Intelligent diagnosis method and system

Abstract

The embodiment of the present application relates to the technical field of vehicle fault diagnosis, and discloses an intelligent diagnosis method and system. When the vehicle automatically triggers a fault detection instruction, the method first obtains the temperature rise rate of the power system and the braking response time difference of the brake system of the target vehicle, and then performs a correlation detection based on the temperature rise rate of the power system and the braking response time difference of the brake system to obtain a correlation detection result. When the correlation detection result is a strong correlation, the temperature rise rate of the power system and the braking response time difference of the brake system are input into a pre-trained brake fault probability prediction model to obtain a brake fault probability prediction value; when the brake fault probability prediction value is greater than or equal to the probability threshold, it is judged that the target vehicle has a brake fault risk and triggers a warning signal; finally, when the target vehicle meets the dangerous driving conditions, the warning signal is sent to the vehicle-mounted terminal of the target vehicle. In this way, the efficiency and accuracy of early warning of vehicle faults are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle fault diagnosis, and in particular to an intelligent diagnosis method and system. Background Art

[0002] With the sustained and rapid development of my country's economy in recent years, the e-commerce and logistics industries have ushered in an unprecedented period of prosperity. This trend has directly led to the rapid expansion of the commercial vehicle market. As the core carrier of logistics and transportation, the number of commercial vehicles has achieved a leap-forward growth in just a few years. They not only play a vital role in urban distribution and long-distance transportation, but also have a profound impact on every link of supply chain management. However, with the surge in the number of commercial vehicles and the continuous increase in vehicle usage, the vehicle failure rate has also shown an upward trend, which has posed a more severe challenge to vehicle maintenance and troubleshooting, especially when the vehicle is about to fail, the efficiency and accuracy of early warning of failures need to be improved urgently. Summary of the invention

[0003] The main purpose of the present invention is to provide an intelligent diagnosis method and system, aiming to solve the technical problem of low efficiency and accuracy of early warning of vehicle failure in the prior art.

[0004] To achieve the above-mentioned purpose, in a first aspect, an intelligent diagnosis method is provided in an embodiment of the present application, which is applied to a vehicle fault diagnosis system, wherein the vehicle fault diagnosis system includes a fault diagnosis terminal tbox and a data stream fault diagnosis cloud platform, and the method includes:

[0005] Obtaining a fault diagnosis instruction of a target vehicle, wherein the fault diagnosis instruction carries operating data stream information of a first component of the target vehicle, wherein the operating data stream information of the first component includes a braking response time difference of a braking system, wherein the braking response time difference is a difference between a current response time of the braking system and an expected response time;

[0006] Acquiring operating data stream information of a second component of the target vehicle according to the fault diagnosis instruction, wherein the operating data stream information of the second component includes a temperature rise rate of a power system;

[0007] A correlation detection is performed according to the temperature rise rate of the power system and the braking response time difference of the brake system to obtain a correlation detection result, wherein the correlation detection is used to determine whether the temperature rise rate of the power system is correlated with the braking response time difference of the brake system, and the correlation detection result is one of a strong correlation and a weak correlation;

[0008] When the correlation detection result is a strong correlation, the temperature rise rate of the power system and the braking response time difference of the brake system are input into a pre-trained brake failure probability prediction model to obtain a brake failure probability prediction value;

[0009] Determining that the brake failure probability estimate is greater than or equal to a probability threshold, determining that the target vehicle has a brake failure risk and triggering a warning signal;

[0010] When the target vehicle meets the dangerous driving conditions, the warning signal is sent to the vehicle-mounted terminal of the target vehicle.

[0011] In a possible implementation, the acquiring of the fault diagnosis instruction of the target vehicle includes:

[0012] Acquire operating condition data stream information of a first component of a target vehicle, wherein the operating condition data stream information of the first component includes a braking response time difference of a braking system;

[0013] When the braking response time difference of the braking system is greater than a first preset difference and less than a second preset difference, it is preliminarily determined that there is a risk of failure in the braking system of the target vehicle, and the second preset difference is a minimum braking response time difference when a failure occurs in the braking system of the target vehicle;

[0014] Trigger the fault diagnosis instruction of the target vehicle.

[0015] In a possible implementation, the performing correlation detection according to the temperature rise rate of the power system and the braking response time difference of the brake system to obtain the correlation detection result includes:

[0016] Perform noise reduction preprocessing on the power system temperature rise rate data series and the brake system braking response time difference data series;

[0017] Performing time synchronization processing on the power system temperature rise rate data sequence and the brake system brake response time difference data sequence to ensure that time synchronization is considered during correlation analysis;

[0018] The Spearman rank correlation coefficient is used to calculate the correlation between the temperature rise rate of the power system and the braking response time difference of the brake system, and the correlation is used to characterize the strength of the relationship between the two.

