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

Abstract

A user type identification method, an electronic device, and a readable storage medium are disclosed. The method includes a step (2100) of acquiring driving data for a predetermined period, including at least a vehicle driving time and an accumulated driving distance, a step (2200) of acquiring analysis target data including a driving time, a driving distance, and a number of driving times for each period each day based on the driving data for the predetermined period, and a step (2300) of analyzing the analysis target data by a predetermined identification model to obtain a user type.

Description

Technical Field

[0001] This application claims the priority of a Chinese patent application with an application number of 202111676076.2 and a title of "User Type Identification Method, Electronic Device and Readable Storage Medium", which was filed with the China National Intellectual Property Administration on December 31, 2021, and all of its contents are incorporated herein by reference.

[0002] Embodiments of the present disclosure relate to the technical field of data processing, and more specifically, to a user type identification method, an electronic device, and a readable storage medium.

Background Art

[0003] With the popularization of new energy vehicles, the number of users has also increased significantly. Since the driving habits and driving methods of each user are different, the performance of the vehicle's power battery is also different. In order to optimize the battery performance, it is necessary to create a personalized battery management system for each user by analyzing the driving data of the vehicle to obtain user categories.

[0004] However, when classifying user types, the processing flow is complex, and it is necessary to obtain and analyze delicate information such as the user's driving route, so there are significant problems with user information security.

Summary of the Invention

[0005] The user type identification method according to the first aspect of the embodiments of the present disclosure includes: obtaining driving data within a predetermined period, including at least the driving time and cumulative driving distance of the vehicle; obtaining analysis target data including the number of driving hours, driving distance, and number of driving times within each period of each day based on the driving data within the predetermined period; analyzing the analysis target data by a predetermined identification model to obtain a user type.

[0006] In one embodiment of the present invention, the step of acquiring data to be analyzed based on driving data within the predetermined period is: The steps include: extracting daily driving data from the driving data within the predetermined period; The process includes the step of performing calculations on the daily driving data to obtain the driving time, the driving distance, and the number of drives within each period of the day.

[0007] In one embodiment of the present invention, the user type identification result includes daytime online ride-hailing user types, nighttime online ride-hailing user types, commuter private car user types, commercial vehicle user types, and non-commuter private car user types.

[0008] In one embodiment of the present application, the display method is: The process further includes the step of performing visualization analysis on user vehicle data based on the user type and obtaining visualization analysis results.

[0009] In one embodiment of the present invention, the vehicle data includes driving data when the vehicle is in a driving state and non-driving data when the vehicle is not in a driving state.

[0010] In one embodiment of the present application, the display method is: The process further includes the step of performing feature analysis on the user vehicle data to obtain information on the user's driving habits.

[0011] In one embodiment of the present application, the display method is: The process further includes the step of creating a control policy for the corresponding battery management system based on the user type.

[0012] In one embodiment of the present invention, the step of analyzing the data to be analyzed using a predetermined identification model to obtain a user type is: The steps include obtaining a training sample set containing pre-processed offline vehicle data, The steps include training the discriminative model based on the aforementioned training sample set, The process includes the step of analyzing the data to be analyzed based on the identification model to obtain a user type.

[0013] In one embodiment of the present application, the display method is: The method further includes the step of updating the training sample set with the data to be analyzed and the corresponding analysis results as training samples after the analysis of the data to be analyzed.

[0014] A user type identification device according to a second embodiment of the embodiments of this disclosure includes an acquisition module and an analysis module, The acquisition module acquires driving data within a predetermined period, including at least the vehicle's driving time and cumulative mileage, and based on the driving data within the predetermined period, acquires data for analysis, including the number of driving hours, mileage, and the number of times the vehicle is driven each day within the period. The analysis module analyzes the data to be analyzed using a predetermined identification model to obtain the user type.

[0015] In one embodiment of the present invention, the analysis module further performs visualization analysis on user vehicle data to obtain visualization analysis results.

