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

Abstract

The invention discloses a jumping processing method and device for a windscreen wiper, electronic equipment and a readable storage medium, and the method comprises the steps: sampling a current signal in a brushless motor, and obtaining at least one windscreen wiper current signal; performing wavelet packet decomposition on at least one windscreen 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 for representing the energy intensity on the sub-band signal; if it is determined that at least one jumping sub-band signal exists in the M sub-band signals according to each sub-band coefficient, the wiping speed of the windscreen wiper rod in the jumping angle range corresponding to the jumping sub-band signal is adjusted to be an anti-jumping speed; wherein the anti-jumping speed is greater than the wiping speed of the windscreen wiper in a jumping angle range. The noise generated by the windscreen wiper rod is reduced, the driving texture of a vehicle is improved, and the situation that attention of a driver is disturbed, and driving safety is affected is avoided.

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 microscopic texture of the rubber strip forms multiple-point uniform contacts with the micro-protrusions on the glass surface, and the vibration energy is efficiently dissipated through the viscoelastic rubber strip.

[0003] However, when there is stain deposition on the glass surface, the oil film / particle pollutants form 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 have an instantaneous bounce at a certain specific windshield wiper angle and generating noise, 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 has an instantaneous bounce at a certain specific windshield wiper angle and generates noise, 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; The bounce processing method includes: Sampling the current signal in the brushless motor to obtain at least one windshield wiper current signal; 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; If it is determined that at least one bounce sub-band signal exists in the M sub-band signals according to each sub-band coefficient, 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.

[0006] In the above solution, sampling the current signal in the brushless motor to obtain at least one windshield wiper current signal includes: 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; 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; Filter at least one of the original current acquisition signals to obtain at least one of the wiper current signals.

[0007] In the above solution, 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 includes: Perform N-layer wavelet packet decomposition on at least one wiper current signal to obtain M sub-band signals; Perform boundary effect processing on each sub-band signal respectively to obtain the extended signal of each sub-band signal; Define a wavelet basis function for each sub-band signal; Measure the similarity degree between each extended signal and its wavelet basis function through inner product operation to obtain the sub-band coefficient of each sub-band.

[0008] In the above solution, if it is determined that there is at least one jumping sub-band signal among the M sub-band signals according to each sub-band coefficient, it includes: If it is determined that there is at least one jumping energy index in the sub-band coefficient of the first sub-band signal, then 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 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 jumping energy indexes meets the preset jumping processing rule, then determine the first sub-band signal as the jumping sub-band signal.

[0009] In the above solution, adjusting the wiping speed of the wiper within the jumping angle range corresponding to the jumping sub-band signal to the anti-jumping speed includes: Set the wiper angle range corresponding to at least one jumping sub-band signal as the jumping angle range; Within the jumping angle range, use a PID controller to adjust the wiping speed of the wiper rod to the anti-jumping speed.

[0010] In the above solution, after 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 method further includes: Adjust the wiping speed of the wiper rod within at least one non-jumping angle range to the wiper matching speed; wherein, the non-jumping angle range refers to the other wiper angle ranges in the overall wiper angle range of the wiper rod on the windshield except the jumping angle range; the wiper matching speed is less than the wiping speed of the wiper rod within the non-jumping angle range.

[0011] In the above solution, setting the wiping speed of the wiper rod within at least one non-jumping angle range to the wiper matching speed includes: Within the non-jumping wiping speed range, use a PID controller to adjust the wiping speed of the wiper rod to the wiper matching speed; On both sides of one non-jumping angle range close to the jumping angle range, respectively set an entering shake angle range and an exiting shake angle range; Based on a first speed curve, gradually change the wiping speed of the wiper rod within the entering shake angle range from the wiper matching speed to the anti-jumping 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 a second speed curve, gradually change the wiping speed of the wiper rod within the exiting shake angle range from the anti-jumping 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.

[0012] To achieve the above object, the present invention further provides a jumping processing device for a wiper, which is installed on the wiper and runs the above-mentioned jumping processing method of the wiper; the wiper rod of the wiper is used to wipe the windshield of the vehicle; The jumping processing device includes: A sampling module, configured to sample the current signal in the brushless motor to obtain at least one wiper current signal; A decomposition module, configured to 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; wherein, the sub-band coefficients are used to characterize the energy intensity on the sub-band signals; An anti-jumping module, configured to, if it is determined according to each sub-band coefficient that there is at least one jumping sub-band signal among the M sub-band signals, adjust the wiping speed of the wiper rod within the jumping angle range corresponding to the jumping sub-band signal to the anti-jumping speed; wherein, the anti-jumping speed is greater than the wiping speed of the wiper within the jumping angle range.

[0013] 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 jitter processing method are implemented.

[0014] To achieve the above object, the present invention further provides a computer-readable storage medium. A computer program is stored on the readable storage medium. When the computer program stored on the readable storage medium is executed by a processor, the steps of the above-mentioned jitter processing method are implemented.

[0015] A jitter processing method, device, electronic device and readable storage medium for a windshield wiper provided by the present invention can accurately draw a curve of a current signal through current sampling, ensuring the accuracy of subsequent identification of the jitter angle range.

[0016] By performing wavelet packet decomposition on at least one windshield wiper current signal, the full-frequency characteristics of the signal are comprehensively reflected, improving the time-frequency resolution of the signal, so as to more accurately identify and analyze the characteristics in the signal.

[0017] When the wiper rod of the windshield wiper brushes the windshield, local jitter of the wiper rod and noise will be generated due to stains on the glass surface or aging of the wiper rod rubber strip. The local jitter of the wiper rod will affect the current signal in the wiper motor, resulting in fluctuations in the current signal.

