AMT gear self-learning method and device

Abstract

The application discloses an AMT gear self-learning method and device, and belongs to the technical field of vehicles. The gear engagement motor is controlled to work at a constant duty ratio in response to a vehicle, and the gear engagement position is moved from the N gear to the gear engagement position. Gear engagement motor data is acquired, wherein the gear engagement motor data comprises working current and gear engagement position signals. The gear engagement motor data is compared with preset conditions to obtain a comparison result. In response to the comparison result, a current gear corresponding to the preset conditions is determined, wherein the preset conditions comprise a first preset condition, a second preset condition and a third preset condition. The application avoids the problems of gear disengagement or gear engagement overshoot and wear of the shift fork.

Description

Technical Field

[0001] This invention discloses an AMT gear self-learning method and device, belonging to the field of vehicle technology. Background Technology

[0002] During vehicle manufacturing, tolerances exist in the production processes of parts, and it's difficult to achieve perfect precision in assembly. These factors cause the gear shift actuator's limit position to be non-constant. If the target position is set improperly, or the gear band is too narrow, the gear can easily disengage unexpectedly while the vehicle is in motion. Conversely, if the target position deviates significantly, or the gear band is too wide, the shift fork will bear excessive pressure, accelerating wear. The previous method of relying solely on setting a fixed offset to determine the target gear position and gear status has revealed significant limitations. It cannot accommodate the complex operating conditions of all gears and is difficult to adapt to various types of transmissions. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention proposes an AMT gear self-learning method and device. Existing methods rely solely on setting a fixed offset to determine the target gear position and gear, which is not applicable to all gears or transmissions.

[0004] The technical solution of the present invention is as follows:

[0005] According to a first aspect of the present invention, an AMT gear self-learning method is provided, comprising:

[0006] In response to the vehicle controlling the gear shift motor to work with a constant duty cycle, the gear shift position moves from N gear to the active gear position, and the gear shift motor data is acquired, including: operating current and gear shift position signal;

[0007] The gear shift motor data is compared with preset conditions to obtain the comparison result. In response to the comparison result, the current gear corresponding to the preset conditions is determined. The preset conditions include: first preset condition, second preset condition and third preset condition.

[0008] Furthermore, in response to the comparison results, the current gear corresponding to the preset conditions is determined, including:

[0009] In response to the comparison result that the gear shift motor data meets the first preset condition, the current gear is the meshing position of the two widest tooth profiles when the shift sleeve tooth and the gear engagement tooth are engaged.

[0010] In response to the comparison result that the gear shifting motor data meets the second preset condition, the current gear position is the edge position of the gear self-locking groove.

[0011] In response to the comparison result that the gear shifting motor data meets the third preset condition, the current gear position is the bottom position of the gear self-locking slot.

[0012] The first preset condition is the point where the motor current increases to the first current value and then decreases rapidly. The second preset condition is the point where the current continues to decrease to the lowest point after the two teeth are engaged at their widest points when the shift sleeve teeth and the gear engagement teeth are engaged. The third preset condition is the point where the current remains at a certain value for a period of time at the edge of the gear self-locking groove and then begins to increase continuously.

[0013] Furthermore, in response to the comparison result indicating that the gear shift motor data meets the third preset condition, and the current gear is after the gear self-locking slot bottom position, the following is also included:

[0014] The bottom position of the gear self-locking slot is the target position. The fluctuation data of the current at the target position within a preset time is obtained. Based on the fluctuation data, the gear self-learning state is obtained. The gear self-learning state is: effective and ineffective.

[0015] When the gear self-learning state is active, continue to drive the gear engagement motor;

[0016] When the gear self-learning state is invalid, self-learning will restart.

[0017] Furthermore, based on the fluctuation data, the gear self-learning state is obtained, including:

[0018] The calibrated current value is a preset value at the target location. If the current deviates within a preset time period, it is determined whether the deviation is less than or equal to the preset value.

[0019] Yes, effective self-learning is effective.

[0020] No, self-learning is considered invalid if it is effective.

