User type identification method, electronic device, and readable storage medium

Abstract

A method for identifying a user type, an electronic device, and a readable storage medium. The method comprises the step (2100) of acquiring driving data in a preset time period - the driving data includes at least the driving time and cumulative driving mileage of a vehicle -; the step (2200) of acquiring data to be analyzed according to the driving data in the preset time period - the data to be analyzed includes the driving duration, driving mileage, and the number of drivings in each time period of each day -; and the step (2300) of acquiring a user type by analyzing the data to be analyzed through a preset identification model.

Description

Technology Field

[0001] Cross-reference regarding related applications

[0002] The present disclosure claims priority to Chinese patent application No. 202111676076.2, filed on December 31, 2021, with the title of the invention "USER TYPE IDENTIFICATION METHOD, ELECTRONIC DEVICE, AND READABLE STORAGE MEDIUM", the entirety of which is incorporated herein by reference.

[0003] Technology field

[0004] The embodiments of the present disclosure relate to the field of data processing technology, and more specifically, to a method for identifying a user type, an electronic device, and a readable storage medium. Background Technology

[0005] As the popularity of new energy vehicles increases, the number of users is also growing. Since each user has different driving habits and modes, the performance of the vehicle's power battery varies. To optimize battery performance, it is necessary to identify user types by analyzing vehicle driving data so that the battery management system can be customized to suit the user.

[0006] However, when user types are classified, the processing procedure is complex, and sensitive information such as users' driving routes needs to be acquired and analyzed, causing significant problems for user information security.

[0007] According to a first aspect of the embodiments of the present disclosure, a user type identification method is provided, said method:

[0008] A step of acquiring driving data over a preset time period - said driving data includes at least the vehicle's driving time and accumulated driving mileage -;

[0009] A step of obtaining to-be-analyzed data based on the driving data in the above-preset time period - the to-be-analyzed data includes driving duration, driving mileage, and the number of drivings in each time period each day -; and

[0010] It includes the step of obtaining a user type by performing an analysis on the data to be analyzed through a pre-set identification model.

[0011] In an embodiment of the present disclosure, the step of acquiring data to be analyzed based on the driving data in the preset time period is:

[0012] A step of extracting daily driving data from the driving data within the above-mentioned preset time period; and

[0013] It includes the step of calculating the daily driving data separately to obtain the driving duration, the driving mileage, and the number of driving times in each time period of each day.

[0014] In an embodiment of the present disclosure, the identification result of the user type includes a daytime online car-hailing user type, a nighttime online car-hailing user type, a commuting private vehicle user type, a commercial vehicle user type, and a non-commuting private vehicle user type.

[0015] In an embodiment of the present disclosure, the method is:

[0016] It further includes the step of performing a visual analysis of the user's vehicle data based on the above user type and obtaining the visual analysis result.

[0017] In an embodiment of the present disclosure, the vehicle data includes the driving data when the vehicle is in a driving state and the non-driving data when the vehicle is in a non-driving state.

[0018] In an embodiment of the present disclosure, the method is:

[0019] It further includes the step of obtaining driving habit information of the user by performing a feature analysis on the vehicle data of the user.

[0020] In an embodiment of the present disclosure, the method is:

[0021] It further includes the step of formulating a control strategy for a corresponding battery management system based on the above-mentioned user type.

[0022] In an embodiment of the present disclosure, the step of obtaining a user type by performing an analysis on the data to be analyzed through a preset identification model is:

[0023] Step of acquiring a training sample set - said training sample set includes preprocessed offline vehicle data -;

[0024] A step of training the identification model based on the above training sample set; and

[0025] It includes the step of obtaining the user type by performing an analysis on the data to be analyzed based on the trained identification model after the step of training the identification model.

[0026] In an embodiment of the present disclosure, the method is:

[0027] After performing an analysis on the data to be analyzed, the method further includes the step of updating the data to be analyzed and the corresponding analysis results as training samples in the training sample set.

[0028] According to a second aspect of the embodiments of the present disclosure, a user type identification device is provided, said identification device:

[0029] An acquisition module configured to acquire driving data in a preset time period—the driving data includes at least the vehicle's driving time and cumulative driving mileage—; and to acquire data to be analyzed based on the driving data in the preset time period—the data to be analyzed includes the driving duration, driving mileage, and the number of drivings in each time period each day; and

[0030] It includes an analysis module configured to obtain a user type by performing an analysis on the data to be analyzed through a pre-configured identification model.

