vehicle data for a plurality of vehicles; extracting data regarding a vehicle battery among the plurality of vehicle data; classifying and storing data regarding a vehicle battery based on at least one of a vehicle type, vehicle identification information, and a data acquisition date; and converting the stored data regarding a vehicle battery into data to be analyzed in a form corresponding to a set analysis purpose.

[0017] A computer-readable non-transient recording medium having a program for executing a vehicle data processing method according to one embodiment on a computer, wherein the vehicle data processing method may include: a step of acquiring a plurality of vehicle data for a plurality of vehicles; a step of extracting data regarding a vehicle battery among the plurality of vehicle data; a step of classifying and storing data regarding a vehicle battery based on at least one of a vehicle type, vehicle identification information and a data acquisition date; and a step of converting the stored data regarding a vehicle battery into data to be analyzed in a form corresponding to a set analysis purpose.

[0018] According to the present disclosure, vehicle data processed into a form that can be optimized for various analysis purposes can be provided.

[0019] In addition, according to the present disclosure, a platform can be constructed to more efficiently collect data regarding vehicle batteries from various sources (vehicle operating entities, vehicle battery pack configurations, etc.), and ultimately provide various insights regarding batteries.

[0020] 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.

[0021] FIG. 1 shows a block diagram of an electronic device according to one embodiment.

[0022] FIG. 2 shows a flowchart of a data processing method for a vehicle according to one embodiment.

[0023] FIG. 3 shows a plurality of vehicle data obtained according to one embodiment.

[0024] FIG. 4 shows data regarding a vehicle battery extracted from a plurality of vehicle data according to one embodiment.

[0025] FIG. 5 shows data regarding a vehicle battery classified and stored according to one embodiment.

[0026] FIG. 6 illustrates a process of converting data regarding a stored vehicle battery according to one embodiment.

[0027] FIG. 7 illustrates a process of merging additional data into data regarding a vehicle battery converted according to one embodiment.

[0028] FIG. 8 shows a structure in which data to be analyzed is stored according to one embodiment.

[0029] FIG. 9 shows a flowchart of a process for extracting data regarding a vehicle battery using an artificial intelligence-based model according to one embodiment.

[0030] FIG. 10 shows a flowchart of a process for classifying data regarding a vehicle battery according to one embodiment.

[0031] FIG. 11 shows a flowchart of a process for converting data regarding a stored vehicle battery into data to be analyzed according to one embodiment.

[0032] The terms used in the embodiments have been selected to be as widely used as possible, taking into account their functions in the present disclosure; however, these may vary depending on the intent of those skilled in the art, case law, the emergence of new technologies, etc. Additionally, in specific cases, terms have been arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant explanatory section. Therefore, terms used in the present disclosure should be defined not merely by their names, but based on their meanings and the overall content of the present disclosure.

[0033] When a part of a specification is described as "comprising" a certain component, this implies that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Furthermore, terms such as "~part" or "~module" as used in the specification refer to a unit that processes at least one function or operation, and this may be implemented in hardware or software, or as a combination of hardware and software.

[0034] 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'.

[0035] 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.

[0036]

[0037] Hereinafter, embodiments of the present disclosure relating to an electronic device for processing data of a vehicle will be described in detail with reference to the drawings.

[0038] FIG. 1 shows a block diagram of an electronic device according to one embodiment.

[0039] Referring to FIG. 1, the electronic device (100) may include a transceiver (110), a processor (120), and a memory (130) according to one embodiment. The electronic device (100) illustrated in FIG. 1 is illustrated only with components related to the present embodiment. Therefore, it will be understood by those skilled in the art related to the present embodiment that other general-purpose components may be included in addition to the components illustrated in FIG. 1.

