Data acquisition device and method of operation thereof

Abstract

A data collection device according to embodiments disclosed in the present document can include a data acquisition unit for acquiring data from a vehicle, and a processor for determining a compression environment for compressing the data based on a type of the data according to a state of the vehicle, and compressing and managing the data based on the compression environment.

Description

Technical Field

[0001] Cross-reference to related applications

[0002] This application claims priority and benefit to Korean Patent Application No. 10-2023-0149332, filed with the Korean Intellectual Property Office on November 1, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The embodiments disclosed herein relate to a data acquisition device and its operating method. Background Technology

[0004] Research and development of rechargeable batteries have been actively underway recently. Rechargeable batteries are batteries capable of being charged and discharged, and include not only traditional Ni / Cd and Ni / MH batteries, but also all recent lithium-ion batteries. Lithium-ion batteries have the advantage of a much higher energy density than traditional Ni / Cd and Ni / MH batteries. Furthermore, because lithium-ion batteries can be manufactured in small and lightweight forms, they are used as power sources for mobile devices, and recently, their application has expanded to powering electric vehicles, making them a highly anticipated next-generation energy storage medium.

[0005] A standalone data acquisition device can be used to analyze the battery's state. This device can be connected to the battery or a battery pack to collect data about its state and send the collected data to a server for analysis.

[0006] In addition, the vehicle's data acquisition equipment can connect to the vehicle network (Controller Area Network (CAN)) to collect data from various controllers and send the data to the server. The vehicle network (CAN) is a bus-based communication method used between microcontrollers, and it communicates with the vehicle's electronic control unit (ECU), transmission control unit (TCU), anti-lock braking system (ABS) microcontroller, etc.

[0007] With the recent proliferation of vehicle data collection devices, the amount of data collected by these devices is likely to increase rapidly. Consequently, the communication costs and server storage costs for transmitting this data to servers may also increase. Therefore, it is necessary to manage the collected data to reduce communication and server storage costs. Summary of the Invention

[0008] Technical issues

[0009] The embodiments disclosed herein aim to provide a data acquisition device and its operating method, wherein a data management method is provided to effectively utilize the storage space of the data acquisition device.

[0010] The embodiments disclosed herein also aim to provide a data acquisition device and a method for operating the same, wherein a data management method is provided to effectively perform communication between the data acquisition device and a server.

[0011] The technical objectives of the embodiments disclosed herein are not limited to the above-described technical objectives, and those skilled in the art will be able to clearly understand other objectives not described in the following description.

[0012] Technical solution

[0013] A data acquisition device according to an embodiment disclosed herein may include: a data acquisition unit configured to acquire data from a vehicle; and a processor configured to determine a compression environment for compressing the data based on the type of the data according to the state of the vehicle, and to compress and manage the data based on the compression environment.

[0014] According to an implementation, the processor can determine the type of data based on whether the data was acquired during the vehicle's charging or driving state.

[0015] According to an implementation, the processor may change one or more compression parameters used to compress the data based on the type of the data to determine the compression environment.

[0016] According to an implementation, the processor can change the window bit and memory level among the one or more compression parameters.

[0017] According to an implementation method, the processor can change the compression parameters so that the data compression rate is above 90%.

[0018] According to an implementation, the processor can compress the data in a streaming manner.

[0019] According to an implementation, the processor can stream process the data while packaging the data into packages of a predetermined size and compressing the packaged data.

[0020] According to an embodiment, the data acquisition device may further include: a memory configured to store the data; and a communication module including storage space configured to store compressed data.

[0021] A data acquisition method according to an embodiment disclosed herein may include the following steps: acquiring data from a vehicle; determining a compression environment for compressing the data based on the type of the data according to the state of the vehicle; and compressing and managing the data based on the compression environment.

[0022] According to an implementation, the step of determining the compression environment may include determining the type of data based on whether the data was acquired during the charging or driving state of the vehicle.

[0023] According to an implementation, the step of determining the compression environment may include changing one or more compression parameters used to compress the data based on the type of the data.