[0018] In response to this, by identifying the jitter sub-band signal according to each sub-band coefficient and identifying the jitter angle range corresponding to the jitter sub-band signal, it is possible to lock the area where stains on the glass surface cause the wiper rod to jitter or the area where the wiper rod rubber strip ages and causes jitter on the windshield based on the fluctuations of the motor current signal; by adjusting the brushing speed of the wiper rod within the jitter angle range corresponding to the jitter sub-band signal to the anti-jitter speed, the wiper rod can quickly sweep through the area where the wiper rod generates jitter, 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

[0019] Figure 1 It is a flowchart of the jitter processing method for the windshield wiper of the present invention; Figure 2 It is a specific method flowchart of the jitter processing method for the windshield wiper of the present invention; Figure 3 It 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; Figure 4It 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 bounce processing method of the present invention, as well as the bounce angle range and non-bounce angle range in the wiper angle, and the anti-bounce speed and wiper matching speed in the wiping speed; Figure 5 It is a schematic diagram of the program module of the bounce processing device of the present invention; Figure 6 It is a schematic structural diagram of the relationship between the bounce processing device of the wiper of the present invention and the wiper; Figure 7 It is a schematic diagram of the hardware structure of the electronic device in the electronic device of the present invention. Specific Embodiments

[0020] In order to make the objectives, technical solutions and advantages of the present invention clearer, 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.

[0021] Embodiment 1: Please refer to Figure 1 , a bounce processing method for a wiper in this embodiment, the wiper is installed on a vehicle, and the wiper rod of the wiper is used to wipe the windshield of the vehicle; The bounce processing method includes: S101: Sample the current signal in the brushless motor to obtain at least one wiper current signal.

[0022] 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 sub-band signal; wherein, the sub-band coefficients are used to characterize the energy intensity on the sub-band signals.

[0023] S103: If it is determined according to each sub-band coefficient that there is at least one bounce sub-band signal among the M sub-band signals, adjust the wiping speed of the wiper rod in the bounce angle range corresponding to the bounce sub-band signal to the anti-bounce speed; wherein, the anti-bounce speed is greater than the wiping speed of the wiper in the bounce angle range.

[0024] In this embodiment, the curve of the current signal can be accurately drawn through current sampling, ensuring the accuracy of subsequent identification of the bounce angle range.

[0025] By performing wavelet packet decomposition on at least one wiper current signal, the full-frequency characteristics of the signal are comprehensively reflected, the time-frequency resolution of the signal is improved, and thus the characteristics in the signal can be more accurately identified and analyzed.

[0026] When the wiper rod of the wiper brushes the windshield, the sudden change in the friction force between the wiper rod and the glass caused by stains on the glass surface or the aging of the wiper strip of the wiper rod will cause an instantaneous change in the motor load. (Generally, the friction force fluctuation caused by local jumping can cause a peak of ±15%-20% in the motor current (duration <50ms).), resulting in local jumping and generating noise. This local jumping will affect the current signal in the wiper motor, resulting in fluctuations in the current signal.

[0027] 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, it is possible to lock the area where the wiper rod jumps due to stains on the glass surface or the area where the wiper strip of the wiper rod ages and causes jumping on the windshield based on the fluctuation of the motor current signal; by adjusting the brushing speed of the wiper rod in 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.

[0028] 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.

[0029] Next, taking the example of performing wavelet packet decomposition on at least one wiper current signal in a wiper running the jumping processing method to obtain M sub-band signals and the sub-band coefficients of each of the sub-band signals, and if at least one jumping sub-band signal is determined among the M sub-band signals according to each of the sub-band coefficients, then adjusting the brushing speed of the wiper rod in the jumping angle range corresponding to the jumping sub-band signal to the anti-jumping speed, the method provided by 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.

[0030] Figure 2 is a specific method flow chart of a jumping processing method provided by an embodiment of the present invention. The method specifically includes steps S201 to S204.

[0031] Please refer to Figure 2 , this application proposes a jumping processing method for a wiper. The wiper is installed on a vehicle, and the wiper rod of the wiper is used to brush the windshield of the vehicle; The jumping processing method includes: S201: Sample the current signal in the brushless motor to obtain at least one wiper current signal.

[0032] In this example, the curve of the current signal can be accurately plotted through current sampling, ensuring the accuracy of subsequent identification of the jumping angle range.

[0033] In a preferred embodiment, the current signal in the brushless motor is sampled to obtain at least one wiper current signal, including: 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; S12: 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; S13: Filter at least one of the original current acquisition signals to obtain at least one of the wiper current signals.

[0034] 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 obtaining 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 obtained. Another method is to sample the command signal to generate the duty cycle of the PWM, thereby indirectly obtaining the PWM synchronization signal.

[0035] Triggering the current signal sampling according to the PWM synchronization signal can ensure obtaining the current signal at the critical moment of motor control, improving the accuracy and real-time performance of sampling. Among them, the sampling methods include: Pulse counting method: Use a counter or timer module to count the occurrence times of the rising edge or falling edge of the PWM pulse, thereby obtaining the frequency and duty cycle of the waveform.

[0036] Incremental encoder: Obtain 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.

[0037] 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.

[0038] 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.

[0039] 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: Limiting filtering method: Determine the maximum allowable deviation value between two samplings based on experience. When a new value is detected each time, judge. 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 use the previous value to replace this value.

[0040] Median filtering method: Continuously sample N times (N is taken as an odd number), arrange the N sampling values in order of size, and take the middle value as the valid value for this time.

