Vehicle window full stroke position ripple number updating method and device and storage medium

Abstract

This application relates to a method, apparatus, and storage medium for updating the number of ripples at the full travel position of a vehicle window. The method determines whether the current number of ripples at the full travel position is valid based on a preset target range for the number of ripples, thereby filtering out obviously invalid ripple counts. If valid, it further determines whether the difference between the current number of ripples at the full travel position and the stored value of the number of ripples triggers self-learning, and updates the target number based on the determination result. When the target number meets a preset threshold, it means that the stored value of the number of ripples can be updated based on the current number of ripples at the full travel position, thereby making the updated stored value of the number of ripples accurate and reliable, eliminating updates under random conditions, and improving the accuracy and reliability of the self-learning of the number of ripples at the full travel position.

Description

Technical Field

[0001] This application relates to the field of vehicle window control technology, specifically to a method, device, and storage medium for updating the number of position ripples throughout the entire travel of a vehicle window. Background Technology

[0002] A car window control system is a system that uses a window motor to raise and lower the car window. Its working principle involves the window controller receiving a request to raise or lower the window. After internal logic processing, the controller controls the window motor to rotate forward or backward, thus raising or lowering the window. As automotive technology continues to evolve towards electrification, intelligence, and connectivity, many functions in smart cockpits require window functionality, such as smoke-induced window lowering, liveness detection window lowering, and automatic rain-induced window raising. Furthermore, window designs and technologies are constantly evolving; for example, new energy vehicles now widely use frameless windows and ripple control technology. These technological advancements place higher demands on window control systems.

[0003] In the process of controlling vehicle windows, the calculation of the actual window position is crucial, as its accuracy directly affects vehicle safety (e.g., anti-pinch function not activating) and the driving experience (e.g., windows not fully opening). The actual window position is calculated based on the number of ripples throughout the window's full travel. Ripple calculation is influenced by many factors, often resulting in missed or over-calculated ripples. Therefore, self-learning of the full travel position is necessary to correct and update the ripple count. Thus, how to accurately and reliably update the ripple count throughout the window's full travel position has become an urgent problem to be solved. Summary of the Invention

[0004] In order to solve the above-mentioned technical problems, or at least partially solve the above-mentioned technical problems, this application provides a method, apparatus and storage medium for updating the number of position ripples of a vehicle window throughout its entire travel.

[0005] Firstly, this application provides a method for updating the number of positional ripples at the full travel distance of a vehicle window, the method comprising:

[0006] Obtain the number of ripples at the current full travel position of the window;

[0007] The validity of the current full-stroke position ripple count is determined based on a preset target range for the number of ripples; wherein, a valid current full-stroke position ripple count is within the target range for the number of ripples.

[0008] If the number of ripples at the current full-stroke position is valid, determine whether to trigger self-learning based on the difference between the number of ripples at the current full-stroke position and the stored value of the number of ripples;

[0009] Update the current target number based on the judgment result;

[0010] If the target number of times meets the preset number of times threshold, the stored value of the number of ripples is updated according to the number of ripples at the current full-stroke position.

[0011] Optionally, the current target count is updated based on the judgment result, including:

[0012] If the judgment result indicates that self-learning has been triggered, then the target number is incremented by one;

[0013] If the judgment result indicates that self-learning is not triggered, then the target number is reduced by one.

[0014] Optionally, if the target number of times meets a preset threshold, the stored value of the ripple count is updated based on the current full-stroke position ripple count, including:

[0015] If the target number of times is equal to a preset number threshold, obtain a preset update step size;

[0016] The stored value of the number of ripples is updated based on the current full-stroke position ripple count and the preset update step size.

[0017] Optionally, updating the stored value of the number of ripples based on the current full-stroke position ripple count and the preset update step size includes:

[0018] If the difference between the current full-stroke position ripple count and the stored ripple count value is greater than or equal to the preset update step size, then the update target value is determined based on the preset update step size and the stored ripple count value.

[0019] If the difference between the current full-stroke position ripple count and the stored ripple count value is less than the preset update step size, then the update target value is determined based on the current full-stroke position ripple count.