[0024] According to an implementation, the step of changing one or more compression parameters may include changing the window bit and memory level among the one or more compression parameters.

[0025] According to an implementation, the step of changing the compression parameters may include changing the compression parameters so that the compression rate of the data is 90% or higher.

[0026] According to an implementation, the step of compressing and managing the data may include compressing the data in a stream processing manner.

[0027] According to an implementation, the step of compressing the data using a streaming method may include: packaging the data into a predetermined size while streaming the data; and compressing the packaged data.

[0028] According to an embodiment, the method may further include storing the compressed data in a storage space included in a memory or communication module.

[0029] Beneficial effects

[0030] Based on the data acquisition device and its operating method according to the embodiments disclosed herein, data can be managed to effectively utilize the storage space of the data acquisition device.

[0031] Based on the data acquisition device and its operating method according to the embodiments disclosed herein, data can be managed to effectively perform communication between the data acquisition device and the server.

[0032] In addition, various effects that can be directly or indirectly identified through this document can be provided. Attached Figure Description

[0033] Figure 1 This is a block diagram illustrating a data acquisition system according to one embodiment disclosed herein.

[0034] Figure 2 This is a block diagram illustrating a data acquisition device according to one embodiment disclosed herein.

[0035] Figure 3 This is a flowchart of the operation of a data acquisition device according to one embodiment disclosed herein.

[0036] Figure 4 This is a flowchart of the operation of a data acquisition device according to one embodiment disclosed herein.

[0037] Figure 5 This is a block diagram illustrating the hardware configuration of a computing system for performing an operation method of a data acquisition device according to one embodiment disclosed herein. Detailed Implementation

[0038] In the following description, various embodiments of the present disclosure will be illustrated with reference to the accompanying drawings. However, it should be understood that this is not intended to limit the present disclosure to a particular embodiment, but rather to include various variations, equivalents, and / or alternatives to the embodiments of the present disclosure.

[0039] It should be understood that the various embodiments and terminology used herein are not intended to limit the technical features described herein to a particular embodiment, but rather to include various modifications, equivalents, or substitutions of the corresponding embodiments. In the description of the drawings, the same reference numerals may be used for the same or related components. Unless the applicable context expressly specifies otherwise, the singular form of a noun corresponding to an item may include one or more items.

[0040] In this document, each of the phrases such as “A or B,” “at least one of A and B,” “at least one of A or B,” “A, B or C,” “at least one of A, B and C,” and “at least one of A, B or C” can include any one of the items listed together in the corresponding phrases within these phrases, or all possible combinations thereof. Terms such as “first,” “second,” “first,” “second,” “A,” “B,” “(a),” or “(b)” can be used simply to distinguish one corresponding component from another, rather than to restrict the corresponding component in another way (e.g., by importance or order).

[0041] In this document, when a particular (e.g., first) component is described as being “connected,” “joined,” “engaged,” “combined,” or “coupled” with another (e.g., second) component, whether or not the terms “functionally” or “communically” are used, it means that the particular component can be connected to the other component directly (e.g., via a wire), wirelessly, or via a third component.

[0042] According to one embodiment, the methods disclosed herein can be provided by being included in a computer program product. The computer program product can be traded as a commodity between a seller and a buyer. The computer program product can be distributed in the form of a device-readable storage medium (e.g., an optical disc read-only memory (CD-ROM)), or distributed through an app store (e.g., downloaded or uploaded), or distributed directly online between two user devices. In the case of online distribution, at least some of the computer program product can be at least temporarily stored or temporarily generated in a device-readable storage medium (e.g., the memory of a manufacturer's server, an app store's server, or a relay server).

[0043] According to various embodiments, each of the above components (e.g., a module or program) may include a single object or multiple objects, and some of the multiple objects may be separately located in another component. According to various embodiments, one or more of the corresponding components or their operations may be omitted, or one or more other components or operations may be added. Alternatively or additionally, multiple components (e.g., modules or programs) may be integrated into one component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as the functions performed by the corresponding components of the multiple components prior to integration. According to various embodiments, the operations performed by modules, programs, or other components may be performed sequentially, in parallel, repeatedly, or heuristically, or may be performed in a different order, or one or more of these operations may be omitted, or one or more other operations may be added.

