Vehicle data processing method and device, computing equipment and vehicle

Abstract

The invention provides a vehicle data processing method and device, computing equipment and a vehicle, and the processing method comprises the steps: obtaining vehicle data of vehicles of the same type, the vehicle data comprising first data and second data; performing feature extraction based on the first data to obtain feature information of each vehicle; and calculating statistical information of the target vehicle in the vehicles of the same type based on the second data and the feature information. By comparing the vehicle data of the target vehicle with the vehicle data of other vehicles of the same type, the position of the target vehicle in the vehicle group of the same type is obtained, the sense of participation and the sense of belonging of a driver are improved, the cognition of the driver on the vehicle performance is improved, and thus the vehicle use experience is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle technology, and in particular to a vehicle data processing method, device, computing equipment and vehicle. Background Art

[0002] At present, improving the experience of vehicle drivers mainly focuses on the following aspects: 1. Through the infotainment system, the infotainment system provides drivers with navigation, music, movies, games, chat or purchase services, and the driver performs related operations through the human-machine interface (HMI); 2. Allowing drivers to independently activate or deactivate certain vehicle functions, such as the electronic stability control system, automatic parking, and other functions present in the car; 3. Providing services through mobile applications, such as remote control of car starting, air conditioning switching, and automatic parking, and displaying vehicle-related information (mileage, charging reminders, etc.) for the driver.

[0003] However, in the prior art, the driver cannot know the position of the vehicle among the same type of vehicles, lacks a sense of participation and belonging, and has a vague understanding of the performance of his own vehicle, which affects the vehicle usage experience. Summary of the invention

[0004] The purpose of the present disclosure is to propose a vehicle data processing method, by comparing the vehicle data of a target vehicle with the vehicle data of other vehicles of the same type, to obtain the position of the target vehicle in a group of vehicles of the same type, to enhance the driver's sense of participation and belonging, to improve the driver's cognition of vehicle performance, and thus to enhance the vehicle use experience.

[0005] To this end, according to the first aspect of the present disclosure, a method for processing vehicle data is provided, including: acquiring vehicle data of vehicles of the same type, the vehicle data including first data and second data; performing feature extraction based on the first data to obtain feature information of each vehicle; and calculating statistical information of the target vehicle among vehicles of the same type based on the second data and the feature information.

[0006] According to the above technical concept, the embodiments of the present disclosure may further include any one or more of the following optional forms.

[0007] In some optional forms, the first data includes driving data, and the method further includes: acquiring driving style characteristic information of each vehicle based on the driving data; and calculating driving style proportion information of the target vehicle among vehicles of the same type based on the driving style characteristic information.

[0008] In certain optional forms, the driving data includes vehicle speed, vehicle acceleration, accelerator pedal depression depth and / or accelerator gear increase / decrease frequency.

[0009] In certain optional forms, the second data includes average power consumption, total mileage or battery health, and the method further includes: obtaining average power consumption ranking information, total mileage ranking information or battery health ranking information of the target vehicle among vehicles of the same type based on the average power consumption, total mileage or battery health.

[0010] In certain optional forms, the vehicle data includes periodic data, and the method further includes:

[0011] Based on the periodic data, periodic statistical information of the target vehicle among vehicles of the same type is obtained.

[0012] In some optional forms, the periodic data includes periodic mileage and periodic energy consumption.

[0013] In some optional forms, the method further includes: sending the statistical information to a vehicle-mounted terminal or a mobile user terminal.

[0014] According to a second aspect of the present disclosure, a vehicle data processing device is provided, including: a data acquisition module, used to acquire vehicle data of vehicles of the same type, the vehicle data including first data and second data; a feature extraction module, used to perform feature extraction based on the first data to obtain feature information of each vehicle; and a statistical information calculation module, used to calculate the statistical information of the target vehicle among vehicles of the same type based on the second data and the feature information.

[0015] According to a third aspect of the present disclosure, a computing device is provided, comprising: at least one processor; and a memory storing machine-readable instructions, which, when executed by the at least one processor, enable the at least one processor to execute the vehicle data processing method.

[0016] According to a fourth aspect of the present disclosure, a vehicle is provided, comprising the vehicle data processing device or the computing equipment.

[0017] The present disclosure compares the vehicle data of a target vehicle with the vehicle data of other vehicles of the same type to obtain the position of the target vehicle in a group of vehicles of the same type, thereby enhancing the driver's sense of participation and belonging, and improving the driver's cognition of vehicle performance, thereby improving the vehicle use experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Other features and advantages of the present disclosure will be better understood through the following detailed description of preferred embodiments in conjunction with the accompanying drawings, in which the same reference numerals represent the same or similar components.

