Electronic device, recording medium, and vehicle data processing method thereof

Abstract

An electronic device for processing vehicle data is provided. The electronic device can be configured to define a data conversion rule for converting each obtained data based on information about a type of a data collection device and information about a type of a vehicle included in the data, obtain a plurality of pieces of vehicle data for a plurality of vehicles based on a charging state of the plurality of vehicles, extract one or more pieces of to-be-converted data from among the plurality of pieces of vehicle data based on a driving state of each of the plurality of vehicles, classify and store the one or more pieces of to-be-converted data, convert each of one or more pieces of first to-be-converted data corresponding to a first vehicle among the plurality of vehicles from among the one or more pieces of to-be-converted data into first converted data based on a first data conversion rule corresponding to the first vehicle, and convert each of the one or more pieces of first converted data into to-be-analyzed data in a form corresponding to a set analysis purpose.

Description

Technical Field

[0001] This disclosure relates to electronic devices, recording media, and methods for processing vehicle data thereof, and more specifically, to a technique for processing vehicle data of electric vehicles based on data characteristics to facilitate easier analysis of the vehicle data.

[0002] This application claims the benefit of Korean Patent Application No. 2024-0134037, filed on October 2, 2024, with the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference in its entirety. Background Technology

[0003] The penetration rate of electric vehicles is increasing dramatically. Therefore, the need to acquire and analyze vehicle data related to electric vehicles in various ways is growing for effective management and operation. Recently, data acquired from electric vehicles has been centralized, managed, and shared on a central server. Besides the benefits of centralized management and sharing, because this vehicle data is collected from heterogeneous collection devices and vehicle types, each piece of data can have a different structure and include different information. Therefore, data preprocessing is necessary to enable integrated processing of various data types. Furthermore, due to these data preprocessing tasks, providing the acquired data for vehicle data analysis can be time-consuming. Therefore, there is a growing need for a data preprocessing method that can overcome the differences in data characteristics between vehicle data, process data in a consistent format, and perform data preprocessing more quickly. Summary of the Invention

[0004] Technical issues

[0005] Example embodiments of the present disclosure are proposed to solve the above-mentioned problems, and an electronic device, a recording medium and a vehicle data processing method thereof are provided.

[0006] The technical objectives to be achieved by this example implementation are not limited to the technical tasks described above, and other technical tasks can be inferred from the following example implementation.

[0007] Technical solution

[0008] According to an example embodiment, an electronic device is provided, comprising: a transceiver, a processor, and one or more memories configured to store one or more instructions, wherein, when executing one or more instructions, the processor is configured to: define a data conversion rule for each piece of acquired data based on information included in the data regarding the type of data collection device and information regarding the type of vehicle; obtain multiple pieces of vehicle data for the multiple vehicles based on the charging status of the multiple vehicles; extract one or more pieces of conversion target data from the multiple pieces of vehicle data based on the operating status of each of the multiple vehicles; classify and store the one or more pieces of conversion target data; convert each of the one or more pieces of first conversion target data corresponding to the first vehicle among the multiple vehicles into first conversion data based on a first data conversion rule corresponding to a first vehicle among the multiple vehicles; and convert each of the one or more pieces of first conversion data into analysis target data in a form corresponding to a set analysis purpose.

[0009] According to an example implementation, the data transformation rule may be a rule configured to transform each piece of data transformed based on a corresponding data transformation rule into data with the same structure that includes information of the same data type.

[0010] According to the example implementation, the data transformation rules can be set for each type of the data collection device and each type of the vehicle.

[0011] According to an example implementation, the data collection device can be configured to collect vehicle data related to a vehicle equipped with the data collection device and send the collected vehicle data to the electronic device.

[0012] According to an example implementation, when executing one or more instructions, the processor can be configured to obtain the plurality of vehicle data at a time point when it is confirmed that the charging state of the plurality of vehicles corresponds to the state at which the charging cycle or discharging cycle of the vehicle is terminated.

[0013] According to an example implementation, when executing one or more instructions, the processor can be configured to identify one or more pieces of vehicle data whose vehicle operating state corresponds to the driving state of the vehicle among the plurality of vehicle data as the one or more pieces of conversion target data.

[0014] According to an example implementation, when executing one or more instructions, the processor can be configured to classify and store the one or more pieces of conversion target data based on at least one of the type of data collection device, vehicle type, and data acquisition date.

