A method and system for predicting the service life of a brake disc

Abstract

The present invention relates to the technical field of brake wear analysis, and specifically to a method and system for predicting the service life of a brake disc. The present invention first obtains the real-time road condition difference based on the difference between the pedal movement speeds corresponding to the current time period and the historical time period; obtains the overall habit influence index by integrating the braking habit influence coefficient and the braking habit wear coefficient; obtains the autocorrelation coefficient based on the pedal movement distance sequence of the reference time period; corrects the autocorrelation coefficient based on the overall habit influence index to obtain the corrected autocorrelation coefficient; and predicts the service life of the brake disc based on the pedal movement distance sequence of the reference time period and the corrected autocorrelation coefficient. The present invention improves the accuracy of the brake disc service life prediction by fully considering the impact of real-time driving habits on brake disc wear and reasonably setting the corrected autocorrelation coefficient.

Description

Technical Field

[0001] The present invention relates to the technical field of brake wear analysis, and in particular to a method and system for predicting the service life of a brake disc. Background Art

[0002] Brake discs are key components of a vehicle's braking system, and their quality directly impacts both braking performance and driver and passenger safety. Therefore, accurately predicting the lifespan of brake discs is crucial. By accurately predicting the lifespan of brake discs, car owners can promptly monitor their wear and replace them when necessary, thus avoiding degradation of braking performance due to excessive wear and ensuring safe driving.

[0003] As brake discs wear, their service life decreases. Increased brake disc wear leads to a gradual decline in braking performance, which is manifested externally as an increase in pedal travel distance. Pedal travel distance can be used as a key indicator for evaluating brake disc service life. The ARMA model is a time series prediction model. When predicting brake disc service life, the ARMA model can be used to make predictions based on the pedal travel distance series. Because different real-time driving habits have different effects on brake disc wear, when using the ARMA model for prediction, the determination of the autocorrelation coefficient does not fully consider driving habits on the current road, affecting the accuracy of the brake disc service life prediction results. Summary of the Invention

[0004] In order to solve the technical problem of inaccurate prediction results of brake disc service life in the prior art, the purpose of the present invention is to provide a method and system for predicting the service life of brake discs. The technical solutions adopted are as follows:

[0005] A method for predicting the service life of a brake disc, the method comprising:

[0006] Acquire a vehicle driving data set; the vehicle driving data set includes a pedal movement distance, a pedal movement speed, and a driving speed corresponding to each braking moment in a reference period; the reference period includes a current period and a historical period;

[0007] According to the difference between the pedal movement speed corresponding to the current time period and the historical time period, a real-time road condition difference is obtained; according to the numerical value of the pedal movement speed in the current time period and the real-time road condition difference, a braking habit influence coefficient is obtained; according to the numerical value of the pedal movement speed and the driving speed corresponding to each braking moment in the current time period, a braking habit wear coefficient is obtained; the braking habit influence coefficient and the braking habit wear coefficient are combined to obtain an overall habit influence index; according to the pedal movement distance sequence in the reference time period, an autocorrelation coefficient is obtained; according to the overall habit influence index, the autocorrelation coefficient is corrected to obtain a corrected autocorrelation coefficient;

[0008] The service life of the brake disc is predicted based on the pedal movement distance sequence during the reference period and the corrected autocorrelation coefficient.

[0009] Furthermore, the method for obtaining the real-time road condition difference includes:

[0010] The current period and the historical period are respectively used as the periods to be analyzed; the pedal movement speeds corresponding to all braking moments in the period to be analyzed are counted in sequence according to the time sequence to obtain a pedal movement speed sequence for the period to be analyzed;

[0011] Based on the DTW algorithm, the DTW distance between the pedal movement speed sequence corresponding to the current time period and the historical time period is used as the real-time road condition difference.