[0019] Figure 1A flowchart of an exemplary vehicle data processing method according to an embodiment of the present disclosure is shown.

[0020] Figure 2 A block diagram of an exemplary vehicle data processing device according to an embodiment of the present disclosure is shown.

[0021] Figure 3 A block diagram of an exemplary computing device according to an embodiment of the present disclosure is shown.

[0022] Figure 4 A schematic diagram of an exemplary hardware architecture according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0023] The following detailed description is merely exemplary in nature and is not intended to limit the application and use. Furthermore, the present invention is not intended to be limited by any stated or implied theory presented in the foregoing technical field, background technology, summary of the invention or the following detailed description.

[0024] As mentioned earlier, current mainstream vehicles do not obtain statistical information (comparison results) among similar vehicles. Drivers cannot know the position of their vehicles among similar vehicles, lack a sense of participation and belonging, and have a vague understanding of their own vehicle performance, which affects the vehicle usage experience.

[0025] Therefore, some exemplary embodiments of the present disclosure provide a method, apparatus, computing device and vehicle for processing vehicle data, which will be described in more detail below in conjunction with the accompanying drawings.

[0026] Reference Figure 1 , shows a flowchart of an exemplary vehicle data processing method 100 according to an embodiment of the present disclosure, including steps 110 to 130. The method 100 may be performed by, for example Figure 2 The battery self-discharge estimation device 200 or Figure 3 The method 100 may be specifically described as follows.

[0027] Step 110, obtaining vehicle data of the same type of vehicle, wherein the vehicle data includes first data and second data. It should be noted that the same type of vehicle in the present disclosure refers to vehicles of the same model and the same manufacturer, which have the same vehicle attribute information, such as maximum speed, acceleration ability, vehicle climbing grade, maximum power, fuel consumption, etc.

[0028] Step 120, feature extraction is performed based on the first data to obtain feature information of each vehicle. The first data is used to extract feature information during vehicle use, such as the driver's driving style, the driver's vehicle equipment usage habits, etc. Optionally, the first data includes vehicle speed, vehicle acceleration, throttle pedal depth, throttle gear increase and decrease frequency, steering wheel rotation data, driving condition data, entertainment information system usage data, ambient light usage data, air conditioning usage data, driving route data, etc.

[0029] Step 130, based on the second data and the characteristic information, calculate the statistical information of the target vehicle among vehicles of the same type. Among them, the second data is directly used to calculate the corresponding statistical information. Optionally, the second data includes battery health state (SOH, State of Health), battery remaining power (SOC, State of Charge), vehicle total energy consumption (such as power consumption, fuel consumption), periodic performance consumption (such as power consumption, fuel consumption), average energy consumption, total driving mileage, periodic mileage, etc. For example, based on the average power consumption, the average power consumption ranking information or proportion information of the target vehicle among vehicles of the same type can be obtained; based on the total mileage, the total mileage ranking information or proportion information of the target vehicle among vehicles of the same type can be obtained; based on the battery health, the battery health ranking information or proportion information of the target vehicle among vehicles of the same type can be obtained.

[0030] Furthermore, in some examples, comprehensive performance ranking information of the vehicle can be calculated based on the statistical information of the second data of the vehicle. For example, the vehicle battery performance ranking can be calculated based on battery-related ranking information such as average power consumption, battery health, and battery usage time.

[0031] In some examples, the first data includes driving data, and the method 100 further includes: obtaining driving style characteristic information of each vehicle based on the driving data; calculating the driving style ratio information of the target vehicle among vehicles of the same type based on the obtained driving style characteristic information. For example, the percentage of the driving style of the target vehicle among vehicles of the same type is calculated. For another example, the ranking of the percentage of the driving style of the target vehicle among vehicles of the same type is calculated. Further, the obtained driving style ratio information can be sent to the vehicle terminal or the user's mobile terminal for the user to read. The information can be displayed in a text display or voice prompt, and part or all of the content can be displayed in response to the user's selection or preset. Optionally, the driving data may include one or more of vehicle speed, vehicle acceleration, throttle pedaling depth, throttle gear increase and decrease frequency, steering wheel rotation data, driving condition data, etc.

[0032] In some examples, the vehicle data includes periodic data, and the method 100 further includes obtaining periodic statistical information of the target vehicle among vehicles of the same type based on the periodic data. Such as periodic mileage, periodic energy consumption, periodic driving data, etc. The statistical period of vehicle data can be customized by the user, or it can be set in stages according to the performance changes of the vehicle, or it can be automatically adjusted according to the changes in the user's usage habits. In some examples, the vehicle data of the same type of vehicles is obtained and processed every month on a monthly basis, and the statistical information of the target vehicle among vehicles of the same type in that month can be obtained.

