Window anti-pinch self-learning method, device, equipment and storage medium

Abstract

The present invention relates to the technical field of window control, and discloses a window anti-pinch self-learning method, device, equipment and storage medium. The method includes: obtaining the top position and the bottom position of a target window; driving the target window to rise according to the top position and the bottom position; during the rising process of the target window, obtaining the input signal characteristics of the target window switch according to the current current and the waveform period of the motor; when the input signal characteristics are target characteristics, determining the self-learning result of the target window anti-pinch according to the current current, the target stall current and the preset duration; since the present invention obtains the input signal characteristics according to the current current and the waveform period during the rising process of the target window, determines whether the input signal characteristics are target characteristics, and if so, determines the self-learning result according to the current current, the target stall current and the preset duration, it can effectively improve the accuracy of the self-learning result for determining window anti-pinch and distinguish the reasons for the failure of self-learning.

Description

Technical Field

[0001] The present invention relates to the technical field of window control, and particularly to a window anti-pinch self-learning method, device, equipment and storage medium. Background Art

[0002] With the continuous popularization of intelligent automotive equipment, how to ensure the matching of intelligent equipment and the whole vehicle during the production process to improve the factory operation efficiency has gradually become a research hotspot. Window lifting is a frequently used function by customers, and it has become an industry trend to configure an anti-pinch function. In order to ensure the customer experience, before mass production, the manufacturer will detect the anti-pinch function of the window and complete the learning of window anti-pinch. Among them, the currently common method for window resistance learning is to control the glass to rise from the bottom position to the top position. During the window resistance learning process, it is easy to fail due to internal or external factors of the window system, resulting in low accuracy of the window anti-pinch function test and inability to distinguish the reasons for the self-learning failure.

[0003] The above content is only used to assist in understanding the technical solution of the present invention, and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of the present invention is to provide a window anti-pinch self-learning method, device, equipment and storage medium, aiming to solve the technical problems that the accuracy of the self-learning result for determining window anti-pinch in the prior art is low and the reasons for the self-learning failure cannot be distinguished.

[0005] To achieve the above purpose, the present invention provides a window anti-pinch self-learning method, and the window anti-pinch self-learning method includes the following steps:

[0006] Obtain the top position and bottom position of the target window;

[0007] Drive the target window to rise according to the top position and the bottom position;

[0008] During the rising process of the target window, obtain the input signal characteristics of the target window switch according to the current current and waveform period of the motor;

[0009] When the input signal characteristics are target characteristics, determine the self-learning result of the target window anti-pinch according to the current current, the target stall current and the preset duration.

[0010] Optionally, the current current includes the current moment current and the previous moment current, and the waveform period includes the current moment waveform period and the previous moment waveform period;

[0011] During the rising process of the target window, obtaining the input signal characteristics of the target window switch according to the current current and waveform period of the motor includes:

[0012] During the upward movement of the target window, determine whether the position of the target window is within the target anti-pinch area according to the current number of waveforms;

[0013] When the position of the target window is within the target anti-pinch area, obtain the voltage at the current moment and the voltage at the previous moment;

[0014] Calculate the current motor torque at the current moment through the target torque calculation strategy based on the current moment current, the voltage at the current moment, and the waveform period at the current moment;

[0015] Calculate the motor torque at the previous moment through the target torque calculation strategy based on the current at the previous moment, the voltage at the previous moment, and the waveform period at the previous moment;

[0016] Determine the resistance change rate according to the current motor torque, the motor torque at the previous moment, and the fixed sampling period;

[0017] When the resistance change rate is greater than or equal to the preset anti-pinch threshold, obtain the input signal characteristics of the target window switch according to the current moment current and the waveform period at the current moment.

[0018] Optionally, after determining whether the position of the target window is within the target anti-pinch area according to the current number of waveforms during the upward movement of the target window, further include:

[0019] When the position of the target window is not within the target anti-pinch area, determine whether the current number of waveforms is less than the first waveform number difference;

[0020] When the current number of waveforms is less than the first waveform number difference, continue to determine whether the current at the current moment is greater than the target determination current;

[0021] When the current at the current moment is greater than the target determination current, determine that the self-learning result of the target window anti-pinch is that the target window rises to the top position, and end the window anti-pinch self-learning.

[0022] Optionally, after determining the resistance change rate according to the current motor torque, the motor torque at the previous moment, and the fixed sampling period, further include:

[0023] When the resistance change rate is greater than or equal to the preset anti-pinch threshold, determine the target current determination range according to the rated current and the locked-rotor current;

[0024] When the current at the current moment is not within the target current determination range and / or the waveform period is greater than or equal to the preset period threshold, obtain the input signal characteristics of the target window switch.

[0025] Optionally, when the current current is not within the target current determination range and / or the waveform period is greater than a preset period threshold, after obtaining the input signal characteristics of the target window switch, the method further includes:

[0026] When the input signal characteristics are not the target characteristics, obtain the current count of the target reverse counter;

[0027] When the current count is the target count, the target window reverses, obtain and record the position where the current position waveform number is the target obstacle point, and the count of the target reverse counter is incremented by 1;

[0028] The target window relearns.

