AI diagnosis method for vehicle braking system and related device

Abstract

The application provides an AI diagnosis method for a vehicle braking system and a related device. The method comprises: obtaining vehicle driving data, historical road condition data and vehicle braking data of a target vehicle within a preset time period; determining an average value of braking pressure data as a reference braking pressure; analyzing the braking pressure data and brake pad wear rate data to obtain a first fitting straight line; determining a vehicle driving style corresponding to the target vehicle according to the vehicle driving data; determining a target adjustment factor according to the vehicle driving style and the historical road condition data through an AI algorithm; adjusting the reference braking pressure according to the target adjustment factor to obtain a target braking pressure; determining a service life of a brake pad of the target vehicle according to brake pad thickness data, the target braking pressure and the first fitting straight line; and generating a diagnosis prompt information according to the service life of the brake pad. The braking life is accurately predicted to improve the accuracy of diagnosing the braking system.

Description

Technical Field

[0001] This application relates to the field of vehicle diagnostic technology, and in particular to an AI diagnostic method and related device for vehicle braking systems. Background Technology

[0002] With the rapid development of the automotive industry, the monitoring and evaluation of vehicle safety performance has become a crucial aspect of improving driving safety. Traditional braking system evaluation and diagnosis typically rely on manual inspection, which is not only inefficient but also unable to monitor vehicle status in real time, resulting in limited efficiency and accuracy.

[0003] Therefore, improving the accuracy of vehicle braking system diagnosis is an urgent issue that needs to be addressed. Summary of the Invention

[0004] This application provides an AI diagnostic method and related device for vehicle braking systems. By combining vehicle usage scenarios with braking system characteristics, the method can accurately predict vehicle braking life, thereby improving the accuracy of vehicle braking system diagnosis.

[0005] In a first aspect, embodiments of this application provide an AI diagnostic method for a vehicle braking system, the method comprising:

[0006] 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 braking pressure data, brake pad thickness data, and brake pad wear rate data;

[0007] The average value of the braking pressure data is determined as the reference braking pressure;

[0008] The braking pressure data and the brake pad wear rate data are analyzed to obtain a first fitted straight line; the horizontal axis of the first fitted straight line is the braking pressure, and the vertical axis is the brake pad wear rate.

[0009] The vehicle driving style corresponding to the target vehicle is determined based on the vehicle driving data.

[0010] The target adjustment factor is determined by a preset AI algorithm based on the vehicle's driving style and historical road condition data.

[0011] The reference braking pressure is adjusted according to the target adjustment factor to obtain the target braking pressure;

[0012] The service life of the brake pads of the target vehicle is determined based on the brake pad thickness data, the target braking pressure, and the first fitted straight line.

[0013] Diagnostic alerts are generated based on the brake pad lifespan; these alerts are used to indicate the brake lifespan of the target vehicle.

[0014] Secondly, embodiments of this application provide an AI diagnostic device for a vehicle braking system, the device comprising an acquisition module, a determination module, an analysis module, an adjustment module, and a generation module, wherein:

[0015] 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 braking pressure data, brake pad thickness data, and brake pad wear rate data.

[0016] The determining module is used to determine the average value of the braking pressure data as the reference braking pressure;

[0017] The analysis module is used to analyze the braking pressure data and the brake pad wear rate data to obtain a first fitted straight line; the horizontal axis of the first fitted straight line is the braking pressure, and the vertical axis is the brake pad wear rate.

[0018] The determining module is further configured to determine the vehicle driving style corresponding to the target vehicle based on the vehicle driving data;

[0019] The determining 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;

[0020] The adjustment module is used to adjust the reference braking pressure according to the target adjustment factor to obtain the target braking pressure;

[0021] The determining module is further configured to determine the service life of the brake pads of the target vehicle based on the brake pad thickness data, the target braking pressure, and the first fitted straight line;

[0022] The generation module is used to generate diagnostic prompt information based on the service life of the brake pads; the diagnostic prompt information is used to indicate the brake life of the target vehicle.

[0023] Thirdly, embodiments of this application provide an electronic device, including 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 include instructions for performing steps in any method of the first aspect of this application.

[0024] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in any method of the first aspect of this application.

[0025] Fifthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in any method of the first aspect of this application. The computer program product may be a software installation package.

[0026] By implementing the embodiments of this application, the vehicle's braking life can be accurately predicted by combining the vehicle's usage scenarios and braking system characteristics, thereby improving the accuracy of diagnosing the vehicle's braking system. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a system architecture diagram of a target diagnostic system provided in an embodiment of this application;

[0029] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0030] Figure 3 This is an application scenario diagram of a target diagnostic system provided in an embodiment of this application;

[0031] Figure 4 This is a flowchart illustrating an AI diagnostic method for a vehicle braking system provided in an embodiment of this application.

[0032] Figure 5 This is a schematic flowchart illustrating the process of determining a target adjustment factor, provided in an embodiment of this application.

[0033] Figure 6 This is a schematic diagram of a process for determining the service life of brake pads provided in an embodiment of this application;

[0034] Figure 7 This is a schematic diagram of a diagnostic prompt information interface provided in an embodiment of this application;

[0035] Figure 8This is a block diagram of the functional modules of an AI diagnostic device for a vehicle braking system provided in an embodiment of this application. Detailed Implementation

[0036] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0037] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0038] It should be understood that the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document indicates that the preceding and following related objects are in an "or" relationship. In the embodiments of this application, "multiple" refers to two or more.

[0039] In the embodiments of this application, "at least one item" or its similar expression refers to any combination of these items, including any combination of a single item or a plurality of items. "One or more" means one or more, while "multiple" means two or more. For example, "at least one item" 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. Each of a, b, and c can be an element or a set containing one or more elements.

[0040] In this application, the term "connection" refers to various connection methods, such as direct connection or indirect connection, to achieve communication between devices. This application does not impose any limitations on this.

[0041] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0042] With the rapid development of the automotive industry, the monitoring and evaluation of vehicle safety performance has become a crucial aspect of improving driving safety. Traditional braking system assessment and diagnosis typically rely on manual inspection, which is not only inefficient but also lacks real-time vehicle status monitoring, resulting in limited efficiency and accuracy. Therefore, improving the accuracy of vehicle braking system diagnosis is an urgent issue to be addressed.