[0033] This example can avoid the influence of old data on the accuracy of statistical information in the current stage by periodic processing of vehicle data. It can also adapt to the influence of special change factors such as changes in vehicle owners, short-term vehicle loans, improvement in driving skills of vehicle owners, and relocation of vehicle owners on the accuracy of statistical information, thereby improving users' vehicle usage experience.

[0034] In some examples, the implementation process of method 100 is described by taking new energy vehicles as an example: First, new energy vehicles of the same type (including the target vehicle) transmit their own vehicle data to the manufacturer's data lake through T-box. T-box (Telematics Box, vehicle-mounted information processing device) is a key component used in the automotive industry to realize the Internet of Vehicles function. It collects various information of the vehicle and transmits the vehicle data to the manufacturer's data lake of the cloud server through wireless communication technology (such as 4G / 5G, Wi-Fi, Bluetooth, etc.). Vehicle data includes data required by GBT32960 (China's regulations on remote services and management of pure electric vehicles) or customized by the vehicle. Vehicle data is transmitted when the vehicle is in a driving or parking charging state. According to a certain period (also known as a time interval or driving cycle, the time from the start of data transmission to the end of data transmission is regarded as the duration of a cycle), the vehicle data of each vehicle is analyzed after each driving cycle (which can be processed in the cloud), and the number of vehicles with less or more periodic data than the target vehicle is calculated, and the percentage information or ranking information of the periodic data is obtained. Of course, the present disclosure does not limit the type and quantity of defined performance data or feature data. Finally, in response to the request sent by the user, the percentage information or ranking information of the corresponding characteristic data is sent to the vehicle terminal or the user's mobile terminal, and is displayed in appropriate text on the human-machine interface. For example, "Your power consumption this month is lower than x% of the vehicles in the same group"; "Your mileage this month is higher than y% of the vehicles in the same group"; "Your current battery health status SOH is higher than z% of the vehicles in the same group"; "Your total mileage is higher than m% of the vehicles in the same group", etc.

[0035] For example, the percentage information of the target vehicle can be calculated as follows:

[0036] x%=(the number of vehicles above or below the target data of the target vehicle / the total number of vehicles)*100%.

[0037] Reference Figure 2 , shows a block diagram of an exemplary vehicle data processing device 200 according to an embodiment of the present disclosure, the processing device 200 includes: a data acquisition module 210, used to acquire vehicle data of the same type of vehicles, the vehicle data including first data and second data; a feature extraction module 220, used to extract features based on the first data to obtain feature information of each vehicle; a statistical information calculation module 230, used to calculate the statistical information of the target vehicle among the same type of vehicles based on the second data and the feature information. The vehicle data processing device 200 is similar to the implementation of the vehicle data processing method 100 described above.

[0038] In some examples, the first data includes driving data, and the feature extraction module 220 is also used to obtain driving style feature information of each vehicle based on the driving data; the statistical information calculation module 230 is also used to calculate the driving style proportion information of the target vehicle among the same type of vehicles based on the driving style feature information.

[0039] In some examples, the data acquisition module 210 is used to acquire driving data, wherein the driving data includes vehicle speed, vehicle acceleration, throttle pedal depression depth, and / or throttle gear increase / decrease frequency.

[0040] In some examples, the second data includes average power consumption, total mileage, or battery health, and the statistical information calculation module 230 is also used to obtain the average power consumption ranking information, total mileage ranking information, or battery health ranking information of the target vehicle among vehicles of the same type based on the average power consumption, total mileage, or battery health.

[0041] In some examples, the vehicle data includes periodic data, and the statistical information calculation module 230 is further used to obtain periodic statistical information of the target vehicle among vehicles of the same type based on the periodic data.

[0042] In some examples, the periodic data includes periodic mileage and periodic energy consumption. The data acquisition module 210 is used to periodically extract periodic data of vehicles of the same type from a data source.

[0043] In some examples, the vehicle data processing device 200 sends the obtained statistical information to a vehicle terminal or a mobile user terminal.

[0044] Reference Figure 3 , shows a block diagram of an exemplary computing device 300 according to an embodiment of the present disclosure, such as Figure 3As shown, the computing device 300 includes at least one processor 310 and a memory 320 coupled to the at least one processor 310. The memory 320 is used to store machine-readable instructions, and when the machine-readable instructions are executed by the at least one processor 310, the processor 310 executes the method in the above embodiment (for example, any one or more steps of the aforementioned method 100).