[0029] Optionally, after obtaining the current count of the target reverse counter when the input signal characteristics are not the target characteristics, the method further includes:

[0030] When the current count is not the target count, obtain the current position waveform number and the waveform number at the last reverse position in history;

[0031] Perform a difference calculation on the current position waveform number and the waveform number at the last reverse position in history to obtain a corresponding second waveform number difference;

[0032] When the second waveform number difference is less than a preset waveform number threshold, obtain the current timing time of the target timer;

[0033] Judge whether the target window meets the rising condition according to the current timing time, where the rising condition is that the current timing time is less than a preset duration, the waveform period changes at the current moment, the resistance change rate is less than a preset anti-pinch threshold, and the current current is less than the target determination current;

[0034] When the rising condition is met, determine that the target window has passed the target obstacle point, and determine that the self-learning result of the target window anti-pinch is to continue driving the target window to rise;

[0035] When the rising condition is not met, determine that the self-learning result of the target window anti-pinch is that the target window is deformed, and end the learning.

[0036] Optionally, after performing a difference calculation on the current position waveform number and the waveform number at the last reverse position in history to obtain a corresponding second waveform number difference, the method further includes:

[0037] When the second waveform number difference is greater than or equal to the preset waveform number threshold, obtain the position of the target obstacle point;

[0038] When the current position is within a preset area of the position of the target obstacle point, determine that the source of the obstacle point at the current position is external to the glass system;

[0039] Trigger the anti-pinch function of the target window and increment the current count by 1;

[0040] When the current count after the increment is greater than the preset count, determine that the self-learning result of the target window anti-pinch is the existence of external self-learning interference factors, and end the window anti-pinch self-learning.

[0041] Optionally, when the difference in the number of the second waveforms is greater than or equal to the preset waveform number threshold, when obtaining the position of the target obstacle point, it further includes:

[0042] When the current position is not within the preset area of the position of the target obstacle point, determine the current position as the position of the target obstacle point, reverse the target window, and increment the count of the target reverse counter by 1;

[0043] The target window re-learns.

[0044] Optionally, when the input signal characteristic is the target characteristic, determining the self-learning result of the target window anti-pinch according to the current current, the target stall current, and the preset duration includes:

[0045] When the input signal characteristic is the target characteristic, calculate the target determination current according to the target stall current and the preset percentage;

[0046] Statistical duration during which the current current is greater than or equal to the target determination current;

[0047] When the duration is greater than the preset duration, trigger the anti-pinch function of the target window, and determine that the self-learning result of the target window anti-pinch is that the door is severely deformed, and end the learning.

[0048] In addition, to achieve the above object, the present invention also proposes a window anti-pinch self-learning device, and the window anti-pinch self-learning device includes:

[0049] An acquisition module for acquiring the top position and the bottom position of the target window;

[0050] A driving module for driving the target window to rise according to the top position and the bottom position;

[0051] The acquisition module is further configured to obtain the input signal characteristic of the target window switch according to the current current and the waveform period of the motor during the rising process of the target window;

[0052] A determination module for determining the self-learning result of the target window anti-pinch according to the current current, the target stall current, and the preset duration when the input signal characteristic is the target characteristic.

[0053] In addition, to achieve the above object, the present invention also provides a window anti-pinch self-learning device, which includes: a memory, a processor, and a window anti-pinch self-learning program stored on the memory and executable on the processor. The window anti-pinch self-learning program is configured to implement the window anti-pinch self-learning method as described above.

[0054] In addition, to achieve the above object, the present invention also provides a storage medium with a window anti-pinch self-learning program stored thereon. When the window anti-pinch self-learning program is executed by a processor, it implements the window anti-pinch self-learning method as described above.

[0055] The window anti-pinch self-learning method provided by the present invention includes: obtaining the top position and bottom position of a target window; driving the target window to rise according to the top position and bottom position; during the rising process of the target window, obtaining the input signal characteristics of the target window switch according to the current current and waveform period of the motor; when the input signal characteristics are target characteristics, determining the self-learning result of the target window anti-pinch according to the current current, the target stall current, and a preset duration. Since the present invention obtains the input signal characteristics according to the current current and waveform period during the rising process of the target window, determines whether the input signal characteristics are target characteristics, and if so, determines the self-learning result according to the current current, the target stall current, and the preset duration, it can effectively improve the accuracy of determining the self-learning result of the window anti-pinch and distinguish the reasons for the failure of self-learning. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a schematic structural diagram of a window anti-pinch self-learning device in a hardware operating environment related to the embodiment solution of the present invention;

[0057] Figure 2 is a schematic flowchart of the first embodiment of the window anti-pinch self-learning method of the present invention;

[0058] Figure 3 is a schematic flowchart of the second embodiment of the window anti-pinch self-learning method of the present invention;

[0059] Figure 4 is a schematic diagram of area division in an embodiment of the window anti-pinch self-learning method of the present invention;

[0060] Figure 5 is a schematic flowchart of the third embodiment of the window anti-pinch self-learning method of the present invention;

[0061] Figure 6 is a schematic diagram of functional modules of the first embodiment of the window anti-pinch self-learning device of the present invention.

[0062] The realization, functional features, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0063] 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.

[0064] Referring to Figure 1 , Figure 1 , which is a schematic structural diagram of a window anti-pinch self-learning device for the hardware operating environment involved in the embodiment solution of the present invention.

[0065] As Figure 1 shown, the window anti-pinch self-learning device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to implement connection communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless-fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0066] Those skilled in the art can understand that Figure 1 the structure shown in

[0067] does not constitute a limitation on the window anti-pinch self-learning device, and may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements. Figure 1 shown, in the memory 1005 as a storage medium, there may be included an operating system, a network communication module, a user interface module, and a window anti-pinch self-learning program.

