Server and operation method thereof

Abstract

According to an embodiment disclosed in the present document, a server comprises a communication circuit, a memory, and a processor operatively connected to the communication circuit and the memory, wherein the processor can: acquire history data related to battery charging of users and information related to the current traveling of a target user; extract characteristics related to charging factors of each user from the history data; set, on the basis of the characteristics, a criterion for determining habits related to the factors; determine habits of the target user on the basis of the characteristics of the target user and the criterion; and provide guide information related to the habits of the target user on the basis of at least one from among the habits, historical data, and information related to the current traveling of the target user.

Description

Server and how it works

[0001] Cross-citation with related applications

[0002] This invention claims the benefit of priority to Korean Patent Application No. 10-2023-0129123, filed on September 26, 2023, and Korean Patent Application No. 10-2023-0151951, filed on November 6, 2023, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to a server and a method of operating the same.

[0005] Recently, research and development on secondary batteries has been actively conducted. Here, secondary batteries are rechargeable and include both conventional Ni / Cd and Ni / MH batteries, as well as recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of having a much 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 for mobile devices. Recently, their use has expanded to include power sources for electric vehicles, attracting attention as a next-generation energy storage medium.

[0006] These batteries are repeatedly charged and discharged. When installed in a vehicle, the charging pattern may vary depending on the driver's preferences. Typically, to avoid battery depletion, many users tend to maintain regular charging habits.

[0007] One purpose of the embodiments disclosed in this document is to provide a server and its operating method that can determine a user's habits and provide guide information related to the habits to strengthen the user's habits.

[0008] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the descriptions below.

[0009] According to an embodiment disclosed in the present document, a server includes a communication circuit, a memory, and a processor operatively connected to the communication circuit and the memory, wherein the processor obtains history data related to battery charging of users and information related to current driving of a target user, extracts a feature related to a charging factor of each user from the history data, sets a criterion for determining a habit related to the factor based on the feature, determines the habit of the target user based on the feature of the target user and the criterion, and provides guide information related to the habit of the target user based on at least one of the habit of the target user and the history data and the information related to the current driving.

[0010] According to an embodiment, the processor can extract a probability density function (PDF) for the factor, which is produced by applying a kernel density estimation (KDE) technique to the history data, as the feature.

[0011] According to an embodiment, the processor may set the criteria based on the result of clustering the features.

[0012] According to an embodiment, the processor may set the criterion based on the maximum probability density value of features included in each cluster of the clustering results.

[0013] According to an embodiment, the processor may apply the kernel density estimation by assigning a higher weight to data acquired at a point in time closer to the current point in time among the history data.

[0014] According to an embodiment, the processor can determine the habit of the target user based on a result of comparing the maximum probability density derived from the characteristics of the target user with the criterion.

[0015] According to an embodiment, the processor may update the characteristics and habits of the target user by reflecting the history data acquired each time the target user charges.

[0016] According to an embodiment, the processor may calculate a remaining discharge depth based on a charging start SOC obtained based on the habit and information related to the current driving, and provide the guide information including the remaining discharge depth.

[0017] According to an embodiment disclosed in this document, a method of operating a server may include a step of acquiring history data related to battery charging of users and information related to current driving of a target user, a step of extracting a feature related to a charging factor of each user from the history data, a step of setting a criterion for determining a habit related to the factor based on the feature, a step of determining a habit of the target user based on the feature of the target user and the criterion, and a step of providing guide information related to the habit of the target user based on at least one of the habit of the target user and the history data and information related to current driving.

[0018] According to an embodiment, the step of extracting the feature may be characterized by extracting a probability density function (PDF) for the factor, which is calculated by applying a kernel density estimation (KDE) technique to the history data, as the feature.

[0019] According to an embodiment, the step of setting the criterion may be characterized by setting the criterion based on the result of clustering the features.

[0020] According to an embodiment, the step of setting the criterion may be characterized by setting the criterion based on the maximum probability density value of the probability density function of the features included in each cluster as a result of the clustering.

