Method for determining driving intensity of vehicle, computer device and readable storage medium

Abstract

The application discloses a driving intensity determination method of a vehicle, computer equipment and a readable storage medium, and the driving intensity determination method of the vehicle comprises the following steps: determining a time period and a sample size according to data statistical needs; acquiring TBOX data in a specific time period through Internet of Vehicles, wherein the TBOX data comprises but is not limited to annual driving mileage, load conditions, GPS information and the like; determining vehicle driving road condition composition and proportion according to the GPS information, and the determination method of the mileage proportion of different road conditions comprises: determining the mileage proportion of different road conditions by calculating the number of data points and the average speed of users in different road condition areas, or determining the mileage proportion of different road conditions by calculating the proportion of driving mileage of users in each road condition area in the total mileage; and determining the driving intensity of the vehicle through annual driving mileage, load conditions, road condition composition and proportion and the like. The annual driving mileage, load conditions, road condition composition and proportion and the like are determined based on the TBOX data, so as to determine the driving intensity of the vehicle.

Description

Technical Field

[0001] This invention relates to the technical field of methods for determining the driving intensity of a vehicle, and in particular to a method for determining the driving intensity of a vehicle, a computer device, and a readable storage medium. Background Technology

[0002] Fatigue durability is a crucial performance indicator for automobiles and must be met in vehicle design. The formulation of fatigue durability targets is closely related to the driving intensity of vehicle users. Determining the vehicle's driving intensity is of decisive significance for the development of automotive fatigue durability performance.

[0003] In related technologies, the driving intensity of a vehicle can be determined through actual user surveys.

[0004] First, analyze the market through sales and after-sales data to understand the main application scenarios and usage environments of automobiles, identify the main user groups (automobile usage) and their proportions, and grasp the overall sales volume and the sales proportion of each province and city nationwide. As needed, specify the vehicle driving time period for the users to be surveyed (e.g., a period of six months or one year). While ensuring the validity and sufficiency of the final sample data, determine the total number of users surveyed, the proportion and number of users in each province and city, and for each user group.

[0005] Then, design a reasonable user survey questionnaire, including but not limited to user groups (automobile usage), common load size, daily driving habits, frequently traveled road sections, road condition classification (such as highways, national and provincial roads, urban roads, rural roads, mountain roads, etc.) and their proportions, monthly / quarterly / annual average mileage and other user driving intensity data that determine vehicle fatigue durability.

[0006] Then, at car dealerships, target markets, or users' homes, questionnaires are distributed to a large number of users targeting the main user groups. The size of each data item in the questionnaire is determined and filled out by users through subjective evaluation.

[0007] Finally, the validity of the questionnaire was evaluated, valid data was selected, and the data was calculated to obtain the final required vehicle driving intensity data.

[0008] However, the above-mentioned vehicle driving intensity method is based on subjective user evaluation, resulting in poor data accuracy. Furthermore, it requires extensive research in different provinces and cities across the country, which is costly and time-consuming. Summary of the Invention

[0009] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention proposes a method for determining vehicle driving intensity, which uses TBOX data to determine data such as annual mileage, load conditions, road condition composition and proportion, thereby determining the vehicle driving intensity. Furthermore, TBOX data has high accuracy, and the results are calculated based on real user data, significantly reducing costs and time.

[0010] The present invention also proposes a computer device.

[0011] The present invention also proposes a computer-readable storage medium.

[0012] According to a first aspect of the present invention, a method for determining the driving intensity of a vehicle includes the following steps: determining the time period and sample size based on data statistics needs; acquiring TBOX data of a selected user within a specific time period through a vehicle network, wherein the TBOX data includes, but is not limited to, annual mileage, load conditions, and determining the composition and proportion of road conditions based on GPS information; the method for determining the proportion of mileage under different road conditions includes: determining the proportion of mileage under different road conditions by calculating the number of data points and average vehicle speed of the user in different road condition areas, or by calculating the proportion of the user's mileage in each road condition area to the total mileage; and determining the driving intensity of the vehicle using data such as annual mileage, load conditions, road condition composition and proportion.

