A vehicle risk warning method and system based on big data

Abstract

The present invention relates to the field of big data technology, and specifically discloses a vehicle risk warning method and system based on big data. The present invention obtains the road friction coefficient based on the road roughness value and the road inclination, and then obtains the braking error influence index, and then obtains the effective braking distance based on the initial speed of the vehicle, the road friction coefficient and the preset gravity acceleration, and then obtains the braking force based on the initial speed of the vehicle, the mass of the vehicle, the braking error influence index and the effective braking distance, and prompts the vehicle risk based on the braking force. In this way, through intuitive indicator light prompts, and different colors and signals correspond to different braking force conditions, the driver can quickly understand the risk status, which helps them adjust the operation in time and reduce the risk of accidents. At the same time, through the above steps, the driver can be intuitively told how much braking force is needed to meet the current braking distance, thereby avoiding the braking risk of the vehicle.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a vehicle risk warning method and system based on big data. Background Art

[0002] As the number of cars increases, driving risks also increase. Vehicle risk warnings use technical means, such as sensors or historical data extracted from big data, to identify and assess various potential hazards that a vehicle may encounter during driving, and issue warnings or recommended measures to the driver in a timely manner. Its purpose is to help drivers understand and respond to safety hazards in advance, thereby reducing the occurrence of traffic accidents and improving road safety.

[0003] Existing vehicle risk warnings can usually only judge the distance between vehicles based on sensors, and then judge the risk based on the distance between vehicles. When the vehicle prompts the driver, it can only remind the driver of the risk, but cannot intuitively tell the driver how much braking force is needed to meet the current braking distance. Therefore, a vehicle risk warning method and system based on big data is needed to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to provide a vehicle risk warning method and system based on big data to solve the technical problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A vehicle risk warning method based on big data, comprising:

[0007] Acquiring vehicle driving information, wherein the vehicle driving information includes road surface information and vehicle braking information;

[0008] Acquiring the mass of the vehicle and the initial speed of the vehicle according to the vehicle braking information;

[0009] Acquiring a road surface roughness value and a road surface inclination according to the road surface information, and acquiring a road surface friction coefficient according to the road surface roughness value and the road surface inclination;

[0010] Acquiring vehicle maintenance information based on big data, acquiring a plurality of brake pad surface wear values according to the vehicle maintenance information, and acquiring a braking error impact index according to the plurality of brake pad surface wear values and a preset replacement cycle;

[0011] Obtaining an effective braking distance based on the initial speed of the vehicle, the road friction coefficient, and a preset gravitational acceleration;

[0012] Obtaining a braking force according to the initial speed of the vehicle, the mass of the vehicle, the braking error influence index, and the effective braking distance;

[0013] The vehicle risk is prompted according to the braking force.

[0014] Preferably, the step of obtaining the road surface friction coefficient according to the road surface roughness value and the road surface inclination includes:

[0015] Acquiring image information according to the road surface information, and performing grayscale processing on the image information to obtain road surface grayscale image information;

[0016] Performing standard pixel unit grid segmentation on the road surface grayscale image information to obtain multiple pixel grids;

[0017] Obtaining corresponding RGB pixel values according to the pixel grid, wherein the RGB pixel values include R pixel values, G pixel values, and B pixel values;

[0018] The pixel grid grayscale value is obtained according to the R pixel value, the G pixel value, and the B pixel value, wherein the calculation formula is:

[0019] ;

[0020] in, Represents the grayscale value of the pixel grid, R represents the R pixel value, G represents the G pixel value, and B represents the B pixel value;

[0021] Repeating the steps of obtaining corresponding RGB pixel values according to the pixel grid, wherein the RGB pixel values include R pixel values, G pixel values, and B pixel values, to obtaining pixel grid grayscale values according to the R pixel values, the G pixel values, and the B pixel values, so as to traverse all pixel grids and obtain multiple pixel grid grayscale values;

[0022] Comparing the plurality of pixel grid grayscale values with a preset grayscale value-roughness value table to obtain a plurality of first road surface roughness values, and averaging the plurality of first road surface roughness values to obtain an average road surface roughness value, and using the average road surface roughness value as the road surface roughness value;

[0023] obtaining a road surface inclination angle according to the road surface inclination;

[0024] The road friction coefficient is calculated according to the road roughness value and the road inclination angle, wherein the calculation formula is:

[0025] ;

[0026] in, represents the road friction coefficient, Indicates the road roughness value, Indicates the road inclination angle.

[0027] Preferably, the step of obtaining a braking error influence index according to a plurality of brake pad surface wear values and a preset replacement cycle comprises:

[0028] Obtain the starting time of use and replacement time of the brake pads according to the preset replacement cycle;

[0029] The average wear degree value of the brake pad surface is calculated based on the plurality of brake pad surface wear values, the starting time of use and the replacement time, wherein the calculation formula is:

[0030] ;

[0031] in, Indicates the average wear degree of the brake pad surface. Indicates the start time of use. Indicates the replacement time. represents the brake pad surface wear value, n represents the number of brake pad surface wear values, where i=1, 2, 3...n;

[0032] The standard brake pad surface wear value is calculated based on the plurality of brake pad surface wear values and the average brake pad surface wear degree value, wherein the calculation formula is:

[0033] ;

[0034] in, Indicates the standard brake pad surface wear value, Indicates the surface wear value of the oth brake pad, Indicates the number of brake pad surface wear values, o indicates the serial number of the brake pad surface wear value, where o=1, 2, 3...k, Indicates the average wear degree of the brake pad surface;

[0035] The brake pad surface wear influence coefficient is calculated based on the standard brake pad surface wear value and the average brake pad surface wear degree value, wherein the calculation formula is:

[0036] ;

[0037] in, Indicates the brake pad surface wear influence coefficient, Indicates the average wear degree of the brake pad surface. Indicates the standard brake pad surface wear value;

[0038] The brake pad surface wear influence coefficient is used as the braking error influence index.

