Vehicle driving detection method, device, equipment and storage medium

Abstract

The application discloses a driving detection method, device and equipment and a storage medium, and belongs to the technical field of vehicle control. The method comprises the following steps: acquiring driving data of a vehicle and weather data during driving; determining influence data of vehicle driving based on the driving data of the vehicle and the weather data during driving, wherein the influence data of vehicle driving comprises at least one of driving safety data, driving speed data, average fuel consumption data, travel habit data or driving mileage data; acquiring first index data of vehicle driving based on the influence data of vehicle driving, determining second index data and total index data based on the first index data of vehicle driving, and determining driving suggestions for the vehicle based on the second index data and the total index data. By detecting various data of driving travel, the characteristics of driving travel can be obtained, the driving suggestions for the vehicle are obtained based on the characteristics of driving travel, and the driving safety is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a method, apparatus, device, and storage medium for detecting vehicle driving. Background Technology

[0002] With the increasing prevalence of vehicles and the growing normalization of driving, data analysis of each driver's trip is essential for improving driving safety and reducing driving expenses.

[0003] In related technologies, a certain aspect of driving is detected, the characteristics of driving are obtained based on the detection results, and driving habits are improved according to the characteristics of driving.

[0004] In related technologies, because the detection of a certain aspect of driving travel results in limitations in the characteristics of driving travel obtained from the detection results, the safety of driving is not high even after improving driving habits according to the characteristics of driving travel. Summary of the Invention

[0005] This application provides a method, apparatus, device, and storage medium for detecting vehicle driving, which can be used to solve problems existing in related technologies. The technical solution is as follows:

[0006] On one hand, embodiments of this application provide a method for detecting vehicle driving, the method comprising:

[0007] Acquire vehicle driving data and weather data during driving;

[0008] The impact data of vehicle driving is determined based on the vehicle's driving data and the weather data during driving. The impact data of vehicle driving includes at least one of driving safety data, driving speed data, average fuel consumption data, travel habit data, or driving mileage data.

[0009] Based on the impact data of the vehicle driving, obtain the primary indicator data of the vehicle driving, determine the secondary indicator data and the total indicator data based on the primary indicator data of the vehicle driving, and determine the driving suggestions for the vehicle based on the secondary indicator data and the total indicator data.

[0010] On the other hand, a vehicle driving detection device is provided, the device comprising:

[0011] The acquisition module is used to acquire vehicle driving data and weather data during driving;

[0012] The first determining module is used to determine the impact data of vehicle driving based on the vehicle's driving data and the weather data during driving. The impact data of vehicle driving includes at least one of driving safety data, driving speed data, average fuel consumption data, travel habit data, or driving mileage data.

[0013] The second determining module is used to obtain primary indicator data of vehicle driving based on the impact data of vehicle driving, determine secondary indicator data and total indicator data based on the primary indicator data of vehicle driving, and determine driving suggestions for the vehicle based on the secondary indicator data and the total indicator data.

[0014] On the other hand, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor to enable the computer device to implement any of the vehicle driving detection methods described above.

[0015] On the other hand, a computer-readable storage medium is also provided, wherein at least one computer program is stored in the computer-readable storage medium, the at least one computer program being loaded and executed by a processor to enable a computer to implement any of the vehicle driving detection methods described above.

[0016] On the other hand, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the vehicle driving detection methods described above.

[0017] The technical solution provided in this application has at least the following beneficial effects:

[0018] This application determines at least one type of vehicle driving impact data, including driving safety data, driving speed data, average fuel consumption data, travel habit data, or mileage data, based on vehicle driving data and weather data during driving. A control data service platform processes this vehicle driving impact data to obtain primary driving indicator data. Secondary and total indicator data are then determined based on the primary indicator data, and recommendations for vehicle driving are derived based on these data. By detecting multiple aspects of driving data, this application can identify the characteristics of driving trips and derive driving recommendations based on these characteristics, thereby improving driving safety. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of an implementation environment provided in an embodiment of this application;

[0021] Figure 2 This is a flowchart of a vehicle driving detection method provided in an embodiment of this application;

[0022] Figure 3 This is a diagram showing the correspondence between primary indicator data, secondary indicator data, primary weights, and secondary weights, as provided in an embodiment of this application.

[0023] Figure 4 This is a five-dimensional radar chart of secondary indicator data provided in an embodiment of this application;

[0024] Figure 5 This is a schematic diagram of the structure of a vehicle driving detection device provided in an embodiment of this application;

[0025] Figure 6 This is a schematic diagram of vehicle driving detection communication provided in an embodiment of this application;

[0026] Figure 7 This is a schematic diagram of the structure of a server provided in an embodiment of this application;

[0027] Figure 8 This is a schematic diagram of the structure of a vehicle driving detection device provided in an embodiment of this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0029] This application provides a method for detecting vehicle driving; please refer to... Figure 1 The diagram illustrates the implementation environment of the method provided in this embodiment. This implementation environment may include: a vehicle 11, a vehicle control system 12, and a data service platform 13.

[0030] The vehicle control system 12 acquires the driving data of the vehicle 11 and the weather data when the vehicle 11 is driven. The vehicle control system 12 determines the impact data of driving the vehicle 11 based on the driving data of the vehicle 11 and the weather data when the vehicle 11 is driven.

[0031] Subsequently, the vehicle control system 12 uploads the impact data of vehicle 11's driving to the data service platform 13, which processes the impact data of vehicle 11's driving to obtain the primary indicator data of vehicle 11's driving. Based on the primary indicator data of vehicle 11's driving, the secondary indicator data and the total indicator data are determined. Based on the secondary indicator data and the total indicator data, driving suggestions for vehicle 11 are determined.

[0032] The vehicle control system 12 receives primary indicator data, secondary indicator data, total indicator data, and driving suggestions for the vehicle 11 from the data service platform 13.

[0033] The vehicle 11 is equipped with an instrument control system capable of displaying primary indicator data, secondary indicator data, total indicator data, and driving suggestions for the vehicle 11. The vehicle control system 12 can store the primary indicator data, secondary indicator data, total indicator data, and driving suggestions for the vehicle 11. The vehicle 11 can retrieve the primary indicator data, secondary indicator data, total indicator data, and driving suggestions for the vehicle 11 from the vehicle control system 12. Alternatively, the vehicle 11 can also store the primary indicator data, secondary indicator data, total indicator data, and driving suggestions for the vehicle 11 itself.

[0034] Optionally, the vehicle control system 12 can be a terminal, such as an in-vehicle terminal. The vehicle 11 and the vehicle control system 12 establish a communication connection via a wired or wireless network.

[0035] Based on the above Figure 1 The implementation environment shown in this application provides a method for detecting vehicle driving, such as... Figure 2 As shown, taking the application of this method to a vehicle control system as an example, the method includes steps 201-203.

[0036] In step 201, vehicle driving data and weather data during driving are acquired.

[0037] For example, vehicle driving data includes at least one of the following: number of times a seatbelt was not worn, degree of driver fatigue, number of collision warnings, number of emergency braking requests, number of tire pressure warnings, number of lane departure warnings, number of high-speed cornerings, number of speed limit warnings, vehicle speed, vehicle acceleration, degree of speeding, number of times of rapid acceleration, speed limit of the road, duration of inactivity, number of times driving with reference to weather conditions, driving time, average monthly fuel consumption per 100 kilometers, or total monthly mileage.

[0038] Next, examples will be given to illustrate the methods for obtaining various driving data.

[0039] (1) Number of times a seatbelt was not worn

[0040] For example, obtaining the number of times a seatbelt was not worn includes: after a first reference period since the vehicle started, identifying the wearing status of the seatbelts in the driver's seat, front passenger seat, or rear passenger seat, and obtaining the number of times a seatbelt was not worn based on the wearing status of any one of the seatbelts in the driver's seat, front passenger seat, or rear passenger seat. For example, if the wearing status of any one of the seatbelts in the driver's seat, front passenger seat, or rear passenger seat is considered as not wearing a seatbelt, it is recorded as one instance of not wearing a seatbelt; if the wearing status of any two of the seatbelts in the driver's seat, front passenger seat, or rear passenger seat is considered as not wearing a seatbelt, it is recorded as two instances of not wearing a seatbelt.

[0041] The methods for determining vehicle startup include, but are not limited to, obtaining the power-on status of the vehicle key. If the vehicle key is powered on, the vehicle is in the startup state; if the vehicle key is not powered on, the vehicle is not in the startup state. Furthermore, this application embodiment does not limit the first reference duration. For example, the first reference duration can be set based on experience or adjusted according to actual circumstances.

[0042] After determining the first reference duration of vehicle startup, the seatbelt wearing status is acquired. This application does not limit the method of acquiring the seatbelt wearing status. For example, the seatbelt wearing status can be acquired through the airbag controller; the vehicle control system can acquire the seatbelt wearing status in the driver's seat, front passenger seat, or rear passenger seat through the airbag controller. Alternatively, images of the driver's seat, front passenger seat, or rear passenger seat can be captured, and the seatbelt wearing status can be acquired through image recognition.