[0021] According to a second aspect of the present invention, an AMT gear self-learning device is provided, comprising:

[0022] The acquisition module is used to acquire gear shift motor data in response to the vehicle controlling the gear shift motor to work with a constant duty cycle and the gear shift position moving from N gear to the active gear position. The gear shift motor data includes: operating current and gear shift position signal.

[0023] The determination module is used to compare the gear shift motor data with preset conditions to obtain the comparison result. In response to the comparison result, the current gear corresponding to the preset conditions is determined. The preset conditions include: a first preset condition, a second preset condition, and a third preset condition.

[0024] Preferably, the acquisition module is used for:

[0025] In response to the comparison result that the gear shift motor data meets the first preset condition, the current gear is the meshing position of the two widest tooth profiles when the shift sleeve tooth and the gear engagement tooth are engaged.

[0026] In response to the comparison result that the gear shifting motor data meets the second preset condition, the current gear position is the edge position of the gear self-locking groove.

[0027] In response to the comparison result that the gear shifting motor data meets the third preset condition, the current gear position is the bottom position of the gear self-locking slot.

[0028] The first preset condition is the point where the motor current increases to the first current value and then decreases rapidly. The second preset condition is the point where the current continues to decrease to the lowest point after the two teeth are engaged at their widest points when the shift sleeve teeth and the gear engagement teeth are engaged. The third preset condition is the point where the current remains at a certain value for a period of time at the edge of the gear self-locking groove and then begins to increase continuously.

[0029] Preferably, the determining module is also used for:

[0030] The bottom position of the gear self-locking slot is the target position. The fluctuation data of the current at the target position within a preset time is obtained. Based on the fluctuation data, the gear self-learning state is obtained. The gear self-learning state is: effective and ineffective.

[0031] When the gear self-learning state is active, continue to drive the gear engagement motor;

[0032] When the gear self-learning state is invalid, self-learning will restart.

[0033] Preferably, the determining module is also used for:

[0034] The calibrated current value is a preset value at the target location. If the current deviates within a preset time period, it is determined whether the deviation is less than or equal to the preset value.

[0035] Yes, effective self-learning is effective.

[0036] No, self-learning is considered invalid if it is effective.

[0037] According to a third aspect of the present invention, a terminal is provided, comprising:

[0038] One or more processors;

[0039] Memory for storing the one or more processor-executable instructions;

[0040] Wherein, the one or more processors are configured as follows:

[0041] Perform the method described in the first aspect of the embodiments of the present invention.

[0042] According to a fourth aspect of the present invention, a non-transitory computer-readable storage medium is provided, wherein when instructions in the storage medium are executed by a processor of a terminal, the terminal is enabled to perform the method described in the first aspect of the present invention.

[0043] According to a fifth aspect of the present invention, an application product is provided that, when the application product is running on a terminal, causes the terminal to execute the method described in the first aspect of the present invention.

[0044] The beneficial effects of this invention are as follows:

[0045] This invention provides an AMT gear self-learning method and device. In response to the vehicle, the gear shift motor is controlled to operate with a constant duty cycle, moving from neutral (N) to engaged (EN) position. Gear shift motor data is acquired, including operating current and gear position signal. The data is compared with preset conditions to obtain a comparison result. Based on the comparison result, the current gear corresponding to the preset conditions is determined. The preset conditions include a first preset condition, a second preset condition, and a third preset condition, preventing issues such as gear slippage or over-adjustment and wear on the shift fork.

[0046] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating an AMT gear self-learning method according to an exemplary embodiment.

[0048] Figure 2 This is a flowchart illustrating an AMT gear self-learning method according to an exemplary embodiment.

[0049] Figure 3 This is a statistical diagram illustrating the gear judgment logic in an AMT gear self-learning method according to an exemplary embodiment.

[0050] Figure 4 This is a statistical graph of the current value at the bottom position of the gear self-locking slot in an AMT gear self-learning method according to an exemplary embodiment.

[0051] Figure 5 This is a schematic block diagram illustrating the structure of an AMT gear self-learning device according to an exemplary embodiment.

