AI diagnosis method for vehicle braking system and related device

Abstract

The invention provides an AI diagnosis method for a vehicle braking system and a related device. The method comprises the steps that vehicle driving data, historical road condition data and vehicle braking data of a target vehicle within a preset time period are acquired; determining the average value of the brake pressure data as reference brake pressure; the brake pressure data and the brake pad abrasion speed data are analyzed, and a first fitting straight line is obtained; determining a vehicle driving style corresponding to the target vehicle according to the vehicle driving data; determining a target regulation factor through an AI algorithm according to the vehicle driving style and the historical road condition data; the reference brake pressure is adjusted according to the target adjusting factor, and target brake pressure is obtained; the brake pad service life of the target vehicle is determined according to the brake pad thickness data, the target brake pressure and the first fitting straight line; and generating diagnosis prompt information according to the service life of the brake pad. The brake service life is accurately predicted, so that the diagnosis accuracy of the brake system is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle diagnosis, in particular to an AI diagnosis method for a vehicle braking system and a related device. BACKGROUND

[0002] With the rapid development of the automotive industry, monitoring and evaluating the safety performance of vehicles has become an important link to improve driving safety. Traditional evaluation and diagnosis of braking systems usually rely on manual detection, which is not only inefficient, but also cannot monitor the vehicle state in real time, and the efficiency and accuracy are limited.

[0003] Therefore, how to improve the accuracy of diagnosing the vehicle braking system needs to be solved. SUMMARY

[0004] The embodiments of the present application provide an AI diagnosis method for a vehicle braking system and a related device, which accurately predicts the service life of the vehicle braking system by combining the vehicle use scenario and the braking system characteristics, thereby improving the accuracy of diagnosing the vehicle braking system.

[0005] In a first aspect, the embodiments of the present application provide an AI diagnosis method for a vehicle braking system, which comprises:

[0006] Obtaining vehicle driving data and historical road condition data of a target vehicle in a preset time period, and vehicle braking data corresponding to the braking system of the target vehicle; the vehicle braking data includes brake pressure data, brake pad thickness data and brake pad wear rate data;

[0007] Determining the average value of the brake pressure data as a reference brake pressure;

[0008] Analyzing the brake pressure data and the brake pad wear rate data to obtain a first fitting straight line; the horizontal axis of the first fitting straight line is the brake pressure, and the vertical axis is the brake pad wear rate;

[0009] Determining the vehicle driving style corresponding to the target vehicle according to the vehicle driving data;

[0010] Determining a target adjustment factor according to the vehicle driving style and the historical road condition data through a preset AI algorithm;

[0011] Adjusting the reference brake pressure according to the target adjustment factor to obtain a target brake pressure;

[0012] Determining the service life of the brake pad of the target vehicle according to the brake pad thickness data, the target brake pressure and the first fitting straight line;

[0013] generate diagnosis prompt information according to the brake pad service life; the diagnosis prompt information is used to prompt the brake life of the target vehicle.

[0014] In a second aspect, the embodiments of the present application provide an AI diagnosis device for a vehicle braking system, the device comprising an acquisition module, a determination module, an analysis module, an adjustment module, a generation module, wherein:

[0015] The acquisition module is configured to acquire vehicle driving data and historical road condition data of a target vehicle in a preset time period, and vehicle braking data corresponding to a braking system of the target vehicle; the vehicle braking data comprises brake pressure data, brake pad thickness data and brake pad wear speed data.

[0016] The determination module is configured to determine an average value of the brake pressure data as a reference brake pressure.

[0017] The analysis module is configured to analyze the brake pressure data and the brake pad wear speed data to obtain a first fitting straight line; an abscissa of the first fitting straight line is brake pressure, and an ordinate is brake pad wear speed.

[0018] The determination module is further configured to determine a vehicle driving style corresponding to the target vehicle according to the vehicle driving data.

[0019] The determination module is further configured to determine a target adjustment factor according to the vehicle driving style and the historical road condition data by using a preset AI algorithm.

[0020] The adjustment module is configured to adjust the reference brake pressure according to the target adjustment factor to obtain a target brake pressure.

[0021] The determination module is further configured to determine a brake pad service life of the target vehicle according to the brake pad thickness data, the target brake pressure and the first fitting straight line.

[0022] The generation module is configured to generate diagnosis prompt information according to the brake pad service life; the diagnosis prompt information is used to prompt the brake life of the target vehicle.

[0023] In a third aspect, the embodiments of the present application provide an electronic device, comprising a processor, a memory, a communication interface and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs comprise instructions for executing steps in any method of the first aspect of the embodiments of the present application.

[0024] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program for electronic data exchange, and the computer program causes a computer to perform some or all of the steps described in any of the methods of the first aspect of the embodiments of the present application.

[0025] In a fifth aspect, an embodiment of the present application provides a computer program product, which comprises a non-transitory computer readable storage medium storing a computer program. The computer program is operable to cause a computer to perform some or all of the steps described in any of the methods of the first aspect of the embodiments of the present application. The computer program product can be a software installation package.

[0026] By implementing the embodiments of the present application, the service life of the vehicle braking system can be accurately predicted by combining the vehicle use scene and the braking system characteristics, thereby improving the accuracy of diagnosing the vehicle braking system. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0028] Figure 1 is a system architecture diagram of a target diagnosis system provided by an embodiment of the present application;

[0029] Figure 2 is a structural schematic diagram of an electronic device provided by an embodiment of the present application;

[0030] Figure 3 is an application scenario diagram of a target diagnosis system provided by an embodiment of the present application;

[0031] Figure 4 is a flowchart of an AI diagnosis method for a vehicle braking system provided by an embodiment of the present application;

[0032] Figure 5 is a flowchart of determining a target adjustment factor provided by an embodiment of the present application;

[0033] Figure 6 is a flowchart of determining the service life of brake pads provided by an embodiment of the present application;

[0034] Figure 7 is a schematic diagram of a diagnosis prompt information interface provided by an embodiment of the present application;

[0035] Figure 8is a functional module composition block diagram of an AI diagnosis device for a vehicle braking system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0036] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0037] The terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish different objects, not to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed or can optionally include other steps or units inherent to the process, method, product or device.

[0038] It should be understood that the term "and / or" herein is only a description of the association relationship between the associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in this paper represents that the front and rear associated objects are a "or" relationship. The "multiple" appearing in the embodiments of the present application means two or more than two.

[0039] The "at least one" or similar expressions in the embodiments of the present application means any combination of these items, including any combination of single item or multiple items, means one or more, and multiple means two or more than two. For example, at least one of a, b or c can represent the following seven cases: a, b, c, a and b, a and c, b and c, a, b and c. Among them, each of a, b and c can be an element or a set containing one or more elements.

[0040] The "connection" appearing in the embodiments of the present application means direct connection or indirect connection and various connection modes to realize communication between devices, which is not limited by the embodiments of the present application.

[0041] Reference to“an embodiment” herein means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase“in one embodiment” in various places in the specification are not necessarily referring to the same embodiment, nor are they necessarily mutually exclusive of one another. It is expressly understood that any of the embodiments described herein can be combined with any of the other embodiments unless specifically noted otherwise.