[0068] In Figure 1In the window anti-pinch self-learning device shown, the network interface 1004 is mainly used for data communication with the network integrated platform workstation; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the window anti-pinch self-learning device of the present invention can be arranged in the window anti-pinch self-learning device. The window anti-pinch self-learning device calls the window anti-pinch self-learning program stored in the memory 1005 through the processor 1001 and executes the window anti-pinch self-learning method provided by the embodiments of the present invention.

[0069] Based on the above hardware structure, an embodiment of the window anti-pinch self-learning method of the present invention is proposed.

[0070] Refer to Figure 2 , Figure 2 which is a schematic flowchart of the first embodiment of the window anti-pinch self-learning method of the present invention.

[0071] In the first embodiment, the window anti-pinch self-learning method includes the following steps:

[0072] Step S10, obtain the top position and the bottom position of the target window.

[0073] It should be noted that the execution subject of this embodiment is the window anti-pinch self-learning device, and it can also be other devices that can achieve the same or similar functions, such as a window controller, etc. This embodiment does not limit this. In this embodiment, the window controller is taken as an example for illustration.

[0074] It should be understood that the top position refers to the topmost position of the target window. This top position can be determined by manually controlling the target window to rise and when the stall time exceeds a preset time threshold, which can be set to 2 seconds. After reaching the top position, the target window already has the rising function, and at this time, the anti-pinch function starts. Before obtaining the top position, the initial state of the target window can be any state. The bottom position refers to the position where the target window automatically descends from the top position to the bottom at one time. The window controller, which is the execution subject of this embodiment, has the function of identifying the current absolute position of the glass and the lifting resistance at each absolute position, so as to achieve anti-pinch within a specified range.

[0075] Step S20, drive the target window to rise according to the top position and the bottom position.

[0076] It can be understood that after obtaining the top position and the bottom position of the target window, the window controller drives the target window to rise from the bottom position to the top position through the torque provided by the vehicle. The target window will be subject to resistance during the rising process, and as the position of the target window rises, the resistance it receives is also constantly changing.

[0077] Step S30, during the upward movement of the target window, obtain the input signal characteristics of the target window switch according to the current current and waveform period of the motor.

[0078] It should be understood that the target window switch refers to the switch that controls the upward or downward movement of the target window. Specifically, when the target window switch is pulled up, the target window starts to rise from the current position. Conversely, when the target window switch is pressed down, the target window starts to descend from the current position. The input signal characteristics refer to the characteristics of the input signal from the target window switch. The input signal characteristics can be continuous or discontinuous. By judging the input signal characteristics of the target window switch, the user's intention can be determined.

[0079] It can be understood that the current current refers to the current at the current moment during the operation of the motor. The current current can be obtained by real-time acquisition using a current detection device. Similarly, the waveform period refers to the period of the number of collected waveforms. The waveform period and the current current can be obtained synchronously.

[0080] Step S40, when the input signal characteristics are the target characteristics, determine the self-learning result of anti-pinch for the target window according to the current current, the target stall current, and the preset duration.

[0081] It can be understood that the target characteristics refer to the continuous characteristics. After obtaining the input signal characteristics of the target window switch, it is necessary to judge whether the input signal characteristics are the target characteristics. If so, it indicates that the user's intention is to continue to raise the target window. It can be considered that there is no possibility of a dangerous accident at the scene, and continue to try to drive the window upward while synchronously monitoring the current and duration to judge whether the target window can continue to rise.

[0082] It should be understood that the target stall current refers to the current when the target window is stalled, and the preset duration refers to the minimum duration for judging whether the target window can continue to rise. The preset duration can be 2 seconds. Determine the self-learning result of anti-pinch for the target window according to the current current, the target stall current, and the preset duration. The self-learning result of anti-pinch for the target window can be that the target window rises to the top position, the target window crosses the target obstacle point and continues to rise, there are external self-learning interference factors, and there are serious deformation defects in the door, etc.

[0083] In this embodiment, the top position and the bottom position of the target window are obtained; the target window is driven to rise according to the top position and the bottom position; during the rising process of the target window, the input signal characteristics of the target window switch are obtained according to the current current and the waveform period of the motor; when the input signal characteristics are the target characteristics, the self-learning result of the anti-pinch of the target window is determined according to the current current, the target stall current and the preset duration. Since in this embodiment, the input signal characteristics are obtained according to the current current and the waveform period during the rising process of the target window, and it is judged whether the input signal characteristics are the target characteristics. If so, the self-learning result is determined according to the current current, the target stall current and the preset duration, which can effectively improve the accuracy of the self-learning result for determining window anti-pinch and distinguish the reasons for the failure of self-learning.

[0084] In one embodiment, as Figure 3 described, based on the first embodiment, the second embodiment of the window anti-pinch self-learning method of the present invention is proposed. The step S30 includes:

[0085] Step S301, during the rising process of the target window, judge whether the position of the target window is located in the target anti-pinch area according to the current waveform number.

[0086] It should be understood that the target anti-pinch area refers to the anti-pinch area within the window area. Refer to Figure 4 , Figure 4 for the area division schematic diagram. Specifically, the window area is divided into an anti-pinch area and a non-anti-pinch area. The non-anti-pinch area is located at the top of the anti-pinch area. The anti-pinch control force of the anti-pinch area is within 100 N, and the spring coefficient can be 10 N / m. During the rising process of the target window, the waveform number is collected in real time by the target waveform acquisition device, and then it is judged whether the position of the target window is located in the target anti-pinch area according to the collected current waveform number.