[0015] According to an example implementation, when executing one or more instructions, the processor can be configured to transform each of the one or more first transformation target data based on a first resource corresponding to the first vehicle.

[0016] According to an example implementation, the analysis objective may include at least one of an analysis objective for each data collection device and an analysis objective for each vehicle type.

[0017] According to an example implementation, a method for processing vehicle data performed by an electronic device is provided. The method includes the following steps: defining a data transformation rule for each piece of acquired data based on information included in the data regarding the type of data collection device and information regarding the type of vehicle; obtaining multiple pieces of vehicle data for multiple vehicles based on the charging status of multiple vehicles; extracting one or more pieces of transformation target data from the multiple pieces of vehicle data based on the operating status of each of the multiple vehicles; classifying and storing the one or more pieces of transformation target data; converting each of the one or more pieces of first transformation target data corresponding to the first vehicle among the multiple vehicles into first transformation data based on a first data transformation rule corresponding to the first vehicle; and converting each of the one or more pieces of first transformation data into analysis target data in a form corresponding to a set analysis purpose.

[0018] According to an example embodiment, a non-transitory computer-readable recording medium is provided having a program for performing a method of processing vehicle data, the method comprising the steps of: defining a data conversion rule for converting each piece of acquired data based on information included in the data regarding the type of data collection device and information regarding the type of vehicle; obtaining multiple pieces of vehicle data for multiple vehicles based on the charging status of multiple vehicles; extracting one or more pieces of conversion target data from the multiple pieces of vehicle data based on the operating status of each of the multiple vehicles; classifying and storing the one or more pieces of conversion target data; converting each of the one or more pieces of first conversion target data corresponding to the first vehicle among the multiple vehicles into first conversion data based on a first data conversion rule corresponding to a first vehicle among the multiple vehicles; and converting each of the one or more pieces of first conversion data into analysis target data in a form corresponding to a set analysis purpose.

[0019] Beneficial effects

[0020] According to the example implementation, vehicle data processed into a form that can be optimized for various vehicle data analysis purposes can be provided more quickly and economically.

[0021] According to the example implementation, consistent analytical performance is possible by converting various vehicle data collected from various sources (vehicle type, data collection device type, etc.) into data with an integrated structure, and thus the accuracy and reliability of vehicle data analysis can ultimately be improved.

[0022] The effects of the exemplary embodiments are not limited to those described above, and those skilled in the art will clearly understand from the description of the claims other effects not described. Attached Figure Description

[0023] Figure 1 A block diagram of an electronic device according to an example embodiment is shown.

[0024] Figure 2 A flowchart of a vehicle data processing method according to an example implementation is shown.

[0025] Figure 3 This illustrates multiple vehicle data entries for which data transformation rules have been applied according to an example implementation.

[0026] Figure 4 A flowchart illustrating the processing of multiple vehicle data based on a charging cycle or discharging cycle according to an exemplary embodiment is shown.

[0027] Figure 5 The illustration shows operations performed by a vehicle, a data collection device, and an electronic device, respectively, for processing multiple vehicle data according to an example implementation.

[0028] Figure 6 The process of processing vehicle data based on resources set for each vehicle, according to an example implementation, is shown. Detailed Implementation

[0029] The terminology used in the example embodiments is selected, to the extent possible, from commonly used terms that are widely available, while taking into account the functionality of this disclosure. However, these terms may vary depending on the intent or precedent of those skilled in the art, the emergence of new technologies, etc. Furthermore, in some cases, there are terms arbitrarily chosen by the applicant, and in these cases, their meanings will be described in detail in the corresponding description. Therefore, the terms used in this disclosure should be defined based on their meanings and the content of this disclosure, rather than on their simple names.

[0030] Throughout the specification, when a part is described as "including" or "containing" a component, it does not exclude another component but may further include another component, unless otherwise stated. Furthermore, terms such as "...unit," "...group," and "...module" described in the specification refer to a unit that performs at least one function or operation, which may be implemented as hardware, software, or a combination thereof.

[0031] The expression “at least one of a, b and c” described throughout the specification may include “only a”, “only b”, “only c”, “a and b”, “a and c”, “b and c” or “all of a, b and c”.

[0032] In the following, 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 them. However, the present disclosure can be implemented in many different forms and is not limited to the exemplary embodiments described herein.

[0033] Hereinafter, with reference to the accompanying drawings, exemplary embodiments of the present disclosure relating to electronic devices for processing vehicle data are described in detail.