[0042] With the rapid development of the automotive industry, monitoring and evaluation of vehicle safety performance has become an important link to improve driving safety. The traditional evaluation and diagnosis of the braking system usually relies on manual detection, which is not only inefficient, but also cannot monitor the vehicle state in real time, and the efficiency and accuracy are limited. Therefore, how to improve the accuracy of diagnosing the vehicle braking system needs to be solved.

[0043] To solve the above problems, the application embodiment provides an AI diagnosis method and related device for a vehicle braking system, vehicle driving data and historical road condition data of a target vehicle in a preset time period are obtained, and vehicle braking data corresponding to the braking system of the target vehicle is obtained; the vehicle braking data includes brake pressure data, brake pad thickness data and brake pad wear rate data; the average value of the brake pressure data is determined as a reference brake pressure; the brake pressure data and the brake pad wear rate data are analyzed to obtain a first fitting straight line; the horizontal axis of the first fitting straight line is the brake pressure, and the vertical axis is the brake pad wear rate; the vehicle driving style corresponding to the target vehicle is determined according to the vehicle driving data; a target adjustment factor is determined according to the vehicle driving style and the historical road condition data through a preset AI algorithm; the target brake pressure is obtained by adjusting the reference brake pressure according to the target adjustment factor; the service life of the brake pad of the target vehicle is determined according to the brake pad thickness data, the target brake pressure and the first fitting straight line; diagnosis prompt information is generated according to the service life of the brake pad; the diagnosis prompt information is used to prompt the brake life of the target vehicle. By combining the vehicle use scene and the braking system characteristics, the service life of the vehicle brake is accurately predicted, so as to improve the accuracy of diagnosing the vehicle braking system.

[0044] For ease of understanding, please refer to Figure 1 , Figure 1 is a system architecture diagram of a target diagnosis system provided by the application embodiment, which is used to execute the AI diagnosis method for the vehicle braking system, including a data acquisition module, a preprocessing module, an analysis and calculation module, and a diagnosis output module.

[0045] Among them, the data acquisition module is responsible for obtaining various basic data of the target vehicle, such as vehicle driving data, historical road condition data and vehicle braking data. Vehicle driving data includes but is not limited to the total number of brakes, total number of accelerations, vehicle speed, acceleration, driving time, and turning frequency. Historical road condition data includes but is not limited to road type, proportion of congested sections, road slope, and traffic light density. Vehicle braking data includes but is not limited to brake pressure data, brake pad thickness data, and brake pad wear rate data, which are not specifically limited here.

[0046] The preprocessing module cleans, converts, and standardizes the collected raw data, providing high-quality data for subsequent analysis and calculations, preventing noise or outliers from interfering with diagnostic logic. First, data cleaning can be performed by removing outliers, filling in missing values, and removing redundant data. Then, data standardization is achieved through unified data formats and normalization. Finally, different types of data can be categorized and stored to create a structured database, facilitating rapid access by the analysis and calculation modules.

[0047] The analysis and calculation module executes the core logic of the diagnostic method based on preprocessed data, completing the critical calculation process from data to diagnostic results. First, the preprocessed brake pressure data is averaged to serve as a baseline value, namely the reference brake pressure. A linear regression algorithm is then used to fit the brake pressure data to the brake pad wear rate data, establishing a quantitative relationship between the two (brake pressure on the horizontal axis and wear rate on the vertical axis). The target vehicle's driving style is then determined based on the vehicle driving data. A pre-set AI algorithm (such as a decision tree or neural network model) is then invoked, inputting the vehicle's driving style and historical road condition data (such as the percentage of congested roads). The algorithm then outputs a target adjustment factor (used to correct the reference brake pressure to reflect actual braking intensity differences in different scenarios). The reference brake pressure is then adjusted using the target adjustment factor (e.g., multiplication: target brake pressure = reference brake pressure × adjustment factor) to obtain a target brake pressure that better reflects actual usage scenarios. Finally, the remaining wearable thickness of the brake pad is determined based on the brake pad thickness data. The wear rate at this target brake pressure is then determined using a first fitted line. This is then combined with the remaining wearable thickness to calculate the remaining service life of the brake pad.

[0048] Among them, the diagnostic output module can convert the service life of the brake pad obtained by the analysis and calculation module into intuitive diagnostic prompt information. According to the analysis of the service life of the brake pad, corresponding graded prompt information is generated (such as "service life of 3 months, it is recommended to replace in advance", "service life of 1 year, can be used normally"), and supplementary auxiliary information, such as "reason for accelerated wear: frequent sudden braking in congested roads". It should be noted that when generating diagnostic prompt information, the service life of the brake pad can be indicated by a time period, or by mileage, which is not specifically limited here. The diagnostic prompt information can be displayed as text or icon warnings through the on-board terminal of the target vehicle (such as the car screen), or it can be displayed through the mobile terminal of the target user (such as a mobile phone), which is not specifically limited here.

[0049] It can be seen that by intelligently collecting and analyzing vehicle driving, road conditions and braking data, combined with AI algorithms to dynamically adapt driving style and road conditions, the accuracy of brake pad service life prediction can be improved, thereby improving the accuracy of vehicle braking system diagnosis.

[0050] The following combination Figure 2 The electronic device in the embodiment of the present application is described. Figure 2 is a structural diagram of an electronic device provided in an embodiment of the present application, such as Figure 2 As shown, the electronic device includes one or more processors, a memory, a communication interface and one or more programs, and the processor is communicatively connected with the memory and the communication interface via an internal communication bus.

[0051] Among them, the processor is mainly used for:

[0052] Acquiring vehicle driving data and historical road condition data of a target vehicle within a preset time period, as well as vehicle braking data corresponding to the braking system of the target vehicle; the vehicle braking data includes brake pressure data, brake pad thickness data, and brake pad wear rate data;

[0053] determining an average value of the brake pressure data as a reference brake pressure;

[0054] Analyzing the brake pressure data and the brake pad wear rate data to obtain a first fitting straight line, wherein the horizontal axis of the first fitting straight line is the brake pressure and the vertical axis is the brake pad wear rate;

[0055] determining a vehicle driving style corresponding to the target vehicle according to the vehicle driving data;

[0056] Determining a target adjustment factor based on the vehicle driving style and the historical road condition data using a preset AI algorithm;

[0057] adjust the reference brake pressure according to the target adjustment factor, to obtain a target brake pressure;

[0058] determine the brake pad service life of the target vehicle according to the brake pad thickness data, the target brake pressure and the first fitting straight line;

[0059] generate diagnostic prompt information according to the brake pad service life; the diagnostic prompt information is used to prompt the brake service life of the target vehicle.

[0060] The one or more programs are stored in the above-mentioned memory and configured to be executed by the above-mentioned processor, and the one or more programs include instructions for executing any step of the above-mentioned method embodiments.

[0061] The processor can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logical blocks, units and circuits described in combination with the disclosure. The processor can also be a combination of computing functions, such as one or more microprocessor combinations, combinations of DSP and microprocessor, etc. The communication unit can be a communication interface, a transceiver, a transceiver circuit, etc., and the storage unit can be a memory.

