Battery data analysis server and operating method thereof

Abstract

A battery data analysis server according to an embodiment disclosed herein includes: a first communication unit that receives battery data from a plurality of vehicles; an analysis unit that determines target vehicles that have driven at a target date from among the plurality of vehicles, analyzes each of the battery data corresponding to each of the target vehicles, and generates daily data based on a result of the analysis; a data lake that stores daily data; a message queue that acquires, in real time, data information including the number of pieces of daily data stored in the data lake; a storage unit that receives and stores at least a portion of the daily data from the data lake; and a scheduler that stores the daily data in the storage unit based on the number of items of the daily data being stored and processed by the storage unit and the data information.

Description

Technical Field

[0001] Cross-references to related applications

[0002] This application claims priority and benefit to Korean Patent Application No. 10-2023-0142228, filed on October 23, 2023, with the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The embodiments disclosed herein relate to a battery data analysis server and its operating methods. Background Technology

[0004] Recently, research and development of rechargeable batteries have been actively pursued. Here, a rechargeable battery is a battery capable of being charged and discharged, and includes not only conventional Ni / Cd and Ni / MH batteries, but also all recently developed lithium-ion batteries. Among rechargeable batteries, lithium-ion batteries have the advantage of significantly higher energy density than conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight form, making them suitable for use as power sources in mobile devices. Moreover, with their application expanding to power electric vehicles, lithium-ion batteries are attracting attention as a next-generation energy storage medium.

[0005] Additionally, secondary batteries can typically be used as battery packs comprising battery modules in which multiple battery cells are connected in series and / or parallel. Furthermore, secondary batteries can be used as battery racks comprising multiple battery modules and a rack housing the battery modules.

[0006] Furthermore, to check the status of the secondary batteries included in electric vehicles (EVs), servers can collect and analyze battery data and provide results to users. When collecting daily data from multiple EVs, the data volume is enormous, resulting in excessively long data analysis times and placing an excessive load on storage units when storing analysis results all at once. Summary of the Invention

[0007] Technical issues

[0008] The embodiments disclosed herein relate to providing a battery data analysis server and its operating method, which is capable of analyzing battery data from multiple vehicles.

[0009] The embodiments disclosed herein also relate to providing a battery data analysis server and a method of operating the same, wherein battery data is stored in consideration of the performance of the data storage unit to adjust the load on the storage unit.

[0010] The embodiments disclosed herein also relate to providing a battery data analysis server and its operating method, which improves the efficiency of data analysis by independently analyzing the battery data of each of a plurality of vehicles.

[0011] The embodiments disclosed herein also relate to providing a battery data analysis server and its operating method, wherein battery data is stored in consideration of the performance of the data storage unit to adjust the load on the storage unit.

[0012] The embodiments disclosed herein are not limited to the objectives described above, and other objectives not described will be readily understood by those skilled in the art to which this disclosure pertains based on the following description.

[0013] Technical solution

[0014] A battery data analysis server according to one embodiment disclosed herein includes: a first communication unit configured to receive battery data from a plurality of vehicles; an analysis unit configured to identify a target vehicle among the plurality of vehicles that is driven on a target date, analyze each battery data corresponding to each of the target vehicles, and generate daily data based on the analysis results; a data lake configured to store the daily data; a message queue configured to acquire data information including the quantity of daily data stored in the data lake in real time; a storage unit configured to receive and store at least some of the daily data sent from the data lake; and a scheduler configured to store the daily data in the storage unit based on the quantity and data information of the daily data being processed by the storage unit.

[0015] According to one implementation, when the amount of daily data being processed by the storage unit is less than a preset amount, the scheduler can store the daily data by sending the daily data from the data lake to the storage unit.

[0016] According to one implementation, the scheduler can examine the data information in the message queue and delete the data information from the data lake that corresponds to the data sent to the storage unit.

[0017] According to one implementation, the scheduler can check the data information of the message queue at a preset period.

[0018] According to one implementation, the storage unit can classify and store the daily data corresponding to each of the target vehicles for each target vehicle.

[0019] According to one implementation, the battery data analysis server may further include a second communication unit configured to send daily data to each corresponding user in the target vehicle.

[0020] According to one implementation, the data information may include location information of the daily data stored in the data lake, and the scheduler may store the daily data by sending the daily data from the data lake to the storage unit based on the location information.

[0021] According to one implementation, the scheduler can send daily data from the data lake to the storage unit in an amount equivalent to a preset daily data transmission quantity.