[0043] (2) Assess the degree of fatigue driving

[0044] Optionally, the degree of driver fatigue can be categorized as no fatigue, fatigue at a first reference level, fatigue at a second reference level, or fatigue at a third reference level. This application does not limit the method of obtaining the degree of driver fatigue. For example, the degree of driver fatigue can be obtained using an OPENCV (Open Source Computer Vision Library) fatigue detection system.

[0045] For example, the degree of driver fatigue is obtained by using the OPENCV fatigue detection system, including: obtaining at least one of the driver's blinking frequency or the driver's yawning frequency by using the OPENCV fatigue detection system, and determining the degree of driver fatigue based on the obtained at least one of the driver's blinking frequency or the driver's yawning frequency.

[0046] The determination of the degree of driver fatigue based on at least one of the driver's blink frequency or yawn frequency includes: when the driver's blink frequency is greater than a first blink threshold or the driver's yawn frequency is less than a first yawn threshold, the driver's fatigue level is no fatigue; when the driver's blink frequency is less than the first blink threshold but greater than a second blink threshold, or the driver's yawn frequency is greater than the first yawn threshold but less than the second yawn threshold, the driver's fatigue level is a first reference level of fatigue; when the driver's blink frequency is less than the second blink threshold but greater than a third blink threshold, or the driver's yawn frequency is greater than the second yawn threshold but less than the third yawn threshold, the driver's fatigue level is a second reference level of fatigue; when the driver's blink frequency is less than the third blink threshold, or the driver's yawn frequency is greater than the third yawn threshold, the driver's fatigue level is a third reference level of fatigue.

[0047] This application embodiment does not limit the first reference level of fatigue, the second reference level of fatigue, and the third reference level of fatigue. For example, it is required that the fatigue level of the third reference level of fatigue is greater than that of the second reference level of fatigue, and that the fatigue level of the second reference level of fatigue is greater than that of the first reference level of fatigue. This application embodiment also does not limit the first blink threshold, the second blink threshold, and the third blink threshold. For example, these can be set based on experience, requiring that the first blink threshold is greater than the second blink threshold, and the second blink threshold is greater than the third blink threshold. This application embodiment also does not limit the first yawn threshold, the second yawn threshold, and the third yawn threshold. For example, these can be set based on experience, requiring that the first yawn threshold is less than the second yawn threshold, and the second yawn threshold is less than the third yawn threshold.

[0048] (3) Number of collision alarms obtained

[0049] This application does not limit the devices used for collision alarms. For example, a vehicle collision avoidance warning system installed in a vehicle can be used to issue an alarm for a collision. For example, obtaining the number of collision alarms includes the vehicle control system acquiring visual and audible collision alarms from the vehicle collision avoidance warning system, and adding the number of visual and audible collision alarms to the number of collision alarms as the total number of collision alarms.

[0050] Specifically, when the time interval between the visual collision alarm and the audible collision alarm is less than the second reference duration, it is recorded as one collision alarm. This embodiment does not limit the second reference duration; it can be set based on experience or adjusted according to actual circumstances. For example, the second reference duration can be set to 5 minutes.

[0051] (4) Number of emergency braking requests received

[0052] In one possible implementation, the vehicle is equipped with AEB (Autonomous Emergency Braking), EBA (Electronic Brake Assist), and AWB (Autonomous Warning Braking). Optionally, acquiring the number of emergency braking requests includes acquiring the first deceleration control request issued by AEB, the second deceleration control request issued by EBA, and the third deceleration control request issued by AWB. The sum of the acquired number of first deceleration control requests, second deceleration control requests, and third deceleration control requests is taken as the number of emergency braking requests acquired.

[0053] For example, each time the vehicle control system receives a third deceleration control request, it is recorded as one first-level emergency braking; each time the vehicle control system receives a second deceleration control request, it is recorded as one second-level emergency braking; and each time the vehicle control system receives a first deceleration control request, it is recorded as one third-level emergency braking. The sum of the number of first-level, second-level, and third-level emergency braking is the number of emergency braking requests received.

[0054] (5) Number of times the tire pressure alarm is detected

[0055] In one possible implementation, a tire pressure monitoring system is installed in the vehicle to monitor the tire pressure of the left front wheel, left rear wheel, right front wheel, and right rear wheel. Optionally, the number of tire pressure alarms is obtained by the vehicle control system monitoring the tire pressure of the left front wheel, left rear wheel, right front wheel, and right rear wheel through the tire pressure monitoring system. Within a third reference time period, when the tire pressure of at least one of the left front wheel, left rear wheel, right front wheel, and right rear wheel triggers a tire pressure alarm, it is recorded as one tire pressure alarm. The total number of tire pressure alarms within the third reference time period is taken as the number of tire pressure alarms in the acquired driving data. This application embodiment does not limit the third reference time period; it can be set based on experience or adjusted according to actual conditions.

[0056] (6) Number of lane departure warnings received

[0057] In one possible implementation, the vehicle is equipped with an LDW (Lane Departure Warning) system, which can alert the driver if the vehicle has deviated from its lane. For example, acquiring the number of lane departure warnings includes: the vehicle control system acquiring the LDW's lane-crossing warning signal and intervention warning signal. The number of times the LDW issued a lane-crossing warning signal and the number of times it issued an intervention warning signal are obtained. The sum of the number of times the LDW issued a lane-crossing warning signal and the number of times it issued an intervention warning signal is taken as the acquired number of lane departure warnings.

[0058] (7) Obtain the number of times the vehicle takes a high-speed corner and the vehicle's speed.

[0059] In one possible implementation, the vehicle is equipped with a lateral acceleration sensor and a speed sensor. The lateral acceleration sensor acquires the lateral acceleration during vehicle movement, and the speed sensor acquires the vehicle's speed. Optionally, acquiring the number of high-speed cornering maneuvers includes: the vehicle control system acquires the lateral acceleration during vehicle movement using the lateral acceleration sensor and the vehicle speed using the speed sensor; determining a high-speed cornering maneuver as one instance based on the absolute value of the lateral acceleration being greater than a reference value and the vehicle speed being greater than a reference speed; and acquiring the number of high-speed cornering maneuvers.

[0060] (8) Obtain the number of speed limit alarms and the speed limit of the road where the vehicle is located.

[0061] In one possible implementation, the vehicle is equipped with an SLA (Speed ​​Limit Assist). The SLA can acquire the speed limit of the road where the vehicle is located and can issue a speed limit signal when the vehicle exceeds a second reference speed. For example, acquiring the number of speed limit warnings includes: the vehicle control system acquiring the speed limit signal issued by the SLA, and recording each acquired speed limit signal as one speed limit warning.

[0062] (9) Obtain the speeding level of the vehicle

[0063] For example, obtaining the degree of speeding of a vehicle includes: comparing the vehicle's speed with the speed limit of the road where the vehicle is located; determining that the vehicle is speeding at a first reference level if its speed exceeds the speed limit of the road where the vehicle is located by a first reference percentage but is less than a second reference percentage; determining that the vehicle is speeding at a second reference level if its speed exceeds the speed limit of the road where the vehicle is located by a second reference percentage but is less than a third reference percentage; and determining that the vehicle is speeding at a third reference level if its speed exceeds the speed limit of the road where the vehicle is located by a third reference percentage.

[0064] This application does not limit the first reference percentage, the second reference percentage, or the third reference percentage. For example, the first reference percentage must be less than the second reference percentage, and the second reference percentage must be less than the third reference percentage.

[0065] (10) Obtain the number of times the vehicle accelerates rapidly and the vehicle's acceleration.

[0066] In one possible implementation, the vehicle is equipped with an accelerometer that can acquire the acceleration of the vehicle.

[0067] For example, obtaining the number of times a vehicle accelerates rapidly includes: obtaining the acceleration of the vehicle by means of an accelerometer installed on the vehicle; and recording a rapid acceleration of the vehicle as one instance based on the obtained acceleration being greater than a reference acceleration.

[0068] This application does not impose any restrictions on the reference acceleration. It can be set based on experience or adjusted according to actual conditions.

[0069] (11) Get the duration of non-startup

[0070] Optionally, the duration of inactivity is obtained, including: obtaining the time when the vehicle key is in the powered-on state, and using the time interval between two instances of the vehicle key being powered on as the duration of inactivity. If the time interval between two instances of the vehicle key being powered on exceeds a fourth reference duration but is less than a fifth reference duration, the vehicle is determined to be a Level 1 long-term inactivity; if the time interval between two instances of the vehicle key being powered on exceeds a fifth reference duration but is less than a sixth reference duration, the vehicle is determined to be a Level 2 long-term inactivity; if the time interval between two instances of the vehicle key being powered on exceeds a sixth reference duration, the vehicle is determined to be a Level 3 long-term inactivity.

[0071] Among them, when the occurrence times of Level 1 long-term non-starting, Level 2 long-term non-starting, or Level 3 long-term non-starting overlap, the vehicle is determined to be at the higher level of non-starting.