[0016] In one embodiment of the present invention, the analysis module further performs feature analysis on the user vehicle data to obtain information on the user's driving habits.

[0017] In one embodiment of the present invention, the identification device further includes a processing module, The processing module creates a control policy for the corresponding battery management system based on the user type.

[0018] In one embodiment of the present invention, the acquisition module further comprises: Obtain a training sample set containing pre-processed offline vehicle data, Based on the aforementioned training sample set, the discrimination model is trained. Based on the identification model, analyze the data to be analyzed to obtain the user type.

[0019] The electronic device according to the third aspect of the embodiments of the present disclosure includes a memory and a processor. The memory stores executable instructions, and the processor executes the user type identification method according to any one of the first aspects of the embodiments of the present disclosure under the control of the instructions.

[0020] The readable storage medium according to the fourth aspect of the embodiments of the present disclosure stores a computer program that, when executed by a processor, realizes the user type identification method according to any one of the first aspects of the embodiments of the present disclosure.

Brief Description of the Drawings

[0021] The drawings incorporated in the specification and constituting a part of the specification illustrate the embodiments of the present disclosure and, together with the description, interpret the principles of the present disclosure.

[0022] [Figure 1] It is a principle block diagram of the hardware configuration of an exemplary electronic device. [Figure 2] It is a schematic diagram showing the steps of the user type identification method according to the embodiments of the present disclosure. [Figure 3] It is a visualization schematic diagram of the user type obtained by the user type identification method according to the embodiments of the present disclosure. [Figure 4] It is a schematic configuration diagram of the user type identification device according to the embodiments of the present disclosure. [Figure 5] It is a schematic configuration diagram of the hardware of an exemplary electronic device.

Modes for Carrying Out the Invention

[0023] Hereinafter, various exemplary embodiments of the present disclosure will be described in detail with reference to the drawings. Unless otherwise specified, the relative arrangements of the members and steps described in these embodiments, numerical expressions, and numerical values do not limit the scope of the present disclosure.

[0024] The following description of at least one exemplary embodiment is illustrative in nature and does not in any way limit the disclosure or its application or use.

[0025] Although the author has not described in detail any techniques, methods, and apparatus known to those skilled in the art, such techniques, methods, and apparatus should be considered as part of the specification where appropriate.

[0026] In all the examples presented and discussed herein, any specific values ​​should be interpreted as illustrative only and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0027] Furthermore, since similar symbols and letters represent similar items in the following drawings, if something is defined in one drawing, it does not need to be considered again in subsequent drawings.

[0028] One objective of the embodiments of this disclosure is to provide a user type identification method that can achieve user classification and improve user information security.

[0029] The beneficial effects of this disclosure are as follows: Driving data is acquired within a predetermined period, including at least driving time, total driving distance, and driving speed during the vehicle driving process; based on the driving data within the predetermined period, data to be analyzed is acquired, including the number of driving hours per day, the distance driven per day, and the number of times the vehicle is driven at each time of day; and the data to be analyzed is analyzed using a predetermined identification model to obtain a user type. In this embodiment, a user type is obtained by analyzing the number of driving hours per day, the distance driven per day, and the number of times the vehicle is driven at each time of day, and since the data used in the calculation does not relate to the user's sensitive privacy data, it is possible to classify users and improve the user's information security.

[0030] <Hardware Configuration> As shown in Figure 1, the electronic device 1000 to which the user type identification method of this disclosure can be applied may include a processor 1100, memory 1200, interface device 1300, communication device 1400, display device 1500, input device 1600, speaker 1700, microphone 1800, and the like.