[0020] The updated ripple count storage value is determined based on the updated target value.

[0021] Optionally, determining the updated ripple count storage value based on the update target value includes:

[0022] Obtain the maximum and minimum values ​​of the target range for the number of ripples;

[0023] If the update target value is greater than the maximum value or less than the minimum value, the updated ripple count storage value is determined based on the maximum value or the minimum value.

[0024] Optionally, if the current full-stroke position ripple count is valid, determining whether to trigger self-learning based on the difference between the current full-stroke position ripple count and the stored ripple count value includes:

[0025] If the number of ripples at the current full-stroke position is valid, obtain the difference between the number of ripples at the current full-stroke position and the stored value of the number of ripples;

[0026] Obtain the preset deviation threshold;

[0027] Whether to trigger self-learning is determined based on the difference and the deviation threshold;

[0028] If the difference is greater than or equal to the deviation threshold, the judgment result is determined to trigger self-learning;

[0029] If the difference is less than the deviation threshold, the judgment result is determined to be that self-learning is not triggered.

[0030] Optionally, obtain the number of ripples at the current full-travel position of the window, including:

[0031] Obtain control commands for the vehicle windows, and the window positions before and after the execution of the control commands;

[0032] The number of ripples at the current full travel position of the window is determined based on the control command and the window position before and after execution.

[0033] Secondly, this application provides a device for updating the number of positional ripples of a vehicle window throughout its entire travel range, the device comprising:

[0034] The acquisition module is used to obtain the number of ripples at the current full-travel position of the window;

[0035] The first judgment module is used to determine whether the current full-stroke position ripple count is valid based on a preset ripple count target interval; wherein, the valid current full-stroke position ripple count is within the ripple count target interval;

[0036] The second judgment module is used to determine whether to trigger self-learning based on the difference between the current full-stroke position ripple count and the stored ripple count value when the current full-stroke position ripple count is valid.

[0037] The target count determination module is used to update the current target count based on the judgment result;

[0038] The update module is used to update the stored value of the number of ripples based on the number of ripples at the current full-stroke position when the target number of times meets the preset number of times threshold.

[0039] Thirdly, this application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0040] Memory, used to store computer programs;

[0041] When the processor executes a program stored in the memory, it implements the method for updating the number of window position ripples throughout the entire travel distance as described in any embodiment of the first aspect.

[0042] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for updating the number of ripples at the full travel position of a vehicle window as described in any embodiment of the first aspect.

[0043] The beneficial effects of this application are:

[0044] The method provided in this application embodiment obtains the number of ripples at the current full-travel position of the vehicle window; determines whether the current full-travel position ripple count is valid based on a preset target range for the number of ripples; wherein, a valid current full-travel position ripple count is within the target range for the number of ripples; if the current full-travel position ripple count is valid, determines whether self-learning is triggered based on the difference between the current full-travel position ripple count and the stored ripple count value; updates the current target number of times based on the determination result; and updates the stored ripple count value based on the current full-travel position ripple count if the target number of times meets a preset threshold number of times. This method determines whether the current full-stroke position ripple count is valid based on a preset target range for ripple count, thereby filtering out obviously invalid ripple counts. If valid, it further determines whether the difference between the current full-stroke position ripple count and the stored ripple count triggers self-learning, and updates the target number based on the determination result. When the target number meets the preset threshold, it means that the stored ripple count can be updated based on the current full-stroke position ripple count, thus making the updated ripple count stored value accurate and reliable, eliminating updates under random conditions, and improving the accuracy and reliability of the full-stroke position ripple count self-learning. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A system architecture diagram of a method for updating the number of position ripples in a vehicle window throughout its entire travel, provided in one embodiment of this application;

[0048] Figure 2 A flowchart illustrating a method for updating the number of position ripples at the full travel distance of a vehicle window, provided in one embodiment of this application;

[0049] Figure 3 This application provides a schematic diagram of a self-learning value update process as an embodiment of the present application.

[0050] Figure 4 This is a schematic diagram illustrating the updating of the number of positional ripples of a vehicle window throughout its entire travel, as provided in one embodiment of this application.

