Brake performance glide analysis method, device and equipment and storage medium

Abstract

The invention discloses a brake performance glide analysis method, device and equipment and a storage medium, and belongs to the technical field of vehicles. The method comprises the steps of collecting vehicle environment data and vehicle data; performing normalization processing on the vehicle environment data and the vehicle data so as to at least generate corresponding standard environment data and standard vehicle data; sequentially calculating weighted scores of each standard environment data and each standard vehicle data at least based on the standard environment data, the standard vehicle data and a preset weighting ratio; determining a score ratio of each weighted score at least according to the weighted scores; and determining the reason of the brake performance glide at least according to the score proportion. According to the embodiment of the invention, by collecting the vehicle environment data and the vehicle data and automatically determining the reason of brake performance glide based on the collected data, the diagnosis efficiency can be improved, subjective errors can be reduced, and the accuracy of the diagnosis result can be improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of vehicles, and in particular to a brake performance degradation analysis method, device, equipment and storage medium. BACKGROUND

[0002] With the development of the automobile industry, the safety performance of vehicles is increasingly valued. As a key component for ensuring the safety of vehicles, the stability of the performance of the brake system is directly related to the driving safety of users. The performance of the brake system can degrade due to various factors. In the prior art, the diagnosis of brake performance degradation usually relies on manual inspection and experience-based judgment. However, the manual inspection and experience-based judgment are not only inefficient but also subjective. Therefore, there is an urgent need for an automatic brake performance diagnosis method based on data analysis to accurately and quickly identify the specific reasons for brake performance degradation. SUMMARY

[0003] Embodiments of the present application provide a brake performance degradation analysis method, device, equipment and storage medium to determine the score proportion of each item of data based on at least standard environment data, standard vehicle data and a preset weighting ratio, so as to automatically diagnose the brake degradation reason according to the score proportion of each item of data, thereby improving the diagnosis efficiency and the accuracy of the diagnosis result.

[0004] In a first aspect, embodiments of the present application provide a brake performance degradation analysis method, at least comprising the following steps:

[0005] Collecting vehicle environment data and vehicle self data;

[0006] Performing normalization processing on the vehicle environment data and the vehicle self data, respectively, to generate at least corresponding standard environment data and standard vehicle data;

[0007] Calculating the weighted score of each standard environment data and each standard vehicle data based on at least the standard environment data, the standard vehicle data and a preset weighting ratio;

[0008] Determining the score proportion of each weighted score based on at least the weighted score;

[0009] Determining the reason for brake performance degradation based on at least the score proportion.

[0010] Optionally, the vehicle environment data at least includes one of the environment temperature, the environment humidity, the road surface condition and the road slope;

[0011] The vehicle self data at least includes one of the tire pressure, the brake pad thickness and the brake duration;

[0012] The standard environment data at least includes one of a standard environment temperature value, a standard environment humidity value, the standard road surface value and the standard slope value;

[0013] The standard vehicle data at least includes one of a standard tire pressure value, a standard brake pad thickness value and a standard brake duration value;

[0014] The standard environment data is generated based on the corresponding vehicle environment data, and the standard vehicle data is generated based on the corresponding vehicle itself data.

[0015] Optionally, after the vehicle environment data and the vehicle itself data are collected, the method further comprises:

[0016] Confirming a road surface condition based on road condition image information.

[0017] Optionally, the confirming of the road surface condition based on the road condition image information specifically comprises:

[0018] Collecting the road condition image information by at least an image collection device, and obtaining feature information of the road condition image information;

[0019] Determining the road surface condition according to at least the feature information;

[0020] The feature information at least includes one of a color feature, a brightness feature and a texture feature.

[0021] Optionally, the performing of the normalization processing on the vehicle environment data and the vehicle itself data respectively to generate corresponding standard environment data and standard vehicle data specifically comprises:

[0022] Determining the standard environment temperature value according to at least the environment temperature and a preset temperature value; and / or

[0023] Determining the standard environment humidity value according to at least the environment humidity and a preset humidity value; and / or

[0024] Determining the standard road surface value according to at least the road surface condition; and / or

[0025] Determining the standard slope value according to at least the road surface slope; and / or

[0026] Determining the standard tire pressure value according to at least the tire pressure and a preset tire pressure value; and / or

[0027] Determining the standard brake pad thickness value according to at least the brake pad thickness; and / or

[0028] Determining the standard brake duration value according to at least the brake duration;

[0029] The brake pad thickness is determined at least by the current capacitance value, the initial capacitance value and the initial thickness value.

[0030] Optionally, the standard ambient temperature value is determined at least by:

[0031]

[0032] wherein T represents the standard ambient temperature value, T o represents the ambient temperature, T i represents the preset temperature value.

[0033] The standard ambient humidity value is determined at least by:

[0034]

[0035] wherein H represents the standard ambient humidity value, H o represents the ambient humidity, H i represents the preset humidity value.

[0036] The standard tire pressure value is determined at least by:

[0037]

[0038] wherein P represents the standard tire pressure value, P o represents the tire pressure value, P i represents the preset tire pressure value.

[0039] The brake pad thickness is determined at least by:

[0040]

[0041] wherein d represents the brake pad thickness, d o represents the initial thickness value, C o represents the initial capacitance value, C represents the current capacitance value.

[0042] Optionally, the score proportion of the weighted score is determined at least by:

[0043]

[0044] wherein W represents the score proportion of any of the standard ambient data or the standard vehicle data, W o represents the weighted score corresponding to any of the standard ambient data or the standard vehicle data, W s represents the sum of all the weighted scores.