[0087] Further, after step S301, it further includes: when the position of the target window is not located in the target anti-pinch area, judge whether the current waveform number is less than the first waveform number difference; when the current waveform number is less than the first waveform number difference, continue to judge whether the current current is greater than the target determination current; when the current current at the current moment is greater than the target determination current, determine that the self-learning result of the anti-pinch of the target window is that the target window rises to the top position, and end the window anti-pinch self-learning.

[0088] It can be understood that the first waveform number difference refers to the maximum waveform number difference for determining whether the window has not reached the top position, and the target determination current refers to the maximum current value for determining whether the window has reached the top position. When it is determined that the position of the target window is not in the target anti-pinch area, continue to determine whether the current waveform number is less than the first waveform number difference. If not, it is determined that the window has not reached the top position, and then continue to drive the target window to rise. If so, continue to determine whether the current current is greater than the target determination current. If so, it indicates that the target window has risen to the top position, and the current window anti-pinch self-learning ends. If not, it indicates that the window has not reached the top position, and the target window still needs to be driven to rise.

[0089] Step S302, when the position of the target window is in the target anti-pinch area, obtain the current voltage and the voltage at the previous moment.

[0090] It can be understood that the current voltage refers to the driving voltage of the motor at the current moment. Similarly, the voltage at the previous moment refers to the driving voltage of the motor at the previous moment. The current voltage and the voltage at the previous moment are obtained by the voltage sensor at different times.

[0091] Step S303, calculate the current motor torque by the target torque calculation strategy for the current current, the current voltage, and the current waveform period.

[0092] It should be understood that the target torque calculation strategy refers to the strategy for calculating the motor torque through specific parameters. The target torque calculation strategy can be expressed by the following formula: N = (U * I * K) / (a * T), where U is the voltage, I is the current, K is the constant affecting motor loss and efficiency, a is the inverse proportional coefficient of the motor speed and the waveform period, and T is the waveform period. The current motor torque is obtained by calculating the current current, the current voltage, and the current waveform period through the target torque calculation strategy. Specifically:

[0093] N1 = (U1 * I1 * K) / (a * T1);

[0094] Where, N1 is the current motor torque, U1 is the current voltage, I is the current at the current moment, K is the constant affecting motor loss and efficiency, a is the inverse proportional coefficient of the motor speed and the waveform period, and T is the current waveform period.

[0095] Step S304, calculate the motor torque at the previous moment by the target torque calculation strategy for the current at the previous moment, the voltage at the previous moment, and the waveform period at the previous moment.

[0096] It can be understood that after obtaining the current at the previous moment, the voltage at the previous moment, and the waveform period at the previous moment, the current at the previous moment, the voltage at the previous moment, and the waveform period at the previous moment are calculated through the target torque calculation strategy to obtain the motor torque at the previous moment, specifically:

[0097] N2 = (U2 * I2 * K) / (a * T2);

[0098] Among them, N2 is the motor torque at the previous moment, U2 is the voltage at the previous moment, I2 is the current at the previous moment, K is the constant affecting motor loss and efficiency, a is the inverse proportional coefficient of motor speed and waveform period, and T2 is the waveform period at the previous moment.

[0099] Step S305, determine the resistance change rate according to the current motor torque, the motor torque at the previous moment, and the fixed sampling period.

[0100] It should be understood that the fixed sampling period refers to the period of collecting the number of waveforms in a fixed time period, and the resistance change rate refers to the change rate of the resistance during the upward movement of the target window. The resistance change rate is obtained by the change value of the motor torque per unit time and the fixed sampling period. This unit time can be the interval time for collecting the voltage at the current moment and the voltage at the previous moment, specifically:

[0101] ΔF = ΔN / ΔT;

[0102] Among them, ΔF is the resistance change rate, ΔN is the change value of the motor torque, and ΔT is the fixed sampling period.

[0103] Furthermore, after step S305, it further includes: when the resistance change rate is greater than or equal to the preset anti-pinch threshold, determine the target current determination range according to the rated current and the locked-rotor current; when the current at the current moment is not within the target current determination range and / or the waveform period at the current moment is greater than or equal to the preset period threshold, obtain the input signal characteristics of the target window switch.

[0104] It can be understood that the rated current refers to the current when the motor operates at the rated power under the rated voltage. The target current determination range refers to the range for determining whether the target window can continue to be driven upward. The target current determination range is determined by the rated current and the locked-rotor current. For example, the rated current is I 额 , the target locked-rotor current is I 堵 , then the target current determination range is [I 额 * 120%, I 堵 * 75%].

[0105] It should be understood that after obtaining the target determination current range, it is necessary to determine whether the current at the current moment is not within the target determination current range, or whether the waveform period at the current moment is greater than or equal to the preset period threshold. If any of the above conditions is met, the target window switch is detected in real time to obtain the corresponding input signal characteristics.

[0106] Further, when the current at the current moment is not within the target current determination range and / or the waveform period at the current moment is greater than or equal to the preset period threshold, after obtaining the input signal characteristics of the target window switch, it further includes: when the input signal characteristics are not the target characteristics, obtaining the current count number of the target reverse counter; when the current count number is the target number, the target window reverses, obtaining and recording the position where the current position waveform number is the target obstacle point, and the count number of the target reverse counter +1; the target window re-learns.

[0107] It can be understood that the target reverse counter refers to a device that counts the number of times the target window reverses. When the determination result is that the input signal characteristics of the target window are not the target characteristics, indicating that the user's intention is not involved, it is necessary to determine whether the target window is performing anti-pinch self-learning for the first time. Specifically, it is determined by judging whether the current count number of the target reverse counter is the target number. If so, it is determined as the first anti-pinch. Considering safety factors, when the target window reverses, the target number can be 0. Then, record the current position waveform number when the target window reverses, and use the position corresponding to the current position waveform number as the position of the target obstacle point, and the count number of the target reverse counter will be +1 on the original basis. Then the target window will re-learn.