[0040] For example, an electronic device (100) may include a communication device comprising one or more transceivers (110), an input device, and an output device. The communication device is a device for performing wired / wireless communication and can communicate with an external electronic device. The external electronic device may be a terminal or a server. Additionally, communication technologies used by the communication device may include GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Bluetooth, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), etc. The input device may be, for example, a traditional type of keypad or keyboard, a mouse, a microphone for receiving voice signals, a camera, and various other types of input means for detecting or receiving various forms of user input. The output unit may be, for example, a display that outputs images, a speaker that outputs sound, a haptic device that generates vibrations, and various other forms of output means.

[0041] According to one embodiment, the electronic device (100) may be a server that acquires and processes a plurality of vehicle data, and the server may be, for example, a cloud server that collects, manages, and processes data regarding various vehicles. The plurality of vehicle data acquired by the electronic device (100) is various data regarding each of a plurality of vehicles including various vehicle types, and it will be understood as a concept that encompasses all types of data regarding vehicles, regardless of type, such as the vehicle battery's SoC, the voltage of the battery pack, driving speed, and gear shift status. Meanwhile, the plurality of vehicle data may be acquired, for example, from at least one of an on-board diagnostics (OBD) device installed in each vehicle, a battery management system (BMS), and a device in which vehicle data is stored (e.g., a database) through a transceiver (110) of the electronic device (100). The method by which the electronic device (100) acquires multiple vehicle data is not limited to the above example, and it will be clearly understood by a person skilled in the art that the electronic device (100) can acquire data from various devices capable of communicating through the transceiver (110). Meanwhile, the type of electronic device (100) is not limited thereto, and various embodiments of the present disclosure can be applied to various devices capable of acquiring and processing data about a vehicle.

[0042] The processor (120) can control the overall operation of the electronic device (100) and process data and signals. The processor (120) may be composed of at least one hardware unit. Additionally, the processor (120) may be operated by one or more software modules generated by executing program code stored in memory (130). The processor (120) may include memory, and the processor (120) can control the overall operation of the electronic device (100) and process data and signals by executing program code stored in memory.

[0043] The processor (120) may be implemented as a computer or a similar device according to hardware, software, or a combination thereof. In terms of hardware, the processor (120) may be implemented in the form of an electronic circuit that processes electrical signals to perform control functions, and in terms of software, it may be implemented in the form of a program that drives the hardware processor (120). Meanwhile, unless otherwise specifically mentioned in the description below, the operation of the electronic device may be interpreted as being performed by the control of the processor (120). That is, when modules implemented in a system for processing vehicle data are executed, the modules may be interpreted as the processor (120) controlling the operation of the electronic device (100) below.

[0044] The memory (130) can store various types of information. The memory (130) can store data temporarily or semi-permanently. For example, the memory (130) of the electronic device (100) may store data such as an operating program (OS: Operating System) for operating the electronic device (100). Examples of memory (130) may include a hard disk drive (HDD), a solid state drive (SSD), a flash memory, a read-only memory (ROM), and a random access memory (RAM). Such memory (130) may be provided as an internal type or a removable type.

[0045] In summary, various embodiments can be implemented through various means. For example, various embodiments can be implemented by hardware, firmware, software, or a combination thereof.

[0046] In the case of implementation by hardware, the method according to various embodiments may be implemented by one or more ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), processors, controllers, microcontrollers, microprocessors, etc.

[0047] In the case of implementation by firmware or software, the method according to various embodiments may be implemented in the form of modules, procedures, or functions that perform the functions or operations described above. For example, software code may be stored in memory and executed by a processor. The memory may be located inside or outside the processor and may exchange data with the processor by various known means.

[0048]

[0049] FIG. 2 shows a flowchart of a data processing method for a vehicle according to one embodiment.

[0050] In step S210, the electronic device (100) can acquire multiple vehicle data for multiple vehicles. The multiple vehicle data can be acquired, for example, in a streaming form, and can be acquired at preset intervals (e.g., 5 minutes). Additionally, considering that an excessive amount of data may flow into the electronic device (100) at a specific time when multiple vehicle data is acquired, the device may be configured to acquire only a preset number of data (e.g., 50,000) at specific times when data is acquired. Accordingly, each of the multiple vehicle data can be acquired by the electronic device (100) regardless of the time the data was generated, and each data can be acquired in various forms that are not uniform.