[0019] Determining that the absolute value of the Spearman rank correlation coefficient is greater than or equal to 0.7, it is determined that there is a strong correlation between the temperature rise rate of the power system and the braking response time difference of the brake system;

[0020] It is determined that the absolute value of the Spearman rank correlation coefficient is less than 0.7, and it is determined that there is a weak correlation between the temperature rise rate of the power system and the braking response time difference of the brake system.

[0021] In a possible implementation, the method of calculating the correlation between the temperature rise rate of the power system and the braking response time difference of the brake system by using the Spearman rank correlation coefficient includes:

[0022] Sort the data of two variables (X, Y) and record the position after sorting (X', Y'). The value of (X', Y') is called the rank. One of the variables X and Y is the temperature rise rate of the power system, and the other is the braking response time difference of the brake system.

[0023] Calculate the rank difference d of each data pair i =X i '-Y i ';

[0024] The Spearman rank correlation coefficient is calculated using the Spearman rank correlation coefficient model, where the Spearman rank correlation coefficient model satisfies the following expression:

[0025] ;

[0026] in, is the Spearman rank correlation coefficient, n is the total number of observed samples, d i is the rank difference of the i-th data pair.

[0027] In a possible implementation, the step of inputting the power system temperature rise rate and the brake system braking response time difference into a pre-trained brake failure probability prediction model to obtain a brake failure probability prediction value includes:

[0028] Obtaining the average value of the temperature rise rate of the power system and the average value of the braking response time difference of the brake system within a preset time period;

[0029] The average value of the temperature rise rate of the power system and the average value of the braking response time difference of the brake system obtained within the preset time period are input into the pre-trained brake failure probability prediction model to obtain a brake failure probability prediction value, wherein the brake failure probability prediction model satisfies the following expression:

[0030] ;

[0031] Where P is the probability of brake failure, is the average temperature rise rate of the power system, is the historical maximum value of the temperature rise rate of the power system, is the power system temperature rise rate threshold, is the mean time difference of the braking response of the braking system, The historical maximum value of the braking response time difference of the braking system. It is the braking response time difference threshold of the braking system.

[0032] In a possible implementation, when the target vehicle meets the dangerous driving condition, sending the warning signal to the vehicle-mounted terminal of the target vehicle includes:

[0033] Get the current speed of the target vehicle;

[0034] When the current driving speed of the target vehicle is greater than or equal to the preset speed, the warning signal is sent to the vehicle-mounted terminal of the target vehicle.

[0035] In a possible implementation, when the target vehicle meets the dangerous driving condition, sending the warning signal to the vehicle-mounted terminal of the target vehicle includes:

[0036] Obtaining the smoothness of the current road surface on which the target vehicle is traveling and the current speed of the target vehicle;

[0037] When the smoothness of the current driving road surface of the target vehicle is greater than or equal to the preset smoothness, and the current driving speed of the target vehicle is greater than or equal to the preset speed, the warning signal is sent to the vehicle-mounted terminal of the target vehicle.

[0038] In a possible implementation, the target vehicle is an electric vehicle, the power system of the target vehicle includes a drive motor, and the step of obtaining the operating condition data stream information of the second component of the target vehicle according to the fault diagnosis instruction includes:

[0039] The temperature rise rate of the target vehicle drive motor is obtained according to the fault diagnosis instruction.

[0040] In a possible implementation, the target vehicle is a fuel vehicle, the power system of the target vehicle includes an engine, and the step of obtaining the operating condition data stream information of the second component of the target vehicle according to the fault diagnosis instruction includes:

[0041] The temperature rise rate of the target vehicle engine is obtained according to the fault diagnosis instruction.

[0042] In a second aspect, an embodiment of the present application further provides a vehicle fault diagnosis system, comprising:

[0043] The fault diagnosis terminal tbox is used to obtain the vehicle's operating data stream information and send it to the data stream fault diagnosis cloud platform through wireless communication;

[0044] A data stream fault diagnosis cloud platform, used to receive the operating condition data stream information sent by the fault diagnosis terminal tbox, and perform vehicle fault analysis based on the operating condition data stream information; and,

[0045] A memory and a processor, wherein the memory is used to store program codes, and the processor is used to call the program codes to execute the method as described in the first aspect.

