Bicycle seat control method and device

Abstract

The application relates to a bicycle seat control method and device, which comprises the following steps: in response to receiving a seat adjustment instruction sent by a Bluetooth key device on a bicycle handle through a Bluetooth receiving device, a dynamic physical model of a seat lifting mechanism is constructed based on a current magnetic angle change rate collected by a magnetic angle sensor; a magnetic angle mutation feature of a motor in a starting process is used to identify a limit position state of a seat lock head; an initial mapping relationship between the seat lock head position and the magnetic angle change rate is established based on the limit position state and the magnetic angle change rate; a target motor magnetic angle is determined according to the initial mapping relationship; the motor is driven to rotate in the direction of the target motor magnetic angle, and a real-time magnetic angle change rate is continuously acquired during the rotation; and based on the real-time matching result of the real-time magnetic angle change rate and the dynamic physical model, the control parameters of the motor are adjusted, so that the seat lock head accurately reaches the target position state. The application solves the problem of seat lifting control precision reduction.

Description

Technical Field

[0001] This application relates to the field of intelligent control technology, and in particular to bicycle seat control methods and devices. Background Technology

[0002] As an important mode of personal transportation, the comfort and adaptability of bicycles have a significant impact on the riding experience. The seat, as a key component in contact between the rider and the bicycle, is crucial for adapting to different riding postures, road conditions, and rider body types through height adjustment.

[0003] Traditional bicycle seats generally use a mechanical structure for height adjustment. The adjustment process typically involves loosening the seat post by rotating a screw, manually adjusting the seat to the desired height, and finally tightening the screw to make the seat fixed. This method prevents riders from adjusting the seat height while riding, failing to meet the need for quick adjustments to accommodate changes in riding conditions. When switching between different road conditions such as flat roads, uphill, and downhill, riders must stop and manually adjust the seat, which not only reduces riding efficiency but also increases safety hazards, especially in busy traffic or riding scenarios requiring rapid reaction time.

[0004] To address these issues, electric height-adjustable seat technology has emerged in the market. Existing electric height-adjustable seat control systems typically employ an open-loop control strategy, relying solely on the absolute angle of the motor's rotation to determine the seat lock position.

[0005] However, this control method has a significant drawback: current bicycle seat control systems use a fixed angle threshold to determine the lock position, which cannot adapt to system characteristic drift, causing the seat lock position control accuracy to decrease significantly over time. Summary of the Invention

[0006] Therefore, it is necessary to provide a bicycle seat control method and device that achieves high-precision, adaptive control of the bicycle seat lock position and solves the problem of decreased control accuracy caused by mechanical wear and environmental changes, in order to address the above-mentioned technical problems.

[0007] In a first aspect, this application provides a bicycle seat control method, the method comprising: In response to receiving a seat adjustment command from a Bluetooth button on the bicycle handlebars via a Bluetooth receiver, a dynamic physical model of the seat lifting mechanism is constructed based on the current rate of change of magnetic angle collected by a magnetic angle sensor; wherein, the dynamic physical model represents the dynamic correspondence between the motor control signal and the seat lock position; The abrupt change in magnetic angle during motor startup is used to identify the extreme position state of the seat lock. Based on the extreme position state and the rate of change of magnetic angle, an initial mapping relationship between the position of the seat lock and the rate of change of magnetic angle is established; Based on the initial mapping relationship, the target motor magnetic angle is determined; whereby the target motor magnetic angle corresponds to the target position state of the seat lock head; The drive motor rotates in the direction of the target motor's magnetic angle, and continuously acquires the real-time rate of change of the magnetic angle during the rotation process; Based on the real-time matching results of the real-time magnetic angle change rate and the dynamic physical model, the control parameters of the motor are adjusted so that the seat lock head can accurately reach the target position.

[0008] Secondly, this application also provides a bicycle seat control device, the device comprising: A magnetic angle sensor is used to collect the rate of change of the current magnetic angle during the rotation of the motor. The central processing unit, electrically connected to the magnetic angle sensor, is used to construct a dynamic physical model of the seat lifting mechanism based on the current rate of change of magnetic angle collected by the magnetic angle sensor in response to the received seat adjustment command; wherein, the dynamic physical model represents the dynamic correspondence between the motor control signal and the seat lock position; The central processing unit is also used to identify the extreme position state of the seat lock head by analyzing the magnetic angle change characteristics of the motor during the startup process; based on the extreme position state and the magnetic angle change rate, an initial mapping relationship between the position of the seat lock head and the magnetic angle change rate is established; and the target motor magnetic angle is determined according to the initial mapping relationship; wherein, the target motor magnetic angle corresponds to the target position state of the seat lock head. The motor drive module is electrically connected to the central processing unit and is used to drive the motor to rotate in the magnetic angle direction of the target motor. During the rotation, the real-time magnetic angle change rate is continuously acquired through the magnetic angle sensor. The central processing unit is also used to adjust the control parameters of the motor drive module based on the real-time matching results of the real-time magnetic angle change rate and the dynamic physical model, so that the seat lock head can accurately reach the target position.