[0021] According to an embodiment, the step of extracting the feature may be characterized by applying the kernel density estimation by giving a higher weight to data acquired at a point in time closer to the current point in time among the history data.

[0022] According to an embodiment, the step of determining the habit of the target user may be characterized by determining the habit of the target user based on a result of comparing the maximum probability density derived from the characteristics of the target user with the criterion.

[0023] According to an embodiment, the step of providing the guide information may be characterized by calculating a remaining discharge depth based on a charging start SOC obtained based on the habit and information related to the current driving, and providing the guide information including the remaining discharge depth.

[0024] The server and its operating method according to the embodiments disclosed in this document can determine the user's habits and provide guide information related to the habits to strengthen the user's habits.

[0025] In addition, various effects may be provided, either directly or indirectly, through this document.

[0026] Figure 1 is a block diagram showing the configuration of an information provision system according to one embodiment disclosed in this document.

[0027] FIG. 2 is a diagram showing an example of extracting user characteristics according to one embodiment disclosed in this document.

[0028] FIG. 3 is a diagram showing an example of extracting features by applying weights according to one embodiment disclosed in this document.

[0029] FIG. 4 is a diagram showing an example of a clustering result of features according to one embodiment disclosed in this document.

[0030] FIG. 5 is a diagram showing an example of setting criteria for determining habituality according to one embodiment disclosed in this document.

[0031] FIG. 6 is a flowchart for explaining a method of operating a server according to one embodiment disclosed in this document.

[0032] FIG. 7 is a block diagram showing the configuration of a server according to one embodiment disclosed in this document.

[0033] Hereinafter, various embodiments of the present invention will be described with reference to the attached drawings. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that the present invention encompasses various modifications, equivalents, and / or alternatives of the embodiments.

[0034] In this document, the singular form of a noun corresponding to an item may include one or more of said items, unless the context clearly indicates otherwise. In this document, 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" may each include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish the corresponding element from other corresponding elements, and do not limit the corresponding elements in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as being “coupled” or “connected” to another component (e.g., a second component), with or without the terms “functionally” or “communicatively,” it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0035] Each component (e.g., a module or a program) described in this document may include one or more entities. According to various embodiments, one or more components or operations of the components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0036] The term "module" or "part" used in this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0037] Various embodiments of the present document may be implemented as software (e.g., a program or an application) including one or more instructions stored in a machine-readable storage medium (e.g., memory). For example, a processor of the device may call at least one instruction among the one or more instructions stored from the storage medium and execute it. This enables the device to operate to perform at least one function according to the at least one instruction called. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, "non-transitory" only means that the storage medium is a tangible device and does not contain a signal (e.g., electromagnetic waves), and this term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily in the storage medium.

[0038]

[0039] Figure 1 is a block diagram showing the configuration of an information provision system according to one embodiment disclosed in this document.

[0040] Referring to FIG. 1, the information provision system (1) may include a server (10), a battery (20), and an electronic device (30).

[0041] The information provision system (1) can obtain and analyze historical data related to users' battery charging, thereby establishing criteria for determining users' driving habits. Furthermore, the information provision system (1) can obtain historical data related to the target user's charging and information related to the current driving, thereby determining the target user's driving habits and providing guidance information related to the user's driving habits. Through this, the information provision system (1) can induce the strengthening of the target user's driving habits.

[0042] The server (10) can acquire user history data and information related to the target user's current driving. The server (10) can analyze the acquired data and information to determine the target user's habits and provide guidance information.

[0043] The battery may be a battery installed in the vehicle, or may be a battery pack or battery module. For example, the vehicle (20) may be equipped with an On-Board Diagnostics (OBD) device that acquires data, and the server (10) may receive charging history data and / or driving-related information from the OBD device.