[0034] Figure 1 A block diagram of an electronic device according to an example embodiment is shown.

[0035] Reference Figure 1 According to an example implementation, electronic device 100 may include transceiver 110, processor 120 and memory 130. Figure 1 The electronic device 100 shown only illustrates elements relevant to this example embodiment. Therefore, those skilled in the art will understand that, in addition to... Figure 1 In addition to the components shown, other general-purpose components may also be included.

[0036] For example, electronic device 100 may include a communication device, which includes one or more transceivers 110, input devices, and output devices. The communication device is a means for performing wired / wireless communication and can communicate with external electronic devices. The external electronic devices may be terminals or servers. Furthermore, the communication technologies used by the communication device may include Global System for Mobile Communications (GSM), Code Division Multiple Access (CDMA), Long Term Evolution (LTE), 5G, Wireless LAN (WLAN), Wi-Fi, Bluetooth, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), ZigBee, Near Field Communication (NFC), etc. Input devices may be, for example, conventional buttons or keyboards, mice, microphones for receiving voice signals, cameras, and various other forms of input devices for detecting or receiving user input. Output devices may be, for example, displays that output images, speakers that output sound, haptic devices that generate vibrations, and various other forms of output devices.

[0037] According to an example implementation, electronic device 100 may be a server for acquiring and processing multiple vehicle data sets, and the server may be, for example, a cloud server for collecting, managing, and processing data about various vehicles. The multiple vehicle data sets acquired by electronic device 100 are different data about each of a plurality of vehicles, including various types of vehicles, and can be understood as including all vehicle-related data such as the vehicle battery's SoC, battery pack voltage, driving speed, and shift state, regardless of type. Furthermore, the multiple vehicle data sets can be obtained via, for example, a transceiver 110 of electronic device 100 from a vehicle data collection device (On-Board Diagnostics, OBD) installed in each vehicle. However, the method by which electronic device 100 acquires multiple vehicle data sets is not limited to the examples described above, and it will be apparent to those skilled in the art that electronic device 100 can be obtained from various devices configured to communicate via transceiver 110. Furthermore, the type of electronic device 100 is not limited thereto, and the various example implementations of this disclosure can be applied to various devices configured to acquire and process vehicle-related data.

[0038] Processor 120 can control the overall operation of electronic device 100 and process data and signals. Processor 120 may include at least one hardware unit. Furthermore, processor 120 may be operated by one or more software modules generated by executing program code stored in memory 130. Processor 120 may include memory, and processor 120 can execute program code stored in memory to control the overall operation of electronic device 100 and process data and signals.

[0039] Processor 120 can be implemented as a computer or similar device using hardware, software, or a combination thereof. In terms of hardware, processor 120 can be implemented as electronic circuitry that processes electrical signals and performs control functions, and in terms of software, processor 120 can be implemented as a program that drives the hardware processor 120. Furthermore, unless otherwise stated in the following description, the operation of electronic equipment can be interpreted as being performed under the control of processor 120. In other words, when modules implemented in a system for processing vehicle data are executed, these modules can be interpreted as processor 120 controlling the operation of the electronic equipment below.

[0040] Memory 130 can store various types of information. Memory 130 can store data temporarily or semi-permanently. For example, memory 130 of electronic device 100 can store data related to the operating system (OS) used to operate electronic device 100. Examples of memory 130 may include hard disk drive (HDD), solid-state drive (SSD), flash memory, read-only memory (ROM), random access memory (RAM), etc. These memories 130 can be provided as built-in or removable types.

[0041] In summary, various exemplary implementations can be implemented by various means. For example, various exemplary implementations can be implemented by hardware, firmware, software, or a combination thereof.

[0042] Regarding hardware implementation, the methods described in various example implementations can be implemented using one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, etc.

[0043] When implemented via firmware or software, the methods according to various example implementations can be implemented in the form of modules, programs, or functions that perform the above-described functions or operations. For example, software code can be stored in memory and executed by a processor. Memory can be located inside or outside the processor and can exchange data with the processor through various known means.

[0044] Figure 2 A flowchart of a vehicle data processing method according to an example implementation is shown.

[0045] In operation S210, based on information included in the data regarding the type of data collection device and the type of vehicle, electronic device 100 can define data transformation rules for each piece of acquired data. Specifically, the defined multiple data transformation rules can be rules configured to convert each piece of transformed data into data with the same structure, including information of the same data type. In other words, the rules can be rules that transform data so that each piece of data has the same structure, regardless of the characteristics of the various data sources (e.g., various devices that can communicate with electronic device 100).