[0039] Preferably, the step of obtaining the effective braking distance according to the initial speed of the vehicle, the road friction coefficient and a preset gravitational acceleration includes:

[0040] The effective braking distance is obtained according to the initial speed of the vehicle, the road friction coefficient and the preset gravity acceleration, wherein the calculation formula is:

[0041] ;

[0042] in, Indicates the effective braking distance, represents the road friction coefficient, represents the initial speed of the vehicle, Indicates the preset gravitational acceleration.

[0043] Preferably, the step of obtaining the braking force according to the initial speed of the vehicle, the mass of the vehicle, the braking error influence index and the effective braking distance includes:

[0044] Acquire historical braking force and historical driver pedaling force based on the big data, and calculate braking efficiency based on the historical braking force and historical driver pedaling force;

[0045] The braking force is obtained according to the initial speed of the vehicle, the mass of the vehicle, the braking efficiency, the braking error influence index and the effective braking distance, wherein the calculation formula is:

[0046] ;

[0047] in, Indicates the braking force of the brake. Indicates the mass of the vehicle, represents the initial speed of the vehicle, Indicates the braking efficiency, Indicates the braking error influence index.

[0048] Preferably, the step of prompting vehicle risk according to the braking force includes:

[0049] Determining whether the braking force meets a preset threshold range;

[0050] When the braking force does not meet the preset threshold range and is less than the minimum value of the preset threshold range, it is determined that the driver's foot is pressing the brake pedal with less force and a first reminder signal is generated. According to the first reminder signal, a corresponding preset yellow indicator light is turned on to provide a risk warning;

[0051] When the braking force meets the preset threshold range, it is determined that the driver's foot braking force is normal and a second reminder signal is generated. According to the second reminder signal, the corresponding preset green indicator light is turned on to provide a safety reminder;

[0052] When the braking force does not meet the preset threshold range and is greater than the maximum value of the preset threshold range, it is determined that the driver's foot is stepping on the brake pedal with great force and a third reminder signal is generated. According to the third reminder signal, the corresponding preset red indicator light is turned on to issue a risk warning.

[0053] This application also provides a vehicle risk warning system based on big data, including:

[0054] A first acquisition module is used to acquire vehicle driving information, wherein the vehicle driving information includes road surface information and vehicle braking information;

[0055] A second acquisition module is used to acquire the mass of the vehicle and the initial speed of the vehicle according to the vehicle braking information;

[0056] a third acquisition module, configured to acquire a road surface roughness value and a road surface inclination according to the road surface information, and acquire a road surface friction coefficient according to the road surface roughness value and the road surface inclination;

[0057] a fourth acquisition module, configured to acquire vehicle maintenance information based on the big data, acquire a plurality of brake pad surface wear values according to the vehicle maintenance information, and acquire a braking error influence index according to the plurality of brake pad surface wear values and a preset replacement cycle;

[0058] a fifth acquisition module, configured to acquire an effective braking distance according to an initial speed of the vehicle, the road friction coefficient, and a preset gravitational acceleration;

[0059] a sixth acquisition module, configured to acquire a braking force according to the initial speed of the vehicle, the mass of the vehicle, the braking error influence index, and the effective braking distance;

[0060] The first prompt module is used to prompt the vehicle risk according to the braking force.

[0061] Preferably, the third acquisition module includes:

[0062] a first acquiring unit, configured to acquire image information according to the road surface information, and perform grayscale processing on the image information to obtain road surface grayscale image information;

[0063] The first segmentation unit is used to perform standard pixel unit grid segmentation on the road surface grayscale image information to obtain a plurality of pixel grids;

[0064] A second acquiring unit, configured to acquire corresponding RGB pixel values according to the pixel grid, wherein the RGB pixel values include R pixel values, G pixel values, and B pixel values;

[0065] A first calculation unit is configured to obtain a pixel grid grayscale value according to the R pixel value, the G pixel value, and the B pixel value, wherein the calculation formula is:

[0066] ;

[0067] in, Represents the grayscale value of the pixel grid, R represents the R pixel value, G represents the G pixel value, and B represents the B pixel value;

[0068] a third acquiring unit, configured to repeat the steps of acquiring corresponding RGB pixel values according to the pixel grid, wherein the RGB pixel values include R pixel values, G pixel values, and B pixel values, to acquiring a pixel grid grayscale value according to the R pixel value, the G pixel value, and the B pixel value, so as to traverse all pixel grids and obtain a plurality of pixel grid grayscale values;

[0069] a first comparison unit, configured to compare the grayscale values of the plurality of pixel grids with a preset grayscale value-roughness value table to obtain a plurality of first road surface roughness values, and average the plurality of first road surface roughness values to obtain an average road surface roughness value, and use the average road surface roughness value as the road surface roughness value;

[0070] a fourth acquiring unit, configured to acquire a road surface inclination angle according to the road surface inclination;

[0071] The second calculation unit is used to calculate the road friction coefficient according to the road roughness value and the road inclination angle, wherein the calculation formula is:

[0072] ;

[0073] in, represents the road friction coefficient, Indicates the road roughness value, Indicates the road inclination angle.