[0051] Figure 5 This is a schematic diagram of a device for updating the number of position ripples in a vehicle window throughout its entire travel, provided in one embodiment of this application.

[0052] Figure 6 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0053] The embodiments of this application will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be understood that the preferred embodiments are only for illustrating this application and are not intended to limit the scope of protection of this application.

[0054] The first embodiment of this application provides a method for updating the number of positional ripples in a vehicle window throughout its entire travel range. This method can be applied to, for example... Figure 1 The system architecture shown includes at least a window 101 and a window control system 102, which controls the raising and lowering of the window 101 and adjusts its parameters. Specifically, this system architecture can be a vehicle, and the type of vehicle is not limited, such as a gasoline-powered vehicle, a pure electric vehicle, a hybrid vehicle, or a fuel cell vehicle, etc. This method can be applied to the window control system 102 within this system architecture.

[0055] Next, based on this system architecture, the method for updating the number of ripples at the full travel position of the window will be explained in detail, such as... Figure 2 The methods for updating the number of ripples at the full travel position of the car window include:

[0056] Step 201: Obtain the number of ripples at the current full travel position of the window.

[0057] The number of ripples at the current full-stroke position of the window can be obtained during the normal full-stroke movement of the window, such as from the window's maximum open position to the window's closed position, or from the window's movement from the closed position to the maximum open position.

[0058] In one embodiment, obtaining the number of ripples at the current full-stroke position of the window includes: obtaining the control command for the window, and the window position before and after the execution of the control command; and determining the number of ripples at the current full-stroke position of the window based on the control command and the window position before and after execution.

[0059] In this implementation, after obtaining the control command for the vehicle window, such as the window opening command or the window closing command, the window position before and after the execution of the control command is obtained. For example, the window position before and after the execution of the control command includes the maximum opening position and the closed position, respectively. Then, it can be determined that the number of ripples corresponding to the movement of the window between the maximum opening position and the closed position during the execution of this control command is the number of ripples at the current full stroke position of the window.

[0060] In this embodiment, after obtaining the number N of ripples at the current full travel position of the window each time, the following judgment can be made on whether the number N of ripples at the current full travel position is valid, so as to monitor whether the number of ripples at the current full travel position is valid in real time.

[0061] Step 202: Determine whether the current full-stroke position ripple count is valid based on the preset target range for the number of ripples; wherein, a valid current full-stroke position ripple count is within the target range for the number of ripples.

[0062] The target range for the number of ripples can be a preset reasonable range. For example, the minimum value of the target range for the number of ripples is Nmin, and the maximum value of the target range for the number of ripples is Nmax. Whether N is valid can be determined by whether N is greater than or equal to Nmin and less than or equal to Nmax.

[0063] Step 203: If the number of ripples at the current full-stroke position is valid, determine whether to trigger self-learning based on the difference between the number of ripples at the current full-stroke position and the stored value of the number of ripples.

[0064] In one embodiment, when the current full-stroke position ripple count is valid, determining whether to trigger self-learning based on the difference between the current full-stroke position ripple count and the stored ripple count value includes: when the current full-stroke position ripple count is valid, obtaining the difference between the current full-stroke position ripple count and the stored ripple count value; obtaining a preset deviation threshold; determining whether to trigger self-learning based on the difference and the deviation threshold; if the difference is greater than or equal to the deviation threshold, determining that the determination result is triggering self-learning; if the difference is less than the deviation threshold, determining that the determination result is not triggering self-learning.

[0065] In this embodiment, the ripple count storage value is the number of ripples currently in use stored in the window control system. When controlling the movement of the window, the window control system controls the opening and closing of the window according to this ripple count storage value.

[0066] In this embodiment, when the current full-stroke position ripple count is valid, the difference between the current full-stroke position ripple count and the stored ripple count value is first determined. Then, based on the obtained deviation threshold and the difference, it is determined whether self-learning should be triggered. If the difference is greater than or equal to the deviation threshold, it indicates a large deviation, and the result is to trigger self-learning. If the difference is less than the deviation threshold, it indicates a small deviation, and the original stored ripple count value meets the usage requirements, and the result is not to trigger self-learning. It should be understood that the difference mentioned here refers to the absolute value of the difference between the current full-stroke position ripple count and the stored ripple count value.

