Seat motor self-learning calibration method, related equipment and vehicle

Abstract

The invention provides a self-learning calibration method of a seat motor, related equipment and a vehicle. The method comprises the following steps: driving a seat motor of a target seat to rotate towards a preset learning direction according to a folding instruction until the seat motor is locked; determining a first stop point of a rotor of the seat motor when the seat motor is locked; determining whether the distance between the first stop point and the expected stop point is smaller than a preset Hall distance; wherein the expected stop point is a pre-calibrated stop point of a rotor of the seat motor in a preset learning direction; in response to the fact that the distance between the first stop point and the expected stop point is smaller than the preset Hall distance, updating the expected stop point as the first stop point; wherein the first stop point represents the stop point of the seat motor rotating towards the preset learning direction next time.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to a self-learning calibration method for a seat motor, related equipment and a vehicle. Background Art

[0002] With the rapid development of the automobile industry and the continuous advancement of intelligent technology, cars have become an indispensable means of transportation in modern life. Among them, the seat memory function, as an important feature to improve driving comfort and convenience, is widely used in various types of vehicles.

[0003] However, in the current position learning method for realizing the seat memory function, the Hall sensor of the seat motor may cause inaccurate counting due to premature shutdown or reversal of the seat motor, which to a certain extent restricts the accuracy of seat adjustment and its self-learning. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the first object of the present invention is to provide a self-learning calibration method for a seat motor, which solves the problem of inaccurate counting of the Hall sensor of the seat motor by continuously calibrating the stop point of the seat motor, thereby effectively improving the accuracy of seat adjustment and its self-learning.

[0005] A second objective of the present invention is to provide a self-learning calibration device for a seat motor.

[0006] A third objective of the present invention is to provide an electronic device.

[0007] A fourth object of the present invention is to provide a non-transitory computer-readable storage medium.

[0008] A fifth object of the present invention is to provide a seat motor.

[0009] A sixth object of the present invention is to provide a vehicle.

[0010] To achieve the above object, a first aspect of the present invention provides a self-learning calibration method for a seat motor, the method comprising:

[0011] According to the folding instruction, the seat motor of the target seat is driven to rotate in a preset learning direction until the seat motor is blocked;

[0012] determining a first stopping point of a rotor of the seat motor when the seat motor is blocked;

[0013] Determine whether the distance between the first stop point and the expected stop point is less than a preset Hall distance; wherein the expected stop point is a stop point pre-calibrated by the rotor of the seat motor in a preset learning direction;

[0014] In response to the distance between the first stop point and the expected stop point being less than the preset Hall distance, the expected stop point is updated to the first stop point; wherein the first stop point represents the stop point of the seat motor when it rotates next time in the preset learning direction.

[0015] In addition, the self-learning calibration method of the seat motor of the above embodiment of the present invention may also have the following additional technical features:

[0016] Optionally, before driving a seat motor of the target seat to rotate in a preset learning direction according to the folding instruction, the method further includes:

[0017] In response to receiving the folding instruction, determining whether the target seat meets a folding condition;

[0018] In response to the target seat satisfying the folding condition, it is determined that a seat motor of the target seat is controlled to rotate in a preset learning direction according to the folding instruction.

[0019] Optionally, determining whether the target seat meets the folding condition includes:

[0020] Determine whether the target seat is occupied;

[0021] In response to the target seat being occupied, it is determined that the target seat does not satisfy a folding condition.

[0022] Optionally, determining whether the target seat meets the folding condition further includes:

[0023] Determine whether there is an obstacle within the travel range of the target seat in the preset learning direction;

[0024] In response to an obstacle existing within a travel range of the target seat for rotation in a preset learning direction, it is determined that the target seat does not satisfy the folding condition.

[0025] Optionally, the obstacle includes other seats, and the method further includes:

[0026] In response to a travel range of the target seat rotating in a preset learning direction being blocked by other seats, the other seats are adjusted to move the other seats away from the travel range.

[0027] Optionally, before driving a seat motor of the target seat to rotate in a preset learning direction according to the folding instruction, the method further includes:

[0028] determining whether an engine for driving a seat motor is in an activated state, and determining whether a desired stop point is stored for the seat motor;

[0029] In response to the engine being in the started state and the seat motor having a desired stop point stored therein, it is determined that the seat motor of the driving target seat is rotated in a preset learning direction.

[0030] Optionally, the method further includes:

[0031] Record the number of the first Hall pulses output by the seat motor from the start-up moment to the moment when the seat motor is blocked;

[0032] The first Hall pulse number is converted into a first Hall value according to a preset ratio.

[0033] Optionally, determining whether the distance between the first stop point and the expected stop point is less than a preset Hall distance includes:

[0034] Calculating a difference Hall distance between the first Hall value and a preset Hall value corresponding to the desired stop point;

[0035] It is determined whether the difference Hall distance is less than the preset Hall distance.

[0036] Optionally, the method further includes:

[0037] In response to the distance between the first stop point and the expected stop point being not less than a preset Hall distance, determining whether the number of times the seat motor is stalled within a preset time interval reaches a preset number threshold;

[0038] In response to the number of times that the seat motor is stalled within a preset time interval reaching a preset number threshold, determining a second stop point when the seat motor is stalled for the last time;

[0039] The expected stop point is updated as a second stop point, wherein the second stop point represents the stop point of the seat motor when it rotates next time in a preset learning direction.

[0040] Optionally, determining the second stop point when the seat motor was last stalled includes:

[0041] Record the number of second Hall pulses output by the seat motor from the start-up time to the last time the seat motor is locked;

[0042] The second stop point is determined according to the second number of Hall pulses.