[0074] The present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0075] The present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0076] The beneficial effects of the present application are as follows: the present invention obtains road surface information and vehicle braking information from vehicle driving information, as well as vehicle maintenance information based on big data, and comprehensively considers multiple factors affecting vehicle braking, such as road surface roughness, inclination, and brake pad wear, thereby enabling a more comprehensive and accurate assessment of vehicle risk. The present invention then obtains road surface roughness and road surface inclination based on the road surface information, and obtains a road friction coefficient based on the road surface roughness and road surface inclination. Vehicle maintenance information is then obtained based on big data, and multiple brake pad surface wear values are obtained based on the vehicle maintenance information. A braking error impact index is obtained based on the multiple brake pad surface wear values and a preset replacement cycle. An effective braking distance is then obtained based on the vehicle's initial speed, the road surface friction coefficient, and a preset gravitational acceleration. Braking force is then obtained based on the vehicle's initial speed, the vehicle's mass, the braking error impact index, and the effective braking distance. Finally, vehicle risk is indicated based on the braking force. This is achieved through intuitive indicator lights (yellow, green, and red), with different colors and signals corresponding to different braking force conditions, allowing drivers to quickly understand risk status, helping them to adjust their operations in a timely manner and reduce accident risks. At the same time, the above steps can intuitively tell the driver how much braking force is needed to meet the current braking distance, thereby avoiding braking risks of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is a schematic diagram of a method flow chart according to an embodiment of the present application.

[0078] Figure 2 This is a schematic diagram of the system structure of an embodiment of the present application.

[0079] Figure 3 This is a schematic diagram of the internal structure of a computer device according to an embodiment of the present application.

[0080] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0081] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0082] like Figure 1-Figure 3 As shown, the present application provides a vehicle risk warning method based on big data, including:

[0083] S1. Acquire vehicle driving information, wherein the vehicle driving information includes road surface information and vehicle braking information;

[0084] S2. acquiring the mass and initial speed of the vehicle according to the vehicle braking information;

[0085] S3. Obtaining a road surface roughness value and a road surface inclination according to the road surface information, and obtaining a road surface friction coefficient according to the road surface roughness value and the road surface inclination;

[0086] S4. Obtaining vehicle maintenance information based on the big data, obtaining a plurality of brake pad surface wear values according to the vehicle maintenance information, and obtaining a braking error impact index according to the plurality of brake pad surface wear values and a preset replacement cycle;

[0087] S5. Obtaining an effective braking distance based on the initial speed of the vehicle, the road friction coefficient, and a preset gravitational acceleration;

[0088] S6. Obtaining a braking force according to the initial speed of the vehicle, the mass of the vehicle, the braking error influence index, and the effective braking distance;

[0089] S7. Prompt vehicle risks based on the braking force.

[0090] As described in the above steps S1-S7, existing vehicle risk prompts can usually only judge the vehicle distance based on sensors, and then judge the risk based on the vehicle distance. When the vehicle prompts the driver, it can only remind the driver of the risk, but cannot intuitively tell the driver how much braking force is needed to meet the current braking distance. At the same time, if the driver suddenly steps on the brake pedal, there will be a certain risk of rollover (sudden braking will cause the vehicle to decelerate rapidly. During emergency braking, the front of the vehicle sinks due to deceleration, and the rear will rise relatively. This transfer of the front and rear center of gravity may cause the vehicle to lose balance, especially when driving at high speeds. The front wheels are subjected to a large braking force, which increases the risk of rollover. Especially in poor road conditions or high speeds, this risk will be more obvious). Therefore, the present invention first obtains vehicle driving information, where the vehicle driving information includes road information and vehicle braking information. In this way, key information during vehicle driving is comprehensively collected to provide basic data for subsequent accurate assessment of vehicle risks. Road surface information reflects the impact of road conditions on vehicle driving, while vehicle braking information is directly related to vehicle deceleration performance and safety control. The vehicle's mass and initial speed are then derived from this braking information. These data are crucial for calculating braking parameters. Mass affects vehicle inertia, while initial speed determines the vehicle's motion prior to braking. These data are crucial for subsequent calculations of braking distance, braking force, and other parameters, and are essential for accurately assessing vehicle braking performance. Road surface conditions are crucial for vehicle safety, as varying road surface roughness and inclination can affect vehicle braking performance. Therefore, the road surface roughness value and road surface inclination can be derived from the road surface information, and the road surface friction coefficient can be derived from these values. The road surface roughness value and road surface inclination reflect the actual road surface conditions. The roughness value affects tire-road friction, while the inclination affects the vehicle's gravity component, which in turn affects the vehicle's braking performance. By calculating the road surface friction coefficient, the impact of road surface conditions on vehicle braking can be quantified, providing key parameters for accurately calculating braking distance and other parameters. Brake pads gradually wear during vehicle use, a key factor affecting braking performance. Considering brake pad wear can more accurately reflect the vehicle's current braking capacity, avoiding inaccurate risk warnings caused by ignoring this factor, thereby better ensuring driving safety. Therefore, vehicle maintenance information is acquired based on big data, and multiple brake pad surface wear values are obtained based on this vehicle maintenance information. Furthermore, a brake error impact index is derived based on these multiple brake pad surface wear values and a preset replacement cycle. Brake pad wear is directly related to the vehicle's braking performance. By acquiring maintenance information from big data and calculating wear values and the brake error impact index, changes in braking performance due to vehicle use can be taken into account.Severely worn brake pads increase braking distance. The Braking Error Impact Index (BDI) quantifies this effect, making risk warnings more comprehensive and accurate. Vehicle maintenance information acquired from big data is filtered using a clustering model within the big data model. This filtering process uses the existing vehicle information as the centroid and the remaining unfiltered data as samples, aligning the samples with the centroid. This allows for rapid screening of multiple brake pad surface wear values. The effective braking distance is then calculated based on the vehicle's initial speed, the road friction coefficient, and a preset gravitational acceleration. Based on physical principles, the theoretical braking distance is calculated under the current road conditions and initial vehicle speed. This is a key indicator for assessing whether a vehicle can stop within a safe distance. This indicator allows drivers to understand the distance required to completely stop the vehicle under current conditions, enabling them to make informed driving decisions. Braking force is then calculated based on the vehicle's initial speed, mass, the BDI, and the effective braking distance. The required braking force to achieve a safe braking distance is then calculated, providing a specific operational reference for the driver. The driver can adjust the braking force based on this prompt, so that the vehicle can brake more effectively under different road conditions and vehicle states, reducing the risk of accidents caused by improper braking. Finally, the vehicle risk is prompted based on the braking force. In this way, the driver can quickly understand the risk status of the current braking operation through the braking force prompt. At the same time, the driver can know whether his braking operation meets the safety requirements without complex calculations, which helps to adjust the operation in time and reduce the risk of accidents.