[0009] The aforementioned bicycle seat control method and device first constructs a physical model representing the dynamic correspondence between the motor control signal and the seat lock position in response to seat adjustment commands, thus eliminating the reliance on fixed angle thresholds. Then, it identifies extreme position states by analyzing the magnetic angle abrupt change characteristics during motor startup, using the essential characteristics of physical phenomena rather than fixed values ​​for judgment. Next, it establishes an initial mapping relationship between the seat lock position and the rate of change of magnetic angle, which reflects the nonlinear characteristics of the system. Based on this mapping relationship, it determines the target motor magnetic angle and continuously acquires the real-time rate of change of magnetic angle during motor drive. Finally, it dynamically adjusts the control parameters based on the matching results between the real-time rate of change of magnetic angle and the dynamic physical model. This series of technical features forms a complete closed-loop adaptive control system, enabling the seat lock to accurately reach the target position. Even under conditions of mechanical wear or changes in environmental conditions due to long-term use, the system can maintain high-precision control through real-time matching and parameter adjustment, effectively overcoming the problem of decreased control accuracy caused by system characteristic drift in traditional technologies. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating the bicycle seat control method provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the steps for adjusting control parameters based on real-time matching results provided in an embodiment of the present invention. Figure 3 This is a flowchart illustrating the adaptive seat control optimization steps provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of the bicycle seat control device provided in an embodiment of the present invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0013] like Figure 1 As shown, this embodiment provides a bicycle seat control method, executed by a central controller. The following will describe the method in conjunction with... Figure 1The steps shown illustrate the bicycle seat control method provided in this embodiment: In step S101, in response to receiving a seat adjustment command from the Bluetooth button device on the bicycle handlebars via the Bluetooth receiver, a dynamic physical model of the seat lifting mechanism is constructed based on the current magnetic angle change rate collected by the magnetic angle sensor.

[0014] Among them, the dynamic physical model represents the dynamic correspondence between the motor control signal and the seat lock position.

[0015] In some embodiments, the dynamic physical model is a mathematical model constructed based on the mechanical characteristics and motor dynamics of the seat lifting mechanism. Specifically, the model describes the dynamic relationship between the input control signal and the output seat lock position, taking into account the influence of factors such as motor inertia, mechanical transmission ratio, spring force, and friction.

[0016] In some embodiments, the dynamic physical model is represented by a first-order or second-order transfer function, and its mathematical expression is as follows: G(s)=K / (τs+1) or G(s)=K / (τ1s²+τ2s+1) Where K represents the system gain, τ, τ1, and τ2 represent the system time constants, and s represents the Laplace transform variable. This model can accurately describe the dynamic response characteristics of the seat lifting mechanism under different control signals.

[0017] In some embodiments, when constructing a dynamic physical model, the basic characteristic parameters of the seat lifting mechanism are first obtained through small-signal excitation tests, and then the model parameters are initially set based on historical adjustment data. This model construction method based on actual tests and historical data ensures a high degree of matching between the model and the actual system, laying the foundation for subsequent precise control.

[0018] By constructing an accurate dynamic physical model, the motion trajectory and final position of the seat lock head can be accurately predicted under different control signals, avoiding the control error caused by inaccurate models in traditional open-loop control, and significantly improving the accuracy and reliability of seat adjustment.

[0019] In step S102, the extreme position state of the seat lock head is identified by analyzing the magnetic angle change characteristics of the motor during the startup process.

[0020] In some embodiments, the limit position states of the seat lock head include two states: "fully protruding" (liftable state) and "fully recessed" (non-liftable state). When the seat lock head reaches the limit position, due to the limitation of the mechanical structure, the motor will be unable to continue rotating, resulting in a significant change in the rate of change of magnetic angle.

[0021] In some embodiments, by monitoring the magnetic angle change rate curve during the initial stage of motor startup, a significant inflection point is detected in the change rate curve, indicating that the seat lock has reached its limit position. Specifically, a significant abrupt change in the absolute value of the magnetic angle change rate indicates that the seat lock has reached the limit position of its mechanical travel.

[0022] In some embodiments, to improve the accuracy of identification, a sliding window method is used to smooth the rate of change of magnetic angle, eliminating sensor noise interference, and then the abrupt change point is determined by calculating the first or second derivative. This method can effectively distinguish between the true mechanical limit position and the transient change caused by temporary resistance, ensuring the reliability of limit position identification.

[0023] By accurately identifying the extreme position of the seat lock, a key reference point is provided for establishing a precise mapping relationship, avoiding the cumulative error problem caused by relying on fixed angle thresholds in traditional methods. In particular, it can still maintain accurate extreme position identification even after mechanical wear.

[0024] In step S103, an initial mapping relationship between the seat lock position and the magnetic angle change rate is established based on the extreme position state and the magnetic angle change rate.

[0025] In some embodiments, the initial mapping relationship is a mathematical expression or lookup table describing the functional relationship between the seat lock position and the rate of change of the magnetic angle. This mapping relationship takes into account the nonlinear characteristics of the seat lifting mechanism, such as spring stiffness variations and mechanical backlash.

[0026] In some embodiments, the process of establishing an initial mapping relationship includes: first, determining the magnetic angle value corresponding to the extreme position; then, selecting multiple test points uniformly or non-uniformly between the two extreme positions, recording the rate of change of magnetic angle corresponding to each test point; and finally, establishing a complete mapping relationship through curve fitting or interpolation methods.

[0027] In some embodiments, the mapping relationship can be represented as: P=f(ω) Where P represents the position of the seat lock, ω represents the rate of change of the magnetic angle, and f represents the mapping function. This function can be a polynomial function, a spline function, or other functional forms suitable for describing nonlinear relationships.

[0028] By establishing a precise initial mapping relationship, the abstract rate of change of magnetic angle is correlated with the actual position of the seat lock, providing a foundation for precise control of the subsequent target position. This method overcomes the cumulative error problem caused by relying solely on absolute magnetic angle values ​​in traditional methods, thus improving control accuracy.

[0029] In step S104, the target motor magnetic angle is determined based on the initial mapping relationship.

[0030] The target motor magnetic angle corresponds to the target position state of the seat lock.

