Brake state recognition method and vehicle

Abstract

The application relates to a brake state recognition method and a vehicle, and relates to the technical field of intelligent cockpits.The method comprises the following steps: acquiring a plurality of brake characteristic index values calculated in a plurality of preset time length windows under a continuous wet and slippery working condition of the vehicle; based on the plurality of brake characteristic index values in the plurality of preset time length windows, a plurality of target brake characteristic index values are obtained by statistics; the plurality of target brake characteristic index values are fused to obtain a brake state quantitative score, and the brake state of the vehicle is recognized according to the brake state quantitative score. The method breaks the limitation of traditional manual subjective feeling for determining the brake state, can objectively quantify the brake softening degree under a wet and slippery environment, simultaneously completes the whole operation in real time based on time sequence data under the continuous wet and slippery working condition, does not need offline equipment detection, can identify the brake decay hidden danger caused by the brake system being wet and the brake disc water film accumulation in advance, and improves the real-time performance, stability and quantitative precision of the wet and slippery road brake fault recognition.

Description

Technical Field

[0001] This application relates to the field of intelligent cockpit technology, and in particular to a braking status recognition method and vehicle. Background Technology

[0002] Soft brakes are a common abnormal braking phenomenon during vehicle operation. Specifically, they manifest as increased brake pedal travel and delayed braking response. Under the same pedal pressure, the vehicle's braking force is significantly reduced. This fault directly increases the vehicle's braking distance, greatly reduces driving safety, and can easily lead to road traffic accidents.

[0003] Currently, the methods for detecting brake softening in vehicles are relatively traditional, mainly relying on the driver's subjective driving experience or periodic offline disassembly and equipment testing to complete the fault diagnosis. These methods cannot capture dynamic braking anomalies in rainy or wet conditions in real time, making it difficult to achieve online identification and early warning of brake softening faults. The fault detection is delayed, and it is impossible to avoid the driving safety hazards caused by brake failure in a timely manner. Summary of the Invention

[0004] This application provides a brake status identification method, device, vehicle, and storage medium. It overcomes the limitations of traditional methods that rely on subjective human perception to determine brake status, objectively quantifying the degree of brake softening in wet and slippery conditions. Furthermore, it utilizes real-time time-series data under continuous wet and slippery conditions to complete the entire calculation process, eliminating the need for offline equipment testing. This allows for early identification of brake fade risks caused by moisture in the braking system and water film buildup on the brake discs, improving the real-time performance, stability, and quantitative accuracy of brake fault identification on wet and slippery roads. The specific technical solution is as follows: In a first aspect of this application, a braking state recognition method is provided, the method comprising: The values ​​of multiple braking characteristic indicators calculated within multiple preset time windows under continuous wet and slippery conditions of the vehicle are obtained. Based on the values ​​of the various types of braking characteristic indicators within multiple preset time windows, the values ​​of the various types of target braking characteristic indicators are statistically obtained. By fusing the braking feature index values ​​of the multiple target categories, a braking state quantitative score is obtained, and the vehicle braking state is identified based on the braking state quantitative score.

[0005] In an optional implementation, the multiple braking characteristic index values ​​include braking response hysteresis index, wet skid braking performance attenuation coefficient, and water film accumulation and insufficient dispersion index. The method of statistically obtaining multiple target braking feature index values ​​based on the multiple types of braking feature index values ​​within multiple preset time windows includes: The average value of the braking response hysteresis index within all preset time windows is taken as the target braking response hysteresis index. The average value of the wet skid braking performance attenuation coefficient within all preset time windows is taken as the target wet skid braking performance attenuation coefficient. The average value of the cumulative water film dispersion insufficiency index within all preset time windows is taken as the target cumulative water film dispersion insufficiency index. The process of fusing the braking feature index values ​​of the multiple target categories to obtain a quantitative score of braking status includes: The target braking response hysteresis index, the target wet skid braking efficiency attenuation coefficient, and the target water film accumulation and dispersion insufficiency index are normalized to obtain three types of normalized indices. The braking state quantitative score is calculated by linearly weighting and summing the three types of normalized indicators according to their respective preset weights.

[0006] In an optional implementation, identifying the vehicle braking state based on the braking state quantification score includes: Determine the scoring range in which the quantitative score of the braking state falls, and match the braking state level corresponding to the scoring range; The method further includes: Output driving prompts and handling suggestions corresponding to the braking status level.

[0007] In an optional implementation, the braking response hysteresis index is calculated in the following manner: Within the preset duration window, the effective braking response time and the braking start speed corresponding to each effective braking response time are statistically analyzed. Find the first median of all effective braking response times and the second median of all braking initiation speeds; The braking response hysteresis index is obtained by normalizing the first median based on the second median.

[0008] In an optional implementation, the step of calculating the effective braking response time within the preset duration window includes: Within the preset time window, monitor the braking initiation time corresponding to each switch of the brake pedal from the unpressed state to the pressed state; Starting from the braking initiation time, extract the minimum value of longitudinal acceleration during this braking process, and the stable time when the longitudinal acceleration reaches the product of the minimum value and a preset ratio; If the minimum value of the longitudinal acceleration is less than the preset effective braking threshold, then the time difference between the stabilization time and the braking start time is calculated. If the time difference falls within a preset difference range, then the time difference is determined as the effective braking response time. If the time difference does not fall within the preset difference range, the time difference is discarded.

[0009] In an optional implementation, the normalization of the first median based on the second median to obtain the braking response hysteresis index includes: Multiply the preset fixed coefficient by the second median to obtain the product term, and add the product term to the preset reference duration to obtain the reference response time; Dividing the first median by the reference response time yields the braking response hysteresis index.

[0010] In an optional implementation, the wet braking performance attenuation coefficient is calculated in the following manner: Within the preset time window, multiple valid sampling moments that meet the valid sampling conditions are selected. The valid sampling conditions include the brake pedal being depressed, the vehicle speed being greater than a preset vehicle speed threshold, and the brake master cylinder pressure being greater than a preset pressure threshold. For any of the aforementioned valid sampling times, determine the theoretical deceleration and the actual deceleration for that valid sampling time; Based on the theoretical deceleration and the actual deceleration, determine the efficiency ratio of the effective sampling time; The wet skid braking efficiency attenuation coefficient is determined based on the efficiency ratio at multiple effective sampling times.

[0011] In an optional implementation, determining the theoretical deceleration at the effective sampling time includes: Obtain the brake master cylinder pressure at the effective sampling time, and multiply the brake master cylinder pressure by a preset braking factor to obtain the product term; Obtain the preset vehicle mass, divide the product term by the preset vehicle mass, and obtain the theoretical deceleration at the effective sampling time.

[0012] In an optional implementation, determining the efficiency ratio at the effective sampling time based on the theoretical deceleration and the actual deceleration includes: Divide the actual deceleration by the theoretical deceleration to obtain the initial efficiency ratio; The initial efficiency ratio is truncated to a preset efficiency ratio range to obtain the efficiency ratio at the effective sampling time.

[0013] In an optional implementation, determining the wet skid braking performance attenuation coefficient based on the performance ratio at multiple effective sampling times includes: Calculate the average energy efficiency ratio at multiple effective sampling times, and obtain the preset baseline attenuation value; Subtracting the average energy efficiency ratio from the preset basic attenuation benchmark value yields the wet skid braking efficiency attenuation coefficient.

[0014] In an optional implementation, the water film cumulative dispersion insufficiency index is calculated in the following manner: Within the preset time window, for any given moment, the basic risk and time factor for that moment are determined respectively; Multiplying the basic risk by the time factor yields the instantaneous water film risk at the specified moment; Based on the instantaneous water film risk at each moment within the preset time window, the cumulative water film dispersion insufficiency index is determined.