[0043] According to the self-learning calibration method of the seat motor of the embodiment of the present invention, the seat motor of the target seat is first driven to rotate in a preset learning direction according to the folding instruction until the seat motor is blocked. Further, the first stop point of the rotor of the seat motor when the seat motor is blocked is determined, and it is determined whether the distance between the first stop point and the expected stop point is less than the preset Hall distance; wherein the expected stop point is a stop point of the rotor of the seat motor pre-calibrated in the preset learning direction, and in response to the distance between the first stop point and the expected stop point being less than the preset Hall distance, the expected stop point is updated to the first stop point; wherein the first stop point represents the stop point of the seat motor when it rotates in the preset learning direction next time. The present invention solves the problem of inaccurate counting of the Hall sensor of the seat motor by continuously calibrating the stop point of the seat motor, thereby effectively improving the accuracy of seat adjustment and its self-learning.

[0044] To achieve the above object, a second aspect of the present invention provides a self-learning calibration device for a seat motor, the device comprising:

[0045] a control module configured to drive a seat motor of a target seat to rotate in a preset learning direction according to a folding instruction until the seat motor is blocked;

[0046] A first determination module is configured to determine a first stop point of a rotor of the seat motor when the seat motor is blocked;

[0047] a second determination module configured to determine whether a distance between the first stop point and an expected stop point is less than a preset Hall distance; wherein the expected stop point is a stop point pre-calibrated by the rotor of the seat motor in a preset learning direction;

[0048] The calibration module is configured to update the expected stop point to the first stop point in response to the distance between the first stop point and the expected stop point being less than a preset Hall distance; wherein the first stop point represents the stop point of the seat motor when it rotates next time in a preset learning direction.

[0049] According to an embodiment of the present invention, a self-learning calibration device for a seat motor is provided. First, a control module is used to drive the seat motor of a target seat to rotate in a preset learning direction according to a folding instruction until the seat motor is blocked. Further, a first determination module is used to determine a first stop point of the rotor of the seat motor when the seat motor is blocked, and a second determination module is used to determine whether the distance between the first stop point and the expected stop point is less than a preset Hall distance. The expected stop point is a stop point of the rotor of the seat motor pre-calibrated in the preset learning direction. Recently, the calibration module is used to update the expected stop point to the first stop point in response to the distance between the first stop point and the expected stop point being less than the preset Hall distance. The first stop point represents the stop point of the seat motor when it rotates in the preset learning direction next time. The present invention solves the problem of inaccurate counting of the Hall sensor of the seat motor by continuously calibrating the stop point of the seat motor, thereby effectively improving the accuracy of seat adjustment and self-learning.

[0050] To achieve the above-mentioned purpose, the third aspect of the present invention proposes an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the self-learning calibration method of the seat motor as described above when executing the program, and solves the problem of inaccurate counting of the Hall sensor of the seat motor by continuously calibrating the stop point of the seat motor, thereby effectively improving the accuracy of seat adjustment and its self-learning.

[0051] In order to achieve the above-mentioned purpose, the fourth aspect of the present invention proposes a non-transitory computer-readable storage medium, which stores computer instructions. The computer instructions are used to enable the computer to execute the self-learning calibration method of the seat motor described above, which solves the problem of inaccurate counting of the Hall sensor of the seat motor by continuously calibrating the stopping point of the seat motor, thereby effectively improving the accuracy of seat adjustment and its self-learning.

[0052] In order to achieve the above-mentioned object, a fifth aspect of the present invention provides a seat motor, and the seat motor includes the electronic device as described above.

[0053] In order to achieve the above-mentioned object, a sixth aspect of the present invention provides a vehicle, which includes the seat motor as described above.

[0054] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 A schematic flow chart of a self-learning calibration method for a seat motor provided in an embodiment of the present invention.

[0057] Figure 2 A schematic diagram of a target seat folding application scenario provided by an embodiment of the present invention.

[0058] Figure 3 A schematic diagram of the seat motor reversal provided in an embodiment of the present invention.

[0059] Figure 4 Another schematic diagram of the seat motor reversal provided by an embodiment of the present invention.

[0060] Figure 5 A side view of a target seat providing a step difference according to an embodiment of the present invention.

[0061] Figure 6 A top view of a target seat providing a step difference according to an embodiment of the present invention.

[0062] Figure 7 A schematic diagram of the overall process of the self-learning calibration method for a seat motor provided in an embodiment of the present invention.

[0063] Figure 8 Schematic diagram of a self-learning calibration device for a seat motor provided in an embodiment of the present invention.

[0064] Fig. 9 A more specific schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention.

[0065] Fig.10 A schematic diagram of a seat motor provided in an embodiment of the present invention.

[0066] Fig.11 A schematic diagram of a vehicle provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0068] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0069] As mentioned in the background technology section, with the rapid development of the automobile industry and the continuous advancement of intelligent technology, automobiles have become an indispensable means of transportation in modern life. Among them, the vehicle seat memory function, as an important feature to improve driving comfort and convenience, is widely used in various types of vehicles.

[0070] In the process of realizing the present invention, the applicant found that the existing vehicle seat learning technology has problems such as inadequate seat movement, step difference phenomenon and seat position offset. The causes of these problems mainly include three aspects: first, the foaming interference of the three-row seat is large, which makes the rear unfolded seat unable to move accurately into place, and then produces the step difference phenomenon; second, after the motor of the seat module stops driving, the Hall interruption acquisition time is short, which may cause the Hall signal to be missed when the motor braking stroke is large, and the error accumulates after multiple folding and unfolding, causing the seat position to gradually shift backward; finally, the three-row seat motor lacks a self-locking function. During the process of stopping driving, the motor may be reversed due to the reverse resistance of the foaming after the backrest is folded, generating a reverse Hall waveform, causing the module to miscount the Hall number, while the actual seat stroke is offset upward. These situations are all problems of deviation in the Hall sensor count during the vehicle seat adjustment and memory learning process, which can be understood as a "Hall drift phenomenon".

[0071] For this reason, the present invention provides a self-learning calibration method for a seat motor, related equipment and a vehicle, which first drives the seat motor of the target seat to rotate in a preset learning direction according to a folding instruction until the seat motor is blocked, and further determines the first stop point of the rotor of the seat motor when the seat motor is blocked, and determines whether the distance between the first stop point and the expected stop point is less than the preset Hall distance; wherein the expected stop point is a stop point of the rotor of the seat motor pre-calibrated in the preset learning direction, and in response to the distance between the first stop point and the expected stop point being less than the preset Hall distance, the expected stop point is updated to the first stop point; wherein the first stop point represents the stop point of the seat motor when it rotates in the preset learning direction next time. The present invention solves the problem of inaccurate counting of the Hall sensor of the seat motor by continuously calibrating the stop point of the seat motor, thereby effectively improving the accuracy of seat adjustment and its self-learning.