[0044] Figure 1 This is a block diagram illustrating a data acquisition system according to one embodiment disclosed herein.

[0045] Reference Figure 1 According to one embodiment of the present disclosure, the data acquisition system 10 may include a vehicle 100, a data acquisition device 200, and a server 300.

[0046] Vehicle 100 may be an electric vehicle (EV) that receives driving force from a battery used for storing electricity, but the scope of this disclosure is not limited thereto, and the technical concepts of this disclosure may be applied to other electric vehicles (e.g., electric scooters) instead of vehicle 100.

[0047] Vehicle 100 may include battery pack 110, vehicle controller 120 and vehicle network 130.

[0048] The battery pack 110 may include a battery (not shown) for storing the power required to drive the vehicle 100 and a battery management system (BMS) (not shown) for controlling the operation of the battery. Here, the BMS can control and / or manage the charging and discharging of the battery. Furthermore, according to embodiments, the BMS can generate battery data regarding the state of the battery. For example, the BMS can send battery data to the vehicle network 130, which includes at least one of data obtained by sensing the battery (e.g., voltage, current, resistance, and temperature of the battery cells) and data generated by processing the obtained data (e.g., state of charge (SOC) and state of health (SOH)).

[0049] The vehicle controller 120 can control the operation and / or functions of the vehicle 100 and generate vehicle data. According to embodiments, in addition to the battery pack 110, the vehicle controller 120 can control the operation of the vehicle 100 by controlling at least one sensor (e.g., a radar sensor or a temperature sensor) and at least one control device (e.g., a drive system, braking system, steering system, automated driving system (ADS), telematics system (TMS)) located in the vehicle 100. According to embodiments, the vehicle controller 120 can be an ECU for controlling some components of the vehicle 100.

[0050] According to the implementation method, the vehicle data may have a CAN message format. The vehicle data may include status values, status change values, output signal values, etc. of the components included in the vehicle 100.

[0051] Furthermore, the vehicle controller 120 can store vehicle identification information unique to the vehicle 100 and send the vehicle identification information in response to requests from external sources (e.g., data acquisition device 200). According to embodiments, the vehicle identification information may include a vehicle identification number (VIN). The vehicle controller 120 can communicate with other devices located inside or outside the vehicle 100 via the vehicle network 130.

[0052] Vehicle network 130 can provide a communication environment in which components within vehicle 100 can send and receive data. Vehicle network 130 may be a CAN where components within vehicle 100 can be connected in parallel to communicate with each other without a master, but the scope of this disclosure is not limited thereto. According to embodiments, vehicle network 130 may allow the connection of external devices (e.g., data acquisition device 200) to provide an environment in which external devices can communicate with another device (e.g., battery pack 110) via vehicle network 130.

[0053] The data acquisition device 200 can collect data from the vehicle 100 and send the collected data to the server 300. According to one embodiment, the data acquisition device 200 can collect data from the battery pack 110 and / or the vehicle controller 120 of the vehicle 100. According to another embodiment, the data acquisition device 200 can be integrally formed with the vehicle 100 and implemented as a separate device, and can be connected to the vehicle 100 via an external connection means.

[0054] According to the implementation, the data acquisition device 200 is a device that can be installed on the vehicle 100 and connected to the vehicle network 130. For example, it can be an OBD device connected to an on-board diagnostic (OBD) port provided on the vehicle 100. The data acquisition device 200 can connect to the vehicle network 130 to send and receive Unified Diagnostic Service (UDS) data, thereby obtaining data about the status of the vehicle 100 from components in the vehicle 100. Here, the data about the status of the vehicle 100 may include vehicle speed data, distance traveled data, travel time data, location data, and battery data.