[0015] In one optional implementation, determining the underlying risk at the stated moment includes: Obtain the instantaneous vehicle speed at the specified moment, and determine whether the instantaneous vehicle speed is less than a preset instantaneous vehicle speed threshold; If the instantaneous vehicle speed is less than the preset instantaneous vehicle speed threshold, obtain the rainfall intensity and windshield wiper intensity level at that moment; The rainfall intensity is divided by a preset first coefficient to obtain a first calculation item, and the wiper strength level is divided by a preset second coefficient to obtain a second calculation item; Add the first calculation term to the second calculation term to obtain the basic risk at the stated time. If the instantaneous vehicle speed is greater than or equal to the preset instantaneous vehicle speed threshold, the basic risk at that moment is assigned a preset benchmark risk value.

[0016] In an optional implementation, determining the time factor of the time moment includes: Obtain the time interval between the time of the last braking and the end of the last braking, divide the time interval by a preset third coefficient, and add it to a preset base factor to obtain the initial time factor; The initial time factor is truncated to a preset factor interval to obtain the time factor of the time.

[0017] In an optional implementation, determining the water film cumulative dispersion insufficiency index based on the instantaneous water film risk at each moment within the preset time window includes: Calculate the average value of the instantaneous water film risk at all times within the preset time window; The cumulative number of trips under continuous wet and slippery conditions is obtained, and the cumulative number of trips is divided by a preset fourth coefficient to obtain the trip correction term. The minimum value between the travel correction item and the preset correction upper limit value is selected as the correction coefficient, and the average value is multiplied by the correction coefficient to obtain the initial water film accumulation and dispersion insufficiency index. The initial water film cumulative dispersion insufficiency index is truncated to a preset index range to obtain the water film cumulative dispersion insufficiency index.

[0018] In a second aspect of this application, a brake state recognition device is also provided, the device comprising: The index value acquisition module is used to acquire multiple types of braking characteristic index values ​​calculated within multiple preset time windows under continuous wet and slippery conditions of the vehicle. The indicator value statistics module is used to statistically obtain multiple target braking feature indicator values ​​based on the multiple types of braking feature indicator values ​​within multiple preset time windows. The index value fusion module is used to fuse the braking feature index values ​​of the multiple types of targets to obtain a quantitative score of braking status; The braking status recognition module is used to identify the vehicle's braking status based on the braking status quantification score.

[0019] In a third aspect of the embodiments of this application, a vehicle is also provided, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When the processor executes a program stored in the memory, it implements the braking state recognition method described in any of the first aspects above.

[0020] In a fourth aspect of the embodiments of this application, a storage medium is also provided, wherein the storage medium stores instructions that, when run on a computer, cause the computer to execute any of the braking state recognition methods described in the first aspect above.

[0021] In a fifth aspect of the embodiments of this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the braking state recognition methods described above.

[0022] The technical solution provided in this application embodiment obtains multiple types of braking characteristic index values ​​calculated within multiple preset time windows under continuous wet and slippery conditions of the vehicle. Based on the multiple types of braking characteristic index values ​​within each preset time window, multiple types of target braking characteristic index values ​​are statistically obtained. The multiple types of target braking characteristic index values ​​are fused to obtain a braking state quantitative score, and the vehicle braking state is identified based on the braking state quantitative score.

[0023] This solution first acquires the values ​​of multiple braking characteristic indicators corresponding to each of the preset time windows under continuous wet and slippery conditions. Then, it statistically processes the values ​​of the same type of indicator across all windows to obtain the values ​​of various target braking characteristic indicators. Finally, it integrates various target indicators to output a quantitative score of braking status and identifies the braking status based on the score. First, by collecting indicators synchronously through multiple windows, it can avoid the problem of indicator distortion caused by the accidental fluctuation of single-segment driving data. The target indicators obtained through multi-window statistical processing can further smooth sampling noise and reduce the calculation deviation caused by instantaneous road conditions or single braking behavior. Then, it integrates and quantifies the multi-dimensional braking indicators to form a unified score, which gets rid of the limitations of traditionally relying on human subjective feelings to judge the braking status. It can objectively quantify the degree of brake softening under wet and slippery conditions. At the same time, it completes the entire calculation in real time based on the time series data under continuous wet and slippery conditions, without the need for offline equipment testing. It can identify the potential brake fade hazards caused by moisture in the braking system and water film accumulation on the brake disc in advance, improving the real-time performance, stability and quantitative accuracy of brake fault identification on wet and slippery roads. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0027] Figure 1 This is a schematic diagram illustrating the basic implementation process of a braking state recognition method shown in the embodiments of this application; Figure 2 This is a schematic diagram illustrating the basic implementation process of another braking state recognition method shown in the embodiments of this application; Figure 3 This is a schematic diagram illustrating the implementation process of a braking response hysteresis index calculation method shown in the embodiments of this application; Figure 4 A schematic diagram illustrating the implementation process of a method for calculating the attenuation coefficient of wet skid braking performance provided in this application embodiment; Figure 5 A schematic diagram illustrating the implementation process of a method for calculating the water film accumulation and dispersion insufficiency index provided in this application embodiment; Figure 6 This is a schematic diagram of the structure of a brake status recognition device shown in an embodiment of this application; Figure 7 This is a schematic diagram of the hardware structure of a vehicle as shown in an embodiment of this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] The following disclosure provides numerous different embodiments or examples for implementing various structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0030] When vehicles are driven for extended periods in continuously slippery conditions such as rain, snow, freezing rain, and melting snow and ice on the road, a water film tends to adhere to the surface of the brake discs. At the same time, the high humidity environment causes the brake fluid to absorb moisture, the brake calipers to corrode, and the friction performance of the brake pads to decrease, resulting in a softening of the brakes. This significantly shortens the effective braking distance and poses a driving safety hazard. Currently, the industry mainly uses two methods to identify brake softening: one relies on the driver's subjective pedal feel, which is highly subjective and has a delayed recognition effect, only becoming noticeable after the fault has significantly worsened, and cannot provide early warning of minor brake fade issues; the other relies on offline maintenance equipment to statically inspect brake system components, which can only achieve periodic, fixed-point inspections and cannot perform dynamic, real-time assessments based on the actual slippery driving conditions of the vehicle.

[0031] To address the shortcomings of existing technologies, the research and development team of this application, combining real-world vehicle road test data from continuous rainy weather trips, identified three core characterization dimensions of brake fade under wet and slippery conditions. They constructed three quantitative indicators: a brake response hysteresis index, a wet and slippery braking efficiency attenuation coefficient, and a water film accumulation and dispersion insufficiency index. Simultaneously, a multi-slip duration window acquisition mechanism was introduced to smooth instantaneous data noise. The three indicators were then normalized and weighted to obtain a unified quantitative score, and multiple score ranges were assigned to match corresponding braking strategies. This addresses the technical challenges of traditional identification methods, such as poor real-time performance, large result fluctuations, and the inability to provide quantitative and graded early warnings.

[0032] This application provides a braking state recognition method. The overall technical logic is as follows: First, multiple braking characteristic index values ​​calculated by multiple preset time windows under continuous wet and slippery conditions are collected. For the same type of braking characteristic index, all window data are integrated to complete statistical processing to obtain various target braking characteristic index values. Then, all target braking characteristic index values ​​are fused and calculated to output a unified braking state quantitative score. Finally, based on the quantitative score, the corresponding braking state level is matched and corresponding driving prompts and maintenance suggestions are output. Among them, the multiple braking characteristic indices include braking response hysteresis index, wet and slippery braking efficiency attenuation coefficient, and water film accumulation and insufficient dispersal index. Each of the three indices is configured with independent and complete sampling and filtering, numerical calculation, and interval truncation logic. Multi-window mean smoothing is used to eliminate interference from single driving data. Then, multi-dimensional degradation features are uniformly quantified through independent normalization and weighted fusion to adapt to various braking softening scenarios caused by moisture and water film accumulation in the braking system under continuous wet and slippery conditions.

