Jumping processing method, device, electronic device and readable storage medium of windshield wiper

Abstract

The present invention discloses a method, device, electronic device and readable storage medium for jitter processing of a windshield wiper, including: sampling a current signal in a brushless motor to obtain at least one wiper current signal; performing wavelet packet decomposition on the at least one wiper current signal to obtain M sub-band signals and sub-band coefficients of each of the sub-band signals; wherein, the sub-band coefficients are used to characterize the energy intensity on the sub-band signals; if it is determined according to each of the sub-band coefficients that at least one jitter sub-band signal exists among the M sub-band signals, then adjusting the scraping speed of the wiper rod within the jitter angle range corresponding to the jitter sub-band signal to an anti-jitter speed; wherein, the anti-jitter speed is greater than the scraping speed of the windshield wiper within the jitter angle range. The present invention reduces the noise generated by the wiper rod, improves the driving texture of the vehicle, and avoids the situation of interfering with the driver's attention and affecting driving safety.

Description

Technical Field

[0001] The present invention relates to the technical field of electric motors, and particularly to a device for starting an electric motor or an electromechanical converter, and more particularly to a method and device for processing the bounce of a windshield wiper, an electronic device, and a readable storage medium. Background Art

[0002] When a new rubber strip contacts a clean glass, the frictional force is evenly distributed along the brushing direction, forming a stable sliding interface. At this time, the micro-texture of the rubber strip forms a multi-point uniform contact with the micro-protrusions on the glass surface, and the vibration energy is efficiently dissipated through the viscoelastic rubber strip.

[0003] However, when stains are deposited on the glass surface, an oil film / particle pollutant forms a non-uniform friction layer on the glass surface, resulting in an increase in the friction coefficient of the contact surface; and when the rubber strip of the windshield wiper rod hardens, the cross-linking of rubber molecular chains causes an increase in the elastic modulus and a decrease in the number of contact points, resulting in an increase in the force on a single point; further leading to a stick-slip effect at the rubber strip-glass interface, causing the windshield wiper rod to produce instantaneous bounce and noise at a certain specific windshield wiper angle, reducing the driving texture of the vehicle, and easily disturbing the driver's attention and affecting driving safety. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and device for processing the bounce of a windshield wiper, an electronic device, and a readable storage medium, which are used to solve the problems in the prior art that the windshield wiper rod produces instantaneous bounce and noise at a certain specific windshield wiper angle, reducing the driving texture of the vehicle and affecting driving safety.

[0005] To achieve the above purpose, the present invention provides a method for processing the bounce of a windshield wiper, the windshield wiper is installed on a vehicle, and the windshield wiper rod of the windshield wiper is used to brush the windshield of the vehicle;

[0006] The bounce processing method includes:

[0007] Sampling the current signal in the brushless motor to obtain at least one windshield wiper current signal;

[0008] Performing wavelet packet decomposition on at least one windshield wiper current signal to obtain M sub-band signals and the sub-band coefficients of each sub-band signal; wherein, the sub-band coefficients are used to characterize the energy intensity on the sub-band signals;

[0009] If it is determined according to each sub-band coefficient that at least one bounce sub-band signal exists in the M sub-band signals, the brushing speed of the windshield wiper rod in the bounce angle range corresponding to the bounce sub-band signal is adjusted to an anti-bounce speed; wherein, the anti-bounce speed is greater than the brushing speed of the windshield wiper in the bounce angle range.

[0010] In the above solution, the current signal in the brushless motor is sampled to obtain at least one wiper current signal, including:

[0011] Obtain the PWM synchronization signal of the motor driver of the brushless motor; wherein, the PWM synchronization signal is a current signal used to adjust the motor speed and torque;

[0012] Trigger the sampling of the current signal in the brushless motor according to the PWM synchronization signal to obtain at least one original current acquisition signal;

[0013] Filter at least one of the original current acquisition signals to obtain at least one of the wiper current signals.

[0014] In the above solution, perform wavelet packet decomposition on at least one wiper current signal to obtain M sub-band signals and the sub-band coefficients of each sub-band signal, including:

[0015] Perform N-layer wavelet packet decomposition on at least one wiper current signal to obtain M sub-band signals;

[0016] Perform boundary effect processing on each of the sub-band signals to obtain the extended signal of each sub-band signal;

[0017] Define a wavelet basis function for each of the sub-band signals;

[0018] Measure the similarity between each of the extended signals and its wavelet basis function through inner product operation to obtain the sub-band coefficient of each sub-band.

[0019] In the above solution, if it is determined that at least one of the M sub-band signals has a jumping sub-band signal according to each sub-band coefficient, including:

[0020] If it is determined that at least one of the jumping energy indicators exists in the sub-band coefficient of the first sub-band signal, obtain the jumping processing rule corresponding to the status information of the wiper; wherein, the first sub-band signal is one of the M sub-bands; the status information reflects the working wiping speed of the wiper lever, the current weather condition of the vehicle, and the service life of the wiper lever;

[0021] If it is determined that at least one of the jumping energy indicators meets the preset jumping processing rule, determine the first sub-band signal as the jumping sub-band signal.

[0022] In the above solution, adjust the wiping speed of the wiper within the jumping angle range corresponding to the jumping sub-band signal to the anti-jumping speed, including:

[0023] Set the wiper angle range corresponding to at least one jumping sub-band signal as the jumping angle range;

[0024] Within the described jitter angle range, a PID controller is used to adjust the wiping speed of the wiper rod to the anti-jitter speed.

[0025] In the above solution, after adjusting the wiping speed of the wiper rod within the jitter angle range corresponding to the jitter sub-band signal to the anti-jitter speed, the method further includes:

[0026] Adjusting the wiping speed of the wiper rod within at least one non-jitter angle range to the wiper matching speed; wherein, the non-jitter angle range refers to other wiper angle ranges in the overall wiper angle range of the wiper rod on the windshield except the jitter angle range; the wiper matching speed is less than the wiping speed of the wiper rod within the non-jitter angle range.

[0027] In the above solution, setting the wiping speed of the wiper rod within at least one non-jitter angle range to the wiper matching speed includes:

[0028] Within the non-jitter wiping speed range, a PID controller is used to adjust the wiping speed of the wiper rod to the wiper matching speed;

[0029] An in-jitter angle range and an out-jitter angle range are respectively set on both sides of one of the non-jitter angle ranges close to the jitter angle range;

[0030] Based on the first speed curve, the wiping speed of the wiper rod within the in-jitter angle range is gradually changed from the wiper matching speed to the anti-jitter speed; wherein, the first speed curve is any one of an S-shaped speed curve, a T-shaped speed curve, an exponential speed curve, and a trigonometric function speed curve;

[0031] Based on the second speed curve, the wiping speed of the wiper rod within the out-jitter angle range is gradually changed from the anti-jitter speed to the wiper matching speed; wherein, the second speed curve is any one of an S-shaped speed curve, a T-shaped speed curve, an exponential speed curve, and a trigonometric function speed curve.

[0032] To achieve the above object, the present invention further provides a jitter processing device for a wiper, which is installed on the wiper and runs the above-mentioned jitter processing method of the wiper; the wiper rod of the wiper is used to wipe the windshield of the vehicle;

[0033] The jitter processing device includes:

[0034] A sampling module, configured to sample the current signal in the brushless motor to obtain at least one wiper current signal;

[0035] A decomposition module for performing wavelet packet decomposition on at least one wiper current signal to obtain M sub-band signals and the sub-band coefficients of each of the sub-band signals; wherein the sub-band coefficients are used to characterize the energy intensity on the sub-band signals.

[0036] An anti-jump module for, if it is determined according to each of the sub-band coefficients that there is at least one jump sub-band signal among the M sub-band signals, adjusting the scraping speed of the wiper rod within the jump angle range corresponding to the jump sub-band signal to an anti-jump speed; wherein the anti-jump speed is greater than the scraping speed of the wiper within the jump angle range.

[0037] To achieve the above object, the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor of the electronic device executes the computer program, the steps of the above-mentioned jump processing method are implemented.

[0038] To achieve the above object, the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program stored on the readable storage medium is executed by a processor, the steps of the above-mentioned jump processing method are implemented.

[0039] A jump processing method, device, electronic device, and readable storage medium for a wiper provided by the present invention can accurately draw the curve of the current signal through current sampling, ensuring the accuracy of subsequent identification of the jump angle range.

[0040] By performing wavelet packet decomposition on at least one wiper current signal, the full-frequency characteristics of the signal are comprehensively reflected, improving the time-frequency resolution of the signal, thereby more accurately identifying and analyzing the characteristics in the signal.

[0041] When the wiper rod of the wiper scrapes the windshield, local jumping of the wiper rod and generation of noise may occur due to stains on the glass surface or aging of the wiper rod rubber strip. The local jumping of the wiper rod will affect the current signal in the wiper motor, resulting in fluctuations in the current signal.