[0044] The electronic device (30) may be a user terminal carried by a user. The electronic device (30) may include various types of devices capable of performing data communication. For example, the electronic device (30) may include a portable device such as a smartphone or tablet, a computer device such as a desktop or laptop, a multimedia device, a camera, a wearable device, a virtual reality (VR) device, etc., but is not limited to the aforementioned devices. For example, the electronic device (30) may include a server or gateway capable of transmitting data packets through an application.

[0045] A user may refer to, for example, a person registered with the information provision system (1). For example, a user may be a person who has subscribed to an application provided by the operator of the information provision system (1). A user may utilize a service provided by the server (10) using an electronic device (30). For example, the service may include an information provision service. To this end, an application for utilizing the information provision service may be installed on the electronic device (30).

[0046] Users can subscribe to information provision services and check their habits and guide information by running an application on their electronic device (30). The application installed on the electronic device (30) may be an application provided by the operator of the information provision system (1) (e.g., a battery manufacturer).

[0047] Below, the operation of the server (10) will be described in detail.

[0048] The server (10) can obtain historical data related to the users' battery charging and information related to the target user's current driving. For example, the server (10) can receive historical data and driving-related information from components such as the battery management system (BMS), vehicle BMS, OBD (On Board Device), and charger / discharger installed in the battery pack via a communication circuit.

[0049] The server (10) can acquire and store historical data related to users' battery charging. The historical data related to battery charging may include various information, such as charging start SOC (State Of Charge), charging end SOC, and charging time.

[0050] The target user may be a user for whom guidance information is provided. More specifically, the target user may be a user who has subscribed to the information provision service. Information related to the current driving may include various information, such as the driving start SOC, the current remaining SOC, driving time, and remaining driving distance.

[0051] The server (10) can extract features related to each user's charging factor from the user's history data. The charging factor may be a factor defining charging-related habits. The server (10) can analyze the data to determine the charging factor's habits. The charging factor may include, for example, the charging start SOC, the charging end SOC, the charging time, the charging day, etc. For convenience of explanation, the charging factor will be described below as the charging start SOC.

[0052] According to an embodiment, the server (10) can extract a probability density function (PDF) for a factor produced by applying a Kernel Density Estimation (KDE) technique to historical data as a feature. Kernel density estimation is a technique used to produce a distribution of data, and can produce a distribution of data to be analyzed using a kernel expressed as a probability density.

[0053] The server (10) can calculate the distribution of historical data as a probability density function using kernel density estimation. The server (10) can calculate a continuous probability density function even when the values ​​of the historical data are discontinuous.

[0054]

[0055] *According to an embodiment, the server (10) can apply kernel density estimation by assigning a higher weight to data acquired closer to the current point in time among the historical data. Through this, the server (10) can improve recent data dependency. That is, since the more recent the user's data is, the more it reflects the user's habits at the current point in time. Therefore, the server (10) can more accurately determine the user's current habits by assigning a higher weight to data acquired at the recent point in time.

[0056] The server (10) can set criteria for determining habituality related to factors based on the extracted features.

[0057] According to an embodiment, the server (10) can establish criteria for determining habitual behavior based on the results of clustering features extracted from each user. The server (10) can obtain probability density functions for each user and perform clustering based on their shape, maximum probability density, etc. Through clustering, the server (10) can classify the user's features into multiple clusters, and each cluster can exhibit different habitual behaviors with respect to the user's charging factor.

[0058] For example, if the charging factor is the charging start SOC, multiple clusters classified as a result of clustering by the server (10) may exhibit different habits for the charging start SOC. For example, the multiple clusters may be classified as a cluster having a habit for the charging start SOC, a cluster not having a habit for the charging start SOC, and a cluster having an ambiguous habit for the charging start SOC.

[0059] According to an embodiment, the server (10) can set a criterion based on the maximum probability density value of the features included in each cluster as a result of clustering the features. The server (10) can analyze the maximum probability density value of the features included in each cluster classified as a result of clustering. The server (10) can set a criterion for determining (classifying) habituality by analyzing the maximum probability density value.