[0046] The information that forms the basis for setting these data transformation rules can include information about the type of data collection device and information about the vehicle type. First, when vehicles are of the same type, information about the vehicle type typically shares similar data structures and vehicle characteristics, and therefore can be included in the identification elements used to set the data transformation rules.

[0047] Furthermore, the data collection device is a means of collecting vehicle data related to a vehicle equipped with the data collection device and transmitting the collected vehicle data to an electronic device, and may be, for example, an OBD device installed in the vehicle. Additionally, the data collection device may be connected to the vehicle's OBD port to collect vehicle data and may send and receive data with the vehicle via Controller Area Network (CAN). When the types of data collection devices differ, even if the vehicles are of the same type, the type of information included in the vehicle data obtained from each vehicle equipped with a data collection device may differ. Therefore, information about the type of data collection device may be included in the identification element along with information about the type of vehicle. Here, the information about the data collection device included in each piece of vehicle data identified by the electronic device 100 may include, for example, the manufacturer of the data collection device, the model of the data collection device, etc.

[0048] Based on the information regarding the type of data collection equipment equipped with the vehicle and the type of vehicle as described above, the electronic device 100 can set data conversion rules according to the type of data collection equipment and the type of vehicle.

[0049] In operation S220, based on the charging status of multiple vehicles, electronic device 100 can acquire multiple vehicle data points for multiple vehicles. These multiple vehicle data points can be acquired at specific time points, such as confirming that the charging status of multiple vehicles corresponds to a specific state, and can be in various non-uniform forms. In the prior art, multiple vehicle data points are acquired at preset intervals and in preset quantities. However, in this case, during peak periods when vehicle data collection is excessively concentrated, a backlog of vehicle data occurs until all data for that period is processed, requiring a significant waiting time, and ultimately, this may overload electronic device 100. Therefore, the overload problem of electronic device 100 can be prevented by setting the vehicle data to be acquired based on the charging status of the vehicle corresponding to each of the multiple acquired vehicle data points.

[0050] According to the example implementation, each of the multiple vehicle data entries may include at least one of the following: information regarding the date and time of obtaining each of the multiple vehicle data entries, information regarding the vehicle type, vehicle identification information, information regarding the corresponding data type, and information regarding the data value. Here, the information regarding the date and time of obtaining the data may include, for example, information regarding the year, month, and day of data acquisition.

[0051] Furthermore, for example, information about the corresponding data type can be identified based on the ID information of the CAN communication-based signal. As a specific example implementation, it can be configured such that when the ID of information included in the vehicle data is 123, this indicates information about the vehicle speed, and when the ID is 456, this could represent the battery pack voltage, and the correlation between the ID information and the data type can vary depending on the vehicle type. In other words, in the example implementation, information included in each of multiple pieces of vehicle data can be identified based on the relationship between the ID information and the data type information based on the vehicle type.

[0052] In operation S230, based on the operating status of each of the multiple vehicles, electronic device 100 can extract one or more conversion target data from multiple vehicle data sets. Specifically, electronic device 100 can identify and extract one or more vehicle data sets corresponding to the vehicle's operating status and driving status from the multiple vehicle data sets, and the vehicle's operating status can be identified based on information about the vehicle's operating status included in each of the multiple vehicle data sets. In other words, all of the one or more conversion target data sets can be data about the corresponding vehicle's driving status. Data about the vehicle's driving status reflects the vehicle's performance, status, and the driver's driving behavior in real time in the actual driving environment. Therefore, data about the vehicle's driving status may be more useful for vehicle management and vehicle data analysis to improve vehicle performance, thus providing effective insights about the vehicle. Therefore, to prevent unnecessary resource abuse, electronic device 100 can extract and utilize data about the vehicle's driving status as one or more conversion target data sets from the multiple vehicle data sets before processing for data analysis.

