Wiper blade aging detection method and system based on optical rain sensor and vehicle

Abstract

The application provides a wiper aging detection method and system based on an optical rain sensor and a vehicle, and relates to the technical field of wiper aging detection. The method comprises the following steps: collecting a rain digital signal sequence in each wiper cycle, synchronously extracting a rain peak value in a preset time window before wiper starting and a minimum rain value in a preset time window after wiper starting, calculating a normalized residual rate by taking the ratio of the two values after effective filtering, and obtaining a normalized residual rate sequence; performing sliding window mean smoothing processing on the sequence, estimating a trend slope, and counting an over-standard count to obtain an aging characteristic parameter; and comparing the aging characteristic parameter with a dynamic threshold value, and outputting a wiper replacement reminder signal when the aging determination condition is met. The application eliminates the interference of rainfall intensity on the judgment of wiping effect through normalization processing, realizes early, accurate and adaptive detection of the wiper aging state by combining an adaptive dynamic threshold value and a double aging determination mechanism, and improves driving safety.

Description

Technical Field

[0001] This invention relates to the field of wiper aging detection technology, and in particular to a wiper aging detection method, system and vehicle based on an optical rain sensor. Background Technology

[0002] Optical rain sensors operate based on the principle of infrared total internal reflection, emitting infrared light towards the windshield via an LED. In rainless conditions, the infrared light undergoes total internal reflection, resulting in high received light intensity. In rainy conditions, some light is refracted, reducing the light intensity, thus enabling rain detection. Wiper blades age over time, leading to reduced wiping performance. Drivers must rely on subjective feeling or periodic inspections to determine replacement, making early detection of aging issues difficult and posing a safety hazard. Existing technologies assess wiping performance by obtaining the absolute difference in rainfall signals before and after wiper action. However, the absolute value of the rainfall signal is affected by both rainfall intensity and wiping performance. Relying solely on the absolute value can lead to false alarms or missed alarms under varying rainfall intensities, failing to accurately detect wiper aging. Summary of the Invention

[0003] To address the technical problems mentioned above, this invention provides a method, system, and vehicle for detecting wiper aging based on an optical rain sensor.

[0004] To achieve the above objectives, a first aspect of the present invention provides a method for detecting wiper aging based on an optical rain sensor, comprising: During each wiper cycle, a sequence of digital rainfall signals output by an optical rain sensor is collected. The peak rainfall within a preset time window before the wipers start and the minimum rainfall within a preset time window after the wipers start are extracted from the digital rainfall signal sequence. After validity filtering, valid signal pairs are obtained. Based on the effective signal pair, the normalized residual rate is calculated to obtain the normalized residual rate sequence; The normalized residual rate sequence is smoothed by sliding window mean to obtain a smoothed normalized residual rate sequence. The trend slope and the number of out-of-range statistics are estimated based on the smoothed normalized residual rate sequence to obtain aging characteristic parameters. The aging characteristic parameters are compared with the dynamic threshold. When the aging judgment conditions are met, the wiper is judged to be aging, and the aging judgment result is obtained. Based on the aging determination results, a wiper replacement reminder signal is output.

[0005] Furthermore, let the first... The sequence of digital rainfall signals output by the optical rain sensor during each wiper cycle is as follows: , ,in, For the first The start time of each wiper cycle For the first The end time of each wiper cycle; the peak rainfall Defined as:

[0006] in, The width of the sampling window before the scraping; the minimum rainfall value. Defined as:

[0007] in, For the first The interruption trigger time after the wiper completes its wiping action. This refers to the stable waiting time after the interrupt is triggered. This represents the width of the sampling window after scraping.

[0008] Furthermore, the conditions for the validity filtering are: and ,in, The lowest effective peak threshold, This is the minimum dynamic range threshold for the signal.

[0009] Furthermore, the calculation method for the sliding window mean smoothing process is as follows:

[0010] in, For the first The normalized residual rate for each effective wiper cycle, which is the ratio of the minimum rainfall after wiping to the peak rainfall before wiping. This is the length of the sliding window.

[0011] Furthermore, the slope of the trend is estimated as follows:

[0012] in, The number of consecutive effective wiper cycles used in trend analysis. The average of the period numbers. To smooth and normalize the mean residual rate, For the first The smoothed normalized residual rate for each period is the value obtained after smoothing the normalized residual rate sequence by applying a sliding window mean.

[0013] Furthermore, the method also includes: Continuous data collection of a preset number Normalized residual rate data for each effective wiper cycle, divided into preset intervals based on peak rainfall. Rainfall intensity level For each intensity level The benchmark mean was calculated for each of the corresponding normalized residual rate subsets. and the benchmark standard deviation Establish an individualized normalized residual rate benchmark distribution database.

[0014] Furthermore, the dynamic threshold includes a dynamic single exceedance threshold and a dynamic trend change threshold; The dynamic single-time exceedance threshold is calculated as follows:

[0015] in, This represents the sensitivity coefficient for a single instance of exceeding the limit. This is a temperature correction factor. The ambient temperature value is read in real time by the in-vehicle temperature sensor; when the residual rate is smoothed and normalized... When the dynamic single-time exceedance threshold is exceeded, the period is marked as an exceedance period.

[0016] Furthermore, the dynamic trend change threshold is calculated as follows:

[0017] in, The mean of the normalized residual rate trend slope during the baseline acquisition phase. The standard deviation of the trend slope during the baseline data collection phase. This is the sensitivity coefficient for trend judgment.

[0018] Furthermore, the temperature correction coefficient The calculation method is as follows:

[0019] in, The ambient temperature value is read in real time by the vehicle's interior temperature sensor. To correct the reference temperature for low temperature, This is the temperature-corrected intensity coefficient.

[0020] Furthermore, the aging determination criteria include at least one of the following: Comprehensive aging assessment criteria: and ; Rapid early warning conditions: ; in, For analysis window The number of cycles in which the smoothed normalized residual rate exceeds the corresponding dynamic single-time exceedance threshold. The threshold for the proportion exceeding the standard. To trigger a rapid early warning multiplier; When the comprehensive aging judgment condition or the rapid warning condition is met, the wiper is judged to be aging.

[0021] Furthermore, the method also includes: When a wiper blade replacement operation is detected, the baseline reset process is triggered, the original baseline distribution database is cleared, and the initialization and acquisition phase is restarted to establish the normalized residual rate baseline distribution corresponding to the new wiper blade.

[0022] A second aspect of the present invention provides a wiper aging detection system based on an optical rain sensor, comprising: The signal acquisition and preprocessing module is used to acquire the rain digital signal sequence output by the optical rain sensor in each wiper cycle, extract the rain peak value within a preset time window before the wiper starts and the rain minimum value within a preset time window after the wiper starts from the rain digital signal sequence, and output valid signal pairs after validity filtering. The normalization calculation module is connected to the signal acquisition and preprocessing module for calculating the normalized residual rate based on the effective signal to obtain a normalized residual rate sequence. The feature extraction module is connected to the normalization calculation module and is used to perform sliding window mean smoothing on the normalized residual rate sequence to obtain a smoothed normalized residual rate sequence. Based on the smoothed normalized residual rate sequence, the trend slope and the number of out-of-range statistics are estimated to obtain aging feature parameters. The aging determination module is connected to the feature extraction module and is used to compare the aging feature parameters with dynamic thresholds. When the preset aging determination conditions are met, the wiper is determined to be aging, and the aging determination result is obtained. The reminder output module is electrically connected to the aging determination module and is used to output a reminder signal based on the aging determination result.