[0108] Further, when the input signal characteristics are not the target characteristics, after obtaining the current count number of the target reverse counter, it further includes: when the current count number is not the target number, obtaining the current position waveform number and the waveform number at the previous reverse position in history; performing a difference calculation on the current position waveform number and the waveform number at the previous reverse position in history to obtain the corresponding second waveform number difference; when the second waveform number difference is less than the preset anti-pinch threshold, obtaining the current timing time of the target timer; judging whether the target window meets the rising condition according to the current timing time, and the rising condition is that the current timing time is less than the preset duration, and the waveform period at the current moment changes, and the resistance change rate is less than the preset anti-pinch threshold, and the current current is less than the target determination current; when the rising condition is met, it is determined that the target window has passed the target obstacle point, and the self-learning result of the target window anti-pinch is determined to continue driving the target window to rise. When the rising condition is not met, the self-learning result of the target window anti-pinch is determined that the target window is deformed, and the learning ends.

[0109] It should be understood that the number of waveforms at the current position refers to the number of waveforms of the target window at the current position. Similarly, the number of waveforms at the last reverse position in history refers to the number of waveforms of the target window when the last reverse occurred. When it is determined that the current counting number is not the target number, the second waveform number difference refers to the result obtained by subtracting the number of waveforms at the current position from the number of waveforms at the last reverse position in history. By comparing the second waveform number difference with the preset waveform number threshold, it can be confirmed whether it is a subjective obstacle such as the body of a non-operator user. If the second waveform number difference is less than the preset waveform number threshold, the target timer starts to work, and then it is judged whether the target window meets the rising condition according to the current timing time. If so, it is determined that the target window has crossed the target obstacle point. At this time, the self-learning result of the anti-pinch function of the target window is to continue driving the target window to rise. The rising condition is that the current timing time is less than the preset duration, and the waveform period changes at the current moment, and the resistance change rate is less than the preset anti-pinch threshold, and the current current is less than the preset anti-pinch threshold of the target determination current. If any of the above conditions is not met, it is determined that the self-learning result of the anti-pinch function of the target window is that the target window is deformed, and this learning will end at this time.

[0110] Further, after calculating the difference between the number of waveforms at the current position and the number of waveforms at the last reverse position in history to obtain the corresponding second waveform number difference, it further includes: when the second waveform number difference is greater than or equal to the preset waveform number threshold, obtaining the position of the target obstacle point; when the current position is within the preset area of the position of the target obstacle point, determining that the obstacle point at the current position originates from outside the glass system; triggering the anti-pinch function of the target window and adding 1 to the current counting number; when the current counting number after adding 1 is greater than the preset number, determining that the self-learning result of the anti-pinch function of the target window is that there are external self-learning interference factors, and ending the self-learning of the anti-pinch function of the window.

[0111] It can be understood that the target obstacle point refers to the obstacle point determined in the previous self-learning process, and the preset area refers to the area below the target obstacle point. When it is determined that the second waveform number difference is greater than or equal to the preset waveform number threshold, it is further judged whether the current position is within the preset area of the position of the target obstacle point. If not, it is determined that the current position is a new obstacle point. At this time, the safety anti-pinch function is triggered, the target window reverses, and then the number of waveforms at the reversed position is recorded, and the current counting number of the target reverse counter is incremented by 1.

[0112] It should be understood that when determining that the current position is within the preset area of the position of the target obstacle point, it is determined that the obstacle point at the current position originates from outside the glass system, rather than being caused by the glass system itself. At this time, the safety anti-pinch function also needs to be triggered, the target window reverses, the current count of the target reverse counter is incremented by 1, and then it is determined whether the accumulated current count is greater than the preset number of times. This preset number of times can be 3. If so, it indicates that there are interference conditions from outside the glass system for 3 consecutive times, and the window anti-pinch self-learning cannot proceed smoothly at this time. Therefore, it is determined that the self-learning result of the target window anti-pinch is that there are external self-learning interference factors, and the window anti-pinch self-learning is terminated.

[0113] Further, when the difference in the number of the second waveforms is greater than or equal to the preset waveform number threshold and obtaining the position of the target obstacle point, it further includes: when the current position is not within the preset area of the position of the target obstacle point, determining the current position as the position of the target obstacle point, reversing the target window, and incrementing the count of the target reverse counter by 1; the target window re-learns.

[0114] It can be understood that when determining that the current position is not within the preset area of the position of the target obstacle point, that is, when the current position is above the target obstacle point, the current position is taken as the position of the target obstacle point at this time, and then the target window triggers the anti-pinch function and reverses. At this time, the count of the target reverse counter is incremented by 1, and then the target window re-learns.

[0115] Step S306, when the rate of change of the resistance is greater than or equal to the preset anti-pinch threshold, obtaining the input signal characteristics of the target window switch according to the current at the current moment and the waveform period at the current moment.

[0116] It can be understood that the preset anti-pinch threshold refers to the default value for judging whether to trigger the anti-pinch action before the self-learning is completed. This preset anti-pinch threshold also refers to the rate of change. After obtaining the rate of change of the resistance, it is necessary to determine whether the rate of change of the resistance is greater than or equal to the preset anti-pinch threshold. If not, it indicates that the target window can continue to be driven upward, and then it returns to the steps of collecting the current, voltage, and waveform period. If so, the input signal characteristics of the target window switch are obtained according to the current at the current moment and the waveform period at the current moment.