[0052] Figure 6 This is a schematic block diagram of a terminal structure according to an exemplary embodiment. Detailed Implementation

[0053] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] In the description of this invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0055] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0056] This invention provides an AMT gear self-learning method, which is implemented by a terminal, such as a desktop computer or a laptop computer, and includes at least a CPU.

[0057] Example 1

[0058] Figure 1 This is a flowchart illustrating an AMT gear self-learning method according to an exemplary embodiment. The method is used in a terminal and includes the following steps:

[0059] Step 101: In response to the vehicle controlling the gear shift motor to work with a constant duty cycle, the gear shift position moves from N gear to the active gear position, and the gear shift motor data is acquired. The gear shift motor data includes: operating current and gear shift position signal. The gear shift motor drives the shift fork to move, causing the sliding sleeve engagement teeth to gradually approach the gear engagement teeth.

[0060] Step 102: Based on the comparison between the gear shift motor data and preset conditions, a comparison result is obtained. In response to the comparison result, the current gear corresponding to the preset conditions is determined. The preset conditions include: a first preset condition, a second preset condition, and a third preset condition, the specific contents of which are as follows:

[0061] The comparison results are obtained by comparing the gear shift motor data with preset conditions, wherein:

[0062] In response to the comparison result that the gear shift motor data meets the first preset condition, the current gear is the meshing position of the two teeth at their widest points when the shift sleeve teeth and the gear engagement teeth are engaged. The first preset condition is the extreme point where the gear shift motor current increases to the first current value and then decreases rapidly.

[0063] In response to the comparison result that the gear shift motor data meets the second preset condition, the current gear is at the edge of the gear self-locking groove. The second preset condition is that when the shift sleeve tooth and the gear engagement tooth are engaged, the current continuously decreases to the lowest point of the current after the two teeth are engaged at their widest points.

[0064] In response to the comparison result that the gear shift motor data meets the third preset condition, the current gear is the bottom position of the gear self-locking slot, and the third preset condition is that the current at the edge position of the gear self-locking slot remains at a certain value for a period of time and then begins to increase continuously.

[0065] The bottom position of the gear self-locking slot is the target position. The fluctuation data of the current at the target position within a preset time is obtained. Based on the fluctuation data, the gear self-learning state is obtained. The gear self-learning state is: effective and ineffective.

[0066] The calibrated current value is a preset value at the target location. If the current deviates within a preset time period, it is determined whether the deviation value is less than or equal to the preset value.

[0067] Yes, effective self-learning is effective.

[0068] No, self-learning is considered invalid if it is effective.

[0069] When the gear self-learning state is active, continue to drive the gear engagement motor;

[0070] When the gear self-learning state is invalid, self-learning will restart.

[0071] Select the absolute value of the maximum current value within a preset time period and the absolute value of the minimum current value within a preset time period, calculate the average value within the preset time period, determine the instantaneous value that is close to the average value within the preset time period, and set the position corresponding to the instantaneous value as the bottom position of the gear self-locking groove.

[0072] After completing the self-learning of the bottom position of the gear self-locking slot, continue to drive the gear engagement motor. When the gear engagement current increases sharply but the gear engagement position no longer changes, record the last position.

[0073] Example 2

[0074] Figure 2This is a flowchart illustrating an AMT gear self-learning method according to an exemplary embodiment. The method is used in a terminal and includes the following steps:

[0075] The shift motor is controlled with a constant duty cycle, moving from neutral (N) to neutral (H) position. The operating current and shift position signal of the shift motor are recorded. The shift motor drives the shift fork to move, causing the sliding sleeve engagement teeth to gradually approach the gear engagement teeth. Figure 3 and 4 As shown.

[0076] When the first preset condition is met, determine and record the current position as Pos1, the meshing position of the two widest points of the gear shift sleeve tooth and the gear engagement tooth;

[0077] Continue driving the gear shifting motor. When the second preset condition is met, determine and record the current position as the edge position Pos2 of the gear shift self-locking groove.