[0062] The memory can be a volatile memory or a nonvolatile memory, or can include both volatile and nonvolatile memory. Among them, the nonvolatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM) used as an external cache. By way of example, and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DR RAM).

[0063] It can be understood that the electronic device can include more or less structural elements than those in the above structural block diagram, for example, including a power module, a physical key, a Wi-Fi module, a speaker, a Bluetooth module, a sensor, a display module, etc., which are not limited herein. It can be understood that the electronic device can be equipped with a system architecture as described above. Figure 1

[0064] For ease of understanding, please refer to Figure 3 , Figure 3 ​is an application scenario of a target diagnosis system provided by an embodiment of the present application, wherein the "target vehicle" represents a vehicle that needs to be diagnosed, and the "target diagnosis system" represents a system for analyzing and processing vehicle data and performing diagnosis. The first arrow points from the "target vehicle" to the "target diagnosis system", and the "target vehicle data" is marked on the first arrow, which means that the target vehicle will send its relevant data to the target diagnosis system. The "target vehicle data" includes but is not limited to vehicle driving data, historical road condition data, vehicle braking data and various other vehicle-related information. The second arrow points from the "target diagnosis system" to the "target vehicle", and the "diagnosis prompt information" is marked on the second arrow, which means that the target diagnosis system will generate corresponding diagnosis prompt information after analyzing and processing the received vehicle data, and feed back to the target vehicle. The diagnosis prompt information includes fault warnings, maintenance suggestions and other content about the target vehicle, helping the target user or maintenance personnel to understand the vehicle status in time.

[0065] After understanding the software and hardware architecture of the present application, the following will be combined with Figure 4 An AI diagnosis method for a vehicle braking system in an embodiment of the present application is described, Figure 4 is a flowchart of an AI diagnosis method for a vehicle braking system provided by an embodiment of the present application, which specifically includes the following steps:

[0066] Step S401, obtaining vehicle driving data and historical road condition data of a target vehicle in a preset time period, and vehicle braking data corresponding to a braking system of the target vehicle.

[0067] The vehicle braking data includes brake pressure data, brake pad thickness data and brake pad wear rate data. The corresponding vehicle driving data and vehicle braking data can be obtained through the sensor module of the target vehicle, and the historical road condition data can be obtained through the vehicle-mounted navigation system of the target vehicle. The vehicle-mounted navigation system can analyze the real-time traffic data and the average vehicle speed change of the map to determine the congestion of the road section (e.g., less than 50% of the road speed limit is determined as congestion).

[0068] Step S402, determining the average value of the brake pressure data as a reference brake pressure.

[0069] The brake pressure data collected in the preset time period can be calculated by arithmetic mean to obtain an average value reflecting the overall level of brake pressure in the preset time period, i.e., the reference brake pressure, which provides a basic reference value for subsequent brake pressure adjustment and brake pad life calculation.

[0070] Step S403, analyzing the brake pressure data and the brake pad wear rate data to obtain a first fitting straight line.

[0071] The horizontal axis of the first fitting straight line is the brake pressure, and the vertical axis is the brake pad wear speed. The brake pressure data and the brake pad wear speed data are analyzed to obtain the first fitting straight line. The specific steps include:

[0072] A1, dividing the preset time period into a plurality of reference time periods according to a preset length;

[0073] A2, dividing the brake pressure data and the brake pad wear speed data into a plurality of reference brake pressure data sets and a plurality of reference brake pad wear speed data sets according to the plurality of reference time periods; each reference time period corresponds to a reference brake pressure data set and a reference brake pad wear speed data set;

[0074] A3, determining the average value of each reference brake pressure data set in the plurality of reference brake pressure data sets to obtain a plurality of brake pressures;

[0075] A4, determining the average value of each reference brake pad wear speed data set in the plurality of reference brake pad wear speed data sets to obtain a plurality of brake pad wear speeds;

[0076] A5, fitting according to the plurality of brake pressures and the plurality of brake pad wear speeds to obtain the first fitting straight line.

[0077] In specific embodiments, first, the preset time period is divided into a plurality of reference time periods according to a preset length. The preset length can be one day or one week, and the preset time period can be one month or half a year, which is not limited here. Then, the brake pressure data and the brake pad wear speed data are divided according to the plurality of reference time periods to obtain a plurality of reference brake pressure data sets and a plurality of reference brake pad wear speed data sets. Each reference time period corresponds to a reference brake pressure data set and a reference brake pad wear speed data set.

[0078] Next, the average value of each reference brake pressure data set in the plurality of reference brake pressure data sets is calculated to obtain a plurality of brake pressures. The average value of each reference brake pad wear speed data set in the plurality of reference brake pad wear speed data sets is calculated to obtain a plurality of brake pad wear speeds. Then, fitting is performed according to the plurality of brake pressures and the plurality of brake pad wear speeds to obtain the first fitting straight line. The brake pressure and the brake pad wear speed of each reference time period can be paired to form a plurality of data points, and then a preset least squares method is used to fit a straight line to the plurality of data points to finally obtain the first fitting straight line.

[0079] It can be seen that by time segmentation and mean calculation, the linear law between the brake pressure and the brake pad wear speed is determined, the accidental interference of single data is avoided, and the wear speed of the brake pad is accurately predicted in combination with the actual brake pressure data.

[0080] In step S404, the vehicle driving style corresponding to the target vehicle is determined according to the vehicle driving data.

[0081] The vehicle driving data includes the total number of braking and the total number of acceleration, and the vehicle driving style corresponding to the target vehicle is determined according to the vehicle driving data, and the specific steps include:

[0082] B1, determining a first brake pressure threshold according to a preset ratio and a rated brake pressure of the target vehicle;

[0083] B2, obtaining the number of times of emergency braking from the total number of braking, wherein the brake pressure is greater than the first brake pressure threshold;

[0084] B3, determining the proportion of emergency braking according to the number of times of emergency braking and the total number of braking;

[0085] B4, obtaining the number of times of emergency acceleration from the total number of acceleration, wherein the accelerator opening degree is greater than a preset first accelerator opening degree threshold; the accelerator opening degree represents the degree of depression of the accelerator pedal of the target vehicle;

[0086] B5, determining the proportion of emergency acceleration according to the number of times of emergency acceleration and the total number of acceleration;

[0087] B6, determining the vehicle driving style according to the proportion of emergency braking, the proportion of emergency acceleration, a preset first proportion threshold and a second proportion threshold.

[0088] In specific embodiments, first, the first brake pressure threshold is calculated according to the rated brake pressure of the target vehicle and the preset ratio, for example, the rated brake pressure is 10 MPa, and the preset ratio is 70%, then the first brake pressure threshold = 7.0 MPa. The rated brake pressure represents the maximum brake pressure or standard pressure value designed for the target vehicle, and the preset ratio can be set according to the actual situation of the target vehicle, for example, for a family car, the preset ratio can be set to 60%, and the family car is usually driven for urban commuting or gentle road conditions, and the demand for emergency braking is low. The first brake pressure threshold of 60% of the rated brake pressure can more accurately identify the emergency braking condition exceeding the normal braking intensity, and for a sports car, the preset ratio can be increased to 70% to avoid misjudging the normal braking as emergency braking. Herein, no specific limitation is made.