[0033] Based on the above inventive concept, such as Figure 1 The diagram shown is a schematic representation of the implementation process of a braking state recognition method provided in this application. This method is applied to a vehicle and may specifically include the following steps: S101, obtain the values ​​of multiple braking characteristic indicators calculated within multiple preset time windows under continuous wet and slippery conditions of the vehicle.

[0034] In this embodiment, the data acquisition in this step relies on the time-series data from onboard sensors under continuous slippery conditions. These continuous slippery conditions include scenarios such as continuous rain, continuous snowfall, continuous freezing rain, and continuous snow melting and icing on the road surface. Specific criteria are set for continuous rain: for example, a single day's rainfall exceeding 0.5 mm, at least one hour of rainfall exceeding 0.2 mm / h on a single day, and at least two consecutive natural days meeting these rainfall conditions. Similar criteria apply to other continuous slippery conditions.

[0035] In actual data collection, complete time-series data of the last driving trip under the working condition (e.g., the last 30 minutes of driving trip during continuous rainy days) are selected. Several independent preset time windows are divided according to fixed duration and fixed sliding step size. Each preset time window reads raw driving data such as brake pedal switch signal, longitudinal acceleration, brake master cylinder pressure, vehicle speed, rainfall intensity, and wiper gear position. Then, through corresponding calculation logic, multiple braking characteristic index values ​​such as brake response hysteresis index, wet skid braking efficiency attenuation coefficient, and water film accumulation and dispersion insufficiency index are calculated. Each preset time window can output a complete set of multiple braking characteristic index values. Finally, the multiple braking characteristic index values ​​corresponding to each of the multiple preset time windows are summarized.

[0036] By using multiple windows to calculate the indicators separately, we can avoid large fluctuations in the values ​​of a single driving segment due to factors such as single braking, instantaneous road surface water film, and short-term vehicle speed changes. This provides basic data support for subsequent elimination of sampling interference and stabilization of indicator calculation results through mean smoothing.

[0037] S102, based on the multi-type braking characteristic index values ​​of each of the multiple preset time windows, statistically obtain the multi-type target braking characteristic index values.

[0038] In this embodiment, the braking characteristic indexes, such as the braking response hysteresis index, wet skid braking performance attenuation coefficient, and water film accumulation and dispersion insufficiency index, calculated separately within multiple preset time windows in the previous step, are summarized and processed separately according to index type to obtain multiple target braking characteristic index values. Specifically, braking characteristic index values ​​of the same type within multiple preset time windows are grouped together, and their arithmetic mean is calculated. The calculated average value is used as the target braking characteristic index value for the corresponding category.

[0039] By averaging the same type of indicators across multiple windows, we can filter out sudden changes in indicators caused by single braking behavior, short-term water film changes, and instantaneous vehicle speed fluctuations. This reduces the interference of randomness in single-segment window data on the results, and yields a stable indicator that can objectively reflect the degree of brake attenuation throughout the wet and slippery travel. This provides reliable basic parameters for subsequent multi-indicator fusion scoring.

[0040] S103, integrates multiple target braking characteristic index values ​​to obtain a braking state quantitative score, and identifies the vehicle braking state based on the braking state quantitative score.

[0041] In this embodiment of the application, the braking status quantification score can be obtained by fusing the multiple target braking feature index values ​​from the previous step, and the vehicle braking status can be identified based on the braking status quantification score.

[0042] The process involves first independently normalizing the values ​​of various target braking characteristic indicators to unify the numerical ranges of each indicator. Then, a linear weighted summation is performed according to preset weights to calculate a unique quantitative braking status score. This quantitative score is then compared with a pre-defined scoring range to match the corresponding brake softening level, and driving alerts and vehicle maintenance suggestions corresponding to that level are output simultaneously. Here, the brake softening level quantifies the different degrees of brake softening.

[0043] Separate normalization can eliminate calculation bias caused by inconsistencies in the dimensions and numerical ranges of multiple indicators. Weighted fusion can comprehensively take into account three types of slippery fault causes: brake response lag, brake performance decay, and road surface water film accumulation. By relying on segmented interval matching levels, the degree of fault can be quantitatively distinguished. It can not only intuitively remind the driver of the current braking risk, but also provide targeted maintenance guidance, thus solving the shortcomings of traditional solutions that rely solely on subjective feelings to judge the braking status.

[0044] Based on the above description of the technical solution provided in the embodiments of this application, multiple types of braking characteristic index values ​​are obtained by calculating multiple preset time windows under continuous wet and slippery conditions of the vehicle. Based on the multiple types of braking characteristic index values ​​in each of the multiple preset time windows, multiple types of target braking characteristic index values ​​are statistically obtained. The multiple types of target braking characteristic index values ​​are fused to obtain a braking state quantitative score, and the vehicle braking state is identified according to the braking state quantitative score.

[0045] This solution first acquires the values ​​of multiple braking characteristic indicators corresponding to each of the preset time windows under continuous wet and slippery conditions. Then, it statistically processes the values ​​of the same type of indicator across all windows to obtain the values ​​of various target braking characteristic indicators. Finally, it integrates various target indicators to output a quantitative score of braking status and identifies the braking status based on the score. First, by collecting indicators synchronously through multiple windows, it can avoid the problem of indicator distortion caused by the accidental fluctuation of single-segment driving data. The target indicators obtained through multi-window statistical processing can further smooth sampling noise and reduce the calculation deviation caused by instantaneous road conditions or single braking behavior. Then, it integrates and quantifies the multi-dimensional braking indicators to form a unified score, which gets rid of the limitations of traditionally relying on human subjective feelings to judge the braking status. It can objectively quantify the degree of brake softening under wet and slippery conditions. At the same time, it completes the entire calculation in real time based on the time series data under continuous wet and slippery conditions, without the need for offline equipment testing. It can identify the potential brake fade hazards caused by moisture in the braking system and water film accumulation on the brake disc in advance, improving the real-time performance, stability and quantitative accuracy of brake fault identification on wet and slippery roads.

[0046] like Figure 2 The diagram shown illustrates the implementation flow of another braking state recognition method provided in this application. This method is applied to a vehicle and may specifically include the following steps: S201, obtain the braking response hysteresis index, wet braking performance attenuation coefficient, and water film accumulation and dispersion insufficiency index calculated in multiple preset time windows under continuous wet and slippery conditions of the vehicle.

[0047] In this embodiment, this step relies on various onboard sensors to collect driving time-series data under continuous slippery conditions. Continuous slippery conditions include scenarios such as continuous rain, continuous snowfall, continuous freezing rain, and continuous snowmelt and icy road surfaces. Continuous rain requires a daily rainfall exceeding 0.5 mm, at least one hour of rainfall intensity exceeding 0.2 mm / h, and two or more consecutive days meeting these criteria. Similar conditions apply to other continuous slippery conditions.

[0048] The data is selected from all time-series records of the last driving trip within the working condition (e.g., the last 30 minutes of driving trip during continuous rainy days). Multiple independent time windows are divided according to fixed duration and fixed sliding interval. For the raw data such as pedal switch, longitudinal acceleration, brake master cylinder pressure, vehicle speed, rainfall, and wiper gear in each window, three independent calculation logics are run to calculate the corresponding brake response hysteresis index, wet braking efficiency attenuation coefficient, and water film accumulation and dispersion insufficiency index for each window. Each window will generate a complete set of three indicators.

[0049] By using a method of calculating three types of indicators independently in separate windows, braking characteristic data under different driving conditions at different times can be retained. This avoids the distortion of results caused by single-segment data being easily affected by single braking, instantaneous water film, and short-term vehicle speed interference. It also provides sufficient samples for subsequent multi-window mean smoothing processing, ensuring that the final indicators can objectively reflect the braking attenuation of the entire wet and slippery journey.