[0053] In operation S240, electronic device 100 can classify and store one or more pieces of conversion target data. Specifically, electronic device 100 can classify and store one or more pieces of conversion target data based on at least one of the type of data collection device, vehicle type, and data acquisition date. For example, electronic device 100 can classify conversion target data by the type of data collection device, classify conversion target data classified by the type of data collection device by the type of vehicle, and classify conversion target data classified by both the type of data collection device and the type of vehicle by the data acquisition date. In such an example implementation, the conversion target data classified by electronic device 100 can ultimately be classified into a more detailed form where data from the same data collection device, the same vehicle type, and the same acquisition date are grouped together. Furthermore, the criteria used to classify the conversion target data are not limited to this, and according to the example implementation, classification can be based on more appropriately set criteria, which are based on the set analysis purpose.

[0054] When the target data for transformation is not categorized after extraction, it is difficult to identify interconnections because each data point is unstructured and lacks a fixed structure, and therefore the target data for transformation can be in a meaningless and mixed form. However, as described with respect to the example implementation, when data about the same vehicle is categorized based on at least one of the data collection device type, vehicle type, and data acquisition date, even if the data is unstructured, related data are grouped together, which may have the advantage that data preprocessing (transformation) and other processing operations that can be performed later can be performed more efficiently and economically.

[0055] After classifying the target data for transformation in this way, electronic device 100 can store the classified target data for transformation by utilizing a storage system such as a data lake that stores unprocessed raw data. By storing the classified data, electronic device 100 can immediately retrieve the classified data to be used from the storage system when further analysis or processing is required.

[0056] In operation S250, based on a first data conversion rule corresponding to a first vehicle among a plurality of vehicles, electronic device 100 can convert each of the one or more first conversion target data corresponding to the first vehicle into first conversion data. In other words, electronic device 100 can convert each piece of conversion target data into conversion data by applying the data conversion rule corresponding to each vehicle to the classified and stored conversion target data. The data conversion rule corresponding to each vehicle can be one of a plurality of data conversion rules corresponding to the type of each vehicle and the type of data collection device installed in each vehicle. Each of the multiple pieces of conversion data generated in this way can have the same data structure; therefore, consistent data processing and analysis are possible.

[0057] In operation S260, electronic device 100 can convert each of one or more pieces of transformed data into target data in a form corresponding to the set analysis purpose. The conversion to target data can be a process of processing data according to the set analysis purpose to facilitate analysis. Here, the analysis purpose can include, for example, at least one of the analysis purpose for each data collection device and the analysis purpose for each vehicle type, and the analysis purpose can be ultimately set to improve the efficiency of battery performance and lifespan management of electric vehicles, taking action based on various criteria to facilitate vehicle data analysis through battery management algorithms. Based on the set analysis purpose, electronic device 100 can, for example, categorize each piece of transformed data by the type of data collection device or by vehicle type, and at this time, the structure of each piece of transformed data can be converted into a column-based form. Here, the column-based form (Parquet) is a form that provides the content of each piece of information based on columns, and the data is stored in columns, thus it has high compression efficiency and can be a schema-supporting form.

[0058] In another example implementation, the electronic device 100 may add at least one additional data type and corresponding information to each transformed data based on a set analysis purpose. Here, the additional data type and corresponding information can be set according to the set analysis purpose, and the analysis purpose may include at least one of data analysis purpose by vehicle type, data analysis purpose by vehicle, and data analysis purpose by data acquisition date. The process by which the electronic device 100 transforms data according to the analysis purpose is not limited to the above example, and according to the example implementation, the transformation process can be performed in various forms for each analysis purpose.

[0059] The series of vehicle data processing operations described above can be performed based on, for example, the Extract, Load, Transform (ELT) method. In the ELT method, data can be extracted, then loaded into a storage device, and processed in the order of transformation as needed.

[0060] Figure 3 This illustrates multiple vehicle data entries for which data transformation rules have been applied according to an example implementation.

[0061] Reference Figure 3The target data for conversion extracted by electronic device 100 may include vehicle data A 301 and vehicle data B 302, which are driving status data of each of vehicles A and B from multiple vehicle data obtained from vehicles A and B. Since the two vehicles have different vehicle types and at least one different data collection device, vehicle data A 301 and vehicle data B 302 may include different data types. Electronic device 100 can generate converted vehicle data A' 311 by applying data conversion rule A corresponding to vehicle data A 301 to vehicle data A 301, and can generate converted vehicle data B' 312 by applying data conversion rule B corresponding to vehicle data B 302 to vehicle data B 302. When the corresponding data conversion rule is applied to the target data for conversion, converted data including the same data type can be created.