[0042] In response to this, by identifying the jump sub-band signal according to each of the sub-band coefficients and identifying the jump angle range corresponding to the jump sub-band signal, it is possible to lock the area on the glass surface where stains cause the wiper rod to jump or the area on the windshield where the wiper rod rubber strip aging causes jumping based on the fluctuations of the motor current signal; by adjusting the scraping speed of the wiper rod within the jump angle range corresponding to the jump sub-band signal to the anti-jump speed, the wiper rod can quickly sweep through the area where the wiper rod jumps, thereby reducing the noise generated by the wiper rod, improving the driving texture of the vehicle, and avoiding the situation of interfering with the driver's attention and affecting driving safety. Description of the Drawings

[0043] Figure 1 This is a flowchart of the jitter processing method for the windshield wiper of the present invention;

[0044] Figure 2 This is a specific method flowchart of the jitter processing method for the windshield wiper of the present invention;

[0045] Figure 3 This is a curve reflecting the wiper angle on each timing signal (Time) and a curve of the wiper current signal on each timing signal (Time) in the jitter processing method for the windshield wiper of the present invention;

[0046] Figure 4 This is the relationship between the curve reflecting the wiper angle on each timing signal (Time) and the curve of the brushing angle in the jitter processing method for the windshield wiper of the present invention, as well as the jitter angle range and non-jitter angle range in the wiper angle, and the anti-jitter speed and wiper matching speed in the brushing speed;

[0047] Figure 5 This is a schematic diagram of the program module of the jitter processing device of the present invention;

[0048] Figure 6 This is a schematic structural diagram of the relationship between the jitter processing device of the windshield wiper of the present invention and the windshield wiper;

[0049] Figure 7 This is a schematic diagram of the hardware structure of the electronic device in the electronic device of the present invention. Detailed implementation manners

[0050] In order to make the purpose, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0051] Embodiment 1: Please refer to Figure 1 , a jitter processing method for a windshield wiper in this embodiment, the windshield wiper is installed on a vehicle, and the wiper rod of the windshield wiper is used to brush the windshield of the vehicle;

[0052] The jitter processing method includes:

[0053] S101: Sample the current signal in the brushless motor to obtain at least one wiper current signal.

[0054] S102: Perform wavelet packet decomposition on at least one wiper current signal to obtain M sub-band signals and the sub-band coefficients of each of the sub-band signals; wherein, the sub-band coefficients are used to characterize the energy intensity on the sub-band signals.

[0055] S103: If it is determined that there is at least one jumping sub-band signal among the M sub-band signals according to each of the sub-band coefficients, adjust the wiping speed of the wiper rod within the jumping angle range corresponding to the jumping sub-band signal to an anti-jumping speed; wherein, the anti-jumping speed is greater than the wiping speed of the wiper in the jumping angle range.

[0056] In this embodiment, the curve of the current signal can be accurately drawn through current sampling, ensuring the accuracy of subsequent identification of the jumping angle range.

[0057] By performing wavelet packet decomposition on at least one wiper current signal, the full-frequency characteristics of the signal are comprehensively reflected, improving the time-frequency resolution of the signal, thereby more accurately identifying and analyzing the characteristics in the signal.

[0058] When the wiper rod of the wiper wipes the windshield, the sudden change in the friction force between the wiper rod and the glass due to stains on the glass surface or aging of the wiper rod rubber strip will cause an instantaneous change in the motor load. (Generally, the friction force fluctuation caused by local jumping can cause a ±15%-20% spike in the motor current (duration < 50 ms).), resulting in local jumping and generating noise. This local jumping will affect the current signal in the wiper motor, causing fluctuations in the current signal.

[0059] In response to this, by identifying the jumping sub-band signal according to each of the sub-band coefficients and identifying the jumping angle range corresponding to the jumping sub-band signal, to lock the area on the glass surface where stains cause the wiper rod to jump, or the area on the windshield where the wiper rod rubber strip aging causes jumping based on the fluctuation of the motor current signal; by adjusting the wiping speed of the wiper rod within the jumping angle range corresponding to the jumping sub-band signal to the anti-jumping speed, the wiper rod can quickly sweep through the area where the wiper rod jumps, thereby reducing the noise generated by the wiper rod, improving the driving texture of the vehicle, and avoiding the situation of interfering with the driver's attention and affecting driving safety.

[0060] Embodiment 2: This embodiment is a specific application scenario of the above Embodiment 1. Through this embodiment, the method provided by the present invention can be more clearly and specifically described.

[0061] Next, taking the example of a wiper running the jitter processing method, performing wavelet packet decomposition on at least one wiper current signal to obtain M sub-band signals and the sub-band coefficients of each of the sub-band signals. If it is determined that at least one jitter sub-band signal exists among the M sub-band signals according to each of the sub-band coefficients, then adjusting the wiping speed of the wiper rod within the jitter angle range corresponding to the jitter sub-band signal to the anti-jitter speed, the method provided in this embodiment will be specifically described. It should be noted that this embodiment is only exemplary and does not limit the scope protected by the embodiments of the present invention.

[0062] Figure 2 It is a specific method flowchart of a jitter processing method provided by an embodiment of the present invention, and this method specifically includes steps S201 to S204.

[0063] Please refer to Figure 2 , this application proposes a jitter processing method for a wiper. The wiper is installed on a vehicle, and the wiper rod of the wiper is used to wipe the windshield of the vehicle;

[0064] The jitter processing method includes:

[0065] S201: Sample the current signal in the brushless motor to obtain at least one wiper current signal.

[0066] In this example, the curve of the current signal can be accurately drawn through current sampling, ensuring the accuracy of subsequent identification of the jitter angle range.

[0067] In a preferred embodiment, sampling the current signal in the brushless motor to obtain at least one wiper current signal includes:

[0068] S11: Obtain the PWM synchronization signal of the motor driver of the brushless motor; wherein, the PWM synchronization signal is a current signal used to adjust the motor speed and torque;

[0069] S12: Trigger the sampling of the current signal in the brushless motor according to the PWM synchronization signal to obtain at least one current original acquisition signal;

[0070] S13: Filter at least one of the current original acquisition signals to obtain at least one of the wiper current signals.

[0071] Exemplarily, the PWM (Pulse Width Modulation) synchronization signal is used to adjust the motor speed and torque and is a key signal in motor control. By acquiring the PWM synchronization signal, the accuracy and effectiveness of current signal sampling can be ensured because the PWM signal directly reflects the control state of the motor. The control signal can be generated by comparing the command signal with the PWM carrier signal, and then the PWM synchronization signal can be acquired. Another method is to sample the command signal to generate the duty cycle of the PWM, thereby indirectly acquiring the PWM synchronization signal.

[0072] Triggering the current signal sampling according to the PWM synchronization signal can ensure that the current signal is acquired at the critical moment of motor control, improving the accuracy and real-time performance of sampling. Among them, the sampling methods include:

[0073] Pulse counting method: Use a counter or timer module to count the number of occurrences of the rising edge or falling edge of the PWM pulse, thereby obtaining the frequency and duty cycle of the waveform.

[0074] Incremental encoder: Acquire the frequency and duty cycle of the PWM wave by collecting the output signal of the encoder. The output signal of the encoder is usually a sine wave, which can be converted into a PWM waveform through a decoding algorithm.

[0075] Analog-to-Digital Converter (ADC): Convert the PWM signal into an analog signal for sampling, and then use the sampled data to analyze the characteristics of the PWM wave.

[0076] Edge detection method: Use an edge detection circuit to detect the rising edge or falling edge of the PWM wave, and then use a counter or timer module to count to obtain the frequency and duty cycle.

[0077] Filtering the original current acquisition signal can remove the noise and interference in the signal, improving the quality and accuracy of the signal. Among them, the filtering methods include:

[0078] Limiting filtering method: Based on experience judgment, determine the maximum allowable deviation value between two samplings. Each time a new value is detected, judge that if the difference between this value and the previous value is less than or equal to the maximum deviation value, then this value is valid; otherwise, this value is invalid, discard this value, and replace this value with the previous value.

[0079] Median filtering method: Continuously sample N times (N is an odd number), arrange the N sampling values in size, and take the middle value as the effective value of this time.

[0080] Arithmetic mean filtering method: Continuously take N sampling values for arithmetic mean operation. When the N value is larger, the signal smoothness is higher, but the sensitivity is lower; when the N value is smaller, the signal smoothness is lower, but the sensitivity is higher.

[0081] Recursive average filtering method: Consider continuously taking N sampling values as a queue, and the length of the queue is fixed at N. Each time a new data is sampled and put into the tail of the queue, and the original data at the head of the queue is discarded. Perform arithmetic average operation on the N data in the queue to obtain a new filtering result.

[0082] Median average filtering method: Equivalent to "median filtering method" + "arithmetic average filtering method". Continuously sample N data, remove one maximum value and one minimum value, and then calculate the arithmetic average of N - 2 data.

[0083] Limited amplitude average filtering method: Equivalent to "limited amplitude filtering method" + "recursive average filtering method". Each time the newly sampled data is first subjected to limited amplitude processing, and then sent to the queue for recursive average filtering processing.

[0084] First-order lag filtering method: Take a = 0~1, the current filtering result = (1 - a) × the current sampling value + a × the previous filtering result.

[0085] Weighted recursive average filtering method: It is an improvement of the recursive average filtering method, that is, different weights are assigned to data at different times. Usually, the closer the data is to the current time, the greater the weight.

[0086] Debounce filtering method: Set a filtering counter, and compare each sampling value with the current valid value. If the sampling value is equal to the current valid value, the counter is cleared; if the sampling value is not equal to the current valid value, the counter is incremented by 1, and it is judged whether the counter is greater than or equal to the upper limit N (overflow). If the counter overflows, the current value is replaced with the current valid value, and the counter is cleared.

[0087] Kalman filtering method: Kalman filtering is a recursive algorithm used for dynamic system state estimation of a series of noisy observation data. It is widely used in fields such as navigation, control systems, and signal processing. Kalman filtering optimizes the estimation result by combining prediction and measurement, and is suitable for real-time systems and embedded systems. Kalman filtering includes two steps: prediction and update. The prediction step uses the dynamic model of the system to estimate the current state, and the update step uses the measured value to correct the predicted value.

[0088] Further, trigger the sampling of the current signal in the brushless motor according to the PWM synchronization signal to obtain at least one original current acquisition signal, including:

[0089] S121: Obtain at least one rising edge and at least one falling edge of at least one of the PWM synchronization signals;

[0090] S122: Determine the synchronization period of at least one of the PWM synchronization signals according to the time difference between two adjacent rising edges, determine the synchronization pulse width of the PWM synchronization signal according to one adjacent rising edge and one falling edge, and determine the acquisition period according to the synchronization period and the synchronization pulse width;

[0091] S123: When a compensation center point of a PWM in the PWM synchronization signal is recognized, sample the current signal in the brushless motor according to the acquisition period to obtain an original current acquisition signal.