[0031] In an embodiment of the present disclosure, the analysis module is further configured to perform a visual analysis of the user's vehicle data based on the user type and to obtain a visual analysis result.

[0032] In an embodiment of the present disclosure, the analysis module is further configured to perform a feature analysis of the user's vehicle data to obtain information on the user's driving habits.

[0033] In an embodiment of the present disclosure, the identification device is:

[0034] It further includes a processing module configured to formulate a control strategy for a corresponding battery management system based on the above-mentioned user type.

[0035] In an embodiment of the present disclosure, the acquisition module is:

[0036] Acquire a training sample set - said training sample set includes preprocessed offline vehicle data -;

[0037] Training the identification model based on the above training sample set;

[0038] It is further configured to perform an analysis on the data subject to analysis based on the trained identification model to obtain the user type.

[0039] According to a third aspect of the embodiments of the present disclosure, an electronic device comprising a memory and a processor is provided. The memory is configured to store an executable instruction; and the processor is configured to execute a user type identification method according to any one of the first aspects of the embodiments of the present disclosure under the control of the instruction.

[0040] According to a fourth aspect of the embodiments of the present disclosure, a readable storage medium storing a computer program is provided. When executed by a processor, the computer program implements a user type identification method according to any one of the first aspects of the embodiments of the present disclosure. Brief explanation of the drawing

[0041] The attached drawings are incorporated into this specification and constitute part of this specification. They are used to illustrate embodiments of this disclosure and, together with their description, to explain the principles of this disclosure. Figure 1 is a block diagram of the principle of hardware configuration of an exemplary electronic device. FIG. 2 is a schematic flowchart of a user type identification method according to an embodiment of the present disclosure. FIG. 3 is a schematic visual diagram of a user type obtained through a user type identification method according to an embodiment of the present disclosure. FIG. 4 is a schematic structural diagram of a user type identification device according to an embodiment of the present disclosure. Figure 5 is a schematic diagram of the hardware structure of an exemplary electronic device. Specific details for implementing the invention

[0042] Now, various exemplary embodiments of the present disclosure are described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specified, reverse arrangements of components and steps, numerical expressions, and numerical values ​​described in the embodiments do not limit the scope of the present disclosure.

[0043] The following description of at least one exemplary embodiment is merely illustrative and does not in any way constitute any limitation to the present disclosure and its application or use.

[0044] Technologies, methods, and devices known to a person skilled in the art may not be discussed in detail, but where appropriate, such technologies, methods, and devices should be considered part of this specification.

[0045] In all examples illustrated and discussed in this specification, any specific value should be interpreted merely as illustrative and not as a limitation. Accordingly, other examples of exemplary embodiments may have different values.

[0046] It should be noted that similar reference numbers and letters refer to similar items in the following attached drawings. Therefore, once an item is defined in the attached drawings, it is no longer necessary to discuss it in subsequent attached drawings.

[0047] The objective of the embodiments of the present disclosure is to provide a method for identifying user types so that user information security can be improved when user classification is implemented.

[0048] An advantageous effect of the present disclosure is that driving data over a preset time period is obtained—the driving data includes at least the driving time, cumulative driving mileage, and driving speed in the vehicle driving process—; data to be analyzed is obtained based on the driving data over the preset time period—the data to be analyzed includes the daily driving duration, daily driving mileage, and the number of drivings at each moment of each day—; and an analysis of the data to be analyzed is performed through a preset identification model to obtain a user type. In the embodiments, the user type is obtained by performing an analysis on the daily driving duration, daily driving mileage, and the number of drivings at each moment of each day. Since the data used in the calculation process is not related to the user's sensitive privacy data, the user's information security can be improved when user classification is implemented.

[0049] <Hardware Configuration>

[0050] As illustrated in FIG. 1, an electronic device (1000) capable of using the user type identification method of the present disclosure may include a processor (1100), memory (1200), interface device (1300), communication device (1400), display device (1500), input device (1600), speaker (1700), microphone (1800), etc.