[0062] The data types included according to the application of data conversion rules can be, for example, data types that are typically included in the data before conversion. In another example implementation, the types can be types that can be extracted as common data types, even if they are not explicitly common types. For example, when vehicle data A 301 includes a data type called "average battery cell voltage" and vehicle data B 302 includes a data type called "battery cell voltage" for each battery cell, even if there is no data type called "average battery cell voltage" in vehicle data B 302, information corresponding to the data type called "average battery cell voltage" can be extracted from the values ​​of the data type called "battery cell voltage" for each battery cell. Therefore, both vehicle data A' 311 and vehicle data B' 312 can be converted to include the data type "average battery cell voltage" by data conversion rules.

[0063] Furthermore, the converted data generated by applying data conversion rules can include data with the same structure. Specifically, the format used to represent the same data type can be different for each vehicle (for each vehicle type and data collection device), and the data type symbol form for each vehicle can be, for example, the actual symbol form of the data type identified based on the relationship between ID information and data type information according to the signals of CAN communication. As a more specific example implementation, relative to the data type concerning the battery pack voltage, vehicle 1 can be represented as "pack_voltage", vehicle 2 as "packVoltage", vehicle 3 as "pack-vol", and vehicle 4 as "pvoltage". Each data type included in the conversion target data can be represented in various inconsistent forms, but by applying data conversion rules, all converted data can be generated to consistently display the data type concerning the battery pack voltage as "pack_voltage".

[0064] Figure 4 A flowchart illustrating the processing of multiple vehicle data based on a charging cycle or discharging cycle according to an exemplary embodiment is shown.

[0065] In operation S410, based on information included in the data regarding the type of data collection device and the type of vehicle, electronic device 100 can define data transformation rules for each piece of acquired data.

[0066] In operation S420, at the point in time when the charging state is identified as corresponding to the termination of a vehicle's charging or discharging cycle, the electronic device 100 can acquire multiple vehicle data points. In other words, the electronic device 100 can acquire vehicle data from a specific vehicle corresponding to the termination of a charging or discharging cycle. Therefore, the electronic device 100 can acquire vehicle data for each vehicle at an independent point in time, and furthermore, considering the need to process vehicle data for each vehicle based on independent resources as described below, rapid data processing can be performed on each vehicle, regardless of the number of vehicles from which vehicle data is acquired.

[0067] In operation S430, based on the operating status of each of the multiple vehicles, electronic device 100 can extract one or more pieces of target data from multiple vehicle data.

[0068] In operation S440, electronic device 100 can classify and store one or more pieces of conversion target data.

[0069] In operation S450, based on the first data conversion rule corresponding to the first vehicle among a plurality of vehicles, the electronic device 100 can convert each of the one or more first conversion target data corresponding to the first vehicle into first conversion data.

[0070] In operation S460, electronic device 100 can convert each of one or more pieces of transformed data into target data in a form corresponding to the set analysis purpose.

[0071] Figure 5 The illustration shows operations performed by a vehicle, a data collection device, and an electronic device, respectively, for processing multiple vehicle data according to an example implementation.

[0072] Reference Figure 5 The data processing method according to this disclosure can be executed based on the relationship between vehicle 500, data collection device 510, and electronic device 100. Figure 5 In this process, vehicle 500 may include multiple vehicles, and data collection device 510 may include all data collection devices corresponding to each vehicle. When discharging and charging begin in a specific vehicle in operation S501, the data collection device installed on the vehicle can be activated in operation S511, and while the vehicle is discharging or charging in operation S520, the activated data collection device can continue collecting vehicle data in operation S512. Furthermore, when discharging or charging of a vehicle is terminated in operation S503, the data collection device can terminate the collection of vehicle data in operation S513, and therefore, in operation S512, the data collection device can continuously send the collected vehicle data to electronic device 100. Thus, electronic device 100 can obtain multiple vehicle data from each vehicle that has completed discharging or charging in operation S520. In operation S530, based on the vehicle data obtained when discharging or charging of each vehicle is completed, electronic device 100 can perform processing on the vehicle data. Furthermore, in operation S540, the processed and generated analysis target data can be stored in electronic device 100 in column-based form, the stored analysis target data itself can be provided in operation S560, the target data can be analyzed according to the analysis purpose in operation S550, and it can be provided to each vehicle in operation S504.