[0046] Different from the prior art, the embodiment of the present application provides an intelligent diagnosis method. When the vehicle automatically triggers the fault detection instruction, the temperature rise rate of the power system and the braking response time difference of the brake system of the target vehicle are first obtained, and then the correlation detection is performed according to the temperature rise rate of the power system and the braking response time difference of the brake system to obtain the correlation detection result. When the correlation detection result is a strong correlation, the temperature rise rate of the power system and the braking response time difference of the brake system are input into the pre-trained brake fault probability prediction model to obtain the brake fault probability prediction value; when the brake fault probability prediction value is greater than or equal to the probability threshold, it is judged that the target vehicle has a brake fault risk and triggers a warning signal; finally, when the target vehicle meets the dangerous driving conditions, the warning signal is sent to the vehicle terminal of the target vehicle. That is, by collecting the data stream of the temperature rise rate of the power system and the braking response time difference of the brake system to analyze the brake fault model to obtain the brake fault probability, the fault risk of the vehicle is predicted in advance, and when the vehicle is in a dangerous driving state, the warning signal will be sent to the vehicle terminal to remind the driver in advance that the vehicle has a fault risk. In this way, the efficiency and accuracy of early warning of vehicle faults are greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 the structures shown in these drawings without paying creative work.

[0048] Figure 1 This is a data flow diagram of a vehicle fault diagnosis system in some embodiments of the present application;

[0049] Figure 2 This is a flowchart of the intelligent diagnosis method in some embodiments of the present application;

[0050] Figure 3 This is a flowchart of step S300 of the intelligent diagnosis method in some embodiments of the present application;

[0051] Figure 4 This is a schematic diagram of the hardware structure of a vehicle fault diagnosis system in some embodiments of the present application.

[0052] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0055] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0056] With the sustained and rapid development of my country's economy in recent years, the e-commerce and logistics industries have ushered in an unprecedented period of prosperity. This trend has directly led to the rapid expansion of the commercial vehicle market. As the core carrier of logistics and transportation, the number of commercial vehicles has achieved a leap-forward growth in just a few years. They not only play a vital role in urban distribution and long-distance transportation, but also have a profound impact on every link of supply chain management. However, with the surge in the number of commercial vehicles and the continuous increase in vehicle usage, the vehicle failure rate has also shown an upward trend, which has posed a more severe challenge to vehicle maintenance and troubleshooting, especially when the vehicle is about to fail, the efficiency and accuracy of early warning of failures need to be improved urgently.

[0057] In view of the above problems, the present application proposes an intelligent diagnosis method, which can be applied to a vehicle local server or a remote cloud server. The following is an example of a vehicle fault diagnosis system executing the intelligent diagnosis method. In the embodiment of the present application, the vehicle fault diagnosis system includes a fault diagnosis terminal tbox and a data flow fault diagnosis cloud platform, such as Figure 1 As shown, Figure 1This is a data flow diagram of a vehicle fault diagnosis system in some embodiments of the present application, wherein the fault diagnosis terminal tbox is used to obtain the vehicle's operating data flow information and send it to the data flow fault diagnosis cloud platform via wireless communication; the data flow fault diagnosis cloud platform is used to receive the operating data flow information sent by the fault diagnosis terminal tbox, and perform vehicle fault analysis based on the operating data flow information. It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than here. Please refer to the attached Figure 2 The method includes the following steps S100 to S600:

[0058] S100, obtaining a fault diagnosis instruction of a target vehicle, wherein the fault diagnosis instruction carries operating data stream information of a first component of the target vehicle, wherein the operating data stream information of the first component includes a braking response time difference of a braking system, wherein the braking response time difference is a difference between a current response time of the braking system and an expected response time;

[0059] In the embodiment of the present application, the fault diagnosis instruction of the target vehicle can be triggered in real time or at a preset time, can be manually triggered by the user, or can be automatically triggered based on abnormal conditions detected by sensor data.

[0060] For example, it can be triggered based on the current operating status of the vehicle or the driver's operation; or triggered at a specific time point or time interval, such as a certain time every day, week or month; or manually triggered by the owner through a handheld terminal; or triggered when a sensor detects that certain parameters are abnormal.

[0061] In one embodiment, the step S100: obtaining a fault diagnosis instruction for a target vehicle includes: obtaining operating condition data stream information of a first component of the target vehicle, the operating condition data stream information of the first component including a braking response time difference of a braking system; when the braking response time difference of the braking system is greater than a first preset difference and less than a second preset difference, preliminarily determining that there is a failure risk in the braking system of the target vehicle, the second preset difference being the minimum braking response time difference when a failure occurs in the braking system of the target vehicle; and triggering a fault diagnosis instruction for the target vehicle.

[0062] Specifically, in the embodiment of the present application, when it is detected that the braking response time difference of the braking system is greater than the first preset difference, it means that the response speed of the braking system is slower than normal expectations, but has not yet reached the level of failure. In other words, although the performance of the braking system has declined, it is not serious enough to completely fail. When the above two conditions are met at the same time (that is, the braking response time difference is greater than the first preset difference and less than the second preset difference), the system preliminarily determines that there is a risk of failure in the braking system of the target vehicle and automatically triggers a fault diagnosis instruction. This instruction will initiate a cloud model check of the braking system to determine whether there is indeed a fault.