[0089] Then, the number of times of braking with a brake pressure greater than the first brake pressure threshold is filtered from the total number of times of braking to obtain the number of times of hard braking. The proportion of hard braking is calculated according to the number of times of hard braking and the total number of times of braking. The number of times of acceleration with a throttle opening greater than a preset first throttle opening threshold is filtered from the total number of times of acceleration to obtain the number of times of hard acceleration. The throttle opening represents the degree to which the throttle pedal of the target vehicle is depressed. The first throttle opening threshold can be set to 80%, which means that the degree to which the throttle pedal is depressed exceeds 80%. The proportion of hard acceleration is determined according to the number of times of hard acceleration and the total number of times of acceleration. Finally, the driving style of the vehicle is determined according to the proportion of hard braking, the proportion of hard acceleration, the first proportion threshold and the second proportion threshold. The first proportion threshold is used to define the critical proportion of whether the hard braking is frequent, that is, when the proportion of the number of times of hard braking to the total number of times of braking exceeds the first proportion threshold, it is determined that the operation of hard braking is more aggressive. The second proportion threshold is used to define the critical proportion of whether the hard acceleration is frequent, that is, when the proportion of the number of times of hard acceleration to the total number of times of acceleration exceeds the second proportion threshold, it is determined that the operation of hard acceleration is more aggressive.

[0090] It can be seen that by setting reasonable thresholds in combination with the actual situation of the vehicle, the proportions of hard braking and hard acceleration are quantified, so as to objectively and accurately determine the driving style of the vehicle.

[0091] The driving style of the vehicle includes any one of the following: smooth driving type, balanced driving type and aggressive driving type. The determination of the driving style of the vehicle according to the proportion of hard braking, the proportion of hard acceleration, the first proportion threshold and the second proportion threshold includes the following steps.

[0092] C1. If the proportion of hard braking is less than or equal to the first proportion threshold, and the proportion of hard acceleration is less than or equal to the second proportion threshold, it is determined that the driving style of the vehicle is the smooth driving type.

[0093] C2. If the proportion of hard braking is greater than the first proportion threshold, and the proportion of hard acceleration is less than or equal to the second proportion threshold, or the proportion of hard braking is less than or equal to the first proportion threshold, and the proportion of hard acceleration is greater than the second proportion threshold, it is determined that the driving style of the vehicle is the balanced driving type.

[0094] C3. If the proportion of hard braking is greater than the first proportion threshold, and the proportion of hard acceleration is greater than the second proportion threshold, it is determined that the driving style of the vehicle is the aggressive driving type.

[0095] In specific embodiments, if the sudden braking proportion is less than or equal to the first proportion threshold value, and the sudden acceleration proportion is less than or equal to the second proportion threshold value, it indicates that the frequencies of sudden braking and sudden acceleration are both controlled at a low level, the driving operation is gentle, the comfort and economy are emphasized, the target vehicle is less worn, and the vehicle driving style is determined to be a smooth driving type. If the sudden braking proportion is greater than the first proportion threshold value, and the sudden acceleration proportion is less than or equal to the second proportion threshold value, or the sudden braking proportion is less than or equal to the first proportion threshold value, and the sudden acceleration proportion is greater than the second proportion threshold value, it indicates that only one of the sudden braking or the sudden acceleration has a high frequency, the operation style is biased to one side (such as frequent sudden acceleration but gentle braking, or frequent sudden braking but gentle acceleration), the overall driving intensity is moderate, and the vehicle driving style is determined to be a balanced driving type. If the sudden braking proportion is greater than the first proportion threshold value, and the sudden acceleration proportion is greater than the second proportion threshold value, it indicates that the frequencies of sudden braking and sudden acceleration are both high, the driving style is aggressive, and the vehicle braking system is worn more, and the vehicle driving style is determined to be an aggressive driving type.

[0096] It can be seen that by combining the first proportion threshold value and the second proportion threshold value which can be flexibly set, the vehicle driving style is judged to obtain an accurate vehicle driving style, which is convenient for subsequent diagnosis of the target vehicle.

[0097] Step S405, determining a target adjustment factor according to the vehicle driving style and the historical road condition data by a preset AI algorithm.

[0098] For better understanding, please refer to Figure 5 , Figure 5 is a flowchart of determining a target adjustment factor provided by an embodiment of the present application, wherein the target adjustment factor is determined according to the vehicle driving style and the historical road condition data by a preset AI algorithm, and the specific steps include:

[0099] D1, determining a first characteristic value corresponding to the vehicle driving style according to a mapping relationship between a preset driving style and a characteristic value; the first characteristic value is used to reflect the wear tendency of the vehicle driving style on the braking system of the target vehicle;

[0100] D2, determining a first proportion of congested road sections and a second proportion of smooth road sections according to the historical road condition data;

[0101] D3, determining second characteristic values and third characteristic values corresponding to the congested road sections and the smooth road sections respectively according to a preset rule; the second characteristic values and the third characteristic values are both used to reflect the wear tendency of the historical road condition data on the braking system of the target vehicle;

[0102] D4, determining a fourth characteristic value according to the first proportion, the second characteristic value, the second proportion and the third characteristic value;

[0103] D5, obtaining the target adjustment factor by processing the first characteristic value and the fourth characteristic value through the AI algorithm.

[0104] In specific embodiments, first, the driving style is converted into a first characteristic value according to a preset mapping relationship between the driving style and the characteristic value, and the first characteristic value is used to reflect the wear tendency of the vehicle driving style on the brake system of the target vehicle. For example, the first characteristic value corresponding to smooth driving type is 0.8, the first characteristic value corresponding to balanced driving type is 1.0, and the first characteristic value corresponding to aggressive driving type is 1.2, which is not limited here. Then, the historical road condition data is statistically analyzed to obtain a first proportion of congested road sections and a second proportion of smooth road sections, the first proportion representing the proportion of the mileage corresponding to the congested road sections in the total mileage, and the second proportion representing the proportion of the mileage corresponding to the smooth road sections in the total mileage, and the sum of the first proportion and the second proportion is 1.

[0105] Then, the second characteristic value and the third characteristic value corresponding to the congested road sections and the smooth road sections are determined according to a preset rule, wherein the second characteristic value and the third characteristic value are both used to reflect the wear tendency of the historical road condition data on the brake system of the target vehicle. The preset rule can be a fixed numerical setting, for example, it is specified that the second characteristic value corresponding to the congested road sections is 1.1 (indicating that the wear is higher than the baseline level), and the third characteristic value corresponding to the smooth road sections is 0.9 (indicating that the wear is lower than the baseline level), or it can be a hierarchical dynamic adjustment, i.e. refining the rules according to the congestion degree (such as light congestion, severe congestion) or the smoothness level (such as general smoothness, high-speed smoothness), for example, the second characteristic value corresponding to severe congestion is 1.3, the second characteristic value corresponding to light congestion is 1.1, the second characteristic value corresponding to high-speed smoothness is 0.7, and the second characteristic value corresponding to ordinary smoothness is 0.9, so that the characteristic value is more in line with the actual road condition difference, which is not limited here. It should be noted that the second characteristic value is greater than 1 because the wear of the congested road sections is higher than the baseline due to frequent braking, and the second characteristic value is less than 1 because the wear of the smooth road sections is lower than the baseline due to low braking frequency.