[0012] Furthermore, the method for obtaining the braking habit influence coefficient includes:

[0013] In the current period, if the pedal movement speed is greater than the preset standard speed value, it is marked as an emergency braking speed; if the pedal movement speed is not greater than the preset standard speed value, it is marked as a slow braking speed;

[0014] Calculating the average of all the sudden braking speeds in the current period as the overall sudden braking speed value; calculating the average of all the slow braking speeds in the current period as the overall slow braking speed value; calculating the difference between the overall sudden braking speed value and the overall slow braking speed value to obtain the habitual first influence coefficient;

[0015] Calculating the ratio of the total number of all the sudden braking speeds to the total number of all the pedal movement speeds in the current period to obtain a habitual second influence coefficient;

[0016] The first influence coefficient of the habit, the second influence coefficient of the habit and the real-time road condition difference are forwardly integrated to obtain the braking habit influence coefficient.

[0017] Furthermore, the preset standard speed value is set to 5.

[0018] Furthermore, the method for obtaining the brake habit wear coefficient includes:

[0019] In the current period, the product of the pedal movement speed and the driving speed corresponding to each braking moment is calculated to obtain the wear index corresponding to the braking moment; the cumulative sum of the wear indexes corresponding to all the braking moments in the current period is calculated and normalized to obtain the braking habit wear coefficient.

[0020] Furthermore, the method for obtaining the overall habit influence index includes:

[0021] The braking habit influence coefficient and the braking habit wear coefficient are forwardly integrated to obtain an overall habit influence index.

[0022] Furthermore, the method for obtaining the autocorrelation coefficient includes:

[0023] The ARIMA model method is used to obtain the autocorrelation coefficient based on the pedal movement distance series during the reference period.

[0024] Furthermore, the method for obtaining the corrected autocorrelation coefficient includes:

[0025] The product of the negative correlation mapping result of the autocorrelation coefficient corresponding to the lag order and the overall habit influence index is calculated to obtain an adjustment parameter; the product of the adjustment parameter and the autocorrelation coefficient is calculated and normalized to obtain a corrected autocorrelation coefficient.

[0026] Furthermore, the method for predicting the service life of the brake disc includes:

[0027] The ARIMA model method is used to predict the service life of brake discs based on the time series data of movement distance in the reference period and the corrected autocorrelation coefficient.

[0028] A service life prediction system for a brake disc comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a method for predicting the service life of a brake disc are implemented.

[0029] The present invention proposes a service life prediction system for brake discs, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the service life prediction method for brake discs are implemented.

[0030] The present invention has the following beneficial effects:

[0031] First, a real-time road condition difference is constructed to reflect the degree of change in real-time road conditions compared to historical conditions. The braking habit influence coefficient is then derived based on the pedal speed during the current period and the real-time road condition difference. This braking habit influence coefficient initially reflects the impact of real-time driving braking habits on brake disc wear. The braking habit wear coefficient further reflects the physical extent of real-time driving habits on brake disc wear. Combining the braking habit influence coefficient and the braking habit wear coefficient yields an overall habit influence index, which is used to assess the overall impact of the driver's braking habits on brake disc wear. The autocorrelation coefficient is then corrected based on the overall habit influence index to obtain a corrected autocorrelation coefficient. This corrected autocorrelation coefficient fully accounts for the impact of real-time driving habits on brake disc wear, thereby improving the accuracy and reliability of brake disc life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1 A flowchart of a method for predicting the service life of a brake disc provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0034] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a brake disc service life prediction method and system according to the present invention, including its specific implementation, structure, features, and effectiveness. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0035] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0036] The following describes in detail a method and system for predicting the service life of a brake disc provided by the present invention with reference to the accompanying drawings.

[0037] The embodiment of the present invention provides a method and system for predicting the service life of a brake disc. Figure 1 , which shows a flow chart of a method for predicting the service life of a brake disc provided by one embodiment of the present invention, the method comprising the following steps:

[0038] Step S1: Acquire a vehicle driving data set; the vehicle driving data set includes a pedal movement distance, a pedal movement speed, and a driving speed corresponding to each braking moment in a reference period; the reference period includes a current period and a historical period.

[0039] In order to make a reasonable prediction of the service life of the brake disc, we first obtain a set of car driving data to provide basic data support for subsequent analysis of driving habits and prediction.