[0072] The following describes the self-learning calibration method of the seat motor according to an exemplary embodiment of the present invention in combination with a specific application scenario. It should be noted that the above application scenario is only shown to facilitate understanding of the spirit and principle of the present invention, and the embodiments of the present invention are not limited in this regard. On the contrary, the embodiments of the present invention can be applied to any applicable scenario.

[0073] refer to Figure 1 , is a schematic flow chart of a self-learning calibration method for a seat motor provided in an embodiment of the present invention.

[0074] Step S102, driving the seat motor of the target seat to rotate in a preset learning direction according to the folding instruction until the seat motor is blocked;

[0075] Step S104, determining a first stopping point of the rotor of the seat motor when the seat motor is blocked;

[0076] Step S106, determining whether the distance between the first stop point and the expected stop point is less than a preset Hall distance; wherein the expected stop point is a stop point pre-calibrated by the rotor of the seat motor in a preset learning direction;

[0077] Step S108, in response to the distance between the first stop point and the expected stop point being less than the preset Hall distance, the expected stop point is updated to the first stop point; wherein the first stop point represents the stop point of the seat motor when it rotates in the preset learning direction next time.

[0078] refer to Figure 2 , is a schematic diagram of a target seat folding application scenario provided by an embodiment of the present invention.

[0079] First, seat foam interference refers to the physical contact or conflict between the seat foam material (usually polyurethane foam) and the seat frame, other components or surrounding space during the seat folding, unfolding or adjustment process. This interference may cause the seat to fail to reach the predetermined position accurately, resulting in step difference (i.e. step difference), affecting the flatness and aesthetics of the seat, and may even cause the seat to fail.

[0080] The seat memory function relies on a built-in memory chip to store and retrieve the seat position and angle set by the user. However, if the seat is affected by foam interference during adjustment, the seat may not reach the preset position accurately. The contact or conflict between the foam material and the seat frame or other components will cause the seat movement to be blocked, resulting in position deviation. This deviation will directly affect the accuracy of the seat memory function, making it impossible for the seat to return to the user's desired position. To address this problem, the size, shape and installation position of the foam material can be optimized to reduce the interference between the foam and the seat frame or other components. It can also improve the assembly accuracy of the various components of the seat, ensure that the matching clearance between the components is reasonable, and avoid interference problems caused by assembly errors.

[0081] In addition, if the Hall interrupt collection time is set too short after the motor stops driving, the Hall signal may be missed when the motor has a large braking stroke. The Hall signal is an important basis for the motor position and speed control. Missing the signal will cause the control system to be unable to accurately determine the actual position of the motor. After multiple folding and unfolding, this error will accumulate, eventually causing the seat position to gradually shift backward, affecting the accuracy and stability of the seat. To address this problem, the time for the seat module to turn off the Hall interrupt collection after the motor stops driving can be appropriately increased to ensure that all Hall signals can be accurately collected when the motor has a large braking stroke. The collected Hall signals can also be filtered and denoised to improve the accuracy and reliability of the signals. Alternatively, the signal can be analyzed and processed in real time through algorithm optimization to ensure that the position and speed of the motor can be accurately calculated.

[0082] If the seat motor lacks a self-locking function during the stop driving process (such as the third row of seats in a vehicle), it may be affected by the reverse resistance of the foam after the backrest is folded and reverse. The reversal will produce a reverse Hall waveform, causing the Hall sensor to miscount the Hall number, while the actual seat travel is offset upward. This problem not only affects the accuracy and stability of the seat, but may also cause damage to the motor and control system.

[0083] refer to Figure 3 , is a schematic diagram of the seat motor reversal provided in an embodiment of the present invention.

[0084] In some cases, if the number of poles of the seat motor and the layout of the Hall sensors are designed properly, reversal may not change the number of times the Hall sensor passes the magnetic poles (i.e., the Hall number). The Hall number does not change, and the seat motor position is reversed from the P1 position to the P2 position. This is because the Hall sensor detects changes in the magnetic field, and the magnetic field may pass through the Hall sensor in the same way when the seat motor is rotating forward and reverse. However, although the Hall number may not change, the actual stroke of the seat motor (i.e., the rotation distance or position of the seat motor shaft) will change due to reversal. This is because the stroke is calculated based on the rotation direction of the seat motor and the accumulated Hall signal. Reversal means that the direction of movement of the seat motor shaft is opposite, so even if the Hall number is the same, the final position of the seat motor shaft will be different.

[0085] refer to Figure 4 , is another schematic diagram of the seat motor reversal provided in an embodiment of the present invention.

[0086] In certain seat motor and Hall sensor configurations, reversal may cause a change in the Hall signals. There is a change in the Hall count and the seat motor position reverses from the P1 position to the P3 position (registering an increase in the Hall count, crossing the P2 position). This may be because the direction of the magnetic field changes during reversal, causing the Hall sensor to detect a different signal sequence. If the change in the Hall signals does not match the expected sequence (for example, the signals are reversed due to reversal), the control system may erroneously register additional Hall signals. This may be because the control system has difficulty trying to interpret the Hall signals and thus erroneously identifies additional signal changes. The seat motor's travel calculations are also affected due to the change in the Hall signals and possible erroneous registration. This may cause the control system to misunderstand the actual position of the seat motor, which may affect the performance and positioning accuracy of the seat motor.

[0087] refer to Figure 5 , is a side view of a target seat with a step difference provided by an embodiment of the present invention. Figure 6 , which is a top view of the step difference of the target seat provided by the embodiment of the present invention. Since the left and right seats of the vehicle will generate step differences due to the above reasons after executing different flip-up and folding commands, after accumulation, the left and right seat backs of the vehicle cannot be leveled and aligned.