[0106] Then, the fourth characteristic value is determined according to the first proportion, the second characteristic value, the second proportion and the third characteristic value, i.e. the first proportion, the second characteristic value, the second proportion and the third characteristic value are weighted and calculated to obtain the fourth characteristic value. Finally, the target adjustment factor is obtained by processing the first characteristic value and the fourth characteristic value through the AI algorithm.

[0107] It can be seen that by converting the vehicle driving style and the historical road condition data into characteristic values and then dynamically fusing them through the AI algorithm, the target adjustment factor can accurately reflect the influence of the actual use scenario on the brake system, providing a more realistic wear correction basis for subsequent brake pad service life prediction and diagnosis.

[0108] The first characteristic value and the fourth characteristic value are processed by the AI algorithm to obtain the target adjustment factor, and the specific steps include:

[0109] E1, the first characteristic value and the fourth characteristic value are weighted and summed according to the preset first weight and second weight to obtain a reference adjustment factor; the first weight corresponds to the first characteristic value; the second weight corresponds to the fourth characteristic value;

[0110] E2, obtaining the target vehicle load of the target vehicle in the preset time period;

[0111] E3, if the target vehicle load is greater than a preset vehicle load threshold, a correction factor corresponding to the target vehicle load is obtained; the greater the target vehicle load, the greater the correction factor;

[0112] E4, the reference adjustment factor is corrected according to the correction factor to obtain the target adjustment factor;

[0113] E5, if the target vehicle load is less than or equal to the vehicle load threshold, the reference adjustment factor is taken as the target adjustment factor.

[0114] In specific embodiments, the AI algorithm includes a weighted fusion mechanism and a load correction mechanism, which can be used to integrate multi-dimensional information and realize rapid adaptation to complex scenes through weighting, correction and other mechanisms. In the basic weighted fusion mechanism, the first characteristic value and the fourth characteristic value can be weighted and summed by the preset first weight and second weight to obtain a reference adjustment factor, the first weight corresponding to the first characteristic value, and the second weight corresponding to the fourth characteristic value. It should be noted that the AI algorithm can dynamically update the first weight or the second weight through machine learning (such as neural network, decision tree) combined with real-time data of the target vehicle, for example, the vehicle driving style of the target vehicle changes from smooth driving type to aggressive driving type, the number of sudden accelerations or sudden brakes increases by 50% within 3 days, the weight acceleration update can be started, the first weight is increased from 0.6 to 0.7, and the target adjustment factor can more quickly reflect the influence of the new vehicle driving style on the braking system.

[0115] Then, in the load correction mechanism, the target vehicle load of the target vehicle in a preset time period can be obtained. If the target vehicle load is greater than a preset vehicle load threshold, a correction factor corresponding to the target vehicle load is obtained, and the greater the target vehicle load, the greater the correction factor. Wherein, the target vehicle load represents the load condition of the target vehicle, such as the weight of the carried goods, and the vehicle load threshold represents a threshold at which the target vehicle load begins to affect the braking system. The vehicle load threshold can be set to 50% of the rated load of the target vehicle, which is not limited here. It should be noted that when the target vehicle load exceeds the vehicle load threshold, that is, the ratio between the target vehicle load and the rated load exceeds 50%, the brake pad wear acceleration effect is significantly enhanced. For example, when the target vehicle load is 60% of the rated load, the correction factor can be 1.05, which is not limited here. Wherein, since the wet road surface in rainy weather will cause the braking distance to be prolonged, in order to achieve the same braking effect, the braking system needs to exert greater pressure, and at the same time the friction efficiency of the brake pad and the brake disc is reduced, resulting in a faster wear speed than in sunny weather. Therefore, the weather data can be analyzed by AI algorithm to obtain the weather type, and the target adjustment factor can be further corrected according to the weather type to adapt to the actual scene of reduced road friction coefficient and increased braking load in rainy weather.

[0116] Finally, the reference adjustment factor is corrected according to the correction factor to obtain the target adjustment factor. Wherein, if the target vehicle load is less than or equal to the vehicle load threshold, it means that the influence of the target vehicle load on the braking system is small and can be ignored, and the reference adjustment factor is directly taken as the target adjustment factor.

[0117] It can be seen that by fusing the multi-dimensional feature values of the target vehicle and dynamically adjusting, the precise prediction of the wear of the vehicle braking system is realized, which is convenient for subsequent adjustment of the braking pressure to provide the accuracy of diagnosing the braking system.

[0118] Step S406, adjust the reference braking pressure according to the target adjustment factor to obtain the target braking pressure.

[0119] Specifically, the target adjustment factor and the reference braking pressure can be multiplied to obtain the target braking pressure. For example, the target adjustment factor is 1.3 and the reference braking pressure is 5.0MPa, then the target braking pressure can be calculated to be 6.5MPa, which is not limited here.

[0120] Step S407, determining the service life of the brake pad of the target vehicle according to the brake pad thickness data, the target braking pressure and the first fitting straight line.

[0121] For ease of understanding, please refer to Figure 6 , Figure 6is a flowchart of a process for determining the service life of brake pads provided by the present application, wherein the service life of the brake pads of the target vehicle is determined according to the brake pad thickness data, the target braking pressure and the first fitted straight line, and the specific steps include:

[0122] F1, determining the target brake pad wear rate according to the target braking pressure and the first fitted straight line;

[0123] F2, determining the remaining wearable thickness of the brake pads of the target vehicle according to the brake pad thickness data;

[0124] F3, determining the service life of the brake pads according to the target brake pad wear rate and the remaining wearable thickness of the brake pads.

[0125] In specific embodiments, first, the target braking pressure can be substituted into the first fitted straight line to determine the target brake pad wear rate corresponding to the target braking pressure, wherein in the first fitted straight line, the brake pad wear rate is positively correlated with the braking pressure (the greater the braking pressure, the more intense the friction between the brake pad and the brake disc, and the faster the wear). Then, the current thickness of the brake pads of the target vehicle is determined according to the brake pad thickness data, and the brake pad discard thickness (brake pad thickness safety threshold, usually 2-3 mm) corresponding to the target vehicle is obtained. When the brake pad thickness is lower than the brake pad discard thickness, it will cause abnormal braking sound, braking force decline, and even brake disc wear. Therefore, the safety redundancy must be deducted in the calculation process of the remaining wearable thickness of the brake pads, that is, the current thickness of the brake pads is subtracted from the brake pad discard thickness to obtain the remaining wearable thickness of the brake pads of the target vehicle. Finally, the service life of the brake pads is determined according to the target brake pad wear rate and the remaining wearable thickness of the brake pads.