[0045] Reference Figure 4 , showing a schematic diagram of an exemplary hardware architecture 400 according to an embodiment of the present disclosure. The hardware architecture 400 includes: vehicles of the same type (target vehicle 410 and other vehicles of the same type 420), a data lake 430, a data processing device 440 and a mobile user terminal 450. Among them, vehicles 410 and 420 are connected to the data lake 430 in communication, and the vehicle data is transmitted to the data lake through a T-box; the data processing device 440 obtains vehicle data (including first data and second data) from the data lake 430, and processes the obtained vehicle data (feature extraction based on the first data to obtain feature information of each vehicle; based on the second data and the obtained feature information, the statistical information of the target vehicle among vehicles of the same type, such as proportion information, ranking information, etc.), and sends the corresponding processing results to the user in response to the user's request sent from the on-board terminal 410 of the target vehicle or the user's mobile terminal 450.

[0046] According to an embodiment of the present disclosure, an exemplary vehicle is provided. The exemplary vehicle includes a vehicle data processing device 200 as described above or a computing device 300 as described above.

[0047] In summary, the vehicle data processing method, device, computing equipment and vehicle proposed in the present invention compare the vehicle data of the target vehicle with the vehicle data of other vehicles of the same type to obtain the position of the target vehicle in the group of vehicles of the same type, thereby enhancing the driver's sense of participation and belonging, and improving the driver's understanding of vehicle performance, thereby improving the vehicle usage experience.

[0048] In general, various example embodiments of the present disclosure may be implemented in hardware or dedicated circuits, software, firmware, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device. When various aspects of the embodiments of the present disclosure are illustrated or described as block diagrams, flow charts, or using some other graphical representation, it will be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general hardware or controllers or other computing devices, or some combination thereof as non-limiting examples.

[0049] Computer-readable program instructions or computer program products for executing the various embodiments of the present disclosure can also be stored in the cloud. When needed, users can access the computer-readable program instructions for executing an embodiment of the present disclosure stored in the cloud through mobile Internet, fixed network or other networks, thereby implementing the technical solutions disclosed in accordance with the various embodiments of the present disclosure.

[0050] The foregoing discussion is merely the disclosure and description of the exemplary embodiments of the present invention. Through these discussions and the accompanying drawings and claims, those skilled in the art will readily appreciate that various changes, modifications and variations may be made to the present invention without departing from the spirit and scope of the present invention as defined by the appended claims.

Claims

1. A method for processing vehicle data, comprising: Acquire vehicle data of vehicles of the same type, wherein the vehicle data includes first data and second data; Performing feature extraction based on the first data to obtain feature information of each vehicle; Statistical information of the target vehicle among vehicles of the same type is calculated based on the second data and the feature information.

2. The method according to claim 1, wherein: The first data includes driving data, and the method further includes: Acquiring driving style characteristic information of each vehicle based on the driving data; The driving style ratio information of the target vehicle among the vehicles of the same type is calculated based on the driving style characteristic information.

3. The method according to claim 2, wherein: The driving data includes vehicle speed, vehicle acceleration, accelerator pedal depression depth and / or accelerator gear increase / decrease frequency.

4. The method according to claim 1, wherein: The second data includes average power consumption, total mileage or battery health, and the method further includes: Based on the average power consumption, total mileage or battery health, the average power consumption ranking information, total mileage ranking information or battery health ranking information of the target vehicle among vehicles of the same type is obtained.

5. The method according to claim 1, wherein the vehicle data comprises periodic data, and the method further comprises: Based on the periodic data, periodic statistical information of the target vehicle among vehicles of the same type is obtained.

6. The method according to claim 5, wherein: The periodic data includes periodic driving mileage and periodic energy consumption.

7. The method according to claim 1, further comprising: The statistical information is sent to a vehicle-mounted terminal or a mobile user terminal.

8. A vehicle data processing device, comprising: A data acquisition module, used to acquire vehicle data of vehicles of the same type, wherein the vehicle data includes first data and second data; A feature extraction module, used for performing feature extraction based on the first data to obtain feature information of each vehicle; A statistical information calculation module is used to calculate the statistical information of the target vehicle among vehicles of the same type based on the second data and the characteristic information.

9. A computing device comprising: at least one processor; as well as A memory storing machine-readable instructions, which, when executed by the at least one processor, cause the at least one processor to execute the vehicle data processing method according to any one of claims 1-7. 10 . A vehicle comprising the vehicle data processing device according to claim 8 or the computing device according to claim 9 .

Images