[0088] In view of the above problems, especially the problem that the Hall sensor miscounts the Hall number due to the reversal of the seat motor, the present invention proposes a self-learning calibration method for the seat motor. By enabling the seat motor to "self-learn" each time it is adjusted, the seat motor can "re-learn" the optimal stop point during each folding process. For multiple seats in the same row, the left and right seats can be restored to their initial positions after each folding, and the initial positions can be self-learned. After the same adjustment procedure is performed again, the left and right backrests can be adjusted flush each time.

[0089] The controller plays a vital role in the operation mechanism of the seat motor. It is the "brain" of the entire motor control system and is responsible for receiving folding instructions from the vehicle's central control unit (such as ECU) or other command sources. Once these instructions are received, the controller will send corresponding current or voltage signals to the seat motor according to the preset program and logic to drive the motor to rotate in the specified direction (such as folding or unfolding). The folding instructions can come from the control panel, remote control or central control unit (ECU) inside the vehicle.

[0090] When the controller receives a folding instruction, it drives the seat motor of the target seat to start rotating in the preset learning direction. This rotation process will continue until the seat motor encounters resistance and becomes blocked, that is, the motor can no longer rotate. The preset learning direction can be the folding direction or the flipping direction of the target seat. The target seat can be any seat in the vehicle, or any row of seats. When the target seat represents any row of seats in the target vehicle, all individual seats in the row are driven synchronously.

[0091] Taking the case where the seat cushion of the target seat does not move (i.e., the target seat does not adjust forward and backward) as an example, the rotation of the target seat in the preset learning direction means that the backrest of the target seat rotates in the folding direction or the flipping direction under the drive of the corresponding seat motor. During the whole process, the travel range of the target seat in the preset learning direction is limited. For example, the maximum flipping angle of the backrest of the target seat is 135°, and the corresponding travel range of the seat motor may be 1800 Hall. In theory, when the seat back of the target seat is flipped up to 135°, the seat motor may run from 0 to 1800 Hall. Of course, due to the existence of foaming interference, Hall sensor leakage, and seat motor reversal, the seat motor may not reach the maximum Hall distance during each adjustment process and will be blocked. When the seat motor is blocked, the position of the rotor of the seat motor at this time is recorded, and this position is defined as the first stop point. Next, the system will calculate the distance between this first stop point and the expected stop point. The expected stop point here is the position where the rotor of the seat motor is expected to stop in advance in the preset learning direction (i.e., the expected stop point). It can be understood that the expected stop point is the point where the seat motor "should" stop in theory. Continuing with the above example, the maximum tilting angle of the target seat backrest is 135°, and the corresponding seat motor travel range may be 1800 Hall. Then when the seat motor runs to 1800 Hall, the corresponding position of the seat electronic rotor can be understood as the expected stop point.

[0092] However, due to reasons such as foaming interference, Hall sensor leakage, and seat motor reversal, when the seat motor runs to a stroke of 1750 Halls, the seat motor is blocked by resistance and cannot continue to move. The stop point (i.e., the first stop point) when the seat motor runs to the stroke of "1750 Halls" is recorded. If the calculated distance between the first stop point and the expected stop point is less than the preset Hall distance, the system will consider the first stop point to be a more accurate or more appropriate stop point. Therefore, the system will update the position of the expected stop point and set it to the position of the first stop point. The updated stop point will be used as the stop point when the seat motor rotates in the preset learning direction next time.

[0093] It should be noted that the preset Hall distance is a preset threshold used to compare the difference between the actual stop position and the expected stop position. The most accurate stop point of the seat motor is determined through actual operation and detection, and the stop position of the motor is continuously optimized by comparing and updating with the preset value, thereby improving the control accuracy and stability of the seat motor, which is the "self-learning" of the seat calibration position.

[0094] As an optional embodiment, before driving the seat motor of the target seat to rotate in a preset learning direction according to the folding instruction, it also includes: in response to receiving the folding instruction, determining whether the target seat meets the folding condition; in response to the target seat meeting the folding condition, determining to control the seat motor of the target seat to rotate in the preset learning direction according to the folding instruction.

[0095] In a specific implementation, before the motor of the target seat responds to the folding instruction, the system first checks whether the target seat meets the folding conditions.

[0096] As an optional embodiment, the determining whether the target seat satisfies the folding condition includes: determining whether the target seat is occupied; and in response to the target seat being occupied, determining that the target seat does not satisfy the folding condition.

[0097] In specific implementations, these conditions may include, but are not limited to, the need to determine whether there are passengers or objects on the target seat to ensure that the folding operation does not cause any injury or damage. For the above situation, a weight sensor or infrared sensor can be installed on the target seat to detect whether there are passengers or objects on the target seat. When the sensor detects weight or objects, the system will automatically lock the folding function to avoid misoperation. The target seat area can also be monitored in real time using an on-board camera or radar system. Image recognition technology can be used to determine whether there are passengers or objects on the target seat, thereby avoiding the folding operation. In addition, before the folding operation, the system can prompt the user to confirm whether there are people or objects on the target seat through a display or voice. The system will only perform the folding operation after the user manually confirms.

[0098] As an optional embodiment, the determination of whether the target seat meets the folding condition also includes: determining whether there is an obstacle within the range of rotation of the target seat in a preset learning direction; in response to the presence of an obstacle within the range of rotation of the target seat in the preset learning direction, determining that the target seat does not meet the folding condition.

[0099] As an optional embodiment, the obstacles include other seats, and in response to the rotation range of the target seat in the preset learning direction being blocked by other seats, the other seats are adjusted to move them away from the travel range.

[0100] In a specific implementation, whether the folding conditions are met may also include the need to determine whether there is enough space around the target seat for folding to avoid collision with other parts of the vehicle. For the above situation, ultrasonic sensors, laser sensors, radars and other devices can be used to measure the space around the target seat. When insufficient space is detected, the system will automatically stop the folding operation and give a warning. Or the target seat folding system can be combined with the vehicle's parking assistance system, radar system, etc. to achieve information sharing. When the vehicle is in a narrow space or there are obstacles nearby (such as the front seats are too far back), the system will automatically limit the folding operation of the target seat. Or allow the user to set the minimum space requirement for folding the target seat in the system according to actual needs. When the actual space is less than the value set by the user, the system will automatically stop the folding operation.