[0041] Arithmetic mean filtering method: Continuously take N sampling values for arithmetic mean operation. When the value of N is large, the signal smoothness is high, but the sensitivity is low; when the value of N is small, the signal smoothness is low, but the sensitivity is high.

[0042] Recursive mean 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 end of the queue, and the original data at the head of the queue is discarded. Perform arithmetic mean operation on the N data in the queue to obtain a new filtering result.

[0043] Median mean filtering method: Equivalent to "median filtering method" + "arithmetic mean filtering method". Continuously sample N data, remove one maximum value and one minimum value, and then calculate the arithmetic mean of the N - 2 data.

[0044] Limiting mean filtering method: Equivalent to "limiting filtering method" + "recursive mean filtering method". Each time the newly sampled data is first subjected to limiting processing, and then sent to the queue for recursive mean filtering processing.

[0045] First-order lag filtering method: Take a = 0~1, the filtering result for this time = (1 - a) × the sampling value for this time + a × the filtering result for the previous time.

[0046] Weighted recursive mean filtering method: It is an improvement of the recursive mean filtering method, that is, different weights are given to data at different times. Usually, the closer the data is to the current time, the greater the weight.

[0047] 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, replace the current valid value with this value and clear the counter.

[0048] Kalman Filtering Method: Kalman filtering is a recursive algorithm used for estimating the state of a dynamic system from a series of noisy observation data. It is widely applied in fields such as navigation, control systems, signal processing, etc. Kalman filtering optimizes the estimation result by combining prediction and measurement, and is suitable for real-time systems and embedded systems. Kalman filtering consists of 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 measurement value to correct the predicted value.

[0049] 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: S121: Obtain at least one rising edge and at least one falling edge of at least one of the PWM synchronization signals; 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 an adjacent rising edge and a falling edge, and determine the acquisition period according to the synchronization period and the synchronization pulse width; S123: When identifying a compensation center point of a PWM in the PWM synchronization signal, sample the current signal in the brushless motor according to the acquisition period to obtain an original current acquisition signal.

[0050] Exemplarily, through a high-speed signal detection circuit or the input capture function of a microprocessor, monitor the voltage change of the PWM synchronization signal in real time. Use a Schmitt trigger or the edge detection function of a microprocessor to accurately identify 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. 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.

[0051] 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 an adjacent rising edge and a 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.

[0052] 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.

[0053] When the compensation center point is recognized, a current signal sampling operation is triggered. Current sampling methods such as current transformers, shunt resistors, Hall sensors or Rogowski coils are used to convert the current signal into a voltage signal, which is then processed through amplification, filtering, etc. and sent to an analog-to-digital converter (ADC) for sampling. Sampling is carried out according to a pre-determined acquisition period to ensure that a raw current acquisition signal is obtained within each acquisition period.

[0054] The raw current acquisition signal obtained through ADC sampling is a digital signal, which reflects the magnitude and direction of the current in the brushless motor. The raw current acquisition signal is stored in the memory of the microprocessor for subsequent filtering, analysis and control algorithm processing.

[0055] Optionally, filtering at least one of the raw current acquisition signals to obtain at least one of the wiper current signals includes: S131: Obtain the wiping speed of the brushless motor; 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; S133: If it is determined that the wiping speed is not less than the speed threshold, shorten the window width to maintain dynamic response.

[0056] Exemplarily, the rotating shaft of the brushless motor drives the permanent magnet on the shaft to rotate, changing the magnetic field magnitude. The change in the magnetic field 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 wiping speed can be calculated by the microcontroller's timer / counter to calculate the number of pulses within a unit time. Or combine the advantages of the M method (measuring distance at a fixed time) and the T method (measuring 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.

[0057] By experimentally measuring 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.

[0058] When the wiper speed is less than the speed threshold, the window width increase algorithm is triggered. The amount of window width increase can be determined according to the difference between the wiper speed and the speed threshold. The greater the difference, the more the window width increases. By adjusting the window function parameters 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.

[0059] When the wiper speed is not less than the speed threshold, the window width shortening algorithm is triggered. The amount of window width shortening can be determined according to the difference between the wiper speed and the speed threshold. The greater the difference, the more the window width shortens. By adjusting the window function parameters 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.

[0060] 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.

[0061] In this step, by performing wavelet packet decomposition on at least one wiper current signal, the full-frequency characteristics of the signal are comprehensively reflected, the time-frequency resolution of the signal is improved, and thus the characteristics in the signal can be more accurately identified and analyzed.

[0062] In a preferred embodiment, 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 includes: S21: Perform N-layer wavelet packet decomposition on at least one wiper current signal to obtain M sub-band signals; S22: Perform boundary effect processing on each of the sub-band signals to obtain the extended signal of each of the sub-band signals; S23: Define a wavelet basis function for each of the sub-band signals; 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.

[0063] In this example, the wiper current signal is usually a non-stationary signal, and its frequency 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.

[0064] Wavelet decomposition is prone to distortion (such as Gibbs phenomenon) at the signal boundary, resulting in subband coefficient errors. To address this, boundary effect processing is performed on each subband signal. By extending the signal, the boundary influence is eliminated, enabling the extended signal to smoothly transition with the original signal at the boundary and reducing the decomposition error. The generation methods of the extended signal can include: Symmetric extension: extrapolating the signal boundary into a symmetric form, suitable for periodic signals; Mirror extension: extrapolating the boundary into a mirror form, suitable for abrupt signals.