[0023] Furthermore, the system also includes a benchmark establishment and dynamic threshold generation module, which comprises three sub-modules: a benchmark establishment sub-module, a dynamic threshold generation sub-module, and a benchmark reset sub-module; The benchmark establishment submodule is connected to the normalization calculation module for continuously collecting normalized residual rate data of a preset number of effective wiper cycles during the system initialization phase. The data is grouped according to the rainfall intensity level, and the benchmark mean and benchmark standard deviation of the normalized residual rate under each level are calculated to establish an individualized normalized residual rate benchmark distribution database. The dynamic threshold generation submodule is connected to the benchmark establishment module for generating dynamic single exceedance thresholds and dynamic trend change thresholds based on the benchmark distribution database and real-time ambient temperature. The benchmark reset submodule is connected to the benchmark establishment module. When a wiper blade replacement operation is detected, the benchmark reset process is triggered to clear the original benchmark distribution database, re-enter the initialization acquisition stage, and establish the normalized residual rate benchmark distribution corresponding to the new wiper blade.

[0024] A third aspect of the present invention provides a vehicle including the wiper aging detection system based on an optical rain sensor provided in the second aspect of the present invention.

[0025] Furthermore, the vehicle also includes: a windshield wiper, which is mounted on the vehicle body; An optical rain sensor is mounted on the vehicle body to collect rainfall signals; The in-vehicle display screen is installed inside the vehicle body and is electrically connected to the wiper aging detection system to display wiper replacement reminder information; A voice prompt device is installed inside the vehicle body and is electrically connected to the wiper aging detection system to play a voice reminder for wiper replacement. An in-vehicle temperature sensor is installed inside the vehicle body and is connected to the wiper aging detection system to provide real-time ambient temperature data.

[0026] Compared with existing technologies, this invention provides a wiper aging detection method, system, and vehicle based on an optical rain sensor. It simultaneously extracts the peak rainfall before wiping and the minimum rainfall after wiping within each wiper cycle, constructing a normalized residual rate based on their ratio. This removes rainfall intensity from the wiping performance evaluation, making wiping quality comparable under different rainfall intensities. Furthermore, by combining trend slope estimation and out-of-range count statistics, it performs dual aging feature extraction on the normalized residual rate sequence and compares it with a dynamic threshold to determine the aging state. This achieves accurate, real-time, and quantitative detection of wiper aging status, eliminating false alarms and missed alarms caused by changes in rainfall intensity, and improving driving safety. Attached Figure Description

[0027] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0028] Figure 1 This is a flowchart of a wiper aging detection method based on an optical rain sensor provided in Embodiment 1 of the present invention.

[0029] Figure 2 This is a schematic diagram of the signal waveform during a single wiper cycle provided in Embodiment 1 of the present invention.

[0030] Figure 3 The flowchart for establishing an adaptive dynamic threshold and determining aging is provided in Embodiment 1 of the present invention.

[0031] Figure 4 This is a schematic diagram illustrating the aging trend of the normalized residual rate (NRR) as the number of wiper cycles increases, as provided in Embodiment 1 of the present invention.

[0032] Figure 5 This is an architecture diagram of a wiper aging detection system based on an optical rain sensor provided in Embodiment 2 of the present invention. Detailed Implementation

[0033] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the invention. In the description of the embodiments of the present invention, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "this embodiment" should be understood as "at least one embodiment". The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this embodiment, unless otherwise stated, "a plurality of" means two or more.

[0034] Before describing specific embodiments of the present invention, the key terms involved in the present invention will first be explained: 1. The ECU (Electronic Control Unit) is the core controller in an automotive electronic system, responsible for receiving sensor signals, executing control algorithms, and outputting control commands. In this embodiment, the ECU receives the digital rainfall signal output by the optical rain sensor, executes algorithms such as normalized residual rate calculation, trend analysis, and aging determination, and outputs a wiper replacement reminder signal.

[0035] 2. EEPROM (Electrically Erasable Programmable Read-Only Memory) is a non-volatile memory that retains data even after power is off. It is commonly used in automotive ECUs to store calibration parameters, reference data, and other information that needs to be retained even when power is off. In this embodiment, the individualized normalized residual rate reference distribution database is stored in the ECU's EEPROM or Flash memory, or other non-volatile memory.

[0036] Example 1 This embodiment provides a method for detecting wiper aging based on an optical rain sensor, such as... Figure 1 The method includes steps S101 to S105. It should be understood that by extracting the normalized residual rate and analyzing the trend of the rainfall signal from the optical rain sensor during the wiper reciprocating cycle, a quantitative evaluation of the wiper wiping effect can be achieved.

[0037] First, we introduce the relevant terminology and working principle. The optical rain sensor operates based on the principle of infrared total internal reflection. In this embodiment, the sensor's built-in LED emits infrared light onto the inner surface of the windshield. After passing through a lens and silicone, the light undergoes total internal reflection on the inner surface of the windshield. The reflected light is then focused by the lens and received by a photodiode, which converts it into an electrical signal.

[0038] In this embodiment, when raindrops hit the windshield, some of the infrared light emitted by the LED towards the windshield is refracted into the air, reducing the amount of light reflected back and received by the photodiode. Therefore, by observing the overall trend and variation in the infrared light received by the photodiode in rainy and rainless conditions, the amount of rainfall can be determined.

[0039] In this embodiment, the light intensity received by the photodiode is converted by the ADC (Analog-to-Digital Converter) and output as a digital quantized value, denoted as DN (Digital Number), which is a dimensionless integer. The range of its value is determined by the ADC resolution. It should be understood that a larger signal value indicates less reflected light received by the photodiode, i.e., a thicker water film on the glass surface; a smaller signal value indicates a cleaner glass and stronger reflected light.

[0040] In this embodiment, wiper aging refers to the gradual decline in the performance of wipers after prolonged use due to material wear, hardening, loss of elasticity, or surface aging. Exemplarily, this includes, but is not limited to: poor wiping performance, uneven wiping, and abnormal wiper noise. It should be understood that with aging, the efficiency and effectiveness of wipers will be affected, and in severe cases, they may even completely lose their cleaning function. Exemplarily, the impact of wiper aging on the windshield includes, but is not limited to: glass damage, scratches, and stains. It should be understood that the cleanliness of the windshield directly affects the driver's visibility.

[0041] The overall detection logic of this embodiment is as follows: By extracting the peak rainfall before wiping and the minimum rainfall after wiping for each wiper cycle, a normalized residual rate is calculated, thereby eliminating the interference of rainfall intensity on the judgment of wiping effect; the normalized residual rate sequence is smoothed by a sliding window, and the trend slope and exceedance count are extracted as aging feature parameters; the aging feature parameters are compared with an adaptive dynamic threshold to determine the aging state of the wipers and output a reminder signal. It should be understood that this logic can achieve accurate quantitative detection of the aging state of the wipers.

[0042] S101: Acquire digital rainfall signal sequence. See section S101 for details. Figure 1 The system collects the digital rainfall signal sequence output by the optical rain gauge. In this embodiment, during wiper operation, the rainfall signal output by the optical rain gauge exhibits periodic fluctuations, and this period is synchronized with the reciprocating motion cycle of the wiper.