[0101] As an optional embodiment, whether the folding conditions are met may also include the need to determine whether the mechanical structure of the target seat is in a normal state to ensure that the folding operation can proceed smoothly. The mechanical structure of the seat can be regularly inspected and maintained to ensure that all components are in good condition. If problems are found, they should be repaired or replaced in time to avoid affecting the folding operation. Sensors and diagnostic technologies can also be used to monitor the operating status of the seat mechanical structure in real time. When an abnormality is detected, the system will automatically stop the folding operation and give a fault prompt. The software of the seat folding system can also be regularly upgraded and optimized to improve the stability and reliability of the system. At the same time, through software upgrades, remote monitoring and diagnosis of the seat mechanical structure can be achieved.

[0102] It should be noted that when the seats adjacent to the target seat are in motion, the target seat is not allowed to be adjusted. For example, if the target seat is the third row of seats in the vehicle and you want to fold the target seat, then when the second row of seats in the vehicle are in motion approaching the third row of seats, the target seat may collide and interfere with the second row of seats if it is folded. Therefore, it is determined that the target seat does not meet the folding conditions.

[0103] It should be noted that when the target seat is moving forward or backward, it is not allowed to perform a folding or flipping operation, that is, the folding condition is not met. When the target seat is performing a folding or flipping operation, it is not allowed to perform a forward or backward movement.

[0104] As an optional embodiment, before driving the seat motor of the target seat to rotate in a preset learning direction according to the folding instruction, the method also includes: determining whether the engine for driving the seat motor is in a started state, and determining whether the seat motor stores a desired stop point; in response to the engine being in a started state and the seat motor storing a desired stop point, determining that the seat motor of the target seat is driven to rotate in the preset learning direction.

[0105] In a specific implementation, it is necessary to determine the engine state and the storage status of the expected stop point of the seat motor to ensure that the system is in a proper operating state before driving the seat motor to perform a folding operation.

[0106] Specifically, after receiving the folding instruction, the system will first check whether the engine used to drive the seat motor (which can be understood as the main engine of the vehicle or the auxiliary engine / power supply specifically for the seat motor) is in the starting state. If the engine is not started, the system may give a prompt to ask the user to start the engine first, or automatically try to start the engine. The engine start is a prerequisite for the normal operation of the seat motor because it provides the necessary power or power source. Further, the system will then check whether the seat motor has stored the expected stop point. This expected stop point is the position where the seat motor should stop in the preset learning direction. If the seat motor does not store the expected stop point, the system may perform a self-learning or calibration process to determine this point, or give a prompt to require the user to calibrate. The storage of the expected stop point is the key to the seat motor being able to stop accurately at the expected position, which ensures the accuracy and reliability of the seat folding operation. Only when the engine is in the starting state and the seat motor has stored the expected stop point, the system will drive the seat motor to rotate in the preset learning direction according to the folding instruction.

[0107] As an optional embodiment, it is necessary to record the number of first Hall pulses output by the seat motor from the start-up moment to the moment when the seat motor is stalled; and convert the first Hall pulse number into a first Hall value according to a preset ratio.

[0108] As an optional embodiment, determining whether the distance between the first stop point and the expected stop point is less than a preset Hall distance includes: calculating the difference Hall distance between the first Hall value and the preset Hall value corresponding to the expected stop point; and determining whether the difference Hall distance is less than the preset Hall distance.

[0109] In a specific implementation, when the seat motor starts to rotate from the start-up moment, the system will start to record the number of Hall pulses output by the motor. Hall pulses are generated by the Hall sensor during the rotation of the motor, and they correspond to the rotation angle or position of the motor. The first Hall pulse number refers to the total number of Hall pulses recorded by the system during the period from the start of the motor to the occurrence of stall. The system will convert the first Hall pulse number into a first Hall value according to a preset ratio (this ratio can be determined based on the design of the motor and the characteristics of the Hall sensor). The Hall value is a more abstract representation, which represents the relative position or angle of the motor rotation. Through the conversion, the system can convert the physical quantity of the Hall pulse number into a value that is easier to process and compare. Further, after determining the first Hall value, the system needs to calculate the difference Hall distance between it and the preset Hall value corresponding to the expected stop point. The expected stop point is the position where the seat motor should stop in the preset learning direction, and the preset Hall value is the Hall value corresponding to this position. The difference Hall distance is the difference between the first Hall value and the preset Hall value, which represents the relative distance between the actual stop position and the expected stop position (on the scale of the Hall value). Finally, the system checks whether the difference Hall distance is less than a preset Hall distance threshold. This threshold is determined based on the accuracy requirements of the seat motor, the resolution of the Hall sensor, and the overall design requirements of the system. If the difference Hall distance is less than the preset Hall distance, the system can assume that the difference between the actual stop position and the expected stop position is acceptable, so the expected stop point can be updated to the first stop point (or the position corresponding to the first Hall value).

[0110] As an optional embodiment, in response to the distance between the first stop point and the expected stop point being not less than a preset Hall distance, it is determined whether the number of times the seat motor is stalled within a preset time interval reaches a preset number threshold; in response to the number of times the seat motor is stalled within a preset time interval reaching a preset number threshold, the second stop point when the seat motor was last stalled is determined; the expected stop point is updated to the second stop point; wherein the second stop point represents the stop point when the seat motor rotates next time in a preset learning direction.