[0067] Examples are given below:

[0068] Taking an example with a stored value of 2080 for the number of ripples, a target range of 2000 (min) and 2160 (max) for the number of ripples, and a deviation threshold of 40, if the current total stroke position has a ripple count of 2048, which falls within the target range, then 2048 is a valid value. The difference between the current total stroke position ripple count and the stored value is then determined; this difference is 32. Since 32 is less than the deviation threshold of 40, the deviation is small, and self-learning is not triggered. If the current total stroke position has a ripple count of 2032, which also falls within the target range, then 2032 is a valid value. The difference between the current total stroke position ripple count and the stored value is then determined; this difference is 48. Since 48 is greater than the deviation threshold of 40, the deviation is large, and self-learning is triggered.

[0069] Step 204: Update the current target number based on the judgment result.

[0070] In one embodiment, updating the current target count based on the judgment result includes: if the judgment result indicates that self-learning is triggered, then the target count is incremented by one; if the judgment result indicates that self-learning is not triggered, then the target count is decremented by one.

[0071] In this embodiment, the ripple count storage value is not updated every time self-learning is triggered. Instead, the current target count is updated first, which can be represented by C. If the judgment result indicates that self-learning is triggered, the target count is incremented by one; if the judgment result indicates that self-learning is not triggered, the target count is decremented by one. For example, if the ripple count storage value is 2080, the minimum value Nmin of the target ripple count interval is 2000, the maximum value Nmax is 2160, the deviation threshold is 40, and the current target count C is 1, then if the current total stroke position ripple count is 2048, and 2048 falls within the target ripple count interval, then 2048 is a valid value. The difference between the current total stroke position ripple count and the ripple count storage value is further determined. This difference is 32. Since 32 is less than the deviation threshold 40, it indicates a small deviation, and self-learning is not triggered. Therefore, C is decremented by one, i.e., C changes from 1 to 0. If the number of ripples at the current full-stroke position is 2032, and 2032 falls within the target range for the number of ripples, then 2032 is a valid value. Further determine the difference between the number of ripples at the current full-stroke position and the stored value of the number of ripples. This difference is 48. Since 48 is greater than the deviation threshold of 40, it indicates that the deviation is large, triggering self-learning. Then C is incremented by one, that is, C changes from 0 to 1.

[0072] Step 205: If the target number of times meets the preset number of times threshold, update the stored value of the number of ripples based on the current number of ripples at the full stroke position.

[0073] This method determines whether the current full-stroke position ripple count is valid based on a preset target range for ripple count, thereby filtering out obviously invalid ripple counts. If valid, it further determines whether the difference between the current full-stroke position ripple count and the stored ripple count triggers self-learning, and updates the target number based on the determination result. When the target number meets the preset threshold, it means that the stored ripple count can be updated based on the current full-stroke position ripple count, thus making the updated ripple count stored value accurate and reliable, eliminating updates under random conditions, and improving the accuracy and reliability of the full-stroke position ripple count self-learning.

[0074] When the target number C meets the preset number threshold Cmax (for example, when C equals Cmax), the stored value of the number of ripples can be directly updated to the number of ripples at the current full stroke position. Of course, in order to avoid the adjustment of the stored value of the number of ripples being too large each time, the stored value of the number of ripples can be updated according to the number of ripples at the current full stroke position in combination with the preset update step size Nstep. Nstep can also be called the setting step size or the update step size.

[0075] In one embodiment, when the target number of times meets a preset number of times threshold, the stored value of the number of ripples at the current full-stroke position is updated, including: when the target number of times is equal to the preset number of times threshold, obtaining a preset update step size; and updating the stored value of the number of ripples at the current full-stroke position and the preset update step size.