[0092] Exemplarily, the voltage change of the PWM synchronization signal is monitored in real time through a high-speed signal detection circuit or the input capture function of a microprocessor. The rising edge (the moment when the voltage jumps from a low level to a high level) and the falling edge (the moment when the voltage jumps from a high level to a low level) of the PWM synchronization signal are accurately recognized by using a Schmitt trigger or the edge detection function of a microprocessor. When a rising edge or a falling edge is detected, record the current timestamp for subsequent calculation of the synchronization period and the synchronization pulse width.

[0093] Calculate the difference between the timestamps of two adjacent rising edges, which is the synchronization period of the PWM synchronization signal. Calculate the difference between the timestamps of one adjacent rising edge and one falling edge, which is the synchronization pulse width of the PWM synchronization signal. The acquisition period should be determined according to the synchronization period and the synchronization pulse width to ensure current signal sampling at critical moments of motor control.

[0094] The compensation center point of the PWM refers to the critical moment point in the PWM signal for compensating motor control errors, usually located at the midpoint of the PWM period or at a position dynamically adjusted according to the control algorithm. Determine the position of the compensation center point by calculating the synchronization period and the synchronization pulse width and combining the control algorithm.

[0095] When the compensation center point is recognized, trigger the current signal sampling operation. Adopt current sampling methods such as current transformers, manganese copper shunts, Hall sensors or Rogowski coils to convert the current signal into a voltage signal, and then send it to an analog-to-digital converter (ADC) for sampling after amplification, filtering and other processing. Sampling is carried out according to the pre-determined acquisition period to ensure obtaining an original current acquisition signal within each acquisition period.

[0096] The original current acquisition signal obtained through ADC sampling is a digital signal, reflecting the magnitude and direction of the current in the brushless motor. Store the original current acquisition signal in the memory of the microprocessor for subsequent filtering, analysis and control algorithm processing.

[0097] Optionally, filtering at least one of the original current acquisition signals to obtain at least one of the wiper current signals includes:

[0098] S131: Obtain the wiping speed of the brushless motor;

[0099] S132: If it is determined that the wiping speed is less than a preset speed threshold, increase the window width to suppress low-frequency drift;

[0100] S133: If it is determined that the wiping speed is not less than the speed threshold, shorten the window width to maintain dynamic response.

[0101] Exemplarily, the rotating shaft of the brushless motor drives the permanent magnet on the shaft to rotate, changing the magnetic field magnitude. The magnetic field change is converted into a pulse signal through a Hall circuit, and a rectangular pulse signal is output after amplification and shaping. The frequency of the pulse signal is proportional to the rotational speed, and the wiping speed can be calculated by measuring the pulse frequency. Or a speed measurement system composed of a grid disk and a photoelectric gate is used. The brushless motor drives the grid disk to rotate through the transmission part, and the photoelectric gate obtains a series of pulse signals. The number of pulses within a unit time is calculated by the single-chip microcomputer timer / counter, and the wiping speed can be obtained. Or combine the advantages of the M method (measuring the distance at a fixed time) and the T method (measuring the time at a fixed distance). By capturing the pulse signal of the encoder, calculating the position pulse increment and the time pulse count, the wiping speed can be obtained. This method is applicable to medium and high-speed measurements and has high measurement accuracy.

[0102] Through experimental measurement of the system performance at different wiping speeds, determine the speed range where the system is prone to low-frequency drift when the wiping speed is low, and use the lower limit of this speed range as the speed threshold. Or set a speed threshold according to the dynamic response and stability requirements of the system. When the wiping speed is lower than this threshold, the system needs to increase the window width to suppress low-frequency drift.

[0103] When the wiping speed is less than the speed threshold, trigger the window width increase algorithm. The amount of window width increase can be determined according to the difference between the wiping speed and the speed threshold. The larger the difference, the more the window width increases. By adjusting the window function parameter in the signal processing algorithm, the window width is increased. For example, in the Fourier transform or filtering algorithm, increasing the window width can smooth the signal and suppress low-frequency drift.

[0104] When the wiping speed is not less than the speed threshold, trigger the window width shortening algorithm. The amount of window width shortening can be determined according to the difference between the wiping speed and the speed threshold. The larger the difference, the more the window width shortens. By adjusting the window function parameter in the signal processing algorithm, the window width is shortened. For example, in the Fourier transform or filtering algorithm, shortening the window width can improve the time resolution and maintain the dynamic response of the system.

[0105] S202: Perform wavelet packet decomposition on at least one wiper current signal to obtain M sub-band signals and the sub-band coefficients of each of the sub-band signals; wherein, the sub-band coefficients are used to characterize the energy intensity on the sub-band signals.

[0106] In this step, at least one wiper current signal is decomposed by wavelet packet decomposition to comprehensively reflect the full-frequency characteristics of the signal, improve the time-frequency resolution of the signal, and thus more accurately identify and analyze the features in the signal.

[0107] In a preferred embodiment, at least one wiper current signal is decomposed by wavelet packet decomposition to obtain M sub-band signals and the sub-band coefficients of each of the sub-band signals, including:

[0108] S21: Perform N-layer wavelet packet decomposition on at least one wiper current signal to obtain M sub-band signals;

[0109] S22: Perform boundary effect processing on each of the sub-band signals to obtain the extended signal of each of the sub-band signals;

[0110] S23: Define a wavelet basis function for each of the sub-band signals;

[0111] S24: Measure the similarity between each of the extended signals and its wavelet basis function through inner product operation to obtain the sub-band coefficients of each of the sub-bands.

[0112] In this example, the wiper current signal is usually a non-stationary signal, and its spectrum changes with time. The traditional Fourier transform cannot effectively analyze the time-frequency local characteristics. Therefore, through N-layer wavelet packet decomposition, the signal is decomposed into 2N sub-band signals with equal-width frequency bands (M = 2N) to achieve time-frequency localization analysis. Each sub-band signal corresponds to a specific frequency band, which is convenient for subsequent targeted processing. For example: decomposing a 50Hz current signal into 10 sub-bands, each sub-band covering a 5Hz bandwidth, can accurately analyze the characteristics of frequency bands such as 5 - 10Hz and 10 - 15Hz.

[0113] Wavelet decomposition is prone to distortion (such as Gibbs phenomenon) at the signal boundary, resulting in errors in sub-band coefficients. To address this, by performing boundary effect processing on each sub-band signal, the boundary influence is eliminated through the extended signal, enabling the extended signal to smoothly transition with the original signal at the boundary and reducing the decomposition error. The generation method of the extended signal can include: symmetric extension: extrapolating the signal boundary into a symmetric form, which is suitable for periodic signals; mirror extension: extrapolating the boundary into a mirror form, which is suitable for abrupt signals.

[0114] The time-frequency characteristics of different sub-band signals vary greatly, and a single wavelet basis function cannot globally adapt. To address this, an independent wavelet basis function is defined for each sub-band signal to improve the decomposition accuracy. Among them, for low-frequency sub-bands, a wavelet basis with good smoothness (such as Daubechies db4) is selected to avoid high-frequency noise interference; for high-frequency sub-bands, a wavelet basis with good compact support (such as Symlet sym8) is selected to retain abrupt features.

[0115] Regarding how to quantify the similarity between the sub-band signal and the wavelet basis function to extract effective features. To this end, the sub-band coefficients are calculated through inner product operations, so that the sub-band coefficients can project the signal energy onto the corresponding wavelet basis and are used for subsequent classification, reconstruction, or fault diagnosis.

[0116] Specifically, perform N-layer wavelet packet decomposition on at least one wiper current signal to obtain M sub-band signals, including:

[0117] S211: Determine the wavelet order according to the motor characteristics of the brushless motor, and determine the decomposition layer number according to the number of the at least one wiper current signal and the wavelet order; wherein, the number of signals is the number of wiper current signals;

[0118] S212: Use at least one wiper current signal as the input signal, convolve the input signal with a pre-computed filter bank to obtain convolution data, and perform downsampling on the convolution data to obtain the first-layer decomposition signal; use the P-layer decomposition signal as the input signal, convolve the input signal with a pre-computed filter bank to obtain convolution data, and perform downsampling on the convolution data to obtain the (P + 1)-layer decomposition signal; iterate in turn until the N-layer decomposition signal is obtained to obtain M sub-band signals.

[0119] Exemplarily, using the Mallat fast algorithm, the signal is decomposed layer by layer through a low-pass / high-pass filter bank (h[n] / g[n]) to obtain M = 2^N sub-band signals, denoted as {X1, X2,..., XM}, and each sub-band corresponds to a specific frequency band and time-frequency resolution. Among them, the Mallat algorithm realizes the multi-resolution decomposition of the signal through an iterative filter bank. Each decomposition divides the signal into two parts: low-frequency approximation (A) and high-frequency detail (D). For wavelet packet decomposition, all sub-bands of each layer need to be further decomposed to form a binary tree structure; extension (such as periodic extension) or threshold method is used to reduce the boundary effect of the obtained sub-band signal.

[0120] Specifically, perform boundary effect processing on each of the sub-band signals to obtain the extended signal of each sub-band signal, including:

[0121] S221: Perform extension processing on the first sub-band signal to obtain a left extended signal and a right extended signal;

[0122] S222: Perform soft threshold processing on the left extended signal and the right extended signal respectively to obtain a left splicing signal and a right splicing signal;

[0123] S223: Splice the left splicing signal on the left side of the first sub-band signal, and splice the right splicing signal on the right side of the first sub-band signal to obtain the first extended processing signal.