[0051] The processor (1100) may be a mobile version processor. The memory (1200) includes non-volatile memory such as, for example, ROM (read-only memory), RAM (random access memory), and a hard disk. The interface device (1300) includes, for example, a USB interface and an earphone interface. The communication device (1400) may perform wired or wireless communication, for example. The communication device (1400) may include any device that performs short-range wireless communication based on short-range wireless communication protocols such as Hilink protocol, WiFi (IEEE 802.11 protocol), Mesh, Bluetooth, ZigBee, Thread, Z-Wave, NFC, UWB, and LiFi. The communication device (1400) may also include any device that performs remote communication, for example, WLAN, GPRS, or 2G / 3G / 4G / 5G remote communication. The display device (1500) is, for example, a liquid crystal display screen or a touch display screen. The input device (1600) may include, for example, a touch screen and a keyboard. The electronic device (1000) can output audio information through a speaker (1700) and collect audio information through a microphone (1800).

[0052] In this embodiment, the memory (1200) of the electronic device (1000) is configured to store instructions, and the instructions are configured to control the processor (1100) to perform an operation that implements a user type identification method. A person skilled in the art may design instructions according to the solutions disclosed by this disclosure. How instructions control the processor to perform an operation is well known in the art and is therefore not described again in detail in this specification.

[0053] Although a number of devices of the electronic device (1000) are illustrated in FIG. 1, the present disclosure may relate to only some of the devices therein. For example, the electronic device (1000) relates only to the memory (1200), processor (1100), communication device (1400), and display device (1500).

[0054] It should be understood that although FIG. 1 illustrates only one electronic device (1000), this does not mean that the number of electronic devices (1000) is limited.

[0055] <Method Example>

[0056] FIG. 2 is a schematic flowchart of a user type identification method according to an embodiment of the present disclosure.

[0057] As illustrated in FIG. 2, the user type identification method in this embodiment can specifically be performed by the electronic device (1000) illustrated in FIG. 1.

[0058] Specifically, the user type identification method in this embodiment may include the following steps (2100) to (2300).

[0059] Step (2100): Driving data is obtained over a preset time period, wherein the driving data includes at least the driving time, accumulated driving mileage, and driving speed in the vehicle driving process.

[0060] Acquiring vehicle data within a preset time period may mean acquiring vehicle data within one week, acquiring vehicle data within one month, or acquiring vehicle data within three months. The preset time period may be set according to actual requirements. This is not specifically limited in this specification.

[0061] It can be understood that when the vehicle state is in a driving state, the acquired vehicle data is driving data, and when the vehicle state is in a charging state, the acquired vehicle data is non-driving data. In this embodiment, users are classified based on driving data. Therefore, driving data over a preset time period needs to be selected from the vehicle data according to the vehicle state.

[0062] Specifically, the electronic device (1000) first obtains vehicle data for a preset time period - vehicle data includes driving time, cumulative driving mileage, vehicle status, and driving speed; vehicle status includes driving status and charging status -; then, based on the vehicle status, can obtain driving data for a preset time period from the vehicle data.

[0063] In the example, the time interval for collecting vehicle data can be 30 seconds.

[0064] Step (2200): Data to be analyzed is obtained based on driving data over a preset time period, wherein the data to be analyzed includes the daily driving duration, daily driving mileage, and the number of drivings at each moment of each day.

[0065] Specifically, after the electronic device acquires driving data over a preset time period, daily driving data can be extracted from the driving data over the preset time period; and the daily driving data is calculated separately to acquire the daily driving duration, daily driving mileage, and the number of drives at each moment of each day within the preset time period.

[0066] For example, assuming User A drives a vehicle three times a day, driving for 1 hour at 8:00 AM with a mileage of 5 kilometers; driving for 0.5 hours at 12:00 PM with a mileage of 2 kilometers; and driving for 2 hours at 12:45 PM with a mileage of 10 kilometers, the daily driving duration obtained through calculation is 3.5 hours, the daily driving mileage is 17 kilometers, the number of drives at 8:00 is 1, the number of drives at 12:00 is 2, and the number of drives at all other moments of the day is 0.

[0067] Step (2300): Analysis of the data to be analyzed is performed through a pre-set identification model to obtain the user type.

[0068] Specifically, the electronic device (1000) can acquire a training sample set containing preprocessed offline vehicle data; train an identification model based on the training sample set; and perform an analysis on the data to be analyzed based on a preset identification model to acquire a corresponding user type.

[0069] After the analysis of the data to be analyzed is completed, the data to be analyzed and the corresponding analysis results can be updated in the training sample set as training samples, and thus the electronic device (1000) optimizes the identification model by training the identification model based on the updated training sample set.