[0031] In some embodiments, the target position state includes two basic states: "liftable state" (lock head protruding) and "non-liftable state" (lock head recessed), as well as an intermediate position state set according to user needs.

[0032] In some embodiments, the process of determining the target motor magnetic angle includes: first, determining the target position state according to the user instruction, and then calculating the corresponding target magnetic angle value in reverse through an initial mapping relationship. For example, when the user instruction is "switch to the liftable state", the target position state is "lock head protruding", and the corresponding target magnetic angle value is determined through an initial mapping relationship.

[0033] In some embodiments, to improve the accuracy of the target magnetic angle determination, the influence of environmental factors such as motor temperature and battery voltage on system characteristics is considered, and the initial mapping relationship is compensated in real time. For example, when the battery voltage is detected to be lower than a threshold, the target magnetic angle value is appropriately adjusted to compensate for the decrease in motor output torque.

[0034] By precisely determining the target motor magnetic angle, the seat lock head can accurately reach the expected position, avoiding the inaccurate control problem caused by fixed angle settings in traditional methods, and maintaining stable control performance even under different environmental conditions.

[0035] In step S105, the drive motor rotates in the magnetic angle direction of the target motor, and the real-time magnetic angle change rate is continuously acquired during the rotation.

[0036] In some embodiments, the process of driving the motor adopts a phased control strategy: in the initial stage, a larger control signal is used to quickly approach the target position; when approaching the target position, the control signal is reduced to avoid overshoot; and in the final stage, fine adjustment is used to ensure precise positioning.

[0037] In some embodiments, the sampling frequency for continuously acquiring the real-time magnetic angle change rate is dynamically adjusted according to the motor speed. When the motor speed is high, the sampling frequency is increased to capture rapid changes; when the motor speed is low, the sampling frequency is decreased to save computational resources. This adaptive sampling strategy effectively reduces system resource consumption while ensuring control accuracy.

[0038] In some embodiments, to eliminate sensor noise interference, the raw magnetic angle data is digitally filtered, for example using Kalman filtering or moving average filtering, to obtain smooth magnetic angle change rate data. This method significantly improves the quality of real-time data, providing a reliable basis for subsequent precise control.

[0039] By dynamically adjusting the sampling strategy and filtering process, the accuracy and reliability of the real-time magnetic angle change rate data are ensured, providing high-quality input data for adjusting control parameters based on real-time matching results.

[0040] In step S106, based on the real-time matching results of the real-time magnetic angle change rate and the dynamic physical model, the control parameters of the motor are adjusted so that the seat lock head can accurately reach the target position.

[0041] In some embodiments, the real-time matching result is characterized by calculating the deviation between the real-time magnetic angle change rate and the expected magnetic angle change rate predicted based on the dynamic physical model. A large deviation indicates a discrepancy between the actual system and the model, requiring adjustment of the control parameters.

[0042] In some embodiments, the process of adjusting the control parameters includes: firstly calculating a correction amount based on the magnitude and direction of the deviation, and then applying the correction amount to the current control signal to form new control parameters. For example, when the real-time magnetic angle change rate is less than the expected value, the duty cycle of the PWM signal is increased to improve the motor output torque.

[0043] In some embodiments, to avoid system oscillations caused by excessive adjustment of control parameters, a proportional-integral control strategy is adopted, which combines the proportional and integral terms of the deviation to calculate the correction. This method effectively suppresses system oscillations and improves control stability while ensuring fast response.

[0044] By dynamically adjusting control parameters based on real-time matching results, precise closed-loop control of the seat lock position is achieved, effectively overcoming the problem of decreased control accuracy caused by factors such as mechanical wear and environmental interference, and ensuring that the seat can accurately reach the target position under various conditions.

[0045] In some embodiments, the bicycle seat control method is applied to a BLE Bluetooth wireless height-adjustable seat device, which includes a Bluetooth motor drive device and a Bluetooth button device. The Bluetooth button device receives user commands and sends them to the Bluetooth motor drive device, which includes components such as a central processing unit, a magnetic angle sensor, and a brushless DC motor, and is used to execute the control method provided in this embodiment.

[0046] In practical applications, when a user presses a button on the Bluetooth button device, a seat adjustment command is generated and sent to the Bluetooth motor drive device via BLE Bluetooth. After receiving the command, the central processor of the Bluetooth motor drive device executes the steps S101-S106 above to precisely control the position of the seat lock head, thereby achieving real-time adjustment of the seat height.

[0047] The bicycle seat control method provided in this embodiment allows cyclists to adjust the seat height in real time without stopping by simply pressing buttons, adapting to different riding environments and posture requirements, thus significantly improving the riding experience and safety.

[0048] In some embodiments, the extreme position state of the seat lock head is identified by analyzing the abrupt change in magnetic angle during motor startup. A magnet is fixed to the central shaft of a brushless DC motor, rotating with it as the motor rotates. A high-precision absolute magnetic angle sensor accurately detects the absolute position angle of the permanent magnet on the rotating shaft. When the seat lock head reaches its extreme position, due to the physical limitations of the mechanical structure, the motor cannot continue rotating in its original direction, resulting in a significant change in the rate of change of magnetic angle. Specifically, when the magnetic angle sensor detects a sudden change in the amplitude of the rate of change of magnetic angle within a short period (e.g., within 5ms) (the change exceeds 40% of a preset threshold), and the direction of change reverses (e.g., from positive to negative), the system determines that the seat lock head has reached its extreme position. This identification mechanism fully utilizes the characteristic that "the angle rotated by the motor is proportional to the displacement of the mechanical seat lock head," judging the extreme state by monitoring the dynamic characteristics of the rate of change of magnetic angle rather than the absolute position, effectively avoiding the problem of absolute position drift caused by mechanical wear or temperature changes.