[0043] Let the sequence of digital rainfall signals output by the optical rain sensor during the i-th wiper cycle be . , ,in, For the first The start of a wiper cycle, that is, the moment when the wipers begin to wipe. For the first The end of a wiper cycle is the moment when the wiper completes its reciprocating motion and returns to its initial position.

[0044] After the wiper completes one wiping motion, the wiper position sensor or wiper motor controller sends an interrupt signal when the wiper returns to its initial position. The ECU receives the interrupt trigger and records the moment as . This interruption signal indicates the completion of the wiping action in this wiper cycle, providing a time reference for subsequent extraction of the minimum rainfall after wiping.

[0045] It should be understood that in the digital signal sequence of rainfall In this process, the signal value directly reflects the thickness of the water film on the glass surface. For example, a larger signal value indicates that the photodiode receives less reflected light, meaning the water film on the glass surface is thicker; a smaller signal value indicates that the glass is cleaner and the reflected light is stronger.

[0046] Therefore, this step provides a continuous, periodic digital rainfall signal for the entire detection process, serving as the data basis for subsequent feature extraction and aging assessment. It should be understood that by synchronously acquiring wiper cycle and rainfall signals, the system can accurately pinpoint the critical time windows before and after wiping, laying the foundation for accurately extracting the pre-wiping peak value and post-wiping minimum value.

[0047] S102: Extract the peak rainfall before the sweep and the minimum rainfall after the sweep, and perform effectiveness filtering. See S102 for details. Figure 1 and Figure 2 Extract the peak rainfall before the scraping. Minimum rainfall after wind And perform validity filtering. Figure 2 This is a schematic diagram illustrating the calculation principle of normalized residual rate (NRR), with the horizontal axis representing time. The vertical axis represents the rainfall signal (DN), and the graph marks the range of a wiper cycle as well as key moments and parameters.

[0048] Regarding the peak rainfall before the wind The extraction is defined as follows:

[0049] In this embodiment, The width of the sampling window before wiping, i.e., the time from when the wipers start wiping. The length of time spent looking back is used to capture the maximum water accumulation on the windshield surface before the wipers move. For example, The typical value range is 0.05s to 0.2s, and it needs to be calibrated according to the sensor sampling frequency and wiper action response time.

[0050] It should be understood that The unit is DN, reflecting the current rainfall intensity. For example, the stronger the rainfall, the higher the intensity. The larger the value, the more water accumulates on the glass surface due to heavy rainfall, resulting in less reflected light received by the photodiode and a higher DN value output by the sensor.

[0051] Combination Figure 2 illustrate: This corresponds to the maximum value point of the rainfall signal curve within the corresponding window. It should be understood that... Figure 2 The starting time for scraping is marked in the middle. and the time window before the scraping. This window is located before the wipers start wiping, at which point the water film thickness on the glass surface reaches its peak for this cycle.

[0052] thus, The residual rainfall, used as a quantitative representation of the intensity of the current rainfall event, serves as the denominator in the normalization process. It should be understood that by dividing the residual rainfall after wiping by the peak value before wiping, the interference of rainfall intensity on the judgment of wiping effectiveness can be eliminated. In some embodiments, even if the absolute value of the residual rainfall after wiping differs under different rainfall intensities, the normalized residual rate can still reflect the true wiping efficiency of the wipers, thereby making the wiping quality comparable across different scenarios.

[0053] Minimum rainfall after a blow The extraction is defined as follows:

[0054] in, For the first The interrupt trigger time received by the ECU after the wiper completes the wiping action is issued by the wiper position sensor or the wiper motor controller when the wiper returns to the initial position.

[0055] In this embodiment, This is the stabilization wait time after the interruption is triggered, used to skip the brief transition period when the water film on the glass surface has not yet stabilized immediately after the wiper finishes wiping. It should be understood that at the instant the wiper finishes its wiping action, the water film on the glass surface may still be flowing or redistributing, and the sensor signal has not yet reached a stable state. For example, The typical value range is 0.05s to 0.15s to ensure that the signal has been sufficiently stable within the sampling window.

[0056] In some embodiments, The sampling window width after shaving typically ranges from 0.1s to 0.3s. It should be understood that this window must ensure the signal has sufficiently stabilized within the window and that new rainfall accumulation has not yet significantly affected the signal. For example, if the window is too wide, new rainfall may have already begun to accumulate on the glass surface, leading to… Too high a level can affect the accuracy of judgment.

[0057] It should be understood that The unit is DN. For example, The larger the value, the thicker the residual water film, and the worse the scraping effect. The smaller the value, the cleaner the glass and the better the wiping effect.

[0058] Combination Figure 2 illustrate: Figure 2 The interrupt trigger time is marked in the text. and the subsequent post-scraping stabilization time window , This corresponds to the minimum point of the rainfall signal curve within the corresponding window. It should be understood that this window is located after the wiper has completed its wiping action and the water film has stabilized. At this point, the water film thickness on the glass surface reaches the minimum value after this wiping cycle, truly reflecting the wiping effect of the wiper.

[0059] As a result, aging wiper blades become less effective at cleaning, leaving more residual rainwater in each cycle. It should be understood that as the degree of aging increases, the amount of residual rainwater in each cycle will decrease. It will also show an upward trend, directly reflecting the decline in wiping effectiveness. In some embodiments, through continuous monitoring... Changes in wipers can be detected in the early stages, before they are easily visible to the naked eye, revealing abnormal trends in wiper aging.

[0060] Regarding validity filtering, in this embodiment, to exclude abnormal periods (such as excessively low peak values ​​leading to division by zero or insufficient signal-to-noise ratio), a validity filtering condition is introduced: a valid signal pair is valid if and only if... and .

[0061] In this embodiment, This is the lowest effective peak threshold, expressed in densities (DN). It should be understood that this indicates the current rainfall intensity is too low, the sensor signal dynamic range is insufficient, and the calculation of the normalized residual rate may be distorted. For example, a typical value for is 10% to 15% of the ADC's full scale. For instance, if the ADC has a 12-bit resolution (full scale 4095), a typical value is 410 to 614.

[0062] In some embodiments, This is the minimum signal dynamic range threshold, measured in nanometers (DN). It should be understood that when... This indicates that the signal change before and after the wipe is too small, which may be due to sensor noise or invalid wiping (such as the wiper not actually contacting the glass). In this case, the calculated normalized residual rate is not representative. For example, Typical values ​​are 5% to 10% of the ADC's full scale, i.e., 205 to 410 (for a 12-bit ADC).

[0063] It should be understood that only the normalized residual rate of the period that meets the validity filtering criteria is included in subsequent analysis. In some embodiments, periods that do not meet the criteria are marked as invalid periods and are not included in trend slope estimation and out-of-range count statistics.

[0064] Therefore, effective filtering eliminates abnormal cycles in scenarios with insufficient signal-to-noise ratio, such as drizzle and sensor noise, avoiding distortion of the normalized residual rate and improving the reliability of subsequent trend analysis. In some embodiments, by setting Threshold, to prevent [damage] when rainfall intensity is too low. Too small a value can cause division by zero errors or distortion of the normalized residual rate; this can be addressed by setting... A threshold is set to prevent sensor noise or invalid scratches from being mistakenly identified as valid data. It should be understood that this filtering mechanism ensures that all data included in the analysis has sufficient signal quality, serving as the first line of defense for accurate aging assessment.