[0013] The vehicle driving intensity determination method according to embodiments of the present invention determines the vehicle driving intensity based on TBOX data and the annual mileage, load conditions, road condition composition and proportion determined through processing. TBOX data has high accuracy, is calculated from real user data, and has small error. Furthermore, the function can be integrated into the Internet of Vehicles (IoV) using existing TBOX data to automatically calculate and analyze the final result, greatly reducing costs and time.

[0014] According to some embodiments of the present invention, the step of determining the time period and sample size according to data statistics needs further includes: determining the proportion of samples from each province based on the sales share of each province in the country, and obtaining the sample size of each province.

[0015] According to some embodiments of the present invention, the method for determining the annual mileage includes: obtaining the mileage within a specific time period through a vehicle network, wherein the annual mileage = annual period / specific time period * mileage within the specific time period, and thus obtaining the annual mileage.

[0016] According to some embodiments of the present invention, the method for determining the load condition includes: acquiring the user's load condition through a vehicle load sensor.

[0017] According to some embodiments of the present invention, the method for determining the load condition includes: by having the vehicle carry different loads and drive under different road conditions, and using data such as vehicle speed, acceleration, fuel consumption, throttle opening, and engine speed, a benchmarking method is used to determine the relationship between the load condition and the data such as vehicle speed, acceleration, fuel consumption, throttle opening, and engine speed, and an algorithm is formulated to determine the load.

[0018] According to some embodiments of the present invention, road conditions are classified into highways, national and provincial highways, urban roads, rural roads, mountain roads, etc.; or, road conditions are classified into highways, national and provincial highways, urban roads, rural roads, mountain roads, construction sites, etc.; and, the road condition area division method includes: dividing the national geographical location into different road condition type areas according to road conditions, and using road type information marked on the map within the area and information such as mountains, plains, and urban settlements in satellite images to assist in the division.

[0019] According to some embodiments of the present invention, the step of determining the proportion of mileage in different road conditions by calculating the proportion of the user's driving mileage in each road condition area to the total mileage further includes: taking each segment of TBOX data of the user in a specific time period, according to the location and mileage information such as longitude and latitude, starting from the starting position, sequentially determining the road condition area to which each data point in the TBOX data belongs, and recording the real-time mileage, until the ending position.

[0020] According to some embodiments of the present invention, the step of determining the road condition mileage ratio by calculating the number of data points and average vehicle speed of the user in different road condition areas further includes: taking all TBOX data of the user for a specific time period, determining the road condition area to which the location of the data point belongs according to location information such as longitude and latitude, and recording the real-time vehicle speed to obtain the road condition area and real-time vehicle speed to which all data points belong; calculating the number of data points in different road condition areas respectively, and averaging the corresponding real-time vehicle speeds to obtain the average vehicle speed in each road condition area; obtaining the mileage ratio of each road condition by (number of data points in different road conditions * average vehicle speed in different road conditions) / (number of data points in road condition a * average vehicle speed a + number of data points in road condition b * average vehicle speed b + number of data points in road condition c * average vehicle speed c + ... number of data points in road condition n * average vehicle speed n); where n is the number of road conditions.

[0021] A computer device according to a second aspect of the present invention includes: a processor, a memory, and a vehicle driving intensity determination program stored in the memory and executable on the processor, wherein the vehicle driving intensity determination program implements the vehicle driving intensity determination method when executed by the processor.

[0022] According to a third aspect of the present invention, a computer-readable storage medium stores a vehicle driving intensity determination program, which, when executed by a processor, implements the vehicle driving intensity determination method.

[0023] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0024] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0025] Figure 1 This is a flowchart of a method for determining the driving intensity of a vehicle according to an embodiment of the present invention;

[0026] Figure 2 This is a flowchart illustrating how, according to an embodiment of the present invention, a user determines the percentage of road condition mileage by calculating the number of data points and average vehicle speed in different road condition areas. Detailed Implementation

[0027] The embodiments of the present invention are described in detail below. The embodiments described with reference to the accompanying drawings are exemplary. The embodiments of the present invention are described in detail below.

[0028] The following is for reference. Figures 1-2 The present invention describes a method for determining the driving intensity of a vehicle according to an embodiment of the present invention. The present invention also provides a computer device having the above-described method for determining the driving intensity of a vehicle, and a computer-readable storage medium having the above-described method for determining the driving intensity of a vehicle.