[0049] In practical implementation, the system employs a two-stage verification mechanism to ensure the accuracy of identification. First, when the derivative of the rate of change of the magnetic angle (i.e., angular acceleration) exceeds a preset threshold (e.g., 0.8 rad / s²), preliminary detection is triggered. Subsequently, the system checks whether the direction of change of the next three consecutive sampling points is opposite to the previous one. If the direction reversal lasts for more than 10 ms, the system confirms that an extreme position state has been detected. This design takes into account the physical characteristics of the mechanical seat post and mechanical linkage, and can effectively distinguish between the true mechanical extreme position and the temporary changes caused by temporary resistance (such as bumps during riding).

[0050] like Figure 2 As shown, the specific process of adjusting control parameters based on real-time matching results includes steps S201~S203: In step S201, the deviation between the real-time magnetic angle change rate and the expected magnetic angle change rate predicted based on the dynamic physical model is calculated.

[0051] In some embodiments, the system acquires the real-time magnetic angle change rate through high-precision sampling and filtering. Specifically, the magnetic angle sensor acquires magnetic angle data during motor rotation at a sampling frequency of 1 kHz. The central processing unit applies a second-order Butterworth low-pass filter (cutoff frequency 100 Hz) to smooth the raw data, eliminating high-frequency noise interference. Then, the magnetic angle change rate is calculated using the center difference method. ω(t)=[θ(t+Δt)-θ(t-Δt)] / (2Δt) Where θ represents the magnetic angle and Δt represents the sampling interval (1ms).

[0052] To improve calculation accuracy, the system uses the sliding window averaging method to average the magnetic angle change rate of five consecutive sampling points, and obtains the final real-time magnetic angle change rate ω_real.

[0053] The process of predicting the expected rate of change of magnetic angle based on a dynamic physical model involves real-time solution of the model. The system employs a discretized first-order dynamic model. G(z) = K·(1-e (-Δt / τ) ) / (1-e (-Δt / τ) ·z (-1) ) Where K is the model gain coefficient, τ is the time constant, and Δt is the sampling period. When the current control signal u(t) is received, the system calculates the expected rate of change of magnetic angle using the model's difference equation: ω_predicted(t)=e (-Δt / τ) ·ω_predicted(t-1)+K·(1-e (-Δt / τ) )·u(t) To ensure prediction accuracy, the system performs boundary checks on the model parameters before each calculation to prevent parameters from exceeding reasonable ranges (e.g., K∈[0.8,1.2], τ∈[0.01,0.1]). In this way, the system can accurately predict the dynamic response of the motor under the current control signal, providing a reliable basis for subsequent deviation calculations.

[0054] In step S202, the parameters of the dynamic physical model are updated according to the magnitude and trend of the deviation.

[0055] The parameters of the dynamic physics model include the model gain coefficient and the time constant.

[0056] In some embodiments, the system employs an adaptive parameter update mechanism to dynamically adjust model parameters based on the magnitude and trend of the deviation. Specifically, when the absolute value of the deviation |ε| is greater than a first threshold (e.g., 0.05 rad / s), the system initiates a fast update mechanism, using a smaller forgetting factor λ_fast (value 0.95) for parameter updates; when the absolute value of the deviation is between the first and second thresholds (e.g., 0.02 rad / s), the system employs a conventional update mechanism, using a larger forgetting factor λ_normal (value 0.99) for parameter updates; when the absolute value of the deviation is less than the second threshold, the system pauses parameter updates to maintain model stability. The update formula for the model gain coefficient K is: K_new = K_old + μ·ε·u·λ, where μ is the learning rate (value 0.05), u is the current control signal, and λ is the forgetting factor; the update formula for the time constant τ is: τ_new = τ_old + μ·ε·dω / dt·λ, where dω / dt is the rate of change of the magnetic angle. This hierarchical update strategy can quickly respond to significant changes in system characteristics while avoiding over-adjustment when the system is stable.

[0057] To prevent oscillations and divergences during parameter updates, the system implements several protective measures. First, physical constraints are imposed on the updated parameters: the model gain coefficient K is limited to the range [0.7, 1.3], and the time constant τ is limited to the range [0.005, 0.2], ensuring that the model parameters conform to the characteristics of the actual physical system. Second, the system monitors the historical trend of parameter updates. When a parameter is detected to exhibit a monotonically increasing or decreasing trend in five consecutive updates, the learning rate μ is automatically reduced to suppress potential divergence risks. Furthermore, the system calculates a confidence index for parameter updates based on the statistical characteristics of the deviation (such as standard deviation and kurtosis). When the confidence index falls below a threshold, the system temporarily freezes parameter updates and triggers a diagnostic process. Test results show that this parameter update mechanism reduces the prediction error of the dynamic physical model from an initial 15% to less than 3% in a stable state, significantly improving the accuracy and stability of the seat lock position control.

[0058] In step S203, the control parameters of the motor are adjusted according to the updated dynamic physical model so that the seat lock head can accurately reach the target position.