[0065] S103: Calculate the normalized residual rate. See [link to S103] for details. Figure 1 and Figure 2 The normalized residual rate is calculated. It should be understood that the normalized residual rate is the core technical feature of this invention, as the normalization process eliminates the interference of rainfall intensity on the judgment of the wiping effect.

[0066] Normalized residual rate Defined as:

[0067] It should be understood that The value range is [0,1].

[0068] In this embodiment, The smaller the value, the cleaner the scraping. For example, when... When the time is right, it means that the residual rain after wiping is close to zero, the glass surface is almost completely clean, and the wiper performance is excellent. Conversely, The larger the value, the more residual water film remains, and the lower the wiping efficiency. For example, when... When the time is right, it means that the residual rainfall after wiping is close to the peak before wiping, and the wipers have done almost no cleaning work, resulting in extremely poor wiping performance.

[0069] Traditional methods directly use the absolute value of rainfall after the windbreak. The method for judging wiper effectiveness has a fundamental flaw: the absolute value of the rainfall signal is simultaneously affected by both rainfall intensity and wiper effectiveness. For example, under heavy rainfall, even if the wiper effectiveness is normal, The absolute value may also be high; even if the windshield wipers are worn out, when the rainfall intensity is low, The absolute value may also be too low. Therefore, under different rainfall intensities, relying solely on the absolute value for aging assessment is prone to false alarms or missed alarms, making it impossible to achieve accurate and reliable wiper aging detection.

[0070] In this embodiment, the following is introduced After being used as the normalized denominator, the wiping effect is separated from the rainfall intensity. It should be understood that regardless of the current rainfall intensity... Whether the rainfall is high or low, the normalized residual rate always reflects the "proportion of residual rainfall after wiping to the peak rainfall before wiping," that is, the wiping efficiency of the wipers under the current rainfall conditions. This makes the wiping quality comparable under different rainfall intensities, fundamentally solving the problem of rainfall intensity interference.

[0071] For example, under light rain conditions ( If residue remains after scraping ,but Under heavy rain conditions ( If residue remains after scraping ,but It should be understood that, although The absolute values ​​differ by a factor of four, but the normalized residual rates are the same, indicating that the wiper efficiency is consistent under both rainfall conditions. For example, if the wipers become worn out, the residual wiping efficiency after wiping in heavy rain may increase to [a higher percentage]. ,at this time, The increase in normalized residual rate clearly reflects the decrease in wiping efficiency, and is not masked by changes in rainfall intensity.

[0072] It should be understood that optical rain sensors can detect tiny raindrops or water films that have not been wiped away early in the aging process of windshield wipers. In some embodiments, even if the residual water film is very thin and imperceptible to the naked eye, the infrared detection mechanism of the optical rain sensor can still sensitively capture changes in reflected light intensity, reflecting this in… The slight increase in the residual rate. Therefore, by observing the trend change in the normalized residual rate, a trend judgment can be made earlier, enabling early warning of wiper aging.

[0073] Regarding the sliding window mean smoothing process, in this embodiment, the normalized residual rate sequence with continuous effective periods is subjected to sliding window mean smoothing to obtain a smoothed normalized residual rate sequence:

[0074] In this embodiment, This represents the length of the sliding window, i.e., the number of consecutive wiper cycles included in the mean calculation. It should be understood that... The larger the value, the stronger the smoothing effect, but the greater the response latency; A smaller value results in a more sensitive response, but also weaker noise immunity. For example, The typical value is 5 to 10 cycles.

[0075] Therefore, transient and occasional disturbances such as water splashes on the road or insect strikes can lead to abnormally high single-event normalized residual rates. For example, when driving at high speed, water spray from a vehicle ahead may briefly adhere to the windshield, causing a certain period of time to show an abnormally high normalized residual rate. Too high, which in turn makes An abnormal increase. It should be understood that if based directly on a single instance... Making a judgment might lead to misinterpreting this occasional disturbance as wiper aging.

[0076] In this embodiment, the sliding window mean smoothing is achieved by applying continuous... The normalized residual rate is averaged across multiple periods, suppressing the impact of occasional disturbances. It should be understood that occasional disturbances appear as isolated peaks in the time series, which are "diluted" by surrounding normal values ​​after window averaging, significantly reducing their impact on the smoothed series. Conversely, the increase in normalized residual rate caused by wiper aging is continuous, showing an increase in value across multiple consecutive periods. Even after window averaging, it maintains its upward trend and is not smoothed out.

[0077] Therefore, trend analysis is based on the smoothed sequence. This process reduces the probability of false alarms triggered by occasional abnormal events and improves the stability of aging assessment. It should be understood that the sliding window mechanism ensures the system's sensitivity to the actual aging trend; after wiper aging begins, the smooth sequence can gradually reflect the upward trend within a few cycles, achieving early warning.

[0078] S104: Estimating the trend slope Statistical count of exceeding the standard This yields aging characteristic parameters. It should be understood that this step extracts quantitative features reflecting the degree of aging from the smoothed normalized residual rate sequence.

[0079] In this embodiment, for continuous The trend slope is estimated using a smoothed, normalized residual rate sequence for each effective period and least-squares linear regression. :

[0080] In this embodiment, The number of consecutive effective wiper cycles participating in the trend analysis (trend analysis window size). It should be understood that... The larger the trend, the more stable it is estimated, but the slower it responds to recent changes; A smaller value responds more quickly to recent changes, but the stability of trend estimation decreases. For example, The typical value is 20 to 50 cycles.

[0081] The average of the period numbers. . To smooth and normalize the mean residual rate, . This represents the average rate of change of the smoothed normalized residual rate with increasing wiper cycle number. It should be understood that... This indicates that the smoothed normalized residual rate is on the rise, meaning that the wiping efficiency is continuously decreasing and the wipers may be aging. The larger the value, the faster the aging rate. This indicates that the wiping efficiency is stable and the wipers are in good working order.

[0082] In some embodiments, the least squares method, compared to simple difference (e.g.) Or compare using moving averages, making full use of all values ​​within the window. The statistical information of each data point is used to estimate the trend by fitting an optimal straight line. It should be understood that this method is more robust to random fluctuations in the data, the trend estimation results are more robust, and it can effectively distinguish between normal fluctuations and monotonous upward trends caused by continuous aging.

[0083] Combination Figure 4 Explain the physical meaning of the trend slope: Figure 4 This is a schematic diagram illustrating the aging trend of normalized residual rate as the number of wiper cycles increases. It should be understood that the horizontal axis in the diagram represents the wiper cycle number. The vertical axis represents the normalized residual rate (NRR). Solid dots represent the smoothed normalized residual rate data points for each period. The solid line represents the trend line, and the slope of the trend line is... .

[0084] exist Figure 4 In the middle, the previous data points are on the dashed line (dynamic single-time exceedance threshold). Below this line, a low normalized residual rate and a relatively flat trend line indicate that the wipers are in good condition and wiping efficiency is stable. It should be understood that as the usage cycle increases, the data points gradually rise, and some data points (marked with hollow circles) exceed the dotted line and are marked as exceeding the standard cycle. Trend line slope The increase indicates a significant upward trend in the normalized residual rate, a continuous decline in wiper efficiency, and the gradual emergence of aging phenomena.