[0060] For example, the server (10) may set the maximum value of the maximum probability density among the features included in the cluster that does not have a habit of charging start SOC as the first reference value, and may set the minimum value of the maximum probability density among the features included in the cluster that has a habit of charging start SOC as the second reference value.

[0061] The server (10) can determine the habits of the target user based on the characteristics of the target user and set criteria.

[0062] According to an embodiment, the server (10) can determine the target user's habitual behavior based on the results of comparing the maximum probability density derived from the target user's characteristics with a reference. The target user's habitual behavior may include, for example, information such as the presence or absence of a habit for charging start SOC and the charging start habit SOC.

[0063] For example, if the maximum probability density of the target user is less than or equal to a first reference value, the server (10) may determine that the target user does not have a habit of charging start SOC. In another example, if the maximum probability density of the target user is greater than or equal to a second reference value, the server (10) may determine that the target user has a habit of charging start SOC, and may determine the SOC having the maximum probability density as the charging start habit SOC of the target user.

[0064] In one embodiment, the server (10) can update the characteristics and habits of the target user by reflecting the history data acquired each time the target user charges. The server (10) can update the characteristics and habits of the target user each time additional history data is acquired, thereby providing more accurate habitual information and guidance information for the target user. In one embodiment, the server (10) can assign a higher weight to newly acquired history data than to previously acquired history data.

[0065] The server (10) may provide guidance information related to the target user's habits based on at least one of the target user's habits, history data, and information related to the current driving. The server (10) may analyze and provide guidance information by comparing data related to charging factors included in the target user's habits and history data and / or driving-related information.

[0066] According to an embodiment, the server (10) can calculate the remaining discharge depth based on the charging start SOC obtained based on the habit of the target user and information related to the current driving.

[0067] For example, the server (10) can calculate the remaining depth of discharge (DOD) from the charging start habit SOC and the driving start SOC according to the user's habit, and provide the user with the remaining depth of discharge from the driving start time to the charging time according to the user's habit.

[0068] In another example, the server (10) can calculate the remaining discharge depth from the charging start habit SOC and the current SOC according to the user's habit and provide the user with the remaining discharge depth from the current point in time to the charging point according to the habit.

[0069] In another example, the server (10) can calculate the remaining discharge depth from the charging start habit SOC according to the user's habit and the driving end SOC included in the recent history data, and provide the user with the remaining discharge depth up to the charging point according to the user's habit during subsequent driving.

[0070] In this way, the server (10) can provide guide information including the remaining discharge depth, thereby inducing the strengthening of the user's charging-related habits.

[0071] FIG. 2 is a diagram showing an example of extracting user characteristics according to one embodiment disclosed in this document.

[0072] Referring to FIG. 2, the server (10) can extract features related to the charging factors of each user from the users' history data.

[0073] The server (10) can extract data on charging factors from the history data of each user. 210 of FIG. 2 exemplarily shows data on a user's charging factor when the charging factor is a charging start SOC, and the x-axis represents the charging start SOC and the y-axis represents the number of samples.

[0074] The server (10) can extract user characteristics from charging factor data. In an embodiment, the server (10) can apply the kernel density estimation (KDE) technique to the charging factor data to derive a probability density function, which can then be extracted as user characteristics. For example, the server (10) can express each user's characteristics as a probability density function for the charging factor, as shown at 220 in FIG. 2 .

[0075] FIG. 3 is a diagram showing an example of extracting features by applying weights according to one embodiment disclosed in this document.

[0076] Referring to FIG. 3, the server (10) can apply kernel density estimation by assigning a higher weight to data acquired at a point in time closer to the current point in time among the history data.

[0077] The server (10) can apply a weight function (320) to data (310) regarding a charging factor. The weight function (320) can be set to have a higher value the closer it is to the current point in time, and for example, the server (10) can multiply the data (310) regarding a charging factor by the weight function (320) to express the data regarding a charging factor as a histogram (330).

[0078] The server (10) can apply a kernel density estimation technique to a histogram (330) for a charging factor to produce a probability density function (340).