[0124] In this example, due to the truncation effect at the signal boundary, wavelet decomposition causes energy leakage and artifacts (such as Gibbs phenomenon), which affects the accuracy of subsequent feature extraction. Therefore, by means of extension processing, the boundary truncation effect is reduced and the accuracy of wavelet decomposition is improved. Extending the signal may introduce noise and cause distortion of the subband coefficients. Therefore, soft threshold processing effectively suppresses the high-frequency noise in the extended signal and avoids noise interference in feature extraction. Directly splicing the extended signal and the original signal may result in discontinuity and cause boundary mutation. Therefore, splicing processing realizes the smooth transition between the extended signal and the original signal and avoids signal distortion caused by boundary mutation.

[0125] Furthermore, perform extension processing on the first subband signal to obtain a left extended signal and a right extended signal, including:

[0126] Determine the number of extensions according to the number of filters of the wavelet filter;

[0127] Copy the signal points of the number of extensions close to the left boundary in the first subband signal to obtain the left extended signal, and copy the signal points of the number of extensions close to the right boundary in the first subband signal to obtain the right extended signal.

[0128] Furthermore, perform soft threshold processing on the left extended signal and the right extended signal to obtain a left splicing signal and a right splicing signal, including:

[0129] Generate a left soft threshold signal through a preset soft threshold function according to the signal points on the left side of the left extended signal with the number equal to the number of influential points;

[0130] Generate a right soft threshold signal through a preset soft threshold according to the signal points on the right side of the right extended signal with the number equal to the number of influential points;

[0131] Replace the signal points on the left side of the left extended signal with the number equal to the number of influential points with the left soft threshold signal to obtain the left splicing signal; and replace the signal points on the right side of the right extended signal with the number equal to the number of influential points with the right soft threshold signal to obtain the right splicing signal.

[0132] This example takes into account both accuracy and robustness and reduces boundary jumps.

[0133] Exemplarily, process the left extended signal and the right extended signal through a soft threshold function to obtain a left soft threshold signal and a right soft threshold signal.

[0134] Soft Thresholding is a non-linear shrinkage operator in signal processing and statistics, which realizes signal denoising or sparse representation by suppressing small amplitude coefficients and retaining significant features.

[0135] For the input coefficient x, the soft threshold function is as follows:

[0136] y = sign(x) × max(|x| - θ, 0);

[0137] where: x is the current value of the signal point in the left extended signal and the right extended signal, y is the current value of the soft signal point; θ > 0 is the threshold parameter; sign(x) is the sign function;

[0138] When |x| < θ, y = 0 (noise suppression); this means that when the absolute value of the current value of the signal point is less than or equal to the threshold, the soft threshold function sets it to zero.

[0139] When |x| ≥ θ, y = (|x| - θ) × sign(x) (retain the signal but weaken the amplitude). This means that when the absolute value of the current value of the signal point is greater than the threshold, the soft threshold function will shrink it towards zero, and the shrinkage amplitude is θ.

[0140] The soft signal points y that affect the number are aggregated to form a left soft threshold signal or a right soft threshold signal.

[0141] Specifically, a wavelet basis function is defined for each of the sub-band signals, including:

[0142] S231: Determine the energy distribution of the first sub-band signal to obtain the energy distribution data of the first sub-band signal;

[0143] S232: Determine the kurtosis and / or entropy value of the first sub-band signal to obtain the signal feature data of the first sub-band signal;

[0144] S233: Determine the wavelet basis function of the first sub-band signal according to the energy distribution data and the signal feature data.

[0145] In this example, first, a general wavelet basis function (such as Daubechies wavelet, Symlet wavelet, etc.) is initially selected to perform wavelet decomposition on the original signal. According to the signal characteristics and analysis requirements, the decomposition level is determined, and the first sub-band signal is extracted.

[0146] The coefficients of the first sub-band signal are squared and summed to obtain the energy of the first sub-band signal. The energy of the first sub-band is normalized to obtain the energy distribution data of the first sub-band signal.

[0147] Kurtosis is used to measure the peakedness and tail thickness of a probability distribution. Among them, the peakedness reflects the concentration degree of signal points in the first sub-band signal relative to the normal distribution (whether the peak is sharper or flatter), and the tail thickness reflects the probability of extreme values (outliers) of signal points in the first sub-band signal (whether the tail is thicker or thinner). The peakedness describes the concentration degree of the signal near the mean value, reflecting whether the signal is "sharp" or "flat". High peakedness: The signal energy is concentrated near the mean value, and the distribution is narrow (such as the normal distribution). Low peakedness: The signal energy is dispersed, and the distribution is wide (such as the uniform distribution). The tail thickness describes the probability of extreme values (outliers) of signal points in the sub-band signal, reflecting the heavy-tailed or light-tailed nature of the signal. Heavy-tailed distribution: The probability of extreme values of signal points is high (such as the Cauchy distribution, Pareto distribution). Light-tailed distribution: The probability of extreme values of signal points is low (such as the normal distribution).

[0148] The entropy value is used to quantify the uncertainty or information content of signal points in the first sub-band signal. The higher the entropy value, the more complex the fluctuations of the signal in the sub-band (such as containing noise, rapidly changing components); the lower the entropy value, the simpler the signal in the sub-band (such as a single frequency component or a constant value). Among them, the entropy value of signal points in the first sub-band signal is calculated by using the Shannon entropy formula. The sub-band with a high entropy value may contain random noise or chaotic signals. The sub-band with a low entropy value may correspond to a signal with strong regularity (such as a sine wave).

[0149] The kurtosis and / or entropy value is used as the characteristic data of the first sub-band signal for subsequent wavelet basis function selection.

[0150] Optionally, according to the energy distribution data and signal characteristic data, determine the wavelet basis function of the first sub-band signal, including: Step 1: According to the energy distribution data, screen out the wavelet basis functions that can match the energy distribution of the first sub-band signal.

[0151] Step 2: According to the kurtosis and / or entropy value, further screen out the wavelet basis functions that can match the signal characteristics.

[0152] Step 3: Perform performance evaluation (such as reconstruction error, computational complexity, etc.) on the candidate wavelet basis functions, and select the optimal wavelet basis function.

[0153] Among them, for energy distribution matching: Select the wavelet basis function that can better capture the energy distribution of the first sub-band signal. For example, if the energy of the first sub-band signal is concentrated in the low-frequency part, a wavelet basis function with low-frequency characteristics can be selected.

[0154] For kurtosis matching: If the signal kurtosis is high (sharp), a wavelet basis function with a compact support property can be selected, such as the Daubechies wavelet.

[0155] For entropy value matching: If the signal entropy value is high (complex), a wavelet basis function with multi-scale analysis ability can be selected, such as the Symlet wavelet.

[0156] Common wavelet basis functions include:

[0157] Daubechies wavelet (dbN): It has the property of compact support and is suitable for analyzing sharp signals.

[0158] Symlet wavelet (symN): Approximately symmetric and suitable for analyzing complex signals.

[0159] Coiflet wavelet (coifN): It has a high vanishing moment and is suitable for analyzing smooth signals.

[0160] Biorthogonal wavelet (biorNr.Nd): A biorthogonal wavelet suitable for signal reconstruction.

[0161] Specifically, the similarity degree between each of the extended signals and its wavelet basis function is measured through inner product operation to obtain the subband coefficients of each of the subband signals, including:

[0162] S241: Traverse the position information of each first signal point in the first extended signal; wherein, the first extended signal is one of at least one of the extended signals; the first signal point is one of at least one signal point in the first extended signal.

[0163] S242: Calculate the product of the first signal point at the first position information in the first extended signal and the wavelet signal point at the first position in the wavelet basis function of the first extended signal to obtain the product value of the first signal point.

[0164] S243: If it is determined that the product value of the first signal point exceeds the preset product threshold, then take the difference between the product value and the product threshold as the signal energy index of the first signal point.

[0165] S244: If it is determined that the product value of the first signal point does not exceed the product threshold, then take the preset initial value as the signal energy index of the first signal point.

[0166] S245: Aggregate the signal energy indexes of each signal point to obtain an energy index vector, and take the obtained energy index vector as the subband coefficient of the subband signal corresponding to the first extended signal.

[0167] Exemplarily, for the signal traversal mechanism in S241: The SIMD instruction set (such as AVX2) is used to accelerate the traversal process; the edge region of the extended signal (such as the first and last 10% lengths) is double-checked to avoid extension distortion.

[0168] Optimization of the product calculation in S242: Use FFT to implement the frequency-domain product of the wavelet basis and the signal segment, reducing the complexity from O(N²) to O(NlogN); Quantize the floating-point operations (such as INT16), which is suitable for embedded low-power scenarios.

[0169] For the threshold comparison logic in S243 and S244: Reduce the conditional branch overhead by pre-sorting or threshold interval partitioning; Use the absolute value or the square difference, and select according to the application scenario (for example, the square difference is more sensitive to outliers).

[0170] For the energy metric aggregation in S245: Use SIMD instructions to batch process signal points to generate a sub-band coefficient vector; Normalize the energy metric vector with the L2 norm to eliminate the influence of the signal amplitude.

[0171] S203: If it is determined from each of the sub-band coefficients that at least one of the M sub-band signals is a jumping sub-band signal, then adjust the wiping speed of the wiper rod within the jumping angle range corresponding to the jumping sub-band signal to an anti-jumping speed; wherein, the anti-jumping speed is greater than the wiping speed of the wiper in the jumping angle range.

[0172] In this step, when the wiper rod of the wiper wipes the windshield, local jumping of the wiper rod and noise will occur due to stains on the glass surface or aging of the wiper rod rubber strip, and the local jumping of the wiper rod will affect the current signal in the wiper motor, resulting in fluctuations in the current signal.