[0031] The processor 1100 may be a mobile processor. The memory 1200 includes, for example, ROM (read-only memory), RAM (random access memory), and non-volatile memory such as a hard disk. The interface device 1300 includes, for example, a USB interface and an earphone interface. The communication device 1400 can perform, for example, wired communication or wireless communication, and the communication device 1400 may include any short-range communication device, such as 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, LiFi, etc., and the communication device 1400 may also include any long-range communication device, such as any device that performs WLAN, GPRS, 2G / 3G / 4G / 5G long-range communication. The display device 1500 is, for example, a liquid crystal display, a touch display, etc. The input device 1600 may include, for example, a touchscreen, a keyboard, etc. The electronic device 1000 can output audio information using the speaker 1700 and collect audio information using the microphone 1800.

[0032] In this embodiment, the memory 1200 of the electronic device 1000 stores instructions, which control and operate the processor 1100 to implement a user type identification method. Those skilled in the art can design instructions according to the technical means disclosed herein. How the instructions control and operate the processor is known in the art and will not be described in detail here.

[0033] Figure 1 shows multiple devices of the electronic device 1000, but this disclosure may relate to only some of these devices, for example, the electronic device 1000 may relate only to the memory 1200 and processor 1100, the communication device 1400 and the display device 1500.

[0034] Although Figure 1 shows only one electronic device 1000, it should be understood that this does not limit the number of electronic devices 1000.

[0035] <Example of the method> Figure 2 is a schematic diagram showing the steps of the user type identification method according to an embodiment of the present disclosure.

[0036] As shown in Figure 2, the user type identification method of this embodiment may be specifically performed by the electronic device 1000 shown in Figure 1.

[0037] Specifically, the user type identification method of this embodiment may include the following steps 2100 to 2300.

[0038] In step 2100, driving data is acquired for a predetermined period, including at least the driving time, cumulative mileage, and driving speed during the vehicle driving process.

[0039] Acquiring vehicle data within a specified period may mean acquiring vehicle data within one week, within one month, or within three months. The specified period can be set according to actual needs and is not specifically limited here.

[0040] To make it clear, when the vehicle state is in a driving state, the acquired vehicle data is the driving data described above, and when the vehicle state is in a charging state, the acquired vehicle data is non-driving data. In this embodiment, in order to classify users based on the driving data described above, it is necessary to select the driving data within the predetermined period described above from the vehicle data based on the vehicle state described above.

[0041] Specifically, the electronic device 1000 may first acquire vehicle data within a predetermined period, including driving time, total driving distance, vehicle condition, and driving speed. The vehicle condition includes driving condition and charging condition. Next, based on the vehicle condition, it acquires driving data within the predetermined period from the vehicle data.

[0042] For example, the time interval for collecting the above vehicle data may be 30 seconds.

[0043] In step 2200, based on the driving data within the predetermined period, analysis data is obtained, including the number of hours driven each day, the distance driven each day, and the number of times driven at each time of day.

[0044] Specifically, the electronic device may, after acquiring driving data within the predetermined period, extract daily driving data from the driving data within the predetermined period, and perform calculations on the daily driving data to obtain the daily driving hours, daily driving distance, and the number of times the vehicle is driven at each time of day within the predetermined period.

[0045] For example, suppose user A drives a vehicle three times on a given day: 1 hour at 8am with a distance of 5 kilometers, 0.5 hours at noon with a distance of 2 kilometers, and 2 hours at 12:45pm with a distance of 10 kilometers. Then, the calculated daily driving time is 3.5 hours, the daily driving distance is 17 kilometers, the number of drives at 8am is 1, the number of drives at noon is 2, and the number of drives at any other time on the day is 0.

[0046] In step 2300, the data to be analyzed is analyzed using a predetermined identification model to obtain the user type.

[0047] Specifically, the electronic device 1000 may acquire a training sample set containing pre-processed offline vehicle data, train the identification model based on the training sample set, and then analyze the data to be analyzed based on the predetermined identification model to obtain the corresponding user type.

[0048] To make it easier to understand, after the analysis of the data to be analyzed is completed, the electronic device 1000 can train the identification model based on the updated training sample set by updating the training sample set with the data to be analyzed and the corresponding analysis results.

[0049] In this embodiment, the predetermined identification model may be a kmean clustering model.