[0051] According to one embodiment, each of the plurality of vehicle data may include, for example, information regarding the time at which each of the plurality of vehicle data is acquired by the electronic device (100), information regarding the vehicle type, identification information of the vehicle, information regarding the corresponding data type, and information regarding the data value. Additionally, according to an embodiment, each of the plurality of vehicle data may further include information regarding the order in which each of the plurality of vehicle data is acquired by the electronic device (100), and the information regarding the acquired order may be information unrelated to the vehicle type, vehicle identification information, and information regarding the time at which it is acquired.

[0052] Information regarding the date and time obtained by the electronic device (100) includes information regarding the year, month, day, and time of data acquisition, wherein the information regarding time may be displayed down to the second, for example. In this example, as numerous data are acquired simultaneously by the electronic device (100), multiple identical data may exist down to the second. Therefore, by setting the electronic device (100) to acquire information regarding the acquisition order of each of the multiple vehicle data, the electronic device (100) can acquire multiple identical data down to the second in a more detailed manner. This information regarding the acquisition order may be information included in each of the multiple vehicle data according to one embodiment, or, according to another embodiment, may be a value generated independently for each data as it is pre-set on the electronic device (100) to automatically increase according to the order in which it is input into the electronic device (100).

[0053] In step S220, the electronic device (100) can extract data regarding a vehicle battery from a plurality of vehicle data. Here, the data regarding a vehicle battery may be data that collectively refers to all types of data related to a vehicle battery. Specifically, to extract data regarding a vehicle battery, the electronic device (100) identifies a data type corresponding to each of the plurality of vehicle data, and among the plurality of vehicle data, identifies at least one vehicle data corresponding to at least one type related to a vehicle battery as data regarding a vehicle battery. At this time, the type related to a vehicle battery may be a predefined type and may include, for example, types such as SoC, voltage of a battery pack, temperature of a battery pack, etc., and according to an embodiment, may be configured to include more appropriate data types required for analysis regarding a vehicle battery. Through such a process, the electronic device (100) can extract data regarding a vehicle battery from various vehicle data. This extraction of data may be performed, for example, by extracting only data containing a specific data type based on a predefined filter.

[0054] In addition, according to the embodiment, the electronic device (100) can extract vehicle data corresponding to specific vehicle identification information among data corresponding to a specific data type regarding a vehicle battery. For example, if an analysis of the vehicle battery corresponding to each of vehicle A and vehicle B is scheduled to be performed, the electronic device (100) can extract some vehicle data corresponding to at least one predefined data type and at least one predefined vehicle identification information among a plurality of acquired vehicle data.

[0055] Meanwhile, according to one embodiment, the electronic device (100) may utilize an artificial intelligence-based model for extracting data regarding a vehicle battery. Specifically, the electronic device (100) may input a plurality of vehicle data into an artificial intelligence-based model and obtain data regarding a vehicle battery output from the artificial intelligence-based model. Here, the artificial intelligence-based model may be a machine learning model constructed by modeling the correlation between the input data for training and the output data for training, using the data values ​​obtained for each of the plurality of vehicles and the data type notation forms corresponding to each of the obtained data values ​​as input data for training, and the data types corresponding to the data obtained for each of the plurality of vehicles as output data for training.

[0056] More specifically, for each data type, the data source (meaning various devices capable of communication with the electronic device (100) as described above) from which the data was generated may differ, and accordingly, the format for indicating each data type may differ. For example, regarding the data type concerning the voltage of a battery pack, data source A may indicate it as 'pack_voltage', data source B as 'packVoltage', data source C as 'pack-vol', and data source D as 'pvoltage'. Despite inconsistent notation formats for the same data type, the electronic device (100) must extract vehicle battery data corresponding to specific data types that are pre-set based on multiple unstructured vehicle data. Therefore, an artificial intelligence-based model trained to identify data types based on the notation formats of various data sources for a single data type may be utilized.