[0117] In this embodiment, during the upward movement of the target window, it is determined whether the position of the target window is in the target anti-pinch area according to the current number of waveforms; if so, the voltage at the current moment and the voltage at the previous moment are collected in real time, and then the target torque calculation strategy is used to calculate the current at the current moment, the voltage at the current moment, and the waveform period at the current moment, as well as the current at the previous moment, the voltage at the previous moment, and the waveform period at the previous moment respectively. Then, the resistance change rate is determined according to the current motor torque, the motor torque at the previous moment, and the fixed sampling period. Then, it is determined whether the resistance change rate is greater than or equal to the preset anti-pinch threshold. If so, the input signal characteristics of the target window switch are obtained according to the current at the current moment and the waveform period at the current moment, so as to be able to determine whether there is a user intention, and further effectively improve the accuracy of the self-learning result of determining window anti-pinch.

[0118] In one embodiment, as Figure 5 described, based on the first embodiment, the third embodiment of the window anti-pinch self-learning method of the present invention is proposed. The step S40 includes:

[0119] Step S401, when the input signal characteristic is the target characteristic, calculate the target determination current according to the target stall current and the preset percentage.

[0120] It can be understood that the preset percentage refers to the percentage for calculating the target determination current. When it is determined that the input signal characteristic of the target window switch is the target characteristic, the target determination current is calculated through the target stall current and the preset percentage. For example, the target stall current is I 堵 , and the preset percentage is 85%, then the target determination current is I 堵 *85%.

[0121] Step S402, count the duration during which the current current is greater than or equal to the target determination current.

[0122] It should be understood that after obtaining the target determination current, it is necessary to determine whether the current current is greater than or equal to the target determination current. If so, the duration of this condition is counted in real time.

[0123] Step S403, when the duration is greater than the preset duration, trigger the anti-pinch function of the target window, and determine that the self-learning result of the target window anti-pinch is that the door is severely deformed, and end the learning.

[0124] It can be understood that the preset duration refers to the minimum duration for determining whether the target window can continue to move upward. The preset duration can be 2 seconds. When the duration during which the current current is greater than or equal to the target determination current is greater than the preset duration, it indicates that the target window cannot cross the target obstacle point, and it is determined that the self-learning result of the target window anti-pinch is a severe deformation defect of the door.

[0125] In this embodiment, when the input signal characteristic is the target characteristic, the target determination current is calculated according to the target stall current and a preset percentage; the duration during which the current current is greater than or equal to the target determination current is statistically counted; when the duration is greater than a preset duration, the anti-pinch function of the target window is triggered, and it is determined that the self-learning result of the target window anti-pinch is that the door is severely deformed, and the learning is ended; because in this embodiment, when the input signal characteristic is the target characteristic, the target stall current and the preset percentage are calculated, and then when the current current is greater than or equal to the target determination current, the duration of this condition is statistically counted, and then it is judged whether the duration is greater than the preset duration. If so, the anti-pinch function of the target window is triggered, and it is determined that the self-learning result of the target window anti-pinch is that the door is severely deformed, and the learning is ended, so that the accuracy of determining the self-learning result of the target window anti-pinch can be effectively improved.

[0126] In addition, an embodiment of the present invention further provides a storage medium, on which a window anti-pinch self-learning program is stored. When the window anti-pinch self-learning program is executed by a processor, the steps of the window anti-pinch self-learning method as described above are implemented.

[0127] Since this storage medium adopts all the technical solutions of the above-mentioned all embodiments, it has at least all the beneficial effects brought by the technical solutions of the above-mentioned embodiments, which will not be elaborated one by one here.

[0128] In addition, referring to Figure 6 , an embodiment of the present invention further provides a window anti-pinch self-learning device, and the window anti-pinch self-learning device includes:

[0129] An acquisition module 10, configured to acquire the top position and the bottom position of the target window.

[0130] A driving module 20, configured to drive the target window to rise according to the top position and the bottom position.

[0131] The acquisition module 10 is further configured to acquire the input signal characteristic of the target window switch according to the current current and the waveform period of the motor during the rising process of the target window.

[0132] A determination module 30, configured to determine the self-learning result of the target window anti-pinch according to the current current, the target stall current, and the preset duration when the input signal characteristic is the target characteristic.

[0133] In this embodiment, the top position and the bottom position of the target window are obtained; the target window is driven to rise according to the top position and the bottom position; during the rising process of the target window, the input signal characteristics of the target window switch are obtained according to the current current and the waveform period of the motor; when the input signal characteristics are target characteristics, the self-learning result of anti-pinch of the target window is determined according to the current current, the target stall current and the preset duration; since in this embodiment, the input signal characteristics are obtained according to the current current and the waveform period during the rising process of the target window, and it is judged whether the input signal characteristics are target characteristics, if so, the self-learning result is determined according to the current current, the target stall current and the preset duration, which can effectively improve the accuracy of the self-learning result for determining window anti-pinch and distinguish the reasons for the failure of self-learning.

[0134] It should be noted that the above-described work flow is only illustrative and does not limit the protection scope of the present invention. In actual applications, those skilled in the art can select some or all of them according to actual needs to achieve the purpose of the solution of this embodiment, and no limitation is made here.

[0135] In addition, for the technical details not described in detail in this embodiment, reference can be made to the window anti-pinch self-learning method provided in any embodiment of the present invention, which will not be elaborated here.