[0043] To address the aforementioned issues, this application provides an AI diagnostic method and related apparatus for vehicle braking systems. The method acquires 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 target vehicle's braking system. 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 brake pad wear rate data are analyzed to obtain a first fitted straight line. The horizontal axis of the first fitted straight line represents brake pressure, and the vertical axis represents brake pad wear rate. The vehicle driving style corresponding to the target vehicle is determined based on the vehicle driving data. A target adjustment factor is determined using a preset AI algorithm based on the vehicle driving style and the historical road condition data. The reference brake pressure is adjusted according to the target adjustment factor to obtain a target brake pressure. The brake pad lifespan of the target vehicle is determined based on the brake pad thickness data, the target brake pressure, and the first fitted straight line. Diagnostic prompt information is generated based on the brake pad lifespan. The diagnostic prompt information is used to indicate the brake lifespan of the target vehicle. By combining vehicle usage scenarios with braking system characteristics, the vehicle's braking life can be accurately predicted, thereby improving the accuracy of vehicle braking system diagnosis.

[0044] For easier understanding, please refer to Figure 1 , Figure 1 This is a system architecture diagram of a target diagnostic system provided in an embodiment of this application. The target diagnostic system is used to execute the AI ​​diagnostic method for the vehicle braking system and includes a data acquisition module, a preprocessing module, an analysis and calculation module, and a diagnostic output module.

[0045] The data acquisition module is responsible for acquiring 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, total number of braking strokes, total number of acceleration strokes, vehicle speed, acceleration, driving time, and turning frequency. Historical road condition data includes, but is not limited to, road type, percentage of congested road sections, road surface 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, without specific limitations.

[0046] The preprocessing module cleans, transforms, and standardizes the collected raw data, providing high-quality data for subsequent analysis and calculations, and preventing noise or outliers from interfering with diagnostic logic. First, the data is cleaned by removing outliers, filling in missing values, and removing redundant data. Then, the data is standardized through unified data formats and normalization. Finally, different types of data are categorized and stored to establish a structured database, facilitating rapid retrieval 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 key calculation process from data to diagnostic results. First, the average value of the preprocessed brake pressure data is taken as a baseline, i.e., the reference brake pressure. Then, a linear regression algorithm is used to fit the brake pressure data and brake pad wear rate data, establishing a quantitative relationship between the two (horizontal axis represents brake pressure, and vertical axis represents wear rate). Next, the vehicle driving style corresponding to the target vehicle is determined based on vehicle driving data, and a preset AI algorithm (such as a decision tree or neural network model) is invoked. The vehicle driving style and historical road condition data (such as the percentage of congested road sections) are input, and a target adjustment factor is output (used to correct the reference brake pressure, reflecting the differences in actual braking intensity under different scenarios). Then, the reference brake pressure is adjusted using the target adjustment factor (e.g., multiplication operation: target brake pressure = reference brake pressure × adjustment factor) to obtain a target brake pressure that better reflects the actual usage scenario. Finally, the remaining wearable thickness of the brake pads is determined based on the brake pad thickness data. The wear rate under the target brake pressure is then determined using a first fitted straight line, and combined with the remaining wearable thickness of the brake pads, the remaining service life of the brake pads is calculated.

[0048] The diagnostic output module converts the brake pad lifespan obtained from the analysis and calculation module into intuitive diagnostic prompts. Based on the brake pad lifespan analysis, it generates corresponding graded prompts (e.g., "Lifespan 3 months, early replacement recommended," "Lifespan 1 year, normal use allowed"), and supplements auxiliary information, such as "Cause of accelerated wear: frequent emergency braking in congested areas." It should be noted that when generating diagnostic prompts, brake pad lifespan can be indicated by a time period or by mileage; no specific limitation is made here. This diagnostic prompt can be displayed as text or an icon warning on the target vehicle's in-vehicle terminal (e.g., the car's infotainment screen), or on the target user's mobile terminal (e.g., a mobile phone); no specific limitation is made here either.

[0049] It is evident that by intelligently collecting and analyzing vehicle driving, road conditions, and braking data, and combining AI algorithms to dynamically adapt to driving styles and road conditions, the accuracy of brake pad life prediction can be improved, thereby enhancing the accuracy of vehicle braking system diagnosis.

[0050] The following is combined Figure 2 The electronic devices in the embodiments of this application will be described. Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this 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. The processor is connected to the memory and the communication interface via an internal communication bus.

[0051] The processor is mainly used for:

[0052] 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 braking pressure data, brake pad thickness data, and brake pad wear rate data;

[0053] The average value of the braking pressure data is determined as the reference braking pressure;

[0054] The braking pressure data and the brake pad wear rate data are analyzed to obtain a first fitted straight line; the horizontal axis of the first fitted straight line is the braking pressure, and the vertical axis is the brake pad wear rate.

[0055] The vehicle driving style corresponding to the target vehicle is determined based on the vehicle driving data.

[0056] The target adjustment factor is determined by a preset AI algorithm based on the vehicle's driving style and historical road condition data.

[0057] The reference braking pressure is adjusted according to the target adjustment factor to obtain the target braking pressure;

[0058] The service life of the brake pads of the target vehicle is determined based on the brake pad thickness data, the target braking pressure, and the first fitted straight line.

[0059] Diagnostic alerts are generated based on the brake pad lifespan; these alerts are used to indicate the brake lifespan of the target vehicle.

[0060] The one or more programs are stored in the aforementioned memory and configured to be executed by the aforementioned processor, and the one or more programs include instructions for performing any step in the above 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 the various exemplary logic blocks, cells, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The communication unit can be a communication interface, transceiver, transceiver circuitry, etc., and the storage unit can be a memory.

[0062] The memory can be volatile or non-volatile, or a combination of both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but 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 synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0063] It is understood that the electronic device may include more or fewer structural elements than those shown in the block diagram above, such as a power module, physical buttons, a Wi-Fi module, a speaker, a Bluetooth module, sensors, a display module, etc., without limitation. It is understood that the electronic device may incorporate elements such as... Figure 1 The system architecture described above.