[0073] The data conversion process of operation S530 performed by electronic device 100 may include several operations. Specifically, in order to set data conversion rules corresponding to each vehicle data, in operation S531, electronic device 100 may first identify the type of the corresponding data collection device and the type of vehicle. Furthermore, in operation S532, based on the driving cycle of the vehicle corresponding to each vehicle data, vehicle operation data (vehicle data regarding driving status) may be extracted. Each extracted vehicle driving data may be one or more conversion target data of this disclosure, and in operation S533, electronic device 100 may classify and store such operation data. Here, classification and storage may be a task of temporarily storing the original data itself before conversion. Furthermore, in operation S534, electronic device 100 may apply data conversion rules corresponding to each stored conversion target data based on the type of data collection device and the type of vehicle. In operation S535, additional data conversion for each conversion data may be performed in a form corresponding to the set analysis purpose.

[0074] The analysis target data generated by performing the series of data transformation operations described above can be stored and provided within the electronic device 100 or used for analysis. As a specific example implementation, the analysis target data stored in a column-based format can be stored in a hive-style partition structure, where data is divided into multiple partitions to efficiently store large amounts of data and find necessary data more quickly. In other words, each piece of analysis target data, categorized according to specific criteria based on the analysis purpose, can be stored in a different directory. For example, the provision of the column-based analysis target data stored in this manner in operation S560 can be performed in response to user input. In the example implementation, the provided analysis target data can also be stored in a cloud environment using storage such as cloud storage. In such an example implementation, the analysis target data can be used by various query tools to search for data, and by various visualization tools to visualize the stored data in the form of graphs or charts. Furthermore, operation S550, which performs analysis according to the analysis purpose, can be performed on the electronic device 100, and according to the example implementation, the analysis can also be performed via another terminal or server capable of communicating with the electronic device 100.

[0075] When an analysis is performed in operation S550 according to the analysis purpose, the results identified from the analysis data can be sent to each corresponding vehicle, and therefore the analysis results can be identified for each vehicle in operation S504. Since the data processing in operation S530 is performed based on the charging state of each vehicle, the analysis results in the vehicles in operation S504 can be identified without spending a long time from the time each vehicle is discharging or charging.

[0076] Furthermore, the process of generating analysis target data based on the transformation target data in operation S570 includes operations S534 and S535, and operation S570 can be performed on each piece of data corresponding to each vehicle in the classified and stored transformation target data based on independent resources for each vehicle. Here, resources can refer to resources that can be used for data processing, and for example, can be serverless resources, provided as a type of cloud service that allows it to run independently of servers in a cloud computing environment. Therefore, electronic device 100 can perform the task of transforming each piece of transformation target data for each vehicle in the resources set for each vehicle more quickly (e.g., within 1 minute), and can process data at a consistent speed and process even as the number of analysis target vehicles increases.

[0077] Figure 6 The process of processing vehicle data based on resources set for each vehicle, according to an example implementation, is shown.

[0078] Reference Figure 6 The electronic device 100 can convert one or more conversion target data 601 from multiple vehicles into analysis target data. Specifically, a first resource 610 corresponding to the first vehicle and a second resource 620 corresponding to the second vehicle can be set. In other words, resources can be set for each vehicle. Therefore, in operation S611, the electronic device 100 can convert each of the one or more first conversion target data corresponding to the first vehicle into first conversion data based on the first data conversion rule in the first resource 610 corresponding to the first vehicle, and in operation S612, each of the one or more first conversion data can be converted into analysis target data in a form corresponding to the set analysis purpose. Simultaneously or in parallel, in operation S621, the electronic device 100 converts each of the one or more second conversion target data corresponding to the second vehicle into second conversion data based on the second data conversion rule in the second resource 620 corresponding to the second vehicle, and in operation S622, each of the one or more second conversion data can be converted into analysis target data in a form corresponding to the set analysis purpose.

[0079] In this example implementation, where the resource set for each vehicle is a serverless resource, each resource can be activated for a specific amount of time to perform a data transformation task, and then automatically terminate and return. Therefore, the data transformation process, handled as a vehicle-based independent resource, can be performed more efficiently and can also significantly contribute to cost reduction.

[0080] The apparatus according to the above-described exemplary embodiments may include a processor, a memory for storing and executing program data, a permanent storage device such as a disk drive, a communication port for communicating with external devices, and a user interface device such as a touch panel, buttons, and keypads. The method of implementation as a software module or algorithm is computer-readable code or program instructions executable on a processor and stored on a computer-readable recording medium. Here, computer-readable recording media include magnetic storage media (e.g., read-only memory (ROM), random access memory (RAM), floppy disks, and hard disks) and optically readable media (e.g., CD-ROMs, digital versatile discs (DVDs)). The computer-readable recording medium can be distributed among network-connected computer systems, allowing computer-readable code to be stored and executed in a distributed manner. The medium can be read by a computer, stored in memory, and executed on a processor.