[0063] S200, acquiring operating condition data stream information of a second component of the target vehicle according to the fault diagnosis instruction, wherein the operating condition data stream information of the second component includes a temperature rise rate of a power system;

[0064] After the system automatically triggers the fault diagnosis command based on the sensor data, in order to improve the accuracy of the judgment, it is necessary to integrate other sensor data to further determine whether there is indeed a fault. For example, the temperature sensor data can be integrated to perform model analysis to accurately detect whether the vehicle has a fault.

[0065] In one embodiment, taking the target vehicle as an electric vehicle, the power system of the target vehicle includes a drive motor, and the step S200: obtaining the operating data stream information of the second component of the target vehicle according to the fault diagnosis instruction includes: obtaining the temperature rise rate of the drive motor of the target vehicle according to the fault diagnosis instruction. That is, the temperature sensor data of the vehicle drive motor is integrated to perform model analysis to accurately detect whether the vehicle has a fault.

[0066] In other embodiments, taking the target vehicle as a fuel vehicle, such as a fuel vehicle, the power system of the target vehicle includes an engine, the step S200: obtaining the operating data stream information of the second component of the target vehicle according to the fault diagnosis instruction includes: obtaining the temperature rise rate of the engine of the target vehicle according to the fault diagnosis instruction. That is, the temperature sensor data of the vehicle engine is integrated to perform model analysis to accurately detect whether the vehicle has a fault.

[0067] It is understandable that when the temperature rise rate of the vehicle's motor or engine is too fast, it may affect the brake system connected to it, such as the increase in brake fluid temperature, thereby affecting the braking effect. Therefore, when the system automatically triggers the brake system fault detection command, it is necessary to further obtain the temperature rate of the vehicle's motor or engine to perform a fusion judgment of the fault based on the two parameters of the power system temperature rise rate and the brake system brake response time difference.

[0068] S300, performing correlation detection according to the temperature rise rate of the power system and the braking response time difference of the brake system to obtain a correlation detection result, wherein the correlation detection is used to determine whether the temperature rise rate of the power system is correlated with the braking response time difference of the brake system, and the correlation detection result is one of a strong correlation and a weak correlation;

[0069] In order to improve the accuracy of vehicle fault diagnosis, after obtaining the two parameters of power system temperature rise rate and brake system brake response time difference, it is necessary to pre-judge the correlation between the power system temperature rise rate and the brake system brake response time difference, that is, to judge whether the power system temperature rise rate is correlated with the brake system brake response time difference. If there is no correlation or a weak correlation between the two, fusing the two parameters of power system temperature rise rate and brake system brake response time difference will result in an incorrect result.

[0070] like Figure 3 As shown, in one embodiment, the step S300: performing correlation detection according to the temperature rise rate of the power system and the braking response time difference of the brake system to obtain a correlation detection result includes:

[0071] S310, performing noise reduction preprocessing on the power system temperature rise rate data sequence and the brake system braking response time difference data sequence;

[0072] S320, performing time synchronization processing on the power system temperature rise rate data sequence and the brake system brake response time difference data sequence to ensure that time synchronization is considered during correlation analysis;

[0073] S330, calculating the correlation between the temperature rise rate of the power system and the braking response time difference of the brake system by using the Spearman rank correlation coefficient, wherein the correlation is used to characterize the strength of the relationship between the two;

[0074] S340, determining that the absolute value of the Spearman rank correlation coefficient is greater than or equal to 0.7, and determining that there is a strong correlation between the temperature rise rate of the power system and the braking response time difference of the brake system;

[0075] S350. Determine that the absolute value of the Spearman rank correlation coefficient is less than 0.7, and determine that there is a weak correlation between the temperature rise rate of the power system and the braking response time difference of the brake system.

[0076] Specifically, firstly, filtering technology, data smoothing or other signal processing technology are used to reduce random fluctuations and outliers in the data, so as to remove noise in the data series of the temperature rise rate of the power system and the brake response time difference of the brake system, so as to improve the accuracy of subsequent analysis; then, through interpolation, resampling or timestamp matching, the time points of the two data series are made to correspond one by one, ensuring that the two data series are consistent in time, so as to accurately reflect the synchronization relationship between the two in the correlation analysis; and then the Spearman rank correlation coefficient is used to calculate the correlation between the temperature rise rate of the power system and the brake response time difference of the brake system. The Spearman rank correlation coefficient is a non-parametric statistic used to measure the monotonic relationship between two variables. It does not require the data to follow a normal distribution and is applicable to various types of data. When calculating, the data is first arranged in ascending order, and then the rank of each data point (i.e., its position in the sorted data) is calculated, and finally the correlation coefficient is calculated based on the rank. If the absolute value of the Spearman rank correlation coefficient is greater than or equal to 0.7, it indicates that the relationship between the two is relatively close, and the change of one variable may significantly affect the change of another variable. At this time, the system determines that there is a strong correlation between the temperature rise rate of the power system and the brake response time difference of the brake system. If the absolute value of the Spearman rank correlation coefficient is less than 0.7, it indicates that the relationship between the two is not close enough, and the change of one variable may have a small or unobvious impact on the other variable. At this time, the system judges that there is a weak correlation between the temperature rise rate of the power system and the braking response time difference of the brake system.