[0064] For easier understanding, please refer to Figure 3 , Figure 3This diagram illustrates an application scenario of a target diagnostic system provided in this application. The "target vehicle" represents the vehicle requiring diagnostics, and the "target diagnostic system" represents the system used to analyze and process vehicle data for diagnostic purposes. The first arrow points from the "target vehicle" to the "target diagnostic system," and is labeled "target vehicle data," indicating that the target vehicle will send its relevant data to the target diagnostic system. "Target vehicle data" includes, but is not limited to, various vehicle-related information such as vehicle driving data, historical road condition data, and vehicle braking data. The second arrow points from the "target diagnostic system" to the "target vehicle," and is labeled "diagnostic prompt information," indicating that after analyzing and processing the received vehicle data, the target diagnostic system will generate corresponding diagnostic prompt information and send it back to the target vehicle. This diagnostic prompt information includes fault warnings and maintenance suggestions for the target vehicle, helping the target user or maintenance personnel to understand the vehicle's condition in a timely manner.

[0065] After understanding the software and hardware architecture of this application, the following will be combined with... Figure 4 This application describes an AI diagnostic method for a vehicle braking system. Figure 4 This is a flowchart illustrating an AI diagnostic method for a vehicle braking system provided in an embodiment of this application, specifically including the following steps:

[0066] Step S401: Obtain the vehicle driving data and historical road condition data of the target vehicle within a preset time period, as well as the vehicle braking data corresponding to the braking system of the target vehicle.

[0067] The vehicle braking data includes braking pressure data, brake pad thickness data, and brake pad wear rate data. Relevant vehicle driving and braking data can be acquired through the target vehicle's sensor module, and historical traffic data can be obtained through the target vehicle's in-vehicle navigation system. This in-vehicle navigation system can combine real-time map traffic data and changes in average vehicle speed for analysis to determine the congestion level of a road segment (e.g., speeds below 50% of the road speed limit are considered congested).

[0068] Step S402: Determine the average value of the braking pressure data as the reference braking pressure.

[0069] Among them, the arithmetic mean of the brake pressure data collected within a preset time period can be calculated to obtain an average value that reflects the overall level of brake pressure within the preset time period, namely the reference brake pressure, which provides a basic benchmark value for subsequent adjustment of brake pressure and calculation of brake pad life.

[0070] Step S403: Analyze the braking pressure data and the brake pad wear rate data to obtain a first fitted straight line.

[0071] Wherein, the horizontal axis of the first fitted straight line represents braking pressure, and the vertical axis represents brake pad wear rate. The specific steps for analyzing the braking pressure data and the brake pad wear rate data to obtain the first fitted straight line include:

[0072] A1. Divide the preset time period into multiple reference time periods according to the preset duration;

[0073] A2. Based on the multiple reference time periods, the braking pressure data and the brake pad wear rate data are respectively divided into multiple reference braking pressure datasets and multiple reference brake pad wear rate datasets; each reference time period corresponds to one reference braking pressure dataset and one reference brake pad wear rate dataset.

[0074] A3. Determine the average value of each reference braking pressure dataset in the plurality of reference braking pressure datasets to obtain multiple braking pressures;

[0075] A4. Determine the average value of each reference brake pad wear rate dataset in multiple reference brake pad wear rate datasets to obtain multiple brake pad wear rates;

[0076] A5. Fit the multiple braking pressures and the multiple brake pad wear rates to obtain the first fitted straight line.

[0077] In a specific embodiment, firstly, the preset time period is divided into multiple reference time periods according to a preset duration. This preset duration can be one day, one week, one month, or half a year; no specific limitation is made here. Then, the brake pressure data and brake pad wear rate data are divided into multiple reference time periods respectively, resulting in multiple reference brake pressure datasets and multiple reference brake pad wear rate datasets. Each reference time period corresponds to one reference brake pressure dataset and one reference brake pad wear rate dataset.

[0078] Next, the average value of each reference brake pressure dataset in the multiple reference brake pressure datasets is calculated to obtain multiple brake pressures. Then, the average value of each reference brake pad wear rate dataset in the multiple reference brake pad wear rate datasets is calculated to obtain multiple brake pad wear rates. Then, the multiple brake pressures and multiple brake pad wear rates are fitted to obtain the first fitted straight line. Specifically, the brake pressure and brake pad wear rate for each reference time period can be paired to form multiple data points, and then a pre-defined least squares method is used to fit a straight line to the multiple data points to finally obtain the first fitted straight line.

[0079] As can be seen, by segmenting time and calculating the mean, the linear relationship between braking pressure and brake pad wear rate is determined, avoiding the random interference of single data, and making it easier to accurately predict the wear rate of brake pads by combining actual braking pressure data.

[0080] Step S404: Determine the vehicle driving style corresponding to the target vehicle based on the vehicle driving data.

[0081] The vehicle driving data includes the total number of braking actions and the total number of acceleration actions. The specific steps for determining the vehicle driving style corresponding to the target vehicle based on the vehicle driving data include:

[0082] B1. Determine the first braking pressure threshold based on the preset ratio and the rated braking pressure of the target vehicle;

[0083] B2. Obtain the number of emergency braking operations from the total number of braking operations where the braking pressure is greater than the first braking pressure threshold.

[0084] B3. Determine the percentage of emergency braking based on the number of emergency braking incidents and the total number of braking incidents;

[0085] B4. Obtain the number of accelerations in the total number of accelerations where the throttle opening is greater than a preset first throttle opening threshold, and obtain the number of rapid accelerations; the throttle opening represents the degree to which the accelerator pedal of the target vehicle is pressed.

[0086] B5. Determine the percentage of rapid acceleration based on the number of rapid accelerations and the total number of accelerations;

[0087] B6. Determine the vehicle driving style based on the percentage of emergency braking, the percentage of rapid acceleration, and the preset first and second percentage thresholds.