[0070] In this embodiment, the pre-set identification model may be a K means clustering model.

[0071] User types include at least daytime online ride-hailing user types, nighttime online ride-hailing user types, commuting private vehicle user types, commercial vehicle user types, and non-commuting private vehicle user types.

[0072] Optionally, after an analysis of the data to be analyzed is performed and a user type is obtained, the electronic device (1000) can additionally separate the user's corresponding vehicle data from the vehicle data based on the user type; and can perform a visual analysis of the user's vehicle data to obtain a visual analysis result.

[0073] As illustrated in FIG. 3, the visual analysis results corresponding to the daytime online vehicle call user type are shown in Cluster 1, and their characteristics are mainly characterized by a long average daily driving time and daily driving mileage, and a fixed driving time period. The visual analysis results corresponding to the nighttime online vehicle call user type are shown in Cluster 2, and their characteristics are mainly characterized by a long average daily driving time and daily driving mileage, and a fixed driving time period. The visual analysis results corresponding to the commuting private vehicle user type are shown in Cluster 3, and their characteristics are mainly characterized by a driving time period concentrated between working and non-working hours, and a fixed driving mileage. The visual analysis results corresponding to the commercial vehicle user type are shown in Cluster 4, and their characteristics are mainly characterized by an intermediate driving time and driving mileage, and a non-fixed driving time period. The visual analysis results corresponding to the non-commuting private vehicle user type are shown in Cluster 5, and their characteristics are mainly characterized by a short driving time and driving mileage, and a non-fixed driving time period.

[0074] Optionally, after an analysis of the data to be analyzed is performed and a user type is obtained, the electronic device (1000) may additionally perform a feature analysis of the user's vehicle data to obtain information on the user's driving habits.

[0075] In this embodiment, after step (2300), the electronic device (1000) may further formulate a control strategy for the corresponding battery management system based on the user type. For example, the estimation accuracy of the SOC may be adjusted.

[0076] In this technical solution of this embodiment, driving data over a preset time period is obtained—the driving data includes at least the driving time, cumulative driving mileage, and driving speed in the vehicle driving process—; data to be analyzed is obtained based on the driving data over the preset time period—the data to be analyzed includes the daily driving duration, daily driving mileage, and the number of drives at each moment of each day—; and an analysis of the data to be analyzed is performed through a preset identification model to obtain a user type. In this embodiment, the user type is obtained by performing an analysis on the daily driving duration, daily driving mileage, and the number of drives at each moment of each day. Since the data used in the calculation process is not related to the user's sensitive privacy data, user information security can be improved when user classification is implemented.

[0077] <Device Example>

[0078] FIG. 4 is a schematic structural diagram of a user type identification device according to an embodiment of the present disclosure. As shown in FIG. 4, the user type identification device (4000) in this embodiment may include an acquisition module (4100) and an analysis module (4200).

[0079] The acquisition module (4100) is configured to acquire driving data in a preset time period - the driving data includes at least the driving time, cumulative driving mileage, and driving speed in the vehicle driving process -; and to acquire data to be analyzed based on the driving data in the preset time period - the data to be analyzed includes the daily driving duration, daily driving mileage, and the number of drivings at each moment of each day -;

[0080] The analysis module (4200) is configured to perform an analysis on the data to be analyzed through a preset identification model to obtain a user type.

[0081] User types include at least daytime online ride-hailing user types, nighttime online ride-hailing user types, commuting private vehicle user types, commercial vehicle user types, and non-commuting private vehicle user types.

[0082] In this embodiment, the acquisition module (4100) is specifically configured to extract daily driving data from driving data over a preset time period; and to calculate the daily driving data separately in order to acquire the daily driving duration, daily driving mileage, and the number of drivings at each moment of each day over the preset time period.

[0083] In this embodiment, the user type identification device (4000) may further include a separation module (4300) configured to separate the user's corresponding vehicle data from the vehicle data based on the user type; and the analysis module (4200) may be further configured to perform a visual analysis of the user's vehicle data and obtain a visual analysis result.

[0084] In this embodiment, the analysis module (4200) may be further configured to perform feature analysis on the user's vehicle data to obtain information on the user's driving habits.

[0085] In this embodiment, the user type identification device (4000) may further include a processing module (4400) configured to formulate a control strategy for a corresponding battery management system based on the user type.