[0111] Specifically, when the system detects that the distance between the first stop point and the expected stop point is not less than the preset Hall distance, it will further check whether the number of times the seat motor is blocked within the preset time interval has reached the preset number threshold. This preset time interval and number threshold are determined according to the characteristics of the seat motor, the design requirements of the system, and the actual application scenario. If the number of blocking times does not reach the threshold, the system may continue to try to adjust the rotation parameters of the motor or perform other operations to try to get closer to the expected stop point. If the number of times the seat motor is blocked within the preset time interval has reached the preset number threshold, the system will determine the second stop point when the seat motor was blocked for the last time. The second stop point is the position where the motor finally stops after multiple attempts, and it may be closer to the expected stop point. Further, the system will update the expected stop point to the second stop point. This means that in the subsequent seat folding operation, when the seat motor rotates in the preset learning direction, it will try to stop at the second stop point. The purpose of updating the expected stop point is to adjust the system's expectations according to the actual performance of the motor to ensure that the seat can be accurately folded to the expected position. The second stop point not only represents the stop point of the seat motor when it rotates in the preset learning direction next time, but also reflects the best stop position of the motor under the current conditions. By updating the desired stop point as the second stop point, the system is able to more accurately control the folding position of the seat, improving the performance and reliability of the entire system.

[0112] It should be noted that the system can also evaluate the operating status and performance of the motor by checking the number of stalls within a preset time interval. If the number of stalls is too high, it may indicate that the motor is faulty or needs maintenance.

[0113] In summary, by determining the second stop point, the system can adjust the expected stop point according to the actual performance of the motor, thereby ensuring that the seat can be accurately folded to the expected position. Updating the expected stop point is a key step in the seat motor self-learning calibration. It enables the system to optimize the control strategy according to the actual operation of the motor and changes in the external environment, improving the performance and reliability of the entire system.

[0114] As an optional embodiment, determining the second stop point when the seat motor is last stalled includes: recording the number of second Hall pulses output by the seat motor from the start-up time to the last stall-occurrence time of the seat motor; and determining the second stop point according to the second Hall pulse number.

[0115] Specifically, when the seat motor starts to rotate from the start-up moment and the system detects that the motor is stalled for the last time, the number of Hall pulses output from the start-up moment to the stalling moment will be recorded, which can be called the second Hall pulse number. The Hall pulse number is generated by the Hall sensor during the rotation of the motor, and they correspond to the rotation angle or position of the motor. By recording these pulse numbers, the system can track the rotation of the motor. Based on the recorded second Hall pulse number, the system can determine the stopping position of the seat motor when it was stalled for the last time, that is, the second stopping point. Similarly, this process involves converting the Hall pulse number into the angle or position information of the motor rotation, which can be achieved through a preset ratio or mapping relationship. The second stopping point represents the position where the motor can stop stably under the current conditions. By recording the second Hall pulse number, the system can accurately track the rotation of the motor and determine the position of the motor when it was stalled for the last time.

[0116] In the previous step, the system has determined the first stop point and checked the distance between it and the expected stop point. If the distance between the first stop point and the expected stop point is not less than the preset Hall distance, the system will further check the number of stalls of the motor. When the number of stalls reaches the preset number threshold, the system will execute the steps in this optional embodiment to determine the second stop point and update the expected stop point accordingly.

[0117] refer to Figure 7 , which is a schematic diagram of the overall process of the self-learning calibration method of the seat motor provided in an embodiment of the present invention.

[0118] As an optional embodiment, when the controller receives the folding instruction, it will first check whether the target seat is occupied. If the target seat is occupied, the system will consider that the folding condition is not met and immediately terminate the entire operation process. If the target seat is not occupied, the system will further check whether the engine used to drive the seat motor has been started. At the same time, the system will also check whether the seat motor has stored the expected stop point. If the engine of the seat motor has not been started or the expected stop point has not been stored, the system will also terminate the entire operation process. If the engine has been started and the seat motor has stored the expected stop point, the system will further determine whether the current position of the seat back of the target seat meets the movement condition. The movement condition includes the conditions for the seat to move forward and backward, up and down, and the conditions for the seat back to fold and turn up. For example, the seat back of the target seat needs to have enough space during the rotation process and should not be blocked by adjacent seats or components. If the rotation travel of the seat back of the target seat is blocked by an adjacent seat, the system will try to drive the adjacent seat to meet the movement condition of the target seat. Before driving the adjacent seat, it is also necessary to check whether the adjacent seat meets the movement condition. If the adjacent seats do not meet the motion conditions, the system will issue a prompt to the vehicle's central control and continue to detect whether the adjacent seats meet the conditions. Once all conditions are met, the system will drive the seat motor according to the folding instruction. During the driving process, the system will continue to monitor whether the seat motor is blocked. If the seat motor is blocked, the system will immediately stop driving the motor. Next, the system will detect whether the differential Hall distance of the seat motor is less than the preset Hall distance (used to compare the difference between the actual stop position and the expected stop position). If the differential Hall distance is less than the preset value, the system will recalibrate the stop point of the seat motor and use the current stop point as the stop point for the next rotation in the preset learning direction. If the differential Hall distance is not less than the preset value, the system will continue to monitor whether the seat motor is blocked for a preset number of times within a preset time interval, for example, if it is blocked three times within 5 seconds, the stop point of the seat motor will also be recalibrated if it is blocked three times within 5 seconds. If the seat motor does not block three times within 5 seconds, the system will consider that the folding operation is successfully completed and end the entire operation process. Through the above series of inspection and monitoring steps, the safety and effectiveness of the seat folding operation are ensured. At the same time, by recalibrating the stopping point of the seat motor, the system can adapt to different usage scenarios and conditions, improving the accuracy and reliability of seat folding.

[0119] As can be seen from the above description, the present invention provides a self-learning calibration method for a seat motor, which first drives the seat motor of the target seat to rotate in a preset learning direction according to a folding instruction until the seat motor is blocked, and further determines the first stop point of the rotor of the seat motor when the seat motor is blocked, and determines whether the distance between the first stop point and the expected stop point is less than the preset Hall distance; wherein the expected stop point is a stop point of the rotor of the seat motor pre-calibrated in the preset learning direction, and in response to the distance between the first stop point and the expected stop point being less than the preset Hall distance, the expected stop point is updated to the first stop point; wherein the first stop point represents the stop point of the seat motor when it rotates in the preset learning direction next time. The present invention solves the problem of inaccurate counting of the Hall sensor of the seat motor by continuously calibrating the stop point of the seat motor, thereby effectively improving the accuracy of seat adjustment and its self-learning.