[0050] S202, calculate the average value of the braking response hysteresis index within all preset time windows, and use it as the target braking response hysteresis index.

[0051] In this embodiment of the application, the braking response hysteresis indices calculated within all preset time windows are aggregated together and calculated as an arithmetic average. The average value obtained is the target braking response hysteresis index.

[0052] A single braking operation and instantaneous speed change during different driving periods will cause the single-window index to fluctuate. Averaging the indices of the same type across all windows can offset the random fluctuations caused by short-term driving data, weaken the impact of accidental braking behavior on the final result, and output a stable index that can objectively reflect the degree of braking response lag throughout the wet and slippery journey, providing a reliable single-item basic parameter for subsequent comprehensive scoring.

[0053] S203, calculate the average value of the wet skid braking performance attenuation coefficient within all preset time windows, and use it as the target wet skid braking performance attenuation coefficient.

[0054] In this embodiment, the wet skid braking performance attenuation coefficients calculated by each of the preset time windows are aggregated, the arithmetic mean is calculated, and the average value is determined as the target wet skid braking performance attenuation coefficient.

[0055] Fluctuations in instantaneous vehicle speed and single braking pressure during vehicle operation can cause deviations in the efficiency attenuation coefficient within a single window. Averaging the coefficients of the same type across multiple windows can smooth out random errors caused by single sampling, avoid judgment biases caused by the randomness of data in a single time period, and obtain a stable coefficient that can truly reflect the friction attenuation level of the entire wet and slippery journey, thus ensuring the accuracy of subsequent multi-indicator fusion scoring.

[0056] S204, calculate the average value of the water film cumulative dispersion insufficiency index within all preset time windows, and use it as the target water film cumulative dispersion insufficiency index.

[0057] In this embodiment of the application, the cumulative water film dispersion insufficiency index calculated by all preset time windows is collected, the arithmetic mean is calculated, and the average value is set as the target cumulative water film dispersion insufficiency index.

[0058] The intensity of short-term rainfall and the mileage of a single trip can cause fluctuations in the index of a single window. Averaging the indices of the same type across all windows can reduce the data disturbance caused by instantaneous road surface water film conditions, objectively reflect the overall degree of continuous accumulation and difficulty in dissipation of water film under wet and slippery conditions, and provide stable and reliable dimensional parameters for subsequent comprehensive quantitative analysis of braking status.

[0059] S205, the target braking response hysteresis index, the target wet skid braking efficiency attenuation coefficient, and the target water film accumulation dispersion insufficiency index are normalized to obtain three types of normalized indices.

[0060] In this embodiment, the three target indicators are normalized to unify their numerical ranges and generate corresponding normalized indicators. Since the upper and lower limits of different indicators are different, different upper and lower limits can be used to normalize the three target indicators to unify their numerical ranges and generate corresponding normalized indicators.

[0061] Specifically, for the target braking response hysteresis index, its normal lower limit is, for example, 0.9, and its normal upper limit is 2.0. Then its normalization process is as follows, where norm1 is the normalization index of the target braking response hysteresis index. The target braking response hysteresis index.

[0062] .

[0063] For the target wet braking performance attenuation coefficient, its normal lower limit is, for example, 0.1, and its normal upper limit is, for example, 0.6. Then its normalization process is as follows: The normalized index corresponding to the target wet braking performance attenuation coefficient. The target wet braking performance attenuation coefficient.

[0064] .

[0065] For the target water film accumulation and dispersion insufficiency index, its normal lower limit is, for example, 0.3, and its normal upper limit is, for example, 1.2. Then its normalization process is as follows: The normalized index corresponding to the target water film accumulation and insufficient dispersion index. The target water film accumulation and dispersion insufficiency index.

[0066] .

[0067] The braking response hysteresis index, wet braking efficiency attenuation coefficient, and water film accumulation and dispersion insufficiency index each have different value ranges and dimensions. Direct weighting would lead to a single-dimensional parameter dominating the final score. By normalizing them separately, the difference in dimensions is eliminated, so that the three types of indicators have an equal basis for comparison when they are integrated in the subsequent calculation. This ensures that the three influencing factors of braking hysteresis, friction attenuation, and water film accumulation can all participate in the comprehensive evaluation in a reasonable manner, thereby improving the balance and objectivity of the quantitative score.

[0068] S206. The braking state quantitative score is calculated by linearly weighting and summing the three types of normalized indicators according to their respective preset weights.

[0069] In this embodiment, based on a pre-set weighting ratio, corresponding weight coefficients are assigned to the three types of normalized indicators. Then, each normalized indicator is multiplied by its own weight and summed to finally calculate the braking state quantitative score. For example, S = 0.35 × norm1 + 0.45 × norm2 + 0.20 × norm3, where S is the braking state quantitative score, and norm1, norm2, and norm3 are the three types of normalized indicators.

[0070] The three categories of indicators correspond to three independent influencing factors: braking response lag, braking friction decay, and road surface water film accumulation. Setting differentiated weights can distinguish the degree of influence of different deterioration factors on brake softening. Linear weighted fusion can integrate multi-dimensional braking loss characteristics into a single value, realizing the unified quantification of the degree of brake decay under wet and slippery conditions, which facilitates the intuitive differentiation of the severity of the fault.

[0071] S207 determines the scoring range of the braking status quantitative score, matches the braking status level corresponding to the scoring range, and outputs driving prompts and handling suggestions corresponding to the braking status level.

[0072] In this embodiment of the application, the calculated braking state quantitative score is compared with the pre-divided score intervals to determine the score belonging interval, the braking state level corresponding to the interval is matched, and then the driving prompts and handling suggestions corresponding to the braking state level are output, as shown in Table 1 below.

[0073]

[0074] Table 1 By dividing the data into intervals, continuous quantitative values ​​are transformed into graded states, which can clearly distinguish the severity of brake fade. Different levels correspond to different prompts and handling measures. Slight fade only reminds drivers to drive cautiously, while severe fade prompts drivers to go to the store for inspection in time. This allows drivers to intuitively understand the current braking risk and provides tiered and targeted operation and maintenance guidance, solving the problem that relying on a single value cannot intuitively determine the risk level and there is no corresponding handling plan.

[0075] In the embodiments of this application, the calculation of braking response hysteresis index, wet skid braking performance attenuation coefficient, and water film accumulation and dispersion insufficiency index within each preset time window will be explained below.

[0076] When a vehicle experiences brake softness, the interval between the driver pressing the brake pedal and the vehicle establishing stable longitudinal deceleration becomes significantly longer. The brake response hysteresis index quantifies the speed of response from brake triggering to stable deceleration output. When a vehicle is driven in wet and slippery conditions such as continuous rain, a water film continuously adheres to the brake disc surface, and a rust layer is easily formed. This water film acts as a lubricant, significantly reducing the initial contact friction coefficient between the brake pads and the brake disc. As a result, it is difficult to quickly generate effective braking force when the pedal is pressed, requiring a longer time to establish stable deceleration, thus exacerbating the brake response hysteresis. This index quantifies this hysteresis duration, directly reflecting the brake trigger delay problem caused by wet and slippery environments.

[0077] This index is calculated based solely on data of effective braking actions within each time window under continuous wet and slippery conditions. It only characterizes the braking trigger delay caused by water film on wet and slippery surfaces and brake disc corrosion. It does not cover response lag caused by mechanical component failures such as vacuum booster malfunction, brake line leakage, and brake master cylinder wear. The larger the index value, the longer the interval between pressing the brake pedal and the stable braking force output, and the higher the degree of braking response degradation under wet and slippery conditions.