[0077] In one embodiment, the step S330: using the Spearman rank correlation coefficient to calculate the correlation between the temperature rise rate of the power system and the braking response time difference of the brake system, includes: sorting the data of the two variables (X, Y), recording the sorted position (X', Y'), and the value of (X', Y') is called the rank, one of the variables X and Y is the temperature rise rate of the power system, and the other is the braking response time difference of the brake system; calculating the rank difference d of each data pair i =X i '-Y i '; The Spearman rank correlation coefficient is calculated using the Spearman rank correlation coefficient model, where the Spearman rank correlation coefficient model satisfies the following expression:

[0078] ;

[0079] In the formula, is the Spearman rank correlation coefficient, n is the total number of observed samples, d i is the rank difference of the i-th data pair.

[0080] Since the Spearman rank correlation coefficient can show better performance in small sample data and can provide more reliable results, the embodiment of the present application uses the Spearman rank correlation coefficient to measure the correlation between the temperature rise rate of the power system and the braking response time difference of the brake system, which can improve the efficiency of obtaining the correlation results.

[0081] S400, when the correlation detection result is a strong correlation, inputting the temperature rise rate of the power system and the braking response time difference of the brake system into a pre-trained brake failure probability estimation model to obtain a brake failure probability estimation value;

[0082] When the correlation detection result is a strong correlation, it indicates that there is a strong causal correlation between the temperature rise rate of the power system and the braking response time difference of the brake system. At this time, the temperature rise rate of the power system and the braking response time difference of the brake system are input into the pre-trained brake failure probability prediction model to obtain the brake failure probability prediction value, which can ensure the effectiveness of the brake failure probability prediction value.

[0083] In one embodiment, the step S400: inputting the power system temperature rise rate and the brake system braking response time difference into a pre-trained brake failure probability prediction model to obtain a brake failure probability prediction value includes:

[0084] S410, obtaining an average value of the temperature rise rate of the power system and an average value of the braking response time difference of the brake system within a preset time period;

[0085] S420, inputting the average value of the temperature rise rate of the power system and the average value of the braking response time difference of the brake system obtained within a preset time period into a pre-trained brake failure probability prediction model to obtain a brake failure probability prediction value, wherein the brake failure probability prediction model satisfies the following expression:

[0086] ;

[0087] Where P is the probability of brake failure, is the average temperature rise rate of the power system, is the historical maximum value of the temperature rise rate of the power system, is the power system temperature rise rate threshold, is the mean time difference of the braking response of the braking system, The historical maximum value of the braking response time difference of the braking system. It is the braking response time difference threshold of the braking system.

[0088] The embodiment of the present application uses a large amount of data for model training, and continuously performs parameter correction and adjustment during the model training process to obtain a brake failure probability prediction model. Specifically, by collecting a series of data on the change of motor temperature rise rate and brake response time, and recording the corresponding brake failure conditions. Then, statistical methods (such as least squares method, gradient descent method, etc.) are used to fit these coefficients. By dividing ΔT and Δt by their historical maximum values ​​T_max and t_max, respectively, these variables are normalized to the range of 0 to 1. This helps to ensure that the formula works stably under input data of different scales. and The choice can be based on actual data and experience. 0.5℃ / S, is 0.09s. This threshold defines when it is considered that the change in motor temperature or brake response time has a positive effect on the probability of brake failure. In practical applications, these thresholds may need to be adjusted according to specific circumstances. After the brake failure probability prediction model is obtained, it is pre-stored on the cloud server. After receiving the average temperature rise rate of the power system and the average brake response time difference of the brake system within the current preset time period (for example, within 1 minute or within 5 minutes), the cloud server can obtain the probability value of brake failure. In this way, the brake failure probability prediction model realizes the ability to predict the probability of brake failure based on the changes in motor temperature rise rate and brake response time.

[0089] S500, determining that the brake failure probability estimate is greater than or equal to a probability threshold, determining that the target vehicle has a brake failure risk and triggering a warning signal;

[0090] When the brake failure probability estimation value calculated by the brake failure probability estimation model is greater than or equal to the probability threshold, for example, the probability estimation value reaches 80%, it means that the vehicle's brake system has a greater risk of failure, and the system triggers a warning signal. The warning signal can include sound alarms, light alarms, text prompts on the display screen, etc. The warning signal can be sent to the driver user or a third party (such as background supervision, maintenance party or insurance party).