[0040] The present invention first initiates an authorization request to obtain driving records from the vehicle to be processed. After the driver agrees, the vehicle driving data set for the vehicle to be processed is obtained from the cloud service platform of the intelligent vehicle computer. The vehicle driving data set includes the pedal travel distance, pedal travel speed, and driving speed corresponding to each braking moment during a reference period. The specific acquisition process includes: During the reference period, the driver will perform multiple braking operations. For each braking operation, the data collected includes: Braking duration: The total time from the moment the brake pedal is depressed to the moment it is fully released; Pedal travel distance: The total distance the brake pedal travels during the braking operation. This data directly reflects the effectiveness of the braking system and the friction between the tire and the ground, and is a key indicator for predicting brake disc life; Braking moment: The first moment corresponding to the braking operation, reflecting the moment the pedal is first depressed during the braking operation, serving as the starting time of the braking event; Pedal travel speed: The ratio of pedal travel distance to braking duration can be used to determine pedal travel speed, a key parameter for measuring vehicle deceleration during braking; and Driving speed: The vehicle's speed at the moment immediately before the braking moment. Driving speed reflects the vehicle's dynamic state before braking and is also important for analyzing driving habits and predicting brake disc life. The pedal travel distance, pedal speed, and driving speed corresponding to each braking moment during the reference period are uploaded to a cloud service platform for storage and management. In one embodiment of the present invention, the reference period is selected from a preset time range from the current moment to the current moment. The preset time range is set to 100 hours, and the implementer can customize it based on the implementation scenario.

[0041] To analyze real-time driving habits, the reference period is divided into a current period and a historical period. In one embodiment of the present invention, the current period is defined as the preset time range from the current moment to the current moment. The preset current time range is set to 5 hours, which can be customized by the implementer based on the implementation scenario. Within the reference period, the time range outside the current period is defined as the historical period.

[0042] For the above steps, a relatively short time window is selected as the current period. This time window closely matches the current moment and is used to capture and analyze the driver's recent driving behavior. Compared to the current period, the historical period spans a longer time span and covers the driver's driving records over a period of time. This historical period data can be used to understand the driver's long-term driving habits.

[0043] The data collection in this invention is authorized by the user and does not violate relevant laws and regulations or public order and good morals. To facilitate calculations, all indicator data involved in the calculations in the embodiments of this invention undergo data preprocessing to eliminate dimensionality effects. The specific means of eliminating dimensionality effects are well known to those skilled in the art and are not limited here.

[0044] Step S2: According to the difference between the pedal movement speeds corresponding to the current time period and the historical time period, obtain the real-time road condition difference; according to the numerical value of the pedal movement speed in the current time period and the real-time road condition difference, obtain the braking habit influence coefficient; according to the numerical value of the pedal movement speed and the driving speed corresponding to each braking moment in the current time period, obtain the braking habit wear coefficient; comprehensively obtain the braking habit influence coefficient and the braking habit wear coefficient to obtain the overall habit influence index; according to the pedal movement distance sequence of the reference time period, obtain the autocorrelation coefficient; according to the overall habit influence index, correct the autocorrelation coefficient to obtain the corrected autocorrelation coefficient.

[0045] First, a real-time road condition difference is constructed to reflect the degree of change in real-time road conditions compared to historical conditions. The braking habit influence coefficient is then derived based on the pedal speed during the current period and the real-time road condition difference. This braking habit influence coefficient initially reflects the impact of real-time driving braking habits on brake disc wear. The braking habit wear coefficient further reflects the physical extent of real-time driving habits on brake disc wear. Combining the braking habit influence coefficient and the braking habit wear coefficient yields an overall habit influence index, which is used to assess the overall impact of the driver's braking habits on brake disc wear. The autocorrelation coefficient is then corrected based on the overall habit influence index to obtain a corrected autocorrelation coefficient. This corrected autocorrelation coefficient fully accounts for the impact of real-time driving habits on brake disc wear, thereby improving the accuracy and reliability of brake disc life prediction.