[0081] The example implementation can be represented by functional block elements and various processing steps. Functional blocks can be implemented in any number of hardware and / or software configurations that perform a specific function. For example, the example implementation can employ integrated circuit configurations such as memory, processing, logic, and / or lookup tables, which can perform various functions by controlling one or more microprocessors or other control devices. Similar to elements that can be implemented as software programming or software elements, the example implementation can be implemented in programming or scripting languages ​​such as C, C++, Java, assembler, including various algorithms implemented as combinations of data structures, procedures, routines, or other programming constructs. Functional aspects can be implemented in algorithms that run on one or more processors. Furthermore, the example implementation can employ existing techniques for electronic environment setup, signal processing, and / or data processing. Terms such as “mechanism,” “element,” “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 processors, etc.

[0082] The above-described exemplary embodiments are merely examples, and other embodiments may be implemented within the scope of the claims described later.

Claims

1. An electronic device, the electronic device comprising: transceiver; processor; as well as One or more memories, the one or more memories being configured to store one or more instructions. When executing one or more of the instructions, the processor is configured to: Based on the information included in the data regarding the type of data collection equipment and the type of vehicle, define data transformation rules for each piece of data obtained; Based on the charging status of multiple vehicles, obtain multiple vehicle data for the multiple vehicles; Based on the operating status of each of the multiple vehicles, extract one or more pieces of target data from the multiple vehicle data; Classify and store the one or more target data entries for transformation; Based on a first data conversion rule corresponding to a first vehicle among the plurality of vehicles, each of the one or more first conversion target data corresponding to the first vehicle is converted into first conversion data; and Each of one or more of the first transformed data is converted into target data in a form corresponding to the set analysis purpose.

2. The electronic device according to claim 1, in, The data transformation rule is a rule that is set so that each piece of data transformed based on the corresponding data transformation rule in the data transformation rule is transformed into data with the same structure that includes information of the same data type.

3. The electronic device according to claim 1, in, The data transformation rules are set for each type of data collection device and each type of vehicle.

4. The electronic device according to claim 1, in, The data collection device is configured to collect vehicle data related to vehicles equipped with the data collection device and to send the collected vehicle data to the electronic device.

5. The electronic device according to claim 1, in, When executing one or more of the instructions, the processor is configured to, At the point in time when the charging state of the plurality of vehicles corresponds to the termination of the vehicle's charging or discharging cycle, the plurality of vehicle data are obtained.

6. The electronic device according to claim 1, in, When executing one or more of the instructions, the processor is configured to: One or more vehicle data entries whose vehicle operating status corresponds to the driving status of the vehicle among the multiple vehicle data entries are identified as the one or more target data entries for conversion.

7. The electronic device according to claim 1, in, When executing one or more of the instructions, the processor is configured to: The one or more pieces of target data for conversion are classified and stored based on at least one of the following: the type of data collection device, the type of vehicle, and the date the data was acquired.

8. The electronic device according to claim 1, in, When executing one or more of the instructions, the processor is configured to: Each of the one or more first transformation target data is transformed based on the first resource corresponding to the first vehicle.

9. The electronic device according to claim 1, in, The analytical objectives include at least one of the analytical objectives for each data collection device and the analytical objectives for each vehicle type.

10. A method for processing vehicle data performed by an electronic device, the method comprising the following steps: Based on the information included in the data regarding the type of data collection equipment and the type of vehicle, define data transformation rules for each piece of data obtained; Based on the charging status of multiple vehicles, obtain multiple vehicle data for the multiple vehicles; Based on the operating status of each of the multiple vehicles, extract one or more pieces of target data from the multiple vehicle data; Classify and store the one or more target data entries for transformation; Based on a first data conversion rule corresponding to a first vehicle among the plurality of vehicles, each of the one or more first conversion target data corresponding to the first vehicle is converted into first conversion data; and Each of one or more of the first transformed data is converted into target data in a form corresponding to the set analysis purpose.

11. A non-transitory computer-readable recording medium having a program for performing the method of claim 10 on a computer.

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