[0120] It should be noted that the method of the embodiment of the present invention can be executed by a single device, such as a computer or a server. The method of this embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only execute one or more steps in the method of the embodiment of the present invention, and the multiple devices will interact with each other to complete the self-learning calibration method of the seat motor.

[0121] It should be noted that some embodiments of the present invention are described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0122] Based on the same inventive concept, corresponding to the method provided in any of the above embodiments, the present invention also provides a self-learning calibration device for a seat motor.

[0123] refer to Figure 8 , is a schematic diagram of a self-learning calibration device for a seat motor provided in an embodiment of the present invention.

[0124] The device comprises:

[0125] a control module configured to drive a seat motor of a target seat to rotate in a preset learning direction according to a folding instruction until the seat motor is blocked;

[0126] A first determination module is configured to determine a first stop point of a rotor of the seat motor when the seat motor is blocked;

[0127] a second determination module configured to determine whether a distance between the first stop point and an expected stop point is less than a preset Hall distance; wherein the expected stop point is a stop point pre-calibrated by the rotor of the seat motor in a preset learning direction;

[0128] The calibration module is configured to update the expected stop point to the first stop point in response to the distance between the first stop point and the expected stop point being less than a preset Hall distance; wherein the first stop point represents the stop point of the seat motor when it rotates next time in a preset learning direction.

[0129] In a possible implementation manner, the first determining module is further configured to:

[0130] In response to receiving the folding instruction, determining whether the target seat satisfies a folding condition;

[0131] In response to the target seat satisfying the folding condition, it is determined that a seat motor of the target seat is controlled to rotate in a preset learning direction according to the folding instruction.

[0132] In a possible implementation manner, the first determining module is further configured to:

[0133] Determining whether the target seat is occupied;

[0134] In response to the target seat being occupied, it is determined that the target seat does not satisfy a folding condition.

[0135] In a possible implementation manner, the first determining module is further configured to:

[0136] Determining whether there is an obstacle within the travel range of the target seat rotating in the preset learning direction;

[0137] In response to an obstacle existing within a travel range of the target seat rotating toward the preset learning direction, it is determined that the target seat does not meet a folding condition.

[0138] In a possible implementation, the obstacle includes other seats, and the first determining module is further configured to:

[0139] In response to a travel range of the target seat rotating toward the preset learning direction being blocked by other seats, the other seats are adjusted to move the other seats away from the travel range.

[0140] In a possible implementation manner, the first determining module is further configured to:

[0141] determining whether an engine for driving the seat motor is in a started state, and determining whether the seat motor stores the expected stop point;

[0142] In response to the engine being in the started state and the seat motor storing the desired stop point, it is determined that the seat motor of the driving target seat is rotated in a preset learning direction.

[0143] In a possible implementation, the control module is further configured to:

[0144] Recording the number of first Hall pulses output by the seat motor from the start-up time to the time when the seat motor is stalled;

[0145] The first Hall pulse number is converted into a first Hall value according to a preset ratio.

[0146] In a possible implementation, the control module is further configured to:

[0147] Calculating a difference Hall distance between the first Hall value and a preset Hall value corresponding to the expected stop point;

[0148] It is determined whether the difference Hall distance is less than the preset Hall distance.

[0149] In a possible implementation, the calibration module is further configured to:

[0150] In response to the distance between the first stop point and the expected stop point being not less than the preset Hall distance, determining whether the number of times the seat motor is stalled within a preset time interval reaches a preset number threshold;

[0151] In response to the number of times that the seat motor is stalled within a preset time interval reaching the preset number threshold, determining a second stop point when the seat motor is stalled for the last time;

[0152] The expected stop point is updated to the second stop point; wherein the second stop point represents the stop point of the seat motor when it rotates toward the preset learning direction next time.

[0153] In a possible implementation, the calibration module is further configured to:

[0154] Record the number of second Hall pulses output by the seat motor from the start-up time to the last time the seat motor is locked;

[0155] The second stop point is determined according to the second number of Hall pulses.

[0156] For the convenience of description, the above system is described by dividing it into various modules according to its functions. Of course, when implementing the present invention, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0157] The system of the above embodiment is used to implement the corresponding self-learning calibration method of the seat motor in any of the above embodiments, and has the beneficial effects of the corresponding self-learning calibration method embodiment of the seat motor, which will not be repeated here.

[0158] Based on the same inventive concept, corresponding to the self-learning calibration method of the seat motor described in any of the above embodiments, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the self-learning calibration method of the seat motor described in any of the above embodiments when executing the program.

[0159] Fig. 9 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1110, a memory 1120, an input / output interface 1130, a communication interface 1140, and a bus 1150. The processor 1110, the memory 1120, the input / output interface 1130, and the communication interface 1140 are connected to each other through the bus 1150 in the device.

[0160] The processor 1110 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0161] The memory 1120 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1120 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1120 and are called and executed by the processor 1110.

[0162] The input / output interface 1130 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0163] The communication interface 1140 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0164] The bus 1150 comprises a pathway that transmits information between the various components of the device (eg, the processor 1110 , the memory 1120 , the input / output interface 1130 , and the communication interface 1140 ).

[0165] It should be noted that, although the above device only shows the processor 1110, the memory 1120, the input / output interface 1130, the communication interface 1140 and the bus 1150, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0166] The electronic device of the above embodiment is used to implement the corresponding self-learning calibration method of the seat motor in any of the above embodiments, and has the beneficial effects of the corresponding self-learning calibration method embodiment of the seat motor, which will not be repeated here.

[0167] Based on the same inventive concept, corresponding to the self-learning calibration method of the seat motor described in any of the above embodiments, the present invention also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the self-learning calibration method of the seat motor described in any of the above embodiments.

[0168] The above-mentioned non-transitory computer-readable storage medium can be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)), etc.

[0169] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the self-learning calibration method of the seat motor as described in any embodiment in the above exemplary method part, and have the corresponding beneficial effects of the self-learning calibration method embodiment of the seat motor, which will not be repeated here.