[0126] It can be seen that by accurately calculating the service life of the brake pads to diagnose the braking system of the vehicle, the braking life of the brake pads can be warned in advance, and the decline of the braking force, abnormal braking sound or brake disc damage caused by excessive wear of the brake pads can be avoided, thereby reducing accidents caused by braking system failures from the root.

[0127] Step S408, generating diagnosis prompt information according to the service life of the brake pads; the diagnosis prompt information is used to prompt the braking life of the target vehicle.

[0128] Specifically, different diagnostic prompt information can be generated according to specific values of the service life of the brake pad. The diagnostic prompt information includes, but is not limited to, a regular prompt, a warning prompt, and an emergency prompt, which are not specifically limited here. For example, when the service life of the brake pad is greater than 3000 kilometers, a regular prompt can be generated: "The service life of the brake pad is 5000 kilometers, and it is recommended to check the thickness during the next maintenance"; when the service life of the brake pad is greater than 1000 kilometers and less than or equal to 3000 kilometers, a warning prompt can be generated: "The service life of the brake pad is 2000 kilometers, and it needs to be replaced in the near future (about 2 weeks), to avoid affecting the braking effect"; and when the service life of the brake pad is less than or equal to 1000 kilometers, an emergency prompt can be generated: "The brake pad is severely worn, and the service life of the brake pad is only 800 kilometers, please replace it immediately, otherwise it may cause brake failure".

[0129] For ease of understanding, please refer to Figure 7 , Figure 7 is a schematic diagram of a diagnostic prompt information interface provided by an embodiment of the present application, wherein the diagnostic prompt information belongs to an emergency prompt, and the specific prompt content is "The brake pad is severely worn, and the service life of the brake pad is only 800 kilometers, please replace it immediately, otherwise it may cause brake failure". It can be known that if the target user does not replace the brake pad of the target vehicle in time, the vehicle may be braked to failure as the brake pad continues to wear, thereby causing a serious traffic safety accident. Below the prompt content, functional buttons such as "Book a maintenance" and "Learn more" are arranged, which are not specifically limited here. The "Book a maintenance" button provides a convenient operation entrance for the target user, and the target user can quickly book a vehicle maintenance service through the button to replace the brake pad in time. The "Learn more" button allows the target user to further obtain detailed information about the wear and replacement of the brake pad, to help the target user better understand the vehicle condition and maintenance needs.

[0130] It can be seen that through the simple and clear interface design and direct and effective information transmission, important vehicle diagnostic prompts and convenient operation options are provided for the target user, which helps to ensure the braking safety and normal operation of the vehicle.

[0131] The above describes the scheme of the embodiments of the present application mainly from the perspective of the process of executing the method. It can be understood that, in order to implement the above functions, the electronic device comprises a hardware structure and / or a software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the unit and algorithm steps of each example described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is implemented in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0132] The embodiments of the present application can divide the functional units of the electronic device according to the above method examples. For example, each functional unit can be divided according to each function, or two or more functions can be integrated in one processing unit. The integrated unit can be implemented in the form of hardware or software functional unit. It should be noted that the division of units in the embodiments of the present application is illustrative, and is only a logical functional division. When actually implemented, there can be another division method.

[0133] In the case of dividing each functional module according to each function, Figure 8 is a functional module composition block diagram of an AI diagnosis device for a vehicle braking system provided by the embodiments of the present application. The AI diagnosis device for the vehicle braking system 800 comprises an acquisition module 810, a determination module 820, an analysis module 830, an adjustment module 840, and a generation module 850, wherein:

[0134] The acquisition module 810 is configured to acquire vehicle driving data and historical road condition data of a target vehicle in a preset time period, and vehicle braking data corresponding to a braking system of the target vehicle. The vehicle braking data comprises braking pressure data, brake pad thickness data, and brake pad wear speed data.

[0135] The determination module 820 is configured to determine an average value of the braking pressure data as a reference braking pressure.

[0136] The analysis module 830 is configured to analyze the braking pressure data and the brake pad wear speed data to obtain a first fitting straight line. The horizontal axis of the first fitting straight line is the braking pressure, and the vertical axis is the brake pad wear speed.

[0137] The determination module 820 is further configured to determine a vehicle driving style corresponding to the target vehicle according to the vehicle driving data.

[0138] The determination module 820 is further configured to determine a target adjustment factor according to the vehicle driving style and the historical road condition data by using a preset AI algorithm.

[0139] The adjustment module 840 is configured to adjust the reference brake pressure to obtain a target brake pressure according to the target adjustment factor.

[0140] The determination module 820 is further configured to determine a brake pad service life of the target vehicle according to the brake pad thickness data, the target brake pressure and the first fitting straight line.

[0141] The generation module 850 is configured to generate a diagnostic prompt information according to the brake pad service life, and the diagnostic prompt information is used to prompt a brake service life of the target vehicle.

[0142] Optionally, in the step of analyzing the brake pressure data and the brake pad wear speed data to obtain a first fitting straight line, the analysis module 830 is specifically configured to:

[0143] divide the preset time period into a plurality of reference time periods according to a preset time length;

[0144] divide the brake pressure data and the brake pad wear speed data into a plurality of reference brake pressure data sets and a plurality of reference brake pad wear speed data sets according to the plurality of reference time periods, and each reference time period corresponds to a reference brake pressure data set and a reference brake pad wear speed data set;

[0145] determine an average value of each reference brake pressure data set in the plurality of reference brake pressure data sets to obtain a plurality of brake pressures;

[0146] determine an average value of each reference brake pad wear speed data set in the plurality of reference brake pad wear speed data sets to obtain a plurality of brake pad wear speeds;

[0147] fit the plurality of brake pressures and the plurality of brake pad wear speeds to obtain the first fitting straight line.

[0148] Optionally, the vehicle driving data includes a total brake number and a total acceleration number, and in the step of determining the vehicle driving style corresponding to the target vehicle according to the vehicle driving data, the determination module 820 is specifically configured to:

[0149] determine a first brake pressure threshold according to a preset ratio and a rated brake pressure of the target vehicle;

[0150] obtain a number of emergency brakes from the total brake number, wherein a brake pressure of each emergency brake is greater than the first brake pressure threshold;

[0151] determine a sudden braking ratio according to the sudden braking times and the total braking times;

[0152] obtain a sudden acceleration times from the total acceleration times, wherein the sudden acceleration times is the times that the accelerator opening degree is greater than a preset first accelerator opening degree threshold; the accelerator opening degree represents a degree that an accelerator pedal of the target vehicle is stepped on;

[0153] determine a sudden acceleration ratio according to the sudden acceleration times and the total acceleration times;

[0154] determine the vehicle driving style according to the sudden braking ratio, the sudden acceleration ratio, a preset first ratio threshold and a second ratio threshold.