[0045] In a second aspect, the embodiments of the present application further provide a brake performance decline analysis device, comprising at least:

[0046] a data collection module, configured to collect vehicle environment data and vehicle self data;

[0047] a standard processing module, configured to perform normalization processing on the vehicle environment data and the vehicle self data respectively, to generate corresponding standard environment data and standard vehicle data at least;

[0048] a weighting calculation module, configured to calculate a weighting score of each of the standard environment data and the standard vehicle data based on the standard environment data, the standard vehicle data and a preset weighting ratio at least;

[0049] a proportion calculation module, configured to determine a score proportion of each of the weighting scores based on the weighting scores at least;

[0050] a decline analysis module, configured to determine a cause of brake performance decline based on the score proportion at least.

[0051] In a third aspect, the embodiments of the present application further provide an electronic device, comprising a processor and a memory, wherein the memory stores computer readable instructions, and when the computer readable instructions are executed by the processor, the steps of the brake performance decline analysis method in the first aspect are executed.

[0052] In a fourth aspect, the embodiments of the present application further provide a computer readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the steps of the brake performance decline analysis method in the first aspect are executed.

[0053] The technical scheme provided by the embodiments of the present application firstly collects vehicle environment data and vehicle self data; further, normalization processing is performed on the vehicle environment data and the vehicle self data respectively, to generate corresponding standard environment data and standard vehicle data at least; further, a weighting score of each of the standard environment data and the standard vehicle data is calculated based on the standard environment data, the standard vehicle data and a preset weighting ratio at least; further, a score proportion of each of the weighting scores is determined based on the weighting scores at least; finally, a cause of brake performance decline is determined based on the score proportion at least.

[0054] It can be seen that, the embodiment of the present application generates corresponding standard environment data or standard vehicle data based on the collected vehicle environment data and vehicle self data in turn, so as to determine the score proportion of each data based on the preset weighting ratio and the above standard data, and finally realize the automatic diagnosis of the brake performance decline reason according to the score proportion of all data, which at least solves the problems of low efficiency and strong subjectivity in the prior art, artificial inspection and experience judgment, is beneficial to improve the diagnosis efficiency, reduce the subjective error and improve the accuracy of the diagnosis result. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the specific embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0056] Figure 1 is a flow chart of a brake performance decline analysis method provided by the embodiment of the present application;

[0057] Figure 2 is a flow chart of another brake performance decline analysis method provided by the embodiment of the present application;

[0058] Figure 3 is a structural schematic diagram of a brake performance decline analysis device provided by the embodiment of the present application;

[0059] Figure 4 is a structural schematic diagram of another brake performance decline analysis device provided by the embodiment of the present application;

[0060] Figure 5 is a structural schematic diagram of an electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0062] The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in the description of the embodiments of the present application and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0063] It should be understood that the term "and / or" as used herein merely describes associated objects, which can exist in three relationships, for example, A and / or B can represent three cases: A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are in an "or" relationship.

[0064] It should be understood that although the terms first, second, third, etc. can be used in the embodiments of the present application to describe, these descriptions should not be limited to these terms. These terms are only used to distinguish the description. For example, the first can also be called the second without departing from the scope of the embodiments of the present application, and similarly, the second can also be called the first.

[0065] Depending on the context, the word "if" as used herein can be interpreted as meaning "when" or "upon" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if it is determined" or "if (a stated condition or event) is detected" can be interpreted as meaning "when it is determined" or "in response to determining" or "when (a stated condition or event) is detected" or "in response to detecting (a stated condition or event)".

[0066] It should also be noted that the terms "comprise", "comprising", or any other variant thereof are intended to cover non-exclusive inclusions, so that a product or device that comprises a list of elements does not exclude the presence of other elements not explicitly listed, or the presence of elements inherent to such product or device. Without more limitations, the element defined by the phrase "comprising a" does not exclude the presence of another identical element in the product or device comprising the element.

[0067] It should be particularly noted that the symbols and / or numbers present in the specification, if not marked in the description of the drawings, are not drawing reference numbers.

[0068] Figure 1 is a flowchart of a brake performance degradation analysis method provided by an embodiment of the present application. The embodiment is applicable to at least various brake performance degradation analysis scenarios of various vehicles. The brake performance degradation analysis method can be executed by a brake performance degradation analysis device in the embodiments of the present application as an execution subject, and the execution subject can be realized in the form of software and / or hardware. As shown in FIG. 1, the brake performance degradation analysis method comprises the following steps.Figure 1 As shown, the brake performance decline analysis method at least includes the following steps:

[0069] S1, collect vehicle environment data and vehicle self data.

[0070] The collection of vehicle environment data and vehicle self data can be through a vehicle-mounted camera or various types of sensors (e.g., temperature sensor, humidity sensor, etc.). It can be understood that the implementation stage of the present embodiment can determine whether the brake performance of the vehicle has indeed declined when the decline of the brake performance of the vehicle is detected, which can be based on the scheme disclosed in the patent document "CN202411199754.4 - A brake performance real-time monitoring method".

[0071] S2, performing normalization processing on the vehicle environment data and the vehicle self data respectively to generate at least corresponding standard environment data and standard vehicle data.

[0072] The purpose of normalization processing is to convert the data form of vehicle environment data and vehicle self data into numerical form, ensuring that data of different dimensions can be compared fairly. In a specific embodiment, the vehicle environment data at least includes one of the environment temperature, the environment humidity, the road surface condition and the road slope; the vehicle self data at least includes one of the tire pressure, the brake pad thickness and the brake duration; the standard environment data at least includes one of the standard environment temperature value, the standard environment humidity value, the standard road surface value and the standard slope value; the standard vehicle data at least includes one of the standard tire pressure value, the standard brake pad thickness value and the standard brake duration value.