[0029] The method for determining the driving intensity of a vehicle according to an embodiment of the present invention includes the following steps:

[0030] S01: Determine the time period and sample size based on the data statistics requirements;

[0031] S02: Through vehicle networking, acquire TBOX data for a selected user within a specific time period. TBOX data includes, but is not limited to, annual mileage, load conditions, and GPS information. Among these, data such as annual mileage, load conditions, road condition composition and percentage can be calculated from various types of data, including VIN, data time, longitude, latitude, altitude, vehicle speed, mileage, throttle opening, engine speed, engine instantaneous consumption rate, friction torque percentage, engine intake air mass flow rate, intake pressure signal, clutch switch status, and brake switch status.

[0032] Among them, the vehicle's driving intensity is related to the vehicle's annual mileage; that is, the more mileage a vehicle travels in a year, the greater its driving intensity.

[0033] Furthermore, the driving intensity of a vehicle is related to its load. That is, the heavier the vehicle is, the greater the load it bears, and the greater the driving intensity of the vehicle for the same mileage.

[0034] S03: Determine the composition and proportion of road conditions used by the vehicle based on GPS information. Furthermore, the vehicle's driving intensity is also related to the composition and proportion of road conditions. Specifically, the vehicle's driving intensity varies under different road conditions. For example, when road conditions are good and there are few surrounding vehicles, the vehicle's driving intensity is lower for the same distance traveled. Conversely, when road conditions are complex, there are many surrounding vehicles, and significant external interference, the vehicle's driving intensity is higher for the same distance traveled.

[0035] According to an optional embodiment of the present invention, the method for determining the proportion of mileage under different road conditions includes: determining the proportion of mileage under different road conditions by calculating the proportion of the user's mileage in each road condition area to the total mileage. Thus, when a vehicle forms a driving path within a certain time period, the mileage of that driving path within a certain road condition area is the mileage under that road condition. That is, the total mileage can be divided into mileage under different road condition areas, and then the mileage under each road condition is divided by the total mileage to obtain the proportion of mileage under each road condition.

[0036] According to another optional embodiment of the present invention, the method for determining the proportion of mileage under different road conditions includes: determining the proportion of mileage under different road conditions by calculating the number of data points and the average vehicle speed of the user in different road condition areas. The TBOX data collection interval is fixed, that is, TBOX data is collected once every certain period of time, so that multiple TBOX data points are displayed on the map, where each TBOX data point can represent a period. The multiple TBOX data points are divided into different road condition areas, and the average speed under different road condition areas is calculated. Since distance equals average speed multiplied by time, the mileage under different road conditions can be obtained. Then, the mileage under each road condition is divided by the total mileage to obtain the proportion of mileage under each road condition.

[0037] S04: The vehicle's driving intensity is determined by data such as annual mileage, load conditions, road condition composition and proportion. That is, the vehicle's driving intensity can be obtained by reasonably summarizing and aggregating data such as annual mileage, load conditions, and GPS information.

[0038] Therefore, based on vehicle network data and specific processing methods, information such as annual mileage, load conditions, and GPS information is obtained to determine the vehicle's driving intensity. TBOX data has high accuracy, as the results are calculated based on real user data, eliminating errors caused by human perception and subjective evaluation. Furthermore, existing TBOX data can be integrated into the vehicle network for automatic calculation and analysis to determine the final result, significantly reducing costs and time.

[0039] The steps for determining the time period and sample size, based on data statistics needs, include: determining the sample proportion for each province based on the sales share of each province nationwide, and thus obtaining the sample size for each province. Because different provinces have different geographical environments such as longitude, latitude, altitude, temperature, humidity, road slope, and oxygen content in the air, the driving intensity will differ across provinces even with the same annual mileage, load conditions, road condition composition, and proportions. Therefore, it is necessary to obtain the sample size for each province based on its sales share to more accurately determine the vehicle's driving intensity.