[0173] In response, by identifying the jumping sub-band signal according to each of the sub-band coefficients and identifying the jumping angle range corresponding to the jumping sub-band signal, it is possible to lock the area on the glass surface where stains cause the wiper rod to jump or the area on the windshield where the wiper rod rubber strip aging causes jumping based on the fluctuations of the motor current signal; By adjusting the wiping speed of the wiper rod within the jumping angle range corresponding to the jumping sub-band signal to the anti-jumping speed, the wiper rod can quickly sweep through the area where the wiper rod jumps, thereby reducing the noise generated by the wiper rod, improving the driving texture of the vehicle, and avoiding the situation of interfering with the driver's attention and affecting driving safety.

[0174] In a preferred embodiment, if it is determined from each of the sub-band coefficients that at least one of the M sub-band signals is a jumping sub-band signal, it includes:

[0175] S31: If it is determined that there is at least one jump energy index among the subband coefficients of the first subband signal, obtain a jump processing rule corresponding to the status information of the wiper; wherein, the first subband signal is one of the M subbands; the status information reflects the working wiping speed of the wiper lever, the current weather condition of the vehicle, and the service life of the wiper lever.

[0176] S32: If it is determined that at least one of the jump energy indices satisfies a preset jump processing rule, determine that the first subband signal is a jump subband signal.

[0177] In this example, for the jump rule acquisition mechanism in S31: Use Kalman filtering to fuse multi-sensor data (speed, rainfall, vibration); use D-S evidence theory to handle the uncertainty of the service life (such as mileage error).

[0178] For the determination of the jump subband in S32: Based on multiple indicators, jointly determine whether at least one of the jump energy indices satisfies a preset jump processing rule.

[0179] The multiple indicators include:

[0180] Time domain index: The variance of three consecutive windows exceeds the threshold.

[0181] Frequency domain index: The energy proportion of a specific frequency band > a predefined pattern (such as the increase in the high-frequency energy proportion during heavy rain).

[0182] Nonlinear index: Entropy value mutation (indicating that the system enters a chaotic state).

[0183] The method for jointly determining whether multiple indicators satisfy the jump processing rule includes: Using Dempster-Shafer theory to fuse the confidence levels of the three indicators; Final determination threshold: Σ confidence level > 0.75; If it is higher than the preset determination, determine that at least one of the jump energy indices satisfies the jump processing rule.

[0184] Specifically, if it is determined that there is at least one jump energy index among the subband coefficients of the first subband signal, obtaining a jump processing rule corresponding to the status information of the wiper includes:

[0185] S311: Calculate the variance of the signal energy index of the subband coefficients of the first subband signal in each time domain to obtain the coefficient variance value of the first subband signal; wherein, the coefficient variance value reflects the fluctuation degree of each signal energy index in the subband coefficients.

[0186] S312: Perform a fast Fourier transform on the subband coefficients of the first subband signal to obtain the energy proportion value of the first subband signal; wherein, the energy proportion value reflects the proportion of the signal energy index in a specific frequency range in the subband coefficients among the subband coefficients.

[0187] S313: If it is determined that the coefficient variance value exceeds a preset variance threshold, or the energy proportion value exceeds a preset proportion preset, it is determined that the first subband signal has at least one jitter energy indicator.

[0188] S314: Acquire the wiping speed of the wiper, and acquire the weather conditions of the current area of the vehicle and the service life of the wiper rod from the vehicle computer system to obtain the status information of the wiper;

[0189] S315: Acquire a beat processing rule corresponding to the state information from a preset rule library.

[0190] In this example, for the coefficient variance value calculation in S311: the subband coefficients of the first subband signal are divided into multiple time windows of equal length (for example, one window every 50 milliseconds) in chronological order, ensuring that each window contains enough data points to reflect the signal characteristics. In each time window, the energy index of the subband coefficient is calculated. The energy index can characterize the strength of the signal segment by calculating the sum of squares (or the sum of absolute values) of all coefficient values in the window. The energy index values of all time windows are counted and their variance is calculated. The larger the variance value, the more drastic the fluctuation of the energy index in different time windows, that is, the worse the stability of the subband coefficient.

[0191] For the calculation of the energy proportion value in S312: perform fast Fourier transform (FFT) on the subband coefficients of the first subband signal, convert the time domain signal into a frequency domain signal, and obtain the amplitude of each frequency component. According to the vibration characteristics of the wiper during normal operation, a specific frequency range is predefined (for example, a narrow band of ±2Hz of the motor base frequency). This frequency range is related to the inherent vibration of the wiper mechanical structure or the motor operating frequency. Calculate the sum of the energies of all frequency components within the specific frequency range and the proportion of the total energy of the entire frequency domain. This proportion is the energy proportion value, which reflects the dominance of a specific frequency component in the signal.

[0192] For the determination of the beating energy index in S313: preset the variance threshold and the proportion threshold. The variance threshold is determined through historical data or experiments, and characterizes the allowable range of energy index fluctuations under normal working conditions; the proportion threshold is set according to the normal vibration characteristics of the wiper, and characterizes the normal energy proportion of a specific frequency component. If the coefficient variance value exceeds the variance threshold, it indicates that the signal energy fluctuates abnormally over time, and intermittent beating may exist. If the energy proportion value exceeds the proportion threshold, it indicates that the energy proportion of a specific frequency component is too high, and periodic beating may exist. If any threshold exceeding condition is met, it is determined that the first sub-band signal has at least one beating energy index, triggering the subsequent processing flow.

[0193] For the acquisition of wiper status information in S314: The current wiping speed (unit: revolutions per minute) is obtained in real time through the wiper motor Hall sensor. The weather data (such as rainfall level or visibility classification) of the current area of the vehicle is obtained from the vehicle head unit system, or the rainfall intensity is sensed in real time through the on-vehicle camera / radar. The vehicle mileage data is read from the vehicle head unit system, and combined with the designed life of the wiper lever, its service life is estimated (for example: assuming an average annual mileage of 20,000 kilometers, the total mileage divided by 20,000 is the service life).

[0194] For the matching of jitter processing rules in S315: A rule library is preset, which contains jitter processing rules corresponding to different combinations of wiper status (wiping speed, weather conditions, service life). The rules include: The jitter determination threshold adjustment strategy under different states (for example: a higher variance threshold is allowed during high-speed wiping). The warning level or maintenance suggestions when jitter occurs (for example: when jitter is detected in a heavy rain environment, it is preferred to prompt an inspection of the wiper lever connection parts). According to the real-time obtained wiper status information, the matching jitter processing rules are retrieved in the rule library. If there are multiple matching rules, they are loaded according to the priority (for example: the rules in bad weather have a higher priority than the rules based on service life).

[0195] Specifically, if it is determined that at least one of the jitter energy indicators meets the preset jitter processing rules, it is determined that the first sub-band is a jitter sub-band signal, including:

[0196] S321: If it is determined that at least one of the jitter energy indicators has at least one target energy indicator, calculate the number of the target energy indicators; wherein, the target energy indicator refers to the jitter energy indicator that exceeds the indicator threshold in the jitter processing rules;

[0197] S322: If it is determined that the number of the target energy indicators exceeds the number threshold in the jitter processing rules, it is determined that the first sub-band is a jitter sub-band signal.

[0198] In this example, for the calculation of the target energy index quantity in S321: According to the method described above (S311 - S313), the coefficient variance value and the energy occupancy ratio of the first sub - band signal have been obtained. These two indexes respectively characterize the fluctuation characteristics of the signal energy in the time domain and the frequency domain, and jointly constitute the beating energy index system. From the preset beating processing rules (S315), obtain the coefficient variance threshold and the energy occupancy ratio threshold for the current wiper state (wiper speed, weather condition, service life). For example: Exemplarily, in a heavy rain environment (rainfall level 4), high - speed wipering (60 rpm), and service life > 3 years, the variance threshold is 1.5 and the occupancy ratio threshold is 25%. Compare the actually calculated coefficient variance value with the variance threshold, and at the same time compare the energy occupancy ratio with the occupancy ratio threshold: Single - index exceeding the standard: If only the variance or only the occupancy ratio exceeds the corresponding threshold, it is counted as 1 target energy index. Double - index exceeding the standard: If both the variance and the occupancy ratio exceed the threshold, it is counted as 2 target energy indexes. Count the number of all exceeding target energy indexes. For example: In a certain detection, if the variance exceeds the standard and the occupancy ratio does not exceed the standard, the number of target energy indexes is 1; if both the variance and the occupancy ratio exceed the standard, the number is 2.

[0199] For the determination of the beating sub - band signal in S322: A preset quantity threshold (for example: 2) in the beating processing rules. This threshold is determined by historical data or experiments and represents the minimum number of exceeding indexes required to determine a beat. If the quantity of target energy indexes ≥ the quantity threshold (such as 2), then determine that the first sub - band signal is a beating sub - band signal. If the quantity < the threshold (such as 1), it is not determined as a beat for the time being, but the current index value is recorded for trend analysis. Introduce a time - dimension accumulation mechanism: If in 3 consecutive detections, there are 2 times when the quantity of target energy indexes ≥ 1, then even if the quantity does not reach the threshold in a single detection, it is still determined as a beating sub - band signal. This mechanism is used to capture intermittent beats.

[0200] In a preferred embodiment, adjusting the wiper speed in the beating angle range corresponding to the beating sub - band signal to the anti - beating speed includes:

[0201] S33: Set at least one wiper angle range corresponding to the beating sub - band signal as the beating angle range;

[0202] S34: In the beating angle range, use a PID controller to adjust the wiper speed of the wiper rod to the anti - beating speed.