[0046] In order to analyze the difference between the driving road conditions in the current period and the historical period, preferably, in one embodiment of the present invention, the method for obtaining the difference in real-time road conditions includes:

[0047] The current period and the historical period are respectively used as the periods to be analyzed; the pedal movement speed corresponding to each braking moment in the period to be analyzed is counted in sequence according to the time sequence to obtain the pedal movement speed sequence of the period to be analyzed;

[0048] Based on the DTW (Dynamic Time Warping) algorithm, the DTW distance between the pedal speed sequences corresponding to the current time period and the historical time period is used as the difference in real-time road conditions. It should be noted that the DTW (Dynamic Time Warping) algorithm is well known to those skilled in the art and will not be described in detail here.

[0049] Regarding the above steps, considering that different brake pedal movement speeds reflect different car driving conditions, by comparing the difference between the pedal movement speeds corresponding to the current period and the historical period, the degree of difference in the car driving conditions corresponding to the current period and the historical period is indirectly reflected. The DTW distance reflects the difference in the pedal movement speed sequence corresponding to the current period and the historical period. Finally, the DTW distance of the pedal movement speed sequence corresponding to the current period and the historical period is used as the real-time road condition difference. The real-time road condition difference quantifies the degree of difference in the driving conditions between the current period and the historical period.

[0050] In other embodiments of the present invention, the method for obtaining the real-time road condition difference further includes:

[0051] The current period and the historical period are respectively used as the periods to be analyzed; based on a two-dimensional coordinate system, that is, the horizontal axis of the two-dimensional coordinate system is time and the vertical axis is pedal movement speed, each pedal movement speed and its corresponding braking moment constitute a data point, and all pedal movement speed data points in the period to be analyzed are counted as fitting data points; using the least squares method, curve fitting is performed on all fitting data points in the period to be analyzed to obtain the braking degree fitting curve for the period to be analyzed;

[0052] Using a curve matching algorithm, the real-time road condition difference is obtained based on the difference between the braking degree fitting curves of the current time period and the historical time period. It should be noted that the least squares method and curve matching algorithm are well known to those skilled in the art and will not be described in detail here.

[0053] Considering that different braking behaviors in the current period cause different wear on the brake disc, for example, sudden braking causes greater wear on the brake disc than slow braking. A braking habit influence coefficient is constructed to reflect the impact of real-time braking behavior and real-time road conditions on brake disc wear. Preferably, in one embodiment of the present invention, the method for obtaining the braking habit influence coefficient includes:

[0054] In the current period, if the pedal movement speed is greater than the preset standard speed value, it is marked as the emergency braking speed; if the pedal movement speed is not greater than the preset standard speed value, it is marked as the slow braking speed;

[0055] Calculate the average of all sudden braking speeds in the current period as the overall sudden braking speed value; calculate the average of all slow braking speeds in the current period as the overall slow braking speed value; calculate the difference between the overall sudden braking speed value and the overall slow braking speed value to obtain the habit first influence coefficient;

[0056] Calculate the ratio of the total number of all sudden braking speeds to the total number of all pedal movement speeds in the current period to obtain the habitual second influence coefficient;

[0057] The first influence coefficient of habit, the second influence coefficient of habit and the difference of real-time road conditions are forwardly integrated to obtain the influence coefficient of braking habit. It should be noted that forward fusion is an existing technology well known to those skilled in the art, and forward fusion can adopt simple product, arithmetic average or other suitable fusion methods. In one embodiment of the present invention, the product of the first influence coefficient of habit, the second influence coefficient of habit and the difference of real-time road conditions is calculated and normalized to obtain the influence coefficient of braking habit. In one embodiment of the present invention, the normalization process can adopt linear normalization, etc., which is not limited here. In one embodiment of the present invention, the preset standard speed value is 5, and the implementer can set it according to the implementation scenario.