[0072] (12) Obtain the driving time period

[0073] In one possible implementation, obtaining the driving time period includes: the vehicle control system obtaining the vehicle driving time from the vehicle's instrument control system, and determining the vehicle driving time period based on the vehicle driving time.

[0074] For example, the time periods for vehicle driving include daytime, nighttime, and late nighttime. This application does not limit the time division of daytime, nighttime, and late nighttime; for example, the time division of daytime, nighttime, and late nighttime can be set based on experience.

[0075] (13) Obtain the average monthly fuel consumption per 100 kilometers

[0076] For example, obtaining the average monthly fuel consumption per 100 kilometers includes: the vehicle control system obtaining the vehicle's fuel consumption for the current month from the vehicle's instrument control system, dividing the current month's fuel consumption by the number of kilometers driven, and then multiplying by 100 to obtain the average monthly fuel consumption per 100 kilometers.

[0077] (14) Obtain the total monthly mileage

[0078] Optionally, obtaining the total monthly mileage includes: the vehicle control system obtaining the total mileage of the vehicle on the last day of the previous month and the total mileage on the last day of the current month from the vehicle's instrument control system, and subtracting the total mileage of the last day of the previous month from the total mileage of the last day of the current month to obtain the total monthly mileage.

[0079] (15) Obtain the number of times the vehicle was driven in reference weather (reference weather includes rainy or snowy days, i.e., the situation of the vehicle traveling in rainy or snowy weather).

[0080] In one possible implementation, the vehicle control system obtains weather data during vehicle driving from the vehicle's instrument control system. The weather data includes at least one of rain, snow, fog, sunshine, overcast, cloudy, or sandstorm conditions. Optionally, the number of times driving under reference weather conditions is obtained includes the number of times driving under rain, snow, fog, or sandstorm conditions.

[0081] In step 202, the impact data of vehicle driving is determined based on the vehicle's driving data and the weather data during driving. The impact data of vehicle driving includes at least one of driving safety data, driving speed data, average fuel consumption data, travel habit data, or driving mileage data.

[0082] In one possible implementation, vehicle driving impact data is determined based on vehicle driving data and weather data during driving, including: determining driving safety data based on the number of times seatbelts were not worn, the degree of fatigue driving, the number of collision warnings, the number of emergency braking requests, the number of tire pressure warnings, the number of lane departure warnings, and the number of high-speed cornerings; determining driving speed data based on the number of speed limit warnings, the degree of speeding, and the number of rapid accelerations; determining travel habit data based on the duration of inactivity, the number of times driving during a reference time period, and the number of times driving under reference weather conditions, with the number of times driving during a reference time period determined based on the driving time period; determining average fuel consumption data based on a fuel consumption index, with the fuel consumption index determined based on the average monthly fuel consumption per 100 kilometers; and determining mileage data based on the total monthly mileage.

[0083] For example, when the reference period is late night, the number of times the driver drives during the reference period is determined based on the driving time, which is to obtain the number of times the driver drives during the late night. This application embodiment does not limit the time range of the late night period. For example, 11 PM to 5 AM the next day can be defined as the late night period. When the driver drives the vehicle between 11 PM and 5 AM the next day, it is recorded as one driving session during the late night period.

[0084] For example, the fuel consumption index is determined based on the average monthly fuel consumption per 100 kilometers, including: substituting the average monthly fuel consumption per 100 kilometers into the fuel consumption index formula to obtain the fuel consumption index corresponding to the average monthly fuel consumption per 100 kilometers.

[0085] The formula for the fuel consumption index is as follows: Fuel consumption index = (average monthly fuel consumption per 100 kilometers / standard fuel consumption per 100 kilometers - 1) × 100%.

[0086] If the fuel consumption index is greater than the fourth reference percentage, the vehicle's fuel consumption is classified as first reference level; if the fuel consumption index is less than the fourth reference percentage but greater than the fifth reference percentage, the vehicle's fuel consumption is classified as second reference level; if the fuel consumption index is less than the fifth reference percentage but greater than the sixth reference percentage, the vehicle's fuel consumption is classified as third reference level; if the fuel consumption index is less than the sixth reference percentage but greater than the seventh reference percentage, the vehicle's fuel consumption is classified as fourth reference level; if the fuel consumption index is less than the seventh reference percentage but greater than the eighth reference percentage, the vehicle's fuel consumption is classified as fifth reference level; and if the fuel consumption index is less than the eighth reference percentage but greater than a negative eighth reference percentage, the vehicle's fuel consumption is classified as sixth reference level.

[0087] If the fuel consumption index is less than the eighth reference percentage and greater than the seventh reference percentage, the vehicle is considered to be fuel-efficient at the fifth reference level; if the fuel consumption index is less than the seventh reference percentage and greater than the eighth reference percentage, the vehicle is considered to be fuel-efficient at the fourth reference level; if the fuel consumption index is less than the sixth reference percentage and greater than the fifth reference percentage, the vehicle is considered to be fuel-efficient at the third reference level; if the fuel consumption index is less than the fifth reference percentage and greater than the fourth reference percentage, the vehicle is considered to be fuel-efficient at the second reference level; and if the fuel consumption index is less than the fourth reference percentage, the vehicle is considered to be fuel-efficient at the first reference level.

[0088] The embodiments of this application do not limit the fourth reference percentage, fifth reference percentage, sixth reference percentage, seventh reference percentage and eighth reference percentage. They can be set based on experience, or adjusted according to actual conditions.

[0089] The vehicle's total monthly mileage is determined as follows: if the total monthly mileage is greater than 0 and less than the first reference mileage, then the vehicle's total monthly mileage is determined as the first reference mileage; if the total monthly mileage is greater than the first reference mileage and less than the second reference mileage, then the vehicle's total monthly mileage is determined as the second reference mileage; if the total monthly mileage is greater than the second reference mileage and less than the third reference mileage, then the vehicle's total monthly mileage is determined as the fourth reference mileage.

[0090] The embodiments of this application do not impose restrictions on the first reference mileage, the second reference mileage, the third reference mileage, and the fourth reference mileage. They can be set based on experience or adjusted according to actual conditions.

[0091] In step 203, primary indicator data for vehicle driving is obtained based on the impact data of vehicle driving; secondary indicator data and total indicator data are determined based on the primary indicator data of vehicle driving; and driving suggestions for the vehicle are determined based on the secondary indicator data and total indicator data.

[0092] In one possible implementation, the process involves obtaining primary indicator data for vehicle driving based on the impact data of vehicle driving; determining secondary indicator data and total indicator data based on the primary indicator data; and determining driving recommendations for the vehicle based on the secondary indicator data and total indicator data. This includes: uploading the impact data of vehicle driving to a data service platform for processing to obtain primary indicator data; determining secondary indicator data and total indicator data based on the primary indicator data; and determining driving recommendations for the vehicle based on the secondary indicator data and total indicator data; and receiving the primary indicator data, secondary indicator data, total indicator data, and driving recommendations for the vehicle from the data service platform.

[0093] Uploading the impact data of vehicle driving to the data service platform includes: controlling a remote communication terminal to send the impact data of vehicle driving to a remote service provider for the vehicle, wherein the remote communication terminal is located on the vehicle; and controlling the remote service provider for the vehicle to upload the impact data of vehicle driving to the data service platform.

[0094] This application does not limit the remote communication terminal. For example, a TBOX (Telematic Box) is installed below the vehicle's dashboard, and the TBOX has wireless communication capabilities. This application also does not limit the vehicle's remote service provider. Optionally, a TSP (Telematics Service Provider) platform can be used as the vehicle's remote service provider, forwarding the vehicle driving impact data uploaded by the remote communication terminal to the data service platform.

[0095] For example, the vehicle control system controls the TBOX to upload the impact data of vehicle driving to the TSP, which then forwards it to the data service platform.

[0096] After receiving the vehicle driving impact data, the data service platform obtains primary indicator data for vehicle driving based on this data. These primary indicator data include: primary indicator data corresponding to the following from driving safety data: the number of times seatbelts were not worn, the degree of fatigued driving, the number of collision warnings, the number of emergency braking requests, the number of tire pressure warnings, the number of lane departure warnings, and the number of high-speed cornering incidents; primary indicator data corresponding to the number of speed limit warnings, the degree of speeding, and the number of rapid accelerations from driving speed data; primary indicator data corresponding to the following from travel habit data: the duration of inactivity, the number of times driving during the reference time period, and the number of times driving under the reference weather conditions; primary indicator data corresponding to the fuel consumption index from average fuel consumption data; and primary indicator data corresponding to the total monthly mileage from mileage data.

[0097] In one possible implementation, determining the primary indicator data for the number of times a seatbelt was not worn includes: setting the primary indicator data for the number of times a seatbelt was not worn to 100 before the vehicle is started; after the vehicle has been started for a first reference period of time, subtracting the first reference value from the primary indicator data for the number of times a seatbelt was not worn for each instance; and recording the primary indicator data for the number of times a seatbelt was not worn as 0 when the primary indicator data for the number of times a seatbelt was not worn is less than or equal to 0.