[0136] In one embodiment, the obtaining module 10 is further configured that the current current includes the current moment current and the previous moment current, and the waveform period includes the current moment waveform period and the previous moment waveform period; during the rising process of the target window, it is judged whether the position of the target window is located in the target anti-pinch area according to the current waveform number; when the position of the target window is located in the target anti-pinch area, the current moment voltage and the previous moment voltage are obtained; the current motor torque is obtained by calculating the current moment current, the current moment voltage and the current moment waveform period through the target torque calculation strategy; the previous moment motor torque is obtained by calculating the previous moment current, the previous moment voltage and the previous moment waveform period through the target torque calculation strategy; the resistance change rate is determined according to the current motor torque, the previous moment motor torque and the fixed sampling period; when the resistance change rate is greater than or equal to the preset anti-pinch threshold, the input signal characteristics of the target window switch are obtained according to the current moment current and the current moment waveform period.

[0137] In one embodiment, the obtaining module 10 is further configured to, when the position of the target window is not in the target anti-pinch area, determine whether the current number of waveforms is less than the first waveform number difference; when the current number of waveforms is less than the first waveform number difference, continue to determine whether the current current is greater than the target determination current; when the current at the current moment is greater than the target determination current, determine that the self-learning result of the target window anti-pinch is that the target window rises to the top position, and end the window anti-pinch self-learning.

[0138] In one embodiment, the obtaining module 10 is further configured to, when the resistance change rate is greater than or equal to the preset anti-pinch threshold, determine the target current determination range according to the rated current and the locked-rotor current; when the current current is not within the target current determination range and / or the waveform period is greater than or equal to the preset period threshold, obtain the input signal characteristics of the target window switch.

[0139] In one embodiment, the obtaining module 10 is further configured to, when the input signal characteristic is not the target characteristic, obtain the current count of the target reverse counter; when the current count is the target count, reverse the target window, obtain and record the current position waveform number as the position of the target obstacle point, and increment the count of the target reverse counter by 1; re-learn the target window.

[0140] In one embodiment, the obtaining module 10 is further configured to, when the current count is not the target count, obtain the current position waveform number and the waveform number at the previous reverse position in history; perform a difference calculation on the current position waveform number and the waveform number at the previous reverse position in history to obtain the corresponding second waveform number difference; when the second waveform number difference is less than the preset waveform number threshold, obtain the current timing time of the target timer; determine whether the target window meets the rising condition according to the current timing time, where the rising condition is that the current timing time is less than the preset duration, and the waveform period changes at the current moment, and the resistance change rate is less than the preset anti-pinch threshold, and the current current is less than the target determination current; when the rising condition is met, determine that the target window has passed the target obstacle point, and determine that the self-learning result of the target window anti-pinch is to continue driving the target window to rise; when the rising condition is not met, determine that the self-learning result of the target window anti-pinch is that the target window is deformed, and end the learning.

[0141] In one embodiment, the obtaining module 10 is further configured to obtain the position of the target obstacle point when the difference in the number of the second waveforms is greater than or equal to a preset waveform number threshold; when the current position is within a preset area of the position of the target obstacle point, determine that the obstacle point at the current position comes from outside the glass system of the target window; trigger the anti-pinch function of the target window, and increment the current count by 1; when the current count after the increment is greater than a preset number, determine that the self-learning result of the anti-pinch of the target window is that there are external self-learning interference factors, and end the self-learning of the anti-pinch of the window.

[0142] In one embodiment, the obtaining module 10 is further configured to determine that the current position is the position of the target obstacle point when the current position is not within the preset area of the position of the target obstacle point, reverse the target window, and increment the count of the target reverse counter by 1; the target window relearns.

[0143] In one embodiment, the determining module 30 is further configured to calculate a target determination current according to a target locked-rotor current and a preset percentage when the input signal characteristic is a target characteristic; count the duration during which the current current is greater than or equal to the target determination current; when the duration is greater than a preset duration, trigger the anti-pinch function of the target window, and determine that the self-learning result of the anti-pinch of the target window is a serious deformation defect of the door.

[0144] Other embodiments or implementation methods of the window anti-pinch self-learning device of the present invention can refer to the above method embodiments, and will not be repeated here.

[0145] In addition, it should be noted that in this article, the terms "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or system including that element.

[0146] 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.