[0079] FIG. 4 is a diagram showing an example of a clustering result of features according to one embodiment disclosed in this document.

[0080] Referring to FIG. 4, the server (10) can cluster features. 410 of FIG. 4 represents features extracted from the history data of each user, and 420 represents an example of the result of clustering the features.

[0081] According to an embodiment, the server (10) may perform clustering based on the maximum probability density value included in the characteristics of each user. For example, the server (10) may classify users into clusters with weak charging habits, clusters with strong charging habits, and clusters with ambiguous charging habits based on the clustering results of the users' characteristics, as shown in 420 of FIG. 4 . For example, users with a large maximum probability density value may be classified into the cluster with strong charging habits.

[0082] FIG. 5 is a diagram showing an example of setting criteria for determining habituality according to one embodiment disclosed in this document.

[0083] Referring to FIG. 5, the server (10) can set a criterion for determining habituality based on the maximum probability density value of each cluster as a result of clustering.

[0084] 510 of Fig. 5 is a graph showing the maximum probability density value of each user, where the x-axis can represent the maximum probability density and the y-axis can represent the number of samples. The server (10) can set a standard for judging habituality based on the clustering result and the maximum probability density value.

[0085] The server (10) can express the maximum probability density of each user as a graph, such as 520 of FIG. 5 . For example, the server (10) can cluster the characteristics of users and express the maximum probability density of the characteristics included in each cluster by distinguishing them. For example, the data indicated as C1 in 520 of FIG. 5 may be data of users included in a cluster without a charging start habit, and the data indicated as C3 may be data of users included in a cluster with a charging start habit.

[0086] In one embodiment, the server (10) may set the maximum value among the maximum probability densities of users included in a cluster (C1) without a charging initiation habit as a first reference value for determining habit. Furthermore, the server (10) may set the minimum value among the maximum probability densities of users included in a cluster (C3) with a charging initiation habit as a second reference value for determining habit. For example, in 520 of FIG. 5 , the first reference value may be set to 0.020, and the second reference value may be set to 0.028.

[0087] The server (10) can determine the target user's charging habit based on the results of comparing the maximum probability density derived from the target user's characteristics with a standard. For example, in FIG. 5, the server (10) can determine that the target user has a weak charging habit if the maximum probability density value is less than 0.020, and can determine that the target user has a strong charging habit if the maximum probability density value is greater than 0.028.

[0088] FIG. 6 is a flowchart for explaining a method of operating a server according to one embodiment disclosed in this document.

[0089] Referring to FIG. 6, the operating method of the server may include a step (S100) of acquiring history data related to battery charging of users and information related to the current driving of the target user, a step (S200) of extracting features related to charging factors of each user from the history data, a step (S300) of setting criteria for determining habits related to factors based on the features, a step (S400) of determining habits of the target user based on the characteristics and criteria of the target user, and a step (S500) of providing guide information related to the habits of the target user based on the habits of the target user and the history data and information related to the current driving.

[0090] At step S100, the server (10) can obtain users' charging history data and information related to the target user's current driving. For example, the server (10) can obtain information from the battery's BMS, charger, and the like.

[0091] At step S200, the server (10) can extract features related to the charging factors of each user from the history data. According to an embodiment, the server (10) can extract a probability density function (PDF) produced by applying kernel density estimation (KDE) to the users' history data as features of each user.

[0092] At step S300, the server (10) may set criteria for determining habituality related to factors based on features. In an embodiment, the server (10) may set criteria based on the results of clustering features.

[0093] At step S400, the server (10) can determine the target user's habits based on the target user's characteristics and criteria. According to an embodiment, the server (10) can compare the maximum probability density value contained in the target user's characteristics with the criteria and determine the user's habits based on the results.

[0094] At step S500, the server (10) may provide guidance information related to the target user's habits based on at least one of the target user's habits, historical data, and information related to the current driving. According to an embodiment, the server (10) may calculate a remaining discharge depth based on the charging start habit SOC acquired based on the target user's habits and information related to the current driving, and provide guidance information including the remaining discharge depth.