[0057] In the present disclosure, deep learning is a process of training a neural network model using experience in processing data sets, through which software can improve its own data processing capabilities. An AI-based model is a model created by modeling the correlation between data sets, and such correlation can be expressed by multiple parameters. An AI-based model can derive correlations between data sets by extracting and analyzing features from a given data set, and deep learning can be defined as the process of optimizing the parameters of the AI-based model by repeating this process. Specifically, an AI-based model can learn the correlation between input and output for a data set given as input-output pairs according to a deep learning algorithm. Alternatively, an AI-based model can derive regularities between data and learn the relationship even when only input data is given. Here, the deep learning algorithm may be one of a deep neural network, a recurrent neural network, a convolutional neural network, a machine learning model for classification-regression analysis, or a reinforcement learning model.

[0058] The electronic device (100) can build an artificial intelligence model by modeling the correlation between the aforementioned training input data set and training output data set according to such a deep learning algorithm. That is, the electronic device (100) can build an artificial intelligence-based model by modeling the correlation between the training input data set of data type notation forms and data values ​​for each data source and the training output data set (label) of the data type corresponding to each training input data set. Accordingly, the artificial intelligence-based model can classify the data type of the vehicle data based on the data type notation forms and data values ​​of unlabeled vehicle data input from a new data source. The electronic device (100) can perform a data extraction operation regarding the vehicle battery more quickly and efficiently by inputting various vehicle data into this artificial intelligence-based model and obtaining data regarding the vehicle battery output from the artificial intelligence-based model.

[0059] In step S230, the electronic device (100) may classify and store data regarding a vehicle battery based on at least one of a vehicle type, vehicle identification information, and a data acquisition date. For example, the electronic device (100) may classify data regarding a vehicle battery by data acquisition date, classify each of the data regarding a vehicle battery classified by date by vehicle type, and classify each of the data regarding a vehicle battery classified by date and vehicle type by vehicle identification information. In such an example, the data regarding a vehicle battery classified by the electronic device (100) may be classified into a more subdivided form in which data of the same acquisition date, the same vehicle type, and the same vehicle are finally grouped together. Meanwhile, according to an embodiment, the classified data regarding a vehicle battery may be sorted based on information regarding the order of acquisition, and accordingly, may be sorted in the order acquired by the electronic device (100). Furthermore, the criteria for classifying data regarding a vehicle battery are not limited thereto, and according to an embodiment, may be classified based on criteria more appropriately set considering the set analysis purpose.

[0060] If data regarding vehicle batteries is extracted but not classified, it may be in a mixed form without meaning, as it is difficult to identify interrelationships due to the fact that each data is unstructured data that does not have a consistent structure. However, as in the present disclosure, if data regarding vehicle batteries is classified together based on at least one of the acquisition date, vehicle type, and vehicle identification information, data with interrelationships are grouped together despite being unstructured data, thereby providing the advantage that processing operations such as data preprocessing and conversion that may be performed later can be carried out more efficiently and economically.

[0061] After classifying data regarding vehicle batteries in this manner, the electronic device (100) can store the classified data regarding vehicle batteries by utilizing a temporary storage system, such as a data lake, which temporarily stores unprocessed raw data. By temporarily storing the classified data, the electronic device (100) can provide the classified data so that it can be utilized immediately from the temporary storage system when analysis or processing is required thereafter.

[0062] In step S240, the electronic device (100) can convert data regarding a stored vehicle battery into data for analysis in a form corresponding to a set analysis purpose. Specifically, the electronic device (100) can perform merging among data regarding at least one vehicle battery that has the same data acquisition time among the data regarding a stored vehicle battery. More specifically, the electronic device (100) can perform an operation to merge data that is identical down to the second unit of the acquisition time among the classified and stored data regarding a vehicle battery. As mentioned above, the data acquisition time included in each vehicle data includes the data acquisition time down to the second unit; since there may be multiple data that have the same data acquisition time but differ in the order in which they were acquired by the electronic device (100), such multiple data can be merged into one data. A more specific embodiment regarding the process of merging data will be described in detail below with respect to FIG. 6.