[0073] Specifically, the standard environment data is generated based on the corresponding vehicle environment data, and the standard vehicle data is generated based on the corresponding vehicle self data. It can be understood that the standard data (i.e., the aforementioned standard environment data and standard vehicle data) and the vehicle data (i.e., the aforementioned vehicle environment data and vehicle self data) are in a one-to-one correspondence. For example, when only the environment temperature value is collected, only the corresponding standard environment temperature value can be generated by performing step S2; when only the brake pad thickness and the brake duration are collected, only the corresponding standard brake pad thickness value and the standard brake duration value can be generated by performing step S2; no further description is given.

[0074] S3, at least based on the standard environment data, the standard vehicle data and the preset weighting ratio, the weighted score of each standard environment data and each standard vehicle data is calculated in turn.

[0075] The preset weighting ratio can refer to a weight defined by the user based on actual vehicle data for different standards. The calculation method of the weighted score can be the standard data multiplied by the weighting ratio corresponding to the standard data. For example, if the standard brake pad thickness value is 8 and the weighting ratio corresponding to the standard brake pad thickness value is 20%, the weighted score of the standard brake pad thickness value is 8*20% = 1.6.

[0076] It is known that the temperature rise of the brake pad will cause the friction coefficient of the brake pad material to decrease, especially under high temperature conditions (for example, 300°C), the friction coefficient of most brake pad materials will decrease by 20%-50%. In addition, high temperature will also cause the brake fluid to boil, causing the brake fluid to form bubbles, thereby reducing the transmission efficiency of the hydraulic pressure. Correspondingly, under extremely cold conditions (for example, below -20°C), the brake pad material will harden due to environmental supercooling, which not only reduces the friction of the brake pad material, but also lowers the flowability of the brake fluid, affecting the response of the brake system. Brake fluid is easy to evaporate and vaporize in a high-temperature environment, and forms air resistance in the brake pipeline, causing the brake pad to be easily ablated and causing brake failure. In a low-temperature environment, the tire rubber hardens, causing the adhesion between the tire and the road surface to decrease, increasing the braking distance. In view of this, temperature has a direct and significant impact on brake performance, which is particularly pronounced in extreme high-temperature or low-temperature environments.

[0077] In addition, the friction coefficient of a dry asphalt road surface is 0.7-0.9, while the friction coefficient of an icy road surface can be as low as 0.1-0.3, which means that the brake distance will increase by 2-3 times when braking on an icy road surface. For the same reason, in gravel or muddy road conditions, the particulate matter or mud on the road surface will further reduce the tire grip, thereby increasing the brake distance. Therefore, road conditions have the most direct impact on brake performance, especially in harsh road conditions (such as icy road conditions, muddy road conditions, etc.).

[0078] It can be understood that when the environmental humidity is too high, the road surface friction coefficient will decrease; the friction coefficient of a wet road surface is usually 30%-40% lower than that of a dry road surface, and a low road surface friction coefficient will significantly increase the brake distance. In one possible scenario, high humidity weather (such as rainy weather) can cause a water film to form on the surface of the brake disc, affecting the friction between the brake pad and the brake disc in a short period of time, causing the vehicle brake performance to decline. In addition, brake oil is easy to mix with water in a humid environment, and water vapor accumulates on the brake tank, causing water to enter the brake fluid, which will affect the brake performance of the vehicle. In view of the fact that the influence of humidity on brake performance is mainly reflected in the case of a wet road surface, its influence on brake performance is obviously lower than the influence of temperature or road conditions on brake performance.

[0079] For example, when the vehicle is going downhill, the gravity of the vehicle itself will force the vehicle to accelerate, at which time the vehicle needs greater braking force to decelerate. Studies have shown that when the downhill slope exceeds 10°, the braking distance is 10%-20% longer than that on a horizontal ground. Conversely, when the vehicle is going uphill, the gravity of the vehicle itself helps the vehicle to decelerate, thereby affecting the braking distance. Therefore, continuous braking of the vehicle on a long downhill slope will cause the braking system to work continuously, resulting in an increase in the temperature of the braking system and aggravating the heat fade phenomenon. Thus, the influence of the slope on the braking performance mainly manifests in specific driving conditions (such as long downhill driving), and the influence is relatively small in normal driving.

[0080] It can be understood that excessively low tire pressure will increase the ground contact area of the tire, thereby increasing the friction between the tire and the ground, increasing the rolling resistance of the tire, making the steering heavy, increasing fuel consumption, and the like. Conversely, if the tire pressure is too high, the ground contact area of the tire will be reduced, and the tire grip will be reduced, thereby affecting the braking effect. Studies have shown that when the tire pressure is insufficient, the braking distance will increase by 5%-15%, and both excessively high and excessively low tire pressure will affect the stiffness of the tire and the braking response speed. Although the influence of the tire pressure on the braking performance is relatively small, it still has a certain influence under extreme conditions.

[0081] It can be known that a decrease in the thickness of the brake pad will reduce the amount of effective friction material and increase the braking distance. Experiments have shown that when the thickness of the brake pad decreases to a critical value (usually 2-3 mm), the braking distance will increase by 10%-30%, and a severely worn brake pad can directly damage the brake disc, thereby aggravating the rate of decline in the braking performance. However, an excessively thick brake pad will affect the uniform distribution of the braking force during braking. Therefore, the thickness of the brake pad is not the thicker the better, and it is generally considered that the thickness of the brake pad is best when it is between 10 mm and 15 mm. Based on this, the thickness of the brake pad will directly affect the braking performance, especially when the brake pad is severely worn.

[0082] For example, if the user brakes for a long time or frequently, the temperature of the brake pad will increase, triggering the heat fade effect, and the heat fade effect will increase as the continuous braking time increases. Experiments have shown that if the continuous braking time exceeds 90 s, the friction coefficient of the brake pad will decrease significantly. If the heat dissipation performance of the braking system is poor, the braking effect will be further affected. Therefore, heat fade has a significant influence on the braking performance under the condition of long-time braking or frequent braking.