[0050] The user type identification results described above include daytime online ride-hailing user types, nighttime online ride-hailing user types, commuter private car user types, commercial vehicle user types, and non-commuter private car user types.

[0051] Preferably, after analyzing the data to be analyzed to obtain a user type, the electronic device 1000 may separate the corresponding user vehicle data from the vehicle data based on the user type, perform visualization analysis on the user vehicle data, and obtain visualization analysis results.

[0052] As shown in Figure 3, the visualization analysis results corresponding to daytime online ride-hailing users are shown in Cluster 1, and are mainly characterized by long average daily driving time and distance, and a constant driving period. The visualization analysis results corresponding to nighttime online ride-hailing users are shown in Cluster 2, and are mainly characterized by long average daily driving time and distance, and a constant driving period. The visualization analysis results corresponding to commuter private car users are shown in Cluster 3, and are mainly characterized by driving periods concentrated during commuting hours and constant driving distance. The visualization analysis results corresponding to commercial vehicle users are shown in Cluster 4, and are mainly characterized by moderate driving time and distance, and an inconsistent driving period. The visualization analysis results corresponding to non-commuter private car users are shown in Cluster 5, and are mainly characterized by short driving time and distance, and an inconsistent driving period.

[0053] Preferably, after analyzing the data to be analyzed to obtain the user type, the electronic device 1000 may further perform feature analysis on the user vehicle data to obtain information on the user's driving habits.

[0054] In the embodiment, after step 2300, the electronic device 1000 may further create a control policy for the corresponding battery management system based on the user type. For example, the estimation accuracy of the SOC may be adjusted.

[0055] In this embodiment, the technical means acquires driving data within a predetermined period, including at least driving time, cumulative driving distance, and driving speed during the vehicle driving process. Based on the driving data within the predetermined period, analysis target data is acquired, including the number of driving hours per day, the distance driven per day, and the number of times the vehicle is driven at each time of day. The analysis target data is then analyzed using a predetermined identification model to obtain a user type. In this embodiment, the user type is obtained by analyzing the number of driving hours per day, the distance driven per day, and the number of times the vehicle is driven at each time of day. Since the data used in the calculation does not relate to the user's sensitive privacy data, user classification can be achieved, and user information security can be improved.

[0056] <Examples of the device> Figure 4 is a schematic diagram of a user type identification device according to an embodiment of the present disclosure. As shown in Figure 4, the user type identification device 4000 of this embodiment may include an acquisition module 4100 and an analysis module 4200.

[0057] The acquisition module 4100 acquires driving data within a predetermined period, including at least driving time, cumulative driving distance, and driving speed during the vehicle driving process, and based on the driving data within the predetermined period, it acquires data to be analyzed, including the number of driving hours per day, the distance driven per day, and the number of times the vehicle is driven at each time of day. The analysis module 4200 analyzes the data to be analyzed using a predetermined identification model to obtain the user type.

[0058] The user type identification results described above include daytime online ride-hailing user types, nighttime online ride-hailing user types, commuter private car user types, commercial vehicle user types, and non-commuter private car user types.

[0059] In the embodiment, the acquisition module 4100 specifically extracts daily driving data from the driving data within the predetermined period, and performs calculations on the daily driving data to obtain the daily driving hours, daily driving distance, and the number of times each day is driven within the predetermined period.

[0060] In this embodiment, the user type identification device 4000 may further include a separation module 4300, which separates the corresponding user vehicle data from the vehicle data based on the user type, and the analysis module 4200 may further perform visualization analysis on the user vehicle data to obtain visualization analysis results.

[0061] In the embodiment, the analysis module 4200 may further perform feature analysis on the user vehicle data to obtain information on the user's driving habits.

[0062] In this embodiment, the user type identification device 4000 may further include a processing module 4400, which creates a control policy for the corresponding battery management system based on the user type.