[0085] exist Figure 4 The baseline acquisition phase is divided below the horizontal axis. The system consists of two phases: a baseline acquisition phase (located on the left side of the graph) and an aging detection phase. The baseline acquisition phase occurs when the wipers are in good condition, with a low and stable normalized residual rate, and is used to establish an individualized baseline distribution database. The aging detection phase occurs on the right side of the graph. During this phase, the system monitors changes in the normalized residual rate in real time, and triggers an aging warning when the trend slope and the number of exceedances meet the criteria.

[0086] Regarding the statistics of exceeding the standard, in this embodiment, the number of exceeding the standard is... Define as analysis window The number of periods in which the smoothed normalized residual rate exceeds the corresponding dynamic single-out threshold:

[0087] In this embodiment, This is an indicator function, also known as a pointer function. It should be understood that it takes the value 1 when the condition within the parentheses is true, and 0 when it is false. For the first The rainfall intensity level corresponding to each wiper cycle is used to match the dynamic single-time exceedance threshold of the corresponding level from the benchmark database. .

[0088] Therefore, the out-of-range count statistically represents the proportion of periods within the analysis window where the normalized residual rate exceeds the dynamic threshold. It should be understood that when the proportion exceeding the standard exceeds the threshold... When the value is typically 0.3, it indicates that the aging phenomenon has become statistically significant rather than an occasional anomaly.

[0089] In some embodiments, relying solely on a single instance of exceeding the standard to determine aging may misclassify occasional disturbances as aging. For example, a splash of water on the road surface might cause an abnormally high normalized residual rate in a single cycle, exceeding a threshold, but this does not necessarily mean the wipers are worn out. It should be understood that by statistically analyzing the exceedances over multiple cycles and calculating the exceedance ratio, occasional anomalies can be effectively distinguished from continuous aging. For example, if the exceedance occurs only once or twice occasionally, the exceedance ratio is very low and will not trigger an aging determination; if the wipers are truly worn out, the normalized residual rate will be high for multiple consecutive cycles, and the exceedance ratio will reach the threshold, at which point the system will determine it as aging.

[0090] Combination Figure 4 , Figure 4 Data points marked with hollow circles represent periods exceeding the standard, i.e. The cycle. It should be understood that during the aging test phase, as the wipers age more rapidly, the number of cycles exceeding the standard gradually increases, and the proportion of cycles exceeding the standard also increases. Rising. When the proportion exceeding the standard reaches... And the trend slope When the trend threshold is exceeded, the system determines that the wipers are aging and outputs a reminder signal.

[0091] See Figure 3 Adaptive dynamic threshold establishment is another core technical feature of this invention. It should be understood that... Figure 3 The flowchart for dynamic threshold and aging determination includes two parallel branches: the left baseline establishment branch S201-S204 and the right real-time data branch S211-S212. The two branches merge into S301 to generate the dynamic threshold.

[0092] The necessity of replacing the traditional fixed threshold scheme with an adaptive dynamic threshold mechanism is as follows: Individual differences such as different vehicle models, different sensor installation positions, and different regional water quality lead to insufficient universality of fixed thresholds. For example, if the sensor of a certain vehicle model is installed at a higher position, the residual rainfall after normal wiping will already be higher, and using a fixed threshold may produce false alarms; if the water quality in a certain region is hard, scale will easily form on the glass surface, affecting the sensor signal, and a fixed threshold cannot adapt.

[0093] In some embodiments, seasonal temperature variations cause natural changes in rubber elasticity, and using a fixed threshold may result in seasonal false alarms. For example, in winter, low temperatures naturally decrease rubber elasticity, leading to a slight decrease in wiping performance and a higher normalized residual rate. However, this is not due to wiper aging, but rather the effect of ambient temperature. It should be understood that using a fixed threshold may misjudge normal performance degradation at low temperatures as aging.

[0094] Therefore, the adaptive dynamic threshold mechanism establishes an individualized baseline distribution during the initial use of the vehicle and dynamically corrects it in conjunction with real-time ambient temperature, enabling the threshold to adapt to different vehicles and different usage environments, thus significantly improving the accuracy of judgment.

[0095] Regarding baseline data collection and statistics, in S201, the system continuously collects normalized residual rate data for each effective wiper cycle during the initial vehicle use phase (baseline collection phase). For example, A typical value is 100 cycles, corresponding to approximately several weeks to several months of normal use. It should be understood that the wipers are in good condition during this period, and the collected data reflects the normalized residual rate baseline level of the vehicle under normal wiping conditions.

[0096] S202: By rainfall intensity level ( Group the sensor signals. (The peak values ​​of the sensor signals are then grouped.) Divided into preset intervals Each level. Typical values ​​range from 3 to 5, for example: drizzle( ), Light rain ( ), Moderate rain ( ), Heavy rain and above ( ).

[0097] In this embodiment, each intensity level The corresponding normalized residual rate subset is denoted as , This represents the number of samples in this subset. It should be understood that the significance of grouping is that, under different rainfall intensities, even if the wipers are healthy, the baseline level of the normalized residual rate may differ slightly. For example, under heavy rain conditions, the water flow velocity on the glass surface is fast, increasing the difficulty of wiping, and the normalized residual rate of a healthy wiper may be slightly higher than under light rain conditions. Therefore, by establishing benchmarks for each level through grouping, the threshold can be adapted to different rainfall intensities.

[0098] In S203, the baseline mean is calculated for each intensity level. and the benchmark standard deviation :

[0099]

[0100] In this embodiment, Rainfall intensity level The normalized residual rate benchmark mean under the rainfall intensity level represents the typical normalized residual rate level when the wipers are in good condition, and is the benchmark center of the dynamic threshold. The standard deviation of the corresponding normalized residual rate represents the normal fluctuation range of the normalized residual rate of the health status under this level, and is the statistical basis for the dynamic threshold width.

[0101] In some embodiments, during the baseline acquisition phase, the system estimates the trend slope for each sliding window. , Obtain the trend slope sequence (the number of windows from which the trend slope can be calculated in the baseline phase). Calculate the mean trend slope in the baseline phase. and standard deviation :

[0102]

[0103] In this embodiment, The natural drift rate, which reflects the normalized residual rate of healthy windshield wipers under normal usage conditions, is ideally... . It reflects the normal fluctuation range of slope estimation under healthy conditions.

[0104] In S204, the rainfall intensity levels are divided into different categories. , as well as , The individualized normalized residual rate benchmark distribution database for the vehicle is established by storing it in the non-volatile memory (such as EEPROM or Flash) of the ECU.

[0105] Therefore, individualized benchmarks allow subsequent dynamic threshold generation to be based on the actual health status of the vehicle, solving the problem of insufficient universality of fixed thresholds across different vehicle models and usage environments. For example, the sensor installation location and water quality conditions of vehicle A result in a normalized residual rate of 0.15 under healthy conditions, while for vehicle B it is 0.10. It should be understood that if a fixed threshold of 0.12 is used, vehicle A may frequently generate false alarms, while vehicle B may miss alarms. With individualized benchmarks, the thresholds for vehicle A are generated based on 0.15, and for vehicle B based on 0.10, each adapting to its own vehicle characteristics and significantly improving the accuracy of judgments.