[0078] Continue driving the gear shifting motor. When the third preset condition is met, determine and record the current position as the bottom position Pos3 of the gear self-locking slot, set Pos3 as the target position point, and complete the self-learning process.

[0079] Initially, the motor current is relatively small when engaging the gear. As the gears mesh, the current will start to increase. The first preset condition is the gear engagement position Pos1, where the motor current increases to the first current value A1 and then decreases rapidly.

[0080] Point Pos1 is the meshing position where the two teeth are widest when the sliding sleeve tooth and the gear engagement tooth are engaged. The resistance is the greatest, and the current is the greatest under the same PWM. After this point, the tooth profile of the engagement tooth will begin to converge, and the resistance will decrease.

[0081] The second preset condition is that the current continues to decrease after passing the Pos1 position. When the current reaches the lowest point A2, the current position is recorded as the edge position Pos2 of the gear self-locking groove.

[0082] Point Pos2 is the edge of the gear shift self-locking groove. After passing this point, the gear will automatically slide to the lower end of the self-locking groove, Pos3, under the action of the self-locking spring and the self-locking ball, locking the gear shifting position. The gear shifting motor basically does not need to be handled.

[0083] The third preset condition is that after passing through Pos2, the current remains at the value of A3 for a period of time and then begins to increase continuously. The position of the gear that records the value of A3 is the bottom position of the gear self-locking slot, Pos3.

[0084] The calibrated current value is set to a preset value M at Pos3. If A3 deviates within a time period T, i.e., the current A3 at Pos3 fluctuates by A3±M within the time period T, it is determined that this is due to a backlash between the gear engagement teeth, and the self-learning is considered effective. The absolute value of the maximum value of A3 within the time period T, |Max(A3)|, and the absolute value of the minimum value of A3 within the time period T, |Min(A3)|, are selected, and the average value A3 within the time period T is calculated. AVERAGE＝ 1 / 2(|Max(A3)|+|Min(A3)|), determine if it is close to A3 within time T. AVERAGE The corresponding A3 value Value, set A3 value The position corresponding to the value is Pos3.

[0085] If the fluctuation value of A3 within time T exceeds the preset value M, the self-learning process is deemed invalid and self-learning restarts.

[0086] After completing the self-learning of the Pos3 position, continue driving the gear shifting motor. When the gear shifting current increases sharply but the gear shifting position no longer changes, record the Pos4 position.

[0087] To ensure the accuracy of the learning, self-learning is performed N times. If the error is greater than the preset value, the self-learning is considered invalid. If the error is less than the preset value, the average value of the N self-learning positions is taken as the final result.

[0088] Example 3

[0089] Figure 5 This is a schematic block diagram illustrating the structure of an AMT gear self-learning device according to an exemplary embodiment. The device includes:

[0090] The acquisition module 210 is used to acquire gear shift motor data in response to the vehicle controlling the gear shift motor to work with a constant duty cycle and the gear shift position moving from N gear to the gear position. The gear shift motor data includes: operating current and gear shift position signal.

[0091] The determination module 220 is used to compare the gear shift motor data with preset conditions to obtain the comparison result, and in response to the comparison result, determine the current gear corresponding to the preset conditions, wherein the preset conditions include: a first preset condition, a second preset condition and a third preset condition.

[0092] Preferably, the acquisition module 210 is used for:

[0093] In response to the comparison result that the gear shift motor data meets the first preset condition, the current gear is the meshing position of the two widest tooth profiles when the shift sleeve tooth and the gear engagement tooth are engaged.

[0094] In response to the comparison result that the gear shifting motor data meets the second preset condition, the current gear position is the edge position of the gear self-locking groove.

[0095] In response to the comparison result that the gear shifting motor data meets the third preset condition, the current gear position is the bottom position of the gear self-locking slot.

[0096] The first preset condition is the point where the motor current increases to the first current value and then decreases rapidly. The second preset condition is the point where the current continues to decrease to the lowest point after the two teeth are engaged at their widest points when the shift sleeve teeth and the gear engagement teeth are engaged. The third preset condition is the point where the current remains at a certain value for a period of time at the edge of the gear self-locking groove and then begins to increase continuously.

