User type identification method, electronic device and readable storage medium

Abstract

The disclosed embodiment provides a method for identifying user types, an electronic device, and a readable storage medium. The method includes: obtaining driving data within a preset time period; the driving data at least includes driving time, accumulated driving mileage, and driving speed during vehicle driving; obtaining data to be analyzed based on the driving data within the preset time period; wherein the data to be analyzed includes daily driving time, daily driving mileage, and driving frequency at various times of the day; analyzing the data to be analyzed using a preset recognition model to obtain the user type.

Description

Technical Field

[0001] 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 Art

[0002] As new energy vehicles become more popular, the number of users has also increased significantly. Due to the different driving habits and driving methods of each user, the performance of the vehicle's power battery is also different. In order to optimize battery performance, it is necessary to analyze the vehicle driving data to obtain the user category, so as to develop a personalized battery management system for the user.

[0003] However, when using relevant technologies to classify user types, the processing flow is complicated and requires obtaining and analyzing sensitive information such as the user's driving route, which poses a huge problem for user information security. Summary of the invention

[0004] An objective of the embodiments of the present disclosure is to provide a method for identifying user types, which can improve user information security while achieving user classification.

[0005] According to a first aspect of an embodiment of the present disclosure, a method for identifying a user type is provided, comprising:

[0006] Acquiring driving data within a preset time period; the driving data at least includes driving time, cumulative driving mileage and driving speed during vehicle driving;

[0007] Acquire the data to be analyzed according to the driving data in the preset time period; wherein the data to be analyzed includes daily driving time, daily driving mileage, and driving frequency at various times of the day;

[0008] The data to be analyzed is analyzed by using a preset recognition model to obtain the type of user.

[0009] Optionally, obtaining the data to be analyzed according to the driving data within the preset time period includes:

[0010] extracting daily driving data from the driving data within the preset time period;

[0011] The daily driving data are calculated respectively to obtain the daily driving duration, the daily driving mileage, and the driving frequency at each time of the day within the preset time period.

[0012] Optionally, the identification results of the user type include: daytime online car-hailing user type, nighttime online car-hailing user type, commuting private car user type, commercial vehicle user type and non-commuting private car user type.

[0013] Optionally, after calculating the data to be analyzed by using a preset recognition model to obtain the type of user, the method further includes:

[0014] Separating corresponding user vehicle data from the vehicle data according to the type of the user;

[0015] Perform visual analysis on the user vehicle data to obtain a visual analysis result.

[0016] Optionally, after separating the corresponding user vehicle data from the vehicle data according to the type of the user, the method further includes:

[0017] Perform feature analysis on the user's vehicle data to obtain the user's driving habit information.

[0018] Optionally, after calculating the data to be analyzed by using a preset recognition model to obtain the type of user, the method further includes:

[0019] According to the type of the user, a corresponding control strategy of the battery management system is formulated.

[0020] Optionally, analyzing the data to be analyzed by using a preset recognition model to obtain the user type includes:

[0021] Acquire a training sample set, wherein the training sample set includes preprocessed offline vehicle data;

[0022] Training the recognition model according to the training sample set;

[0023] The data to be analyzed is analyzed based on the recognition model to obtain the user type.

[0024] According to a second aspect of an embodiment of the present disclosure, there is provided a device for identifying a user type, including:

[0025] An acquisition module is used to acquire driving data within a preset time period; the driving data at least includes driving time, accumulated driving mileage and driving speed during vehicle driving; based on the driving data within the preset time period, data to be analyzed is acquired; wherein the data to be analyzed includes daily driving time, daily driving mileage, and driving frequency at various times of the day;

[0026] The analysis module is used to analyze the data to be analyzed through a preset recognition model to obtain the user type.

[0027] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory and a processor, wherein the memory is used to store executable instructions; the processor is used to execute the user type identification method described in any one of the first aspects of the embodiments of the present disclosure under the control of the instructions.

[0028] According to a fourth aspect of an embodiment of the present disclosure, a readable storage medium is provided, storing a computer program, which, when executed by a processor, implements a method for identifying a user type as described in any one of the first aspects of an embodiment of the present disclosure.