[0203] In this example, for the set jitter angle range in S33: After detecting the jitter sub-band signal through the method described above (S31 - S32), further analyze the spatio-temporal characteristics of the signal. Since the wiper's wiping motion is periodic, a complete wiping cycle (from the lowest point through the highest point and back to the lowest point) can be divided into multiple angle intervals (for example: every 10 degrees as an interval). Count the occurrence frequency or energy intensity of the jitter sub-band signal in different angle intervals. If the occurrence frequency of the jitter sub-band signal in a certain angle interval exceeds a preset threshold (for example: the jitter is detected in 80% of the time windows in this interval), or the jitter energy index is significantly higher than other intervals, then this interval is determined as the jitter-related angle interval. If multiple consecutive angle intervals are determined as jitter-related intervals, they are merged into the jitter angle range. For example: If jitter is detected in two consecutive intervals of 20° - 50° and 50° - 80°, the merged jitter angle range is 20° - 80°.

[0204] For the PID control anti-jitter speed adjustment in S34: Pre-define an anti-jitter speed curve, which is designed according to the jitter angle range. For example: Low-speed area: Within the jitter angle range, reduce the wiping speed to 60% - 80% of the normal speed. Smooth transition: When entering and exiting the jitter angle range, set a speed gradual change interval (for example: 5° in advance / lag), to avoid new jitters caused by sudden speed changes.

[0205] For the design of the PID controller: Control objective: Make the actual wiping speed of the wiper rod track the anti-jitter speed curve within the jitter angle range. Input quantity: The difference between the current wiping speed (obtained in real time through the motor encoder) and the anti-jitter speed curve. Output quantity: The adjustment quantity output by the PID controller, used to control the motor drive voltage or PWM duty cycle, thereby changing the wiping speed.

[0206] Parameter tuning of the PID controller: Proportional term (P): Quickly respond to the speed deviation, but too large will cause overshoot. Integral term (I): Eliminate the steady-state error, but too large will lead to integral saturation. Derivative term (D): Suppress overshoot and improve system stability, but is sensitive to noise.

[0207] Optionally, according to the historical control effect (such as jitter suppression rate, speed tracking error), use a simple adaptive algorithm (such as MIT Rule) to online adjust the PID parameters.

[0208] Pre-set PID parameter groups under different weather / speed conditions, and switch the control mode according to real-time state information (such as rainfall level, vehicle speed).

[0209] Specifically, setting the wiper angle range corresponding to at least one jitter sub-band signal as the jitter angle range includes:

[0210] S331: Obtain at least one timing signal segment corresponding to the first jumping sub-band signal; wherein, the first jumping sub-band signal is one of at least one of the jumping sub-band signals; the timing signal segment is a time interval of the jumping sub-band signal in the wiper cycle; the wiper cycle is the time period for the wiper rod to complete one wiping action on the windshield.

[0211] S332: Determine the wiper angle range of each jumping sub-band signal according to the position of the timing signal segment of each jumping sub-band signal in the wiper cycle.

[0212] In this example, the wiper cycle is the time period for the wiper rod to complete one wiping action on the windshield. This time period is an important parameter of the wiper's working characteristics and determines the ability of the wiper to remove rainwater, dust, and dirt on the windshield under specific conditions. During the wiper cycle, the jumping sub-band signal may appear in different time intervals. By obtaining the timing signal segment corresponding to the jumping sub-band signal, the occurrence position of the jumping phenomenon in the wiper cycle can be accurately located, providing data support for subsequent jumping analysis and suppression.

[0213] The position of the jumping sub-band signal in the wiper cycle reflects the time point when the wiper encounters the jumping phenomenon during the wiping process. By analyzing the position of the timing signal segment of the jumping sub-band signal in the wiper cycle, the wiper angle range corresponding to the jumping phenomenon can be determined. The wiper angle range refers to the interval in which the wiper angle deviates from the normal range due to the jumping phenomenon during the wiping process. By determining the wiper angle range, important information can be provided for subsequent jumping suppression and wiper control.

[0214] Please refer to Figure 3 , Figure 3 which shows the wiper angle on each timing signal (Time) and the wiper current signal on each timing signal (Time); in Figure 3 , the timing signal segment corresponding to the jumping sub-band signal is the jumping area, and the wiper angle range corresponding to the jumping area is represented by Angle1 - Angle2.

[0215] Furthermore, obtaining the timing signal segment corresponding to the first jumping sub-band signal includes:

[0216] Using a sensor or signal acquisition device, identify the current value, time point, and wiper angle of each wiper current signal during the wiping process of the wiper.

[0217] Extract the jumping time points of each wiper current signal in the first jumping sub-band signal, and summarize at least one adjacent jumping time point to obtain a timing signal segment of the first jumping sub-band signal.

[0218] Specifically, within the range of the jitter angle, a PID controller is used to adjust the wiper bar's wiping speed to the anti-jitter speed, including:

[0219] S341: Obtain the current wiping speed of the wiper bar from the wiper and set the obtained wiping speed as the reference speed;

[0220] S342: Determine the anti-jitter calculation control quantity of the PID controller according to the status information of the wiper;

[0221] S343: Adjust the reference speed according to the anti-jitter calculation control quantity to obtain the anti-jitter speed;

[0222] S344: Output the anti-jitter speed to the wiper, so that the wiper adjusts the wiping speed of the wiper bar to the anti-jitter speed.

[0223] In this example, the wiping speed of the wiper can usually be adjusted through the control device in the vehicle, such as toggling the windshield switch or adjusting the joystick. The operation methods of different vehicle models may vary, but usually there are clear speed gear markings, such as slow, medium, fast, etc. By reading the status of these control devices, the current wiping speed of the wiper bar can be obtained. The obtained wiping speed will be used as the reference speed of the PID controller. This reference speed will be used in the subsequent adjustment process to ensure that the wiper bar maintains a stable wiping speed within the jitter angle range. By obtaining the current wiping speed of the wiper bar and setting it as the reference speed, a stable reference point can be provided for the PID controller. This reference point will be used to calculate the control quantity to adjust the wiping speed of the wiper bar, thereby suppressing the occurrence of the jitter phenomenon.

[0224] The status information of the wiper usually includes its working modes, such as stop (OFF), intermittent (INT), continuous (LO), and high-speed (HI) working states. These information can be identified by letters or icons on the wiper, or obtained by reading the signals of the vehicle control unit. According to the status information of the wiper, the target value or set value of the PID controller can be determined. For example, in the intermittent working state, the PID controller may need to adjust the wiping speed according to the rainfall amount; in the continuous or high-speed working state, the PID controller may need to maintain a stable wiping speed to suppress jitter. The status information of the wiper reflects its current working mode and conditions, such as rainfall amount, vehicle speed, etc. These information are crucial for determining the calculation control quantity of the PID controller. By reasonably using this information, the PID controller can more accurately adjust the wiping speed of the wiper bar, thereby more effectively suppressing the jitter phenomenon.

[0225] The PID controller adjusts the reference speed according to the calculated control amount. This adjustment process may involve the combined action of three parameters: proportional, integral, and derivative, to ensure the stability and accuracy of the adjustment process. By adjusting the reference speed, the wiping speed that the wiper lever should maintain within the jitter angle range can be obtained, that is, the anti-jitter speed. This speed will be used to suppress the occurrence of jitter phenomena, improve the working efficiency of the wiper system and driving safety. Through the combined action of the three parameters of proportional, integral, and derivative, the PID controller can accurately adjust the reference speed according to the calculated control amount. This adjustment process will keep the wiping speed of the wiper lever at the anti-jitter speed within the jitter angle range, thus effectively suppressing the jitter phenomenon.

[0226] Output the calculated anti-jitter speed to the actuator of the wiper, such as a motor or a transmission device. This output process can be achieved through the vehicle's electrical system or electronic control unit. The wiper adjusts the wiping speed of the wiper lever according to the received anti-jitter speed signal. This adjustment process will ensure that the wiper lever maintains a stable wiping speed within the jitter angle range, thus suppressing the occurrence of jitter phenomena. By outputting the anti-jitter speed to the actuator of the wiper and adjusting the wiping speed of the wiper lever, the wiper system can work more stably. This will improve the working efficiency of the wiper system and driving safety, especially under conditions where jitter phenomena are likely to occur.

[0227] Please refer to Figure 4 , Figure 4 It is the relationship between the curve of the wiper angle and the curve of the wiping angle on each timing signal (Time) in the jitter processing method of the wiper of the present invention, as well as the jitter angle range and non-jitter angle range in the wiper angle, and the anti-jitter speed and wiper matching speed in the wiping speed.

[0228] S204: Adjust the wiping speed of the wiper lever on at least one non-jitter angle range to the wiper matching speed; wherein, the non-jitter angle range refers to other wiper angle ranges in the overall wiper angle range of the wiper lever on the windshield except the jitter angle range; the wiper matching speed is less than the wiping speed of the wiper lever on the non-jitter angle range.

[0229] In this step, by setting the wiping speed of the wiper lever on at least one non-jitter angle range to the wiper matching speed, the wiping speed of the wiper lever on the non-jitter angle range is reduced to ensure that the wiping cycle of the wiper lever on the windshield remains unchanged, that is: the wiping cycle of the wiper lever adjusted to the anti-jitter speed and the wiper matching speed is the same as the wiping cycle of the wiper lever before the wiping speed is adjusted, avoiding the problem of the wiping cycle of the wiper being chaotic due to adjusting the wiping speed of the wiper lever.

[0230] Please refer to Figure 4, Figure 4 It is the relationship between the curve reflecting the wiper angle and the curve of the wiping angle on each timing signal (Time) in the wiper jitter processing method of the present invention, as well as the jitter angle range and non-jitter angle range in the wiper angle, and the anti-jitter speed and wiper matching speed in the wiping speed.