[0095] FIG. 7 is a block diagram showing the configuration of a server according to one embodiment disclosed in this document.

[0096] Referring to FIG. 7, a server (10) according to one embodiment may include a communication circuit (11), a processor (12), and a memory (13).

[0097] The communication circuit (11) can support the establishment of a wired or wireless communication connection between the server (10) and an external electronic device (e.g., a vehicle, an electronic device, etc.), and the performance of communication through the established connection. According to one embodiment, the communication circuit (11) includes a wireless communication circuit (e.g., a cellular communication circuit, a short-range wireless communication circuit, or a GNSS (global navigation satellite system) communication circuit) or a wired communication circuit (e.g., a LAN (local area network) communication circuit, or a power line communication circuit), and using the corresponding communication circuit, can communicate with the external electronic device through a short-range communication network such as Bluetooth, WiFi direct, or IrDA (infrared data asSOCiation), or a long-range communication network such as a cellular network, the Internet, or a computer network. The various types of communication circuits (11) described above can be implemented as one chip or can be implemented as separate chips. According to one embodiment, the communication circuit (11) of the server (10) can communicate with the vehicle (20) and the electronic device (30) of FIG. 1.

[0098] The processor (12) can control the overall operation of the server (10). In various embodiments, the processor (12) may include a single processor core or a plurality of processor cores. For example, the processor (12) may include a multi-core such as a dual-core, a quad-core, or a hexa-core. According to embodiments, the processor (12) may further include a cache memory located internally or externally. According to embodiments, the processor (12) may be configured with one or more processors. For example, the processor (12) may include at least one of an application processor, a communication processor, or a graphical processing unit (GPU).

[0099] All or part of the processor (12) may be electrically or operatively coupled with or connected to other components (e.g., communication circuitry (11) or memory (13)) within the server (10). The processor (12) may receive commands from other components, interpret the received commands, and perform calculations or process data according to the interpreted commands. The processor (12) may interpret and process messages, data, commands, or signals received from the communication circuitry (11) and memory (13). The processor (12) may generate new messages, data, commands, or signals based on the received messages, data, commands, or signals. The processor (12) may provide the processed or generated messages, data, commands, or signals to the communication circuitry (11) or memory (13).

[0100] The processor (12) can process data or signals generated or produced by a program. For example, the processor (12) can request commands, data, or signals from the memory (13) to execute or control the program. The processor (12) can record (or store) or update commands, data, or signals in the memory (13) to execute or control the program. According to one embodiment, the processor (12) can analyze the history data of users stored in the memory (13) to set criteria for determining habitual behavior.

[0101] The memory (13) can store commands, control command codes, control data, or user data for controlling the server (10). For example, the memory (13) can include at least one of an application program, an operating system (OS), middleware, or a device driver. The memory (13) can include one or more of volatile memory or non-volatile memory. The volatile memory can include dynamic random access memory (DRAM), static RAM (SRAM), synchronous DRAM (SDRAM), phase-change RAM (PRAM), magnetic RAM (MRAM), resistive RAM (RRAM), ferroelectric RAM (FeRAM), etc. The non-volatile memory can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, etc. The memory (13) may further include a non-volatile medium such as a hard disk drive (HDD), a solid state disk (SSD), an embedded multi-media card (eMMC), or a universal flash storage (UFS). According to one embodiment, the memory (13) may store history data related to users' charging.

[0102]

[0103] Although all components constituting the embodiments disclosed in this document have been described as being combined or operating in combination as one, the embodiments disclosed in this document are not necessarily limited to such embodiments. That is, within the scope of the purpose of the embodiments disclosed in this document, all of the components may be selectively combined and operated one or more times.