[0055] According to the implementation, the data acquisition device 200 can send data about the vehicle 100 to the server 300. The data acquisition device 200 can send data to the server 300 via any wireless communication method. For example, the data acquisition device 200 can send data to the server 300 via the Long Term Evolution (LTE) communication method.

[0056] According to the implementation method, the data acquisition device 200 can send the acquired data to the server 300 in various ways. For example, the data acquisition device 200 can send the raw data of the acquired data, or send the data after processing and / or modification of the acquired data.

[0057] However, due to the recent proliferation and development of data acquisition devices 200 installed on vehicle 100, the amount of data collected by these devices is increasing. Therefore, the communication costs and throughput for sending the data collected by the data acquisition device 200 to the server 300 may increase. Furthermore, the storage space and costs used to store data in both the data acquisition device 200 and the server 300 may also increase. Therefore, to reduce communication and storage costs, it is crucial for the data acquisition device 200 to manage the collected data, and for this purpose, reference can be made to… Figure 2 To describe the operation of the data acquisition device 200.

[0058] According to the implementation method, server 300 can send and receive data with data acquisition device 200. Therefore, server 300 can receive data about vehicle 100 from data acquisition device 200, and store and analyze the data to provide management services for vehicle 100 or battery pack 110.

[0059] Server 300 can acquire data about vehicle 100 from data acquisition device 200. Server 300 can then store the acquired data in a database. The data stored in the database can be used to analyze and / or manage the status of vehicle 100.

[0060] Server 300 can pre-configure the data to be collected by data acquisition device 200 and manage data acquisition device 200. For example, before data acquisition device 200 collects data from vehicle 100, server 300 can select the target from which to collect data (e.g., battery pack 110 and / or vehicle controller 120), set the data items to be collected from the selected target and the collection period, and send them to data acquisition device 200.

[0061] Figure 2 This is a block diagram illustrating a data acquisition device according to one embodiment disclosed herein.

[0062] Reference Figure 2 The data acquisition device 200 may include a data acquisition unit 210, a memory 220, a communication module 230, and a processor 240. However, this disclosure is not limited thereto, and some components may be omitted from the data acquisition device 200, or the data acquisition device 200 may also include other general components. See also... Figure 1 describe Figure 2 The following description.

[0063] Data acquisition unit 210 can be connected to vehicle network 130 to acquire and / or collect data from internal components of vehicle 100 (e.g., battery pack 110 and vehicle controller 120). Here, the data acquired from vehicle 100 can be referred to as raw data. According to an embodiment, data acquisition unit 210 can acquire data from vehicle 100 per unit time. For example, data acquisition unit 210 can acquire data continuously or acquire data at different acquisition cycles according to data items.

[0064] The memory 220 can store data received from the vehicle 100 (i.e., raw data). For example, the memory 220 can store vehicle 100 speed data, travel distance data, travel time data, travel location data, and battery data. In addition, the memory 220 can store data acquisition setting information received from the server 300.

[0065] According to one embodiment, the memory 220 can divide the raw data acquired from the data acquisition unit 210 into multiple data files and store these data files. According to another embodiment, the memory 220 can divide the raw data into 5-minute files and store the 5-minute files.

[0066] According to one embodiment, memory 220 may store data used by at least one component of data acquisition device 200 (e.g., data acquisition unit 210, communication module 230, and processor 240). For example, the data may include software (or related instructions), input data, or output data. According to one embodiment, when executed by processor 240, the instructions may cause data acquisition device 200 to perform operations defined by the instructions.

[0067] According to an embodiment, memory 220 may include volatile memory and / or non-volatile memory. Here, memory 220 may include at least one storage medium selected from flash memory, hard disk memory, multimedia card micro, card-type memory (e.g., SD or XD memory), magnetic memory, magnetic disk, optical disk, random access memory (RAM), static RAM (SRAM), read-only memory (ROM), programmable ROM (PROM), and electrically erasable PROM (EEPROM).