[0231] In a preferred embodiment, setting the wiping speed of the wiper lever in at least one non-jitter angle range to the wiper matching speed includes:

[0232] S41: In the non-jitter wiping speed range, use a PID controller to adjust the wiping speed of the wiper lever to the wiper matching speed;

[0233] S42: Set a jitter-in angle range and a jitter-out angle range on both sides of one of the non-jitter angle ranges close to the jitter angle range;

[0234] S43: Based on the first speed curve, gradually change the wiping speed of the wiper lever in the jitter-in angle range from the wiper matching speed to the anti-jitter speed; wherein, the first speed curve is any one of an S-shaped speed curve, a T-shaped speed curve, an exponential speed curve, and a trigonometric function speed curve;

[0235] S44: Based on the second speed curve, gradually change the wiping speed of the wiper lever in the jitter-out angle range from the anti-jitter speed to the wiper matching speed; wherein, the second speed curve is any one of an S-shaped speed curve, a T-shaped speed curve, an exponential speed curve, and a trigonometric function speed curve.

[0236] In this example, for the PID control of the non-jitter speed range in S41: Pre-define the non-jitter wiping speed range according to the characteristics of the wiper system (for example: 40 rpm - 80 rpm). This range is determined by historical data or experiments, indicating that the wiper can operate stably within this speed range without the risk of jitter.

[0237] Calculate the wiper matching speed by integrating the following factors: Rainfall level: Obtained through the vehicle system or rain sensor (for example, light rain corresponds to 50 rpm, heavy rain corresponds to 70 rpm). Vehicle speed compensation: The higher the vehicle speed, the appropriate increase in the wiping speed is required to ensure the clarity of the field of vision. Decay due to service life: Old wiper levers (service life > 3 years) need to reduce the speed to avoid overload. For the implementation of PID control: Target value setting: Set the wiper matching speed as the target value of the PID controller. Feedback mechanism: Obtain the current wiping speed in real time through the motor encoder as the feedback value. Control output: The PID controller calculates the adjustment amount according to the speed deviation and outputs it to the motor driver to adjust the voltage or PWM duty cycle to make the actual speed approach the target value.

[0238] For the setting of the incoming shake / outgoing shake angle range in S42: At the junction of the jumping angle range (such as 30° - 70°) and the non - jumping angle range (such as 0° - 30° and 70° - 180°), set the incoming shake angle range (such as 25° - 30°) and the outgoing shake angle range (such as 70° - 75°) respectively. The width of the incoming shake / outgoing shake angle range needs to balance the control smoothness and response speed. If the width is too small, it may lead to insufficient speed gradual change; if the width is too large, it may affect the wiping efficiency. The typical value is set to 5° - 10°.

[0239] For the speed gradual change of the incoming shake angle range in S43 (based on the first speed curve): Select one of the S - shaped, T - shaped, exponential, or trigonometric function speed curves to achieve a smooth transition of the wiping speed from the wiper - matching speed (such as 70 rpm) to the anti - jumping speed (such as 56 rpm). Among them, for the S - shaped curve: The Sigmoid function is adopted, and the speed change rate is slow first, then fast, and then slow again, which is suitable for scenarios that require a smooth transition. For the T - shaped curve: The speed change rate is trapezoidal, and the middle section maintains a constant change rate, which is suitable for scenarios with high real - time requirements. For the exponential curve: The speed approaches the target value according to the exponential law, with a fast change at the beginning and a slow change later. For the trigonometric function curve: Utilize the characteristics of the sine / cosine function to achieve a periodic smooth transition. Within the incoming shake angle range, calculate the target speed corresponding to the current angle in real - time according to the selected curve, and use the PID controller to track this target speed to achieve speed gradual change.

[0240] For the speed gradual change of the outgoing shake angle range in S44 (based on the second speed curve): Similar to the incoming shake angle range, select one of the S - shaped, T - shaped, exponential, or trigonometric function speed curves to achieve a smooth transition of the wiping speed from the anti - jumping speed (such as 56 rpm) back to the wiper - matching speed (such as 70 rpm). To ensure the smoothness of the control process, the speed curve adopted in the outgoing shake angle range should be symmetric with that in the incoming shake angle range. For example: If the incoming shake uses an S - shaped curve, the outgoing shake also uses an S - shaped curve but in the opposite direction. Within the outgoing shake angle range, calculate the target speed corresponding to the current angle in real - time according to the selected curve, and use the PID controller to track this target speed to achieve speed recovery.

[0241] Specifically, within the non - jumping wiping speed range, use the PID controller to adjust the wiping speed of the wiper rod to the wiper - matching speed, including:

[0242] S411: Obtain the current wiping speed of the wiper rod from the wiper and set the obtained wiping speed as the reference speed;

[0243] S412: Determine the matching calculation control quantity of the PID controller according to the status information of the wiper;

[0244] S413: Adjust the reference speed according to the calculated control quantity for matching, and obtain the wiper matching speed;

[0245] S414: Output the wiper matching speed to the wiper, so that the wiper adjusts the wiping speed of the wiper rod to the wiper matching speed.

[0246] In this embodiment, within the non-jumping wiping speed range, the PID controller adjusts the wiping speed of the wiper rod according to the state information of the wiper, such as the amount of rainfall, vehicle speed, etc. The PID controller measures the error between the current wiping speed of the wiper rod and the wiper matching speed, and calculates the control quantity using the three parameters of proportional, integral, and differential to adjust the wiping speed of the wiper rod to keep it at the wiper matching speed. The PID controller is a classic control algorithm, consisting of three parts: proportional, integral, and differential. The proportional term is used to quickly respond to the error, the integral term is used to eliminate the steady-state error, and the differential term is used to predict the change trend of the error to improve the system stability. Under non-jumping conditions, the PID controller can accurately adjust the wiping speed of the wiper rod to maintain the wiper matching speed within the non-jumping wiping speed range.

[0247] Specifically, an incoming shake angle range and an outgoing shake angle range are respectively set on both sides of a non-jumping angle range close to the jumping angle range, including:

[0248] S421: Determine the incoming shake angle value of the incoming shake angle range and the outgoing shake angle value of the outgoing shake angle range according to the state information of the wiper;

[0249] S422: In the area where the wiper rod is about to enter the jumping angle range from the non-jumping angle range, set an incoming shake angle range with a range difference of the incoming shake angle value;

[0250] S423: According to the outgoing shake angle value, in the area where the wiper rod just enters the non-jumping angle range from the jumping angle range, set an outgoing shake angle range with a range difference of the outgoing shake angle value.

[0251] Specifically, based on the first speed curve, gradually change the wiping speed of the wiper rod in the incoming shake angle range from the wiper matching speed to the anti-jumping speed, including:

[0252] S431: Take the wiper matching speed as the starting speed, and the anti-jumping speed as the target speed, and calculate the speed difference between the target speed and the starting speed;

[0253] S432: Based on the S-shaped speed curve, generate an S-shaped speed adjustment strategy according to the incoming shake angle range, the starting speed, and the target speed;

[0254] S433: Based on the T-shaped speed curve, generate a T-shaped speed adjustment strategy according to the input jitter angle range, the starting speed, and the target speed;

[0255] S434: Based on the exponential speed curve, generate an exponential speed adjustment strategy according to the input jitter angle range, the starting speed, and the target speed;

[0256] S435: Based on the trigonometric function speed curve, generate a trigonometric function speed adjustment strategy according to the input jitter angle range, the starting speed, and the target speed;

[0257] S436: When it is monitored that the wiper lever enters the input jitter angle range, execute any one of the S-shaped speed adjustment strategy, the T-shaped speed adjustment strategy, the exponential speed adjustment strategy, and the trigonometric function speed adjustment strategy to make the wiper speed transition from the wiper matching speed to the anti-jump speed.

[0258] Specifically, based on the second speed curve, gradually changing the wiper speed of the wiper lever in the output jitter angle range from the anti-jump speed to the wiper matching speed includes:

[0259] S431: Take the anti-jump speed as the starting speed, and take the wiper matching speed as the target speed, and calculate the speed difference between the target speed and the starting speed;

[0260] S432: Based on the S-shaped speed curve, generate an S-shaped speed adjustment strategy according to the input jitter angle range, the starting speed, and the target speed;

[0261] S433: Based on the T-shaped speed curve, generate a T-shaped speed adjustment strategy according to the input jitter angle range, the starting speed, and the target speed;

[0262] S434: Based on the exponential speed curve, generate an exponential speed adjustment strategy according to the input jitter angle range, the starting speed, and the target speed;

[0263] S435: Based on the trigonometric function speed curve, generate a trigonometric function speed adjustment strategy according to the input jitter angle range, the starting speed, and the target speed;

[0264] S436: When it is monitored that the wiper lever enters the input jitter angle range, execute any one of the S-shaped speed adjustment strategy, the T-shaped speed adjustment strategy, the exponential speed adjustment strategy, and the trigonometric function speed adjustment strategy to make the wiper speed transition from the anti-jump speed to the wiper matching speed.

[0265] In this example, the S-type speed curve presents an "S" shape, with the rate of change gradually increasing first, then flattening, and finally gradually decreasing, forming a smooth transition. This curve can ensure the continuous change of the wiper rod speed during the wiping process, avoiding the impact of sudden speed changes on the wiper system and driving safety. The S-type speed curve is suitable for occasions with high requirements for smoothness, such as mobile warehousing, robot movement, precision machining machine tools, etc. In the wiper system, it can provide a stable and continuous wiping speed to improve driving safety.

[0266] The T-type speed curve is similar in shape to a "T", with a higher starting torque and a lower starting current, which can provide a sufficiently large torque for the load. However, its acceleration is discontinuous, and there is a sudden change at the junction of the acceleration and deceleration stage and the uniform speed stage. The T-type speed curve is suitable for occasions that require rapid start and acceleration, such as some industrial equipment, machine tools, etc. In the wiper system, although there is a problem of discontinuous acceleration, gradual speed control can still be achieved through reasonable control algorithms and parameter adjustments.