[0091] In one embodiment, the step S3 of obtaining the road surface friction coefficient according to the road surface roughness value and the road surface inclination includes:

[0092] S301, acquiring image information according to the road surface information, and performing grayscale processing on the image information to obtain road surface grayscale image information;

[0093] S302, performing standard pixel unit grid segmentation on the road surface grayscale image information to obtain multiple pixel grids;

[0094] S303, acquiring corresponding RGB pixel values according to the pixel grid, wherein the RGB pixel values include R pixel values, G pixel values, and B pixel values;

[0095] S304: Obtain a pixel grid grayscale value according to the R pixel value, the G pixel value, and the B pixel value, wherein the calculation formula is:

[0096] ;

[0097] in, Represents the grayscale value of the pixel grid, R represents the R pixel value, G represents the G pixel value, and B represents the B pixel value;

[0098] S305: Repeat the steps of obtaining corresponding RGB pixel values according to the pixel grid, where the RGB pixel value includes an R pixel value, a G pixel value, and a B pixel value, to obtaining a pixel grid grayscale value according to the R pixel value, the G pixel value, and the B pixel value, so as to traverse all pixel grids and obtain multiple pixel grid grayscale values.

[0099] S306: Compare the grayscale values of the plurality of pixel grids with a preset grayscale value-roughness value table to obtain a plurality of first road surface roughness values, average the plurality of first road surface roughness values to obtain an average road surface roughness value, and use the average road surface roughness value as the road surface roughness value;

[0100] S307, obtaining a road surface inclination angle according to the road surface inclination;

[0101] S308: Calculate the road friction coefficient based on the road roughness value and the road inclination angle, wherein the calculation formula is:

[0102] ;

[0103] in, represents the road friction coefficient, Indicates the road roughness value, Indicates the road inclination angle.

[0104] As described in steps S301-S308 above, the present invention obtains image information based on the road surface information and performs grayscale processing on the image information to obtain road surface grayscale image information. After obtaining the road surface information, the image data is often complex and contains a large amount of redundant information. Grayscale processing can simplify the data structure without losing key information (road surface texture and brightness characteristics), making subsequent image-based analysis more efficient. Grayscale images are also easier to manipulate and calculate in many image processing algorithms, providing a more suitable data format for subsequent steps. Image information is extracted from the road surface information, and converting color images into grayscale images reduces the image data volume while highlighting the image's texture and brightness information, facilitating subsequent processing. Grayscale images contain only brightness information, simplifying the calculation process. Brightness information is also correlated with characteristics such as road surface roughness, providing essential data for accurate analysis of road conditions. Next, the road surface grayscale image information is segmented using a standard pixel-by-pixel grid to produce multiple pixel grids. This divides the image into multiple small pixel grids, facilitating local analysis of the image. Each pixel grid can be considered a small area of the road surface. By analyzing these small areas, we can gain a more detailed understanding of the overall characteristics of the road surface, such as the variation in roughness in different areas. The corresponding RGB pixel values are then obtained based on the pixel grid. RGB pixel values include R, G, and B pixel values. The RGB pixel values obtained for each pixel grid reflect the brightness information of the area in the three color channels of red, green, and blue. Although the image has been grayscaled, the original RGB values may still be useful in certain calculations and feature extraction. For example, they may be useful in assisting color-based road condition assessments or in comparisons with other image data. Converting RGB values to grayscale is a common operation in image processing, but different conversion methods can affect the accuracy of the grayscale values. By using a weighted average formula based on the visual characteristics of the human eye, a grayscale image that is closer to the actual perception of the human eye can be obtained. This is very important for road condition assessment based on visual information and can improve the accuracy and reliability of subsequent analysis. Therefore, the pixel grid grayscale value is obtained based on the R pixel value, the G pixel value, and the B pixel value, and the RGB pixel value is converted into a grayscale value using a specific weighted calculation formula. This formula is based on the weights determined by the human eye's sensitivity to different colors and can more accurately reflect the brightness perceived by the human eye.The grayscale values obtained in this way are more consistent with actual visual perception and are more accurate and reliable when subsequently analyzing features such as road roughness based on grayscale values. The steps of obtaining corresponding RGB pixel values based on the pixel grid, where the RGB pixel values include R pixel values, G pixel values, and B pixel values, to obtaining pixel grid grayscale values based on the R pixel values, the G pixel values, and the B pixel values, are then repeated to traverse all pixel grids and obtain multiple pixel grid grayscale values. In order to accurately assess the overall roughness of the road surface, each part of the road surface image needs to be analyzed. Traversing all pixel grids is a necessary means to obtain complete road surface information. Only in this way can a comprehensive and accurate grayscale value distribution be obtained, and then the road surface roughness value can be accurately calculated based on this data, avoiding misjudgment of road conditions due to local analysis. At the same time, by traversing all pixel grids, the grayscale value of each small area of the entire road surface image is obtained, thereby comprehensively understanding the distribution of brightness of the road surface image. These grayscale values constitute a grayscale feature map of the road surface image, reflecting information such as the differences in road roughness at different locations. This provides sufficient data samples for accurately calculating the road roughness value. The grayscale values of the pixel grids are then compared with a preset grayscale value-roughness value table to obtain multiple first road roughness values. These first road roughness values are then averaged to obtain an average road roughness value, which is used as the road roughness value. By comparing the grayscale values of the pixel grids with the preset grayscale value-roughness value table, the grayscale values of the pixel grids are converted to corresponding road roughness values, achieving a quantitative conversion from image data to the actual road roughness. Averaging the multiple first road roughness values reduces roughness value deviations caused by localized abnormal grayscale values (such as image noise or localized special textures), making the resulting road roughness value more representative of the average roughness of the entire road surface and improving data stability and reliability. The road inclination angle is then obtained based on the road inclination, and the road inclination angle is converted to the road tilt angle, a parameter that is more convenient for use in physical calculations. The road surface inclination angle needs to be considered when calculating the road surface friction coefficient, because the inclination angle affects the components of the vehicle's gravity in the directions parallel and perpendicular to the road surface, which in turn affects the vehicle's braking effect. Accurately obtaining the road surface inclination angle can more accurately calculate the road surface friction coefficient, thereby more accurately assessing the vehicle's braking risk on inclined roads. Finally, the road surface friction coefficient is calculated based on the road surface roughness value and road surface inclination angle. In this way, the road surface friction coefficient is calculated using a specific formula using the road surface roughness value and inclination angle. This coefficient comprehensively considers the impact of the road surface roughness and inclination condition on the friction between the vehicle tire and the road surface, and is a key indicator for measuring road braking conditions. An accurate road surface friction coefficient can provide an important basis for subsequent calculations of braking distance, braking force, etc., making risk assessment more accurate and scientific.