[0065] The time-frequency characteristics of different subband signals vary greatly, and a single wavelet basis function cannot globally adapt. To address this, independent wavelet basis functions are defined for each subband signal to improve the decomposition accuracy. Among them, for the low-frequency subband, a wavelet basis with good smoothness (such as Daubechies db4) is selected to avoid high-frequency noise interference; for the high-frequency subband, a wavelet basis with good compact support (such as Symlet sym8) is selected to retain the abrupt characteristics.

[0066] Regarding how to quantify the similarity between the subband signal and the wavelet basis function to extract effective features. To address this, the subband coefficients are calculated through inner product operations, enabling the subband coefficients to be the projection of the signal energy on the corresponding wavelet basis and used for subsequent classification, reconstruction, or fault diagnosis.

[0067] Specifically, perform N-layer wavelet packet decomposition on at least one wiper current signal to obtain M subband signals, including: 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; S212: Use at least one wiper current signal as the input signal, convolve the input signal with the 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 the pre-computed filter bank to obtain convolution data, and perform downsampling on the convolution data to obtain the (P + 1)-layer decomposition signal; iterate sequentially until the N-layer decomposition signal is obtained to obtain M subband signals.

[0068] 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 = 2N 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 signals.

[0069] Specifically, boundary effect processing is respectively performed on each of the sub-band signals to obtain the extended signals of each of the sub-band signals, including: S221: Perform extension processing on the first sub-band signal to obtain a left extended signal and a right extended signal; 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; 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.

[0070] In this example, wavelet decomposition causes energy leakage and artifacts (such as Gibbs phenomenon) due to truncation effect at the signal boundary, which affects the accuracy of subsequent feature extraction. Therefore, extension processing is used to reduce the boundary truncation effect and improve the accuracy of wavelet decomposition. The extended signal may introduce noise, resulting in distortion of sub-band coefficients. Therefore, soft threshold processing effectively suppresses the high-frequency noise in the extended signal and avoids noise interference with feature extraction. Direct splicing of the extended signal and the original signal may cause discontinuity, resulting in 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.

[0071] Furthermore, performing extension processing on the first sub-band signal to obtain a left extended signal and a right extended signal includes: Determine the extension number according to the number of filterings of the wavelet filter; Copy the signal points of the extension number near the left boundary in the first sub-band signal to obtain the left extended signal, and copy the signal points of the extension number near the right boundary in the first sub-band signal to obtain the right extended signal.

[0072] Furthermore, performing soft threshold processing on the left extended signal and the right extended signal to obtain a left splicing signal and a right splicing signal includes: Generate a left soft-threshold signal according to the signal points on the left side of the left extended signal whose quantity is the number of influencing signals through a preset soft-threshold function; Generate a right soft-threshold signal according to the signal points on the right side of the right extended signal whose quantity is the number of influencing signals through a preset soft threshold; Replace the signal points on the left side of the left extended signal whose quantity is the number of influencing signals with the left soft-threshold signal to obtain the left spliced signal; and replace the signal points on the right side of the right extended signal whose quantity is the number of influencing signals with the right soft-threshold signal to obtain the right spliced signal.

[0073] This example takes into account both accuracy and robustness and reduces boundary jumps.

[0074] 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.

[0075] 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.

[0076] For the input coefficient x, the soft-threshold function is: y = sign(x) × max(|x| - θ, 0); where: x is the current value of the signal points in the left extended signal and the right extended signal, y is the current value of the soft signal points; θ > 0 is the threshold parameter; sign(x) is the sign function; When |x| < θ, y = 0 (suppress noise); 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.

[0077] 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 shrinking amplitude is θ.

[0078] Summarize the soft signal points y with the number of influencing signals to form a left soft-threshold signal or a right soft-threshold signal.

[0079] Specifically, define a wavelet basis function for each of the sub-band signals, including: S231: Determine the energy distribution of the first sub-band signal to obtain the energy distribution data of the first sub-band signal; 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; S233: Determine the wavelet basis function of the first sub-band signal according to the energy distribution data and the signal feature data.

[0080] 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, determine the decomposition level and extract the first sub-band signal.

[0081] Sum the squares of the coefficients of the first sub-band signal to obtain the energy of the first sub-band signal. Normalize the energy of the first sub-band to obtain the energy distribution data of the first sub-band signal.

[0082] Kurtosis is used to measure the peak sharpness and tail thickness of a probability distribution; among them, the peak sharpness 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); 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 peak sharpness describes the concentration degree of the signal near the mean value, reflecting whether the signal is "sharp" or "flat". High peak sharpness: The signal energy is concentrated near the mean value and the distribution is narrow (such as the normal distribution). Low peak sharpness: The signal energy is dispersed and the distribution is wide (such as the uniform distribution). The tail thickness describes the probability of extreme values (outlier points) 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).

[0083] 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 using the Shannon entropy formula. A sub-band with a high entropy value may contain random noise or chaotic signals. A sub-band with a low entropy value may correspond to a signal with strong regularity (such as a sine wave).

[0084] Use kurtosis and / or entropy value as the feature data of the first sub-band signal for subsequent wavelet basis function selection.

[0085] Optionally, determining the wavelet basis function of the first sub-band signal according to the energy distribution data and the signal feature data includes: 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.

[0086] Step 2: Further screen out the wavelet basis functions that can match the signal characteristics according to kurtosis and / or entropy value.

[0087] Step 3: Evaluate the performance of candidate wavelet basis functions (such as reconstruction error, computational complexity, etc.), and select the optimal wavelet basis function.