[0022] A battery data analysis method according to one embodiment disclosed herein includes the following operations: receiving battery data from multiple vehicles; identifying a target vehicle among the multiple vehicles that is driven on a target date; analyzing each of the battery data corresponding to each of the target vehicles and generating daily data based on the analysis results; acquiring data information in real time, including the quantity of daily data stored in a data lake; and storing daily data in a storage unit based on the quantity and data information of daily data being stored by the storage unit.

[0023] According to one implementation, when the amount of daily data being processed by the storage unit is less than a preset amount, the storage operation may include storing the daily data by sending the daily data from the data lake to the storage unit.

[0024] According to one implementation, the battery data analysis method may further include operations such as checking data information in the message queue and deleting data information from the data lake that corresponds to the data sent to the storage unit.

[0025] According to one implementation, the battery data analysis method may further include sending daily data to each corresponding user in the target vehicle.

[0026] According to one implementation, the data information may include location information of daily data stored in the data lake, and the storage operation may include the operation of storing daily data by sending daily data from the data lake to a storage unit based on the location information.

[0027] According to one implementation, the storage operation may include sending daily data from the data lake to the storage unit in an amount equivalent to a preset daily data transmission quantity.

[0028] Details of other implementation methods are included in the specific embodiments and accompanying drawings.

[0029] Beneficial effects

[0030] According to the battery data analysis server and its operation method disclosed herein, battery data of multiple vehicles can be analyzed.

[0031] According to the battery data analysis server and its operation method disclosed herein, the performance of the data storage unit can be taken into account when storing battery data to adjust the load on the storage unit.

[0032] According to the battery data analysis server and its operating method disclosed herein, the efficiency of data analysis can be improved by independently analyzing the battery data of each of multiple vehicles.

[0033] According to the battery data analysis server and its operation method disclosed herein, the performance of the data storage unit can be taken into account when storing battery data to adjust the load on the storage unit.

[0034] The effects of the battery data analysis server and its operation method disclosed in this document are not limited to the effects described above, and based on the disclosure of this document, those skilled in the art will be able to clearly understand other effects not described. Attached Figure Description

[0035] Figure 1 This is a block diagram illustrating a battery data analysis system according to one embodiment disclosed herein.

[0036] Figure 2 This is a diagram illustrating the operation of a server analyzing and storing battery data according to one embodiment disclosed herein.

[0037] Figure 3 This is a diagram illustrating the operation of analyzing and storing battery data by a server according to another embodiment disclosed herein.

[0038] Figure 4 This is a flowchart illustrating a battery data analysis method according to one embodiment disclosed herein.

[0039] Figure 5 This is a flowchart illustrating in detail the operation of storing daily data by a server based on data information according to one embodiment disclosed herein.

[0040] Figure 6 This is a flowchart illustrating in detail the operation of storing daily data by a server based on data information according to another embodiment disclosed herein.

[0041] In the description of the accompanying drawings, the same or similar reference numerals may be used for the same or similar components. Detailed Implementation

[0042] In the following description, embodiments of the invention will be illustrated with reference to the accompanying drawings. However, it should be understood that this is not intended to limit the disclosure to specific embodiments, and includes various modifications, equivalents, and / or alternatives to the embodiments of the disclosure.

[0043] It should be understood that the embodiments described in this document and the terminology used herein are not intended to limit the technical features described herein to a particular embodiment and include various modifications, equivalents, or alternatives to the corresponding embodiments. In the description of the drawings, similar reference numerals may be used for similar or related components. Unless the applicable context clearly specifies otherwise, the singular form of the noun corresponding to an item may include one or more items.

[0044] 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 item listed together in the corresponding phrases within these phrases, or all possible combinations thereof. Terms such as “first,” “second,” “primary,” “secondary,” “A,” “B,” “(a),” or “(b)” can be used simply to distinguish the corresponding components from each other and do not impose limitations on the corresponding components in any other way (e.g., importance or order).

[0045] In this document, with or without the terms “functionally” or “communically”, when a particular (e.g., first) component is described as being “connected,” “linked,” or “joined” to another (e.g., second) component, or is described as being “linked” or “attached” to another (e.g., second) component, it means that the particular component can be connected to the other component directly (e.g., via wired or wireless means) or indirectly (e.g., via a third component).

[0046] Methods according to various embodiments disclosed herein can be provided for inclusion 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)), 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 such as the memory of a manufacturer's server, an app store's server, or a relay server.

[0047] According to the embodiments disclosed herein, 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 the embodiments disclosed herein, one or more of the corresponding components or 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 a single 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 the embodiments disclosed herein, operations performed by modules, programs, or other components may be performed sequentially, in parallel, repeatedly, or heuristically, or one or more operations may be performed in a different order or omitted, or one or more other operations may be added.