[0078] Based on this, such as Figure 3 The diagram shown is a schematic representation of the implementation process of a braking response hysteresis index calculation method provided in this application embodiment. This method is applied to vehicles and specifically includes the following steps: S301, within a preset time window, calculates the effective braking response time and the braking start speed corresponding to each effective braking response time.

[0079] In this embodiment, the effective braking response time and the corresponding initial braking speed are statistically analyzed within each preset time window. Different initial vehicle speeds under wet conditions alter the brake disc water film thickness and tire grip, directly affecting the braking response time. Simultaneously storing the initial braking speed eliminates interference from vehicle speed when calculating the braking response hysteresis index, ensuring that the index only reflects braking hysteresis caused by water film and corrosion, thus avoiding distortion in the index calculation due to speed differences.

[0080] Within each preset time window, the braking initiation time corresponding to the transition of the brake pedal from an unpressed state to a pressed state is monitored. Starting from the braking initiation time, the minimum value of the first longitudinal acceleration during this braking process is extracted, along with the stabilization time when the longitudinal acceleration reaches the product of the minimum value and a preset ratio (e.g., 90%). If the minimum value of the longitudinal acceleration is less than a preset effective braking threshold (e.g., -0.5 m / s²), the time difference between the stabilization time and the braking initiation time is calculated. If the time difference falls within a preset difference range, it is determined as the effective braking response time. If the time difference does not fall within the preset difference range (e.g., greater than 2 s, or less than 0.05 s), the time difference is discarded.

[0081] Within each preset duration window, the brake pedal switch signal is continuously monitored, capturing the transition point from pedal release to pedal depression, and recording this point as the braking start time for a single braking action. Using this start time as the timing starting point, longitudinal acceleration data for the entire braking process is continuously read, and the minimum longitudinal acceleration value for this braking phase is selected. This minimum value represents the maximum deceleration achievable during this braking action. This minimum value is then multiplied by a preset proportional coefficient to obtain a stability threshold, and the stable moment corresponding to the longitudinal acceleration decreasing to this threshold is determined. First, the maximum deceleration of this braking action is verified. If the minimum longitudinal acceleration value is less than the preset effective braking threshold, it indicates that this braking action is a complete braking action with effective deceleration. The time difference between the stable moment and the braking start time is calculated to obtain the original braking response time. This time difference is then compared with a preset difference range. If it falls within the range, it indicates that the braking process was smooth, without sudden braking, low-speed creep, or other abnormal conditions, and this time difference is determined as the effective braking response time. If it exceeds the range, it is determined that this braking action involved extreme operation or data distortion, and this set of duration data is directly discarded and not included in subsequent index calculations.

[0082] The entire set of screening rules first filters out light pedal presses that have no deceleration effect by relying on the deceleration threshold, then locks the node where the deceleration enters the stable range by the proportional coefficient, accurately measures the time taken to build up braking force, and at the same time uses the difference range to eliminate interference samples such as starting braking and low-speed coasting braking, so as to avoid abnormal braking data from skewing the final braking response hysteresis index, and ensure that the response time involved in the calculation can truly reflect the braking trigger hysteresis characteristics caused by water film and disc corrosion under wet and slippery conditions.

[0083] S302, calculate the first median of all effective braking response times and the second median of all braking starting speeds.

[0084] In this embodiment, all effective braking response times retained within each preset duration window are aggregated to calculate the first median; simultaneously, the braking start speeds recorded for each group of effective braking response times are collected to calculate the second median.

[0085] Using the median instead of the average can avoid interference from extreme outliers caused by individual emergency braking or special vehicle speeds, and weaken the influence of discrete data on the results. The first median can represent the typical lag level of braking response within the window, and the second median serves as the typical braking starting speed of the window. The two are used together to solve the braking response hysteresis index, reducing the interference of vehicle speed fluctuations on the index calculation and ensuring that the index can truly reflect the response lag characteristics caused by brake disc water film and corrosion under wet and slippery conditions.

[0086] S303, the braking response hysteresis index is obtained by normalizing the first median based on the second median.

[0087] In this embodiment, the second median of the braking start speed within each preset time window is used as a reference value. The first median of the effective braking response time is normalized and converted, and the braking response hysteresis index corresponding to the window is finally output.

[0088] There are significant differences in the water film coverage of brake discs and tire grip at different vehicle speeds. For the same response time, the severity of braking lag is not equal at high and low vehicle speeds. By normalizing based on typical vehicle speeds, the numerical interference caused by vehicle speed differences can be eliminated, allowing the index to focus only on the deterioration of braking response caused by water film and disc corrosion under wet and slippery conditions, thus ensuring that the indexes calculated under different vehicle speed conditions are comparable.

[0089] The process involves multiplying a preset fixed coefficient (e.g., 0.01) by the second median to obtain a product term, adding the product term to a preset reference duration (e.g., 0.2) to obtain a reference response time, and dividing the first median by the reference response time to obtain the braking response hysteresis index.

[0090] First, multiply the preset fixed coefficient by the second median representing the typical braking speed of the window to calculate the product term. Then, add the preset reference duration to the product term to obtain the reference response time adapted to the current vehicle speed. Subsequently, divide the first median of the effective braking response time of the window by the reference response time. The result is the braking response hysteresis index corresponding to this window.

[0091] At different vehicle speeds, there are significant differences in the thickness of the brake disc water film and the tire adhesion state. The degree of degradation corresponding to the same braking delay time at high and low vehicle speeds cannot be directly compared. This calculation method uses a fixed coefficient to convert the median vehicle speed into a vehicle speed compensation amount, and superimposes the benchmark time to construct a dynamic reference standard, so as to achieve linear compensation of vehicle speed for response time. Then, normalization is completed by division conversion to eliminate numerical interference caused by vehicle speed differences. This ensures that the final index only reflects the braking response lag caused by water film and brake disc corrosion under wet and slippery conditions, and ensures that the index calculated at different vehicle speed ranges has horizontal comparability, avoiding interference from vehicle speed fluctuations in the objective assessment of the degree of brake fade.

[0092] Furthermore, prolonged driving in continuous rainy weather causes a water film to adhere to the brake disc surface, significantly reducing the friction coefficient between the brake pads and the brake disc. Under the same master cylinder pressure, the actual deceleration output by the vehicle is significantly lower than the ideal level. The wet braking performance attenuation coefficient is derived from the brake fluid pressure and vehicle mass to obtain the theoretical expected deceleration under conditions without water accumulation and corrosion. By comparing the difference between the measured actual deceleration and the theoretical deceleration, the degree of brake friction performance loss caused by rainwater accumulation is quantified.

[0093] This coefficient quantifies the brake friction performance loss caused by the water film on the brake disc surface under wet and slippery conditions. Under the same master cylinder pressure, a dry brake disc without water accumulation can output the ideal theoretical deceleration. However, a water film on the brake disc lubricates the friction contact surface and reduces the coefficient of friction, resulting in an actual deceleration lower than the theoretical deceleration. The coefficient is calculated based on the difference between the theoretical and measured deceleration. A larger value indicates a more severe brake performance loss due to the water film; a smaller value indicates that the current brake friction state is closer to the ideal level under dry conditions. This coefficient only reflects brake fade caused by the formation of a water film on the road surface and does not cover insufficient braking force caused by hardware damage such as brake pad wear, brake caliper failure, or pressure leakage in the lines.

[0094] Based on this, such as Figure 4 The diagram shown is a schematic representation of the implementation process of a method for calculating the attenuation coefficient of wet braking performance according to an embodiment of this application. This method is applied to vehicles and may specifically include the following steps: S401, within a preset time window, select multiple valid sampling moments that meet the valid sampling conditions. Valid sampling conditions include the brake pedal being depressed, the vehicle speed being greater than a preset vehicle speed threshold, and the brake master cylinder pressure being greater than a preset pressure threshold.