[0068] The communication module 230 can establish a wireless communication channel between the data acquisition device 200 and the server 300, and send and receive data with the server 300 through the established communication channel. For example, the communication module 230 can send and receive data with another device based on at least one radio access technology (RAT). Here, RAT may include 3G (WCDMA, HSDPA, etc.), 4G (LTE, etc.), and 5G. According to the implementation, the communication module 230 may include a module that supports wireless Internet access (e.g., wireless LAN (WLAN), Wibro, Wi-Fi, WiMAX, or HSDPA).

[0069] In addition, the communication module 230 can send and receive data with internal components of the vehicle 100 (battery pack 110, vehicle controller 120, etc.) connected via the vehicle network 130.

[0070] According to an embodiment, the communication module 230 may include storage space 231. Here, storage space 231 may be a storage space different from memory 220. For example, storage space 231 may be the RAM storage space of the communication module 230. According to an embodiment, the communication module 230 can send data stored in storage space 231 to server 300.

[0071] The processor 240 can manage and / or control the operation and / or state of the data acquisition device 200. According to an embodiment, the processor 240 can control the data acquisition device 200 to perform reference... Figure 1 The operation of the data acquisition device 200 is described. Furthermore, the processor 240 can process various data and / or signals required to perform the operation of the data acquisition device 200.

[0072] According to the implementation, the processor 240 can compress and manage the data acquired from the vehicle 100. Here, the processor 240 can compress the data using any compression algorithm. For example, the processor 240 can use a compression algorithm that includes the ZLIB compression library. When the processor 240 uses the ZLIB compression library, data can be compressed at high speed using minimal memory 220.

[0073] According to the implementation, the processor 240 can determine the compression environment for compressing the data based on the type of data. Here, the processor 240 can determine the type of data based on the state of the vehicle 100.

[0074] For example, processor 240 can determine the data type based on whether the data was acquired while vehicle 100 was charging or driving (or discharging). Furthermore, for example, processor 240 can determine the data type based on whether the data was acquired while vehicle 100 was in a high-speed driving state (e.g., speed above 70 km / h), a medium-speed driving state (e.g., speed range of 30 to 70 km / h), or a low-speed driving state (e.g., speed range of 0 to 30 km / h). The data type is not limited to the above embodiments, and processor 240 can classify the data type based on various operating states of vehicle 100.

[0075] According to various implementations, the attributes of the data can vary depending on the state of the vehicle 100. For example, when the vehicle 100 is charging, the voltage data of the vehicle 100 can increase steadily. However, when the vehicle 100 is driving (or discharging), the voltage data of the vehicle 100 may fluctuate more significantly and frequently. That is, compared to the data acquired in the charging state, the data acquired in the driving state may have irregular data patterns. Therefore, when the processor 240 compresses the data acquired in the driving state and the data acquired in the charging state into the same compression environment, there may be a problem of reduced compression ratio of the data acquired in the driving state. Here, the compression ratio can be the ratio of the size of the compressed data to the size of the original data. Therefore, the processor 240 can determine the type of data reflecting the attributes of the data based on the state of the vehicle 100. Therefore, the processor 240 can optimize the compression environment and improve the compression ratio based on the type of data.

[0076] According to the implementation, the processor 240 can check the status of the vehicle 100 at the time of data acquisition by using CAN UDS messages obtained from the vehicle network 130. Furthermore, the processor 240 can add identifiers to the data based on the status of the vehicle 100 to distinguish the data type. For example, the processor 240 can add an identifier related to the status of the vehicle 100 to the filename of the data file to distinguish the data type. Therefore, the processor 240 can easily distinguish the data type by using the data identifier.

[0077] According to the implementation, the processor 240 can determine the compression environment based on the type of data. Here, the compression environment can refer to the method used to compress the data. For example, the compression environment may include the type of compression algorithm, the parameters used in each compression algorithm, the size and quantity of data to be compressed, etc. The processor 240 can determine an appropriate compression environment based on the type of data to improve the compression ratio.