[0063] In this embodiment, the acquisition module 4100 specifically acquires a training sample set including pre-processed offline vehicle data, the user type identification device 4000 may further include a training module which trains the identification model based on the training sample set, and the analysis module 4200 may further analyze the data to be analyzed based on the identification model to obtain the user type.

[0064] The user type identification device of this embodiment can perform the technical means of the embodiment of the above method, and its implementation principle and technical effects are similar, so a detailed explanation is omitted here.

[0065] <Examples of electronic devices> This embodiment further provides an electronic device 5000.

[0066] As shown in Figure 5, the electronic device 5000 may include a processor 5100 and a memory 5200, the memory 5200 storing executable instructions, and the processor 5100 operating the electronic device 5000 in accordance with the control of the instructions to execute the user type identification method of the embodiment described in Figure 2.

[0067] <Examples of media> The computer-readable storage medium according to the embodiments of this disclosure stores a computer program that, when executed by a processor, implements the user type identification method according to any of the embodiments described above.

[0068] This disclosure may be a system, method, and / or a computer program product. The computer program product may include a computer-readable storage medium which a processor stores computer-readable program instructions for implementing each aspect of this disclosure.

[0069] A computer-readable storage medium may be a tangible device capable of holding and storing instructions used by an instruction execution device. A computer-readable storage medium may be, but is 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 thereof. More specific examples (a non-exhaustive 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 compact disk read-only memory (CD-ROM), digital multipurpose disks (DVDs), memory sticks, floppy disks, mechanically encoded devices such as punch cards or grooved projection structures on which instructions are stored, and any suitable combination thereof. The computer-readable storage medium used herein is not interpreted as a transient signal itself, such as, for example, radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through optical fiber cables), or electrical signals transmitted through wires.

[0070] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or they can 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, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface of each computing / processing device receives computer-readable program instructions from the network and transfers them for storage in the computer-readable storage medium of each computing / processing device.

[0071] Computer-readable program instructions for performing the operations of the Disclosure may be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent 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 and C++, and conventional procedural programming languages ​​such as C or similar programming languages. Computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer, partially on a remote computer, or fully on a remote computer or server. In the remote computer scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or wide area network (WAN), or it may be connected to an external computer (for example, via the Internet using an Internet service provider). In some embodiments, state information of computer-readable program instructions can be used to personalize electronic circuits such as programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), and these electronic circuits can implement each aspect of the disclosure by executing computer-readable program instructions.

[0072] Each aspect of this disclosure will be described with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products relating to embodiments of this disclosure. It should be understood that each block in the flowcharts and / or block diagrams, and each combination of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0073] These computer-readable program instructions are provided to the processor of a general-purpose computer, a dedicated computer, or other programmable user type identification device to generate a machine, thereby generating a device that implements the functions / operations defined in one or more blocks in a flowchart and / or block diagram when these instructions are executed by the processor of the computer or other programmable user type identification device. These computer-readable program instructions may be stored on a computer-readable storage medium, which instructs the computer, programmable user type identification device, and / or other device to operate in a particular manner, thereby the computer-readable medium on which the instructions are stored contains a product containing instructions that implement each aspect of the functions / operations defined in one or more blocks in a flowchart and / or block diagram.

[0074] Computer-readable program instructions may be loaded into a computer, another programmable user type identification device, or other device to execute a series of operational steps on the computer, another programmable user type identification device, or other device, thereby generating a process implemented by the computer, the instructions executed on the computer, another programmable user type identification device, or other device to implement a function / operation defined in one or more blocks in a flowchart and / or block diagram.

[0075] The flowcharts and block diagrams in the drawings illustrate possible architectures, functions, and operations of systems, methods, and computer program products relating to multiple embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or part of an instruction, and such module, program segment, or part of an instruction may include one or more executable instructions for realizing a defined logical function. In some alternative implementations, the functions represented by blocks may occur in an order different from the order shown in the drawings. For example, two consecutively shown blocks may actually be executed substantially simultaneously, or they may be executed in reverse order depending on the related functions. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, may be implemented by a dedicated hardware system that performs a defined function or operation, or by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.