[0058] In the above steps, braking events in which the pedal movement speed exceeds a preset standard speed value are marked as sudden braking speed, indicating that the driver performed a relatively abrupt braking operation. Braking events in which the pedal movement speed does not exceed the preset standard speed value are marked as gentle braking speed, indicating that the driver performed a relatively gentle braking operation. The difference between the overall sudden braking speed value and the overall gentle braking speed value is calculated to obtain the habit-first impact coefficient. The habit-first impact coefficient reflects the degree of speed difference between the driver's sudden braking and gentle braking behaviors during the current time period. The larger the habit-first impact coefficient, the greater the wear potential of the driver's braking behavior on the brake disc. The ratio of the total number of sudden braking speeds to the total number of pedal movement speeds is calculated to obtain the habit-second impact coefficient. The habit-second impact coefficient reflects the frequency of sudden braking behaviors during the current time period. The real-time road condition difference reflects the difference between the current time period's road conditions and historical road conditions. The greater the real-time road condition difference, the more attention should be paid to the braking behavior during the current time period. The braking habit-first impact coefficient is calculated by combining the habit-first impact coefficient, the habit-second impact coefficient, and the real-time road condition difference. The higher the braking habit influence coefficient, the greater the impact of the driver's braking habit on the wear of the brake disc in the current period.

[0059] Considering that the driving speed of a car has a significant impact on the degree of brake disc wear, especially when driving at high speeds, even if the number of emergency brakes is small, the wear on the brake disc will be relatively severe due to the huge braking force required. The driving speed not only reflects the movement state of the vehicle, but also reflects the characteristics of driving habits. Therefore, combining the pedal movement speed and driving speed corresponding to the braking moment, a braking habit wear coefficient is constructed to accurately quantify the impact of the driver's driving habits on brake disc wear. Preferably, in one embodiment of the present invention, the method for obtaining the braking habit wear coefficient includes:

[0060] During the current period, the product of the pedal movement speed and the driving speed corresponding to each braking moment is calculated to obtain the wear index corresponding to the braking moment. The cumulative sum of the wear indexes corresponding to all braking moments in the current period is calculated and normalized to obtain the braking habit wear coefficient. In one embodiment of the present invention, the normalization process can adopt linear normalization or other methods, which is not limited here.

[0061] For the above steps, the wear index represents the degree of wear on the brake system during braking operations, influenced by the vehicle's driving speed and brake pedal speed. The wear index corresponding to all braking events during the current period is calculated and normalized to produce the braking habit wear coefficient. This coefficient reflects the impact of the driver's braking habits on brake disc wear by comprehensively considering the pedal movement speed and driving speed corresponding to each braking event during the current period, as well as the combined effect of these speeds on the degree of brake system wear.

[0062] In order to comprehensively analyze the impact of real-time braking habits on brake disc wear, preferably, in one embodiment of the present invention, the method for obtaining the overall habit impact index includes:

[0063] The braking habit influence coefficient and the braking habit wear coefficient are forward-fused to obtain an overall habit influence index. It should be noted that forward fusion is a prior art well known to those skilled in the art, and forward fusion can employ simple multiplication, arithmetic mean, or other suitable fusion methods. In one embodiment of the present invention, the mean of the braking habit influence coefficient and the braking habit wear coefficient is calculated to obtain an overall habit influence index.

[0064] For the above steps, the overall habit impact index comprehensively reflects the impact of real-time braking habits on brake disc wear.

[0065] In order to adjust the autocorrelation coefficient, it is first necessary to obtain the autocorrelation coefficient. Preferably, in one embodiment of the present invention, the method for obtaining the autocorrelation coefficient includes:

[0066] Using the ARIMA model, the autocorrelation coefficient is obtained from the pedal distance sequence during the reference period. It should be noted that the method for obtaining the autocorrelation coefficient is well known to those skilled in the art and is briefly described here: All pedal distances during the reference period are counted sequentially in time sequence to obtain the pedal distance sequence for the reference period. Based on the ARMA (AutoRegressive Moving Average) model, the autocorrelation coefficient is obtained from the pedal distance sequence.