[0088] In a specific embodiment, firstly, a first braking pressure threshold is calculated based on the target vehicle's rated braking pressure and a preset ratio. For example, if the rated braking pressure is 10 MPa and the preset ratio is 70%, then the first braking pressure threshold = 7.0 MPa. Here, the rated braking pressure represents the maximum or standard braking pressure designed for the target vehicle. The preset ratio can be set according to the actual situation of the target vehicle. For example, for a family sedan, the preset ratio can be set to 60%. Family sedans are mostly used for urban commuting or on smooth roads, with low demand for emergency braking. Using 60% of the rated braking pressure as the first braking pressure threshold can more accurately identify emergency braking situations exceeding the normal braking intensity. For a sports sedan, the preset ratio can be increased to 70% to avoid misjudging normal braking as emergency braking situations. No specific limitation is made here.

[0089] Then, the number of braking operations with braking pressure exceeding a first braking pressure threshold is selected from the total number of braking operations to obtain the number of emergency braking operations. The proportion of emergency braking is then calculated based on the number of emergency braking operations and the total number of braking operations. The number of acceleration operations with throttle opening exceeding a preset first throttle opening threshold is selected from the total number of acceleration operations to obtain the number of rapid acceleration operations. Throttle opening represents the degree to which the accelerator pedal of the target vehicle is depressed. The first throttle opening threshold can be set to 80%, indicating that the accelerator pedal is depressed more than 80%. The proportion of rapid acceleration is then determined based on the number of rapid acceleration operations and the total number of acceleration operations. Finally, the vehicle driving style is determined based on the proportion of emergency braking, the proportion of rapid acceleration, and preset first and second proportion thresholds. The first proportion threshold defines the critical ratio for "frequent emergency braking"; when the proportion of emergency braking operations to total braking operations exceeds this first proportion threshold, the emergency braking operation is considered aggressive. The second proportion threshold defines the critical ratio for "frequent rapid acceleration"; when the proportion of rapid acceleration operations to total acceleration operations exceeds this second proportion threshold, the rapid acceleration operation is considered aggressive.

[0090] It is evident that by setting reasonable thresholds based on the actual conditions of the vehicle, the proportion of sudden braking and rapid acceleration can be quantified, thereby objectively and accurately determining the vehicle's driving style.

[0091] The vehicle driving style includes any one of the following: smooth driving, balanced driving, and aggressive driving. The specific steps for determining the vehicle driving style based on the percentage of emergency braking, the percentage of rapid acceleration, and preset first and second percentage thresholds include:

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

[0093] C2. If the proportion of emergency braking is greater than the first proportion threshold and the proportion of rapid acceleration is less than or equal to the second proportion threshold, or if the proportion of emergency braking is less than or equal to the first proportion threshold and the proportion of rapid acceleration is greater than the second proportion threshold, then the vehicle driving style is determined to be the balanced driving type.

[0094] C3. If the proportion of emergency braking is greater than the first proportion threshold and the proportion of rapid acceleration is greater than the second proportion threshold, then the vehicle driving style is determined to be the aggressive driving type.

[0095] In a specific embodiment, if the proportion of emergency braking is less than or equal to the first proportion threshold, and the proportion of rapid acceleration is less than or equal to the second proportion threshold, it indicates that the frequency of both emergency braking and rapid acceleration is controlled at a low level, the driving operation is smooth, focusing on comfort and economy, and the wear and tear on the target vehicle is small, thus determining the vehicle's driving style as a smooth driving type. If the proportion of emergency braking is greater than the first proportion threshold, and the proportion of rapid acceleration is less than or equal to the second proportion threshold, or if the proportion of emergency braking is less than or equal to the first proportion threshold, and the proportion of rapid acceleration is greater than the second proportion threshold, it indicates that only one of emergency braking or rapid acceleration has a high frequency, the driving style is biased towards one side (such as frequent rapid acceleration but smooth braking, or frequent emergency braking but gentle acceleration), the overall driving intensity is moderate, thus determining the vehicle's driving style as a balanced driving type. If the proportion of emergency braking is greater than the first proportion threshold, and the proportion of rapid acceleration is greater than the second proportion threshold, it indicates that the frequency of both emergency braking and rapid acceleration is high, the driving style is aggressive, and the wear and tear on the vehicle's braking system is large, thus determining the vehicle's driving style as an aggressive driving type.

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

[0097] Step S405: Determine the target adjustment factor based on the vehicle driving style and the historical road condition data using a preset AI algorithm.

[0098] For easier understanding, please refer to Figure 5 , Figure 5 This is a flowchart illustrating a method for determining a target adjustment factor according to an embodiment of this application. The step of determining the target adjustment factor using a preset AI algorithm based on the vehicle's driving style and historical road condition data includes the following steps:

[0099] D1. Based on the preset mapping relationship between driving style and feature value, determine the first feature value corresponding to the vehicle driving style; the first feature value is used to reflect the wear tendency of the vehicle driving style on the braking system of the target vehicle.

[0100] D2. Determine the first percentage of congested road sections and the second percentage of uncongested road sections based on the historical traffic data;

[0101] D3. Determine the second and third characteristic values ​​corresponding to the congested road segment and the unobstructed road segment respectively according to preset rules; the second and third characteristic values ​​are used to reflect the wear tendency of the target vehicle's braking system caused by the historical road condition data;

[0102] D4. Determine the fourth characteristic value based on the first proportion, the second characteristic value, the second proportion, and the third characteristic value;

[0103] D5. The first feature value and the fourth feature value are processed by the AI ​​algorithm to obtain the target adjustment factor.

[0104] In a specific embodiment, firstly, based on a preset mapping relationship between driving style and feature value, the driving style is converted into a first feature value. The first feature value reflects the tendency of the vehicle's driving style to cause wear on the target vehicle's braking system. For example, the first feature value corresponding to a smooth driving style is 0.8, the first feature value corresponding to a balanced driving style is 1.0, and the first feature value corresponding to an aggressive driving style is 1.2; no specific limitation is made here. Then, historical road condition data is statistically analyzed to obtain a first proportion of congested road segments and a second proportion of unobstructed road segments. The first proportion represents the percentage of mileage corresponding to congested road segments out of the total mileage, and the second proportion represents the percentage of mileage corresponding to unobstructed road segments out of the total mileage. The sum of the first and second proportions is 1.