[0155] Optionally, the vehicle driving style includes any one of the following: a smooth driving type, a balanced driving type, and an aggressive driving type. In the determination of the vehicle driving style according to the sudden braking ratio, the sudden acceleration ratio, the preset first ratio threshold and the second ratio threshold, the determination module 820 is further specifically configured to:

[0156] if the sudden braking ratio is less than or equal to the first ratio threshold and the sudden acceleration ratio is less than or equal to the second ratio threshold, it is determined that the vehicle driving style is the smooth driving type;

[0157] if the sudden braking ratio is greater than the first ratio threshold and the sudden acceleration ratio is less than or equal to the second ratio threshold, or the sudden braking ratio is less than or equal to the first ratio threshold and the sudden acceleration ratio is greater than the second ratio threshold, it is determined that the vehicle driving style is the balanced driving type;

[0158] if the sudden braking ratio is greater than the first ratio threshold and the sudden acceleration ratio is greater than the second ratio threshold, it is determined that the vehicle driving style is the aggressive driving type.

[0159] Optionally, in the determination of the target adjustment factor according to the vehicle driving style and the historical road condition data by using the preset AI algorithm, the determination module 820 is further specifically configured to:

[0160] determine a first characteristic value corresponding to the vehicle driving style according to a preset mapping relationship between the driving style and the characteristic value; the first characteristic value is used to reflect a loss tendency of the vehicle driving style on the braking system of the target vehicle;

[0161] determine a first proportion of congested road sections and a second proportion of smooth road sections according to the historical road condition data;

[0162] determine second characteristic values and third characteristic values corresponding to the congested road section and the smooth road section respectively according to a preset rule; the second characteristic values and the third characteristic values are both used to reflect a loss tendency of the historical road condition data on a braking system of the target vehicle;

[0163] determine a fourth characteristic value according to the first proportion, the second characteristic value, the second proportion and the third characteristic value;

[0164] process the first characteristic value and the fourth characteristic value through the AI algorithm to obtain the target adjustment factor.

[0165] Optionally, in the processing of the first characteristic value and the fourth characteristic value through the AI algorithm to obtain the target adjustment factor, the determining module 820 is further specifically used for:

[0166] weight and sum the first characteristic value and the fourth characteristic value according to preset first and second weights to obtain a reference adjustment factor; the first weight corresponds to the first characteristic value; and the second weight corresponds to the fourth characteristic value;

[0167] obtain a target vehicle load of the target vehicle in the preset time period;

[0168] if the target vehicle load is greater than a preset vehicle load threshold, obtain a correction factor corresponding to the target vehicle load; the greater the target vehicle load, the greater the correction factor;

[0169] correct the reference adjustment factor according to the correction factor to obtain the target adjustment factor;

[0170] if the target vehicle load is less than or equal to the vehicle load threshold, take the reference adjustment factor as the target adjustment factor.

[0171] Optionally, in the determination of the brake pad service life of the target vehicle according to the brake pad thickness data, the target brake pressure and the first fitted straight line, the determining module 820 is further specifically used for:

[0172] determine a target brake pad wear speed according to the target brake pressure and the first fitted straight line;

[0173] determine a remaining wearable thickness of the brake pad of the target vehicle according to the brake pad thickness data;

[0174] determine the brake pad service life according to the target brake pad wear speed and the remaining wearable thickness of the brake pad.

[0175] It can be seen that by combining the vehicle use scene and the brake system characteristics, the vehicle brake life is accurately predicted, thereby improving the accuracy of diagnosing the vehicle brake system.

[0176] It should be noted that the specific implementation of each operation can adopt the corresponding description of the method embodiments described above, and the AI diagnosis device 800 of the vehicle brake system can be used to execute the method embodiments described above, and details are not repeated.

[0177] The embodiments of the present application also provide a computer readable storage medium, wherein the computer readable storage medium stores a computer program for electronic data exchange, and the computer program causes a computer to execute part or all of the steps of any method described in the above method embodiments, and the computer includes an electronic device.

[0178] The embodiments of the present application also provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any method described in the above method embodiments. The computer program product can be a software installation package, and the computer includes an electronic device.

[0179] It should be noted that, for the above-mentioned various embodiments, in order to simply describe, they are all expressed as a series of action combinations. Those skilled in the art should know that the present application is not limited to the order of the actions described, because some steps in the embodiments of the present application can be performed in other order or simultaneously. In addition, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions, steps, modules or units involved are not necessarily required in the embodiments of the present application.

[0180] In the above embodiments, the description of each embodiment of the embodiments of the present application has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0181] Those of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be implemented by a computer program instructing relevant hardware, and the program can be stored in a computer readable storage medium. The program, when executed, can include the processes of the above method embodiments. The aforementioned storage medium includes: ROM or random access memory (RAM), magnetic disk or optical disk, and various media that can store program codes.

[0182] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM, flash memory, ROM, EPROM, EEPROM, registers, hard disk, a removable media, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. Alternatively, the processor and the storage medium can be located in a terminal device or an access device. The processor and the storage medium can also be located in any other

[0183] Those skilled in the art should clearly understand that, in one or more examples described above, the functions described in the embodiments of the present application can be implemented entirely or partially by software, hardware, firmware, or any combination thereof. When implemented by software, the functions can be implemented in the form of a computer program product entirely or partially. The computer program product includes one or more computer instructions. When loaded and executed on a computer, the computer instructions entirely or partially generate the processes or functions described in the embodiments of the present application. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer instructions can be transferred from one website, computer, server, or data center to another via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. that includes one or more available media sets. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid state disk (SSD)), etc.

[0184] The various modules / units included in the various devices and products described in the above embodiments can be software modules / units or hardware modules / units, or partially software modules / units and partially hardware modules / units. For example, for the various devices and products applied to or integrated into a chip, the various modules / units included therein can all be implemented in the form of hardware such as circuitry, or at least some of the modules / units can be implemented in the form of software programs running on a processor integrated in the chip, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuitry; for the various devices and products applied to or integrated into a chip module, the various modules / units included therein can all be implemented in the form of hardware such as circuitry, and different modules / units can be located in the same component (e.g., a chip, a circuit module, etc.) or different components of the chip module, or at least some of the modules / units can be implemented in the form of software programs running on a processor integrated in the chip module, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuitry; for the various devices and products applied to or integrated into a terminal device, the various modules / units included therein can all be implemented in the form of hardware such as circuitry, and different modules / units can be located in the same component (e.g., a chip, a circuit module, etc.) or different components of the terminal device, or at least some of the modules / units can be implemented in the form of software programs running on a processor integrated in the terminal device, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuitry.

[0185] The above detailed description of the specific implementation of the embodiments of the present application has further explained the purposes, technical solutions, and beneficial effects of the embodiments of the present application. It should be understood that the above description is only a specific implementation of the embodiments of the present application and is not intended to limit the protection scope of the embodiments of the present application. Any modification, equivalent replacement, improvement, etc. made on the basis of the technical solutions of the embodiments of the present application shall be included in the protection scope of the embodiments of the present application.