[0067] In order to adjust the autocorrelation coefficient, preferably, in one embodiment of the present invention, the method for obtaining the corrected autocorrelation coefficient includes:

[0068] The adjustment parameter is calculated by multiplying the negative correlation mapping result corresponding to the lag order of the autocorrelation coefficient with the overall habit influence index. The product of the adjustment parameter and the autocorrelation coefficient is calculated and normalized to obtain the corrected autocorrelation coefficient. It should be noted that the normalization method used is: normalization using the tanh normalization function.

[0069] In the above steps, the autocorrelation coefficient corresponding to the lag order reflects the historical data's proximity to the current moment, while the overall habit impact index comprehensively reflects the impact of real-time braking habits on brake disc wear. A higher overall habit impact index indicates a stronger influence of the driver's braking habits on brake disc wear. In this case, it is more likely that the current brake disc condition is more influenced by recent driving behavior than by the lag effect of distant historical data. Therefore, the adjustment parameter is calculated by multiplying the negative correlation mapping result corresponding to the lag order of the autocorrelation coefficient with the overall habit impact index. In brake disc life prediction, the adjustment parameter is used to correct the autocorrelation coefficient to more accurately reflect brake disc wear. Combining the adjustment parameter with the autocorrelation coefficient yields a corrected autocorrelation coefficient, which more accurately reflects the real-time wear trend of the brake disc and facilitates accurate prediction of brake disc life.

[0070] Step S3: predicting the service life of the brake disc based on the pedal movement distance sequence during the reference period and the corrected autocorrelation coefficient.

[0071] The above steps are used to construct a corrected autocorrelation coefficient that fully considers the impact of real-time driving habits on brake disc wear, thereby improving the accuracy and reliability of brake disc service life prediction.

[0072] In order to accurately predict the service life of the brake disc, preferably, in one embodiment of the present invention, the method for predicting the service life of the brake disc includes:

[0073] The ARIMA model method is used to predict the service life of brake discs based on the time series data of movement distance in the reference period and the corrected autocorrelation coefficient.

[0074] It should be noted that the ARMA model method for prediction is an existing technology well known to those skilled in the art. Here, only a brief description is given: Based on the travel distance time series data of the reference period and the corrected autocorrelation coefficient, appropriate ARIMA model parameters are selected, including the autoregressive term p, the difference order d, and the moving average term q; the prediction model is constructed using the selected ARIMA model parameters. The ARIMA model is trained using the data within the reference period and validated using cross-validation or other validation methods to ensure the accuracy and reliability of the model. The trained ARIMA model is applied to the travel distance time series data within the prediction period to obtain the travel distance value at the prediction moment. Based on the predicted travel distance value, combined with the law of brake disc wear and the empirical formula, the predicted brake disc service life is calculated and output. For example, if the predicted travel distance value is greater than the preset distance threshold, the brake disc service life is output and a life warning is issued. The preset distance threshold in the present invention is 32, and the implementer can set it according to the implementation scenario.

[0075] The present invention proposes a service life prediction system for brake discs, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor implements the steps of a method for predicting the service life of brake discs.

[0076] In summary, embodiments of the present invention provide a method and system for predicting the service life of a brake disc. The method first determines the difference in real-time road conditions based on the difference in pedal movement speed between the current time period and the corresponding historical time period. The method then combines the braking habit influence coefficient and the braking habit wear coefficient to determine the overall habit influence index. The method then determines the autocorrelation coefficient based on the pedal movement distance sequence during a reference time period. The method then corrects the autocorrelation coefficient based on the overall habit influence index to determine the corrected autocorrelation coefficient. Finally, the method predicts the service life of the brake disc based on the pedal movement distance sequence during the reference time period and the corrected autocorrelation coefficient. By fully considering the impact of real-time driving habits on brake disc wear and rationally setting the corrected autocorrelation coefficient, the method improves the accuracy of brake disc service life prediction.