[0083] According to the influence degree of the above data on the brake performance, in a normal case, the weight of the standard ambient temperature value can be set to 20%, the weight of the standard road surface value can be configured to 25%, the weight of the standard ambient humidity value can be 10%, the weight of the standard slope value can be configured to 10%, the weight of the standard tire pressure value can be configured to 5%, the weight of the standard brake pad thickness value can be configured to 15%, and the weight of the standard brake duration value can be configured to 15%.

[0084] S4, determining a score proportion of each weighted score according to at least the weighted score.

[0085] The score proportion can refer to the ratio of the weighted score of any one standard data to the sum of the weighted scores of all standard data. For example, if the weighted score of the standard brake duration value is 2 and the sum of the weighted scores of all standard data is 10, the score proportion of the standard brake duration value is 2 / 10, i.e. 0.2.

[0086] S5, determining the cause of the decline in brake performance according to at least the score proportion.

[0087] The determination of the cause of the decline in brake performance can be that the user determines whether the score proportion of each vehicle environment data and vehicle self-data exceeds a preset threshold, and if the score proportion of any vehicle environment data or vehicle self-data exceeds the preset threshold, the corresponding vehicle environment data or vehicle self-data is determined as the cause of the decline in brake performance. Similarly, the determination of the cause of the decline in brake performance can also be that the user obtains the highest value in all score proportions, and determines the vehicle environment data or vehicle self-data corresponding to the highest value as the cause of the decline in brake performance.

[0088] The technical solution provided by the embodiment first collects vehicle environment data and vehicle self-data, further performs normalization processing on the vehicle environment data and vehicle self-data to generate corresponding standard environment data and standard vehicle data, further calculates the weighted score of each standard environment data and each standard vehicle data based on at least the standard environment data, the standard vehicle data and the preset weighting ratio, further determines the score proportion of each weighted score according to at least the weighted score, and finally determines the cause of the decline in brake performance according to at least the score proportion.

[0089] It can be seen that, based on the collected vehicle environment data and vehicle self data, the corresponding standard environment data or standard vehicle data is generated in sequence, so that the score proportion of each item of data is determined based on the preset weighting ratio and the above standard data, and finally the automatic diagnosis of the brake performance decline reason is realized according to the score proportion of all data, at least solving the problems of low efficiency and strong subjectivity existing in the manual inspection and experience judgment in the prior art, which is beneficial to improve the diagnosis efficiency, reduce the subjective error, and improve the accuracy of the diagnosis result.

[0090] On the basis of the above embodiments or implementation manners, Figure 2 is a flowchart of another brake performance decline analysis method provided by the embodiments of the present application, which is additionally added on the basis of the above embodiments. As shown in Figure 2 , the brake performance decline analysis method at least includes the following steps:

[0091] S1, collecting vehicle environment data and vehicle self data.

[0092] S6, confirming the road surface condition based on the road condition image information.

[0093] In another specific implementation manner, step S6 specifically includes:

[0094] (601) at least collecting road condition image information through an image collection device, and obtaining feature information of the road condition image information.

[0095] The image collection device can be a high-definition dynamic range camera to ensure that the road condition image information can be clearly collected under strong light, low light, night, etc. Optionally, the feature information at least includes one of color features, brightness features and texture features. The user can also select a 120° wide-angle lens to cover a wider road surface area. The road condition image information can be pictures, videos, etc. containing road condition feature information. After collecting the road condition image information, a preprocessing operation is usually required to be performed on the road condition image information, and the preprocessing operation at least includes a denoising operation, a grayscale conversion operation and a contrast enhancement operation, etc. It can be understood that the denoising operation can be a smoothing operation on the road condition image information using a Gaussian filter or a median filter, etc. Through the denoising operation, the noise interference in the road condition image information can be removed. The grayscale conversion operation can be to convert the road condition image information into a grayscale image, so as to further perform brightness analysis and texture analysis. The contrast enhancement operation can be to enhance the contrast of the road condition image information through histogram equalization, so as to adapt the road condition image information to different light conditions.

[0096] (602) determining the road surface condition according to at least the feature information.

[0097] The road surface condition can be classified into dry road condition, wet road condition, gravel road condition, muddy road condition, icy road condition and other road conditions.

[0098] It can be understood that the road surface of the dry road condition is usually uniform in color, and the uniformity of the road surface can be determined by calculating the color histogram of the image. In addition, the brightness of the dry road condition is relatively stable, and there is no obvious highlight or dark area. The standard deviation of the gray value can be calculated. When the standard deviation of the gray value is less than a set threshold, it can be determined that the road condition is dry. In addition, the surface texture of the dry road condition is smooth. The Laplace operator can be used to detect the edges of the image. If there are fewer edges, it indicates that the road surface is smooth and has no obvious roughness, and it can be determined that the road condition is dry. On the contrary, the road condition image information of the wet road condition often has a highlight area. Therefore, whether the current road condition is a wet road condition can be determined by calculating the proportion of the highlight area in the road condition image information. If the proportion of the highlight area exceeds a certain threshold, it can be determined that the road condition is wet. Of course, whether the highlight area is concentrated in an irregular area can also be observed to determine whether it is a highlight area. If the size of the highlight area exceeds a threshold, it can be determined that the road condition is wet. In addition, the surface of the wet road condition often has sharp and irregular highlight shapes in edge detection. Therefore, the highlight edges can be obtained by combining Canny edge detection, and then it can be determined whether the current road condition is a wet road condition.