[0095] In this embodiment of the application, the data of each sampling moment is verified point by point within each preset time window, and only multiple valid sampling moments that simultaneously meet three conditions are retained: the brake pedal signal is in the depressed state, the vehicle speed is greater than the preset vehicle speed threshold (e.g., 10km / h), and the brake master cylinder pressure is greater than the preset pressure threshold (e.g., 0.5bar).

[0096] Only when all three conditions are met simultaneously can the vehicle be considered to be under real braking conditions with intensity and speed. This filters out invalid sampling data such as light pedal touch, low-speed coasting and intermittent braking, and failure to establish brake fluid pressure. It also avoids small fluctuations in pressure and acceleration caused by low-intensity braking from interfering with subsequent coefficient calculations, ensuring that the data involved in the calculation can truly reflect the friction performance of the brake pads and brake discs under wet and slippery conditions.

[0097] S402, for any valid sampling time, determine the theoretical deceleration and actual deceleration at the valid sampling time.

[0098] S403, based on the theoretical deceleration and the actual deceleration, determines the efficiency ratio at the effective sampling time.

[0099] In the embodiments of this application, for each selected valid sampling time, the theoretical deceleration and the actual deceleration of the vehicle corresponding to that time are calculated respectively, and the braking efficiency ratio of that valid sampling time is solved based on the two sets of deceleration values.

[0100] The theoretical deceleration is calculated based on the current brake master cylinder pressure and represents the ideal deceleration level that should be achieved when there is no water film, no corrosion, and good friction performance. The actual deceleration is the negative of the actual longitudinal acceleration collected by the sensor. Under wet and slippery conditions, the water film on the brake disc will reduce the coefficient of friction, and the actual deceleration will be lower than the theoretical value. The ratio of the two is used to form the efficiency ratio, which intuitively quantifies the degree of brake friction loss caused by water film coverage, and provides single-point basic data for subsequent calculation of the wet and slippery braking efficiency attenuation coefficient.

[0101] Specifically, for the theoretical deceleration at the above effective sampling time, the brake master cylinder pressure at the effective sampling time can be obtained, and the brake master cylinder pressure can be multiplied by a preset braking factor (e.g., 0.8) to obtain the product term; the preset vehicle mass (e.g., 1750kg) can be obtained, and the product term can be divided by the preset vehicle mass to obtain the theoretical deceleration at the effective sampling time.

[0102] The brake master cylinder pressure value corresponding to the valid sampling moment is read, and this pressure value is multiplied by the pre-calibrated braking factor to obtain the product term. Then, the fixed vehicle mass parameter is retrieved, and the aforementioned product term is divided by the vehicle mass to calculate the theoretical deceleration corresponding to the valid sampling moment. The brake master cylinder pressure directly determines the magnitude of the braking force output by the braking system. The braking factor integrates hardware structural parameters such as calipers and brake pads to convert hydraulic pressure into braking force. According to Newtonian mechanics, the braking force divided by the vehicle mass yields the theoretical deceleration under ideal conditions. This value represents the maximum deceleration that the current hydraulic pressure can achieve when the brake disc is free of water accumulation, corrosion, and has good friction performance, serving as a benchmark for evaluating actual braking performance.

[0103] Furthermore, for the efficiency ratio at the above effective sampling time, the actual deceleration can be divided by the theoretical deceleration to obtain the initial efficiency ratio; the initial efficiency ratio can be truncated to a preset efficiency ratio range to obtain the efficiency ratio at the effective sampling time.

[0104] Here, the initial efficiency ratio is obtained by dividing the actual deceleration measured at the effective sampling time by the theoretical deceleration calculated at the same point. The initial efficiency ratio is then limited to a pre-defined efficiency ratio range; if it exceeds the upper limit, the upper limit value is used; if it falls below the lower limit, the lower limit value is used. The processed value is the efficiency ratio at that effective sampling time. In wet and slippery conditions, sensor fluctuations and momentary slippage may occasionally occur, causing the initial efficiency ratio to be too high or too low, exceeding the reasonable range. Interval truncation can eliminate these abnormal extreme values, avoiding distorted samples interfering with subsequent calculations, ensuring that the efficiency ratio at each sampling point stably reflects the braking performance loss caused by the reduced friction coefficient due to the water film.

[0105] S404 determines the wet skid braking performance attenuation coefficient based on the performance ratio at multiple effective sampling times.

[0106] In this embodiment, the efficiency ratio calculated at all valid sampling times within each preset time window is summarized, and the wet skid braking efficiency attenuation coefficient, which characterizes the overall braking friction loss level of the window, is statistically obtained.

[0107] The water film on the road surface will continuously reduce the friction coefficient between the brake pads and the brake disc, causing the actual deceleration to be lower than the theoretical deceleration in the long term, resulting in a lower overall efficiency ratio. The efficiency ratio of a large number of sampling points within the comprehensive window can smooth out the single-point data deviation caused by instantaneous slippage and oil pressure fluctuations, and comprehensively reflect the overall situation of the decline in braking friction efficiency during the entire wet and slippery period, and participate in the subsequent comprehensive scoring as a single feature parameter.

[0108] The process involves calculating the average energy efficiency ratio at multiple effective sampling times and obtaining a preset baseline attenuation value. The average energy efficiency ratio is then subtracted from the preset baseline attenuation value to obtain the wet skid braking efficiency attenuation coefficient.

[0109] First, the efficiency ratios corresponding to all valid sampling moments within the preset time window are statistically analyzed, and the arithmetic mean is calculated to obtain the average efficiency ratio. Then, the preset baseline attenuation value is retrieved and the average efficiency ratio is subtracted from the baseline value. The final calculation result is the wet skid braking efficiency attenuation coefficient for that window.

[0110] Under dry conditions, braking friction is good, and the average energy efficiency ratio is close to the baseline attenuation value, with a small difference between the two. When vehicles are driven on wet and slippery roads for extended periods, the water film adhering to the brake discs reduces the coefficient of friction, resulting in a lower actual deceleration and a consequent decrease in the average energy efficiency ratio. The difference between the baseline value and the energy efficiency ratio then increases accordingly. This calculation method can intuitively quantify the degree of braking friction attenuation caused by the water film; a larger coefficient value indicates more severe braking performance loss under wet and slippery conditions. Using a multi-point average energy efficiency ratio within a window for calculation also offsets data disturbances caused by single sampling and instantaneous slippage, ensuring that the coefficient objectively reflects the overall level of braking friction degradation during that period.

[0111] Furthermore, when vehicles are driven for extended periods in continuous rainy conditions, especially at low speeds with windshield wipers at high settings and without the brakes applied for a long time, rainwater on the brake disc surface cannot be dislodged by centrifugal force due to vehicle speed, nor can it evaporate through the heat generated by braking friction. This leads to a continuous buildup and thickening of the water film, directly causing a significant decrease in subsequent braking efficiency. The Water Film Accumulation and Dispersion Insufficient Index integrates multiple parameters, including instantaneous rainfall, vehicle speed, duration of no-braking intervals, and the cumulative number of trips in rainy weather, using a linear weighted calculation method to simultaneously quantify the probability of a water film on the brake disc surface and the combined deterioration effect of its continuous accumulation.

[0112] This index only represents the problem of water film buildup caused by continuous rain and prolonged lack of braking. It does not reflect mechanical failures such as brake pad wear, brake caliper corrosion and jamming, or pipeline leakage. The larger the index value, the more severe the water film buildup on the brake disc and the more difficult it is to dissipate. The negative impact of wet and slippery conditions on braking performance is stronger. During the calculation process, intermediate variables and the original index are truncated to remove distorted data interference such as extreme vehicle speeds, excessively long parking times, and sensor malfunctions, to ensure that the index results are stable and reliable.