[0048] Figure 1 This is a block diagram illustrating a battery data analysis system according to one embodiment disclosed herein.

[0049] Reference Figure 1 The battery data analysis system 10 may include a target vehicle 100 and a server 200. The battery data analysis system 10 can analyze the battery data of the target vehicle 100 in the server 200.

[0050] The target vehicle 100 can be an electric vehicle. The target vehicle 100 can be an electric vehicle including a battery 120. That is, the target vehicle 100 can be an electric vehicle (EV), a hybrid electric vehicle (HEV), a plug-in hybrid electric vehicle (PHEV), or a fuel cell electric vehicle (FCEV) including a battery 120.

[0051] The target vehicle 100 may include an information acquisition device 110. Here, the information acquisition device 110 may include an on-board diagnostic device. That is, the information acquisition device 110 can acquire data from the target vehicle 100 and data from the battery 120 included in the target vehicle 100.

[0052] The information acquisition device 110 can generate battery data. According to an embodiment, the information acquisition device 110 can generate battery data based on the state of charge (SOC), state of health (SOH), open circuit voltage (OCV), charging capacity, discharging capacity, etc. of the battery 120 acquired from the battery 120.

[0053] According to the implementation method, the information acquisition device 110 can generate battery data at a predetermined cycle. The information acquisition device 110 can generate battery data at a preset fixed cycle while the target device is running, or it can generate battery data on a daily cycle by integrating multiple pieces of information about the battery 120 within a day. That is, the information acquisition device 110 can generate battery data once a day at a predetermined specific time.

[0054] The information acquisition device 110 can transmit battery data. According to one embodiment, the information acquisition device 110 can send the battery data to the server 200. For example, the information acquisition device 110 can send the battery data to the server 200 via Long Term Evolution (LTE) communication.

[0055] Server 200 may include a first communication unit 210, an analysis unit 220, a data lake 230, a message queue 240, a scheduler 250, a storage unit 260, and a second communication unit 270. Server 200 may use each component to receive, analyze, and store battery data from information acquisition device 110.

[0056] The first communication unit 210 can receive battery data from multiple vehicles. According to an embodiment, the first communication unit 210 can receive battery data from multiple vehicles, including the target vehicle 100. For example, the first communication unit 210 can communicate with the information acquisition device 110 included in each of the multiple vehicles to receive battery data from each of the multiple vehicles.

[0057] Analysis unit 220 can identify target vehicle 100. Analysis unit 220 can identify the target vehicle 100 that was driven on a target date among a plurality of vehicles. According to an embodiment, analysis unit 220 can determine whether each of the plurality of vehicles has been driven on the target date based on battery data received from each of the plurality of vehicles, and determine the target vehicle 100 based on the determination result. That is, analysis unit 220 can determine that the vehicle driven on the target date among the plurality of vehicles is the target vehicle 100, and vehicles that were not driven on the target date may not be considered the target vehicle 100. According to an embodiment, analysis unit 220 can generate an analysis target list based on the identified target vehicle 100.

[0058] Analysis unit 220 can analyze battery data. Analysis unit 220 can analyze the battery data of target vehicle 100. According to the embodiment, analysis unit 220 can analyze the data of the target vehicle by referring to an analysis target list.

[0059] Analysis unit 220 can independently analyze the battery data of target vehicle 100. Analysis unit 220 can independently analyze the battery data of each of multiple target vehicles 100. For example, analysis unit 220 can simultaneously analyze the battery data corresponding to each of the target vehicles 100 in parallel. That is, analysis unit 220 can independently analyze first battery data and second battery data, instead of first analyzing the first battery data of the first target vehicle 100 and then analyzing the second battery data as the battery data of the second target vehicle 100. Therefore, analysis unit 220 can simultaneously analyze first battery data and second battery data, where the analysis time of the first battery data can partially overlap with the analysis time of the second battery data, and where the analysis time of the first battery data can completely not overlap with the analysis time of the second battery data. In other words, analysis unit 220 can analyze the battery data of multiple target vehicles 100 in a distributed parallel manner. According to the embodiment, analysis unit 220 can allocate battery data corresponding to each target vehicle 100 based on the size of the battery data of each of the target vehicles 100 and analyze the allocated battery data.

[0060] The analysis unit 220 can generate daily data. The analysis unit 220 can generate daily data based on the results of analyzing the battery data of the target vehicle 100. According to the embodiment, the analysis unit 220 can generate daily data corresponding to each battery data point of each of the target vehicles 100. That is, the analysis unit 220 can generate daily data for each target vehicle 100.