[0099] It can be understood that the road condition image information of the gravel road condition usually has rough texture. The particle features of the road condition image information can be extracted by methods such as Local Binary Patterns (LBP) to determine the roughness of the gravel road condition. In addition, the color and brightness of the gravel road condition are not uniform. The gray scale variance of the local area can be calculated. If the gray scale variance is large, it can be determined that the current road condition is a gravel road condition. In addition, Canny edge detection algorithm can be used to observe whether there are a large number of irregularly distributed edge points in the road condition image information. If the number of irregularly distributed edge points exceeds a threshold, it can be determined that the current road condition is a gravel road condition.

[0100] More, the color features of the muddy road condition are mostly dark, usually presenting uneven color blocks such as yellow and brown. Therefore, the uneven color blocks can be converted to the HSV color space to detect specific color regions (such as yellow regions, brown regions, etc.) of the uneven color blocks, so as to determine that the current road condition is a muddy road condition. In addition, the road surface of the muddy road condition also shows high low-frequency texture features. Therefore, the frequency domain features of the image can also be analyzed by Fourier transform. If the low-frequency component is high, it can be determined that the current road condition is a muddy road surface. In addition, brightness distribution detection can also be used. If a large range of low brightness area is detected, it can be further determined that the current road condition is a muddy road condition.

[0101] In addition, the icy road condition is similar to the aforementioned wet road condition, and has irregular highlight reflection areas, but the reflection areas are generally uniform and concentrated, and the surface of the areas is relatively smooth. Therefore, the Sobel operator can be used to detect the edges of the road condition image information, and if the edges are sparse and uniform, it can be determined that the current road condition is an icy road condition. Adaptively, the sharpness of the highlight part in the road condition image information can also be detected, and if there is a significant mirror effect (i.e., the reflection area is relatively stable), it can be determined that the current road condition is an icy road condition.

[0102] S21, determining a standard ambient temperature value according to at least the ambient temperature and a preset temperature value.

[0103] The preset temperature value can be determined according to the test data of the German Automobile Industry Association. Tests show that when the ambient temperature is between 15°C and 25°C, the braking performance of the vehicle is best, and the temperature deviating from this interval will cause the braking performance of the vehicle to decrease, so the value range of the preset temperature value can be between 15°C and 25°C. Of course, the preset temperature value can also be a temperature range, which is not limited in the embodiment.

[0104] In another specific embodiment, the standard ambient temperature value is determined at least by the following method:

[0105]

[0106] In the formula, T represents the standard ambient temperature value, T o represents the ambient temperature, T i represents the preset temperature value.

[0107] For example, assuming that the ambient temperature is 28°C and the preset temperature value is 20°C, since 28°C is in the temperature interval range of [10, 30], the standard ambient temperature value is 1 at this time.

[0108] S22, determining a standard ambient humidity value according to at least the ambient humidity and a preset humidity value.

[0109] The preset humidity value can be determined according to the research of the U.S. National Highway Traffic Safety Administration. Research shows that when the humidity exceeds 80%, the braking performance of the vehicle will decrease significantly. When the ambient humidity is between 30% and 60%, the braking performance of the vehicle is best, so the value range of the preset humidity value can be between 30% and 60%. Of course, the preset humidity value can also be a humidity range, which is not limited in the embodiment.

[0110] In another specific embodiment, the standard ambient humidity value is determined at least by the following method:

[0111]

[0112] H = H0+ (H0- H1) * (H2- H1) / (H2- H1) o H = H0+ (H0- H1) * (H2- H1) / (H2- H1) i H = H0+ (H0- H1) * (H2- H1) / (H2- H1)

[0113]

[0114] S23, determining a standard road value according to at least the road condition.

[0115] In one embodiment, the standard road value is determined according to a self-defined road value assignment rule, for example, the self-defined road value assignment rule can be that dry road condition is assigned a value of 2 (i.e. the standard road value), wet and slippery road condition is assigned a value of 5, gravel road condition is assigned a value of 6, muddy road condition is assigned a value of 8, icy road condition is assigned a value of 9, and other road conditions are assigned a value of 3.

[0116] S24, determining a standard slope value according to at least the road slope.

[0117] In one embodiment, the standard slope value is determined according to a self-defined slope value assignment rule, for example, the self-defined slope value assignment rule can be that:

[0118]

[0119] In one embodiment, the standard slope value is determined according to a self-defined slope value assignment rule, for example, the self-defined slope value assignment rule can be that:

[0120] S25, determining a standard tire pressure value according to at least the tire pressure and a preset tire pressure value.

[0121] In one embodiment, the preset tire pressure value is determined according to the research of Michelin Tire Laboratory, which shows that the best range of tire pressure is between 30psi and 35psi. Therefore, the preset tire pressure value can be in the range of 30psi to 35psi. Of course, the preset tire pressure value can also be a tire pressure range, which is not limited in the embodiment.

[0122] In another specific embodiment, the standard tire pressure value is determined at least by the following way:

[0123]

[0124] In one embodiment, the standard tire pressure value is determined according to a self-defined tire pressure value assignment rule, for example, the self-defined tire pressure value assignment rule can be that: o In one embodiment, the standard tire pressure value is determined according to a self-defined tire pressure value assignment rule, for example, the self-defined tire pressure value assignment rule can be that: i In one embodiment, the standard tire pressure value is determined according to a self-defined tire pressure value assignment rule, for example, the self-defined tire pressure value assignment rule can be that:

[0125] Exemplarily, assuming the tire pressure value is 32 psi, and the preset tire pressure value is 32.5 psi, the standard tire pressure value P can be determined as follows:

[0126]

[0127] S26, determining a standard brake pad thickness value according to at least the brake pad thickness.

[0128] The standard brake pad thickness value determined according to the brake pad thickness can be directly assigned according to a self-defined brake pad thickness assignment rule. For example, the self-defined brake pad thickness assignment rule can be:

[0129]

[0130] In the formula, d represents the brake pad thickness (unit: mm), and R represents the standard brake pad thickness value.