[0105] Next, based on preset rules, the second and third characteristic values ​​corresponding to congested and unobstructed road sections are determined, respectively. Both the second and third characteristic values ​​reflect the wear tendency of the target vehicle's braking system due to historical road condition data. These preset rules can be fixed values, for example, specifying a second characteristic value of 1.1 for congested road sections (indicating wear higher than the baseline level) and a third characteristic value of 0.9 for unobstructed road sections (indicating wear lower than the baseline level). Alternatively, they can be dynamically adjusted in stages, refining the rules according to the degree of congestion (e.g., light congestion, severe congestion) or the level of unobstructed traffic (e.g., moderately unobstructed, high-speed unobstructed). For example, a second characteristic value of 1.3 for severe congestion, 1.1 for light congestion, 0.7 for high-speed unobstructed traffic, and 0.9 for moderately unobstructed traffic, making the characteristic values ​​more closely reflect actual road condition differences. No specific limitations are made here. It should be noted that congested road sections experience frequent braking, resulting in wear higher than the baseline, thus the second characteristic value is greater than 1; while unobstructed road sections have lower braking frequency and lower wear than the baseline, thus the second characteristic value is less than 1.

[0106] Then, the fourth eigenvalue is determined based on the first proportion, the second eigenvalue, the third eigenvalue, and the first proportion. This is achieved by weighting the first proportion, the second eigenvalue, the third eigenvalue, and the first eigenvalue. Finally, the first and fourth eigenvalues ​​are processed using an AI algorithm to obtain the target adjustment factor.

[0107] It is evident that by converting vehicle driving style and historical road condition data into feature values ​​and then dynamically fusing them through AI algorithms, the target adjustment factor can accurately reflect the impact of actual usage scenarios on the braking system, providing a more accurate basis for correcting wear and tear in subsequent brake pad life prediction and diagnostic prompts.

[0108] The specific steps of processing the first feature value and the fourth feature value using the AI ​​algorithm to obtain the target adjustment factor include:

[0109] E1. The first feature value and the fourth feature 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 feature value; the second weight corresponds to the fourth feature value.

[0110] E2. Obtain the target vehicle load within the preset time period;

[0111] E3. If the target vehicle load is greater than a preset vehicle load threshold, then obtain the correction factor corresponding to the target vehicle load; the greater the target vehicle load, the greater the correction factor.

[0112] E4. Correct the reference adjustment factor 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, then the reference adjustment factor shall be used as the target adjustment factor.

[0114] In a specific embodiment, the AI ​​algorithm includes a weighted fusion mechanism and a load correction mechanism, which can be used to integrate multi-dimensional information and achieve rapid adaptation to complex scenarios through weighting and correction mechanisms. In the basic weighted fusion mechanism, the first feature value and the fourth feature value can be weighted and summed using preset first and second weights to obtain a reference adjustment factor. The first weight corresponds to the first feature value, and the second weight corresponds to the fourth feature value. It should be noted that the AI ​​algorithm can dynamically update the first or second weight through machine learning (such as neural networks and decision trees) and combined with real-time data of the target vehicle. For example, if the driving style of the target vehicle changes from a smooth driving type to an aggressive driving type, and the number of rapid accelerations or hard brakings increases by 50% within 3 days, the weight acceleration update can be initiated, increasing the first weight from 0.6 to 0.7, so that the target adjustment factor can more quickly reflect the impact of the new vehicle driving style on the braking system.

[0115] Then, in the load correction mechanism, the target vehicle load within 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; the larger the target vehicle load, the larger the correction factor. Here, the target vehicle load represents the load condition of the target vehicle, such as the weight of the cargo it carries. The vehicle load threshold represents the threshold at which the target vehicle load begins to affect the braking system; this threshold can be set to 50% of the target vehicle's rated load, without specific limitation. It should be noted that when the target vehicle load exceeds the vehicle load threshold, i.e., 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, without specific limitation. In particular, since wet roads in rainy weather can lead to longer braking distances, the braking system needs to apply greater pressure to achieve the same braking effect. At the same time, the friction efficiency between the brake pads and brake discs decreases, resulting in faster wear compared to sunny days. Therefore, AI algorithms can be used to analyze weather data, determine the weather type, and make secondary corrections to the target adjustment factor based on the weather type to adapt to the actual scenario where the road friction coefficient is reduced and the braking load is increased in rainy weather.

[0116] Finally, the reference adjustment factor is adjusted based on the correction factor to obtain the target adjustment factor. If the target vehicle load is less than or equal to the vehicle load threshold, it indicates that the target vehicle load has a small impact on the braking system and can be ignored; in this case, the reference adjustment factor is directly used as the target adjustment factor.

[0117] It is evident that by integrating and dynamically adjusting the multi-dimensional feature values ​​of the target vehicle, accurate prediction of the vehicle's braking system wear can be achieved, facilitating subsequent adjustment of braking pressure and providing accuracy for the diagnosis of 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 braking pressure can be obtained by multiplying the target adjustment factor and the reference braking pressure. For example, if the target adjustment factor is 1.3 and the reference braking pressure is 5.0 MPa, the target braking pressure can be calculated to be 6.5 MPa. No specific limit is set here.

[0120] Step S407: Determine the service life of the brake pads of the target vehicle based on the brake pad thickness data, the target braking pressure, and the first fitted straight line.

[0121] For easier understanding, please refer to Figure 6 , Figure 6This is a flowchart illustrating a method for determining the service life of brake pads according to an embodiment of this application. The specific steps for determining the service life of the brake pads of the target vehicle based on the brake pad thickness data, the target braking pressure, and the first fitted straight line include:

[0122] F1. Determine the target brake pad wear rate based on the target braking pressure and the first fitted straight line;

[0123] F2. Determine the remaining wearable thickness of the brake pads of the target vehicle based on the brake pad thickness data;

[0124] F3. Determine the service life of the brake pad based on the target brake pad wear rate and the remaining wearable thickness of the brake pad.