[0098] For example, determining the primary indicator data for the degree of fatigued driving includes: setting the primary indicator data for the degree of fatigued driving to 100 before starting the vehicle; after starting the vehicle, subtracting a second reference value from the primary indicator data for the degree of fatigued driving corresponding to a first reference level of fatigue, subtracting a third reference value from the primary indicator data for the degree of fatigued driving corresponding to a second reference level of fatigue, and subtracting a fourth reference value from the primary indicator data for the degree of fatigued driving corresponding to a third reference level of fatigue; when the primary indicator data for the degree of fatigued driving is less than or equal to 0, the primary indicator data for the degree of fatigued driving is recorded as 0.

[0099] Optionally, before starting the vehicle, the primary indicator data for the number of collision alarms is set to 100; after starting the vehicle, the primary indicator data for the number of collision alarms corresponding to one collision alarm is reduced by the fifth reference value; when the primary indicator data for the number of collision alarms is less than or equal to 0, the primary indicator data for the number of collision alarms is recorded as 0.

[0100] In one possible implementation, determining the primary index data for the number of emergency braking requests includes: setting the primary index data for the number of emergency braking requests to 100 before vehicle startup; after vehicle startup, subtracting a sixth reference value from the primary index data for the number of emergency braking requests corresponding to a first reference level emergency braking, subtracting a seventh reference value from the primary index data for the number of emergency braking requests corresponding to a second reference level emergency braking, and subtracting an eighth reference value from the primary index data for the number of emergency braking requests corresponding to a third reference level emergency braking; when the primary index data for the number of emergency braking requests is less than or equal to 0, the primary index data for the number of emergency braking requests is recorded as 0.

[0101] For example, before starting the vehicle, the primary indicator data for the number of tire pressure alarms is set to 100; after starting the vehicle, the primary indicator data for the number of tire pressure alarms corresponding to one tire pressure alarm is reduced by the ninth reference value; when the primary indicator data for the number of tire pressure alarms is less than or equal to 0, the primary indicator data for the number of tire pressure alarms is recorded as 0.

[0102] Optionally, before the vehicle starts, the primary indicator data for the number of lane departure warnings is set to 100; after the vehicle starts, the primary indicator data for the number of lane departure warnings corresponding to one lane crossing warning signal is reduced by the tenth reference value, and the primary indicator data for the number of lane departure warnings corresponding to one intervention warning signal is reduced by the eleventh reference value; when the primary indicator data for the number of lane departure warnings is less than or equal to 0, the primary indicator data for the number of lane departure warnings is recorded as 0.

[0103] In one possible implementation, before the vehicle starts, the primary indicator data for the number of high-speed cornerings is set to 100; after the vehicle starts, the primary indicator data for the number of high-speed cornerings corresponding to one high-speed cornering is reduced by the twelfth reference value; when the primary indicator data for the number of high-speed cornerings is less than or equal to 0, the primary indicator data for the number of high-speed cornerings is recorded as 0.

[0104] For example, before the vehicle is started, the primary indicator data for the number of speed limit alarms is set to 100; after the vehicle is started, the primary indicator data for the number of speed limit alarms corresponding to one speed limit alarm is reduced by the thirteenth reference value; when the primary indicator data for the number of speed limit alarms is less than or equal to 0, the primary indicator data for the number of speed limit alarms is recorded as 0.

[0105] In one possible implementation, determining the primary index data for the degree of speeding includes: setting the primary index data for the degree of speeding to 100 before the vehicle starts; after the vehicle starts, deducting the fourteenth reference value from the primary index data for a first reference level of speeding, deducting the fifteenth reference value from the primary index data for a second reference level of speeding, and deducting the sixteenth reference value from the primary index data for a third reference level of speeding; when the primary index data for the degree of speeding is less than or equal to 0, the primary index data for the degree of speeding is recorded as 0.

[0106] For example, before the vehicle is started, the primary indicator data for the number of rapid accelerations is set to 100; after the vehicle is started, the primary indicator data for the number of emergency accelerations is reduced by the seventeenth reference value for each rapid acceleration; when the primary indicator data for the number of rapid accelerations is less than or equal to 0, the primary indicator data for the number of rapid accelerations is recorded as 0.

[0107] For example, before the vehicle is started, the primary indicator data for the number of times the vehicle is driven during the reference period is set to 100; after the vehicle is started, the primary indicator data for the number of times the vehicle is driven during the reference period is reduced by the eighteenth reference value; when the primary indicator data for the number of times the vehicle is driven during the reference period is less than or equal to 0, the primary indicator data for the number of times the vehicle is driven during the reference period is recorded as 0.

[0108] For example, before starting the vehicle, the primary indicator data for the number of times the vehicle is driven in reference weather is set to 100; after starting the vehicle, the primary indicator data for the number of times the vehicle is driven in reference weather is reduced by the nineteenth reference value; when the primary indicator data for the number of times the vehicle is driven in reference weather is less than or equal to 0, the primary indicator data for the number of times the vehicle is driven in reference weather is recorded as 0.

[0109] For example, before starting the vehicle, the primary indicator data of the fuel consumption index is set to 100; after starting the vehicle, the primary indicator data of the fuel consumption index corresponding to the first reference level of fuel consumption is reduced by the twentieth reference value; the primary indicator data of the fuel consumption index corresponding to the second reference level of fuel consumption is reduced by the twenty-first reference value; the primary indicator data of the fuel consumption index corresponding to the third reference level of fuel consumption is reduced by the twenty-second reference value; the primary indicator data of the fuel consumption index corresponding to the fourth reference level of fuel consumption is reduced by the twenty-third reference value; the primary indicator data of the fuel consumption index corresponding to the fifth reference level of fuel consumption is reduced by the twenty-fourth reference value; the primary indicator data of the fuel consumption index corresponding to the sixth reference level of fuel consumption remains unchanged; the primary indicator data of the fuel consumption index corresponding to the first reference level of fuel saving is increased by the twenty-fourth reference value; the primary indicator data of the fuel consumption index corresponding to the second reference level of fuel saving is increased by the twenty-third reference value; the primary indicator data of the fuel consumption index corresponding to the third reference level of fuel saving is increased by the twenty-second reference value; the primary indicator data of the fuel consumption index corresponding to the fourth reference level of fuel saving is increased by the twenty-first reference value; and the primary indicator data of the fuel consumption index corresponding to the fifth reference level of fuel saving is increased by the twentieth reference value. When the primary indicator data of the fuel consumption index is less than or equal to 0, the primary indicator data of the fuel consumption index is recorded as 0.

[0110] For example, before starting the vehicle, the primary indicator data for the total monthly mileage is set to 100. After starting the vehicle, the primary indicator data for the total monthly mileage is incremented by a 25th reference value for each first reference mileage, a 26th reference value for each second reference mileage, a 27th reference value for each third reference mileage, and a 28th reference value for each fourth reference mileage. When the primary indicator data for the total monthly mileage is less than or equal to 0, the primary indicator data for the total monthly mileage is recorded as 0.

[0111] For example, before the vehicle is started, the primary indicator data for the duration of non-starting is set to 100; after the vehicle is started, the primary indicator data for the duration of non-starting corresponding to a first-level long-term non-starting is reduced by the twenty-ninth reference value; the primary indicator data for the duration of non-starting corresponding to a second-level long-term non-starting is reduced by the thirtieth reference value; and the primary indicator data for the duration of non-starting corresponding to a third-level long-term non-starting is reduced by the thirty-first reference value.

[0112] This application does not impose any restrictions on the first, second, third, fourth, fifth, sixth, seventh, eighth, ninth, tenth, eleventh, twelfth, thirteenth, fourteenth, fifteenth, sixteenth, seventeenth, eighteenth, nineteenth, twentieth, twenty-first, twenty-second, twenty-third, twenty-fourth, twenty-fifth, twenty-sixth, twenty-seventh, twenty-eighth, twenty-ninth, thirtieth, and thirty-first reference values. For example, they must all be less than 100.

[0113] In one possible implementation, after obtaining the primary indicator data for vehicle driving, secondary indicator data and total indicator data are determined based on the primary indicator data, including: secondary indicator data for driving safety data are determined based on the primary indicator data and primary weights corresponding to the number of times seatbelts were not worn, the degree of fatigued driving, the number of collision warnings, the number of emergency braking requests, the number of tire pressure warnings, the number of lane departure warnings, and the number of high-speed cornerings, respectively; secondary indicator data for driving speed data are determined based on the primary indicator data and primary weights corresponding to the number of speed limit warnings, the degree of speeding, and the number of rapid accelerations, respectively; based on... The primary indicators and their weights for the travel habit data—such as the duration of inactivity, the number of times driving during the reference time period, and the number of times driving under the reference weather—are used to determine the secondary indicators for travel habit data. Similarly, the primary indicators and their weights for the average fuel consumption index are used to determine the secondary indicators for average fuel consumption data. The primary indicators and their weights for the total monthly mileage are used to determine the secondary indicators for mileage data. Finally, the secondary indicators and their weights for driving safety data, driving speed data, average fuel consumption data, travel habit data, and mileage data are used to determine the overall indicator data.