[0131] In another specific embodiment, the brake pad thickness can be determined at least by the current capacitance value, the initial capacitance value and the initial thickness value. Specifically, the brake pad thickness can be determined at least by the following method:

[0132] (i.e. the brake pad thickness calculation formula below).

[0133] In the formula, d represents the brake pad thickness, d o represents the initial thickness value, C o represents the initial capacitance value, and C represents the current capacitance value. It can be understood that the calculation principle of the brake pad thickness calculation formula can be based on the characteristic that the capacitance changes with the distance between the electrodes. When the brake pad thickness wears out, the distance between the electrodes gradually decreases, thereby causing the capacitance value to change accordingly. The calculation principle can be expressed as follows:

[0134]

[0135] In the formula, ε represents the dielectric constant of the dielectric (a known parameter at the factory), A represents the effective area of the electrode (a known parameter at the factory), and d represents the distance between the electrodes (i.e. the brake pad thickness).

[0136] S27, determining a standard brake pad thickness value according to at least the brake pad thickness.

[0137] The standard brake pad thickness value can be directly assigned according to the actual brake time, and the specific assignment rule can be:

[0138]

[0139] In the formula, t represents the brake time (unit: s), and T represents the standard brake time value.

[0140] S3, calculating a weighted score of each standard environment data and each standard vehicle data based on the standard environment data, the standard vehicle data and a preset weighting ratio.

[0141] S4, determining a score proportion of each weighted score based on the weighted score.

[0142] In another specific embodiment, the score proportion of the weighted score is determined by at least the following method:

[0143]

[0144] wherein W represents the score proportion of any standard environment data or standard vehicle data, W o represents the weighted score corresponding to any standard environment data or standard vehicle data, W s represents the sum of all weighted scores. For example, assuming that the weighted score of the standard tire pressure value is 0.2, W s and 5.28, the score proportion of the standard tire pressure value can be determined by the following method:

[0145]

[0146] S5, determining the cause of the brake performance decline based on the score proportion.

[0147] In determining the cause of the brake performance decline, a classification list, a pie chart, a column chart, etc. can be generated first to list the possible causes of the brake performance decline and the corresponding score proportion, or the cause of the brake performance decline can be directly given to the user on the human-computer interaction interface of the vehicle machine. For example, the classification list can be shown in Table 1.

[0148] Table 1

[0149]

[0150] From the score proportion in Table 1, it can be understood that the road condition has the greatest impact on the brake performance, with a proportion of 38%, and thus it can be determined that the cause of the brake performance decline this time is the road condition.

[0151] The technical scheme provided by the embodiment firstly collects vehicle environment data and vehicle self data; further, at least collects road condition image information through an image collection device and obtains feature information of the road condition image information; further, at least determines a road surface condition according to the feature information; further, at least determines a standard environment temperature value according to an environment temperature and a preset temperature value; further, at least determines a standard environment humidity value according to an environment humidity and a preset humidity value; further, at least determines a standard road surface value according to the road surface condition; further, at least determines a standard slope value according to a road surface slope; further, at least determines a standard tire pressure value according to a tire pressure and a preset tire pressure value; further, at least determines a standard brake pad thickness value according to a brake pad thickness; further, at least determines a standard brake time value according to a brake time; further, at least calculates a weighted score of each standard environment data and each standard vehicle data in turn based on the standard environment data, the standard vehicle data and a preset weighting ratio; further, at least determines a score proportion of each weighted score; and finally, at least determines a reason for brake performance decline according to the score proportion.

[0152] It can be seen that, on the one hand, the embodiment generates corresponding standard environment data or standard vehicle data in turn based on the collected vehicle environment data and vehicle self data, determines a score proportion of each data based on a preset weighting ratio and the above standard data, and finally realizes automatic diagnosis of the reason for brake performance decline according to the score proportion of all data, thereby at least solving the problems of low efficiency and strong subjectivity in the existing manual inspection and experience judgment mode, improving the diagnosis efficiency, reducing subjective errors and improving the accuracy of the diagnosis result. On the other hand, when determining the reason for brake performance decline, the embodiment can automatically generate a classification list or a statistical table based on the calculated various standard values, weighting ratios, score proportions and other data, so that the user can intuitively feel the brake performance decline condition, reduce the implementation difficulty and improve the user experience.

[0153] It should be noted that the step execution order shown in steps S21-S27 is only illustrative, and the user can exchange or skip any step to realize the principle analysis of the vehicle brake performance decline according to the actual situation, and the present application does not limit this.

[0154] Figure 3 is a structural schematic diagram of a brake performance decline analysis device provided by an embodiment of the present application. The brake performance decline analysis device can be realized in the form of software and / or hardware and is suitable for various vehicle brake performance decline analysis scenes. Figure 4 is a structural schematic diagram of another brake performance decline analysis device provided by an embodiment of the present application, as shown in Figure 4 . As shown in Figure 3 , the brake performance decline analysis device 100 at least comprises:

[0155] The data collection module 110 is configured to collect vehicle environment data and vehicle self data.

[0156] The standard processing module 120 is configured to perform normalization processing on the vehicle environment data and the vehicle self data respectively to generate corresponding standard environment data and standard vehicle data.

[0157] The weighting calculation module 130 is configured to calculate a weighting score of each standard environment data and each standard vehicle data in sequence based on the standard environment data, the standard vehicle data and a preset weighting ratio.

[0158] The proportion calculation module 140 is configured to determine a score proportion of each weighting score based on the weighting score.

[0159] The decline analysis module 150 is configured to determine a cause of brake performance decline based on the score proportion.