[0106] In S211, the system reads the ambient temperature value from the vehicle's interior temperature sensor in real time. (Unit: °C). It should be understood that in-vehicle temperature sensors are usually located near the dashboard or air conditioning system and can reflect the ambient temperature inside the vehicle.

[0107] In S212, the temperature correction factor is calculated. :

[0108] In this embodiment, This is the low-temperature correction reference temperature, i.e., the upper temperature limit that triggers temperature correction. For example, The typical value is 5℃, which corresponds to the temperature range in which the elasticity of rubber materials begins to decrease significantly. It should be understood that when the ambient temperature is below 5℃, the elasticity of the rubber strip naturally decreases, the wiping effect decreases slightly, and the normalized residual rate is higher. However, this is not due to wiper aging, but rather the effect of the low temperature environment.

[0109] This is the temperature correction intensity coefficient, with a value range of (0,1), which controls the maximum extent of threshold relaxation under low temperature conditions. The typical value is 0.3. When hour, That is, the threshold can be relaxed by up to 30%.

[0110] when Time does not correct ( ),when The time threshold is relaxed ( ).

[0111] Therefore, the natural decrease in rubber elasticity at low temperatures leads to a higher normalized residual rate, which, if not corrected for the threshold, can result in seasonal false alarms. It should be understood that the temperature correction factor moderately relaxes the threshold at low temperatures to tolerate the higher normalized residual rate caused by the natural decrease in rubber elasticity at low temperatures, thus avoiding seasonal false alarms. For example, at 25℃ in summer, the threshold is... At 0℃ in winter, the threshold is relaxed to The system can tolerate the natural increase in normalized residual rate at low temperatures and will not misjudge it as aging.

[0112] This ensures that the system maintains stable judgment accuracy under different seasonal temperature conditions, avoiding the problems of high false alarm rate in winter and high false alarm rate in summer.

[0113] In S301, the baseline establishment branch and the real-time data branch merge to generate a dynamic single-time exceedance threshold. and dynamic trend change threshold .

[0114] Dynamic single-time exceedance threshold:

[0115] In this embodiment, This is the sensitivity coefficient for a single instance of exceeding the limit, controlling the deviation factor of the single instance of exceeding the limit threshold relative to the baseline mean. It should be understood that when the normalized residual rate follows a normal distribution... This corresponds to a 97.7% confidence interval (meaning that approximately 2.3% of cycles under healthy conditions would be misjudged as exceeding the standard). This corresponds to a 99.7% confidence interval. For example, The typical value is .

[0116] Therefore, the benchmark mean Statistical confidence interval and temperature correction factor The three factors are combined to adaptively adjust the threshold under different rainfall intensity levels and temperature conditions. It should be understood that, for rainfall intensity levels... The threshold center is The threshold width is determined by Determine the temperature correction factor. The threshold is dynamically adjusted based on real-time temperature. This solves the problem of high false alarm rates when using fixed thresholds in diverse application scenarios, and enables accurate identification of single periods of exceeding the limit.

[0117] Dynamic trend change threshold:

[0118] In this embodiment, The mean of the normalized residual rate trend slope during the baseline data collection phase reflects the natural drift rate of the normalized residual rate of healthy wipers under normal usage conditions. Ideally, this represents the average value of the normalized residual rate trend slope during the baseline data collection phase. . The standard deviation of the trend slope during the baseline data collection phase reflects the normal fluctuation range of the slope estimate under healthy conditions. For example, This is the sensitivity coefficient for trend judgment, with a typical value of [value missing]. .

[0119] Therefore, based on the trend slope statistics constructed under baseline healthy conditions, it is possible to distinguish between the natural drift and accelerated increase caused by aging of the normalized residual rate under normal use conditions. It should be understood that the sensor itself may have weak signal drift, causing a slight increase in the normalized residual rate after prolonged use, but this increase rate is very slow, and the trend slope at the baseline stage... This is already reflected in [the data]. The dynamic trend change threshold is based on [the following]. The generation process can tolerate this natural drift, thereby avoiding misjudging sensor drift as aging and improving the specificity of trend judgment.

[0120] See S105. Figure 1 and Figure 3 The process involves aging assessment and alert signal output. It should be understood that this step compares aging characteristic parameters with dynamic thresholds, and outputs an alert signal when the assessment conditions are met.

[0121] In S302, the aging characteristic parameters (trend slope) obtained in S104 are... and exceeding the standard count ) and the dynamic threshold generated by S301 ( and Compare the results to determine whether the aging criteria are met.

[0122] Comprehensive aging assessment criteria: and

[0123] In this embodiment, This refers to the threshold percentage for exceeding the standard, which is the minimum percentage of periods exceeding the standard required to trigger an aging assessment. It should be understood that... This indicates that the aging phenomenon is statistically significant and not an isolated anomaly. For example, The typical value is 0.3, which means that more than 30% of the cycles within the analysis window exceed the limit.

[0124] Therefore, the comprehensive aging judgment criteria require both the exceeding percentage and the trend slope to be met simultaneously, specifically for gradual aging scenarios. It should be understood that gradual aging is a typical pattern of wiper aging, characterized by a gradual increase in the normalized residual rate and a gradual increase in the number of exceeding periods. By requiring both the exceeding percentage and the trend slope to be met simultaneously, the reliability of the judgment is ensured while reducing the false alarm rate. In some embodiments, if only requiring the exceeding percentage to meet the standard, short-term occasional disturbances may be misjudged as aging; if only requiring the trend slope to meet the standard, warnings may be triggered prematurely before exceeding periods have accumulated. Thus, the dual conditions ensure that the aging phenomenon is both statistically significant (high exceeding percentage) and persistent (large trend slope).

[0125] Rapid early warning conditions:

[0126] In this embodiment, Typical values ​​for trigger multipliers for rapid early warning It should be understood that when the trend slope... Reaching the trend threshold Even if the excess ratio has not yet reached the specified level, the time limit is doubled. This also directly triggers aging warnings.

[0127] Therefore, the rapid warning condition targets scenarios of rapid aging. It should be understood that rapid aging may be caused by sudden damage or large-scale detachment of the wiper blades, manifested as a rapid increase in the normalized residual rate within a short period, with an extremely steep trend slope. In this situation, the proportion exceeding the standard may not yet have accumulated to a certain level. However, the trend slope has far exceeded the threshold, indicating an extremely rapid aging rate that requires immediate warning. The rapid warning condition achieves a rapid response to abrupt aging by relaxing the requirement for the excess ratio. Thus, the dual judgment mechanism covers both gradual and sudden aging modes, ensuring reliable judgment of common gradual aging while guaranteeing a rapid response to sudden, abrupt aging.

[0128] In some embodiments, the judgment logic is as follows: If the number of times exceeding the standard is small, but the normalized residual rate increases rapidly (meeting the rapid warning condition), the wiping ability is directly judged to be declining, and the aging judgment result is output. If the rate of increase is rapid but has not yet reached the trend threshold, more rounds of wiping data are collected before judgment is made to avoid premature triggering. If the comprehensive aging judgment condition is met, the result of continuously declining wiping ability is output.