[0097] Preferably, the determining module 220 is also used for:

[0098] The bottom position of the gear self-locking slot is the target position. The fluctuation data of the current at the target position within a preset time is obtained. Based on the fluctuation data, the gear self-learning state is obtained. The gear self-learning state is: effective and ineffective.

[0099] When the gear self-learning state is active, continue to drive the gear engagement motor;

[0100] When the gear self-learning state is invalid, self-learning will restart.

[0101] Preferably, the determining module 220 is also used for:

[0102] The calibrated current value is a preset value at the target location. If the current deviates within a preset time period, it is determined whether the deviation is less than or equal to the preset value.

[0103] Yes, effective self-learning is effective.

[0104] No, self-learning is considered invalid if it is effective.

[0105] Example 4

[0106] Figure 6 This is a structural block diagram of a terminal provided in an embodiment of this application. The terminal can be the terminal in the above embodiments. The terminal 300 can be a portable mobile terminal, such as a smartphone or tablet computer. The terminal 300 may also be referred to as user equipment, portable terminal, or other names.

[0107] Typically, terminal 300 includes a processor 301 and a memory 302.

[0108] Processor 301 may include one or more processing cores, such as a quad-core processor or an octa-core processor. Processor 301 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 301 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 301 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 301 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0109] The memory 302 may include one or more computer-readable storage media, which may be tangible and non-transitory. The memory 302 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 302 are used to store at least one instruction, which is executed by the processor 301 to implement an AMT gear self-learning method provided in this application.

[0110] In some embodiments, the terminal 300 may also optionally include: a peripheral device interface 303 and at least one peripheral device. Specifically, the peripheral device includes at least one of: a radio frequency circuit 304, a touch display screen 305, a camera 306, an audio circuit 307, a positioning component 308, and a power supply 309.

[0111] The peripheral device interface 303 can be used to connect at least one I / O (Input / Output) related peripheral device to the processor 301 and the memory 302. In some embodiments, the processor 301, memory 302, and peripheral device interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, memory 302, and peripheral device interface 303 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0112] The radio frequency (RF) circuit 304 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 304 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 304 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 304 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 304 can communicate with other terminals through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 304 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0113] The touch display screen 305 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. The touch display screen 305 also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to the processor 301 for processing. The touch display screen 305 is used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one touch display screen 305, which is located on the front panel of the terminal 300; in other embodiments, there may be at least two touch display screens, respectively located on different surfaces of the terminal 300 or in a folded design; in still other embodiments, the touch display screen 305 may be a flexible display screen, located on a curved or folded surface of the terminal 300. Furthermore, the touch display screen 305 may be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. The touch display screen 305 may be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0114] Camera assembly 306 is used to acquire images or videos. Optionally, camera assembly 306 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is used for video calls or selfies, and the rear-facing camera is used for taking photos or videos. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, and a wide-angle camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, and panoramic shooting and VR (Virtual Reality) shooting by fusion of the main camera and the wide-angle camera. In some embodiments, camera assembly 306 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash is a combination of a warm light flash and a cool light flash, which can be used for light compensation at different color temperatures.

[0115] Audio circuit 307 provides an audio interface between the user and terminal 300. Audio circuit 307 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to processor 301 for processing, or input to radio frequency circuit 304 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each located at a different part of terminal 300. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from processor 301 or radio frequency circuit 304 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, audio circuit 307 may also include a headphone jack.

[0116] The positioning component 308 is used to determine the current geographic location of the terminal 300 in order to enable navigation or LBS (Location Based Service). The positioning component 308 can be a positioning component based on the US GPS (Global Positioning System), China's BeiDou system, or Russia's Galileo system.

[0117] The power supply 309 is used to power the various components in the terminal 300. The power supply 309 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When the power supply 309 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired connection, while a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0118] In some embodiments, the terminal 300 further includes one or more sensors 310. The one or more sensors 310 include, but are not limited to: an accelerometer 311, a gyroscope 312, a pressure sensor 313, a fingerprint sensor 314, an optical sensor 315, and a proximity sensor 316.