[0086] In this embodiment, the acquisition module (4100) is specifically configured to acquire a training sample set—the training sample set includes preprocessed offline vehicle data—and the user type identification device (4000) may further include a training module configured to train an identification model based on the training sample set; and the analysis module (4200) may be further configured to acquire a user type by performing an analysis on the data to be analyzed based on the trained identification model.

[0087] The user type identification device in this embodiment may be configured to perform the technical solution of the aforementioned method embodiment, and its implementation principle and technical effect are similar. Details are not described again in this specification.

[0088] <Examples of Electronic Devices>

[0089] In the embodiment, an electronic device (5000) is additionally provided.

[0090] As illustrated in FIG. 5, the electronic device (5000) may include a processor (5100) and a memory (5200). The memory (5200) is configured to store executable instructions; and the processor (5100) is configured to execute the electronic device (5000) based on the control of instructions to perform a user type identification method according to the embodiment described in FIG. 2.

[0091] <Media Example>

[0092] An embodiment of the present disclosure provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements a user type identification method according to any one of the embodiments described above.

[0093] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium, and computer-readable program instructions are carried on the medium to enable a processor to implement various aspects of the present disclosure.

[0094] A computer-readable storage medium may be a tangible device capable of holding and storing instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof, but is not limited thereto. More specific examples (non-comprehensive list) of computer-readable storage media include portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compressed disk read-only memory (CD-ROM), digital versatile disk (DVD), memory sticks, floppy disks, mechanical coding devices, for example, punch cards or in-groove bump structures on which instructions are stored, or any suitable combination thereof. Computer-readable storage media used in this specification shall not be interpreted as instantaneous signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.

[0095] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to various computing / processing devices, or may be downloaded to an external computer or external storage device via a network such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface within each computing / processing device receives computer-readable program instructions from the network and transmits the computer-readable program instructions to be stored on a computer-readable storage medium within each computing / processing device.

[0096] Computer-readable program instructions configured to execute the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source code or target code written in any combination of one or more programming languages, wherein the programming languages ​​include object-oriented programming languages, e.g., Smalltalk and C++, and conventional procedural programming languages, e.g., the "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user computer, partially on a user computer, as an independent software package, partially on a user computer and partially on a remote computer, or entirely on a remote computer or server. In situations involving a remote computer, the remote computer may be connected to the user computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., connected via the Internet through an Internet service provider). In some embodiments, an electronic circuit, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized using state information of computer-readable program instructions, and the electronic circuit may implement various aspects of the present disclosure by executing computer-readable program instructions.

[0097] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present disclosure. It should be understood that both individual blocks of the flowcharts and / or block diagrams and combinations of blocks of the flowcharts and / or block diagrams may be implemented by computer-readable program instructions.

[0098] Computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or another programmable user-type identification device, and when executed by the processor of the computer or other programmable user-type identification device, may produce a machine capable of creating a device that implements functions / operations specified in one or more blocks of flowcharts and / or block diagrams. Computer-readable program instructions may be additionally stored in a computer-readable storage medium, and the instructions enable the computer, the programmable user-type identification device, and / or other device to operate in a particular manner, and thus the computer-readable medium storing the instructions comprises a manufactured article, and the manufactured article comprises instructions that implement various modes of functions / operations specified in one or more blocks of flowcharts and / or block diagrams.

[0099] Computer-readable program instructions may be additionally loaded into a computer, another programmable user-type identification device, or another device so that a series of operation steps are performed on the computer, another programmable user-type identification device, or another device to create a computer-implemented process in which instructions executed on the computer, another programmable user-type identification device, or other device implement functions / operations specified in one or more blocks of flowcharts and / or block diagrams.

[0100] The flowcharts and block diagrams in the accompanying drawings illustrate architectures, functions, and operations that may be implemented by systems, methods, and computer program products according to a number of embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, a program segment, or a part of an instruction, and the module, program segment, or part of an instruction comprises one or more executable instructions configured to implement a specified logical function. In some alternative implementations, the functions represented by the blocks may occur in a different order than that shown in the accompanying drawings. For example, two consecutive blocks may actually be executed nearly in parallel, and two consecutive blocks may sometimes be executed in reverse order. This depends on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts and combinations of blocks in the block diagrams and / or flowcharts may be implemented using a dedicated hardware-based system that performs the specified function or operation, or may be implemented using a combination of special-purpose hardware and computer instructions. It is well known to those skilled in the art that implementations achieved through hardware, software, and combinations of software and hardware are equivalent.