[0147] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described 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. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as Read Only Memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, integrated platform workstation, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0148] 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 in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A window anti-pinch self-learning method, characterized in that, The window anti-pinch self-learning method includes the following steps: Obtain the top position and bottom position of the target window; Drive the target window to rise according to the top position and the bottom position; During the rising process of the target window, obtain the input signal characteristics of the target window switch according to the current current and waveform period of the motor; When the input signal characteristic is the target characteristic, determine the self-learning result of the target window anti-pinch according to the current current, the target stall current, and the preset duration; When the input signal characteristic is the target characteristic, determining the self-learning result of the target window anti-pinch according to the current current, the target stall current, and the preset duration includes: When the input signal characteristic is the target characteristic, calculate the target determination current according to the target stall current and the preset percentage; Statistical duration during which the current current is greater than or equal to the target determination current; When the duration is greater than the preset duration, trigger the anti-pinch function of the target window, and determine that the self-learning result of the target window anti-pinch is that the door is severely deformed, and end the learning; 2. The window pinch protection self-learning method according to claim 1, characterized in that The current current includes the current moment current and the previous moment current, and the waveform period includes the current moment waveform period and the previous moment waveform period; During the rising process of the target window, obtaining the input signal characteristics of the target window switch according to the current current and waveform period of the motor includes: During the rising process of the target window, judge whether the position of the target window is located in the target anti-pinch area according to the current waveform number; When the position of the target window is located in the target anti-pinch area, obtain the current moment voltage and the previous moment voltage; Calculate the current motor torque by calculating the current moment current, the current moment voltage, and the current moment waveform period through the target torque calculation strategy; Calculate the previous moment motor torque by calculating the previous moment current, the previous moment voltage, and the previous moment waveform period through the target torque calculation strategy; Determine the resistance change rate according to the current motor torque, the previous moment motor torque, and the fixed sampling period; When the resistance change rate is greater than or equal to the preset anti-pinch threshold, obtain the input signal characteristics of the target window switch according to the current moment current and the current moment waveform period; 3. The window pinch protection self-learning method according to claim 2, wherein, After judging whether the position of the target window is located in the target anti-pinch area according to the current waveform number during the rising process of the target window, it further includes: When the position of the target window is not located in the target anti-pinch area, judge whether the current waveform number is less than the first waveform number difference; When the current waveform number is less than the first waveform number difference, continue to judge whether the current current is greater than the target determination current; When the current moment current is greater than the target determination current, determine that the self-learning result of the target window anti-pinch is that the target window rises to the top position, and end the window anti-pinch self-learning; 4. The window anti-pinch self-learning method according to claim 2, characterized in that, After determining the resistance change rate according to the current motor torque, the previous moment motor torque, and the fixed sampling period, it further includes: When the resistance change rate is greater than or equal to the preset anti-pinch threshold, determine the target current determination range according to the rated current and the stall current; When the current current is not within the target current determination range and / or the waveform period is greater than or equal to a preset period threshold, obtain the input signal characteristics of the target window switch.

5. The window anti-pinch self-learning method according to claim 4, wherein After obtaining the input signal characteristics of the target window switch when the current current is not within the target current determination range and / or the waveform period is greater than a preset period threshold, it further includes: When the input signal characteristics are not the target characteristics, obtain the current count of the target reverse counter; When the current count is the target count, the target window reverses, obtain and record the position where the current position waveform number is the target obstacle point, and increment the count of the target reverse counter by 1; The target window relearns.

6. The window pinch protection self-learning method according to claim 5, characterized in that After obtaining the current count of the target reverse counter when the input signal characteristics are not the target characteristics, it further includes: When the current count is not the target count, obtain the current position waveform number and the waveform number at the previous reverse position in history; Perform a difference calculation on the current position waveform number and the waveform number at the previous reverse position in history to obtain the corresponding second waveform number difference; When the second waveform number difference is less than a preset waveform number threshold, obtain the current timing time of the target timer; Judge whether the target window meets the rising condition according to the current timing time, where the rising condition is that the current timing time is less than a preset duration, the waveform period changes at the current moment, the resistance change rate is less than a preset anti-pinch threshold, and the current current is less than the target determination current; When the rising condition is met, determine that the target window has passed the target obstacle point, and determine that the self-learning result of the target window anti-pinch is to continue driving the target window to rise; When the rising condition is not met, determine that the self-learning result of the target window anti-pinch is that the target window is deformed, and end the learning.

7. The window pinch protection self-learning method according to claim 6, characterized in that, After performing the difference calculation on the current position waveform number and the waveform number at the previous reverse position in history to obtain the corresponding second waveform number difference, it further includes: When the second waveform number difference is greater than or equal to a preset waveform number threshold, obtain the position of the target obstacle point; When the current position is within the preset area of the position of the target obstacle point, determine that the source of the obstacle point at the current position is outside the glass system; Trigger the anti-pinch function of the target window, and increment the current count by 1; When the current count after incrementing by 1 is greater than a preset count, determine that the self-learning result of the target window anti-pinch is that there are external self-learning interference factors, and end the window anti-pinch self-learning.

8. The window pinch protection self-learning method according to claim 7, characterized in that, When obtaining the position of the target obstacle point when the second waveform number difference is greater than or equal to a preset waveform number threshold, it further includes: When the current position is not within the preset area of the position of the target obstacle point, determine the current position as the position of the target obstacle point, reverse the target window, and increment the count of the target reverse counter by 1; The target window relearns.

9. A window anti-pinch self-learning device, characterized in that, The window anti-pinch self-learning device includes: An acquisition module for acquiring the top position and the bottom position of the target window; A driving module for driving the target window to rise according to the top position and the bottom position; The obtaining module is further configured to obtain the input signal characteristics of the target window switch according to the current current and waveform period of the motor during the rising process of the target window; The determining module is configured to determine the self-learning result of the target window anti-pinch according to the current current, the target stall current, and a preset duration when the input signal characteristic is a target characteristic; The determining module is further configured to calculate a target determination current according to the target stall current and a preset percentage when the input signal characteristic is a target characteristic; count the duration during which the current current is greater than or equal to the target determination current; when the duration is greater than a preset duration, trigger the anti-pinch function of the target window, and determine that the self-learning result of the target window anti-pinch is that the door is severely deformed, and end the learning.

10. A window anti-pinch self-learning device, characterized in that, The window anti-pinch self-learning device includes: a memory, a processor, and a window anti-pinch self-learning program stored on the memory and executable on the processor, and the window anti-pinch self-learning program is configured to implement the window anti-pinch self-learning method according to any one of claims 1 to 8.

11. A storage medium, characterized in that, A window anti-pinch self-learning program is stored on the storage medium, and when the window anti-pinch self-learning program is executed by a processor, it implements the window anti-pinch self-learning method according to any one of claims 1 to 8.

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