[0105] In one embodiment, the step S4 of obtaining the brake error influence index according to the plurality of brake pad surface wear values and the preset replacement cycle includes:

[0106] S401, obtaining the starting time of use and the replacement time of the brake pad according to a preset replacement cycle;

[0107] S402: Calculate the average brake pad surface wear value based on the plurality of brake pad surface wear values, the starting time of use, and the replacement time, wherein the calculation formula is:

[0108] ;

[0109] in, Indicates the average wear degree of the brake pad surface. Indicates the start time of use. Indicates the replacement time. represents the brake pad surface wear value, n represents the number of brake pad surface wear values, where i=1, 2, 3...n;

[0110] S403, calculating a standard brake pad surface wear value based on the plurality of brake pad surface wear values and the average brake pad surface wear degree value, wherein the calculation formula is:

[0111] ;

[0112] in, Indicates the standard brake pad surface wear value, Indicates the surface wear value of the oth brake pad, Indicates the number of brake pad surface wear values, o indicates the serial number of the brake pad surface wear value, where o=1, 2, 3...k, Indicates the average wear degree of the brake pad surface;

[0113] S404. Calculate the brake pad surface wear influence coefficient based on the standard brake pad surface wear value and the brake pad surface average wear degree value, wherein the calculation formula is:

[0114] ;

[0115] in, Indicates the brake pad surface wear influence coefficient, Indicates the average wear degree of the brake pad surface. Indicates the standard brake pad surface wear value;

[0116] S405: Using the brake pad surface wear influence coefficient as a braking error influence index.

[0117] As described in steps S401-S405 above, the present invention first obtains the brake pad's initial use time and replacement time based on a preset replacement cycle. This clarifies the brake pad's usage time range, which serves as the temporal basis for evaluating brake pad wear. The initial use time and replacement time define the time period within the brake pad's entire usage cycle, providing a time reference for subsequent wear calculations and facilitating accurate analysis of brake pad wear at different stages of use. The average brake pad surface wear value is then calculated based on the multiple brake pad surface wear values, the initial use time, and the replacement time. This average wear value is calculated by comprehensively considering the brake pad's wear values throughout its entire usage cycle and its usage time. This value reflects the average wear rate of the brake pad over a period of time, providing a macroscopic understanding of the overall wear of the brake pad, rather than simply the wear state at a specific moment. Furthermore, the average wear value can be compared with the standard wear of other vehicles or similar brake pads to determine whether the current brake pad wear is normal or whether there is abnormal wear (such as excessively rapid or slow wear). This provides an important quantitative indicator for further evaluating changes in braking performance. A standard brake pad surface wear value is then calculated based on the multiple brake pad surface wear values and the average brake pad surface wear value. This calculation of the standard brake pad surface wear value measures the dispersion of each brake pad surface wear value relative to the average wear value. A low dispersion indicates relatively uniform wear across the brake pads, while a high dispersion indicates uneven wear, possibly indicating abnormal wear on individual brake pads. Next, a brake pad surface wear influence coefficient is calculated based on the standard brake pad surface wear values and the average brake pad surface wear value. The brake pad surface wear influence coefficient reflects the proportional relationship between the average wear value and the standard wear value, quantifying the impact of brake pad wear on braking performance. The larger the wear impact coefficient, the greater the potential impact of wear on braking performance, and vice versa. This coefficient directly links brake pad wear to its impact on braking performance, providing a key intermediate parameter for the subsequent calculation of the Braking Error Impact Index. This allows the system to more accurately assess changes in braking performance due to brake pad wear, factoring this factor into risk warnings. Finally, the brake pad surface wear impact coefficient is used as the Braking Error Impact Index. This Braking Error Impact Index comprehensively considers brake pad wear and serves as the ultimate indicator for measuring changes in braking performance due to brake pad wear. This index directly reflects the potential deviation between the vehicle's actual braking performance and its ideal braking performance under the current brake pad wear condition.

[0118] In one embodiment, the step S5 of obtaining the effective braking distance according to the initial speed of the vehicle, the road friction coefficient, and the preset gravitational acceleration includes:

[0119] S501: Obtain an effective braking distance based on the initial speed of the vehicle, the road friction coefficient, and a preset gravitational acceleration, wherein the calculation formula is:

[0120] ;

[0121] in, Indicates the effective braking distance, represents the road friction coefficient, represents the initial speed of the vehicle, Indicates the preset gravitational acceleration.