[0078] According to one implementation, processor 240 can change one or more compression parameters used to compress data to determine the compression environment. Alternatively, processor 240 can dynamically adopt compression parameters based on the type of data. Here, the compression parameters can vary depending on the compression algorithm. For example, when using a compression algorithm from the ZLIB compression library, the compression parameters may include at least one of a compression level, a window bit, and a memory level. According to one implementation, processor 240 can change the window bit and memory level among one or more compression parameters based on the type of data.

[0079] According to the embodiment, the processor 240 can determine the compression environment such that the data compression rate is 90% or higher. Here, the compression rate can be the ratio of the size of the compressed data to the size of the original data. According to the embodiment, the processor 240 can change the compression parameters according to the type of data so that the data compression rate is 90% or higher. Preferably, the processor 240 can change the compression parameters so that the data compression rate is 93% or higher. Therefore, the processor 240 can reduce the amount of data sent from the data acquisition device 200 to the server 300 by approximately 90% or more.

[0080] According to one implementation, the processor 240 can change the compression parameters of data acquired in the driving state to be greater than the compression parameters of data acquired in the charging state. Alternatively, the processor 240 can achieve a high compression ratio by changing the compression parameters based on the attributes of the data.

[0081] According to the implementation, the processor 240 can change the compression parameters related to the memory used in the compression process based on the type of data. Here, the compression parameters related to the memory can be the memory level. Furthermore, the memory used in the compression process can be the memory 220 of the data acquisition device 200. For example, the processor 240 can change the compression parameters considering the maximum and minimum memory levels required to achieve a predetermined compression ratio.

[0082] According to the implementation, when compressing data acquired in the charging state, the processor 240 can change the compression parameters to use the minimum memory level during the compression process. Here, the data acquired in the charging state may have a regular (or simple) data pattern. Therefore, the processor 240 can set the window bit to 12, set the memory level to the minimum memory level (e.g., 4), and compress the data acquired in the charging state.

[0083] Conversely, when compressing data acquired while driving, processor 240 can change the compression parameters to utilize the maximum memory level during compression. Here, data acquired while driving may have irregular data patterns. Therefore, processor 240 can change the window bit and set it to 14, change the memory level and set it to the maximum memory level (e.g., 6), and compress the data acquired while driving. Thus, processor 240 can determine appropriate compression parameters for each type of data, thereby optimizing compression performance.

[0084] According to the implementation, the processor 240 can change the compression parameters based on the type of data, using larger compression parameters to compress data with irregular patterns and smaller compression parameters to compress data with regular patterns. Here, larger compression parameters may indicate the use of more computation to compute the compression algorithm, thereby increasing the data compression ratio.

[0085] As described above, data acquired while driving may exhibit irregular patterns compared to data acquired while charging. Therefore, data acquired while driving may have a lower compression ratio than data acquired while charging. Thus, processor 240 can adjust compression parameters to ensure that the compression ratios of different data types are at similar levels. Therefore, processor 240 can improve the compression ratio of data with irregular patterns and save communication costs and storage space.

[0086] According to the implementation, the processor 240 can compress the data in a streaming manner. Here, the streaming manner can be a method of reading and compressing the data to be compressed in real time. Therefore, the processor 240 can process the data sequentially in the time domain without downloading the entire large volume of raw data (i.e., the data obtained from the vehicle 100).

[0087] According to one implementation, processor 240 can compress data acquired within a predetermined time period in a streaming manner, according to a predetermined size. Alternatively, processor 240 can simultaneously stream process the data, package the data to a predetermined size, and compress the packaged data. Here, data packaging can mean dividing the data into small units and managing the divided data. For example, processor 240 can compress 5 minutes of data acquired from vehicle 100 in 16KB units using a streaming manner. Therefore, processor 240 can divide large amounts of data into small units in the time domain and compress and manage the divided data.