[0113] Based on this, such as Figure 5 The diagram shown is a schematic representation of the implementation process of a method for calculating the insufficient water film dispersion index according to an embodiment of this application. This method is applied to vehicles and may specifically include the following steps: S501, within a preset time window, determines the basic risk and time factor for any given moment.

[0114] S502 multiplies the basic risk by the time factor to obtain the instantaneous water film risk at a given moment.

[0115] In this embodiment of the application, each moment (e.g., each second) is traversed within each preset time window, and the basic risk and time factor of each moment are determined. The basic risk and time factor are multiplied to obtain the instantaneous water film risk at each moment.

[0116] Specifically, the basic risk at each moment can be calculated by obtaining the instantaneous vehicle speed at that moment and determining whether the instantaneous vehicle speed is less than a preset instantaneous vehicle speed threshold (e.g., 40 km / h); if the instantaneous vehicle speed is less than the preset instantaneous vehicle speed threshold, the rainfall intensity and wiper intensity level at that moment (e.g., 0~3) are obtained; the rainfall intensity is divided by a preset first coefficient (e.g., 10) to obtain a first calculation item, and the wiper intensity level is divided by a preset second coefficient (e.g., 4) to obtain a second calculation item; the first calculation item and the second calculation item are added together to obtain the basic risk at that moment; if the instantaneous vehicle speed is greater than or equal to the preset instantaneous vehicle speed threshold, the basic risk at that moment is assigned a preset baseline risk value (e.g., 0).

[0117] The system reads the vehicle's instantaneous speed at each moment and compares it with a preset instantaneous speed threshold. If the instantaneous speed is below the threshold, it indicates that the vehicle is traveling at low speed, and the wheels cannot effectively remove water from the brake disc surface using centrifugal force. In this case, the system reads the real-time rainfall intensity and wiper speed level. The rainfall intensity is divided by a preset first coefficient to obtain the first calculation item, and the wiper speed level is divided by a preset second coefficient to obtain the second calculation item. The two items are added together to obtain the basic risk at that moment. If the instantaneous speed is greater than or equal to the threshold, the centrifugal force of the wheels can quickly peel off the water film on the brake disc surface during high-speed driving, and the impact of water accumulation is minimal. The basic risk at that moment is directly fixed as the preset benchmark risk value.

[0118] At low speeds, the vehicle speed is low and the drainage capacity is poor. The thickness of the water film is mainly determined by the intensity and duration of the rainfall. The basic risk is calculated by superimposing two parameters: rainfall and wiper setting, which accurately represents the rate of water accumulation. At high speeds, a fixed benchmark value is used to simplify the calculation and avoid interference from water accumulation at high speeds. This ensures that the basic risk only reflects the real cause of water film accumulation on the brake disc and avoids judgment bias caused by different vehicle speeds.

[0119] Furthermore, the time factor for each of the above moments can be obtained by taking the time interval between that moment and the end of the last braking. Dividing the time interval by a preset third coefficient (e.g., 20) and adding it to a preset base factor yields the initial time factor. The initial time factor is then truncated to a preset factor interval (e.g., less than or equal to 3) to obtain the time factor for that moment. It should be noted that if there was no last braking within the window, the time interval is, for example, assigned a value of 0.

[0120] Read the time interval between the current monitoring time and the end of the last braking action, divide the interval by the preset third coefficient to obtain the compensation amount, add the preset basic factor to calculate the initial time factor; then constrain the initial time factor within the preset factor range, take the upper limit if it exceeds the upper limit, take the lower limit if it is below the lower limit, and obtain the final time factor at that time after the range is truncated.

[0121] When a vehicle is not braked for an extended period, rainwater on the brake disc surface cannot evaporate through frictional heating, leading to continuous water film buildup and a thickening of the corrosion layer. The difficulty of dispersing the water film increases proportionally with the duration of no braking. This calculation method quantifies the accumulated water time using the interval since the last braking action, performs linear conversion using a fixed coefficient, and adds a base factor to form a quantified term for the accumulated water film time. Simultaneously, upper and lower limits are set for the factor to filter out abnormal extreme values ​​caused by prolonged parking or extremely long periods of no braking, preventing distorted values ​​from excessively amplifying the risk of instantaneous water film accumulation. This ensures that the time factor objectively reflects only the characteristics of increased water film buildup due to prolonged no braking under continuous wet and slippery conditions.

[0122] S503 determines the water film cumulative dispersion insufficiency index based on the instantaneous water film risk at each moment within a preset time window.

[0123] In this embodiment, the instantaneous water film risk calculated at all times within each preset time window is collected, and the water film accumulation and insufficient dissipation index corresponding to that window is obtained through comprehensive calculation. The instantaneous water film risk is combined with real-time rainfall, vehicle speed, and the interval since the last braking, reflecting the current degree of water film accumulation on the brake disc. By integrating the instantaneous risks at all times within the window, it can comprehensively reflect the overall state of continuous water film accumulation and difficulty in dissipation throughout the entire cycle, eliminating fluctuations caused by individual extreme situations such as short-term heavy rain or temporary low speed, forming a stable index that intuitively quantifies the negative impact of long-term water film adhesion on braking performance under continuous rainy conditions.

[0124] Specifically, the average value of the instantaneous water film risk at all times within the preset time window is calculated; the cumulative number of trips under continuous wet and slippery conditions is obtained (e.g., the cumulative number of trips during consecutive rainy days, with each trip counted as one trip from vehicle ignition to shutdown); the cumulative number of trips is divided by a preset fourth coefficient (e.g., 3) to obtain the trip correction term; the minimum value between the trip correction term and the preset correction upper limit (e.g., 1.5) is selected as the correction coefficient; the average value is multiplied by the correction coefficient to obtain the initial water film cumulative dispersion insufficiency index; the initial water film cumulative dispersion insufficiency index is truncated to a preset index range (e.g., 0~2) to obtain the water film cumulative dispersion insufficiency index.

[0125] The instantaneous water film risk is statistically analyzed for all moments within each preset time window, and the arithmetic mean is calculated. The cumulative number of trips by the vehicle during consecutive rainy days is read, and the number is divided by the preset fourth coefficient to obtain the trip correction term. The trip correction term is compared with the preset correction upper limit value, and the smaller value between the two is taken as the correction coefficient. The average instantaneous water film risk is multiplied by the correction coefficient to obtain the initial water film accumulation and dispersion insufficiency index. Finally, the initial index is truncated, and the upper limit is taken if it exceeds the upper limit and the lower limit is taken if it is below the lower limit. The constrained value is output as the final water film accumulation and dispersion insufficiency index for this window.

[0126] Repeated driving in continuous rain will cause water film and rust to build up on the brake discs. The more trips, the more severe the degradation of the brake disc friction surface. Therefore, a coefficient is introduced to correct for the number of trips, amplifying the cumulative effect of water film from long-term, repeated rainy driving. Setting an upper limit for correction can prevent the index from being infinitely amplified due to excessive trips, while interval truncation filters out distorted extreme values ​​caused by sensor malfunctions and extreme rainfall. The entire calculation integrates the instantaneous water accumulation risk in a single window with the frequency of driving in long-term rainy weather, accurately quantifying the comprehensive degradation degree of the continuous and difficult-to-disperse water film on the brake discs, providing stable and comparable characteristic parameters for subsequent multi-indicator fusion evaluation of brake softening.

[0127] Corresponding to the above method embodiments, this application also provides a brake state recognition device, such as... Figure 6 As shown, the device may include: an indicator value acquisition module 610, an indicator value statistics module 620, an indicator value fusion module 630, and a brake status recognition module 640.