[0129] In S303, when the comprehensive aging judgment condition or the rapid warning condition is met, the ECU outputs a wiper replacement reminder signal. It should be understood that the reminder signal is presented in a multimodal manner: Visual reminders: Replacement reminders are displayed on in-vehicle displays (such as the central control screen or instrument panel). For example, the reminder information may include text prompts (such as "Wiper blades are aging, please replace them in time"), icons (such as the wiper icon with a warning symbol), progress bars (indicating the degree of aging), etc.

[0130] Auditory reminder: Play a replacement reminder voice message through a voice prompt device (such as a car audio system). For example, the voice message could be "The wiper blades are worn out and it is recommended to replace them as soon as possible to ensure driving safety."

[0131] Therefore, multimodal alerts (visual + auditory) ensure drivers are promptly informed of windshield wiper aging. It should be understood that visual alerts are suitable when the driver's attention is on the center console screen or instrument panel, while auditory alerts are suitable when the driver is focused on driving. The two methods complement each other, improving the effectiveness of the alerts. Timely replacement of worn wiper blades prevents poor wiping performance from causing obstructed vision and compromising driving safety.

[0132] Combination Figure 4 The intuitive representation of aging assessment: Figure 4 This visually illustrates the quantitative process of windshield wiper aging. It should be understood that the horizontal axis represents the wiper cycle number. The vertical axis represents the normalized residual rate (NRR). The smoothed normalized residual rate for each period is marked with solid dots. Initially, the wipers are in good condition, resulting in a low normalized residual rate and a stable trend line. All data points are within the dynamic single-cycle exceedance threshold. Below (dashed line).

[0133] It should be understood that as the usage cycle increases, the elasticity of the adhesive strip decreases, the adhesion deteriorates, and the normalized residual rate after each scraping becomes higher and higher, resulting in a steeper slope of the trend line. Increase. Some data points (marked with hollow circles) extend beyond the dashed line and are marked as exceeding the limit period. When the exceeding ratio... and When both the trend slope and the trend slope are true, the system determines that the wipers are worn out and outputs a reminder signal.

[0134] Figure 4 The label below indicates "Baseline Acquisition Phase" The graph is divided into two phases: the baseline acquisition phase (located on the left side of the graph) and the aging detection phase. The baseline acquisition phase occurs when the wipers are in good condition, with a low and stable normalized residual rate, used to establish an individualized baseline distribution database. The aging detection phase occurs on the right side of the graph. During this phase, the system monitors changes in the normalized residual rate in real time, triggering an aging warning when the trend slope and exceedance count meet the criteria. The entire process clearly demonstrates the quantitative evolution from a healthy state to an aging state.

[0135] Regarding the baseline reset procedure, in this embodiment, the ECU triggers the baseline reset procedure after the driver confirms through the in-vehicle interactive interface (such as the central control screen menu, voice commands, etc.) that the wiper blades have been replaced. It should be understood that the baseline reset procedure includes the following steps: (1) Clear the original baseline distribution database: Delete the baseline mean values ​​for each rainfall intensity level stored in the ECU's non-volatile memory. Benchmark Standard Deviation and the mean slope of the trend in the baseline phase Standard deviation .

[0136] (2) Re-entering the initialization acquisition phase: The system re-enters the baseline acquisition phase and continuously acquires data. The normalized residual rate data for each effective wiper cycle are grouped by rainfall intensity level, and the baseline mean, baseline standard deviation, and trend slope statistics for each new wiper cycle are calculated.

[0137] (3) Establish a normalized residual rate benchmark distribution for the new wipers: Store the benchmark statistics of the new wipers in the ECU and update the benchmark distribution database. Subsequent aging determination is based on the new benchmark.

[0138] This avoids false alarms caused by using the old baseline after replacing the wiper blades. It should be understood that without a baseline reset, the normalized residual rate of the new wiper blades may be significantly lower than the baseline of the old wiper blades, causing the system to remain in a "normal" state and failing to detect the aging of the new wiper blades in a timely manner. By resetting the baseline, it ensures that subsequent judgments are always based on the actual health condition of the current wiper blades.

[0139] It should be understood that benchmark reset enables the system to adapt to individual differences between different brands and models of wiper blades. In some embodiments, wiper blades of different brands or models may differ in material, hardness, and fit, and their normalized residual rate benchmark under healthy conditions may also differ. By resetting the benchmark, the system can learn the characteristics of new wiper blades and achieve individualized adaptation.

[0140] Example 2 This embodiment provides a wiper aging detection system based on an optical rain sensor, used to perform the detection method described in Embodiment 1. Figure 5As shown, the system comprises five main modules (100-500) and one auxiliary module (600). It should be understood that the modules are sequentially connected to form a complete signal processing link.

[0141] Specifically, the signal acquisition and preprocessing module (100) is used to acquire the rain digital signal sequence output by the optical rain sensor in each wiper cycle, extract the rain peak value within a preset time window before the wiper starts and the rain minimum value within a preset time window after the wiper starts from the rain digital signal sequence, and output a valid signal pair after validity filtering. The normalization calculation module (200) is connected to the signal acquisition and preprocessing module for calculating the normalized residual rate based on the effective signal to obtain the normalized residual rate sequence. The feature extraction module (300) is connected to the normalization calculation module and is used to perform sliding window mean smoothing on the normalized residual rate sequence to obtain a smoothed normalized residual rate sequence. Based on the smoothed normalized residual rate sequence, the trend slope and the number of out-of-range statistics are estimated to obtain aging feature parameters. The aging determination module (400) is connected to the feature extraction module and is used to compare the aging feature parameters with the dynamic threshold. When the preset aging determination conditions are met, the wiper is determined to be aging, and the aging determination result is obtained. The reminder output module (500) is electrically connected to the aging determination module and is used to output a reminder signal according to the aging determination result.

[0142] Specifically, the system further includes a benchmark establishment and dynamic threshold generation module (600), which includes three sub-modules: a benchmark establishment sub-module, a dynamic threshold generation sub-module, and a benchmark reset sub-module.

[0143] The benchmark establishment submodule is connected to the normalization calculation module for continuously collecting normalized residual rate data of a preset number of effective wiper cycles during the system initialization phase. The data is grouped according to the rainfall intensity level, and the benchmark mean and benchmark standard deviation of the normalized residual rate under each level are calculated to establish an individualized normalized residual rate benchmark distribution database. The dynamic threshold generation submodule is connected to the benchmark establishment module for generating dynamic single exceedance thresholds and dynamic trend change thresholds based on the benchmark distribution database and real-time ambient temperature. The benchmark reset submodule is connected to the benchmark establishment module. When a wiper blade replacement operation is detected, the benchmark reset process is triggered to clear the original benchmark distribution database, re-enter the initialization acquisition stage, and establish the normalized residual rate benchmark distribution corresponding to the new wiper blade.

[0144] Example 3 Embodiment 3 of the present invention provides a vehicle including the wiper aging detection system based on an optical rain sensor described in Embodiment 2; Windshield wipers are mounted on the vehicle body; An optical rain sensor is mounted on the vehicle body to collect rainfall signals; The in-vehicle display screen is installed inside the vehicle body and is electrically connected to the wiper aging detection system to display wiper replacement reminder information; A voice prompt device is installed inside the vehicle body and is electrically connected to the wiper aging detection system to play a voice reminder for wiper replacement. An in-vehicle temperature sensor is installed inside the vehicle body and is connected to the wiper aging detection system to provide real-time ambient temperature data.