[0088] Among them, for energy distribution matching: Select a wavelet basis function that can better capture the energy distribution of the first subband signal. For example, if the energy of the first subband signal is concentrated in the low-frequency part, a wavelet basis function with low-frequency characteristics can be selected.

[0089] For kurtosis matching: If the signal kurtosis is high (sharp), a wavelet basis function with compact support characteristics, such as the Daubechies wavelet, can be selected.

[0090] For entropy value matching: If the signal entropy value is high (complex), a wavelet basis function with multi-scale analysis ability, such as the Symlet wavelet, can be selected.

[0091] Common wavelet basis functions include: Daubechies wavelet (dbN): It has compact support characteristics and is suitable for analyzing sharp signals.

[0092] Symlet wavelet (symN): Approximately symmetric and suitable for analyzing complex signals.

[0093] Coiflet wavelet (coifN): It has a high vanishing moment and is suitable for analyzing smooth signals.

[0094] Biorthogonal wavelet (biorNr.Nd): A biorthogonal wavelet suitable for signal reconstruction.

[0095] Specifically, the similarity degree between each of the extended signals and its wavelet basis function is measured through an inner product operation to obtain the subband coefficients of each of the subband signals, including: 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.

[0096] 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.

[0097] S243: If it is determined that the product value of the first signal point exceeds a preset product threshold, then use the difference between the product value and the product threshold as the signal energy index of the first signal point.

[0098] S244: If it is determined that the product value of the first signal point does not exceed the product threshold, then use the preset initial value as the signal energy index of the first signal point.

[0099] S245: Aggregate the signal energy indexes of each signal point to obtain an energy index vector, and use the obtained energy index vector as the subband coefficient of the subband signal corresponding to the first extended signal.

[0100] Exemplarily, for the signal traversal mechanism in S241: Use the SIMD instruction set (such as AVX2) to accelerate the traversal process; perform secondary verification on the edge area of the extended signal (such as the first and last 10% lengths) to avoid extension distortion.

[0101] For the product calculation optimization 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.

[0102] For the threshold comparison logic in S243 and S244: Reduce the conditional branch overhead through pre-sorting or threshold interval division; use the absolute value or the square difference, and select according to the application scenario (such as the square difference is more sensitive to outliers).

[0103] For the energy index aggregation in S245: Use SIMD instructions to batch process signal points to generate a subband coefficient vector; perform L2 norm normalization on the energy index vector to eliminate the influence of the signal amplitude.

[0104] S203: If it is determined from each subband coefficient that there is at least one jumping subband signal among the M subband signals, then adjust the wiper speed of the wiper rod within the jumping angle range corresponding to the jumping subband signal to the anti-jumping speed; where the anti-jumping speed is greater than the wiper speed of the wiper in the jumping angle range.

[0105] In this step, when the wiper rod of the wiper scrapes the windshield, local jumping of the wiper rod and noise will occur due to stains on the glass surface or aging of the wiper strip. The local jumping of the wiper rod will affect the current signal in the wiper motor, resulting in fluctuations in the current signal.

[0106] In response to this, by identifying the jumping sub-band signals according to each of the sub-band coefficients and identifying the corresponding jumping angle range of the jumping sub-band signals, it is possible to lock the area where the wiper bar jumps due to stains on the glass surface or the jumping area generated by the aging of the wiper bar rubber strip on the windshield based on the fluctuations of the motor current signal; by adjusting the scraping speed of the wiper bar within the jumping angle range corresponding to the jumping sub-band signal to the anti-jumping speed, the wiper bar can quickly sweep through the area where the wiper bar jumps, thereby reducing the noise generated by the wiper bar, improving the driving texture of the vehicle, and avoiding the situation of interfering with the driver's attention and affecting driving safety.

[0107] In a preferred embodiment, if it is determined that at least one of the M sub-band signals has a jumping sub-band signal according to each of the sub-band coefficients, it includes: S31: If it is determined that at least one of the jumping energy indicators exists in the sub-band coefficients 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 scraping speed of the wiper bar, the current weather condition of the vehicle, and the service life of the wiper bar; S32: If it is determined that at least one of the jumping energy indicators satisfies the preset jumping processing rule, determine the first sub-band signal as the jumping sub-band signal.

[0108] In this example, for the jumping rule acquisition mechanism in S31: Kalman filtering is used to fuse multi-sensor data (speed, rainfall, vibration); the D-S evidence theory is used to handle the uncertainty of the service life (such as mileage error).

[0109] For the determination of the jumping sub-band in S32: Based on multi-index joint determination, it is determined whether at least one of the jumping energy indicators satisfies the preset jumping processing rule.

[0110] The multi-indices include: Time-domain index: The variance of three consecutive windows exceeds the threshold Frequency-domain index: The energy ratio in a specific frequency band > predefined pattern (such as the increase in the high-frequency energy ratio during heavy rain) Nonlinear index: Entropy value mutation (indicating that the system enters a chaotic state) The method for determining whether the multi-indices satisfy the jumping processing rule includes: Using the Dempster-Shafer theory to fuse the confidence degrees of the three indices; Final determination threshold: Σ confidence degree > 0.75; If it is higher than the preset determination, it is determined that at least one of the jumping energy indicators satisfies the jumping processing rule.

[0111] Specifically, if it is determined that the subband coefficient of the first subband signal has at least one jitter energy indicator, obtaining a jitter processing rule corresponding to the state information of the wiper includes: S311: Calculate the variance of the signal energy index of the subband coefficient 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 coefficient; S312: Performing a fast Fourier transform on the subband coefficients of the first subband signal to obtain an energy proportion value of the first subband signal; wherein the energy proportion value reflects the proportion of a signal energy index of a specific frequency range in the subband coefficients in the subband coefficients; 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.