[0125] In a specific embodiment, firstly, 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. In the first fitted straight line, the brake pad wear rate is positively correlated with the braking pressure (the higher the braking pressure, the more intense the friction between the brake pad and the brake disc, and the faster the wear). Then, the current brake pad thickness of the target vehicle is determined based on the brake pad thickness data, and the corresponding brake pad scrap thickness (a safe threshold for brake pad thickness, typically 2-3 mm) is obtained. When the brake pad thickness is lower than this scrap thickness, it will lead to abnormal braking noise, reduced braking force, and even brake disc wear. Therefore, this safety redundancy must be deducted during the calculation of the remaining wearable thickness of the brake pad, i.e., the current brake pad thickness minus the scrap thickness of the brake pad to obtain the remaining wearable thickness of the brake pad for the target vehicle. Finally, the brake pad service life is determined based on the target brake pad wear rate and the remaining wearable thickness of the brake pad.

[0126] It is evident that by accurately calculating the lifespan of brake pads and diagnosing the vehicle's braking system, the braking lifespan of brake pads can be predicted in advance, avoiding reduced braking force, abnormal brake noise, or brake disc damage caused by excessive wear of brake pads, thus reducing accidents caused by braking system failures at the source.

[0127] Step S408: Generate diagnostic prompt information based on the brake pad lifespan; the diagnostic prompt information is used to indicate the brake lifespan of the target vehicle.

[0128] Specifically, different diagnostic prompts can be generated based on the specific lifespan of the brake pads. These prompts include, but are not limited to, routine prompts, warning prompts, and emergency prompts. For example, when the brake pad lifespan is greater than 3000 kilometers, a routine prompt can be generated: "Brake pad lifespan is 5000 kilometers; it is recommended to check the thickness at the next maintenance." When the brake pad lifespan is greater than 1000 kilometers but less than or equal to 3000 kilometers, a warning prompt can be generated: "Brake pad lifespan is 2000 kilometers; it needs to be replaced soon (within approximately 2 weeks) to avoid affecting braking performance." When the brake pad lifespan is less than or equal to 1000 kilometers, an emergency prompt can be generated: "Brake pads are severely worn; brake pad lifespan is only 800 kilometers; please replace immediately, otherwise it may lead to brake failure."

[0129] For easier understanding, please refer to Figure 7 , Figure 7 This is a schematic diagram of a diagnostic prompt information interface provided in an embodiment of this application. The diagnostic prompt is an emergency warning, specifically stating, "Brake pads are severely worn; brake pad lifespan is only 800 kilometers. Please replace them immediately, otherwise brake failure may occur." It is clear that if the target user does not replace the brake pads of the target vehicle in time, the continued wear of the brake pads may lead to brake failure, potentially causing a serious traffic accident. Below the prompt content, there are functional buttons such as "Schedule Maintenance" and "Learn More," which are not specifically limited here. The "Schedule Maintenance" button provides a convenient entry point for the target user, allowing them to quickly schedule vehicle maintenance services to replace the brake pads promptly. The "Learn More" button allows the target user to obtain further detailed information about brake pad wear and replacement, helping them better understand the vehicle's condition and maintenance needs.

[0130] It is evident that the concise and clear interface design and direct and effective information delivery provide target users with important vehicle diagnostic prompts and convenient operation options, which helps to ensure the braking safety and normal operation of the vehicle.

[0131] The above primarily describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the electronic device includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by 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 this application.

[0132] This application embodiment can divide the electronic device into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0133] When dividing each function into modules according to its corresponding function. Figure 8 This is a functional module block diagram of an AI diagnostic device for a vehicle braking system provided in an embodiment of this application. The AI ​​diagnostic device 800 for a vehicle braking system includes 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 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 braking pressure data, brake pad thickness data, and brake pad wear rate data.

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

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

[0137] The determining module 820 is further configured to determine the vehicle driving style corresponding to the target vehicle based on the vehicle driving data;

[0138] The determining module 820 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;

[0139] The adjustment module 840 is used to adjust the reference braking pressure according to the target adjustment factor to obtain the target braking pressure;

[0140] The determining module 820 is further configured to determine the service life of the brake pads of the target vehicle based on the brake pad thickness data, the target braking pressure, and the first fitted straight line.

[0141] The generation module 850 is used to generate diagnostic prompt information based on the service life of the brake pads; the diagnostic prompt information is used to indicate the brake life of the target vehicle.

[0142] Optionally, in analyzing the braking pressure data and the brake pad wear rate data to obtain a first fitted straight line, the analysis module 830 is specifically used for:

[0143] The preset time period is divided into multiple reference time periods according to the preset duration;

[0144] Based on the multiple reference time periods, the braking pressure data and the brake pad wear rate data are respectively divided into multiple reference braking pressure datasets and multiple reference brake pad wear rate datasets; each reference time period corresponds to a reference braking pressure dataset and a reference brake pad wear rate dataset.

[0145] Determine the average value of each reference braking pressure dataset in the plurality of reference braking pressure datasets to obtain multiple braking pressures;

[0146] Determine the average value of each reference brake pad wear rate dataset in multiple reference brake pad wear rate datasets to obtain multiple brake pad wear rates;

[0147] The first fitted straight line is obtained by fitting the multiple braking pressures and the multiple brake pad wear rates.

[0148] Optionally, the vehicle driving data includes the total number of braking actions and the total number of acceleration actions. In determining the vehicle driving style corresponding to the target vehicle based on the vehicle driving data, the determining module 820 is specifically used for:

[0149] The first braking pressure threshold is determined based on a preset ratio and the rated braking pressure of the target vehicle.

[0150] The number of emergency braking operations is obtained by acquiring the number of braking operations in the total number of braking operations where the braking pressure is greater than the first braking pressure threshold.

[0151] The percentage of emergency braking is determined based on the number of emergency braking incidents and the total number of braking incidents.

[0152] The number of accelerations with a throttle opening greater than a preset first throttle opening threshold is obtained from the total number of accelerations to obtain the number of rapid accelerations; the throttle opening represents the degree to which the accelerator pedal of the target vehicle is pressed.