[0119] Accelerometer 311 can detect the magnitude of acceleration along the three coordinate axes of a coordinate system established with terminal 300. For example, accelerometer 311 can be used to detect the components of gravitational acceleration along the three coordinate axes. Processor 301 can control touchscreen 305 to display the user interface in landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 311. Accelerometer 311 can also be used for games or for acquiring user motion data.

[0120] The gyroscope sensor 312 can detect the orientation and rotation angle of the terminal 300. The gyroscope sensor 312, in conjunction with the accelerometer sensor 311, can collect the user's 3D (3D) movements on the terminal 300. Based on the data collected by the gyroscope sensor 312, the processor 301 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0121] The pressure sensor 313 can be disposed on the side bezel of the terminal 300 and / or on the lower layer of the touch display screen 305. When the pressure sensor 313 is disposed on the side bezel of the terminal 300, it can detect the user's grip signal on the terminal 300 and perform left / right hand recognition or quick operation based on the grip signal. When the pressure sensor 313 is disposed on the lower layer of the touch display screen 305, it can control the operable controls on the UI interface based on the user's pressure operation on the touch display screen 305. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0122] The fingerprint sensor 314 is used to collect a user's fingerprint to identify the user's identity. When the user's identity is identified as trusted, the processor 301 authorizes the user to perform relevant sensitive operations, including unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings. The fingerprint sensor 314 can be located on the front, back, or side of the terminal 300. When the terminal 300 has physical buttons or a manufacturer's logo, the fingerprint sensor 314 can be integrated with the physical buttons or manufacturer's logo.

[0123] An optical sensor 315 is used to collect ambient light intensity. In one embodiment, the processor 301 can control the display brightness of the touch screen 305 based on the ambient light intensity collected by the optical sensor 315. Specifically, when the ambient light intensity is high, the display brightness of the touch screen 305 is increased; when the ambient light intensity is low, the display brightness of the touch screen 305 is decreased. In another embodiment, the processor 301 can also dynamically adjust the shooting parameters of the camera assembly 306 based on the ambient light intensity collected by the optical sensor 315.

[0124] The proximity sensor 316, also known as a distance sensor, is typically located on the front of the terminal 300. The proximity sensor 316 is used to detect the distance between the user and the front of the terminal 300. In one embodiment, when the proximity sensor 316 detects that the distance between the user and the front of the terminal 300 is gradually decreasing, the processor 301 controls the touchscreen display 305 to switch from a screen-on state to a screen-off state; when the proximity sensor 316 detects that the distance between the user and the front of the terminal 300 is gradually increasing, the processor 301 controls the touchscreen display 305 to switch from a screen-off state to a screen-on state.

[0125] Those skilled in the art will understand that Figure 6 The structure shown does not constitute a limitation on terminal 300, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0126] Example 5

[0127] In an exemplary embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements an AMT gear self-learning method as provided in all embodiments of the present application.

[0128] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0129] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0130] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0131] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0132] Example 6

[0133] In an exemplary embodiment, an application product is also provided, including one or more instructions that can be executed by the processor 301 of the aforementioned device to complete the aforementioned AMT gear self-learning method.

[0134] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. It can be applied to various fields suitable for the present invention. Other modifications can be readily made by those skilled in the art. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and examples shown and described herein.

Claims

1. An AMT gear shift self-learning method, characterized in that, include: In response to the vehicle controlling the gear shift motor to operate with a constant duty cycle, the gear shift position moves from N gear to the active gear position, and the gear shift motor data is acquired, wherein the gear shift motor data includes: operating current and gear shift position signal; Based on the comparison between the gear shift motor data and preset conditions, a comparison result is obtained. In response to the comparison result, the current gear corresponding to the preset conditions is determined, wherein the preset conditions include: a first preset condition, a second preset condition, and a third preset condition. Among them, determining the current gear corresponding to the preset condition in response to the comparison result includes: In response to the comparison result that the gear shift motor data meets the first preset condition, the current gear is the meshing position of the two widest points of the gear shift sleeve teeth and the gear engagement teeth when they mesh; In response to the comparison result indicating that the gear shifting motor data meets the second preset condition, the current gear position is the edge position of the gear self-locking groove; In response to the comparison result that the gear shift motor data meets the third preset condition, the current gear position is the bottom position of the gear self-locking slot; The first preset condition is the point where the current of the gear shifting motor increases to a first current value and then decreases rapidly. The second preset condition is the point where the current continues to decrease to the lowest point after the two teeth of the shifting sleeve mesh with the gear engagement teeth at their widest meshing positions. The third preset condition is the point where the current continues to increase after being maintained at a certain value for a period of time at the edge of the gear self-locking groove.