[0061] Analysis unit 220 can compress daily data. That is, analysis unit 220 can compress the generated daily data based on the results of analyzing battery data. Analysis unit 220 can then send the compressed daily data to data lake 230.

[0062] Data lake 230 can store battery data. Data lake 230 can store battery data received from multiple vehicles by the first communication unit 210. In addition, data lake can send battery data to analysis unit 220.

[0063] Data lake 230 can store daily data. Data lake 230 can store daily data generated by analysis unit 220. According to one embodiment, data lake 230 can classify the daily data corresponding to each of the target vehicles 100 for each target vehicle 100 and store the classified daily data. According to another embodiment, data lake 230 can compress the daily data and store the compressed daily data.

[0064] Message queue 240 can retrieve data information. Message queue 240 can retrieve data information including the quantity of daily data stored in data lake 230. According to an embodiment, the data information may include the quantity of daily data stored in data lake 230 and the storage location information of each daily data item. Here, message queue 240 is one of the communication methods for exchanging data between processes or programs and can be a system for implementing message-oriented middleware (MOM). Alternatively, MOM can be middleware that uses asynchronous messages to send and receive data between applications.

[0065] Scheduler 250 can retrieve data information. Scheduler 250 can retrieve data information obtained from message queue 240. For example, scheduler 250 can retrieve data information from message queue 240 to retrieve the quantity of daily data stored in data lake 230 at a corresponding time point and the storage location information of each daily data item. According to an implementation, scheduler 250 can retrieve data information from message queue 240 at a preset period.

[0066] Scheduler 250 can receive data information. Scheduler 250 can receive data information in response to a data information retrieval request. Therefore, scheduler 250 can obtain the quantity of daily data stored in data lake 230 and the storage location information of each daily data item.

[0067] Scheduler 250 can delete data information. Scheduler 250 can delete data information from message queue 240. According to an embodiment, scheduler 250 can request message queue 240 to delete data information corresponding to daily data stored in storage unit 260, thereby deleting data information from message queue 240.

[0068] Scheduler 250 can store daily data in storage unit 260. Scheduler 250 can send daily data to storage unit 260 based on data information. That is, scheduler 250 can control data lake 230 to send daily data to storage unit 260 to store daily data. In addition, scheduler 250 can control data lake 230 to send a set number of daily data items to storage unit 260. For example, the number of daily data items sent from data lake 230 to storage unit 260 can be up to 10, but is not limited to this. According to an embodiment, scheduler 250 can control data lake 230 to delete daily data sent from data lake 230 to storage unit 260.

[0069] According to one implementation, the scheduler 250 can obtain the amount of daily data being processed by the storage unit 260. That is, the scheduler 250 can obtain the amount of daily data being stored by the storage unit 260. Depending on the required storage load and the performance of the storage unit 260, a predetermined time is needed to store the daily data of the storage unit 260, and the scheduler 250 can obtain the amount of daily data currently being stored by the storage unit 260. According to another implementation, the scheduler 250 can obtain the amount of daily data being stored by the storage unit 260 by examining information about the amount of daily data being stored included in a separate sub-storage component (not shown). In this case, the sub-storage component can store the amount of daily data currently being stored by the storage unit 260.

[0070] Scheduler 250 can compare the amount of daily data being stored with a preset quantity. This preset quantity can be set based on the performance of storage unit 260 or the central processing unit (CPU) including storage unit 260. When the amount of daily data being stored in storage unit 260 is less than the preset quantity, scheduler 250 can control data lake 230 to send daily data to storage unit 260. Conversely, when the amount of daily data being stored in storage unit 260 is the preset quantity or more, scheduler 250 can wait for a set time without separate control. In this case, scheduler 250 can recheck the amount of daily data being stored in storage unit 260 after waiting for the set time.

[0071] According to another embodiment, scheduler 250 can check the load status of storage unit 260. Scheduler 250 can check the load of storage unit 260 based on the daily data stored in storage unit 260. According to another embodiment, scheduler 250 can check the load of storage unit 260 by checking the load of storage unit 260 or the load of the CPU including storage unit 260. For example, scheduler 250 can check the load of storage unit 260 by checking the average active sessions (AAS) of the CPU including storage unit 260. Here, AAS can be the average number of connections per unit time.