[0114] This application embodiment does not impose restrictions on the primary weights of the number of times a seatbelt was not worn, the degree of fatigued driving, the number of collision warnings, the number of emergency braking requests, the number of tire pressure warnings, the number of lane departure warnings, and the number of high-speed cornerings in the driving safety data. For example, it is necessary to satisfy that the sum of the primary weights of the number of times a seatbelt was not worn, the degree of fatigued driving, the number of collision warnings, the number of emergency braking requests, the number of tire pressure warnings, the number of lane departure warnings, and the number of high-speed cornerings equals 1.

[0115] For example, taking the number of times a seatbelt is not worn as a primary weight of 30%, the degree of fatigue driving as a primary weight of 15%, the number of collision warnings as a primary weight of 15%, the number of emergency braking requests as a primary weight of 10%, the number of tire pressure warnings as a primary weight of 10%, the number of lane departure warnings as a primary weight of 10%, and the number of high-speed cornering incidents as a primary weight of 10%, the correspondence between the primary indicators and their primary weights in the driving safety data is as follows: Figure 3 As shown.

[0116] After determining the primary weights of the number of times seatbelts were not worn, the degree of fatigued driving, the number of collision warnings, the number of emergency braking requests, the number of tire pressure warnings, the number of lane departure warnings, and the number of high-speed cornerings in the driving safety data, the primary weights of the following are multiplied: the primary weight of the number of times seatbelts were not worn is multiplied by the primary indicator data for the number of times seatbelts were not worn; the primary weight of the degree of fatigued driving is multiplied by the primary indicator data for the degree of fatigued driving; the primary weight of the number of collision warnings is multiplied by the primary indicator data for the number of collision warnings; and the primary weight of the number of emergency braking requests is multiplied by the primary indicator data for the number of emergency braking requests. The secondary indicator data for driving safety is obtained by multiplying the primary weight of the number of tire pressure warnings by the primary indicator data of the number of tire pressure warnings, the primary weight of the number of lane departure warnings by the primary indicator data of the number of lane departure warnings, the primary weight of the number of high-speed cornerings by the primary indicator data of the number of high-speed cornerings, the primary weight of the number of speed limit warnings by the primary indicator data of the number of speed limit warnings, the primary weight of the speeding level by the primary indicator data of the speeding level, and the primary weight of the number of rapid accelerations by the primary indicator data of the number of rapid accelerations.

[0117] This application embodiment does not impose restrictions on the primary weights of the number of speed limit alarms, the degree of speeding, and the number of rapid accelerations in the driving speed data. For example, it is required that the sum of the primary weights of the number of speed limit alarms, the degree of speeding, and the number of rapid accelerations equals 1.

[0118] For example, taking a first-level weight of 50% for the number of speed limit warnings, 30% for the degree of speeding, and 20% for the number of sudden accelerations as an example, the correspondence between each first-level indicator data and its first-level weight in the driving speed data is as follows: Figure 3 As shown.

[0119] After determining the primary weights of the number of speed limit warnings, the degree of speeding, and the number of rapid accelerations in the driving speed data, the secondary index data of the driving speed data is obtained by multiplying the primary weight of the number of speed limit warnings by the primary index data of the number of speed limit warnings, multiplying the primary weight of the degree of speeding by the primary index data of the degree of speeding, and multiplying the primary weight of the number of rapid accelerations by the primary index data of the number of rapid accelerations.

[0120] This application embodiment does not impose restrictions on the primary weights of the duration of inactivity, the number of times driving during the reference time period, and the number of times driving during the reference weather in the travel habit data. For example, it is required that the sum of the primary weights of the duration of inactivity, the number of times driving during the reference time period, and the number of times driving during the reference weather equals 1.

[0121] For example, taking the primary weight of inactivity duration as 60%, the primary weight of driving frequency during the reference time period as 20%, and the primary weight of driving frequency based on weather conditions as 20%, the correspondence between each primary indicator data and its primary weight in the travel habit data is as follows: Figure 3 As shown.

[0122] After determining the primary weights for the duration of inactivity, the number of times driving during the reference time period, and the number of times driving during the reference weather, the secondary indicator data for travel habits is obtained by multiplying the primary weight of the duration of inactivity by the primary indicator data for the duration of inactivity, multiplying the primary weight of the number of times driving during the reference time period by the primary indicator data for the number of times driving during the reference time period, and multiplying the primary weight of the number of times driving during the reference weather by the primary indicator data for the number of times driving during the reference weather.

[0123] In one possible implementation, the primary weight of the fuel consumption index in the average fuel consumption data is determined to be 1. The primary indicator data of the fuel consumption index is then used as the secondary indicator data of the average fuel consumption data. The correspondence between the primary indicator data and the primary weight of the fuel consumption index is as follows: Figure 3 As shown.

[0124] Similarly, the primary weight of the monthly total mileage in the mileage data is also 1. The primary indicator data of monthly total mileage is used as the secondary indicator data of average fuel consumption. The correspondence between the primary indicator data of monthly total mileage and its primary weight is as follows: Figure 3 As shown.

[0125] This application does not impose restrictions on the secondary weights of driving safety data, driving speed data, average fuel consumption data, travel habit data, and mileage data in the vehicle driving impact data. For example, it is required that the sum of the secondary weights of driving safety data, driving speed data, average fuel consumption data, travel habit data, and mileage data equals 1.

[0126] For example, taking a secondary weight of 40% for driving safety data, 20% for driving speed data, 15% for average fuel consumption data, 15% for travel habit data, and 10% for mileage data as an example, the correspondence between the secondary indicator data and the secondary weight in the vehicle driving impact data is as follows: Figure 3 As shown.

[0127] After determining the primary weights of driving safety data, driving speed data, average fuel consumption data, travel habit data, and mileage data, the secondary weights of driving safety data, driving speed data, average fuel consumption data, travel habit data, and mileage data are multiplied by their respective secondary index data, and then summed to obtain the total driving index data.

[0128] In one possible implementation, after determining the primary, secondary, and overall performance indicators (SPIs) for vehicle driving, driving recommendations are determined based on the secondary and overall SPIs. This includes comparing the overall SPI with a reference overall SPI. If the overall SPI is lower than the reference overall SPI, the recommendation could be: "The overall SPI for vehicle driving is low; please regulate your driving behavior." If the overall SPI is higher than the reference overall SPI, the recommendation could be: "The overall SPI for vehicle driving is high; please maintain this level of performance."

[0129] For example, comparing the secondary indicator data of driving safety data with the reference secondary indicator data, if the secondary indicator data of the driving safety data is less than or equal to the reference secondary indicator data, the recommendation for driving safety could be: The secondary indicator data of the driving safety data is low; please drive safely. If the secondary indicator data of the driving safety data is greater than the reference secondary indicator data, the recommendation for driving safety could be: The secondary indicator data of the driving safety data is high; please maintain this level of safety.

[0130] For example, comparing the secondary index data of driving speed with the secondary index data of reference driving speed, if the secondary index data of driving speed is less than or equal to the secondary index data of reference driving speed, the suggestion for driving speed could be: The secondary index data of driving speed is low; please pay attention to driving speed. If the secondary index data of driving speed is greater than the secondary index data of reference driving speed, the suggestion for driving speed could be: The secondary index data of driving speed is high; please maintain this level.

[0131] For example, comparing the secondary indicator data of average fuel consumption with the secondary indicator data of reference average fuel consumption, if the secondary indicator data of the average fuel consumption is less than or equal to the secondary indicator data of the reference average fuel consumption, the suggestion for average fuel consumption could be: the secondary indicator data of the average fuel consumption is low; please pay attention to reducing fuel consumption. If the secondary indicator data of the average fuel consumption is greater than the secondary indicator data of the reference average fuel consumption, the suggestion for average fuel consumption could be: the secondary indicator data of the average fuel consumption is high; please continue to maintain it.

[0132] For example, comparing the secondary indicator data of travel habit data with the secondary indicator data of reference travel habits, if the secondary indicator data of the travel habit data is less than or equal to the secondary indicator data of the reference travel habits, the suggestion for travel habits could be: The secondary indicator data of the travel habit data is low; please pay attention to your travel habits. If the secondary indicator data of the travel habit data is greater than the secondary indicator data of the reference travel habits, the suggestion for travel habits could be: The secondary indicator data of the travel habit data is high; please continue to maintain this.

[0133] For example, comparing the secondary indicator data of mileage with the secondary indicator data of reference mileage, if the secondary indicator data of the mileage is less than or equal to the secondary indicator data of the reference mileage, the suggestion for mileage could be: the secondary indicator data of the mileage is low, please pay attention to increasing mileage. If the secondary indicator data of the mileage is greater than the secondary indicator data of the reference mileage, the suggestion for mileage could be: the secondary indicator data of the mileage is high, please continue to maintain it.