[0063] Subsequently, the electronic device (100) may add information regarding at least one additional data value and information regarding the data type of each of at least one additional data for each of the data regarding the merged vehicle battery, based on a set analysis purpose. Here, the information regarding the additional data value and the information regarding the data type of each of the at least one additional data may be set according to the set analysis purpose, and the analysis purpose may include at least one of a data analysis purpose by vehicle type, a data analysis purpose by vehicle, and a data analysis purpose by data acquisition date. A more specific embodiment in which the electronic device (100) generates data to be analyzed by adding some of the data included for each of the data regarding the merged vehicle battery according to the analysis purpose will be described in detail below with respect to FIG. 7.

[0064] The above series of vehicle data processing processes can be performed, for example, based on the ELT (Extract, Load, Transform) method, and the ELT method is a method of processing data in the order of extracting data, loading it into a storage, and transforming it as needed.

[0065] Meanwhile, the electronic device (100) can store the generated data to be analyzed, for example, by utilizing a storage system such as a data warehouse. The storage system utilized is a system capable of efficiently storing and managing structured data, and may be a storage facility where data refined into a form suitable for high-performance data analysis is stored. Through this, the electronic device (100) can enable higher-performance data analysis of the data to be analyzed to be performed quickly and efficiently.

[0066]

[0067] FIG. 3 shows a plurality of vehicle data obtained according to one embodiment.

[0068] Referring to FIG. 3, the plurality of vehicle data (300) obtained by the electronic device (100) may include vehicle data obtained randomly from three vehicles, and may include 20 vehicle data as the number of vehicles is set to 20. The plurality of vehicle data (300) may be in the form of a list in which information included in each vehicle data is a column name and information corresponding to the column constitutes a column value. The column names may include, for example, a date (310), a vehicle ID (320), a key (330), a value (340), and an event number (350). The date (310) may be information regarding the date obtained by the electronic device (100), the vehicle ID (320) may be identification information of the vehicle, the key (330) may be information regarding the corresponding data type, the value (340) may be information regarding the data value, and the event number (350) may be information regarding the order obtained by the electronic device (100). Meanwhile, when referring to multiple vehicle data (300), the acquired data may be in the form of being listed in the order of event numbers, but this is merely an example, and the vehicle data may be acquired in various forms.

[0069]

[0070] FIG. 4 shows data regarding a vehicle battery extracted from a plurality of vehicle data according to one embodiment.

[0071] Referring to FIG. 4, the electronic device (100) can extract data (410) regarding a vehicle battery from a plurality of vehicle data (300). Data regarding a vehicle battery can be extracted, for example, based on a key value representing a data type and a vehicle ID. In the embodiment of FIG. 4, some vehicle data can be extracted as the SoC and pack voltage types are set as data types corresponding to the data regarding the vehicle battery, and A1111 and C333 are set as specific vehicles. That is, the extracted data regarding a vehicle battery (410) may include only vehicle data where the key value corresponds to either SoC or pack voltage and the vehicle ID corresponds to either A1111 or C333.

[0072] Meanwhile, although not illustrated in FIG. 4, in the same example, even if the key values ​​included in the multiple vehicle data (300) have various notation forms such as pack_voltage, pack-vol, etc., the electronic device (100) can use an artificial intelligence-based model to identify all of the key values ​​as meaning pack voltage and extract them as data (410) regarding the vehicle battery.

[0073]

[0074] FIG. 5 shows data regarding a vehicle battery classified and stored according to one embodiment.

[0075] Referring to FIG. 5, the data (500) may be data that has been classified and temporarily stored regarding extracted vehicle batteries. The data (500) may be in a form where the data acquisition date corresponds to July 1, 2024, the vehicle model (vehicle type) is the same as ㄱㄴㄷ, and the data regarding vehicle batteries having the same vehicle ID are classified together. Data having the same vehicle ID may be stored in a column-based storage format (parquet), where the column-based storage format is a data storage format in which data is stored in column units, has high compression efficiency, and supports schema.