[0091] S600: When the target vehicle meets the dangerous driving condition, the warning signal is sent to the vehicle-mounted terminal of the target vehicle.

[0092] In one embodiment, the step S600: when the target vehicle meets the dangerous driving conditions, sending the warning signal to the vehicle-mounted terminal of the target vehicle includes: obtaining the current driving speed of the target vehicle; when the current driving speed of the target vehicle is greater than or equal to the preset speed, sending the warning signal to the vehicle-mounted terminal of the target vehicle.

[0093] Specifically, when monitoring the driving state of the target vehicle, the embodiment of the present application first obtains its current driving speed. This data can come from the vehicle's own sensor system, such as a speed sensor, or can be obtained through external devices such as GPS. After obtaining the current driving speed, it is compared with a preset speed threshold. This preset speed is set after comprehensive consideration of multiple factors such as road conditions, traffic rules, and vehicle performance, and is intended to ensure that when the speed reaches or exceeds this threshold, the driver needs to be reminded in time to avoid potential dangers. If the current driving speed of the target vehicle is greater than or equal to this preset speed, the system believes that the vehicle has met the dangerous driving conditions. At this time, if the probability of brake failure is high, the system will automatically trigger the early warning mechanism, that is, send an early warning signal to the on-board terminal of the target vehicle. Thereby informing the driver that the current speed has approached or exceeded the safety limit in the case of vehicle failure risk.

[0094] In another embodiment, the step S600: when the target vehicle meets the dangerous driving conditions, the warning signal is sent to the vehicle-mounted terminal of the target vehicle, including: obtaining the smoothness of the current driving road surface of the target vehicle and the current driving speed of the target vehicle; when the smoothness of the current driving road surface of the target vehicle is greater than or equal to the preset smoothness, and the current driving speed of the target vehicle is greater than or equal to the preset speed, the warning signal is sent to the vehicle-mounted terminal of the target vehicle.

[0095] Specifically, when the system detects that the current road surface of the target vehicle is relatively smooth and the vehicle speed is relatively fast, the system considers that the vehicle has met the dangerous driving conditions. At this time, if the probability of brake failure is high, the system will automatically trigger the early warning mechanism, that is, send an early warning signal to the vehicle terminal of the target vehicle. This will inform the driver that the vehicle has approached or exceeded the safety limit when the current road surface is relatively smooth, the vehicle speed is relatively fast, and there is a risk of vehicle failure.

[0096] Based on this, the application embodiment provides an intelligent diagnosis method. When the vehicle automatically triggers the fault detection instruction, the temperature rise rate of the power system and the braking response time difference of the brake system of the target vehicle are first obtained, and then the correlation detection is performed according to the temperature rise rate of the power system and the braking response time difference of the brake system to obtain the correlation detection result. When the correlation detection result is a strong correlation, the temperature rise rate of the power system and the braking response time difference of the brake system are input into the pre-trained brake fault probability prediction model to obtain the brake fault probability prediction value; when the brake fault probability prediction value is greater than or equal to the probability threshold, it is judged that the target vehicle has a brake fault risk and triggers a warning signal; finally, when the target vehicle meets the dangerous driving conditions, the warning signal is sent to the vehicle terminal of the target vehicle. That is, by collecting the data stream of the temperature rise rate of the power system and the braking response time difference of the brake system to analyze the brake fault model to obtain the brake fault probability, the fault risk of the vehicle is predicted in advance, and when the vehicle is in a dangerous driving state, the warning signal will be sent to the vehicle terminal to remind the driver in advance that the vehicle has a fault risk. In this way, the efficiency and accuracy of early warning of vehicle faults are greatly improved.

[0097] Please see attached Figure 4 , Figure 4 A schematic diagram of the hardware structure of a vehicle fault diagnosis system provided for some embodiments of the present application; the vehicle fault diagnosis system provided in the embodiments of the present application includes a memory 110 and a processor 120, wherein the memory 110 is used to store program code, and the processor 120 is used to call the program code to execute the method described above.