[0072] Scheduler 250 can compare the load of storage unit 260 with a set value. When the load of storage unit 260 is less than the set value, scheduler 250 can control data lake 230 to send daily data to storage unit 260. When the load of storage unit 260 is the set value or greater, scheduler 250 can wait for a set time without individual control. In this case, scheduler 250 can re-check the load of storage unit 260 after waiting for the set time.

[0073] Storage unit 260 can store daily data. Storage unit 260 can store daily data received from data lake 230. According to an embodiment, storage unit 260 can store daily data received from data lake 230 under the control of scheduler 250.

[0074] Storage unit 260 can classify and store the classified daily data. Storage unit 260 can classify and store the classified daily data corresponding to each of the target vehicles 100 for each target vehicle 100.

[0075] The second communication unit 270 can send daily data to the user. The second communication unit 270 can send the daily data stored in the storage unit 260 to the user. That is, the second communication unit 270 can send the daily data of each of the target vehicles 100 to the user corresponding to each of the target vehicles 100. For example, the second communication unit 270 can upload the daily data to the cloud and send the daily data to the user by allowing the user to check the daily data in the cloud. The second communication unit 270 can provide the daily data directly to the user terminal or personal computer (PC). Alternatively, the second communication unit 270 can provide the daily data through a display installed in the target vehicle 100.

[0076] Figure 2 This is a diagram illustrating the operation of a server analyzing and storing battery data according to one embodiment disclosed herein.

[0077] Reference Figure 2 The analysis unit 220 can identify the target vehicle 100. The analysis unit 220 can identify the target vehicle 100 that is traveling on a target date from among multiple vehicles. According to the implementation method, the analysis unit 220 can generate an analysis target list based on the identified target vehicle 100.

[0078] Analysis unit 220 can analyze battery data. Analysis unit 220 can analyze the battery data of target vehicle 100. According to the embodiment, analysis unit 220 can analyze the data of the target vehicle by referring to an analysis target list.

[0079] Analysis unit 220 can independently analyze the battery data of target vehicle 100. Analysis unit 220 can independently analyze the battery data of each of multiple target vehicles 100. For example, analysis unit 220 can simultaneously analyze the battery data corresponding to each of the target vehicles 100 in parallel. That is, analysis unit 220 can analyze the battery data of multiple target vehicles 100 in a distributed parallel manner. According to an embodiment, analysis unit 220 can allocate battery data corresponding to each target vehicle 100 based on the size of the battery data of each target vehicle 100 and analyze the allocated battery data.

[0080] The analysis unit 220 can generate daily data. The analysis unit 220 can generate daily data based on the results of analyzing the battery data of the target vehicle 100. According to the implementation, the analysis unit 220 can generate daily data corresponding to each of the battery data of each target vehicle 100. That is, the analysis unit 220 can generate daily data for each target vehicle 100.

[0081] Analysis unit 220 can compress daily data. That is, analysis unit 220 can compress the generated daily data based on the results of analyzing battery data. Analysis unit 220 can then send the compressed daily data to data lake 230.

[0082] Data lake 230 can store daily data. Data lake 230 can store daily data generated by analysis unit 220. According to an implementation, data lake 230 can classify and store the daily data corresponding to each of the target vehicles 100 for each target vehicle 100.

[0083] Message queue 240 can retrieve data information. Message queue 240 can retrieve data information including the quantity of daily data stored in data lake 230. According to an embodiment, the data information may include the quantity of daily data stored in data lake 230 and the storage location information of each daily data item.

[0084] Scheduler 250 can retrieve data information. Scheduler 250 can retrieve data information obtained from message queue 240. For example, scheduler 250 can retrieve data information from message queue 240 to retrieve the quantity of daily data stored in data lake 230 at a corresponding time point and the storage location information of each daily data item. According to an implementation, scheduler 250 can retrieve data information from message queue 240 at a preset period.

[0085] Scheduler 250 can receive data information. Scheduler 250 can receive data information in response to a data information retrieval request. Therefore, scheduler 250 can obtain the quantity of daily data stored in data lake 230 and the storage location information of each daily data item.

[0086] Scheduler 250 can delete data information. Scheduler 250 can delete data information from message queue 240. According to an embodiment, scheduler 250 can request message queue 240 to delete data information corresponding to daily data stored in storage unit 260, thereby deleting data information from message queue 240.

[0087] Scheduler 250 can store daily data in storage unit 260. Scheduler 250 can send daily data to storage unit 260 based on data information. That is, scheduler 250 can control data lake 230 to send daily data to storage unit 260 to store daily data. In addition, scheduler 250 can control data lake 230 to send a set number of daily data items to storage unit 260. For example, the number of daily data items sent from data lake 230 to storage unit 260 can be up to 10, but is not limited to this. According to an embodiment, scheduler 250 can control data lake 230 to delete daily data sent from data lake 230 to storage unit 260.