[0267] The exponential speed curve is a nonlinear curve, where the speed changes with time and shows an exponential growth or decay trend. This curve has the advantage of good smoothness and can achieve continuous speed changes. The exponential speed curve is suitable for occasions with high requirements for smoothness, such as precision machinery, automation equipment, etc. In the wiper system, it can provide a stable and continuous wiper speed and improve driving safety.

[0268] Trigonometric function speed curves, such as sine function and cosine function, have the characteristics of good periodicity and smoothness. By adjusting the parameters of the trigonometric function, speed curves of different shapes can be achieved to meet different control requirements. The trigonometric function speed curve is suitable for occasions that require periodic changes or smooth transitions. In the wiper system, it can provide a stable and continuous wiper speed while avoiding the jitter problem caused by sudden changes in speed.

[0269] Example 3: Please refer to Figure 5 A wiper vibration processing device 5 of this embodiment is installed on a wiper and runs the above-mentioned wiper vibration processing method; the wiper rod of the wiper is used to wipe the windshield of the vehicle;

[0270] The jitter processing device 5 comprises:

[0271] The sampling module 51 is used to sample the current signal in the brushless motor to obtain at least one wiper current signal.

[0272] The decomposition module 52 is used to perform wavelet packet decomposition on at least one wiper current signal to obtain M sub-band signals and a sub-band coefficient of each sub-band signal; wherein the sub-band coefficient is used to characterize the energy intensity of the sub-band signal.

[0273] An anti-jump module 53, configured to adjust the wiper speed of the wiper rod within the jump angle range corresponding to the jump sub-band signal to an anti-jump speed if at least one jump sub-band signal is determined among the M sub-band signals according to each of the sub-band coefficients; wherein, the anti-jump speed is greater than the wiper speed of the wiper within the jump angle range.

[0274] Optionally, the jump processing device 5 further includes:

[0275] A matching module 54, configured to adjust the wiper speed of the wiper rod within at least one non-jump angle range to a wiper matching speed; wherein, the non-jump angle range refers to other wiper angle ranges in the overall wiper angle range of the wiper rod on the windshield except the jump angle range; the wiper matching speed is less than the wiper speed of the wiper rod within the non-jump angle range.

[0276] In Figure 6 a wiper 61, the jump processing device 5 is installed in the wiper 61, and a wiper rod 62 of the wiper 61 is configured to wipe the windshield 63 of the vehicle.

[0277] Embodiment 4: To achieve the above object, the present invention further provides an electronic device 7. The components of the jump processing device in Embodiment 3 can be dispersed in different electronic devices. The electronic device 7 can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server or a cabinet server (including an independent server or a server cluster composed of multiple application servers) that executes a program. The electronic device in this embodiment at least includes, but is not limited to, a memory 71 and a processor 72 that can communicate with each other through a system bus, as Figure 7 shown. It should be noted that Figure 7 only an electronic device with components - is shown, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented.

[0278] In this embodiment, the memory 71 (i.e., the readable storage medium) includes flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disc, etc. In some embodiments, the memory 71 may be an internal storage unit of the electronic device, such as the hard disk or memory of the electronic device. In other embodiments, the memory 71 may also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc., equipped on the electronic device. Of course, the memory 71 may also include both the internal storage unit and the external storage device of the electronic device. In this embodiment, the memory 71 is generally used to store the operating system and various application software installed on the electronic device, such as the program code of the jitter processing device in Embodiment 3. In addition, the memory 71 may also be used to temporarily store various data that have been output or will be output.

[0279] In some embodiments, the processor 72 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 72 is generally used to control the overall operation of the electronic device. In this embodiment, the processor 72 is used to run the program code stored in the memory 71 or process data, such as running the jitter processing device to implement the jitter processing methods in Embodiment 1 and Embodiment 2.

[0280] Embodiment 5: To achieve the above object, the present invention also provides a computer-readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disc, server, App application mall, etc., on which a computer program is stored. When the program is executed by the processor 72, the corresponding functions are implemented. The computer-readable storage medium of this embodiment is used to store the computer program for implementing the jitter processing method, and when executed by the processor 72, it implements the jitter processing methods in Embodiment 1 and Embodiment 2.

[0281] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0282] Through the description of the above embodiments, those skilled in the art can clearly understand that the above method of the embodiment can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases, the former is a better implementation method.

[0283] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied to other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for processing the jitter of a windshield wiper, characterized in that, The wiper is installed on a vehicle, and the wiper rod of the wiper is used to wipe the windshield of the vehicle; The jitter processing method includes: Sampling the current signal in the brushless motor to obtain at least one wiper current signal; Performing wavelet packet decomposition on at least one wiper current signal to obtain M sub-band signals and the sub-band coefficients of each sub-band signal; wherein, the sub-band coefficients are used to characterize the energy intensity on the sub-band signals; If it is determined according to each sub-band coefficient that at least one of the M sub-band signals is a jitter sub-band signal, then adjust the wiping speed of the wiper rod in the jitter angle range corresponding to the jitter sub-band signal to an anti-jitter speed; wherein, the anti-jitter speed is greater than the wiping speed of the wiper in the jitter angle range; Adjust the wiping speed of the wiper rod in at least one non-jitter angle range to a wiper matching speed; wherein, the non-jitter angle range refers to other wiper angle ranges in the overall wiper angle range of the wiper rod on the windshield except the jitter angle range; the wiper matching speed is less than the wiping speed of the wiper rod in the non-jitter angle range.

2. The jitter processing method according to claim 1, wherein Sampling the current signal in the brushless motor to obtain at least one wiper current signal, including: Obtaining the PWM synchronization signal of the motor driver of the brushless motor; wherein, the PWM synchronization signal is a current signal used to adjust the motor speed and torque; Triggering the sampling of the current signal in the brushless motor according to the PWM synchronization signal to obtain at least one current original acquisition signal; Filtering at least one of the current original acquisition signals to obtain at least one of the wiper current signals.

3. The beating processing method according to claim 1, characterized in that, Performing wavelet packet decomposition on at least one wiper current signal to obtain M sub-band signals and the sub-band coefficients of each sub-band signal, including: Performing N-layer wavelet packet decomposition on at least one wiper current signal to obtain M sub-band signals; Performing boundary effect processing on each sub-band signal respectively to obtain the extended signal of each sub-band signal; Defining a wavelet basis function for each sub-band signal; Measuring the similarity between each extended signal and its wavelet basis function through inner product operation to obtain the sub-band coefficient of each sub-band.

4. The jitter processing method according to claim 1, wherein If it is determined according to each sub-band coefficient that at least one of the M sub-band signals is a jitter sub-band signal, including: If it is determined that at least one jitter energy index exists in the sub-band coefficients of the first sub-band signal, then obtain the jitter processing rule corresponding to the state information of the wiper; wherein, the first sub-band signal is one of the M sub-bands; the state information reflects the working wiping speed of the wiper rod, the current weather condition of the vehicle, and the service life of the wiper rod; If it is determined that at least one of the jitter energy indexes meets the preset jitter processing rule, then determine the first sub-band signal as the jitter sub-band signal.

5. The beating processing method according to claim 1, wherein Adjusting the wiping speed of the wiper in the jitter angle range corresponding to the jitter sub-band signal to an anti-jitter speed, including: Setting the wiper angle range corresponding to at least one jitter sub-band signal as the jitter angle range; Within the described jitter angle range, a PID controller is used to adjust the wiping speed of the wiper lever to the anti-jitter speed.

6. The beating processing method according to claim 1, characterized in that Setting the wiping speed of the wiper lever in at least one non-jitter angle range to the wiper matching speed includes: Within the non-jitter wiping speed range, a PID controller is used to adjust the wiping speed of the wiper lever to the wiper matching speed; An incoming jitter angle range and an outgoing jitter angle range are respectively set on both sides of one of the non-jitter angle ranges close to the jitter angle range; Based on the first speed curve, the wiping speed of the wiper lever in the incoming jitter angle range is gradually changed from the wiper matching speed to the anti-jitter speed; wherein, the first speed curve is any one of an S-shaped speed curve, a T-shaped speed curve, an exponential speed curve, and a trigonometric function speed curve; Based on the second speed curve, the wiping speed of the wiper lever in the outgoing jitter angle range is gradually changed from the anti-jitter speed to the wiper matching speed; wherein, the second speed curve is any one of an S-shaped speed curve, a T-shaped speed curve, an exponential speed curve, and a trigonometric function speed curve.

7. A jitter processing device for a windshield wiper, characterized in that, Installed on the wiper and running the jitter processing method according to any one of claims 1-6; the wiper lever of the wiper is used to wipe the windshield of the vehicle; The jitter processing device includes: A sampling module for sampling the current signal in the brushless motor to obtain at least one wiper current signal; A decomposition module for performing wavelet packet decomposition on at least one wiper current signal to obtain M sub-band signals and the sub-band coefficients of each sub-band signal; wherein, the sub-band coefficients are used to characterize the energy intensity on the sub-band signals; An anti-jitter module for, if it is determined according to each sub-band coefficient that there is at least one jitter sub-band signal among the M sub-band signals, adjusting the wiping speed of the wiper lever in the jitter angle range corresponding to the jitter sub-band signal to the anti-jitter speed; wherein, the anti-jitter speed is greater than the wiping speed of the wiper in the jitter angle range.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor of the electronic device executes the computer program, the steps of the jitter processing method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, on which a computer program is stored, characterized in that, When the computer program stored in the readable storage medium is executed by the processor, the steps of the jitter processing method according to any one of claims 1 to 6 are implemented.

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