[0029] One beneficial effect of the present disclosure is that, by obtaining driving data within a preset time period; the driving data at least includes the driving time, cumulative driving mileage and driving speed during vehicle driving; according to the driving data within the preset time period, the data to be analyzed is obtained; wherein the data to be analyzed includes daily driving time, daily driving mileage, and driving frequency at various times of the day; the data to be analyzed is analyzed by a preset recognition model to obtain the type of user. In this embodiment, the user type is obtained based on the analysis of daily driving time, daily driving mileage, and driving frequency at various times of the day. Since the data used in the calculation process does not involve the user's sensitive privacy data, it is possible to improve the user's information security while achieving user classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0031] Figure 1 is a principle block diagram of a hardware configuration of an exemplary electronic device;

[0032] Figure 2 is a flowchart of a method for identifying a user type according to an embodiment of the present disclosure;

[0033] Figure 3 is a visual schematic diagram of user types obtained according to the method for identifying user types in an embodiment of the present disclosure;

[0034] Figure 4 is a schematic diagram of the structure of a device for identifying a user type according to an embodiment of the present disclosure;

[0035] Figure 5 The figure is a schematic diagram of the hardware structure of an exemplary electronic device. DETAILED DESCRIPTION

[0036] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless otherwise specifically stated.

[0037] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0038] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0039] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0040] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0041] <Hardware Configuration>

[0042] like Figure 1 As shown, the electronic device 1000 to which the user type identification method disclosed herein can be applied may include a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600, a speaker 1700, a microphone 1800, and the like.

[0043] The processor 1100 may be a mobile version processor. The memory 1200 may include, for example, a ROM (read-only memory), a RAM (random access memory), a non-volatile memory such as a hard disk, etc. The interface device 1300 may include, for example, a USB interface, a headphone interface, etc. The communication device 1400 may, for example, be capable of wired or wireless communication. The communication device 1400 may include a short-range communication device, for example, any device for 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, LiFi, etc. The communication device 1400 may also include a remote communication device, for example, any device for WLAN, GPRS, 2G / 3G / 4G / 5G remote communication. The display device 1500 may be, for example, a liquid crystal display, a touch display, etc. The input device 1600 may include, for example, a touch screen, a keyboard, etc. The mobile terminal 1000 may output audio information through a speaker 1700, and may collect audio information through a microphone 1800.

[0044] In this embodiment, the memory 1200 of the electronic device 1000 is used to store instructions, and the instructions are used to control the processor 1100 to operate to implement the user type identification method. The technician can design instructions according to the scheme disclosed in the present disclosure. How the instructions control the processor to operate is well known in the art, so it will not be described in detail here.

[0045] Despite Figure 1 Multiple devices of the electronic device 1000 are shown in the figure, but the present disclosure may only involve some of the devices, for example, the terminal device 1000 only involves the memory 1200 and the processor 1100, the communication device 1400 and the display device 1500.

[0046] It should be understood that although Figure 1 Only one electronic device 1000 is shown, but this does not mean to limit the number of electronic devices 1000 .

[0047] <Method Example>

[0048] Figure 2 It is a flowchart of a method for identifying a user type according to an embodiment of the present disclosure.

[0049] like Figure 2 As shown, the user type identification method of this embodiment can be specifically performed as follows Figure 1 The electronic device 1000 shown performs.

[0050] Specifically, the method for identifying the user type in this embodiment may include the following steps 2100 to 2300:

[0051] Step 2100, obtaining driving data within a preset time period; the driving data at least includes the driving time, accumulated driving mileage and driving speed during the vehicle driving process.

[0052] The acquisition of vehicle data within a preset time period may be acquisition of vehicle data within a week, or acquisition of vehicle data within a month, or acquisition of vehicle data within three months. The preset time period may be set according to actual needs and is not specifically limited here.

[0053] It is understandable that when the vehicle state is in the driving state, the vehicle data obtained is the driving data, and when the vehicle state is in the charging state, the vehicle data obtained is non-driving data. This embodiment classifies users based on the driving data, and therefore, it is necessary to filter the driving data within the preset time period from the vehicle data according to the vehicle state.

[0054] Specifically, the electronic device 1000 may first obtain vehicle data within a preset time period; the vehicle data includes driving time, cumulative driving mileage, vehicle status, and driving speed; wherein the vehicle status includes driving status and charging status. Then, according to the vehicle status, the driving data within the preset time period is obtained from the vehicle data.

[0055] In one example, the time interval for collecting the vehicle data may be 30 seconds.

[0056] Step 2200, obtaining data to be analyzed based on the driving data within the preset time period; wherein the data to be analyzed includes daily driving time, daily driving mileage, and driving frequency at various times of the day.

[0057] Specifically, after acquiring the driving data within the preset time period, the electronic device can extract daily driving data from the driving data within the preset time period; calculate the daily driving data respectively to obtain the daily driving time within the preset time period, the daily driving mileage, and the driving frequency at each time of the day.

[0058] For example, assuming that user A drives the vehicle 3 times on a certain day, he drives for 1 hour at 8 a.m. and drives 5 kilometers; he drives for 0.5 hours at 12 noon and drives 2 kilometers; he drives for 2 hours at 12:45 and drives 10 kilometers. The calculated daily driving time is 3.5 hours and the daily driving mileage is 17 kilometers. The driving frequency is 1 time at 8 a.m. and 2 times at 12 p.m., and the driving frequency at other times of the day is 0 times.