[0059] In some embodiments, the system calculates the optimal control signal based on an updated dynamic physical model. Specifically, the system first converts the target position state into a target magnetic angle value, and then calculates the required control signal amplitude based on the difference ΔP between the current position and the target position: when |ΔP|>0.3mm, a large signal control strategy is adopted, and the target torque is set to 70%-80% of the motor's maximum torque; when 0.1mm<|ΔP|≤0.3mm, a medium signal control strategy is adopted, and the target torque is set to 40%-50% of the motor's maximum torque; when |ΔP|≤0.1mm, a fine control strategy is adopted, and the target torque is set to 20%-30% of the motor's maximum torque. Simultaneously, the system calculates the PWM signal duty cycle required to achieve the target torque based on the current parameters (K and τ) of the dynamic physical model: D=T_target / (K·V_bat), where T_target is the target torque and V_bat is the current battery voltage. This model-based control signal calculation method can accurately match the actual output characteristics of the motor, ensuring that the seat lock head smoothly and accurately reaches the target position.

[0060] To achieve precise position control, the system implements a closed-loop feedback adjustment mechanism. During motor operation, the system continuously monitors the deviation between the real-time magnetic angle change rate and the predicted value based on the updated model, and dynamically adjusts the frequency of the PWM signal according to the direction of the deviation. Specifically, when the real-time change rate is less than the predicted value (indicating a slow motor response), the system appropriately increases the PWM frequency to increase the motor speed; when the real-time change rate is greater than the predicted value (indicating an overly fast motor response), the system decreases the PWM frequency to slow down the motor movement. Furthermore, the system considers the nonlinear characteristics of the mechanical system, automatically switching to damping control mode when approaching the target position (e.g., |ΔP| < 0.05 mm), applying a small torque in the opposite direction to suppress possible overshoot. Actual test data shows that this control strategy achieves a seat lock position control accuracy of ±0.03 mm, with a smooth and jitter-free adjustment process, and maintains stable control performance under different environmental conditions (temperature -10℃ to 50℃, battery voltage 6.8V to 8.5V), effectively solving the problem of decreased control accuracy caused by model mismatch in traditional methods.

[0061] The method for adjusting control parameters based on real-time matching results provided in this embodiment forms a complete closed-loop control mechanism through steps such as precise calculation of deviation, adaptive updating of model parameters, and dynamic adjustment of control parameters. On the one hand, due to the adoption of a hierarchical parameter update strategy, the system can quickly adapt to system characteristic drift caused by mechanical wear and environmental changes; on the other hand, through precise control based on the updated model, it ensures that the seat lock can accurately reach the target position under various conditions, thereby significantly improving the accuracy and reliability of bicycle seat control.

[0062] See Figure 3 This embodiment provides a process for an adaptive seat control optimization method, specifically including steps S301~S04: In step S301, the change in the ambient magnetic field is monitored, and the ambient interference signal is separated from the real-time magnetic angle change rate to obtain the separated pure magnetic angle change rate.

[0063] In some embodiments, the system utilizes the output characteristics of a magnetic angle sensor when the motor is stationary to monitor changes in the ambient magnetic field. Specifically, when the motor is stationary (i.e., when no seat adjustment command is received), the system acquires the output data of the magnetic angle sensor at a frequency of 10 Hz and calculates the standard deviation σ_env of 10 consecutive sampling points. When σ_env exceeds a preset threshold (e.g., 0.5°), it is determined that there is significant interference in the ambient magnetic field. The system further analyzes the spectral characteristics of the interference signal, identifies the main interference frequency components (typically 50 Hz or 60 Hz power frequency interference and its harmonics), and constructs an adaptive notch filter to suppress them.

[0064] During seat adjustment, the system employs a dual-channel signal processing method to separate environmental interference signals. First, the real-time acquired magnetic angle data is processed through a bandpass filter (cutoff frequency 1-50Hz) to extract the effective signal frequency band. Then, using adaptive noise cancellation technology, the environmental interference reference signal acquired in a stationary state is used as input, and the least mean square (LMS) algorithm is used to estimate and subtract the environmental interference components in real time. Specifically, the system calculates: ω_pure(t)=ω_raw(t)-Σh(i)·x(ti) Where ω_pure(t) is the pure magnetic angle change rate, ω_raw(t) is the original magnetic angle change rate, x(t) is the environmental interference reference signal, and h(i) are the adaptive filter coefficients. This method can effectively separate environmental interference, improve the signal-to-noise ratio of the magnetic angle change rate by more than 15dB, and significantly improve the accuracy of subsequent control.

[0065] In step S302, during the seat adjustment process, when the deviation exceeds the first preset threshold, the fast update mechanism corresponding to the model parameters is activated; when the deviation is less than the second preset threshold, the conventional update mechanism is used to maintain model stability.

[0066] In some embodiments, the system calculates the deviation ε between the real-time magnetic angle change rate and the expected value predicted based on the dynamic physical model in real time, and dynamically adjusts the model parameter update strategy according to the magnitude of the deviation. Specifically, when |ε|>ε_th1 (a first preset threshold, e.g., 0.08 rad / s), the system initiates a fast update mechanism, using a smaller forgetting factor λ_fast (e.g., 0.92) for parameter updates; when ε_th2<|ε|≤ε_th1 (a second preset threshold ε_th2, e.g., 0.03 rad / s), the system adopts a conventional update mechanism, using a larger forgetting factor λ_normal (e.g., 0.98) for parameter updates; when |ε|≤ε_th2, the system pauses parameter updates to maintain model stability. The model parameter update formula is: K_new=K_old+μ·ε·u·λ τ_new=τ_old+μ·ε·dω / dt·λ Where K is the model gain coefficient, τ is the time constant, μ is the learning rate (value 0.05), u is the current control signal, λ is the forgetting factor, and dω / dt is the rate of change of the magnetic angle.