[0134] In one possible implementation, after the data service platform receives the impact data of vehicle driving, it can also determine the location of charging piles and battery swapping stations within a reference range around the vehicle based on the vehicle's location in the impact data of vehicle driving and the location of charging piles and battery swapping stations.

[0135] This application does not limit the reference range; the reference range can be determined based on experience or adjusted according to actual conditions. This application also does not limit the method of obtaining the locations of charging piles and battery swapping stations. For example, the data service platform can be controlled to obtain a map containing the locations of charging piles and battery swapping stations, and the locations of the charging piles and battery swapping stations can be determined based on the map.

[0136] In one possible implementation, after determining the primary indicator data, secondary indicator data, total indicator data, and driving suggestions for the vehicle, the control data service platform sends the primary indicator data, secondary indicator data, total indicator data, and driving suggestions to the vehicle remote service provider's device; the control vehicle remote service provider then transmits the primary indicator data, secondary indicator data, total indicator data, and driving suggestions to a remote communication terminal for real-time display of the primary indicator data, secondary indicator data, total indicator data, and driving suggestions.

[0137] This application does not limit the remote communication terminal; for example, a TBOX can be used as a remote communication terminal. This application also does not limit the vehicle remote service provider; optionally, a TSP platform can be used as the vehicle remote service provider's equipment.

[0138] For example, the control data service platform sends primary indicator data, secondary indicator data, total indicator data, and driving suggestions for the vehicle to the TSP platform; the control TSP platform then transmits the primary indicator data, secondary indicator data, total indicator data, and driving suggestions for the vehicle to the TBOX.

[0139] After obtaining primary indicator data, secondary indicator data, total indicator data, and driving suggestions for the vehicle from the TBOX, the vehicle control system sends the primary indicator data, secondary indicator data, total indicator data, and driving suggestions for the vehicle to the vehicle's instrument control system for display.

[0140] This application does not limit the way the instrument control system displays secondary indicator data. For example, secondary indicator data can be displayed using a five-dimensional radar chart, as shown in the example below. Figure 4 As shown.

[0141] This application does not limit the way in which overall performance data and driving suggestions for vehicles are displayed. For example, overall performance data and driving suggestions for vehicles can be displayed in text.

[0142] This application embodiment does not limit the way the instrument control system displays primary indicator data. For example, a seven-dimensional radar chart can be used to display primary indicator data of driving safety data, such as the number of times a seatbelt was not worn, the degree of fatigue driving, the number of collision warnings, the number of emergency braking requests, the number of tire pressure warnings, the number of lane departure warnings, and the number of high-speed cornerings.

[0143] For example, a three-dimensional radar chart can be used to display primary indicator data such as the number of speed limit warnings, the degree of speeding, and the number of sudden accelerations in the driving speed data.

[0144] For example, a three-dimensional radar chart can be used to display primary indicator data of travel habit data, such as the duration of inactivity, the number of times driving during the reference time period, and the number of times driving under the reference weather conditions.

[0145] For example, the primary indicator data of fuel consumption index and the primary indicator data of total monthly mileage can be displayed using text.

[0146] In this embodiment, vehicle driving impact data is determined based on vehicle driving data and weather data during driving. This impact data includes at least one of driving safety data, driving speed data, average fuel consumption data, travel habit data, or mileage data. The control data service platform processes this impact data to obtain primary driving indicator data. Secondary and total indicator data are then determined based on the primary indicator data, and recommendations for vehicle driving are derived based on these data. This embodiment, by detecting multiple aspects of driving data, can identify the characteristics of driving trips and derive driving recommendations based on these characteristics, thereby improving driving safety.

[0147] To facilitate understanding, the above vehicle driving detection method is illustrated with the following example. For instance, a driver travels from point A to point B at 11 PM. The weather is snowy, and the time elapsed since the last vehicle start exceeds the fourth reference time but is less than the fifth reference time, indicating the vehicle is at level one (long-term inactivity). The driver fastens their seatbelt after getting into the vehicle and is at level one (fatigue). During the journey from point A to point B, one level one emergency braking request and one speed limit warning occur. At the time of the speed limit warning, the vehicle's speed exceeds the first reference percentage of the road's speed limit but is less than the second reference percentage, indicating level one (speeding). The vehicle's fuel consumption index for the month is less than the negative fourth reference percentage, indicating level one (fuel efficiency). The vehicle's total monthly mileage is greater than 0 and less than the first reference mileage, indicating level one (fuel efficiency).

[0148] For example, taking the second, sixth, thirteenth, fourteenth, eighteenth, nineteenth, twenty-fourth, twenty-fifth, and twenty-ninth reference values ​​all being 10, the calculation process for the driver's primary indicator data, secondary indicator data, and total indicator data for this trip includes: Based on the driver's departure time being 11 PM, meaning driving once within the reference time period, the primary indicator data for the number of drives during the reference time period is 100 minus 10, resulting in 90; based on the driver traveling in snowy weather, the primary indicator data for the number of drives in the reference weather is 100 minus 10, resulting in 90; based on the vehicle being a primary... If the vehicle remains inactive for an extended period, the primary indicator for inactivity duration is 100 minus 10, resulting in 90. Based on the driver's current state of first-reference fatigue, the primary indicator for fatigued driving is 100 minus 10, resulting in 90. Based on one first-reference emergency braking request during driving, the primary indicator for the number of emergency braking requests is 100 minus 10, resulting in 90. Based on one speed limit warning during driving, the primary indicator for the number of speed limit warnings is 100 minus 10, resulting in 90. Based on the vehicle exceeding the speed limit limit at the time of the warning, the primary indicator for the speeding level is 100 minus 10, resulting in 90. Based on the vehicle's fuel consumption index being first-reference fuel-efficient this month, the primary indicator for fuel consumption index is 100 plus 10, resulting in 110. Based on the vehicle's total monthly mileage being first-reference mileage, the primary indicator for total monthly mileage is 100 plus 10, resulting in 110.

[0149] Based on the primary weighting of 30% for the number of times a seatbelt was not worn, 15% for the degree of fatigued driving, 15% for the number of collision warnings, 10% for the number of emergency braking requests, 10% for the number of tire pressure warnings, 10% for the number of lane departure warnings, and 10% for the number of high-speed cornerings, the secondary indicator data for driving safety is the sum of 100 multiplied by 30%, 90 multiplied by 15%, 100 multiplied by 15%, 90 multiplied by 10%, 100 multiplied by 10%, 100 multiplied by 10%, and 100 multiplied by 10%, which equals 97.5.

[0150] Based on the primary weighting of the number of speed limit warnings (50%), the primary weighting of the degree of speeding (30%), and the primary weighting of the number of rapid accelerations (20%), the secondary index data of driving speed is the sum of 90 multiplied by 50%, 90 multiplied by 30%, and 100 multiplied by 20%, which is 92.

[0151] Based on the primary weighting of inactivity duration (60%), the primary weighting of driving frequency during reference time periods (20%), and the primary weighting of driving frequency during reference weather (20%), the secondary indicator data for travel habits is 90 multiplied by 60%, 90 multiplied by 20%, and 90 multiplied by 20%, which equals 90.

[0152] Based on the fact that the primary weight of the fuel consumption index in the average fuel consumption data is 1, the secondary index data of the average fuel consumption data is 110.

[0153] Since the primary weight of the monthly total mileage in the mileage data is also 1, the secondary indicator data of the mileage data is 110.

[0154] Based on the secondary weighting of driving safety data (40%), driving speed data (20%), average fuel consumption data (15%), travel habit data (15%), and mileage data (10%), the total index is calculated by multiplying 97.5 by 40%, 92 by 20%, 90 by 15%, 110 by 15%, and 110 by 10%, which equals 98.4.

[0155] After obtaining the overall indicator data, compare the overall indicator data with the reference overall indicator data. Taking the reference overall indicator data as an example, if the overall indicator data is greater than the reference overall indicator data, the suggestions for vehicle driving can include: The overall indicator data for vehicle driving is high, please continue to maintain it.

[0156] For example, taking a scenario where the reference driving safety secondary indicator data, reference driving speed secondary indicator data, reference average fuel consumption secondary indicator data, reference travel habits secondary indicator data, and reference mileage secondary indicator data are all 90, if the driving safety secondary indicator data is higher than the reference driving safety secondary indicator data, the suggestion for driving safety could be: The driving safety secondary indicator data is high; please maintain this level. If the driving speed secondary indicator data is higher than the reference driving speed secondary indicator data, the suggestion for driving speed could be: The driving speed secondary indicator data is high; please maintain this level. If the average fuel consumption secondary indicator data is higher than the reference average fuel consumption secondary indicator data, the suggestion for average fuel consumption could be: The average fuel consumption secondary indicator data is high; please maintain this level. If the travel habit secondary indicator data is equal to the reference travel habit secondary indicator data, the suggestion for travel habits could be: The driving safety secondary indicator data is low; please pay attention to driving safety. If the mileage secondary indicator data is higher than the reference mileage secondary indicator data, the suggestion for mileage could be: The mileage secondary indicator data is high; please maintain this level.