[0112] 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; S315: Acquire a beat processing rule corresponding to the state information from a preset rule library.

[0113] 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.

[0114] 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.

[0115] Regarding 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, representing the allowable range of energy index fluctuations under normal working conditions; the proportion threshold is set according to the normal vibration characteristics of the windshield wiper, representing the normal energy proportion of specific frequency components. If the coefficient variance value exceeds the variance threshold, it indicates that the signal energy fluctuates abnormally over time, and there may be intermittent beating. If the energy proportion value exceeds the proportion threshold, it indicates that the energy proportion of specific frequency components is too high, and there may be periodic beating. When any threshold exceeds the standard condition is met, it is determined that the first sub-band signal has at least one beating energy index, triggering the subsequent processing flow.

[0116] Regarding the acquisition of windshield wiper status information in S314: obtain the current wiping speed (unit: revolutions per minute) in real time through the Hall sensor of the windshield wiper motor. Obtain the weather data (such as rainfall level or visibility classification) of the current area of the vehicle from the in-vehicle system, or sense the rainfall intensity in real time through the on-vehicle camera / radar. Read the vehicle driving mileage data from the in-vehicle system, and estimate its service life in combination with the designed life of the windshield wiper rod (for example: assuming an annual driving of 20,000 kilometers, the total mileage divided by 20,000 is the service life).

[0117] Regarding the matching of beating processing rules in S315: preset a rule library, which contains beating processing rules corresponding to different combinations of windshield wiper status (wiping speed, weather conditions, service life). The rules include: the adjustment strategy of the beating determination threshold under different states (for example: a higher variance threshold is allowed during high-speed wiping). The warning level or maintenance suggestions when beating occurs (for example: when beating is detected in a heavy rain environment, it is preferred to prompt an inspection of the windshield wiper rod connection). According to the real-time obtained windshield wiper status information, retrieve the matching beating processing rules 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).

[0118] Specifically, if it is determined that at least one of the beating energy indexes meets the preset beating processing rules, it is determined that the first sub-band is a beating sub-band signal, including: S321: If it is determined that there is at least one target energy index among at least one of the beating energy indexes, calculate the number of the target energy indexes; where the target energy index refers to the beating energy index that exceeds the index threshold in the beating processing rules; S322: If it is determined that the number of the target energy indexes exceeds the number threshold in the beating processing rules, it is determined that the first sub-band is a beating sub-band signal.

[0119] 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 indicators respectively characterize the fluctuation characteristics of the signal energy in the time domain and the frequency domain, and jointly constitute the jump energy index system. From the preset jump 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 indices. Count the number of all exceeded target energy indices. 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 indices is 1; if both the variance and the occupancy ratio exceed the standard, the number is 2.

[0120] For the determination of the jump sub - band signal in S322: A preset quantity threshold (for example: 2) in the jump processing rules. This threshold is determined by historical data or experiments and represents the minimum number of exceeded indices required to determine a jump. If the number of target energy indices ≥ the quantity threshold (such as 2), then the first sub - band signal is determined to be a jump sub - band signal. If the number < the threshold (such as 1), it is not determined to be a jump 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 number of target energy indices ≥ 1, then even if the single - time number does not reach the quantity threshold, it is still determined to be a jump sub - band signal. This mechanism is used to capture intermittent jumps.

[0121] In a preferred embodiment, adjusting the wiper speed in the jump angle range corresponding to the jump sub - band signal to the anti - jump speed includes: S33: Set at least one wiper angle range corresponding to the jump sub - band signal as the jump angle range; S34: In the jump angle range, use a PID controller to adjust the wiper speed of the wiper rod to the anti - jump speed.

[0122] 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 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°.

[0123] 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 gradient interval (for example: 5° in advance / lag), to avoid new jitters caused by sudden speed changes.

[0124] 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 amount output by the PID controller, used to control the motor drive voltage or PWM duty cycle, thereby changing the wiping speed.

[0125] 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.

[0126] 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 adjust the PID parameters online.

[0127] Pre-set PID parameter groups under different weather / speed conditions, and switch the control mode according to real-time status information (such as rainfall level, vehicle speed).

[0128] Specifically, setting the wiper angle range corresponding to at least one jitter sub-band signal as the jitter angle range includes: 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. 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.

[0129] 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.

[0130] 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 where 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.

[0131] Please refer to Figure 3 , Figure 3 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.

[0132] Further, obtaining the timing signal segment corresponding to the first jumping sub-band signal includes: 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. Extract the jumping time points of each wiper current signal in the first jumping sub-band signal, and aggregate at least one adjacent jumping time point to obtain a timing signal segment of the first jumping sub-band signal.

[0133] Specifically, within the jumping angle range, adjusting the wiping speed of the wiper rod to the anti-jumping speed using a PID controller includes: S341: Obtain the current wiping speed of the wiper rod from the wiper and set the obtained wiping speed as the reference speed; S342: Determine the anti-jump calculation control quantity of the PID controller according to the status information of the wiper; S343: Adjust the reference speed according to the anti-jump calculation control quantity to obtain the anti-jump speed; S344: Output the anti-jump speed to the wiper, so that the wiper adjusts the wiping speed of the wiper rod to the anti-jump speed.

[0134] In this embodiment, the wiping speed of the wiper can usually be adjusted by a 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 rod 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 rod maintains a stable wiping speed within the jump angle range. By obtaining the current wiping speed of the wiper rod 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 rod, thereby suppressing the occurrence of the jumping phenomenon.