[0076]

[0077] FIG. 6 illustrates a process of converting data regarding a stored vehicle battery according to one embodiment.

[0078] Referring to FIG. 6, the electronic device (100) can convert data (500), in which extracted vehicle battery data is classified and temporarily stored, into a form of data (600) that is easy to analyze in order to generate data to be analyzed. For example, the electronic device (100) can merge data in which the values ​​of the temporary column are identical among the data included in the data (500) (S601), and can convert the column configuration of the data during the data merging process (S602). Specifically, the form of the data can be converted so that information that was the value of the key column in the original data (500) becomes the column name of the converted data (600), and information that was the value of the value column becomes the value information of the corresponding column name. More specifically, SoC, which was the value of the key column in the original data (500), becomes the column name of the converted data (600), and 22%, which was the value of the value column, becomes the value of the SoC column in the converted data (600). By referring to the columns of 610, data regarding multiple vehicle batteries acquired for vehicle ID A1111 at 09:00:01 on July 1, 2024, which was omitted in the data (500), is merged into one and included in the converted data (600). Consequently, the key information and value information included in the data regarding each vehicle battery can be converted to form new columns (pack voltage, pack current, temperature). That is, since multiple pieces of information included in multiple data acquired at the same time down to the second are included in a single row, the data can be converted into a form where multiple pieces of information can be checked at a glance.

[0079] Meanwhile, during the conversion process into the converted data (600), a storage time column of 620 may be added. Here, the storage time may refer to the time when the data (500) and the converted data (600) are stored in a temporary storage system. By adding a storage time column in this way to indicate a separately stored time, if the difference from the time of acquisition to the electronic device (100) exceeds a preset time interval, it can be confirmed that an anomaly has occurred during the data analysis process (e.g., an anomaly such as communication failure in a specific area), thereby making it easy to identify that re-analysis is required for vehicle data acquired on a specific date or time of a specific vehicle. For example, the storage time value of 622 differs from the time of acquisition of the data by more than 3 days, and since this has a significant difference compared to most other vehicle data, it can indicate that re-analysis is required for the data acquired on July 1, 2024, for the C3333 vehicle.

[0080]

[0081] FIG. 7 illustrates a process of merging additional data into data regarding a vehicle battery converted according to one embodiment.

[0082] Referring to FIG. 7, additional data (630 and 631) can be further merged into the data transformed by the merge and column configuration, thereby generating data to be analyzed (700). For example, in a case where the battery data of a specific vehicle (A1111 or C3333) is to be analyzed, and due to the characteristics of the vehicle type, the battery type and the number of cells included in the battery pack must be utilized together for battery analysis, the electronic device (100) may set the battery type and the number of cells included in the battery pack as new column names for each of the data regarding each vehicle, and configure the information corresponding to each type as the value information of each column. That is, new columns with various data types as column names may be added depending on the purpose of analysis, and each added column name is a type added to facilitate analysis work, even though it is not obtained by the electronic device (100), and may include various data types depending on the embodiment, such as the power of the battery pack, the current of the pack, and the temperature.

[0083]

[0084] FIG. 8 shows a structure in which data to be analyzed is stored according to one embodiment.

[0085] Referring to FIG. 8, the data to be analyzed (801) can be stored in the form of a data file by setting a path according to the criteria for the purpose of analysis, thereby having a flexible structure that allows for various schema configurations. For example, data regarding the fleet of vehicles and vehicle batteries can be stored in a classified form based on the date, vehicle model, and vehicle identification information obtained by the electronic device (100). Various criteria or combinations of various criteria, such as the fleet of vehicles, the date, vehicle model, and vehicle identification information obtained by the electronic device (100), can be set according to the purpose of analysis, and the electronic device (100) can provide the data to be analyzed (801) in a form that allows the data to be analyzed to be retrieved quickly regardless of what purpose of analysis is set.