[0157] See Figure 5 This application provides a vehicle driving detection device, which includes:

[0158] The acquisition module 501 is used to acquire vehicle driving data and weather data during driving;

[0159] The first determining module 502 is used to determine the impact data of vehicle driving based on the vehicle's driving data and the weather data during driving. The impact data of vehicle driving includes at least one of driving safety data, driving speed data, average fuel consumption data, travel habit data, or driving mileage data.

[0160] The second determining module 503 is used to obtain primary indicator data of vehicle driving based on the impact data of vehicle driving, determine secondary indicator data and total indicator data based on the primary indicator data of vehicle driving, and determine driving suggestions for the vehicle based on the secondary indicator data and total indicator data.

[0161] In one possible implementation, the second determining module 503 is used to upload the impact data of vehicle driving to the data service platform, whereby the data service platform processes the impact data of vehicle driving to obtain primary indicator data of vehicle driving, determines secondary indicator data and total indicator data based on the primary indicator data of vehicle driving, and determines driving suggestions for the vehicle based on the secondary indicator data and total indicator data; and receives the primary indicator data, secondary indicator data, total indicator data and driving suggestions for the vehicle sent by the data service platform.

[0162] In one possible implementation, the second determining module 503 is used to control the remote communication terminal to send the impact data of vehicle driving to the vehicle remote service provider, the remote communication terminal being located on the vehicle; and to control the vehicle remote service provider to upload the impact data of vehicle driving to the data service platform.

[0163] In one possible implementation, the vehicle's driving data includes at least one of the following: number of times the seatbelt was not worn, degree of driver fatigue, number of collision warnings, number of emergency braking requests, number of tire pressure warnings, number of lane departure warnings, number of high-speed cornerings, number of speed limit warnings, vehicle speed, vehicle acceleration, degree of speeding, number of rapid accelerations, speed limit of the road, duration of inactivity, driving time period, average monthly fuel consumption per 100 kilometers, or total monthly mileage; the first determining module 502 is used to determine the number of times the seatbelt was not worn, degree of driver fatigue, etc. Driving safety data is determined based on the severity of collisions, the number of emergency braking requests, the number of tire pressure warnings, the number of lane departure warnings, and the number of high-speed cornerings; driving speed data is determined based on the number of speed limit warnings, the degree of speeding, and the number of rapid accelerations; travel habit data is determined based on the duration of inactivity, the number of times driving during a reference time period, and the number of times driving under reference weather conditions, with the number of times driving during a reference time period determined based on the driving time; average fuel consumption data is determined based on the fuel consumption index, which is based on the average monthly fuel consumption per 100 kilometers; and mileage data is determined based on the total monthly mileage.

[0164] In one possible implementation, the primary indicator data for vehicle driving includes: primary indicator data corresponding to the number of times seat belts were not worn, the degree of fatigued driving, the number of collision warnings, the number of emergency braking requests, the number of tire pressure warnings, the number of lane departure warnings, and the number of high-speed cornering in driving safety data; primary indicator data corresponding to the number of speed limit warnings, the degree of speeding, and the number of rapid accelerations in driving speed data; primary indicator data corresponding to the duration of inactivity, the number of times driving during the reference time period, and the number of times driving under the reference weather conditions in travel habit data; primary indicator data corresponding to the fuel consumption index in average fuel consumption data; and primary indicator data corresponding to the total monthly mileage in mileage data.

[0165] In one possible implementation, the second determining module 503 is used to determine secondary indicator data of driving safety data based on primary indicator data and primary weights corresponding to the number of times seat belts were not worn, the degree of fatigued driving, the number of collision alarms, the number of emergency braking requests, the number of tire pressure alarms, the number of lane departure alarms, and the number of high-speed cornerings in driving safety data; to determine secondary indicator data of driving speed data based on primary indicator data and primary weights corresponding to the number of speed limit alarms, the degree of speeding, and the number of rapid accelerations in driving speed data; to determine secondary indicator data of travel habit data based on primary indicator data and primary weights corresponding to the duration of inactivity, the number of times driving during the reference time period, and the number of times driving under the reference weather conditions in travel habit data; to determine secondary indicator data corresponding to average fuel consumption data based on primary indicator data and primary weights of fuel consumption index in average fuel consumption data; to determine secondary indicator data corresponding to mileage data based on primary indicator data and primary weights of monthly total mileage in mileage data; and to determine total indicator data based on secondary indicator data and secondary weights corresponding to driving safety data, driving speed data, average fuel consumption data, travel habit data, and mileage data in vehicle driving impact data.

[0166] In one possible implementation, the device further includes: a first control module for controlling the data service platform to send primary indicator data, secondary indicator data, total indicator data, and driving suggestions to the vehicle remote service provider; and a second control module for controlling the vehicle remote service provider to transmit the primary indicator data, secondary indicator data, total indicator data, and driving suggestions to the remote communication terminal, for the remote communication terminal to display the primary indicator data, secondary indicator data, total indicator data, and driving suggestions in real time.

[0167] This device determines the impact data on vehicle driving based on vehicle driving data and weather data during driving. The impact data includes at least one of the following: driving safety data, driving speed data, average fuel consumption data, travel habit data, or mileage data. The control data service platform processes this impact data to obtain primary driving indicator data. Secondary and total indicator data are then determined based on the primary indicator data, and recommendations for vehicle driving are derived based on these data. By detecting multiple aspects of driving data, this device can identify the characteristics of driving trips and, based on these characteristics, provide driving recommendations to improve driving safety.

[0168] It should be noted that the apparatus provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0169] Figure 6 This is a schematic diagram of vehicle driving detection communication provided in an embodiment of this application, wherein the vehicle control system 601 sends the impact data of vehicle driving to the remote communication terminal 602, the remote communication terminal 602 sends the impact data of vehicle driving to the vehicle remote service provider 603, and the vehicle remote service provider 603 sends the impact data of vehicle driving to the data service platform 604.

[0170] Figure 7 This is a schematic diagram of a server structure provided in an embodiment of this application. The server can vary significantly due to differences in configuration or performance. It may include one or more processors 701 and one or more memories 702. The one or more memories 702 store at least one computer program, which is loaded and executed by the one or more processors 701 to enable the server to implement the vehicle driving detection method provided in the various method embodiments described above. Of course, the server may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The server may also include other components for implementing device functions, which will not be elaborated upon here.

[0171] Figure 8 This is a schematic diagram of a vehicle driving detection device provided in an embodiment of this application. The device can be a terminal, such as an in-vehicle terminal, smartphone, tablet computer, media player, laptop computer, or desktop computer. The terminal may also be referred to as user equipment, portable terminal, laptop terminal, desktop terminal, or other names.

[0172] Typically, a terminal includes a processor 1501 and a memory 1502.

[0173] Processor 1501 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 1501 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 1501 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 1501 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the screen. In some embodiments, processor 1501 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0174] The memory 1502 may include one or more computer-readable storage media, which may be non-transitory. The memory 1502 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 1502 is used to store at least one instruction, which is executed by the processor 1501 to cause the terminal to implement the vehicle driving detection method provided in the method embodiments of this application.

[0175] In some embodiments, the terminal may also optionally include: a peripheral device interface 1503 and at least one peripheral device. The processor 1501, memory 1502, and peripheral device interface 1503 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 1503 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of: a radio frequency circuit 1504, a display screen 1505, a camera assembly 1506, an audio circuit 1507, and a power supply 1508.

[0176] Peripheral interface 1503 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 1501 and memory 1502. In some embodiments, processor 1501, memory 1502 and peripheral interface 1503 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 1501, memory 1502 and peripheral interface 1503 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0177] The radio frequency (RF) circuit 1504 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 1504 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 1504 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 1504 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 1504 can communicate with other terminals through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: metropolitan area networks (MANs), various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks (WLANs), and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 1504 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0178] Display screen 1505 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 1505 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 1501 for processing. In this case, display screen 1505 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, display screen 1505 can be a single screen, located on the front panel of the terminal; in other embodiments, display screen 1505 can be at least two screens, respectively located on different surfaces of the terminal or in a folded design; in other embodiments, display screen 1505 can be a flexible display screen, located on a curved or folded surface of the terminal. Furthermore, display screen 1505 can be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. Display screen 1505 can be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0179] The camera assembly 1506 is used to acquire images or videos. Optionally, the camera assembly 1506 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the terminal, and the rear-facing camera is located on the back of the terminal. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 1506 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0180] The audio circuit 1507 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 1501 for processing, or input to the radio frequency circuit 1504 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each positioned at a different location on the terminal. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 1501 or the radio frequency circuit 1504 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 1507 may also include a headphone jack.

[0181] Power supply 1508 is used to power the various components in the terminal. Power supply 1508 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 1508 includes a rechargeable battery, the rechargeable battery can support wired or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0182] In some embodiments, the terminal further includes one or more sensors 1509. The one or more sensors 1509 include, but are not limited to: an acceleration sensor 1510, a gyroscope sensor 1511, a pressure sensor 1512, an optical sensor 1513, and a proximity sensor 1514.