[0088] According to one implementation, the scheduler 250 can obtain the amount of daily data being processed by the storage unit 260. That is, the scheduler 250 can obtain the amount of daily data being stored by the storage unit 260. According to another implementation, the scheduler 250 can obtain the amount of daily data being stored by the storage unit 260 by examining information about the amount of daily data being stored included in a separate sub-storage component (not shown). In this case, the sub-storage component can store the amount of daily data currently being stored by the storage unit 260.

[0089] Scheduler 250 can compare the amount of daily data being stored with a preset quantity. This preset quantity can be set based on the performance of storage unit 260 or the central processing unit (CPU) including storage unit 260. When the amount of daily data being stored in storage unit 260 is less than the preset quantity, scheduler 250 can control data lake 230 to send daily data to storage unit 260. Conversely, when the amount of daily data being stored in storage unit 260 is the preset quantity or more, scheduler 250 can wait for a set time without separate control. In this case, scheduler 250 can recheck the amount of daily data being stored in storage unit 260 after waiting for the set time.

[0090] Storage unit 260 can store daily data. Storage unit 260 can store daily data received from data lake 230. According to an embodiment, storage unit 260 can store daily data received from data lake 230 under the control of scheduler 250.

[0091] Storage unit 260 can classify and store the classified daily data. Storage unit 260 can classify and store the classified daily data corresponding to each of the target vehicles 100 for each target vehicle 100.

[0092] Figure 3 This is a diagram illustrating the operation of analyzing and storing battery data by a server according to another embodiment disclosed herein.

[0093] exist Figure 3 In the middle, omission and Figure 2 The implementation methods will repeat some parts, and the main differences will be described.

[0094] Reference Figure 3 Scheduler 250 can check the load status of storage unit 260. When data information exists in message queue 240, scheduler 250 can check the load status of storage unit 260. That is, scheduler 250 can check the load of storage unit 260 based on the daily data stored in storage unit 260. According to an embodiment, scheduler 250 can check the load of storage unit 260 by checking the load of storage unit 260 or the load of the CPU including storage unit 260. For example, scheduler 250 can check the load of storage unit 260 by checking the average active sessions (AAS) of the CPU including storage unit 260. Here, AAS can be the average number of connections per unit time period.

[0095] Scheduler 250 can compare the load of storage unit 260 with a set value. When the load of storage unit 260 is less than the set value, scheduler 250 can control data lake 230 to send daily data to storage unit 260. When the load of storage unit 260 is the set value or greater, scheduler 250 can wait for a set time without individual control. In this case, scheduler 250 can re-check the load of storage unit 260 after waiting for the set time.

[0096] Figure 4 This is a flowchart illustrating a battery data analysis method according to one embodiment disclosed herein.

[0097] Figure 4 The illustrated embodiment is only one embodiment, and the order of operation of various embodiments according to the present invention may be different. Figure 4 The differences shown can be omitted. Figure 4 Some of the operations shown can change the order of operations or can be combined.

[0098] Reference Figure 4 The battery data analysis method may include: an operation S100 of receiving battery data from multiple vehicles; an operation S200 of identifying a target vehicle 100 that will be driven on a target date among the multiple vehicles; an operation S300 of analyzing the battery data corresponding to each of the target vehicles 100 and generating daily data based on the analysis results; an operation S400 of acquiring data information in real time, including the amount of daily data stored in the data lake 230; and an operation S500 of storing the daily data in the storage unit 260 based on the data information.

[0099] In the following text, reference will be made to Figures 1 to 3 Describe operations S100 to S500 in detail.

[0100] In the operation S100 of the battery data analysis method, the server 200 can receive battery data from multiple vehicles.

[0101] In operation S200, server 200 can determine a target vehicle 100 among multiple vehicles that is traveling on a target date. According to an implementation, server 200 can determine whether each of the multiple vehicles has already traveled on the target date based on battery data received from each of the multiple vehicles, and determine the target vehicle 100 based on the determination result.

[0102] In operation S300, server 200 can analyze each of the battery data corresponding to each of the target vehicles 100 and generate daily data based on the analysis results. Server 200 can analyze the battery data of the target vehicles 100 independently. Server 200 can independently analyze the battery data of each of multiple target vehicles 100. For example, server 200 can analyze the battery data corresponding to each of the target vehicles 100 simultaneously in parallel.