[0153] The percentage of rapid accelerations is determined based on the number of rapid accelerations and the total number of accelerations.

[0154] The vehicle driving style is determined based on the percentage of emergency braking, the percentage of rapid acceleration, and preset first and second percentage thresholds.

[0155] Optionally, the vehicle driving style includes any one of the following: smooth driving, balanced driving, and aggressive driving. In determining the vehicle driving style based on the percentage of emergency braking, the percentage of rapid acceleration, and a preset first and second percentage thresholds, the determining module 820 is further specifically used for:

[0156] If the percentage of emergency braking is less than or equal to the first percentage threshold, and the percentage of rapid acceleration is less than or equal to the second percentage threshold, then the vehicle driving style is determined to be the smooth driving type.

[0157] If the proportion of emergency braking is greater than the first proportion threshold and the proportion of rapid acceleration is less than or equal to the second proportion threshold, or if the proportion of emergency braking is less than or equal to the first proportion threshold and the proportion of rapid acceleration is greater than the second proportion threshold, then the vehicle driving style is determined to be the balanced driving type.

[0158] If the proportion of emergency braking is greater than the first proportion threshold and the proportion of rapid acceleration is greater than the second proportion threshold, then the vehicle driving style is determined to be the aggressive driving type.

[0159] Optionally, in determining the target adjustment factor based on the vehicle driving style and the historical road condition data using a preset AI algorithm, the determining module 820 is further specifically used for:

[0160] Based on a preset mapping relationship between driving style and feature value, a first feature value corresponding to the vehicle driving style is determined; the first feature value is used to reflect the wear tendency of the vehicle driving style on the braking system of the target vehicle.

[0161] Based on the historical traffic data, determine the first percentage of congested road sections and the second percentage of uncongested road sections;

[0162] According to preset rules, the second and third characteristic values ​​corresponding to the congested road sections and the unobstructed road sections are determined respectively; the second and third characteristic values ​​are used to reflect the wear tendency of the target vehicle's braking system caused by the historical road condition data;

[0163] A fourth feature value is determined based on the first proportion, the second feature value, the second proportion, and the third feature value;

[0164] The target adjustment factor is obtained by processing the first feature value and the fourth feature value using the AI ​​algorithm.

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

[0166] A reference adjustment factor is obtained by weighted summation of the first feature value and the fourth feature value according to a preset first weight and second weight; the first weight corresponds to the first feature value; the second weight corresponds to the fourth feature value.

[0167] Obtain the target vehicle load within the preset time period;

[0168] If the target vehicle's load is greater than a preset vehicle load threshold, then a correction factor corresponding to the target vehicle's load is obtained; the greater the target vehicle's load, the greater the correction factor.

[0169] The reference adjustment factor is modified according to the modification factor to obtain the target adjustment factor;

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

[0171] Optionally, in determining the brake pad lifespan of the target vehicle based on the brake pad thickness data, the target braking pressure, and the first fitted straight line, the determining module 820 is further specifically used for:

[0172] The target brake pad wear rate is determined based on the target braking pressure and the first fitted straight line.

[0173] The remaining wearable thickness of the brake pads of the target vehicle is determined based on the brake pad thickness data;

[0174] The service life of the brake pad is determined based on the target brake pad wear rate and the remaining wearable thickness of the brake pad.

[0175] It is evident that by combining vehicle usage scenarios with braking system characteristics, accurate prediction of vehicle braking life can be achieved, thereby improving the accuracy of vehicle braking system diagnosis.

[0176] It should be noted that the specific implementation of each operation can be described in the corresponding description of the method embodiments shown above. The AI ​​diagnostic device 800 for the vehicle braking system can be used to execute the method embodiments of this application, and will not be described again here.

[0177] This application also provides a computer-readable storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes an electronic device.

[0178] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may include an electronic device.

[0179] It should be noted that, for the sake of simplicity, the above embodiments are all described as a series of actions. Those skilled in the art should understand that this application is not limited to the described order of actions, as some steps in the embodiments of this application can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions, steps, modules, or units involved are not necessarily essential to the embodiments of this application.

[0180] In the above embodiments, the descriptions of each embodiment in this application have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0181] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0182] The steps of the methods or algorithms described in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, portable hard disk, read-only optical disk (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Furthermore, the ASIC can reside in a terminal device or management device. Alternatively, the processor and storage medium can exist as discrete components in the terminal device or management device.

[0183] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in the embodiments of this application can be implemented, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0184] The modules / units included in the various devices and products described in the above embodiments can be software modules / units, hardware modules / units, or a combination of both. For example, for devices and products applied to or integrated into a chip, all modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs that run on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits. For devices and products applied to or integrated into a chip module, all modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The implementation is achieved through a software program that runs on a processor integrated within the chip module. The remaining modules / units (if any) can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into terminal equipment, each of their modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components within the terminal equipment. Alternatively, at least some modules / units can be implemented using a software program that runs on a processor integrated within the terminal equipment, while the remaining modules / units (if any) can be implemented using hardware methods such as circuits.

[0185] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the embodiments of this application. It should be understood that the above descriptions are merely specific embodiments of the embodiments of this application and are not intended to limit the protection scope of the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solutions of the embodiments of this application should be included within the protection scope of the embodiments of this application.