[0135] 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 the jump. 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 these information, the PID controller can adjust the wiping speed of the wiper rod more accurately, thereby more effectively suppressing the jumping phenomenon.

[0136] The PID controller adjusts the reference speed according to the calculated control quantity. 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 jumping angle range can be obtained, that is, the anti-jumping speed. This speed will be used to suppress the occurrence of the jumping phenomenon and 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 quantity. This adjustment process will keep the wiping speed of the wiper lever at the anti-jumping speed within the jumping angle range, thus effectively suppressing the jumping phenomenon.

[0137] Output the calculated anti-jumping 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-jumping speed signal. This adjustment process will ensure that the wiper lever maintains a stable wiping speed within the jumping angle range, thus suppressing the occurrence of the jumping phenomenon. By outputting the anti-jumping 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 the jumping phenomenon is likely to occur.

[0138] 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 jumping processing method of the wiper of the present invention, as well as the jumping angle range and non-jumping angle range in the wiper angle, and the anti-jumping speed and wiper matching speed in the wiping speed.

[0139] S204: Adjust the wiping speed of the wiper lever on at least one non-jumping angle range to the wiper matching speed; wherein, the non-jumping angle range refers to the other wiper angle ranges in the overall wiper angle range of the wiper lever on the windshield except the jumping angle range; the wiper matching speed is less than the wiping speed of the wiper lever on the non-jumping angle range.

[0140] In this step, by setting the wiping speed of the wiper lever on at least one non-jumping angle range to the wiper matching speed, the wiping speed of the wiper lever on the non-jumping 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-jumping 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.

[0141] 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 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.

[0142] 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: S41: Within the non-jitter wiping speed range, use a PID controller to adjust the wiping speed of the wiper lever to the wiper matching speed; S42: Respectively 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; 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; 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.

[0143] 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 through historical data or experiments, indicating that the wiper can operate stably within this speed range without the risk of jitter.

[0144] 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 visibility. 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, so that the actual speed approaches the target value.

[0145] Regarding 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, and if the width is too large, it may affect the wiping efficiency. The typical value is set to 5° - 10°.

[0146] Regarding the incoming shake angle range speed gradual change (based on the first speed curve) in S43: 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 requiring 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 in the initial stage and a slow change in the later stage. 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 a PID controller to track this target speed to achieve speed gradual change.

[0147] Regarding the outgoing shake angle range speed gradual change (based on the second speed curve) in S44: 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 the incoming shake angle range. For example: If the S-shaped curve is used for the incoming shake, the S-shaped curve is also used for the outgoing shake, 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 a PID controller to track this target speed to achieve speed recovery.

[0148] Specifically, within the non-jumping wiping speed range, use a PID controller to adjust the wiping speed of the wiper rod to the wiper matching speed, including: S411: Obtain the current wiping speed of the wiper rod from the wiper and set the obtained wiping speed as the reference speed; S412: Determine the matching calculation control quantity of the PID controller according to the status information of the wiper; S413: Adjust the reference speed according to the matching calculation control quantity to obtain the wiper matching speed; 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.

[0149] In this example, within the non-jumping wiping speed range, the PID controller adjusts the wiping speed of the wiper rod according to the status 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.

[0150] 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: 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 status information of the wiper; 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; 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.

[0151] 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: S431: Use 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; 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; S433: Based on the T-shaped speed curve, generate a T-shaped speed adjustment strategy according to the incoming shake angle range, the starting speed, and the target speed; S434: Based on the exponential speed curve, generate an exponential speed adjustment strategy according to the incoming shake angle range, the starting speed, and the target speed; S435: Based on the trigonometric function type speed curve, generate a trigonometric function type speed adjustment strategy according to the incoming shake angle range, the starting speed, and the target speed; S436: When it is monitored that the wiper lever enters the incoming shake angle range, execute any one of the S-type speed adjustment strategy, the T-type speed adjustment strategy, the exponential type speed adjustment strategy, and the trigonometric function speed adjustment strategy, so that the wiping speed transitions from the wiper matching speed to the anti-jump speed.

[0152] Specifically, based on the second speed curve, gradually changing the wiping speed of the wiper lever in the outgoing shake angle range from the anti-jump speed to the wiper matching speed includes: 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; S432: Based on the S-type speed curve, generate an S-type speed adjustment strategy according to the incoming shake angle range, the starting speed, and the target speed; S433: Based on the T-type speed curve, generate a T-type speed adjustment strategy according to the incoming shake angle range, the starting speed, and the target speed; S434: Based on the exponential type speed curve, generate an exponential type speed adjustment strategy according to the incoming shake angle range, the starting speed, and the target speed; S435: Based on the trigonometric function type speed curve, generate a trigonometric function type speed adjustment strategy according to the incoming shake angle range, the starting speed, and the target speed; S436: When it is monitored that the wiper lever enters the incoming shake angle range, execute any one of the S-type speed adjustment strategy, the T-type speed adjustment strategy, the exponential type speed adjustment strategy, and the trigonometric function speed adjustment strategy, so that the wiping speed transitions from the anti-jump speed to the wiper matching speed.

[0153] In this example, the S-type speed curve presents an "S" shape, with the change rate gradually increasing first, then tending to be gentle, and finally gradually decreasing, forming a smooth transition. This curve can ensure the continuous change of the speed of the wiper lever during the wiping process, avoiding the impact of speed mutation 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, improving driving safety.