[0183] Accelerometer 1510 can detect the magnitude of acceleration along the three coordinate axes of a coordinate system established by the terminal. For example, accelerometer 1510 can be used to detect the components of gravitational acceleration along the three coordinate axes. Processor 1501 can control display screen 1505 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 1510. Accelerometer 1510 can also be used for games or for acquiring user motion data.

[0184] The gyroscope sensor 1511 can detect the terminal's orientation and rotation angle. The gyroscope sensor 1511 can work in conjunction with the accelerometer sensor 1510 to collect the user's 3D movements on the terminal. Based on the data collected by the gyroscope sensor 1511, the processor 1501 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0185] The pressure sensor 1512 can be disposed on the side bezel of the terminal and / or the lower layer of the display screen 1505. When the pressure sensor 1512 is disposed on the side bezel of the terminal, it can detect the user's grip signal on the terminal, and the processor 1501 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 1512. When the pressure sensor 1512 is disposed on the lower layer of the display screen 1505, the processor 1501 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 1505. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0186] Optical sensor 1513 is used to collect ambient light intensity. In one embodiment, processor 1501 can control the display brightness of display screen 1505 based on the ambient light intensity collected by optical sensor 1513. Specifically, when the ambient light intensity is high, the display brightness of display screen 1505 is increased; when the ambient light intensity is low, the display brightness of display screen 1505 is decreased. In another embodiment, processor 1501 can also dynamically adjust the shooting parameters of camera assembly 1506 based on the ambient light intensity collected by optical sensor 1513.

[0187] The proximity sensor 1514, also known as a distance sensor, is typically installed on the front panel of the terminal. The proximity sensor 1514 is used to detect the distance between the user and the front of the terminal. In one embodiment, when the proximity sensor 1514 detects that the distance between the user and the front of the terminal is gradually decreasing, the processor 1501 controls the display screen 1505 to switch from a screen-on state to a screen-off state; when the proximity sensor 1514 detects that the distance between the user and the front of the terminal is gradually increasing, the processor 1501 controls the display screen 1505 to switch from a screen-off state to a screen-on state.

[0188] Those skilled in the art will understand that Figure 8 The structure shown does not constitute a limitation on the terminal and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0189] In an exemplary embodiment, a computer device is also provided, comprising a processor and a memory storing at least one computer program. The at least one computer program is loaded and executed by one or more processors to enable the computer device to implement any of the aforementioned vehicle driving detection methods.

[0190] In an exemplary embodiment, a computer-readable storage medium is also provided, which stores at least one computer program, which is loaded and executed by a processor of a computer device to enable the computer to implement any of the above-described vehicle driving detection methods.

[0191] In one possible implementation, the aforementioned computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0192] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the vehicle driving detection methods described above.

[0193] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the vehicle driving data and weather data involved in this application were obtained with full authorization.

[0194] It should be understood that "multiple" as used in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0195] It should be noted that the terms "first," "second," etc. (if applicable) in the specification and claims of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0196] The above description is merely an exemplary embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for detecting vehicle driving, characterized in that, The method includes: Acquire vehicle driving data and weather data during driving; The impact data of vehicle driving is determined based on the vehicle's driving data and the weather data during driving. The impact data of vehicle driving includes at least one of driving safety data, driving speed data, average fuel consumption data, travel habit data, or driving mileage data. The impact data of vehicle driving is uploaded to a data service platform, which processes the impact data to obtain primary indicator data of vehicle driving. Based on the primary indicator data of vehicle driving, secondary indicator data and total indicator data are determined. Based on the secondary indicator data and the total indicator data, driving suggestions for the vehicle are determined. Receive the primary indicator data, the secondary indicator data, the total indicator data, and the driving suggestions for the vehicle sent by the data service platform.

2. The method according to claim 1, characterized in that, Uploading the vehicle driving impact data to the data service platform includes: The remote communication terminal, located on the vehicle, sends the driving impact data of the vehicle to a remote service provider. The remote service provider for the vehicle is controlled to upload the impact data of the vehicle's driving to the data service platform.

3. The method according to claim 1, characterized in that, The vehicle's driving data includes at least one of the following: number of times the seatbelt was not worn, degree of driver fatigue, number of collision warnings, number of emergency braking requests, number of tire pressure warnings, number of lane departure warnings, number of high-speed cornerings, number of speed limit warnings, vehicle speed, vehicle acceleration, degree of speeding, number of times of rapid acceleration, speed limit of the road, duration of inactivity, number of times driving with reference to weather conditions, driving time, and average monthly fuel consumption per 100 kilometers or total monthly mileage. The determination of vehicle driving impact data based on the vehicle's driving data and the weather data during driving includes: The driving safety data is determined based on the number of times the seatbelt was not worn, the degree of fatigued driving, the number of collision warnings, the number of emergency braking requests, the number of tire pressure warnings, the number of lane departure warnings, and the number of high-speed cornerings. The driving speed data is determined based on the number of speed limit warnings, the degree of speeding, and the number of rapid accelerations. The travel habit data is determined based on the duration of non-activation, the number of times driving during the reference time period, and the number of times driving during the reference weather, wherein the number of times driving during the reference time period is determined based on the driving time period; The average fuel consumption data is determined based on the fuel consumption index, which is determined based on the average monthly fuel consumption per 100 kilometers. The mileage data is determined based on the total monthly mileage.

4. The method according to claim 3, characterized in that, The primary indicator data for vehicle driving includes: the primary indicator data corresponding to the number of times the seat belt was not worn, the degree of fatigued driving, the number of collision warnings, the number of emergency braking requests, the number of tire pressure warnings, the number of lane departure warnings, and the number of high-speed cornerings, respectively, in the driving safety data. The number of speed limit warnings, the degree of speeding, and the number of rapid accelerations in the driving speed data correspond to the primary indicator data, respectively. The primary indicator data in the travel habit data, namely the duration of non-activation, the number of times driving during the reference time period, and the number of times driving under the reference weather conditions, respectively correspond to the following: The primary indicator data corresponding to the fuel consumption index in the average fuel consumption data; The primary indicator data corresponding to the total monthly mileage in the mileage data.

5. The method according to claim 4, characterized in that, The determination of secondary indicator data and total indicator data based on the primary indicator data of vehicle driving includes: The secondary indicator data of the driving safety data are determined based on the primary indicator data and primary weights corresponding to the number of times the seat belt was not worn, the degree of fatigued driving, the number of collision warnings, the number of emergency braking requests, the number of tire pressure warnings, the number of lane departure warnings, and the number of high-speed cornerings in the driving safety data. The secondary indicator data of the driving speed data is determined based on the primary indicator data and the primary weight corresponding to the number of speed limit warnings, the degree of speeding, and the number of rapid accelerations in the driving speed data, respectively. The secondary indicator data of the travel habit data are determined based on the primary indicator data and the primary weight corresponding to the duration of non-activation, the number of times driving during the reference time period, and the number of times driving under the reference weather conditions in the travel habit data. The secondary indicator data corresponding to the average fuel consumption data is determined based on the primary indicator data and the primary weight of the fuel consumption index in the average fuel consumption data. The secondary indicator data corresponding to the mileage data is determined based on the primary indicator data and the primary weight of the monthly total mileage in the mileage data. The total indicator data is determined based on the secondary indicator data and secondary weights corresponding to the driving safety data, driving speed data, average fuel consumption data, travel habit data, and driving mileage data in the vehicle driving impact data.

6. The method according to claim 2, characterized in that, The method further includes: The data service platform is controlled to send the primary indicator data, the secondary indicator data, the total indicator data, and suggestions for vehicle driving to the vehicle remote service provider; The remote service provider for the vehicle is controlled to transmit the primary indicator data, the secondary indicator data, the total indicator data, and the driving suggestions for the vehicle to the remote communication terminal, so that the remote communication terminal can display the primary indicator data, the secondary indicator data, the total indicator data, and the driving suggestions for the vehicle in real time.

7. A vehicle driving detection device, characterized in that, The device includes: The acquisition module is used to acquire vehicle driving data and weather data during driving; The first determining module is used to determine the impact data of vehicle driving based on the vehicle's driving data and the weather data during driving. The impact data of vehicle driving includes at least one of driving safety data, driving speed data, average fuel consumption data, travel habit data, or driving mileage data. The second determining module is used to upload the impact data of vehicle driving to a data service platform, whereby the data service platform processes the impact data of vehicle driving to obtain primary indicator data of vehicle driving, determines secondary indicator data and total indicator data based on the primary indicator data of vehicle driving, and determines driving suggestions for the vehicle based on the secondary indicator data and the total indicator data; and receives the primary indicator data, the secondary indicator data, the total indicator data, and the driving suggestions for the vehicle sent by the data service platform.

8. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor to enable the computer device to implement the vehicle driving detection method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor to enable the computer to implement the vehicle driving detection method as described in any one of claims 1 to 6.

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