[0098] Among them, the processor 120 is used to provide computing and control capabilities to control the vehicle fault diagnosis system to perform corresponding tasks, for example, to control the vehicle fault diagnosis system to perform the intelligent diagnosis method in any of the above method embodiments, the method comprising: obtaining a fault diagnosis instruction of the target vehicle, the fault diagnosis instruction carrying the operating data stream information of a first component of the target vehicle, the operating data stream information of the first component including the braking response time difference of the braking system, the braking response time difference being the difference between the current response time and the expected response time of the braking system; obtaining the operating data stream information of the second component of the target vehicle according to the fault diagnosis instruction, the operating data stream information of the second component including the temperature rise rate of the power system; obtaining the operating data stream information of the second component according to the temperature rise rate of the power system A correlation detection is performed on the temperature rise rate of the power system and the braking response time difference of the brake system to obtain a correlation detection result, wherein the correlation detection is used to determine whether the temperature rise rate of the power system is correlated with the braking response time difference of the brake system, and the correlation detection result is one of a strong correlation and a weak correlation; when the correlation detection result is a strong correlation, the temperature rise rate of the power system and the braking response time difference of the brake system are input into a pre-trained brake failure probability prediction model to obtain a brake failure probability prediction value; it is determined that the brake failure probability prediction value is greater than or equal to a probability threshold, and it is determined that the target vehicle has a brake failure risk and a warning signal is triggered; when the target vehicle meets dangerous driving conditions, the warning signal is sent to the on-board terminal of the target vehicle.

[0099] The processor 120 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The above-mentioned PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0100] The memory 110 is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the intelligent diagnosis method in the embodiments of the present application. The processor 120 can implement the intelligent diagnosis method in any of the above method embodiments by running the non-transitory software programs, instructions and modules stored in the memory 110.

[0101] Specifically, the memory 110 may include a volatile memory (VM), such as a random access memory (RAM); the memory 110 may also include a non-volatile memory (NVM), such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD) or other non-transitory solid-state storage device; the memory 110 may also include a combination of the above types of memory.

[0102] In summary, the vehicle fault diagnosis system of the present application adopts the technical solution of any one of the above-mentioned intelligent diagnosis method embodiments, and therefore, has at least the beneficial effects brought by the technical solution of the above-mentioned embodiments, which will not be described one by one here.

[0103] The present application also provides a computer-readable storage medium, such as a memory including a program code, and the program code can be executed by a processor to complete the intelligent diagnosis method in the above embodiment. For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CDROM), a magnetic tape, a floppy disk, and an optical data storage device.

[0104] The present application also provides a computer program product, which includes one or more program codes stored in a computer-readable storage medium. The processor of the vehicle fault diagnosis system reads the program code from the computer-readable storage medium, and the processor executes the program code to complete the steps of the intelligent diagnosis method provided in the above embodiment.

[0105] A person skilled in the art will appreciate that all or part of the steps for implementing the above embodiments may be accomplished by hardware or by hardware associated with a program code, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0106] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0107] Through the description of the above embodiments, it can be clearly understood by those skilled in the art that each embodiment can be implemented by means of software plus a general hardware platform, or by hardware. It can be understood by those skilled in the art that all or part of the processes in the above embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0108] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. An intelligent diagnosis method, applied to a vehicle fault diagnosis system, wherein the vehicle fault diagnosis system comprises a fault diagnosis terminal tbox and a data stream fault diagnosis cloud platform, characterized in that: The method comprises: Obtaining a fault diagnosis instruction of a target vehicle, wherein the fault diagnosis instruction carries operating data stream information of a first component of the target vehicle, wherein the operating data stream information of the first component includes a braking response time difference of a braking system, wherein the braking response time difference is a difference between a current response time of the braking system and an expected response time; Acquiring operating data stream information of a second component of the target vehicle according to the fault diagnosis instruction, wherein the operating data stream information of the second component includes a temperature rise rate of a power system; A correlation detection is performed according to the temperature rise rate of the power system and the braking response time difference of the brake system to obtain a correlation detection result, wherein the correlation detection is used to determine whether the temperature rise rate of the power system is correlated with the braking response time difference of the brake system, and the correlation detection result is one of a strong correlation and a weak correlation; When the correlation detection result is a strong correlation, the temperature rise rate of the power system and the braking response time difference of the brake system are input into a pre-trained brake failure probability prediction model to obtain a brake failure probability prediction value; Determining that the brake failure probability estimate is greater than or equal to a probability threshold, determining that the target vehicle has a brake failure risk and triggering a warning signal; When the target vehicle meets the dangerous driving conditions, the warning signal is sent to the vehicle-mounted terminal of the target vehicle.

2. The intelligent diagnosis method according to claim 1, characterized in that: The step of obtaining the fault diagnosis instruction of the target vehicle includes: Acquire operating condition data stream information of a first component of a target vehicle, wherein the operating condition data stream information of the first component includes a braking response time difference of a braking system; When the braking response time difference of the braking system is greater than a first preset difference and less than a second preset difference, it is preliminarily determined that there is a risk of failure in the braking system of the target vehicle, and the second preset difference is a minimum braking response time difference when a failure occurs in the braking system of the target vehicle; Trigger the fault diagnosis instruction of the target vehicle.