[0076] In this embodiment, when the target number of times meets the preset threshold, the stored value of the ripple count can be updated according to the preset update step size and the current ripple count at the full travel position to avoid excessive adjustment of the stored value of the ripple count each time. For example, if the stored value of the ripple count is 2080, the current ripple count at the full travel position is 2032, and the preset threshold is 2, when the target number of times is equal to 2, the preset update step size is obtained. If the preset update step size is 16, since the current ripple count at the full travel position is 2032, directly replacing 2080 with 2032 would directly adjust by 48, which is too large a step. In this case, we can first determine to adjust downwards based on the fact that 2032 is less than 2080, and then adjust the stored value of the ripple count downwards according to the preset update step size, which is to update the stored value of the ripple count to 2064, thereby making the adjustment smoother.

[0077] In one embodiment, updating the stored value of the ripple count based on the current full-stroke position ripple count and a preset update step size includes: if the difference between the current full-stroke position ripple count and the stored value of the ripple count is greater than or equal to the preset update step size, then determining an update target value based on the preset update step size and the stored value of the ripple count; if the difference between the current full-stroke position ripple count and the stored value of the ripple count is less than the preset update step size, then determining an update target value based on the current full-stroke position ripple count; and determining the updated stored value of the ripple count based on the update target value.

[0078] In this embodiment, if the difference is large, that is, the difference between the current full-stroke position ripple count and the stored ripple count is greater than or equal to the preset update step size, then the update target value is determined according to the preset update step size and the stored ripple count. If the difference is small, that is, the difference between the current full-stroke position ripple count and the stored ripple count is less than the preset update step size, then the update target value is determined according to the current full-stroke position ripple count.

[0079] In one embodiment, determining the updated ripple count storage value based on the update target value includes: obtaining the maximum and minimum values ​​of the target ripple count range; if the update target value is greater than the maximum value or less than the minimum value, determining the updated ripple count storage value based on the maximum or minimum value.

[0080] In this embodiment, if the target value for updating is greater than Nmax or less than Nmin, the updated ripple count storage value is determined based on Nmax or Nmin, so that the updated ripple count storage value is still within the target range for the number of ripples, thereby avoiding exceeding the adjustment limit.

[0081] It should be noted that the specific values ​​of the parameters illustrated in the above specific embodiments are only illustrative and do not represent any specific limitations on them.

[0082] In one specific embodiment, the self-learning value update process is illustrated as follows: Figure 3 As shown.

[0083] In this embodiment, the controller calculates the new number of full-stroke position ripples N, and needs to determine the validity of this value. The number of full-stroke position ripples N must be greater than or equal to Nmin, and cannot exceed Nmax. If it falls within this range, the new number of full-stroke position ripples N is considered valid; otherwise, it is considered invalid.

[0084] The effective new full-stroke position ripple count N is compared with the currently applied full-stroke position ripple count (the currently applied full-stroke position ripple count is the stored value of the ripple count). If the difference is greater than the allowable difference range (i.e., the deviation threshold), self-learning is triggered, and the self-learning judgment process begins. Otherwise, self-learning judgment is not performed.

[0085] If the self-learning judgment process is initiated, and the new full-stroke position ripple count is deemed valid and reliable, then the self-learning value update judgment count C is incremented by 1. If the new full-stroke position ripple count N is deemed valid and reliable, and no self-learning judgment is performed, then the new full-stroke position ripple count N is considered to be within the allowable range and without excessive deviation, and the self-learning value update judgment count C is decremented by 1.

[0086] The number of self-learning value update judgments, C, is calculated. If this value exceeds the set value Cmax, it is determined that the number of full-stroke position ripples in the current application no longer conforms to the actual value, and the number of full-stroke position ripples in the current application needs to be updated. If the number of judgments C does not reach the set value Cmax, the self-learning judgment continues.

[0087] A diagram illustrating the update of the number of ripples at the full travel position of the car window is shown below. Figure 4 When it is determined that the number of full-stroke position ripples needs to be updated, the number of full-stroke position ripples is updated according to the set step size Nstep, and the calculated number of full-stroke position ripples must be within the allowable minimum and maximum values. If the calculated number exceeds the maximum value, the maximum value is output; if the calculated number is less than the minimum value, the minimum value is output.