[0145] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A method for detecting wiper aging based on an optical rain sensor, characterized in that, include: During each wiper cycle, a sequence of digital rainfall signals output by an optical rain sensor is collected. The peak rainfall within a preset time window before the wipers start and the minimum rainfall within a preset time window after the wipers start are extracted from the digital rainfall signal sequence. After validity filtering, valid signal pairs are obtained. Based on the effective signal pair, the normalized residual rate is calculated to obtain the normalized residual rate sequence; The normalized residual rate sequence is smoothed by sliding window mean to obtain a smoothed normalized residual rate sequence. The trend slope and the number of out-of-range statistics are estimated based on the smoothed normalized residual rate sequence to obtain aging characteristic parameters. The aging characteristic parameters are compared with the dynamic threshold. When the aging judgment conditions are met, the wiper is judged to be aging, and the aging judgment result is obtained. Based on the aging determination results, a wiper replacement reminder signal is output.

2. The method according to claim 1, characterized in that, Let the first The sequence of digital rainfall signals output by the optical rain sensor during each wiper cycle is as follows: , ,in, For the first The start time of each wiper cycle For the first The end time of each wiper cycle; the peak rainfall Defined as: in, The width of the sampling window before the scraping; the minimum rainfall value. Defined as: in, For the first The interruption trigger time after the wiper completes its wiping action. This refers to the stable waiting time after the interrupt is triggered. This represents the width of the sampling window after scraping.

3. The method according to claim 2, characterized in that, The conditions for the validity filtering are: and ,in, The lowest effective peak threshold, This is the minimum dynamic range threshold for the signal.

4. The method according to claim 1, characterized in that, The calculation method for the sliding window mean smoothing process is as follows: in, For the first The normalized residual rate for each effective wiper cycle, which is the ratio of the minimum rainfall after wiping to the peak rainfall before wiping. This is the length of the sliding window.

5. The method according to claim 1, characterized in that, The slope of the trend is estimated as follows: in, The number of consecutive effective wiper cycles used in trend analysis. The average of the period numbers. To smooth and normalize the mean residual rate, For the first The smoothed normalized residual rate for each period is the value obtained after smoothing the normalized residual rate sequence by applying a sliding window mean.

6. The method according to claim 1, characterized in that, The method further includes: Continuous data collection of a preset number Normalized residual rate data for each effective wiper cycle, divided into preset intervals based on peak rainfall. Rainfall intensity level For each intensity level The benchmark mean was calculated for each of the corresponding normalized residual rate subsets. and the benchmark standard deviation Establish an individualized normalized residual rate benchmark distribution database.

7. The method according to claim 6, characterized in that, The dynamic threshold includes a dynamic single exceedance threshold and a dynamic trend change threshold; The dynamic single-time exceedance threshold is calculated as follows: in, This represents the sensitivity coefficient for a single instance of exceeding the limit. This is a temperature correction factor. The ambient temperature value is read in real time by the in-vehicle temperature sensor; when the residual rate is smoothed and normalized... When the dynamic single-time exceedance threshold is exceeded, the period is marked as an exceedance period.

8. The method according to claim 7, characterized in that, The dynamic trend change threshold is calculated as follows: in, The mean of the normalized residual rate trend slope during the baseline acquisition phase. The standard deviation of the trend slope during the baseline data collection phase. This is the sensitivity coefficient for trend judgment.

9. The method according to claim 7, characterized in that, The temperature correction factor The calculation method is as follows: in, The ambient temperature value is read in real time by the vehicle's interior temperature sensor. To correct the reference temperature for low temperature, This is the temperature-corrected intensity coefficient.

10. The method according to claim 7, characterized in that, The aging determination criteria include at least one of the following: Comprehensive aging assessment criteria: and ; Rapid early warning conditions: ; in, For analysis window The number of cycles in which the smoothed normalized residual rate exceeds the corresponding dynamic single-time exceedance threshold. The threshold for the proportion exceeding the standard. To trigger a rapid early warning multiplier; When the comprehensive aging judgment condition or the rapid warning condition is met, the wiper is judged to be aging.

11. The method according to claim 6, characterized in that, The method further includes: When a wiper blade replacement operation is detected, the baseline reset process is triggered, the original baseline distribution database is cleared, and the initialization and acquisition phase is restarted to establish the normalized residual rate baseline distribution corresponding to the new wiper blade.

12. A wiper aging detection system based on an optical rain sensor, characterized in that, include: The signal acquisition and preprocessing module is used to acquire the rain digital signal sequence output by the optical rain sensor in each wiper cycle, extract the rain peak value within a preset time window before the wiper starts and the rain minimum value within a preset time window after the wiper starts from the rain digital signal sequence, and output valid signal pairs after validity filtering. The normalization calculation module is connected to the signal acquisition and preprocessing module for calculating the normalized residual rate based on the effective signal to obtain a normalized residual rate sequence. The feature extraction module is connected to the normalization calculation module and is used to perform sliding window mean smoothing on the normalized residual rate sequence to obtain a smoothed normalized residual rate sequence. Based on the smoothed normalized residual rate sequence, the trend slope and the number of out-of-range statistics are estimated to obtain aging feature parameters. The aging determination module is connected to the feature extraction module and is used to compare the aging feature parameters with dynamic thresholds. When the preset aging determination conditions are met, the wiper is determined to be aging, and the aging determination result is obtained. The reminder output module is electrically connected to the aging determination module and is used to output a reminder signal based on the aging determination result.

13. The system according to claim 12, characterized in that, The system also includes a benchmark establishment and dynamic threshold generation module, which includes three sub-modules: benchmark establishment sub-module, dynamic threshold generation sub-module, and benchmark reset sub-module; The benchmark establishment submodule is connected to the normalization calculation module for continuously collecting normalized residual rate data of a preset number of effective wiper cycles during the system initialization phase. The data is grouped according to the rainfall intensity level, and the benchmark mean and benchmark standard deviation of the normalized residual rate under each level are calculated to establish an individualized normalized residual rate benchmark distribution database. The dynamic threshold generation submodule is connected to the benchmark establishment module for generating dynamic single exceedance thresholds and dynamic trend change thresholds based on the benchmark distribution database and real-time ambient temperature. The benchmark reset submodule is connected to the benchmark establishment module. When a wiper blade replacement operation is detected, the benchmark reset process is triggered to clear the original benchmark distribution database, re-enter the initialization acquisition stage, and establish the normalized residual rate benchmark distribution corresponding to the new wiper blade.

14. A vehicle, characterized in that, Includes the wiper aging detection system based on an optical rain sensor as described in claim 12 or 13.

15. The vehicle according to claim 14, characterized in that, Also includes: Windshield wipers are mounted on the vehicle body; An optical rain sensor is mounted on the vehicle body to collect rainfall signals; The in-vehicle display screen is installed inside the vehicle body and is electrically connected to the wiper aging detection system to display wiper replacement reminder information; A voice prompt device is installed inside the vehicle body and is electrically connected to the wiper aging detection system to play a voice reminder for wiper replacement. An in-vehicle temperature sensor is installed inside the vehicle body and is connected to the wiper aging detection system to provide real-time ambient temperature data.

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