[0122] As described in step S501 above, the present invention obtains the effective braking distance based on the initial speed of the vehicle, the road friction coefficient and the preset gravity acceleration. In this way, accurately mastering the braking distance during vehicle driving is an important prerequisite for ensuring safety. Calculating the braking distance based on the classical physical kinematics formula is a scientific and widely recognized method that can accurately reflect the relationship between the vehicle's motion state and road conditions and the braking effect. Incorporating the initial speed, road friction coefficient and gravity acceleration into the calculation takes into account the main factors affecting braking, providing the driver with a reliable braking distance reference value based on physical principles, so that they can make reasonable driving decisions based on this value in actual driving, meeting the actual needs of vehicle safe driving and risk assessment.

[0123] In one embodiment, the step S6 of obtaining the braking force according to the initial speed of the vehicle, the mass of the vehicle, the braking error influence index and the effective braking distance includes:

[0124] S601: Obtain historical braking force and historical driver pedaling force based on the big data, and calculate braking efficiency based on the historical braking force and historical driver pedaling force;

[0125] S602: Obtain a braking force based on the initial speed of the vehicle, the mass of the vehicle, the braking efficiency, the braking error influence index, and the effective braking distance, wherein the calculation formula is:

[0126] ;

[0127] in, Indicates the braking force of the brake. Indicates the mass of the vehicle, represents the initial speed of the vehicle, Indicates the braking efficiency, Indicates the braking error influence index.

[0128] As described in steps S601-S602 above, the vehicle's braking performance depends not only on the vehicle's braking system hardware but also on the driver's operation. To more comprehensively and accurately assess the vehicle's braking performance in actual driving, it is necessary to consider driver operation. Therefore, the present invention first obtains historical braking force and driver pedal force based on big data, and then calculates braking efficiency based on the relationship between these historical braking force and driver pedal force. By mining the vehicle's historical data through big data, historical braking force and driver pedal force information can be obtained, reflecting the vehicle's braking performance under different past driving conditions. Calculating braking efficiency quantifies the relationship between driver pedal force and actual braking force, thereby assessing the effectiveness of driver operation in the vehicle's braking system. Braking efficiency is also an important indicator for measuring the performance of the vehicle's braking system and the degree of driver coordination. Understanding braking efficiency helps more accurately predict the vehicle's braking performance in current circumstances because it considers the combined effects of the vehicle's braking system characteristics and driver operating habits on braking. During vehicle braking, multiple factors interact to determine the required braking force. Initial speed and mass determine the vehicle's kinetic energy, braking efficiency reflects the actual effectiveness of the vehicle's braking system, the brake error impact index accounts for the impact of brake pad wear on braking, and the safe braking distance is the ultimate target. Therefore, the braking force is calculated based on the vehicle's initial speed, mass, braking efficiency, brake error impact index, and effective braking distance. This comprehensively considers multiple factors, including the vehicle's initial motion (initial speed and mass), braking system performance (braking efficiency), brake pad wear (brake error impact index), and the required safe braking distance, to accurately calculate the braking force required for safe braking. This force value provides drivers with a clear operational reference, informing them how much braking force should be applied to ensure the vehicle stops within a safe distance, given the current vehicle state and road conditions. The calculation of braking force also integrates various factors that influence vehicle braking, elevating risk warnings from simple risk notification to providing specific operational guidance for drivers. This helps improve driver operational accuracy, reduce the risk of accidents caused by improper braking, and ensure driving safety.

[0129] In one embodiment, the step S7 of providing a warning of vehicle risk based on the braking force includes:

[0130] S701, determining whether the braking force meets a preset threshold range;

[0131] When the braking force does not meet the preset threshold range and is less than the minimum value of the preset threshold range, it is determined that the driver's foot is pressing the brake pedal with less force and a first reminder signal is generated. According to the first reminder signal, a corresponding preset yellow indicator light is turned on to provide a risk warning;

[0132] When the braking force meets the preset threshold range, it is determined that the driver's foot braking force is normal and a second reminder signal is generated. According to the second reminder signal, the corresponding preset green indicator light is turned on to provide a safety reminder;

[0133] When the braking force does not meet the preset threshold range and is greater than the maximum value of the preset threshold range, it is determined that the driver's foot is stepping on the brake pedal with great force and a third reminder signal is generated. According to the third reminder signal, the corresponding preset red indicator light is turned on to issue a risk warning.

[0134] As described in step S701 above, since excessive braking force is an extremely dangerous situation during driving, the driver needs to react immediately. The red indicator light is widely used to indicate emergency and dangerous situations due to its strong visual warning effect, which is in line with the human instinctive response mechanism to danger signals. In this way, the system can convey the most urgent risk information to the driver in the shortest time, prompting them to take emergency measures to correct incorrect operations and prevent serious accidents. This is one of the key measures to ensure driving safety and is also the core function of the risk warning system when facing emergency and dangerous situations. The present invention determines whether the braking force meets the preset threshold range. By comparing the calculated braking force with the preset threshold range, the rationality of the current braking operation can be quickly evaluated. The preset threshold range is determined based on the vehicle's performance, safety standards, and a large amount of actual driving data and experience. It represents a normal and safe range of braking force. When the braking force does not meet the preset threshold range and is less than the minimum value of the preset threshold range, it is determined that the driver's foot is pressing the brake pedal with less force and a first reminder signal is generated. According to the first reminder signal, the corresponding preset yellow indicator light is turned on to provide a risk warning. In this way, when the braking force is too small, the vehicle may not be able to stop within the expected safe distance, and there is a risk of collision such as rear-end collision. Generating a first reminder signal and turning on the yellow indicator light can attract the driver's attention in time and inform them that the current braking force is insufficient. At the same time, the yellow indicator light serves as a warning signal to remind the driver that the braking force needs to be increased, but it does not indicate an emergency danger like a red indicator light. It gives the driver an appropriate level of warning so that they can adjust their operations relatively calmly while paying attention to risks, avoiding panic due to sudden emergency prompts, and helping to guide the driver to correctly deal with risks while ensuring safety. When the braking force meets the preset threshold range, it is determined that the force of the driver's foot on the brake pedal is normal and a second reminder signal is generated. According to the second reminder signal, the corresponding preset green indicator light is turned on for safety prompts. In this way, when the braking force is within the normal range, it indicates that the driver's braking operation meets safety requirements and the vehicle can stop within the expected safe distance.A second reminder signal is generated and the green indicator light is turned on to give the driver positive feedback, letting them know that the current braking operation is correct, enhancing the driver's confidence in their own operation, and also strengthening the behavioral pattern of correct operation. At the same time, the green indicator light serves as a safety reminder, which helps the driver maintain a good mentality during driving, reduces the anxiety caused by uncertainty about the braking operation, and enables them to focus more on other driving tasks, further improving driving safety and comfort. When the braking force does not meet the preset threshold range and is greater than the maximum value of the preset threshold range, it is determined that the driver's foot is stepping on the brake pedal with a large force and a third reminder signal is generated. According to the third reminder signal, the corresponding preset red indicator light is turned on for risk warning. Excessive braking force may cause dangerous situations such as vehicle loss of control, rollover or passenger discomfort, especially when driving at high speed or on poor road conditions. The risk is higher. Generate a third reminder signal and turn on the red indicator light to warn the driver in the strongest way that the current braking operation involves serious risks and requires immediate adjustment. At the same time, the red indicator light serves as an emergency danger signal, which can quickly attract the driver's attention, make them aware of the seriousness of the problem, and prompt them to release the brake pedal as soon as possible or appropriately reduce the braking force to avoid dangerous situations and maximize the safety of the vehicle and personnel. The above steps can intuitively tell the driver how much braking force is needed to meet the current braking distance, thereby avoiding braking risks of the vehicle.