[0040] The method for determining annual mileage includes: obtaining mileage within a specific time period through vehicle networking; annual mileage = annual period / specific time period * mileage within the specific time period. The data collection period for TBOX data can be adjusted based on the driver's driving habits. For example, if the driver's vehicle usage is the same each month of the year, only 3-4 months of TBOX data are needed; that is, 3-4 months of TBOX data can represent the vehicle's annual usage. Alternatively, if the driver's vehicle usage varies each month of the year, then annual TBOX data is required. Next, since the annual mileage needs to be obtained, the mileage within the specific time period needs to be converted. For example, if 3 months of mileage are obtained, the annual mileage is equal to 12 months divided by 3 months, then multiplied by the mileage within 3 months.

[0041] According to an optional embodiment of the present invention, the method for determining the load condition includes: acquiring the user's load condition through a vehicle load sensor. Wherein, when a load sensor is installed on the vehicle, the vehicle load data in the TBOX data can be directly acquired.

[0042] According to another optional embodiment of the present invention, the method for determining the load condition includes: by having the vehicle carry different loads and drive under different road conditions, and using data such as vehicle speed, acceleration, fuel consumption, throttle opening, and engine speed, a benchmark method is used to determine the relationship between the load condition and the data such as vehicle speed, acceleration, fuel consumption, throttle opening, and engine speed, and an algorithm is formulated to determine the load. That is, when no load sensor is installed on the vehicle, the vehicle's load condition can be calculated based on data such as vehicle speed, acceleration, fuel consumption, throttle opening, and engine speed. In other words, the vehicle's load condition is closely related to data such as vehicle speed, acceleration, fuel consumption, throttle opening, and engine speed. When the vehicle's acceleration, fuel consumption, throttle opening, and engine speed are the same, a higher vehicle speed indicates a lower load. Similarly, when the vehicle's speed, fuel consumption, throttle opening, and engine speed are the same, a higher vehicle acceleration indicates a lower load.

[0043] Furthermore, road conditions can be categorized based on the vehicle's application scenario. Road conditions can be classified as highways, national and provincial roads, urban roads, rural roads, mountain roads, etc., or alternatively, as highways, national and provincial roads, urban roads, rural roads, mountain roads, construction sites, etc. For example, when the vehicle is a light truck, the road conditions can be categorized as "highway, national and provincial roads, urban roads, rural roads, mountain roads, etc." Similarly, when the vehicle is an engineering vehicle, the road conditions can be categorized as "highway, national and provincial roads, urban roads, rural roads, mountain roads, construction sites, etc."

[0044] Next, the road condition zoning method includes dividing the country's geographical location into different road condition types, and using information such as road type markings on maps and satellite imagery of mountains, plains, and urban settlements to assist in the zoning. In other words, based on map road information and satellite imagery of mountains, plains, and urban settlements, the area marked with "orange roads and green 'G' signs" is designated as the expressway zone; the area marked with "light orange roads and red 'G' signs" as national highways and yellow 'S' signs as provincial highways is designated as the national and provincial highway zone; the area with "crisscrossing roads, limited green space, and high building density" is designated as the urban road zone; the area with "sparse mountains, abundant farmland, sparse and straight roads, and scattered villages and settlements" is designated as the rural zone; and the area with "numerous mountains, dense forests, sparse and winding roads" is designated as the mountain road zone. The above zoning needs to be completed based on actual regional conditions and experience.

[0045] According to an optional embodiment of the present invention, for each segment of TBOX data within a specific time period of the user, based on location information such as longitude and latitude, and mileage information, starting from the starting position, the road condition area to which each data point in the TBOX data belongs is determined sequentially, and the real-time mileage is recorded until the ending position. That is, based on location information such as longitude and latitude, and mileage information, the path formed by multiple TBOX data points is the driving path, and the road condition area where a certain TBOX data point is located is determined, i.e., the vehicle is located in that road condition. Thus, multiple TBOX data points are divided into different road conditions, thereby obtaining the real-time mileage under a certain road condition area, and further obtaining the mileage percentage of each road condition.

[0046] According to another optional embodiment of the present invention, all TBOX data for a specific time period of the user are sequentially determined to be in the road condition area according to location information such as longitude and latitude, and the real-time vehicle speed is recorded to obtain the road condition area and real-time vehicle speed of all data points.

[0047] The number of data points in different road condition areas is calculated separately, and the average speed of all real-time vehicle speeds is averaged to obtain the average vehicle speed in each road condition area.