[0067] In some embodiments, the system also implements a dual protection mechanism to ensure the stability of the update process. First, physical constraints are applied to the updated parameters: K∈[0.65,1.35], τ∈[0.004,0.25], to prevent the parameters from exceeding reasonable ranges. Second, the system monitors the convergence of parameter updates. When it detects that the parameter changes in the same direction and the amount of change exceeds a threshold in three consecutive updates, the learning rate μ is automatically reduced to suppress potential divergence risks. Test results show that this adaptive update mechanism enables the prediction error of the dynamic physical model to recover to within 5% within 5 adjustments when the system characteristics change abruptly, and to maintain the prediction error below 2% in a steady state, significantly improving the adaptability and stability of the control system.

[0068] In step S303, after each seat adjustment operation is completed, key process data is stored, and the characteristic change trend of the seat lifting mechanism is analyzed based on the key process data.

[0069] In some embodiments, after each seat adjustment operation is completed, the system stores key process data in non-volatile memory. This data includes: motor control parameter sequence (PWM duty cycle, frequency), magnetic angle change rate sequence, actual achieved lock position state, ambient temperature, battery voltage, and maximum deviation value during the adjustment process. To save storage space, the system uses a compression algorithm to process the data, retaining only feature points and trend information, keeping the amount of data stored for each adjustment within 200 bytes.

[0070] Based on stored key process data, the system periodically (e.g., after every 10 adjustments) analyzes the characteristic change trends of the seat lifting mechanism. Specifically, the system calculates the long-term rate of change of model parameters K and τ and compares it with the mechanical wear theoretical model to determine the current mechanical wear rate; simultaneously, it analyzes the changing trend of the maximum deviation value to evaluate the drift characteristics of system parameters. When the mechanical wear rate is detected to exceed a threshold (e.g., 0.5% per week) or the system parameter drift speed accelerates (e.g., the rate of change of K value increases in three consecutive adjustments), the system determines that the seat lifting mechanism has entered the accelerated aging stage. This long-term characteristic analysis allows the system to identify potential problems in advance, providing a basis for subsequent model optimization and extending the product's service life.

[0071] In step S304, the parameter update rate of the dynamic physical model is dynamically adjusted based on the characteristic change trend.

[0072] In some embodiments, the system dynamically adjusts the update rate of model parameters based on the analysis results of characteristic change trends. Specifically, when the characteristic change trend indicates that the system characteristic changes faster (e.g., the mechanical wear rate exceeds a threshold or the rate of change of K value increases continuously), the system increases the parameter update rate of the dynamic physical model: update rate coefficient α = k·(1+β·v), where k is the baseline update rate, β is the gain coefficient (value 0.5), and v is the characteristic change rate. When the characteristic change trend indicates that the system characteristic change rate is stable (e.g., the mechanical wear rate is below a threshold and the rate of change of K value tends to be stable), the system decreases the parameter update rate of the dynamic physical model: update rate coefficient α = k·(1-γ·s), where γ is the attenuation coefficient (value 0.3), and s is the system stability index.

[0073] In some embodiments, the system also considers the impact of environmental factors on the update rate. When a significant change in environmental conditions is detected (e.g., a temperature change exceeding 10°C or a battery voltage change exceeding 0.5V), the system temporarily increases the update rate to accelerate the model's adaptation to the new environment. After the environmental conditions stabilize for a period of time (e.g., a temperature change of less than 2°C in five consecutive adjustments), the system gradually decreases the update rate to maintain model stability. Test data shows that this dynamic update rate adjustment mechanism keeps the system's control accuracy within ±0.08mm during the accelerated mechanical wear phase, which is 40% higher than the fixed update rate scheme, and maintains stable control performance even after long-term use (5000 adjustment operations).

[0074] The adaptive seat control optimization method provided in this embodiment forms a complete closed-loop optimization system through four key steps: environmental disturbance handling, adaptive parameter updating, long-term characteristic learning, and dynamic update rate adjustment. On the one hand, the environmental disturbance separation technology ensures the quality of input data; on the other hand, the multi-level adaptive mechanism enables the system to adapt to both short-term changes and long-term drift, significantly improving control accuracy and long-term stability.

[0075] In one exemplary embodiment, a multi-layered sleep mechanism and intelligent wake-up strategy are provided, specifically including: First, in non-adjustment state, enter low-power sleep mode.

[0076] The low-power sleep mode includes normal sleep mode and deep sleep mode.

[0077] In some embodiments, the system automatically enters a low-power sleep mode after completing seat adjustment or when no user operation command is received for more than a preset time (e.g., 30 seconds). Specifically, the system first enters a normal sleep mode: the central processing unit's main clock is turned off, but a low-speed clock is maintained; peripheral devices (such as the motor drive module and magnetic angle sensor) are set to a low-power state; and critical data in RAM is retained, enabling the system to quickly resume operation. In normal sleep mode, the system's power consumption drops to about 10% of the operating state (approximately 0.5mA). At this time, the RGB indicator light on the Bluetooth button device is off, but the displacement sensor remains active, continuously monitoring the device's movement.

[0078] If the system remains unwakeable in normal sleep mode for an extended period (e.g., 5 minutes), it automatically enters deep sleep mode: the CPU is completely powered off; only the displacement sensor and a few critical circuits are powered; all operating states and temporary data are cleared, leaving only necessary configuration parameters in non-volatile memory. In deep sleep mode, system power consumption is further reduced to 10% of normal sleep mode (approximately 0.05mA), extending the standby time of the 8.4V / 450mAh lithium battery to over 6 months. At this time, the RGB indicator lights on the Bluetooth motor drive are completely off, but the displacement sensor remains active to ensure the user's intentions are detected.