[0059] Step 2300: Analyze the data to be analyzed using a preset recognition model to obtain the type of user.

[0060] Specifically, the electronic device 1000 can obtain a training sample set including preprocessed offline vehicle data; train the recognition model according to the training sample set, and then analyze the data to be analyzed based on the preset recognition model to obtain the corresponding user type.

[0061] It is understandable that after the analysis of the data to be analyzed is completed, the data to be analyzed and the corresponding analysis results can be updated as training samples to the training sample set, so that the electronic device 1000 trains the recognition model based on the updated training sample set, thereby optimizing the recognition model.

[0062] In this embodiment, the preset recognition model may be a kmeans clustering model.

[0063] Among them, the identification results of the user type include: daytime online car-hailing user type, nighttime online car-hailing user type, commuting private car user type, commercial vehicle user type and non-commuting private car user type.

[0064] Optionally, after analyzing the data to be analyzed to obtain the type of user, the electronic device 1000 may also separate the corresponding user vehicle data from the vehicle data according to the type of user; perform visual analysis on the user vehicle data to obtain a visual analysis result.

[0065] like Figure 3 As shown in the figure, the visualization analysis results corresponding to daytime online car-hailing users are shown in cluster1, and their main characteristics are that the average daily driving time and driving mileage are long, and the driving time period is fixed. The visualization analysis results of the nighttime online car-hailing user type are shown in cluster2, and their main characteristics are that the average daily driving time and driving mileage are long, and the driving time period is fixed. The visualization analysis results of the commuting private car user type are shown in cluster3, and their main characteristics are that the driving time period is concentrated during commuting time, and the driving mileage is relatively fixed. The visualization analysis results corresponding to the commercial vehicle user type are shown in cluster4, and their main characteristics are that the driving time and driving mileage are medium, and the driving time period is not fixed. The visualization analysis results of the non-commuting private car user type are shown in cluster5, and their main characteristics are that the driving time and driving mileage are short, and the driving time period is not fixed.

[0066] Optionally, after analyzing the data to be analyzed to obtain the type of user, the electronic device 1000 may also perform feature analysis on the user's vehicle data to obtain the user's driving habit information.

[0067] In one embodiment, after the above step 2300, the electronic device 1000 may also formulate a corresponding control strategy for the battery management system according to the type of the user, for example, the estimation accuracy of the SOC may be adjusted.

[0068] The technical solution of this embodiment is to obtain driving data within a preset time period; the driving data at least includes the driving time, cumulative driving mileage and driving speed during the vehicle driving process; according to the driving data within the preset time period, the data to be analyzed is obtained; wherein the data to be analyzed includes daily driving time, daily driving mileage, and driving frequency at various times of the day; the data to be analyzed is analyzed by a preset recognition model to obtain the type of user. In this embodiment, the user type is obtained based on the analysis of daily driving time, daily driving mileage, and driving frequency at various times of the day. Since the data used in the calculation process does not involve the user's sensitive privacy data, it is possible to improve the user's information security while achieving user classification.

[0069] <Device Example>

[0070] Figure 4 Schematic diagram of the structure of the user type identification device according to the embodiment of the present disclosure. Figure 4 As shown, the user type identification device 4000 of this embodiment may include: an acquisition module 4100 and an analysis module 4200 .

[0071] The acquisition module 4100 is used to acquire driving data within a preset time period; the driving data at least includes driving time, cumulative driving mileage and driving speed during vehicle driving; and to acquire data to be analyzed based on the driving data within the preset time period; wherein the data to be analyzed includes daily driving time, daily driving mileage, and driving frequency at various times of the day;

[0072] The analysis module 4200 is used to analyze the data to be analyzed by using a preset recognition model to obtain the user type.

[0073] Among them, the identification results of the user type include: daytime online car-hailing user type, nighttime online car-hailing user type, commuting private car user type, commercial vehicle user type and non-commuting private car user type.

[0074] In one embodiment, the acquisition module 4100 is specifically used to: extract daily driving data from the driving data within the preset time period; calculate the daily driving data respectively to obtain the daily driving time within the preset time period, the daily driving mileage, and the driving frequency at each time of the day.

[0075] In one embodiment, the device 4000 may also include a separation module for separating corresponding user vehicle data from the vehicle data according to the type of the user; the analysis module 4200 may also be used to perform visual analysis on the user vehicle data to obtain visual analysis results.

[0076] In one embodiment, the analysis module 4200 can also be used to perform feature analysis on the user's vehicle data to obtain the user's driving habit information.

[0077] In one embodiment, the device 4000 may further include a processing module for formulating a corresponding control strategy of the battery management system according to the type of the user.