Claims

1. An AI diagnostic method for a vehicle braking system, characterized in that: The method comprises: Acquire vehicle driving data and historical road condition data of a target vehicle within a preset time period, as well as vehicle braking data corresponding to the braking system of the target vehicle; the vehicle braking data includes brake pressure data, brake pad thickness data, and brake pad wear rate data; determining an average value of the brake pressure data as a reference brake pressure; Analyzing the brake pressure data and the brake pad wear rate data to obtain a first fitting straight line, wherein the horizontal axis of the first fitting straight line is the brake pressure and the vertical axis is the brake pad wear rate; determining a vehicle driving style corresponding to the target vehicle according to the vehicle driving data; Determining a target adjustment factor based on the vehicle driving style and the historical road condition data using a preset AI algorithm; adjusting the reference brake pressure according to the target adjustment factor to obtain a target brake pressure; determining a brake pad service life of the target vehicle according to the brake pad thickness data, the target braking pressure, and the first fitting straight line; Diagnostic prompt information is generated according to the service life of the brake pad; the diagnostic prompt information is used to prompt the brake life of the target vehicle.

2. The method according to claim 1, wherein The analyzing the brake pressure data and the brake pad wear rate data to obtain a first fitting straight line includes: Dividing the preset time period into a plurality of reference time periods according to preset durations; dividing the brake pressure data and the brake pad wear rate data into a plurality of reference brake pressure data sets and a plurality of reference brake pad wear rate data sets according to the plurality of reference time periods; each reference time period corresponds to a reference brake pressure data set and a reference brake pad wear rate data set; determining an average value corresponding to each reference brake pressure data set in the plurality of reference brake pressure data sets to obtain a plurality of brake pressures; Determine an average value corresponding to each reference brake pad wear speed data set in a plurality of reference brake pad wear speed data sets to obtain a plurality of brake pad wear speeds; The first fitting straight line is obtained by performing fitting based on the multiple braking pressures and the multiple brake pad wear speeds.

3. The method according to claim 1, wherein The vehicle driving data includes a total number of braking times and a total number of acceleration times. Determining the vehicle driving style corresponding to the target vehicle based on the vehicle driving data includes: determining a first brake pressure threshold according to a preset ratio and a rated brake pressure of the target vehicle; Obtaining the number of braking times in which the braking pressure is greater than the first braking pressure threshold from the total number of braking times to obtain the number of emergency braking times; Determine the emergency braking ratio according to the emergency braking number and the total braking number; Obtaining the number of accelerations in which the throttle opening is greater than a preset first throttle opening threshold value from the total number of accelerations to obtain the number of rapid accelerations; the throttle opening represents the extent to which the accelerator pedal of the target vehicle is depressed; Determining a sudden acceleration ratio according to the sudden acceleration times and the total acceleration times; The vehicle driving style is determined according to the sudden braking ratio, the sudden acceleration ratio, a preset first ratio threshold, and a preset second ratio threshold.

4. The method according to claim 3, wherein The vehicle driving style includes any one of the following: a smooth driving type, a balanced driving type, and an aggressive driving type. The determining of the vehicle driving style according to the sudden braking ratio, the sudden acceleration ratio, a preset first ratio threshold, and a preset second ratio threshold includes: If the sudden braking ratio is less than or equal to the first ratio threshold, and the sudden acceleration ratio is less than or equal to the second ratio threshold, determining that the vehicle driving style is the smooth driving type; If the sudden braking ratio is greater than the first ratio threshold, and the sudden acceleration ratio is less than or equal to the second ratio threshold, or if the sudden braking ratio is less than or equal to the first ratio threshold, and the sudden acceleration ratio is greater than the second ratio threshold, then determining that the vehicle driving style is the balanced driving type; If the sudden braking ratio is greater than the first ratio threshold, and the sudden acceleration ratio is greater than the second ratio threshold, it is determined that the vehicle driving style is the aggressive driving type.

5. The method according to claim 1, wherein The determining of the target adjustment factor according to the vehicle driving style and the historical road condition data by a preset AI algorithm includes: Determining a first characteristic value corresponding to the vehicle driving style according to a preset mapping relationship between driving styles and characteristic values; the first characteristic value is used to reflect the wear tendency of the vehicle driving style on the brake system of the target vehicle; Determining a first proportion of congested road sections and a second proportion of unobstructed road sections based on the historical traffic condition data; Determining, according to a preset rule, a second characteristic value and a third characteristic value corresponding to the congested road section and the unobstructed road section, respectively; the second characteristic value and the third characteristic value are both used to reflect the wear tendency of the brake system of the target vehicle caused by the historical road condition data; Determine a fourth eigenvalue according to the first proportion, the second eigenvalue, the second proportion, and the third eigenvalue; The first eigenvalue and the fourth eigenvalue are processed by the AI ​​algorithm to obtain the target adjustment factor.

6. The method according to claim 5, wherein The processing of the first eigenvalue and the fourth eigenvalue by the AI ​​algorithm to obtain the target adjustment factor includes: Performing a weighted summation of the first eigenvalue and the fourth eigenvalue according to a preset first weight and a second weight to obtain a reference adjustment factor; the first weight corresponds to the first eigenvalue; and the second weight corresponds to the fourth eigenvalue; Obtaining a target vehicle load of the target vehicle within the preset time period; If the target vehicle load is greater than a preset vehicle load threshold, a correction factor corresponding to the target vehicle load is obtained; the greater the target vehicle load, the greater the correction factor; Correcting the reference adjustment factor according to the correction factor to obtain the target adjustment factor; If the target vehicle load is less than or equal to the vehicle load threshold, the reference adjustment factor is used as the target adjustment factor.

7. The method according to any one of claims 1 to 6, wherein: The determining the brake pad service life of the target vehicle according to the brake pad thickness data, the target braking pressure, and the first fitting straight line includes: determining a target brake pad wear rate according to the target brake pressure and the first fitting straight line; Determining the remaining wearable thickness of the brake pad of the target vehicle according to the brake pad thickness data; The service life of the brake pad is determined according to the target brake pad wear rate and the remaining wearable thickness of the brake pad.

8. An AI diagnostic device for a vehicle braking system, characterized in that: The device includes an acquisition module, a determination module, an analysis module, an adjustment module, and a generation module, wherein: The acquisition module is used to acquire vehicle driving data and historical road condition data of the target vehicle within a preset time period, as well as vehicle braking data corresponding to the braking system of the target vehicle; the vehicle braking data includes brake pressure data, brake pad thickness data, and brake pad wear rate data; The determining module is configured to determine an average value of the brake pressure data as a reference brake pressure; The analysis module is configured to analyze the brake pressure data and the brake pad wear rate data to obtain a first fitting straight line, wherein the horizontal axis of the first fitting straight line is the brake pressure and the vertical axis is the brake pad wear rate; The determination module is further configured to determine a vehicle driving style corresponding to the target vehicle based on the vehicle driving data; The determination module is further configured to determine a target adjustment factor based on the vehicle driving style and the historical road condition data using a preset AI algorithm; The regulating module is configured to regulate the reference brake pressure according to the target regulating factor to obtain a target brake pressure; The determining module is further configured to determine the service life of the brake pad of the target vehicle based on the brake pad thickness data, the target braking pressure, and the first fitting straight line; The generating module is used to generate diagnostic prompt information according to the service life of the brake pad; the diagnostic prompt information is used to prompt the brake life of the target vehicle.

9. An electronic device, characterized in that: include: a processor, a memory, a communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, wherein the programs include instructions for executing the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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