[0077] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for predicting the service life of a brake disc, characterized in that: The method comprises: Acquire a vehicle driving data set; the vehicle driving data set includes a pedal movement distance, a pedal movement speed, and a driving speed corresponding to each braking moment in a reference period; the reference period includes a current period and a historical period; According to the difference between the pedal movement speed corresponding to the current time period and the historical time period, a real-time road condition difference is obtained; according to the numerical value of the pedal movement speed in the current time period and the real-time road condition difference, a braking habit influence coefficient is obtained; according to the numerical value of the pedal movement speed and the driving speed corresponding to each braking moment in the current time period, a braking habit wear coefficient is obtained; the braking habit influence coefficient and the braking habit wear coefficient are combined to obtain an overall habit influence index; according to the pedal movement distance sequence in the reference time period, an autocorrelation coefficient is obtained; according to the overall habit influence index, the autocorrelation coefficient is corrected to obtain a corrected autocorrelation coefficient; The service life of the brake disc is predicted based on the pedal movement distance sequence during the reference period and the corrected autocorrelation coefficient.

2. A method for predicting the service life of a brake disc according to claim 1, characterized in that: The method for obtaining the real-time traffic condition difference includes: The current period and the historical period are respectively used as the periods to be analyzed; the pedal movement speeds corresponding to all braking moments in the period to be analyzed are counted in sequence according to the time sequence to obtain a pedal movement speed sequence for the period to be analyzed; Based on the DTW algorithm, the DTW distance between the pedal movement speed sequence corresponding to the current time period and the historical time period is used as the real-time road condition difference.

3. A method for predicting the service life of a brake disc according to claim 1, characterized in that: The method for obtaining the braking habit influence coefficient includes: In the current period, if the pedal movement speed is greater than the preset standard speed value, it is marked as an emergency braking speed; if the pedal movement speed is not greater than the preset standard speed value, it is marked as a slow braking speed; Calculating the average of all the sudden braking speeds in the current period as the overall sudden braking speed value; calculating the average of all the slow braking speeds in the current period as the overall slow braking speed value; calculating the difference between the overall sudden braking speed value and the overall slow braking speed value to obtain the habitual first influence coefficient; Calculating the ratio of the total number of all the sudden braking speeds to the total number of all the pedal movement speeds in the current period to obtain a habitual second influence coefficient; The first influence coefficient of the habit, the second influence coefficient of the habit and the real-time road condition difference are forwardly integrated to obtain the braking habit influence coefficient.

4. A method for predicting the service life of a brake disc according to claim 3, characterized in that: The preset standard speed value is set to 5.

5. A method for predicting the service life of a brake disc according to claim 1, characterized in that: The method for obtaining the brake habit wear coefficient includes: In the current period, the product of the pedal movement speed and the driving speed corresponding to each braking moment is calculated to obtain the wear index corresponding to the braking moment; the cumulative sum of the wear indexes corresponding to all the braking moments in the current period is calculated and normalized to obtain the braking habit wear coefficient.

6. A method for predicting the service life of a brake disc according to claim 1, characterized in that: The method for obtaining the overall habit influence index includes: The braking habit influence coefficient and the braking habit wear coefficient are forwardly integrated to obtain an overall habit influence index.

7. A method for predicting the service life of a brake disc according to claim 1, characterized in that: The method for obtaining the autocorrelation coefficient includes: The ARIMA model method is used to obtain the autocorrelation coefficient based on the pedal movement distance series during the reference period.

8. The method for predicting the service life of a brake disc according to claim 1, characterized in that: The method for obtaining the modified autocorrelation coefficient includes: The product of the negative correlation mapping result of the autocorrelation coefficient corresponding to the lag order and the overall habit influence index is calculated to obtain an adjustment parameter; the product of the adjustment parameter and the autocorrelation coefficient is calculated and normalized to obtain a corrected autocorrelation coefficient.

9. A method for predicting the service life of a brake disc according to claim 1, characterized in that: Methods for predicting the service life of brake discs include: The ARIMA model method is used to predict the service life of the brake disc based on the time series data of the moving distance in the reference period and the corrected autocorrelation coefficient.

10. A system for predicting the service life of a brake disc, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for predicting the service life of a brake disc as described in any one of claims 1 to 9 are implemented.

Images