2. The AMT gear self-learning method according to claim 1, characterized in that, In response to the comparison result indicating that the gear shift motor data meets the third preset condition, after the current gear is at the bottom position of the gear self-locking slot, the following is also included: The bottom position of the gear self-locking slot is the target position. The fluctuation data of the current at the target position within a preset time is obtained. Based on the fluctuation data, the gear self-learning state is obtained. The gear self-learning state is: effective and ineffective. When the gear self-learning state is active, the gear shifting motor continues to be driven; When the gear self-learning state is invalid, self-learning restarts.

3. The AMT gear self-learning method according to claim 2, characterized in that, Based on the fluctuation data, the gear self-learning state is obtained, including: The calibrated current value is a preset value at the target location. If the current deviates within a preset time period, it is determined whether the deviation value is less than or equal to the preset value. Yes, the self-learning is effective. No, the self-learning effectiveness stated above is invalid.

4. An AMT gear position self-learning device, characterized in that, include: The acquisition module is used to acquire gear shift motor data in response to the vehicle controlling the gear shift motor to work with a constant duty cycle and the gear shift position moving from N gear to the gear position. The gear shift motor data includes: operating current and gear shift position signal. The determination module is used to compare the gear shift motor data with preset conditions to obtain a comparison result, and in response to the comparison result, determine the current gear corresponding to the preset conditions, wherein the preset conditions include: a first preset condition, a second preset condition, and a third preset condition; The acquisition module is used for: In response to the comparison result that the gear shift motor data meets the first preset condition, the current gear is the meshing position of the two widest points of the gear shift sleeve teeth and the gear engagement teeth when they mesh; In response to the comparison result indicating that the gear shifting motor data meets the second preset condition, the current gear position is the edge position of the gear self-locking groove; In response to the comparison result that the gear shift motor data meets the third preset condition, the current gear position is the bottom position of the gear self-locking slot; The first preset condition is the point where the current of the gear shifting motor increases to a first current value and then decreases rapidly. The second preset condition is the point where the current continues to decrease to the lowest point after the two teeth of the shifting sleeve mesh with the gear engagement teeth at their widest meshing positions. The third preset condition is the point where the current continues to increase after being maintained at a certain value for a period of time at the edge of the gear self-locking groove.

5. The AMT gear self-learning device according to claim 4, characterized in that, The determining module is further configured to: The bottom position of the gear self-locking slot is the target position. The fluctuation data of the current at the target position within a preset time is obtained. Based on the fluctuation data, the gear self-learning state is obtained. The gear self-learning state is: effective and ineffective. When the gear self-learning state is active, the gear shifting motor continues to be driven; When the gear self-learning state is invalid, self-learning restarts.

6. The AMT gear self-learning device according to claim 5, characterized in that, The determining module is further configured to: The calibrated current value is a preset value at the target location. If the current deviates within a preset time period, it is determined whether the deviation value is less than or equal to the preset value. Yes, the self-learning is effective. No, the self-learning effectiveness stated above is invalid.

7. A terminal, characterized in that, include: One or more processors; Memory for storing the one or more processor-executable instructions; Wherein, the one or more processors are configured as follows: Perform the AMT gear self-learning method as described in any one of claims 1 to 3.

8. A non-transitory computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the terminal's processor, the terminal is able to execute the AMT gear self-learning method as described in any one of claims 1 to 3.

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