[0103] In operation S400, server 200 can obtain data information including the quantity of daily data stored in data lake 230. According to an embodiment, the data information may include the quantity of daily data stored in data lake 230 and the storage location information of each daily data item.

[0104] In operation S500, server 200 can send daily data to storage unit 260 based on data information. Server 200 can store data information by sending daily data stored in data lake 230 to storage unit 260 and storing the daily data. The following will refer to... Figure 5 and Figure 6 Detailed description of S500 operation.

[0105] Figure 5 This is a flowchart illustrating in detail the operation of storing daily data by a server based on data information according to one embodiment disclosed herein.

[0106] In operation S511, server 200 can determine whether daily data exists in data lake 230. Server 200 can retrieve data information. Server 200 can retrieve data information obtained from message queue 240. Server 200 can receive data information. Server 200 can receive data information in response to the retrieval of data information by server 200. Therefore, server 200 can obtain the quantity of daily data stored in data lake 230 and the storage location information of each daily data. Server 200 can delete data information. Server 200 can delete data information from message queue 240. Therefore, server 200 can determine whether daily data exists in data lake 230.

[0107] When the daily data stored in data lake 230 exists, operation S512 can be executed. When the daily data stored in data lake 230 does not exist, operation S515 can be executed.

[0108] In operation S512, server 200 can determine whether the amount of daily data being stored in storage unit 260 is less than a preset amount. Here, the preset amount can be set based on the performance of storage unit 260 or the CPU including storage unit 260. When the amount of daily data being processed by storage unit 260 is less than the preset amount, operation S513 can be executed. When the amount of daily data being processed by storage unit 260 is equal to or greater than the preset amount, operation S515 can be executed.

[0109] In operation S513, server 200 can store daily data by sending daily data from data lake 230 to storage unit 260. In this case, server 200 can store daily data by sending a set number of daily data items to storage unit 260.

[0110] In operation S514, server 200 can send daily data to the user corresponding to each of the target vehicles 100. Second communication unit 270 can send the daily data stored in storage unit 260 to the user. That is, second communication unit 270 can send the daily data of each of the target vehicles 100 to the user corresponding to each of the target vehicles 100. For example, second communication unit 270 can upload the daily data to the cloud and send the daily data to the user by allowing the user to check the daily data in the cloud. Second communication unit 270 can provide daily data directly to the user terminal or personal computer (PC). Alternatively, second communication unit 270 can provide daily data through a display installed in the target vehicle 100.

[0111] In operation S515, server 200 can wait for a preset time. In this case, server 200 can recheck the amount of daily data being processed by storage unit 260 after waiting for the preset time.

[0112] Figure 6 This is a flowchart illustrating in detail the operation of storing daily data by a server based on data information according to another embodiment disclosed herein.

[0113] In operation S521, server 200 can determine whether daily data exists in data lake 230. Server 200 can retrieve data information. Server 200 can retrieve data information obtained from message queue 240. Server 200 can receive data information. Server 200 can receive data information in response to the retrieval of data information by server 200. Therefore, server 200 can obtain the quantity of daily data stored in data lake 230 and the storage location information of each daily data item. Server 200 can delete data information. Server 200 can delete data information from message queue 240. Therefore, server 200 can determine whether daily data exists in data lake 230.

[0114] When the daily data stored in data lake 230 exists, operation S522 can be executed. When the daily data stored in data lake 230 does not exist, operation S526 can be executed.

[0115] In operation S522, server 200 can check the load status of storage unit 260 by checking the load status of the CPU including storage unit 260. Server 200 can check the load of storage unit 260 based on the storage unit 260 storing daily data. According to the embodiment, server 200 can check the load of storage unit 260 by checking the load of storage unit 260 or the load of the CPU including storage unit 260. For example, server 200 can check the load of storage unit 260 by checking the AAS of the CPU including storage unit 260. Here, AAS can be the average number of connections per unit time.

[0116] In operation S523, server 200 can determine whether the load of storage unit 260 or the load of the CPU including storage unit 260 is less than a set value. When the load is less than the set value, operation S524 can be executed. When the load is the set value or greater, operation S526 can be executed.

[0117] In operation S524, server 200 can store daily data by sending daily data from data lake 230 to storage unit 260. In this case, server 200 can store daily data by sending a set number of daily data items to storage unit 260.