[0078] In one embodiment, the acquisition module 4100 is specifically used to: acquire a training sample set, which includes preprocessed offline vehicle data; the device 4000 may also include a training module for training the recognition model according to the training sample set; the analysis module 4200 may also be used to analyze the data to be analyzed based on the recognition model to obtain the user type.

[0079] The user type identification device of this embodiment can be used to implement the technical solution of the above method embodiment. Its implementation principle and technical effect are similar and will not be repeated here.

[0080] <Electronic Equipment Embodiment>

[0081] In this embodiment, an electronic device 5000 is also provided.

[0082] like Figure 5 As shown, the electronic device 5000 may include a processor 5100 and a memory 5200, wherein the memory 5200 is used to store executable instructions; the processor 5100 is used to control the electronic device 5000 to execute the above-mentioned Figure 2 The method for identifying the user type in the embodiment.

[0083] <Medium Example>

[0084] An embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for identifying a user type provided in any of the aforementioned embodiments is implemented.

[0085] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0086] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0087] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or 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 can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0088] The computer program instructions for performing the operation of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present invention.

[0089] Various aspects of the present invention are described herein with reference to the flow charts and / or block diagrams of the methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each box of the flow chart and / or block diagram and the combination of each box in the flow chart and / or block diagram can be implemented by computer-readable program instructions.

[0090] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable user-type recognition device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable user-type recognition device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable user-type recognition device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0091] Computer-readable program instructions may also be loaded onto a computer, other programmable user-type recognition device, or other device so that a series of operating steps are performed on the computer, other programmable user-type recognition device, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable user-type recognition device, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0092] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a part of a module, a program segment or an instruction, and a part of the module, a program segment or an instruction contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that it is equivalent to implement it by hardware, implement it by software, and implement it by combining software and hardware.

[0093] Embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the marketplace, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

1. A method for identifying user types, characterized in that: include: Obtain driving data within a preset time period; The driving data at least includes the driving time, accumulated driving mileage and driving speed during the vehicle driving process; Acquire the data to be analyzed according to the driving data in the preset time period; wherein the data to be analyzed includes daily driving time, daily driving mileage, and driving frequency at various times of the day; The data to be analyzed is analyzed by a preset recognition model to obtain the user type, specifically, a training sample set is obtained, the training sample set includes preprocessed offline vehicle data; the recognition model is trained according to the training sample set; the data to be analyzed is analyzed based on the recognition model to obtain the user type; wherein the preset recognition model is a kmeans clustering model.

2. The method according to claim 1, characterized in that The step of obtaining the data to be analyzed according to the driving data within the preset time period includes: extracting daily driving data from the driving data within the preset time period; The daily driving data are calculated respectively to obtain the daily driving duration, the daily driving mileage, and the driving frequency at each time of the day within the preset time period.

3. The method according to claim 1, characterized in that The identification results of the user types include: daytime online car-hailing user type, nighttime online car-hailing user type, commuting private car user type, commercial vehicle user type and non-commuting private car user type.

4. The method according to claim 1, characterized in that After calculating the data to be analyzed by using a preset recognition model to obtain the type of user, the method further includes: Separating corresponding user vehicle data from the vehicle data according to the type of the user; Perform visual analysis on the user vehicle data to obtain a visual analysis result.

5. The method according to claim 4, characterized in that After separating the corresponding user vehicle data from the vehicle data according to the type of the user, the method further includes: Perform feature analysis on the user's vehicle data to obtain the user's driving habit information.

6. The method according to claim 1, characterized in that After calculating the data to be analyzed by using a preset recognition model to obtain the type of user, the method further includes: According to the type of the user, a corresponding control strategy of the battery management system is formulated.

7. A user type identification device, characterized in that: include: An acquisition module, used to acquire driving data within a preset time period; The driving data at least includes the driving time, accumulated driving mileage and driving speed during the vehicle driving process; Acquire the data to be analyzed according to the driving data in the preset time period; wherein the data to be analyzed includes daily driving time, daily driving mileage, and driving frequency at various times of the day; The analysis module is used to analyze the data to be analyzed by using a preset recognition model to obtain the user type, wherein: The acquisition module is specifically used to acquire a training sample set, wherein the training sample set includes pre-processed offline vehicle data; The device also includes a training module, which is used to train the recognition model according to the training sample set; The analysis module is further used to analyze the data to be analyzed based on the recognition model to obtain the user type; The preset recognition model is a kmeans clustering model.

8. An electronic device comprising a memory and a processor, wherein the memory is used to store executable instructions; and the processor is used to execute the user type identification method according to any one of claims 1 to 6 under the control of the instructions.

9. A readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the method for identifying a user type according to any one of claims 1 to 6 is implemented.

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