[0154] The T-shaped speed curve is shaped like a "T" and has a high starting torque and a low starting current, which can provide sufficient torque for the load. However, its acceleration is discontinuous, and there is a sudden change at the connection between the acceleration and deceleration stages and the constant speed stage. The T-shaped speed curve is suitable for occasions that require rapid start-up and acceleration, such as certain industrial equipment, machine tools, etc. In the wiper system, although there is a problem of discontinuous acceleration, through reasonable control algorithms and parameter adjustments, the gradual control of speed can still be achieved.

[0155] The exponential speed curve is a non-linear curve, and the speed changes exponentially with time, showing a trend of growth or decay. 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 wiping speed, improving driving safety.

[0156] Trigonometric function speed curves such as sine functions and cosine functions have the characteristics of periodicity and good smoothness. By adjusting the parameters of the trigonometric functions, speed curves of different shapes can be achieved to meet different control requirements. Trigonometric function speed curves are suitable for occasions that require periodic changes or smooth transitions. In the wiper system, it can provide a stable and continuous wiping speed while avoiding the jumping problem caused by sudden speed changes.

[0157] Embodiment 3: Please refer to Figure 5 , a wiper jitter processing device 5 of this embodiment is installed on the wiper and runs the above-mentioned wiper jitter processing method; the wiper rod of the wiper is used to wipe the windshield of the vehicle; The jitter processing device 5 includes: A sampling module 51 for sampling the current signal in the brushless motor to obtain at least one wiper current signal.

[0158] A decomposition module 52 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.

[0159] An anti-jitter module 53 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 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.

[0160] Optionally, the jitter processing device 5 further includes: A matching module 54 is configured to adjust the wiping speed of the wiper lever within at least one non-jumping angle range to a wiper matching speed; wherein, the non-jumping angle range refers to other wiper angle ranges in the overall wiper angle range of the wiper lever on the windshield except the jumping angle range; and the wiper matching speed is less than the wiping speed of the wiper lever within the non-jumping angle range.

[0161] In Figure 6 a wiper device 61, a jitter processing device 5 is installed in the wiper device 61, and a wiper lever 62 of the wiper device 61 is configured to wipe a windshield 63 of a vehicle.

[0162] Embodiment 4: To achieve the above object, the present invention further provides an electronic device 7. Components of the jitter 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 having 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.

[0163] In this embodiment, the memory 71 (i.e., a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 71 can 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 can 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 can 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 an operating system and various application software installed in the electronic device, such as the program code of the jitter processing device in Embodiment 3. In addition, the memory 71 can also be used to temporarily store various data that have been output or will be output.

[0164] 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 beat processing device, so as to implement the beat processing methods of Embodiment 1 and Embodiment 2.

[0165] Embodiment 5: To achieve the above object, the present invention also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., on which a computer program is stored, and 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 beat processing method, and when it is executed by the processor 72, the beat processing methods of Embodiment 1 and Embodiment 2 are implemented.

[0166] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0167] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method.

[0168] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. 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 handling 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 brush 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 signal; If it is determined that at least one jitter sub-band signal exists among the M sub-band signals according to each sub-band coefficient, then adjust the brushing 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 brushing speed of the wiper within the jitter angle range.

2. The beating 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 original current acquisition signal; Filtering at least one of the original current acquisition signals to obtain at least one wiper current signal.

3. The jitter processing method according to claim 1, wherein 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 degree 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 beating processing method according to claim 1, characterized in that If it is determined that at least one jitter sub-band signal exists among the M sub-band signals according to each sub-band coefficient, 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 brushing 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 brushing speed of the wiper within 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 jitter angle range, using a PID controller to adjust the brushing speed of the wiper rod to the anti-jitter speed.

6. The jitter processing method according to claim 1, characterized in that After adjusting the brushing 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: Adjust the wiping speed of the wiper lever within at least one non-jumping angle range to a wiper matching speed; wherein, the non-jumping angle range refers to the other wiper angle ranges in the overall wiper angle range of the wiper lever on the windshield except the jumping angle range; the wiper matching speed is less than the wiping speed of the wiper lever within the non-jumping angle range.

7. The beating processing method according to claim 6, characterized in that, Setting the wiping speed of the wiper lever within at least one non-jumping angle range to a wiper matching speed includes: Within the non-jumping wiping speed range, use a PID controller to adjust the wiping speed of the wiper lever to the wiper matching speed; On both sides of one of the non-jumping angle ranges close to the jumping angle range, respectively set an entering-jump angle range and an exiting-jump angle range; Based on a first speed curve, gradually change the wiping speed of the wiper lever within the entering-jump angle range from the wiper matching speed to the anti-jumping 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 a second speed curve, gradually change the wiping speed of the wiper lever within the exiting-jump angle range from the anti-jumping 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.

8. A jitter processing device for a windshield wiper, characterized in that Installed on the wiper, and running any one of the jumping processing methods described in claims 1-7; the wiper lever of the wiper is used to wipe the windshield of the vehicle; The jumping processing device includes: A sampling module, configured to sample the current signal in the brushless motor to obtain at least one wiper current signal; A decomposition module, configured to 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; An anti-jumping module, configured to, if it is determined according to each of the sub-band coefficients that there is at least one jumping sub-band signal among the M sub-band signals, adjust the wiping speed of the wiper lever within the jumping angle range corresponding to the jumping sub-band signal to the anti-jumping speed; wherein, the anti-jumping speed is greater than the wiping speed of the wiper within the jumping angle range.

9. 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, it implements the steps of any one of the jumping processing methods described in claims 1 to 7.

10. 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, it implements the steps of any one of the jumping processing methods described in claims 1 to 7.

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