[0118] In operation S525, server 200 can send daily data to the user corresponding to each of the target vehicles 100. Second communication unit 270 can send the daily data stored in storage unit 260 to the user. That is, second communication unit 270 can send the daily data of each of the target vehicles 100 to the user corresponding to each of the target vehicles 100. For example, second communication unit 270 can upload the daily data to the cloud and send the daily data to the user by allowing the user to check the daily data in the cloud. Second communication unit 270 can provide daily data directly to the user terminal or PC. Alternatively, second communication unit 270 can provide daily data through a display installed in the target vehicle 100.

[0119] In operation S526, server 200 can wait for a preset time. In this case, server 200 can recheck the amount of daily data being processed by storage unit 260 after waiting for the set time.

[0120] The above description is merely an exemplary description of the technical spirit disclosed herein, and those skilled in the art to which the embodiments disclosed herein pertain will be able to make various modifications and changes to these embodiments without departing from the basic characteristics of the embodiments disclosed herein.

[0121] Therefore, the embodiments disclosed herein are not intended to limit the technical spirit disclosed herein, but are used to describe it, and the scope of the technical spirit disclosed herein is not limited by these embodiments. The scope of the technical spirit disclosed herein should be interpreted by the appended claims, and all technical spirit within the equivalent scope should be interpreted as included within the scope of this document.

[0122] [Description of reference numerals in the attached figures]

[0123] 10: Battery Data Analysis System

[0124] 100: Target vehicle

[0125] 110: Information Acquisition Device

[0126] 200: Server

[0127] 210: First Communication Unit

[0128] 220: Analysis Unit

[0129] 230: Data Lake

[0130] 240: Message Queues

[0131] 250: Scheduler

[0132] 260: Storage unit

[0133] 270: Second Communication Unit

Claims

1. A battery data analysis server comprising: a first communication unit configured to receive battery data from a plurality of vehicles; an analysis unit configured to determine target vehicles that travel on a target date among the plurality of vehicles, analyze each of the battery data corresponding to each of the target vehicles, and generate daily data based on a result of the analysis; a data lake configured to store the daily data; a message queue configured to acquire data information including a number of the daily data stored in the data lake in real time; a storage unit configured to receive and store at least some of the daily data transmitted from the data lake; and a scheduler configured to store the daily data in the storage unit based on the number of the daily data being processed by the storage unit and the data information. when the number of the daily data being processed by the storage unit is less than a preset number, 2. The battery data analysis server of claim 1, wherein, the scheduler stores the daily data by transmitting the daily data from the data lake to the storage unit. the scheduler checks the data information of the message queue, and 3. The battery data analysis server of claim 1, wherein, the scheduler deletes data information corresponding to data transmitted to the storage unit among the daily data from the data lake. the scheduler checks the data information of the message queue at a preset period.

4. The battery data analysis server of claim 1, wherein, the storage unit classifies the daily data corresponding to each of the target vehicles by each target vehicle and stores the classified daily data.

5. The battery data analysis server of claim 1, wherein, 6.The battery data analysis server of claim 5, further comprising a second communication unit configured to transmit the daily data to a user corresponding to each of the target vehicles. the data information includes location information in which the daily data is stored in the data lake, and 7. The battery data analysis server of claim 1, wherein, the scheduler stores the daily data by transmitting the daily data from the data lake to the storage unit based on the location information. the scheduler transmits the daily data corresponding to a preset number of daily data transmission from the data lake to the storage unit.

8. The battery data analysis server of claim 7, wherein, 9.A battery data analysis method comprising the operations of: receiving battery data from a plurality of vehicles; determining target vehicles that travel on a target date among the plurality of vehicles; analyzing each of the battery data corresponding to each of the target vehicles and generating daily data based on a result of the analysis; acquiring data information including a number of the daily data stored in a data lake in real time; and storing the daily data in a storage unit based on the number of the daily data being stored by the storage unit and the data information. when the number of the daily data being processed by the storage unit is less than a preset number, the storing operation includes storing the daily data by transmitting the daily data from the data lake to the storage unit.

10. The battery data analysis method of claim 9, wherein, ​ ​ 11.The battery data analysis method of claim 9, further comprising an operation of checking data information of a message queue and deleting data information corresponding to data transmitted to the storage unit among the daily data from the data lake. 12.The battery data analysis method of claim 9, further comprising an operation of transmitting the daily data to a user corresponding to each of the target vehicles.

13. The battery data analysis method of claim 9, wherein, the data information includes location information in which the daily data is stored in the data lake, and the storing operation includes an operation of storing the daily data by transmitting the daily data from the data lake to the storage unit based on the location information.

14. The battery data analysis method of claim 13, wherein, the storing operation includes an operation of transmitting daily data corresponding to a preset number of daily data transmission from the data lake to the storage unit.

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