[0076] The embodiments of this disclosure are described above, and the above description is illustrative, not exhaustive, and not limited to the embodiments disclosed. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the embodiments described. The choice of terms used herein is intended to best interpret the principles, practical applications or technical improvements in the market of each embodiment, or to enable those skilled in the art to understand each embodiment disclosed herein. The scope of this disclosure is limited by the appended claims.

Claims

1. Step (2100) of acquiring driving data within a predetermined period, including at least the vehicle's driving time and cumulative mileage, Based on the driving data within the predetermined period, the step (2200) is to acquire data to be analyzed, which includes the number of driving hours, driving distance, and the number of drives within each period each day, but does not include the driving route. The process includes the step (2300) of analyzing the data to be analyzed using a predetermined identification model to obtain a user type, The step of analyzing the data to be analyzed using a predetermined identification model to obtain the user type is: The steps include obtaining a training sample set containing pre-processed offline vehicle data, The steps include training the discriminative model based on the aforementioned training sample set, The steps include: analyzing the data to be analyzed based on the identification model to obtain the user type; After analyzing the data to be analyzed, the step of updating the training sample set with the data to be analyzed and the corresponding analysis results as training samples, The steps include training the discriminative model based on an updated training sample set and optimizing the discriminative model, A method for identifying user types, characterized by including the following:

2. The step of acquiring data to be analyzed based on driving data within the predetermined period is: The steps include: extracting daily driving data from the driving data within the predetermined period; The method according to claim 1, characterized by comprising the step of performing calculations on the daily driving data to obtain the number of driving hours, the driving distance, and the number of driving sessions within each period of the day.

3. The method according to claim 1, characterized in that the user type identification result includes daytime online ride-hailing user types, nighttime online ride-hailing user types, commuter private car user types, commercial vehicle user types, and non-commuter private car user types.

4. The method according to claim 1, further comprising the step of performing visualization analysis on user vehicle data based on the user type and obtaining visualization analysis results.

5. The method according to claim 4, characterized in that the user vehicle data includes driving data when the vehicle is in a driving state and non-driving data when the vehicle is not in a driving state.

6. The method according to claim 4, further comprising the step of performing feature analysis on the user vehicle data to obtain information on the user's driving habits.

7. It includes an acquisition module (4100) and an analysis module (4200), The acquisition module (4100) acquires driving data within a predetermined period, including at least the vehicle's driving time and cumulative mileage, and based on the driving data within the predetermined period, acquires data for analysis, including the number of driving hours, mileage, and the number of drives per day within each period, but not the driving route. The analysis module (4200) analyzes the data to be analyzed using a predetermined identification model to obtain the user type. The acquisition module (4100) further, Obtain a training sample set containing pre-processed offline vehicle data, Based on the aforementioned training sample set, the discrimination model is trained. Based on the aforementioned identification model, the data to be analyzed is analyzed to obtain the user type. After analyzing the aforementioned data to be analyzed, the aforementioned data to be analyzed and the corresponding analysis results are updated in the training sample set as training samples. The discriminative model is trained based on the updated training sample set, and the discriminative model is optimized. A user type identification device (4000) characterized by the following.

8. The identification device (4000) according to claim 7 is further characterized in that the analysis module (4200) performs visualization analysis on user vehicle data to obtain visualization analysis results.

9. The identification device (4000) according to claim 8 is further characterized in that the analysis module (4200) performs feature analysis on the user vehicle data to obtain information on the user's driving habits.

10. Electronic device (5000) comprising a memory (5200) and a processor (5100), wherein the memory (5200) stores executable instructions and the processor (5100) executes the user type identification method according to any one of claims 1 to 6 under the control of the instructions.

11. A readable storage medium characterized in that it stores a computer program that, when executed by a processor, realizes the user type identification method described in any one of claims 1 to 6.

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