[0088] This embodiment provides a method for updating the full-travel position of a vehicle window based on ripple control. This method effectively identifies invalid ripple counts for the full-travel window position, avoiding accidental and frequent updates of the self-learning value. Because this embodiment makes the self-learning value for the number of ripple counts for the full-travel window position more accurate and reliable, the window position calculation is also accurate and reliable. This effectively avoids safety issues such as the window anti-pinch function not triggering or being falsely triggered, and prevents driving and riding experience problems such as windows not being able to rise to the top or lower to the bottom.

[0089] In this embodiment and the above embodiments, a method for determining the validity of the self-learning value of the number of ripples at the full travel position of the window is provided, solving the problem of whether the self-learning value is reliable. The number of self-learning ripples is limited not only by a lower boundary minimum value but also by an upper boundary maximum value, thus considering a more comprehensive approach to reliability judgment. Furthermore, the actual driving environment is complex, including variations in ambient temperature and changes in the state of the window top rubber strip, which may cause self-learning to occur accidentally. If the new learned value replaces the original value at this time, an error will occur because the self-learning is triggered accidentally and does not necessarily indicate that a self-learning update is needed. In various embodiments of this application, by combining the validity of the current number of ripples at the full travel position with whether the target number meets the preset number threshold, the randomness is avoided, and the risk of accidental updates during the self-learning process is resolved. Moreover, in some embodiments of this application, by using a preset update step size, the problem of repeated updates of the self-learning value when the deviation between the new self-learning value and the original stored value is too large, if the original stored value is completely replaced at once, is addressed, making the update of the ripple count stored value smoother.

[0090] Based on the same technical concept, the second embodiment of this application provides a device for updating the number of positional ripples of a vehicle window throughout its entire travel range, such as... Figure 5 The device includes:

[0091] The acquisition module 501 is used to acquire the number of ripples at the current full-travel position of the window;

[0092] The first judgment module 502 is used to determine whether the current full-stroke position ripple count is valid based on a preset ripple count target interval; wherein, the valid current full-stroke position ripple count is within the ripple count target interval;

[0093] The second judgment module 503 is used to determine whether to trigger self-learning based on the difference between the current full-stroke position ripple count and the stored ripple count value when the current full-stroke position ripple count is valid.

[0094] The target count determination module 504 is used to update the current target count based on the judgment result;

[0095] The update module 505 is used to update the stored value of the number of ripples based on the number of ripples at the current full-stroke position when the target number of times meets the preset number of times threshold.

[0096] This device determines whether the current full-stroke position ripple count is valid based on a preset target range for the number of ripples, thereby filtering out obviously invalid ripple counts. If valid, it further determines whether the difference between the current full-stroke position ripple count and the stored ripple count triggers self-learning, and updates the target number based on the determination result. When the target number meets the preset number threshold, it means that the stored ripple count can be updated based on the current full-stroke position ripple count, thus making the updated ripple count stored value accurate and reliable, eliminating updates under random conditions, and improving the accuracy and reliability of the full-stroke position ripple count self-learning.

[0097] like Figure 6 As shown, the third embodiment of this application provides an electronic device, including a processor 111, a communication interface 112, a memory 113, and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114.

[0098] Memory 113 is used to store computer programs;

[0099] In one embodiment, when the processor 111 executes the program stored in the memory 113, it implements the method for updating the number of window full-travel position ripples provided in any of the foregoing method embodiments.

[0100] The memory and processor in the aforementioned electronic device communicate with each other via a communication bus and communication interface. The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc.

[0101] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0102] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0103] The fourth embodiment of this application provides a computer-readable medium having processor-executable non-volatile program code.

[0104] Optionally, in embodiments of this application, the computer-readable medium is configured to store program code for a processor to execute the above-described methods.

[0105] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be repeated here.

[0106] In specific implementation, the embodiments of this application can be referred to the above embodiments and have corresponding technical effects.

[0107] It is understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions of this application, or combinations thereof.

[0108] For software implementation, the techniques described herein can be implemented through units that perform the functions described herein. The software code can be stored in memory and executed by a processor. The memory can be implemented within the processor or external to the processor.