[0160] Optionally, the vehicle environment data at least includes one of an environment temperature, an environment humidity, a road surface condition and a road slope.

[0161] The vehicle self data at least includes one of a tire pressure, a brake pad thickness and a brake duration.

[0162] The standard environment data at least includes one of a standard environment temperature value, a standard environment humidity value, a standard road surface value and a standard slope value.

[0163] The standard vehicle data at least includes one of a standard tire pressure value, a standard brake pad thickness value and a standard brake duration value.

[0164] The standard environment data is generated based on the corresponding vehicle environment data, and the standard vehicle data is generated based on the corresponding vehicle self data.

[0165] Optionally, the method further comprises:

[0166] The road condition confirmation module 160 is configured to confirm a road surface condition based on road condition image information.

[0167] Optionally, the road condition confirmation module 160 is specifically configured to:

[0168] acquire feature information of the road condition image information through at least an image collection device, and determine the road surface condition based on the feature information.

[0169] The feature information at least includes one of a color feature, a brightness feature and a texture feature.

[0170] Optionally, the standard processing module 120 is specifically configured to:

[0171] determining a standard environment temperature value at least according to an environment temperature and a preset temperature value; and / or determining a standard environment humidity value at least according to an environment humidity and a preset humidity value; and / or determining a standard road surface value at least according to a road surface condition; and / or determining a standard slope value at least according to a road surface slope; and / or determining a standard tire pressure value at least according to a tire pressure and a preset tire pressure value; and / or determining a standard brake pad thickness value at least according to a brake pad thickness; and / or determining a standard brake time value at least according to a brake time;

[0172] wherein the brake pad thickness is determined at least by a current capacitance value, an initial capacitance value and an initial thickness value.

[0173] Optionally, the standard environment temperature value is determined at least by:

[0174]

[0175] wherein T represents the standard environment temperature value, T o represents the environment temperature, T i represents the preset temperature value.

[0176] The standard environment humidity value is determined at least by:

[0177]

[0178] wherein H represents the standard environment humidity value, H o represents the environment humidity, H i represents the preset humidity value.

[0179] The standard tire pressure value is determined at least by:

[0180]

[0181] wherein P represents the standard tire pressure value, P o represents the tire pressure value, P i represents the preset tire pressure value.

[0182] The brake pad thickness is determined at least by:

[0183]

[0184] wherein d represents the brake pad thickness, d o represents the initial thickness value, C o represents the initial capacitance value, C represents the current capacitance value.

[0185] Optionally, the score ratio of the weighted score is determined at least by:

[0186]

[0187] In the formula, W represents the score proportion of any standard environment data or standard vehicle data, W o represents the weighted score corresponding to any standard environment data or standard vehicle data, W s represents the sum of all weighted scores.

[0188] The technical solution provided by the embodiment is first, the vehicle environment data and the vehicle self data are collected by the data collection module; further, the vehicle environment data and the vehicle self data are respectively executed normalization processing by the standard processing module (equivalent to Figure 4 the data preprocessing module shown in the figure) to generate corresponding standard environment data and standard vehicle data; further, the weighted score of each standard environment data and each standard vehicle data is calculated based on the standard environment data, the standard vehicle data and the preset weighted ratio by the weighted calculation module (equivalent to Figure 4 the calculation module shown in the figure); further, the score proportion of each weighted score is determined according to the weighted score by the proportion calculation module; finally, the reason of brake performance decline is determined according to the score proportion by the slide analysis module (equivalent to Figure 4 the display module shown in the figure).

[0189] Therefore, the vehicle environment data and the vehicle self data collected are used to generate corresponding standard environment data or standard vehicle data in turn, so that the score proportion of each data is determined based on the preset weighted ratio and the above standard data, and finally the automatic diagnosis of the brake performance decline reason is realized according to the score proportion of all data, which at least solves the problems of low efficiency and strong subjectivity in the manual inspection and experience judgment mode in the prior art, is beneficial to improve the diagnosis efficiency, reduce the subjective error and improve the accuracy of the diagnosis result.

[0190] The embodiment of the application also provides an electronic device, Figure 5 is a structural schematic diagram of an electronic device provided by the embodiment of the application, referring to Figure 5The electronic device 1000 comprises a processor 1001 and a memory 1002, and the memory 1002 stores computer readable instructions, when the computer readable instructions are executed by the processor 1001, the steps in any one of the brake performance degradation analysis methods described above are executed. Through the above technical solution, the processor 1001 and the memory 1002 are interconnected and communicate with each other through a communication bus and / or other forms of connection mechanism (not marked), the memory 1002 stores a computer program executable by the processor, when the electronic device 1000 is running, the processor 1001 executes the computer program to execute the brake performance degradation analysis method in any one of the optional implementation manners of the above embodiments, to at least achieve the following functions: collecting vehicle environment data and vehicle self data; performing normalization processing on the vehicle environment data and the vehicle self data respectively, to at least generate corresponding standard environment data and standard vehicle data; calculating the weighted score of each standard environment data and each standard vehicle data in turn based on at least the standard environment data, the standard vehicle data and a preset weighting ratio; determining the score proportion of each weighted score based on at least the weighted score; and determining the cause of brake performance degradation based on at least the score proportion.

[0191] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the brake performance degradation analysis method provided by all the embodiments of the present application: collecting vehicle environment data and vehicle self data; performing normalization processing on the vehicle environment data and the vehicle self data respectively, to at least generate corresponding standard environment data and standard vehicle data; calculating the weighted score of each standard environment data and each standard vehicle data in turn based on at least the standard environment data, the standard vehicle data and a preset weighting ratio; determining the score proportion of each weighted score based on at least the weighted score; and determining the cause of brake performance degradation based on at least the score proportion.

[0192] Any combination of one or more computer readable medium can be employed. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by an instruction execution system, apparatus, or device to execute the program.