[0088] According to one implementation, processor 240 can use one or more buffers to compress data in a streaming manner. For example, processor 240 can use an input buffer and an output buffer to compress raw data in a streaming manner. According to one implementation, processor 240 can package the raw file (extension: .asc) to a predetermined size (e.g., 16KB) and then copy the packaged data to the input buffer. Furthermore, processor 240 can compress the data in the input buffer and copy the compressed data to the output buffer. Here, processor 240 can check for errors during compression. When it is confirmed that no errors have occurred, processor 240 can store the compressed data from the output buffer as a compressed file (extension: ZLIB).

[0089] According to the implementation, the processor 240 can store compressed data in the memory 220 or the storage space 231 of the communication module 230. Here, the compressed data can be data whose size is reduced to about 1 / 10 compared to the original data. Therefore, the processor 240 can store compressed data, which has been compressed with fewer computations than the original data, in the memory 220 or the storage space 231. Therefore, the processor 240 can stabilize the load of the embedded system and improve the processing speed. In addition, the processor 240 can send the compressed data from the data acquisition device 200 to the server 300 to reduce the communication cost with the server 300 by about 90%.

[0090] Figure 3 This is a flowchart of the operation of a data acquisition device according to one embodiment disclosed herein.

[0091] Reference Figure 3 The data acquisition device 200 can acquire data from the vehicle (S101), determine a compression environment for compressing the data based on the type of data according to the vehicle's status (S102), and compress and manage the data based on the compression environment (S103).

[0092] In operation S101, the processor 240 of the data acquisition device 200 can acquire data from the vehicle 100 (S101). Here, the data acquired from the vehicle 100 can be referred to as raw data.

[0093] In operation S102, the processor 240 of the data acquisition device 200 can determine the compression environment for compressing the data based on the type of data according to the state of the vehicle 100 (S102).

[0094] In operation S103, the processor 240 of the data acquisition device 200 can compress and manage data based on a compression environment (S103).

[0095] Figure 4 This is a flowchart of the operation of a data acquisition device according to one embodiment disclosed herein.

[0096] Reference Figure 4 The data acquisition device 200 can determine the type of data based on whether the data is acquired in a charging state or a driving state (S201), and change one or more compression parameters used to compress the data based on the data type (S202), and change the window bit and memory level in one or more compression parameters to make the data compression rate more than 90% (S203).

[0097] In operation S201, the processor 240 of the data acquisition device 200 can determine the type of data based on whether the data was acquired in a charging state or a driving state (S201).

[0098] In operation S202, processor 240 may change one or more compression parameters used to compress the data based on the type of data (S202).

[0099] In operation S203, processor 240 can change the window bit and memory level in one or more compression parameters to make the data compression rate greater than 90% (S203).

[0100] Figure 5 This is a block diagram illustrating the hardware configuration of a computing system for performing an operation method of a data acquisition device according to one embodiment disclosed herein.

[0101] Reference Figure 5The computing system 2000 according to one embodiment disclosed herein may include a microcontroller unit (MCU) 2010, a memory 2020, an input / output I / F 2030, and a communication I / F 2040.

[0102] The MCU 2010 can execute various programs stored in the memory 2020 (e.g., vehicle data acquisition programs, latent variable extraction programs, and compression programs) to process various pieces of information, including data about the vehicle 100, and perform... Figures 1 to 4 The data acquisition device 200 shown includes the functionality of the processor 240.

[0103] The memory 2020 can store various programs (e.g., vehicle data acquisition programs, latent variable extraction programs, and compression programs). Furthermore, the memory 2020 can store various pieces of information, including data about the vehicle 100.

[0104] Multiple memory units 2020 can be configured as needed. Memory units 2020 can be volatile or non-volatile. As volatile memory, memory units 2020 can use RAM, DRAM, SRAM, etc. As non-volatile memory, memory units 2020 can use ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc. For example, memory units 2020 can include SD cards. The examples of memory units 2020 listed above are merely illustrative and are not limited to these examples.

[0105] The input / output I / F 2030 can be an interface for connecting input devices (not shown) such as a keyboard, mouse, or touch panel and output devices such as a display (not shown) to the MCU 2010, and for allowing the input and output devices to send and receive data with the MCU 2010.