[0109] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0110] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0111] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0112] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0113] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0114] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or parts of the technical solutions, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0115] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0116] The above embodiments are merely preferred embodiments provided to fully illustrate this application, and the scope of protection of this application is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on this application are all within the scope of protection of this application.

Claims

1. A method for updating the number of positional ripples throughout the entire travel of a vehicle window, characterized in that, The method includes: Obtain the number of ripples at the current full travel position of the window; The validity of the current full-stroke position ripple count is determined based on a preset target range for the number of ripples; wherein, a valid current full-stroke position ripple count is within the target range for the number of ripples. If the number of ripples at the current full-stroke position is valid, determine whether to trigger self-learning based on the difference between the number of ripples at the current full-stroke position and the stored value of the number of ripples; Update the current target number based on the judgment result; If the target number of times meets the preset number of times threshold, the stored value of the number of ripples is updated according to the number of ripples at the current full-stroke position.

2. The method according to claim 1, characterized in that, Update the current target count based on the judgment result, including: If the judgment result indicates that self-learning has been triggered, then the target number is incremented by one; If the judgment result indicates that self-learning is not triggered, then the target number is reduced by one.

3. The method according to claim 1 or 2, characterized in that, If the target number of times meets a preset threshold, the stored value of the ripple count is updated based on the current full-stroke position ripple count, including: If the target number of times is equal to a preset number threshold, obtain a preset update step size; The stored value of the number of ripples is updated based on the current full-stroke position ripple count and the preset update step size.

4. The method according to claim 3, characterized in that, The stored value of the number of ripples is updated based on the current full-stroke position ripple count and the preset update step size, including: If the difference between the current full-stroke position ripple count and the stored ripple count value is greater than or equal to the preset update step size, then the update target value is determined based on the preset update step size and the stored ripple count value. If the difference between the current full-stroke position ripple count and the stored ripple count value is less than the preset update step size, then the update target value is determined based on the current full-stroke position ripple count. The updated ripple count storage value is determined based on the updated target value.

5. The method according to claim 4, characterized in that, Determining the updated ripple count storage value based on the updated target value includes: Obtain the maximum and minimum values ​​of the target range for the number of ripples; If the update target value is greater than the maximum value or less than the minimum value, the updated ripple count storage value is determined based on the maximum value or the minimum value.

6. The method according to claim 1, characterized in that, If the current full-stroke position ripple count is valid, determine whether to trigger self-learning based on the difference between the current full-stroke position ripple count and the stored ripple count value, including: If the number of ripples at the current full-stroke position is valid, obtain the difference between the number of ripples at the current full-stroke position and the stored value of the number of ripples; Obtain the preset deviation threshold; Whether to trigger self-learning is determined based on the difference and the deviation threshold; If the difference is greater than or equal to the deviation threshold, the judgment result is determined to trigger self-learning; If the difference is less than the deviation threshold, the judgment result is determined to be that self-learning is not triggered.

7. The method according to claim 1, characterized in that, Obtain the number of ripples at the current full travel position of the window, including: Obtain control commands for the vehicle windows, and the window positions before and after the execution of the control commands; The number of ripples at the current full travel position of the window is determined based on the control command and the window position before and after execution.

8. A device for updating the number of positional ripples throughout the entire travel of a vehicle window, characterized in that, The device includes: The acquisition module is used to obtain the number of ripples at the current full-travel position of the window; The first judgment module is used to determine whether the current full-stroke position ripple count is valid based on a preset ripple count target interval; wherein, the valid current full-stroke position ripple count is within the ripple count target interval; The second judgment module is used to determine whether to trigger self-learning based on the difference between the current full-stroke position ripple count and the stored ripple count value when the current full-stroke position ripple count is valid. The target count determination module is used to update the current target count based on the judgment result; The update module is used to update the stored value of the number of ripples based on the number of ripples at the current full-stroke position when the target number of times meets the preset number of times threshold.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in a memory, it implements the method for updating the number of position ripples of a vehicle window throughout its entire travel distance as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for updating the number of ripples at the full travel position of a vehicle window as described in any one of claims 1-7.

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