[0104] In addition, terms such as "include," "comprise," or "have" described above, unless specifically stated otherwise, mean that the corresponding component can be included, and therefore should be interpreted to include other components rather than excluding other components. All terms, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document belong, unless otherwise defined. Commonly used terms, such as terms defined in a dictionary, should be interpreted to be consistent with the contextual meaning of the relevant technology, and shall not be interpreted in an idealized or overly formal sense, unless explicitly defined in this document.

[0105] The above description is merely an example of the technical idea disclosed in this document, and those skilled in the art to which the embodiments disclosed in this document pertain may make various modifications and variations without departing from the essential characteristics of the embodiments disclosed in this document. Therefore, the embodiments disclosed in this document are not intended to limit the technical idea of ​​the embodiments disclosed in this document, but to explain it, and the scope of the technical idea disclosed in this document is not limited by these embodiments. The scope of protection of the technical idea disclosed in this document should be interpreted by the claims below, and all technical ideas within a scope equivalent thereto should be interpreted as being included in the scope of rights of this document.

Claims

1. Communication circuit; memory; and A processor operatively connected to the communication circuit and the memory, the processor comprising: Obtain historical data related to users' battery charging and information related to the target user's current driving, Extract features related to the charging factors of each user from the above history data, Based on the above characteristics, a standard is set to determine the habitual nature related to the above factors, Based on the characteristics of the target user and the criteria above, the habit of the target user is determined, A server that provides guide information related to the target user's habit based on at least one of the target user's habit, history data, and information related to the current driving.

2. In paragraph 1, The above processor, A server that extracts a probability density function (PDF) for the above factor as a feature by applying the kernel density estimation (KDE) technique to the above history data.

3. In paragraph 2, The above processor, A server that sets the above criteria based on the results of clustering the above features.

4. In paragraph 3, The above processor, A server that sets the criteria based on the maximum probability density value of features included in each cluster as a result of the clustering.

5. In paragraph 2, The above processor, A server that applies the kernel density estimation by giving a higher weight to data acquired at a point in time closer to the current point in time among the above history data.

6. In paragraph 1, The above processor, A server that determines the habit of the target user based on the result of comparing the maximum probability density derived from the characteristics of the target user with the above criteria.

7. In paragraph 1, The above processor, A server that updates the characteristics and habits of the target user by reflecting the history data acquired each time the target user recharges.

8. In paragraph 1, The above processor, A server that calculates a remaining discharge depth based on the charging start SOC obtained based on the above habit and information related to the current driving, and provides the guide information including the remaining discharge depth.

9. A step of acquiring historical data related to the users' battery charging and information related to the target user's current driving; A step of extracting features related to the charging factors of each user from the above history data; A step of setting criteria for determining habituality related to the factor based on the above characteristics; A step of determining the habit of the target user based on the characteristics of the target user and the criteria; and A method of operating a server, comprising the step of providing guide information related to the habit of the target user based on at least one of the habit and history data of the target user and information related to the current driving.

10. In paragraph 9, The step of extracting the above features is: A server operation method characterized in that a probability density function (PDF) for the factor produced by applying a kernel density estimation (KDE) technique to the above history data is extracted as the feature.

11. In paragraph 10, The steps for setting the above criteria are: A method of operating a server, characterized in that the criteria are set based on the results of clustering the above features.

12. In paragraph 11, The steps for setting the above criteria are: A server operation method characterized in that the criterion is set based on the maximum probability density value of the probability density function of the features included in each cluster as a result of the clustering.

13. In paragraph 10, The step of extracting the above features is: A server operation method characterized in that the kernel density estimation is applied by giving a higher weight to data acquired at a point in time closer to the current point in time among the above history data.

14. In paragraph 9, The step of determining the habit of the above target user is: A server operation method characterized in that the habitual nature of the target user is determined based on the result of comparing the maximum probability density derived from the characteristics of the target user with the above criteria.

15. In paragraph 9, The steps for providing the above guide information are: A method of operating a server, characterized in that the method calculates a remaining discharge depth based on the charging start SOC obtained based on the above habit and information related to the current driving, and provides the guide information including the remaining discharge depth.

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