[0128] The index value acquisition module 610 is used to acquire multiple types of braking characteristic index values ​​calculated within multiple preset time windows under continuous wet and slippery conditions of the vehicle. The index value statistics module 620 is used to statistically obtain multiple target braking feature index values ​​based on the multiple types of braking feature index values ​​within multiple preset time windows. The index value fusion module 630 is used to fuse the multiple types of target braking feature index values ​​to obtain a quantitative score of braking status. The brake status recognition module 640 is used to identify the vehicle's braking status based on the brake status quantification score.

[0129] This application also provides a vehicle, such as... Figure 7 As shown, it includes a processor 701, a communication interface 702, a memory 703, and a communication bus 704, wherein the processor 701, the communication interface 702, and the memory 703 communicate with each other through the communication bus 704. Memory 703 is used to store computer programs; When processor 701 executes a program stored in memory 703, it performs the following steps: The system acquires multiple braking characteristic index values ​​calculated within multiple preset time windows under continuous wet and slippery conditions; based on the multiple braking characteristic index values ​​within each preset time window, it statistically obtains multiple target braking characteristic index values; it fuses the multiple target braking characteristic index values ​​to obtain a braking state quantitative score, and identifies the vehicle braking state based on the braking state quantitative score.

[0130] The communication bus mentioned in the above vehicles can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0131] The communication interface is used for communication between the aforementioned vehicle and other devices.

[0132] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0133] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0134] In another embodiment provided in this application, a storage medium is also provided, which stores instructions that, when run on a computer, cause the computer to execute any of the braking state recognition methods described in the above embodiments.

[0135] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the braking state recognition methods described in the above embodiments.

[0136] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the 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 storage medium or transmitted from one storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0137] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0138] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0139] The above description is merely a preferred embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application are included within the scope of protection of this application.

Claims

1. A method for identifying braking status, characterized in that, The method includes: The values ​​of multiple braking characteristic indicators calculated within multiple preset time windows under continuous wet and slippery conditions of the vehicle are obtained. Based on the values ​​of the various types of braking characteristic indicators within multiple preset time windows, the values ​​of the various types of target braking characteristic indicators are statistically obtained. By fusing the braking feature index values ​​of the multiple target categories, a braking state quantitative score is obtained, and the vehicle braking state is identified based on the braking state quantitative score.

2. The method according to claim 1, characterized in that, The various braking characteristic index values ​​include braking response hysteresis index, wet skid braking efficiency attenuation coefficient, and water film accumulation and insufficient dispersion index. The method of statistically obtaining multiple target braking feature index values ​​based on the multiple types of braking feature index values ​​within multiple preset time windows includes: The average value of the braking response hysteresis index within all preset time windows is taken as the target braking response hysteresis index. The average value of the wet skid braking performance attenuation coefficient within all preset time windows is taken as the target wet skid braking performance attenuation coefficient. The average value of the cumulative water film dispersion insufficiency index within all preset time windows is taken as the target cumulative water film dispersion insufficiency index. The process of fusing the braking feature index values ​​of the multiple target categories to obtain a quantitative score of braking status includes: The target braking response hysteresis index, the target wet skid braking efficiency attenuation coefficient, and the target water film accumulation and dispersion insufficiency index are normalized to obtain three types of normalized indices. The braking state quantitative score is calculated by linearly weighting and summing the three types of normalized indicators according to their respective preset weights.

3. The method according to claim 2, characterized in that, The braking response hysteresis index is calculated in the following way: Within the preset duration window, the effective braking response time and the braking start speed corresponding to each effective braking response time are statistically analyzed. Find the first median of all effective braking response times and the second median of all braking initiation speeds; The braking response hysteresis index is obtained by normalizing the first median based on the second median.

4. The method according to claim 3, characterized in that, The step of calculating the effective braking response time within the preset time window includes: Within the preset time window, monitor the braking initiation time corresponding to each switch of the brake pedal from the unpressed state to the pressed state; Starting from the braking initiation time, extract the minimum value of longitudinal acceleration during this braking process, and the stable time when the longitudinal acceleration reaches the product of the minimum value and a preset ratio; If the minimum value of the longitudinal acceleration is less than the preset effective braking threshold, then the time difference between the stabilization time and the braking start time is calculated. If the time difference falls within a preset difference range, then the time difference is determined as the effective braking response time. If the time difference does not fall within the preset difference range, the time difference is discarded.

5. The method according to claim 2, characterized in that, The wet braking performance attenuation coefficient is calculated in the following way: Within the preset time window, multiple valid sampling moments that meet the valid sampling conditions are selected. The valid sampling conditions include the brake pedal being depressed, the vehicle speed being greater than a preset vehicle speed threshold, and the brake master cylinder pressure being greater than a preset pressure threshold. For any of the aforementioned valid sampling times, determine the theoretical deceleration and the actual deceleration for that valid sampling time; Based on the theoretical deceleration and the actual deceleration, determine the efficiency ratio of the effective sampling time; The wet skid braking efficiency attenuation coefficient is determined based on the efficiency ratio at multiple effective sampling times.

6. The method according to claim 5, characterized in that, Determining the efficiency ratio at the effective sampling time based on the theoretical deceleration and the actual deceleration includes: Divide the actual deceleration by the theoretical deceleration to obtain the initial efficiency ratio; The initial efficiency ratio is truncated to a preset efficiency ratio range to obtain the efficiency ratio at the effective sampling time.

7. The method according to claim 5, characterized in that, The determination of the wet skid braking performance attenuation coefficient based on the performance ratio at multiple effective sampling times includes: Calculate the average energy efficiency ratio at multiple effective sampling times, and obtain the preset baseline attenuation value; Subtracting the average energy efficiency ratio from the preset basic attenuation benchmark value yields the wet skid braking efficiency attenuation coefficient.

8. The method according to claim 2, characterized in that, The water film cumulative dispersion insufficiency index is calculated in the following way: Within the preset time window, for any given moment, the basic risk and time factor for that moment are determined respectively; Multiplying the basic risk by the time factor yields the instantaneous water film risk at the specified moment; Based on the instantaneous water film risk at each moment within the preset time window, the cumulative water film dispersion insufficiency index is determined.

9. The method according to claim 8, characterized in that, The underlying risks at the stated time include: Obtain the instantaneous vehicle speed at the specified moment, and determine whether the instantaneous vehicle speed is less than a preset instantaneous vehicle speed threshold; If the instantaneous vehicle speed is less than the preset instantaneous vehicle speed threshold, obtain the rainfall intensity and windshield wiper intensity level at that moment; The rainfall intensity is divided by a preset first coefficient to obtain a first calculation item, and the wiper strength level is divided by a preset second coefficient to obtain a second calculation item; Add the first calculation term to the second calculation term to obtain the basic risk at the stated time. If the instantaneous vehicle speed is greater than or equal to the preset instantaneous vehicle speed threshold, the basic risk at that moment is assigned a preset benchmark risk value.

10. The method according to claim 8, characterized in that, Determining the time factor of the stated moment includes: Obtain the time interval between the time of the last braking and the end of the last braking, divide the time interval by a preset third coefficient, and add it to a preset base factor to obtain the initial time factor; The initial time factor is truncated to a preset factor interval to obtain the time factor of the time.

11. The method according to claim 8, characterized in that, The step of determining the cumulative water film dispersion insufficiency index based on the instantaneous water film risk at each moment within the preset time window includes: Calculate the average value of the instantaneous water film risk at all times within the preset time window; The cumulative number of trips under continuous wet and slippery conditions is obtained, and the cumulative number of trips is divided by a preset fourth coefficient to obtain the trip correction term. The minimum value between the travel correction item and the preset correction upper limit value is selected as the correction coefficient, and the average value is multiplied by the correction coefficient to obtain the initial water film accumulation and dispersion insufficiency index. The initial water film cumulative dispersion insufficiency index is truncated to a preset index range to obtain the water film cumulative dispersion insufficiency index.

12. A vehicle, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method described in any one of claims 1-11.

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