[0135] This application also provides a vehicle risk warning system based on big data, including:

[0136] A first acquisition module is used to acquire vehicle driving information, wherein the vehicle driving information includes road surface information and vehicle braking information;

[0137] A second acquisition module is used to acquire the mass of the vehicle and the initial speed of the vehicle according to the vehicle braking information;

[0138] a third acquisition module, configured to acquire a road surface roughness value and a road surface inclination according to the road surface information, and acquire a road surface friction coefficient according to the road surface roughness value and the road surface inclination;

[0139] a fourth acquisition module, configured to acquire vehicle maintenance information based on the big data, acquire a plurality of brake pad surface wear values according to the vehicle maintenance information, and acquire a braking error influence index according to the plurality of brake pad surface wear values and a preset replacement cycle;

[0140] a fifth acquisition module, configured to acquire an effective braking distance according to an initial speed of the vehicle, the road friction coefficient, and a preset gravitational acceleration;

[0141] a sixth acquisition module, configured to acquire a braking force according to the initial speed of the vehicle, the mass of the vehicle, the braking error influence index, and the effective braking distance;

[0142] The first prompt module is used to prompt the vehicle risk according to the braking force.

[0143] In one embodiment, the third acquisition module includes:

[0144] a first acquiring unit, configured to acquire image information according to the road surface information, and perform grayscale processing on the image information to obtain road surface grayscale image information;

[0145] The first segmentation unit is used to perform standard pixel unit grid segmentation on the road surface grayscale image information to obtain a plurality of pixel grids;

[0146] A second acquiring unit, configured to acquire corresponding RGB pixel values according to the pixel grid, wherein the RGB pixel values include R pixel values, G pixel values, and B pixel values;

[0147] A first calculation unit is configured to obtain a pixel grid grayscale value according to the R pixel value, the G pixel value, and the B pixel value, wherein the calculation formula is:

[0148] ;

[0149] in, Represents the grayscale value of the pixel grid, R represents the R pixel value, G represents the G pixel value, and B represents the B pixel value;

[0150] a third acquiring unit, configured to repeat the steps of acquiring corresponding RGB pixel values according to the pixel grid, wherein the RGB pixel values include R pixel values, G pixel values, and B pixel values, to acquiring a pixel grid grayscale value according to the R pixel value, the G pixel value, and the B pixel value, so as to traverse all pixel grids and obtain a plurality of pixel grid grayscale values;

[0151] a first comparison unit, configured to compare the grayscale values of the plurality of pixel grids with a preset grayscale value-roughness value table to obtain a plurality of first road surface roughness values, and average the plurality of first road surface roughness values to obtain an average road surface roughness value, and use the average road surface roughness value as the road surface roughness value;

[0152] a fourth acquiring unit, configured to acquire a road surface inclination angle according to the road surface inclination;

[0153] The second calculation unit is used to calculate the road friction coefficient according to the road roughness value and the road inclination angle, wherein the calculation formula is:

[0154] ;

[0155] in, represents the road friction coefficient, Indicates the road roughness value, Indicates the road inclination angle.

[0156] The present application also provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned vehicle risk warning method based on big data when executing the computer program.

[0157] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned vehicle risk warning method based on big data.

[0158] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAM bus dynamic RAM (RDRAM).

[0159] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0160] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A vehicle risk warning method based on big data, characterized in that: include: Acquiring vehicle driving information, wherein the vehicle driving information includes road surface information and vehicle braking information; Acquiring the mass of the vehicle and the initial speed of the vehicle according to the vehicle braking information; Acquiring a road surface roughness value and a road surface inclination according to the road surface information, and acquiring a road surface friction coefficient according to the road surface roughness value and the road surface inclination; The steps of obtaining vehicle maintenance information based on big data, obtaining a plurality of brake pad surface wear values according to the vehicle maintenance information, and obtaining a brake error influence index according to the plurality of brake pad surface wear values and a preset replacement cycle specifically include: Obtain the starting time of use and replacement time of the brake pads according to the preset replacement cycle; Calculating an average brake pad surface wear value based on the plurality of brake pad surface wear values, the start time of use, and the replacement time; Calculating a standard brake pad surface wear value based on the plurality of brake pad surface wear values and the average brake pad surface wear degree value; Calculating the brake pad surface wear influence coefficient based on the standard brake pad surface wear value and the brake pad surface average wear degree value; The influence coefficient of brake pad surface wear is used as the braking error influence index; Obtaining an effective braking distance based on the initial speed of the vehicle, the road friction coefficient, and a preset gravitational acceleration; Obtaining a braking force according to the initial speed of the vehicle, the mass of the vehicle, the braking error influence index, and the effective braking distance; The vehicle risk is prompted according to the braking force.