[0048] The percentage of mileage for each road condition is obtained by dividing the result by (number of data points for different road conditions * average speed for different road conditions) / (number of data points for road condition a * average speed a + number of data points for road condition b * average speed b + number of data points for road condition c * average speed c + ... + number of data points for road condition n * average speed n).

[0049] The electronic device according to an embodiment of the present invention may mainly include: a processor, a memory, and a vehicle driving intensity determination program stored in the memory and executable on the processor. When the vehicle driving intensity determination program is executed by the processor, it implements the vehicle driving intensity determination method as described above. Thus, the vehicle driving intensity determination program stored in the memory is applied to the processor. Under the processing of the processor, the vehicle driving intensity determination program can implement the control method described above. To reduce redundancy, it will not be described again here.

[0050] The electronic devices are integrated into the server. Each vehicle has a data collection tbox. The tboxes transmit data to each other and then use relevant programs or apps to determine the vehicle's driving intensity.

[0051] According to embodiments of the present invention, a computer-readable storage medium may mainly include: a vehicle driving intensity determination program stored on the computer-readable storage medium, wherein when the vehicle driving intensity determination program is executed by a processor, the vehicle driving intensity determination method as described above is implemented.

[0052] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0053] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example.

[0054] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for determining the driving intensity of a vehicle, characterized in that, Includes the following steps: Determine the time period and sample size based on the data statistics requirements; Through the Internet of Vehicles, TBOX data of selected users within a specific time period is obtained. TBOX data includes, but is not limited to, annual mileage, load conditions, and GPS information. The composition and proportion of road conditions used by vehicles are determined based on GPS information. Methods for determining the proportion of mileage for different road conditions include: All TBOX data for a specific time period of the user are used to determine the road condition area to which each data point belongs according to its longitude and latitude location information, and the real-time vehicle speed is recorded to obtain the road condition area and real-time vehicle speed of all data points. The number of data points in different road condition areas is calculated separately, and the average speed of all real-time vehicle speeds is averaged to obtain the average vehicle speed in each road condition area. The percentage of mileage for each road condition is obtained by (number of data points for different road conditions × average speed for different road conditions) / (number of data points for road condition a × average speed a + number of data points for road condition b × average speed b + number of data points for road condition c × average speed c + ... number of data points for road condition n × average speed n). Where n represents the number of road conditions.

2. The method for determining the driving intensity of a vehicle according to claim 1, characterized in that, The steps for determining the time period and sample size based on data statistical needs also include: Based on the sales share of each province in the country, the proportion of samples in each province is determined, and the sample size of each province is obtained.

3. The method for determining the driving intensity of a vehicle according to claim 1, characterized in that, The method for determining the annual mileage includes: obtaining the mileage within a specific time period through the Internet of Vehicles, and the annual mileage = (annual period / specific time period) × mileage within the specific time period to obtain the annual mileage.

4. The method for determining the driving intensity of a vehicle according to claim 1, characterized in that, The method for determining the load condition includes: The user's load information is obtained through vehicle load sensors.

5. The method for determining the driving intensity of a vehicle according to claim 1, characterized in that, The method for determining the load condition includes: By testing vehicles under different loads and road conditions, and using data on vehicle speed, acceleration, fuel consumption, throttle opening, and engine speed, an algorithm is developed to determine the relationship between load conditions and these data, and to determine the load.

6. The method for determining the driving intensity of a vehicle according to claim 1, characterized in that, Based on the application scenarios of automobiles, road conditions can be classified into highways, national and provincial roads, urban roads, rural roads, and mountain roads. or, Road conditions can be categorized as highways, national and provincial roads, urban roads, rural roads, mountain roads, and construction sites; as well as, Methods for dividing road condition zones include: The country's geographical location is divided into regions with different road conditions, and the division is further aided by information on road type marked on the map within the region and information on mountains, plains, and urban settlements in satellite images.

7. An electronic device, characterized in that, include: A processor, a memory, and a vehicle driving intensity determination program stored in the memory and executable on the processor, wherein the vehicle driving intensity determination program, when executed by the processor, implements the vehicle driving intensity determination method as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a vehicle driving intensity determination program, which, when executed by a processor, implements the vehicle driving intensity determination method as described in any one of claims 1-6.

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