[0086]

[0087] FIG. 9 shows a flowchart of a process for extracting data regarding a vehicle battery using an artificial intelligence-based model according to one embodiment.

[0088] According to one embodiment, the electronic device (100) can acquire multiple vehicle data for multiple vehicles (step S910) and input the acquired multiple vehicle data into an artificial intelligence-based model (step S920). More specifically, the electronic device (100) can input multiple vehicle data and one or more specific data types (data types related to batteries) to be extracted into the artificial intelligence-based model. Since the artificial intelligence-based model is trained to extract at least some data including data types and data values ​​corresponding to the data types based on the input data, the electronic device (100) can acquire data related to vehicle batteries output from the artificial intelligence-based model (step S930). Subsequently, the electronic device (100) can classify and store the data related to vehicle batteries based on at least one of vehicle type, vehicle identification information, and data acquisition date, and can convert the stored data related to vehicle batteries into data for analysis in a form corresponding to a set analysis purpose (step S950).

[0089]

[0090] FIG. 10 shows a flowchart of a process for classifying data regarding a vehicle battery according to one embodiment.

[0091] According to one embodiment, the electronic device (100) can acquire a plurality of vehicle data for a plurality of vehicles (step S1010) and can extract data regarding vehicle batteries among the plurality of vehicle data (step S1020). Based on the extracted data regarding vehicle batteries, the electronic device (100) can first classify the data regarding vehicle batteries by the date of data acquisition (step S1030), classify each of the data regarding vehicle batteries classified by the same date by vehicle type (step S1040), and then classify each of the data regarding vehicle batteries classified by the same date and vehicle type by vehicle identification information (step S1050). That is, the electronic device (100) can thereby classify the extracted data regarding vehicle batteries into a subdivided form in which data of the same acquisition date, the same vehicle type, and the same vehicle are grouped together. Subsequently, the electronic device (100) can convert the data regarding vehicle batteries classified and stored in this manner into data for analysis in a form corresponding to a set analysis purpose (step S1060).

[0092]

[0093] FIG. 11 shows a flowchart of a process for converting data regarding a stored vehicle battery into data to be analyzed according to one embodiment.

[0094] According to one embodiment, the electronic device (100) can acquire multiple vehicle data for multiple vehicles (step S1110), extract data regarding vehicle batteries among the multiple vehicle data (step S1120), and classify and store data regarding vehicle batteries based on at least one of vehicle type, vehicle identification information, and data acquisition date (step S1130). The classification criteria for data regarding vehicle batteries may be set as a combination of various criteria according to the embodiment. Subsequently, the electronic device (100) can perform merging among at least one data regarding vehicle batteries among the stored data regarding vehicle batteries that has the same data acquisition time (step S1140). The electronic device (100) can convert data into a form that is more efficient for analysis by merging and compressing multiple data that have the same data acquisition time (in seconds) but different acquisition order into one data. Subsequently, the electronic device (100) may add information regarding at least one additional data value and information regarding the data type of at least one additional data for each of the data regarding the merged vehicle battery, based on the set analysis purpose (step S1150). The type and data value of the added data may be set to correspond to the analysis purpose, for example, and may be type and value information related to one or more data that is not obtained from multiple data sources but is required or useful for analysis. Thus, the electronic device (100) can generate data to be analyzed by undergoing a process of merging additional data while considering the analysis purpose and merging at least some of the data regarding the vehicle battery, and can provide it to facilitate analysis.

[0095]

[0096] The electronic device according to the embodiments described above may include a processor, memory for storing and executing program data, permanent storage such as a disk drive, a communication port for communicating with an external device, and user interface devices such as a touch panel, a key, a button, etc. Methods implemented as software modules or algorithms may be stored on a computer-readable recording medium as computer-readable code or program instructions executable on the processor. Here, computer-readable recording media include magnetic storage media (e.g., ROM (read-only memory), RAM (random-access memory), floppy disks, hard disks, etc.) and optical reading media (e.g., CD-ROM, DVD (Digital Versatile Disc)). The computer-readable recording medium may be distributed across networked computer systems, allowing computer-readable code to be stored and executed in a distributed manner. The medium may be readable by a computer, stored in memory, and executed by a processor.