[0170] Based on the same inventive concept, corresponding to the self-learning calibration method of the seat motor described in any of the above embodiments, the present invention also provides a seat motor.

[0171] Fig.10 A schematic diagram of a seat motor provided in this embodiment is shown. The device may include the electronic device described above and has the beneficial effects of the corresponding embodiments, which will not be described in detail here.

[0172] Based on the same inventive concept, corresponding to the self-learning calibration method of the seat motor described in any of the above embodiments, the present invention also provides a vehicle.

[0173] Fig.11 A schematic diagram of a vehicle provided by this embodiment is shown. The device may include the seat motor described above and has the beneficial effects of the corresponding embodiments, which will not be repeated here.

[0174] In addition, although the operations of the method of the present invention are described in a specific order in the accompanying drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted in the flow chart can be performed in a different order. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0175] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0176] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the embodiments of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0177] Although the spirit and principle of the present invention have been described with reference to several specific embodiments, it should be understood that the present invention is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined to benefit, and this division is only for the convenience of expression. The present invention is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the attached claims. The scope of the attached claims conforms to the broadest interpretation, thereby including all such modifications and equivalent structures and functions.

Claims

1. A self-learning calibration method for a seat motor, characterized in that: include: driving a seat motor of a target seat to rotate in a preset learning direction according to a folding instruction until the seat motor is blocked; determining a first stopping point of a rotor of the seat motor when the seat motor is blocked; Determining whether the distance between the first stop point and the expected stop point is less than a preset Hall distance; wherein the expected stop point is a stop point pre-calibrated by the rotor of the seat motor in the preset learning direction; In response to the distance between the first stop point and the expected stop point being less than the preset Hall distance, the expected stop point is updated to the first stop point; wherein the first stop point represents the stop point of the seat motor when it rotates toward the preset learning direction next time.

2. The self-learning calibration method of the seat motor according to claim 1, characterized in that: Before driving the seat motor of the target seat to rotate in a preset learning direction according to the folding instruction, the method further includes: In response to receiving the folding instruction, determining whether the target seat satisfies a folding condition; In response to the target seat satisfying the folding condition, it is determined that a seat motor of the target seat is controlled to rotate in a preset learning direction according to the folding instruction.

3. The self-learning calibration method of the seat motor according to claim 2, characterized in that: The determining whether the target seat meets the folding condition includes: Determining whether the target seat is occupied; In response to the target seat being occupied, it is determined that the target seat does not satisfy a folding condition.

4. The self-learning calibration method of the seat motor according to claim 2, characterized in that: The determining whether the target seat meets the folding condition further includes: Determine whether there are other obstacles within the travel range of the target seat rotating in the preset learning direction; In response to other obstacles existing within the travel range of the target seat rotating toward the preset learning direction, it is determined that the target seat does not meet the folding condition.

5. The self-learning calibration method of the seat motor according to claim 4, characterized in that: The obstacles include other seats; The method further comprises: In response to a travel range of the target seat rotating toward the preset learning direction being blocked by other seats, the other seats are adjusted to move the other seats away from the travel range.

6. The self-learning calibration method of the seat motor according to claim 1, characterized in that: Before driving the seat motor of the target seat to rotate in a preset learning direction according to the folding instruction, the method further includes: determining whether an engine for driving the seat motor is in a started state, and determining whether the seat motor has the expected stop point stored therein; In response to the engine being in the started state and the seat motor storing the desired stop point, it is determined that the seat motor of the driving target seat is rotated in a preset learning direction.

7. The self-learning calibration method of a seat motor according to claim 1, characterized in that: The method further comprises: Recording the number of first Hall pulses output by the seat motor from the start-up time to the time when the seat motor is stalled; The first Hall pulse number is converted into a first Hall value according to a preset ratio.

8. The self-learning calibration method of the seat motor according to claim 7, characterized in that: The determining whether the distance between the first stop point and the expected stop point is less than a preset Hall distance includes: Calculating a difference Hall distance between the first Hall value and a preset Hall value corresponding to the expected stop point; It is determined whether the difference Hall distance is less than the preset Hall distance.

9. The self-learning calibration method of a seat motor according to claim 1, characterized in that: The method further comprises: In response to the distance between the first stop point and the expected stop point being not less than the preset Hall distance, determining whether the number of times the seat motor is stalled within a preset time interval reaches a preset number threshold; In response to the number of times that the seat motor is stalled within a preset time interval reaching the preset number threshold, determining a second stop point when the seat motor is stalled for the last time; The expected stop point is updated to the second stop point; wherein the second stop point represents the stop point of the seat motor when it rotates toward the preset learning direction next time.

10. The self-learning calibration method of the seat motor according to claim 9, characterized in that: The determining of the second stop point when the seat motor is last blocked comprises: Record the number of second Hall pulses output by the seat motor from the start-up time to the last time the seat motor is locked; The second stop point is determined according to the second number of Hall pulses.

11. A self-learning calibration device for a seat motor, characterized in that: include: a control module configured to drive a seat motor of a target seat to rotate in a preset learning direction according to a folding instruction until the seat motor is blocked; A first determination module is configured to determine a first stop point of a rotor of the seat motor when the seat motor is blocked; a second determination module configured to determine whether the distance between the first stop point and the expected stop point is less than a preset Hall distance; wherein the expected stop point is a stop point pre-calibrated by the rotor of the seat motor in the preset learning direction; The calibration module is configured to update the expected stop point to the first stop point in response to the distance between the first stop point and the expected stop point being less than the preset Hall distance; wherein the first stop point represents the stop point of the seat motor when it rotates toward the preset learning direction next time.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the self-learning calibration method for a seat motor as claimed in any one of claims 1 to 10 is implemented.

13. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the self-learning calibration method for a seat motor according to any one of claims 1 to 10.

14. A seat motor, characterized in that: Comprising the electronic device as claimed in claim 12.

15. A vehicle, characterized in that: Comprising the seat motor as claimed in claim 14.

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