[0193] The computer readable medium can include a transmission line, a carrier wave propagating through program instructions, a computer readable storage medium, or memory present in a computing device. A computer readable storage medium can be any media that can be accessed by a computer. By way of example, and not limitation, such computer readable storage medium can comprise RAM, ROM, EEPROM, CD-ROM or other optical disc storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, or twisted pair, then the coaxial cable, fiber optic cable, or twisted pair are included in the definition of medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, Blu-ray disc, and floppy disk used to store software.

[0194] Computer readable program code can be transmitted over the Internet, intranet, extranet, wireless network, wireline network, or any other suitable medium, in the form of signals propagating through a communication medium of those networks and is received by a remote computer system using any of a variety of communications protocols, e.g., TCP / IP, Ethernet, etc., and over any communication medium including wireless media.

[0195] Computer readable program code can be implemented in any of various ways. Key storage and retrieval can be implemented using a variety of programming languages, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. Program code can be executed entirely on the user's computer, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of remote computing, the remote computer can be connected to the user's computer by any of a variety of networks, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0196] The above embodiments are only used to illustrate the technical solutions of the present application, not limit the technical solutions of the present application; even though the above technical solutions are described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand: the technical solutions recorded in the foregoing embodiments can be modified, or some of the technical features can be replaced by equivalent replacements; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for analyzing brake performance degradation, characterized in that: At least the following steps are included: Collect vehicle environment data and vehicle data; performing normalization processing on the vehicle environment data and the vehicle self data respectively to generate at least corresponding standard environment data and standard vehicle data; sequentially calculating a weighted score for each of the standard environment data and each of the standard vehicle data based at least on the standard environment data, the standard vehicle data, and a preset weighting ratio; Determining a score proportion of each weighted score at least based on the weighted scores; The reason for the deterioration in braking performance is determined based on at least the score percentage.

2. The braking performance degradation analysis method according to claim 1, characterized in that: The vehicle environment data includes at least one of the following: ambient temperature, ambient humidity, road condition, and road slope; The vehicle data includes at least one of tire pressure, brake pad thickness, and braking time; The standard environmental data includes at least one of a standard environmental temperature value, a standard environmental humidity value, the standard road surface value, and the standard slope value; The standard vehicle data includes at least one of a standard tire pressure value, a standard brake pad thickness value, and a standard braking duration value; The standard environment data is generated based on the corresponding vehicle environment data, and the standard vehicle data is generated based on the corresponding vehicle self data.

3. The brake performance degradation analysis method according to claim 2, characterized in that: After collecting the vehicle environment data and the vehicle itself data, the method further includes: Confirm road conditions based on road image information.

4. The brake performance degradation analysis method according to claim 3, characterized in that: The determining of the road condition based on the road condition image information specifically includes: At least collecting the road condition image information by an image acquisition device and obtaining feature information of the road condition image information; determining the road surface condition based at least on the characteristic information; The feature information includes at least one of a color feature, a brightness feature and a texture feature.

5. The brake performance degradation analysis method according to claim 2, characterized in that: The normalizing process is performed on the vehicle environment data and the vehicle self data respectively to generate at least corresponding standard environment data and standard vehicle data, specifically including: determining the standard ambient temperature value based at least on the ambient temperature and a preset temperature value; and / or determining the standard ambient humidity value based at least on the ambient humidity and a preset humidity value; and / or determining the standard road surface value based on at least the road surface condition; and / or determining the standard slope value based on at least the road surface slope; and / or determining the standard tire pressure value based at least on the tire pressure and a preset tire pressure value; and / or determining the standard brake pad thickness value based on at least the brake pad thickness; and / or determining the standard braking duration value at least according to the braking duration; The brake pad thickness is confirmed at least by a current capacitance value, an initial capacitance value, and an initial thickness value.

6. The brake performance degradation analysis method according to claim 2, characterized in that: The standard ambient temperature value is confirmed by at least the following means: Where, T represents the standard ambient temperature value, T o represents the ambient temperature, T i Indicates the preset temperature value; The standard ambient humidity value is confirmed by at least the following methods: Wherein, H represents the standard ambient humidity value, H o Represents the ambient humidity, H i Indicates the preset humidity value; The standard tire pressure value is confirmed by at least the following methods: Wherein, P represents the standard tire pressure value, P o Indicates the tire pressure value, P i Indicates the preset tire pressure value; The brake pad thickness is confirmed by at least the following methods: Where, d represents the thickness of the brake pad, d o Indicates the initial thickness value, C o represents the initial capacitance value, and C represents the current capacitance value.

7. The method for analyzing brake performance degradation according to claim 1, characterized in that: The weighted score proportion is confirmed by at least the following methods: Where, W represents the score ratio of any of the standard environment data or the standard vehicle data, W o represents the weighted score corresponding to any of the standard environment data or the standard vehicle data, W s Represents the sum of all the weighted scores.

8. A braking performance degradation analysis device, characterized in that: At least: Data acquisition module, used to collect vehicle environment data and vehicle data; a standard processing module, configured to perform normalization processing on the vehicle environment data and the vehicle self data respectively, so as to generate at least corresponding standard environment data and standard vehicle data; a weighted calculation module, configured to sequentially calculate a weighted score of each of the standard environment data and each of the standard vehicle data based at least on the standard environment data, the standard vehicle data, and a preset weighting ratio; a proportion calculation module, configured to determine a score proportion of each weighted score based at least on the weighted scores; The decline analysis module is used to determine the reason for the decline in braking performance based on at least the score ratio.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps of the method for analyzing the decline in braking performance according to any one of claims 1 to 7 are executed.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the braking performance degradation analysis method according to any one of claims 1 to 7 is implemented.

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