[0101] Various embodiments of the present disclosure have been described above. The foregoing description is illustrative and not comprehensive, and is not limited to the embodiments disclosed. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the illustrative embodiments. The terms used herein are intended to best describe various embodiments, actual applications, or principles of technical improvement in the market, or to enable other persons skilled in the art to understand the embodiments disclosed herein. The scope of the present disclosure is defined by the appended claims.

Claims

Claim 1 A method for identifying a user type performed by an electronic device comprises: a step (2100) of acquiring driving data in a preset time period, wherein the driving data includes at least the driving time and cumulative driving mileage of a vehicle; a step (2200) of acquiring data to be analyzed based on the driving data in the preset time period, wherein the data to be analyzed includes the driving duration, driving mileage, and the number of drivings in each time period each day; and a step (2300) of acquiring a user type by performing an analysis on the data to be analyzed through a preset identification model, wherein the step of acquiring a user type by performing an analysis on the data to be analyzed through a preset identification model comprises: a step of acquiring a training sample set, wherein the training sample set includes preprocessed offline vehicle data; a step of training the identification model based on the training sample set; a step of acquiring the user type by performing an analysis on the data to be analyzed based on the identification model; and a step of updating the data to be analyzed and the corresponding analysis results as training samples in the training sample set after performing an analysis on the data to be analyzed. A method further comprising the step of optimizing the identification model by training the identification model based on the updated training sample set. Claim 2 The method according to claim 1, wherein the step of obtaining data to be analyzed based on the driving data in the preset time period comprises: a step of extracting daily driving data from the driving data in the preset time period; and a step of calculating the daily driving data separately to obtain the driving duration, the driving mileage, and the number of driving times in each time period each day. Claim 3 The method according to claim 1, wherein the user types include at least a daytime online vehicle call user type, a nighttime online vehicle call user type, a commuting private vehicle user type, a commercial vehicle user type, and a non-commuting private vehicle user type. Claim 4 A method according to claim 1, further comprising the step of performing a visual analysis of the user's vehicle data based on the user type and obtaining a visual analysis result. Claim 5 A method according to claim 4, wherein the vehicle data includes driving data when the vehicle is in a driving state and non-driving data when the vehicle is in a non-driving state. Claim 6 A method according to claim 4, further comprising the step of performing a feature analysis on the vehicle data of the user to obtain driving habit information of the user. Claim 7 A method according to claim 1, further comprising the step of formulating a control strategy for a corresponding battery management system based on the user type. Claim 8 delete Claim 9 delete Claim 10 A user type identification device (4000) for acquiring driving data in a preset time period, wherein the driving data includes at least the driving time and cumulative driving mileage of the vehicle; and an acquisition module (4100) configured to acquire data to be analyzed based on the driving data in the preset time period, wherein the data to be analyzed includes the driving duration, driving mileage, and the number of drivings in each time period each day. The identification device (4000) includes an analysis module (4200) configured to obtain a user type by performing an analysis on the data to be analyzed through a pre-set identification model, and the acquisition module (4100) is configured to: obtain a training sample set - the training sample set includes pre-processed offline vehicle data -; train the identification model based on the training sample set; perform an analysis on the data to be analyzed based on the trained identification model to obtain the user type; update the data to be analyzed and the corresponding analysis results as training samples in the training sample set after performing an analysis on the data to be analyzed; and optimize the identification model by training the identification model based on the updated training sample set. Claim 11 In item 10, the above analysis module (4200) is further configured to perform a visual analysis of the user's vehicle data and obtain a visual analysis result, the identification device (4000). Claim 12 In claim 11, the identification device (4000) is further configured such that the analysis module (4200) performs feature analysis on the vehicle data of the user to obtain driving habit information of the user. Claim 13 An identification device (4000) further comprising a processing module (4400) configured to formulate a control strategy for a corresponding battery management system based on the user type in claim 10 or 11. Claim 14 delete Claim 15 An electronic device (5000) comprising a memory (5200) and a processor (5100), wherein the memory (5200) is configured to store executable instructions, and the processor (5100) is configured to execute a user type identification method according to any one of claims 1 to 7 under the control of the instructions. Claim 16 A readable storage medium storing a computer program, wherein the computer program implements a user type identification method according to any one of claims 1 to 7 when executed by a processor.

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