[0079] In some embodiments, the system dynamically adjusts its sleep strategy based on the current battery level: when the battery is fully charged (>80%), the duration of the normal sleep mode is extended; when the battery is low (<20%), the duration of the normal sleep mode is shortened, and the system transitions to deep sleep mode more quickly to save power. Furthermore, the system intelligently adjusts sleep parameters based on usage habits: for example, if it detects that the user typically uses the seat adjustment function at 8 AM and 6 PM daily, the system is automatically woken up before these times to ensure immediate response. This adaptive sleep strategy maximizes battery life while ensuring a good user experience, showing significant advantages, especially in long-distance outdoor riding scenarios.

[0080] Secondly, in response to detecting a displacement signal greater than a preset threshold, it wakes up from deep sleep mode.

[0081] In some embodiments, the displacement sensor employs a high-sensitivity accelerometer, remaining operational even in deep sleep mode to continuously monitor the device's motion. Specifically, the displacement sensor acquires triaxial acceleration data at an extremely low sampling rate (e.g., 1 Hz) and calculates the magnitude of the acceleration vector: a = √(x² + y² + z²). When the change in the magnitude of acceleration at three consecutive sampling points exceeds a preset threshold (e.g., 0.5g), it is determined to be a valid displacement signal, triggering the system to wake up from deep sleep mode.

[0082] To avoid false wake-ups, the system implements a multi-layered verification mechanism. First, it checks the duration of the displacement signal: a valid displacement signal must last at least 100ms. Second, it analyzes the spectral characteristics of the displacement signal: real cycling displacement typically contains specific frequency components (0.5-5Hz), while random vibrations often have broadband characteristics. Finally, it combines ambient light sensor data: when a change in light is detected synchronized with the displacement signal, it further confirms that the user is operating the bicycle. When the system wakes up from deep sleep mode, the RGB indicator light on the Bluetooth motor drive illuminates green for 1 second, indicating successful wake-up and readiness to receive commands. Test data shows that this wake-up mechanism has a false wake-up rate of less than 0.1 times / day, while the effective wake-up success rate is as high as 99.8%, ensuring both low power consumption and immediate responsiveness to user operations.

[0083] Additionally, it wakes up from normal sleep mode in response to receiving a seat adjustment command.

[0084] In some embodiments, the system retains some functionality of the BLE Bluetooth receiver in normal sleep mode to listen for seat adjustment commands from the Bluetooth button on the bicycle handlebars. Specifically, the BLE module enters a "connection interval extension" mode, extending the listening interval from 7.5ms in the normal state to 100ms, significantly reducing power consumption while still responding promptly to user operations. When a valid seat adjustment command (containing the correct device address and checksum) is received, the system immediately wakes up from normal sleep mode, resuming normal operation of the central processing unit and peripheral devices.

[0085] To optimize the energy efficiency of the wake-up process, the system employs a tiered wake-up strategy. First, only the core of the central processing unit is woken up to perform instruction verification and preliminary processing. After confirming the instruction's validity, other functional modules (such as the motor drive module and magnetic angle sensor) are gradually woken up. This strategy keeps the system's full wake-up time from normal sleep mode within 15ms, while the additional power consumption during the wake-up process accounts for only 5% of the operating power, achieving a balance between fast response and low power consumption. When the Bluetooth button is pressed, its seven-color indicator light displays different colors according to the current battery status: green for sufficient power, yellow for insufficient power, and red for extremely low power, providing users with intuitive battery information. Furthermore, the system implements a 10-second protection mechanism: if no release command is received within 10 seconds of pressing the button, the system automatically generates a release command, forcibly retracting the locking mechanism to ensure the seat height is not adjustable, avoiding safety hazards during riding.

[0086] In some embodiments, the system also features a backup button function to provide backup control when the Bluetooth button device's battery is depleted or malfunctions. When the user presses and holds the backup button for more than 3 seconds, the system performs the same function as the Bluetooth button: a short press toggles the lock state, and a long press enters pairing mode. The backup button also retains wake-up capability in deep sleep mode, ensuring that the user can still control the seat height via physical buttons even if the Bluetooth system fails. Furthermore, when the system detects that the motor current exceeds a set threshold (4A), it immediately stops the motor and displays a flashing red light to indicate a system malfunction, effectively protecting the motor from overload damage.

[0087] The low-power management method provided in this embodiment forms a complete low-power optimization system through a dual-layer mechanism of normal sleep and deep sleep, intelligent wake-up based on displacement signals, and rapid response based on seat adjustment commands. On the one hand, the system's standby power consumption is reduced to the extreme through deep sleep mode, significantly extending battery life; on the other hand, the intelligent wake-up mechanism ensures the instant responsiveness of user operations, avoiding the response delay problem common in traditional low-power designs.

[0088] In terms of mechanical structure, the seat height adjustment mechanism in this embodiment employs a precision-designed mechanical seat post and mechanical linkage system. When the locking head protrudes, the seat post is in an adjustable state. Because a spring component is installed inside the mechanical linkage, the seat automatically rises to its highest position without external force. When a person sits on the seat, they can freely adjust the height by adjusting their hip strength. This design fully utilizes the elastic potential energy of the spring, making seat height adjustment more natural and smooth, without requiring additional external force. When the locking head retracts, the mechanical structure locks the seat post, making the seat height non-adjustable. No matter how much force is applied, the seat height cannot be changed, ensuring safety and stability during riding.

[0089] In terms of displacement sensor design, the system employs a special low-power design: the displacement sensor remains active after power-on and does not enter a sleep state. When the detected displacement signal exceeds a set threshold, a signal is output to wake the Bluetooth button device from sleep mode and bring it into operation. This design ensures that even in deep sleep mode, the system can respond promptly to user input, achieving an "always-on" user experience while maintaining extremely low power consumption. The activation threshold of the displacement sensor can be dynamically adjusted according to different usage scenarios. For example, a lower threshold can be set for urban commuting to improve sensitivity, while a higher threshold can be set for long-distance cycling to avoid false triggering.