Claims

1. An AI diagnostic method for vehicle braking systems, characterized in that, The method includes: 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 braking pressure data, brake pad thickness data, and brake pad wear rate data; The average value of the braking pressure data is determined as the reference braking pressure; The braking pressure data and the brake pad wear rate data are analyzed to obtain a first fitted straight line; the horizontal axis of the first fitted straight line is the braking pressure, and the vertical axis is the brake pad wear rate. The vehicle driving style corresponding to the target vehicle is determined based on the vehicle driving data. The target adjustment factor is determined by a preset AI algorithm based on the vehicle's driving style and historical road condition data. The reference braking pressure is adjusted according to the target adjustment factor to obtain the target braking pressure; The service life of the brake pads of the target vehicle is determined based on the brake pad thickness data, the target braking pressure, and the first fitted straight line. Diagnostic alert information is generated based on the brake pad lifespan; the diagnostic alert information is used to indicate the brake lifespan of the target vehicle; The step of determining the target adjustment factor based on the vehicle's driving style and historical road condition data using a preset AI algorithm includes: Based on a preset mapping relationship between driving style and feature value, a first feature value corresponding to the vehicle driving style is determined; the first feature value is used to reflect the wear tendency of the vehicle driving style on the braking system of the target vehicle. Based on the historical traffic data, determine the first percentage of congested road sections and the second percentage of uncongested road sections; According to preset rules, the second and third characteristic values ​​corresponding to the congested road sections and the unobstructed road sections are determined respectively; the second and third characteristic values ​​are used to reflect the wear tendency of the target vehicle's braking system caused by the historical road condition data; A fourth feature value is determined based on the first proportion, the second feature value, the second proportion, and the third feature value; The target adjustment factor is obtained by processing the first feature value and the fourth feature value using the AI ​​algorithm.

2. The method as described in claim 1, characterized in that, The step of analyzing the braking pressure data and the brake pad wear rate data to obtain a first fitted straight line includes: The preset time period is divided into multiple reference time periods according to the preset duration; Based on the multiple reference time periods, the braking pressure data and the brake pad wear rate data are respectively divided into multiple reference braking pressure datasets and multiple reference brake pad wear rate datasets; each reference time period corresponds to a reference braking pressure dataset and a reference brake pad wear rate dataset. Determine the average value of each reference braking pressure dataset in the plurality of reference braking pressure datasets to obtain multiple braking pressures; Determine the average value of each reference brake pad wear rate dataset in multiple reference brake pad wear rate datasets to obtain multiple brake pad wear rates; The first fitted straight line is obtained by fitting the multiple braking pressures and the multiple brake pad wear rates.

3. The method as described in claim 1, characterized in that, The vehicle driving data includes the total number of braking actions and the total number of acceleration actions. Determining the vehicle driving style corresponding to the target vehicle based on the vehicle driving data includes: The first braking pressure threshold is determined based on a preset ratio and the rated braking pressure of the target vehicle. The number of emergency braking operations is obtained by acquiring the number of braking operations in the total number of braking operations where the braking pressure is greater than the first braking pressure threshold. The percentage of emergency braking is determined based on the number of emergency braking incidents and the total number of braking incidents. The number of accelerations with a throttle opening greater than a preset first throttle opening threshold is obtained from the total number of accelerations to obtain the number of rapid accelerations; the throttle opening represents the degree to which the accelerator pedal of the target vehicle is pressed. The percentage of rapid accelerations is determined based on the number of rapid accelerations and the total number of accelerations. The vehicle driving style is determined based on the percentage of emergency braking, the percentage of rapid acceleration, and preset first and second percentage thresholds.

4. The method as described in claim 3, characterized in that, The vehicle driving style includes any one of the following: smooth driving, balanced driving, and aggressive driving. Determining the vehicle driving style based on the percentage of emergency braking, the percentage of rapid acceleration, and preset first and second percentage thresholds includes: If the percentage of emergency braking is less than or equal to the first percentage threshold, and the percentage of rapid acceleration is less than or equal to the second percentage threshold, then the vehicle driving style is determined to be the smooth driving type. If the proportion of emergency braking is greater than the first proportion threshold and the proportion of rapid acceleration is less than or equal to the second proportion threshold, or if the proportion of emergency braking is less than or equal to the first proportion threshold and the proportion of rapid acceleration is greater than the second proportion threshold, then the vehicle driving style is determined to be the balanced driving type. If the proportion of emergency braking is greater than the first proportion threshold and the proportion of rapid acceleration is greater than the second proportion threshold, then the vehicle driving style is determined to be the aggressive driving type.

5. The method as described in claim 1, characterized in that, The step of processing the first feature value and the fourth feature value using the AI ​​algorithm to obtain the target adjustment factor includes: A reference adjustment factor is obtained by weighted summation of the first feature value and the fourth feature value according to a preset first weight and second weight; the first weight corresponds to the first feature value; the second weight corresponds to the fourth feature value. Obtain the target vehicle load within the preset time period; If the target vehicle's load is greater than a preset vehicle load threshold, then a correction factor corresponding to the target vehicle's load is obtained; the greater the target vehicle's load, the greater the correction factor. The reference adjustment factor is modified according to the modification factor to obtain the target adjustment factor; If the target vehicle load is less than or equal to the vehicle load threshold, then the reference adjustment factor is used as the target adjustment factor.

6. The method according to any one of claims 1-5, characterized in that, The step of determining the brake pad lifespan of the target vehicle based on the brake pad thickness data, the target braking pressure, and the first fitted straight line includes: The target brake pad wear rate is determined based on the target braking pressure and the first fitted straight line. The remaining wearable thickness of the brake pads of the target vehicle is determined based on the brake pad thickness data; The service life of the brake pad is determined based on the target brake pad wear rate and the remaining wearable thickness of the brake pad.

7. An AI diagnostic device for a vehicle braking system, used to perform the method as described in any one of claims 1-6, 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 braking pressure data, brake pad thickness data, and brake pad wear rate data. The determining module is used to determine the average value of the braking pressure data as the reference braking pressure; The analysis module is used to analyze the braking pressure data and the brake pad wear rate data to obtain a first fitted straight line; the horizontal axis of the first fitted straight line is the braking pressure, and the vertical axis is the brake pad wear rate. The determining module is further configured to determine the vehicle driving style corresponding to the target vehicle based on the vehicle driving data; The determining 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 adjustment module is used to adjust the reference braking pressure according to the target adjustment factor to obtain the target braking pressure; The determining module is further configured to determine the service life of the brake pads of the target vehicle based on the brake pad thickness data, the target braking pressure, and the first fitted straight line; The generation module is used to generate diagnostic prompt information based on the service life of the brake pads; the diagnostic prompt information is used to indicate the brake life of the target vehicle.

8. An electronic device, characterized in that, include: Processor, memory, 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, the programs including instructions for performing the steps of the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-6.

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