2. The vehicle risk warning method based on big data according to claim 1 is characterized in that: The step of obtaining the road surface friction coefficient according to the road surface roughness value and the road surface inclination includes: Acquiring image information according to the road surface information, and performing grayscale processing on the image information to obtain road surface grayscale image information; Performing standard pixel unit grid segmentation on the road surface grayscale image information to obtain multiple pixel grids; Obtaining corresponding RGB pixel values according to the pixel grid, and obtaining pixel grid grayscale values according to the RGB pixel values; Repeating the steps of obtaining corresponding RGB pixel values according to the pixel grid and obtaining pixel grid grayscale values according to the RGB pixel values, so as to traverse all pixel grids and obtain multiple pixel grid grayscale values; Comparing the plurality of pixel grid grayscale values with a preset grayscale value-roughness value table to obtain a plurality of first road surface roughness values, and averaging the plurality of first road surface roughness values to obtain an average road surface roughness value, which is used as the road surface roughness value; obtaining a road surface inclination angle according to the road surface inclination; The road surface friction coefficient is calculated according to the road surface roughness value and the road surface inclination angle.

3. The vehicle risk warning method based on big data according to claim 1 is characterized in that: The step of obtaining the effective braking distance according to the initial speed of the vehicle, the road friction coefficient and the preset gravitational acceleration includes: The effective braking distance is obtained according to the initial speed of the vehicle, the road friction coefficient and the preset gravity acceleration, wherein the calculation formula is: ； in, Indicates the effective braking distance, represents the road friction coefficient, represents the initial speed of the vehicle, Indicates the preset gravitational acceleration.

4. The vehicle risk warning method based on big data according to claim 1 is characterized in that: The step of obtaining the braking force according to the initial speed of the vehicle, the mass of the vehicle, the braking error influence index and the effective braking distance includes: Acquire historical braking force and historical driver pedaling force based on the big data, and calculate braking efficiency based on the historical braking force and historical driver pedaling force; The braking force is obtained according to the initial speed of the vehicle, the mass of the vehicle, the braking efficiency, the braking error influence index and the effective braking distance.

5. The vehicle risk warning method based on big data according to claim 1 is characterized in that: The step of prompting the vehicle risk according to the braking force includes: Determining whether the braking force meets a preset threshold range; When the braking force does not meet the preset threshold range and is less than the minimum value of the preset threshold range, it is determined that the driver's foot is pressing the brake pedal with less force and a first reminder signal is generated. According to the first reminder signal, a corresponding preset yellow indicator light is turned on to provide a risk warning; When the braking force meets the preset threshold range, it is determined that the driver's foot braking force is normal and a second reminder signal is generated. According to the second reminder signal, the corresponding preset green indicator light is turned on to provide a safety reminder; When the braking force does not meet the preset threshold range and is greater than the maximum value of the preset threshold range, it is determined that the driver's foot is stepping on the brake pedal with great force and a third reminder signal is generated. According to the third reminder signal, the corresponding preset red indicator light is turned on to issue a risk warning.

6. A vehicle risk warning system based on big data, used to execute the vehicle risk warning method based on big data according to any one of claims 1 to 5, characterized in that: include: A first acquisition module is used to acquire vehicle driving information, wherein the vehicle driving information includes road surface information and vehicle braking information; A second acquisition module is used to acquire the mass of the vehicle and the initial speed of the vehicle according to the vehicle braking information; a third acquisition module, configured to acquire a road surface roughness value and a road surface inclination according to the road surface information, and acquire a road surface friction coefficient according to the road surface roughness value and the road surface inclination; a fourth acquisition module, configured to acquire vehicle maintenance information based on the big data, acquire a plurality of brake pad surface wear values according to the vehicle maintenance information, and acquire a braking error influence index according to the plurality of brake pad surface wear values and a preset replacement cycle; a fifth acquisition module, configured to acquire an effective braking distance according to an initial speed of the vehicle, the road friction coefficient, and a preset gravitational acceleration; a sixth acquisition module, configured to acquire a braking force according to the initial speed of the vehicle, the mass of the vehicle, the braking error influence index, and the effective braking distance; The first prompt module is used to prompt the vehicle risk according to the braking force.

7. The vehicle risk warning system based on big data according to claim 6 is characterized in that: The third acquisition module includes: a first acquiring unit, configured to acquire image information according to the road surface information, and perform grayscale processing on the image information to obtain road surface grayscale image information; The first segmentation unit is used to perform standard pixel unit grid segmentation on the road surface grayscale image information to obtain a plurality of pixel grids; A second acquiring unit, configured to acquire corresponding RGB pixel values according to the pixel grid, and acquire a grayscale value of the pixel grid according to the RGB pixel values; a third acquiring unit, configured to repeat the steps of acquiring corresponding RGB pixel values according to the pixel grid, and acquiring pixel grid grayscale values according to the RGB pixel values, so as to traverse all pixel grids and obtain a plurality of pixel grid grayscale values; a first comparison unit, configured to compare the grayscale values of the plurality of pixel grids with a preset grayscale value-roughness value table to obtain a plurality of first road surface roughness values, and average the plurality of first road surface roughness values to obtain an average road surface roughness value, and use the average road surface roughness value as the road surface roughness value; a fourth acquiring unit, configured to acquire a road surface inclination angle according to the road surface inclination; The second calculation unit is used to calculate the road friction coefficient according to the road roughness value and the road inclination angle.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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