[0106] The communication I / F 2040 is a component capable of sending and receiving various types of data from a server, and can be any device capable of supporting wired or wireless communication. For example, the data acquisition device 200 can use the communication I / F 2040 to send and receive various information, including battery cell SOC, OCV, parameters, etc., with a separately configured external server.

[0107] As described above, a computer program according to one embodiment disclosed herein can be implemented, for example, by being recorded in memory 2020 and processed by MCU 2010 for execution. Figure 2 The module that provides the shown functions.

[0108] As described above, although all components constituting the embodiments disclosed herein are described as being coupled or operated by being coupled, the embodiments disclosed herein are not necessarily limited to these embodiments. In other words, one or more of all components may be operated by being selectively coupled without departing from the scope of the objectives of the embodiments disclosed herein.

[0109] Furthermore, unless otherwise stated, terms such as “comprising,” “constituting,” or “having” above indicate that the corresponding component may be inherent and should therefore be interpreted as including another component rather than excluding another component. Unless otherwise defined, all terms including technical or scientific terms have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments disclosed herein pertain. Commonly used terms (e.g., terms defined in dictionaries) should be interpreted as consistent with the meaning in the context of the relevant field and should not be interpreted in an ideal or overly formal sense unless explicitly defined herein.

[0110] The foregoing disclosure illustratively describes features of several embodiments, enabling those skilled in the art to better understand various aspects of this disclosure. It will be readily understood by those skilled in the art that this disclosure can serve as a basis for designing or modifying other structures to perform the same purpose or achieve the same advantages of the embodiments described herein. Furthermore, those skilled in the art should recognize that such equivalent configurations do not depart from the scope of this disclosure, and that various changes, substitutions, and modifications can be made herein without departing from the scope of this disclosure.

Claims

1. A data acquisition device, the data acquisition device comprising: A data acquisition unit, configured to acquire data from a vehicle; as well as A processor configured to determine a compression environment for compressing the data based on the type of data according to the state of the vehicle, and to compress and manage the data based on the compression environment.

2. The data acquisition device according to claim 1, wherein, The processor determines the type of data based on whether the data was acquired while the vehicle was charging or driving.

3. The data acquisition device according to claim 1, wherein, The processor determines the compression environment by changing one or more compression parameters used to compress the data based on the type of the data.

4. The data acquisition device according to claim 3, wherein, The processor changes the window bit and memory level among the one or more compression parameters.

5. The data acquisition device according to claim 3, wherein, The processor changes the compression parameters to achieve a data compression rate of over 90%.

6. The data acquisition device according to claim 1, wherein, The processor compresses the data using a streaming method.

7. The data acquisition device according to claim 6, wherein, The processor packages the data into packets of a predetermined size while stream processing the data, and compresses the packaged data.

8. The data acquisition device according to claim 1, further comprising: A memory configured to store the data; as well as A communication module, the communication module including storage space configured to store compressed data.

9. A data acquisition method, the method comprising the following steps: Data is acquired from vehicles; The compression environment for compressing the data is determined based on the type of data according to the state of the vehicle. as well as The data is compressed and managed based on the compression environment.

10. The method according to claim 9, wherein, The step of determining the compression environment includes determining the type of data based on whether the data was acquired while the vehicle was charging or driving.

11. The method according to claim 9, wherein, The step of determining the compression environment includes changing one or more compression parameters used to compress the data based on the type of the data.

12. The method according to claim 11, wherein, The step of changing one or more compression parameters includes changing the window bit and memory level among the one or more compression parameters.

13. The method according to claim 11, wherein, The step of changing the compression parameters includes changing the compression parameters so that the data compression rate is above 90%.

14. The method according to claim 9, wherein, The steps of compressing and managing the data include compressing the data in a streaming manner.

15. The method according to claim 14, wherein, The steps for compressing the data using streaming processing include the following: While streaming the data, the data is packaged into packets of a predetermined size; and Compress the packaged data.

16. The method of claim 9, further comprising storing the compressed data in a storage space included in a memory or communication module.

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