[0097] The present embodiment 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 embodiment may employ integrated circuit configurations such as memory, processing, logic, look-up tables, etc., capable of executing 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 present embodiment 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 present embodiment 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.

[0098] The aforementioned embodiments are merely examples, and other embodiments may be implemented within the scope of the claims set forth below.

Claims

1. In an electronic device, Transceiver; processor; and It includes one or more memories that store one or more instructions, and When executing the above one or more instructions, the processor, Acquire multiple vehicle data for multiple vehicles, and Extracting data regarding vehicle batteries from the plurality of vehicle data above, and Data regarding the vehicle battery is classified and stored based on at least one of the vehicle type, vehicle identification information, and data acquisition date. An electronic device configured to convert the above-mentioned stored data regarding a vehicle battery into a form of target data for analysis corresponding to a set analysis purpose.

2. In Paragraph 1, Each of the above plurality of vehicle data is, An electronic device wherein each of the plurality of vehicle data includes at least one of information regarding the time of acquisition by the electronic device, information regarding the vehicle type, identification information of the vehicle, information regarding the corresponding data type, and information regarding the data value.

3. In Paragraph 2, An electronic device wherein each of the plurality of vehicle data further includes information regarding the order in which each of the plurality of vehicle data is acquired by the electronic device.

4. In Paragraph 1, When executing the above one or more instructions, the processor, Identify the data type corresponding to each of the above multiple vehicle data, and An electronic device configured to identify at least one vehicle data among the plurality of vehicle data, wherein the identified data type corresponds to at least one type related to a vehicle battery, as data related to the vehicle battery.

5. In Paragraph 1, When executing the above one or more instructions, the processor, Input the above multiple vehicle data into an artificial intelligence-based model, and An electronic device configured to acquire data regarding the vehicle battery output from the above artificial intelligence-based model.

6. In Paragraph 5, The above artificial intelligence-based model is, An electronic device, which is a machine learning model constructed by modeling the correlation between the learning input data and the learning output data, wherein the data values ​​obtained for each of a plurality of vehicles and the data type notation forms corresponding to each of the obtained data values ​​are used as learning input data, and the data types corresponding to the data obtained for each of the plurality of vehicles are used as learning output data.

7. In Paragraph 1, When executing the above one or more instructions, the processor, Classify the data regarding the vehicle battery according to the data acquisition date, Each of the data regarding vehicle batteries classified by the above dates is classified by vehicle type, and An electronic device configured to classify each of the data regarding vehicle batteries classified by the above date and vehicle type according to the above vehicle identification information.

8. In Paragraph 1, When executing the above one or more instructions, the processor, Among the above-mentioned stored data regarding vehicle batteries, merging is performed between at least one data regarding a vehicle battery that has the same data acquisition time, and An electronic device configured to add information regarding at least one additional data value and information regarding the data type of each of the at least one additional data for each of the data regarding the merged vehicle battery, based on the above-set analysis purpose.

9. In Paragraph 8, An electronic device in which information regarding the additional data values ​​and information regarding the data types of each of the at least one additional data are set according to the set analysis purpose.

10. In Paragraph 1, The purpose of the above analysis is, An electronic device comprising at least one of the purposes of data analysis by vehicle type, data analysis by vehicle, and data analysis by data acquisition date.

11. A method for processing data of a vehicle performed by an electronic device, A step of acquiring multiple vehicle data for multiple vehicles; A step of extracting data regarding a vehicle battery from the plurality of vehicle data above; A step of classifying and storing data regarding the vehicle battery based on at least one of the vehicle type, vehicle identification information, and data acquisition date; and A method for processing vehicle data, comprising the step of converting the stored data regarding the vehicle battery into analysis target data in a form corresponding to a set analysis purpose.

12. A computer-readable, non-transient recording medium having a program for executing the method of paragraph 11 on a computer.

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