3. The intelligent diagnosis method according to claim 1, characterized in that: The correlation detection is performed according to the temperature rise rate of the power system and the braking response time difference of the brake system to obtain the correlation detection result, including: Perform noise reduction preprocessing on the power system temperature rise rate data series and the brake system braking response time difference data series; Performing time synchronization processing on the power system temperature rise rate data sequence and the brake system brake response time difference data sequence to ensure that time synchronization is considered during correlation analysis; The Spearman rank correlation coefficient is used to calculate the correlation between the temperature rise rate of the power system and the braking response time difference of the brake system, and the correlation is used to characterize the strength of the relationship between the two. Determining that the absolute value of the Spearman rank correlation coefficient is greater than or equal to 0.7, it is determined that there is a strong correlation between the temperature rise rate of the power system and the braking response time difference of the brake system; It is determined that the absolute value of the Spearman rank correlation coefficient is less than 0.7, and it is determined that there is a weak correlation between the temperature rise rate of the power system and the braking response time difference of the brake system.

4. The intelligent diagnosis method according to claim 3, characterized in that: The method of calculating the correlation between the temperature rise rate of the power system and the braking response time difference of the brake system by using the Spearman rank correlation coefficient includes: Sort the data of two variables (X, Y) and record the position after sorting (X', Y'). The value of (X', Y') is called the rank. One of the variables X and Y is the temperature rise rate of the power system, and the other is the braking response time difference of the brake system. Calculate the rank difference d of each data pair i =X i '-Y i '; The Spearman rank correlation coefficient is calculated using the Spearman rank correlation coefficient model, where the Spearman rank correlation coefficient model satisfies the following expression: ； in, is the Spearman rank correlation coefficient, n is the total number of observed samples, d i is the rank difference of the i-th data pair.

5. The intelligent diagnosis method according to claim 1, characterized in that: The step of inputting the power system temperature rise rate and the brake system braking response time difference into a pre-trained brake failure probability prediction model to obtain a brake failure probability prediction value includes: Obtaining the average value of the temperature rise rate of the power system and the average value of the braking response time difference of the brake system within a preset time period; The average value of the temperature rise rate of the power system and the average value of the braking response time difference of the brake system obtained within the preset time period are input into the pre-trained brake failure probability prediction model to obtain a brake failure probability prediction value, wherein the brake failure probability prediction model satisfies the following expression: ； Where P is the probability of brake failure, is the average temperature rise rate of the power system, is the historical maximum value of the temperature rise rate of the power system, is the power system temperature rise rate threshold, is the mean time difference of the braking response of the braking system, The historical maximum value of the braking response time difference of the braking system. It is the braking response time difference threshold of the braking system.

6. The intelligent diagnosis method according to claim 1, characterized in that: When the target vehicle meets the dangerous driving condition, sending the warning signal to the vehicle-mounted terminal of the target vehicle includes: Get the current speed of the target vehicle; When the current driving speed of the target vehicle is greater than or equal to the preset speed, the warning signal is sent to the vehicle-mounted terminal of the target vehicle.

7. The intelligent diagnosis method according to claim 1, characterized in that: When the target vehicle meets the dangerous driving condition, sending the warning signal to the vehicle-mounted terminal of the target vehicle includes: Obtaining the smoothness of the current road surface on which the target vehicle is traveling and the current speed of the target vehicle; When the smoothness of the current driving road surface of the target vehicle is greater than or equal to the preset smoothness, and the current driving speed of the target vehicle is greater than or equal to the preset speed, the warning signal is sent to the vehicle-mounted terminal of the target vehicle.

8. The intelligent diagnosis method according to claim 1, characterized in that: The target vehicle is an electric vehicle, the power system of the target vehicle includes a drive motor, and the method of obtaining the operating condition data stream information of the second component of the target vehicle according to the fault diagnosis instruction includes: The temperature rise rate of the target vehicle drive motor is obtained according to the fault diagnosis instruction.

9. The intelligent diagnosis method according to claim 1, characterized in that: The target vehicle is a fuel vehicle, the power system of the target vehicle includes an engine, and the method of obtaining the operating condition data stream information of the second component of the target vehicle according to the fault diagnosis instruction includes: The temperature rise rate of the target vehicle engine is obtained according to the fault diagnosis instruction.

10. A vehicle fault diagnosis system, characterized in that: include: The fault diagnosis terminal tbox is used to obtain the vehicle's operating data stream information and send it to the data stream fault diagnosis cloud platform through wireless communication; A data stream fault diagnosis cloud platform, used to receive the operating condition data stream information sent by the fault diagnosis terminal tbox, and perform vehicle fault analysis based on the operating condition data stream information; and, A memory and a processor, wherein the memory is used to store program codes, and the processor is used to call the program codes to execute the method according to any one of claims 1 to 9.

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