[0090] Regarding the working logic of the seven-color indicator light, the light change rules in this embodiment are as follows: When the device is in sleep mode, if the displacement sensor detects a displacement signal greater than a set threshold, it will output a signal to wake the device from sleep. Alternatively, the Bluetooth button device can also be woken from sleep when a button is pressed. When the device wakes from sleep, the RGB indicator light will illuminate green for 1 second.

[0091] When the Bluetooth button device and the Bluetooth motor driver device connect for the first time, they need to exchange identity information and I / O port information to ensure a secure connection; this process is called pairing. During pairing, the blue lights on both the Bluetooth button device and the Bluetooth motor driver device flash. Upon successful pairing, the Bluetooth button device and the Bluetooth motor driver device are bound together; the RGB indicator light flashes green once and then turns off. At this time, the Bluetooth button device wakes up from sleep mode and can only connect to the bound Bluetooth motor driver device. Similarly, the Bluetooth motor driver device, whether waking up from sleep mode or restarting from a power outage, can only connect to the bound Bluetooth button device. If pairing fails or times out (>30 seconds without successful pairing), the RGB indicator light flashes red once and then turns off, and the device enters sleep mode.

[0092] When the Bluetooth button device restarts after a power outage, it automatically loses its Bluetooth pairing information and re-enters the pairing process. The RGB indicator light flashes blue. At this time, it can pair with any Bluetooth motor driver device, or quickly connect to a previously paired Bluetooth motor driver device. When pairing is successful, the RGB indicator light flashes green once and then turns off.

[0093] When the Bluetooth motor driver is powered on, pressing and holding the backup button will unbind the Bluetooth motor driver and remove the Bluetooth pairing information. The Bluetooth motor driver will then re-enter the pairing process, with the RGB indicator light flashing blue. At this time, it can pair with other Bluetooth button devices. The Bluetooth motor driver will not pair with the previously paired Bluetooth button device for one minute.

[0094] When the paired Bluetooth button device and Bluetooth motor drive device are reconnected or disconnected, there will be no more seven-color indicator lights.

[0095] Regarding the backup button function, the system is designed with a comprehensive backup control mechanism. The backup button not only provides basic control functions when the Bluetooth button device's battery is depleted or malfunctions, but also has an unbinding function: when the user presses and holds the backup button for more than 3 seconds, the system performs an unbinding operation, clearing the Bluetooth binding information, allowing the Bluetooth motor drive device to pair with a new Bluetooth button device. This design ensures the system's availability and maintainability under various abnormal conditions, improving product lifespan and user experience.

[0096] In terms of motor current detection, the system implements a comprehensive overload protection mechanism. The hardware employs a resistance voltage difference method to calculate the motor current, using a low-ohm resistor. When current flows through the resistor, a weak voltage is generated. The software reads this ADC and converts it into voltage, then calculates the motor current using the formula I=V / Ω. When the motor current exceeds the set threshold (4A), the system immediately stops the motor and displays a flashing red indicator light to indicate a system fault. This dual protection mechanism effectively prevents motor damage due to overload, extends the motor's lifespan, and also protects the electrical safety of the entire system.

[0097] In the application of magnetic angle sensors, the system fully utilizes the key characteristic that "the angle of rotation of the motor is directly proportional to the displacement of the mechanical seat lock head." A magnet is fixed to the central shaft of the brushless DC motor, and rotates with it whenever the motor rotates. A high-precision absolute magnetic angle sensor accurately detects the absolute position angle of the permanent magnet on the rotating shaft. By calibrating the motor's magnetic angle at the lock head's concave and convex positions, the system can accurately determine the lock head's position based on the real-time detected magnetic angle, achieving closed-loop control. This design avoids the positioning inaccuracies caused by accumulated errors in traditional open-loop control, maintaining high-precision control even after long-term use.

[0098] In terms of software architecture design, this embodiment adopts a strict event-driven architecture to achieve low-power management. Only when an event occurs is an interrupt signal generated immediately, waking the central processing unit (CPU) to handle the event in the interrupt service routine. After handling the event, it enters the main loop and returns to sleep mode. The system supports two sleep modes: Normal hibernation: When the CPU and peripheral devices are idle, the chip enters a default low-power sub-mode. Any interrupt event can wake up the central processing unit, and all state information before hibernation is retained upon wake-up.

[0099] Deep hibernation: The system kernel and all running tasks stop, equivalent to a shutdown state, which is a deep power-saving mode. It can only be woken up by resetting, pressing a key, or a motion wake-up event. After waking up, the CPU restarts, and the state information before hibernation is not retained.

[0100] Regarding the motor control process, the system strictly follows the following control logic: Module power-on: Initialize the motor drive module, magnetic angle sensor and current detection circuit.

[0101] Start the motor: Read the motor magnetic angle data, record the current time, check the battery voltage, determine the lock position according to the trigger source command, and start the motor.

[0102] Wait for the motor to reach the specified termination angle: continuously read magnetic angle data, monitor motor current and running time, and stop the motor when the termination angle is reached.

[0103] Braking: Apply the motor brake to stop the motor from running.

[0104] Power off: Turn off the motor drive module, magnetic angle sensor, and motor current sensor, putting them into sleep mode.

[0105] End: No action is taken; prepare for the next operation.

[0106] In addition, the system also implements the relationship between user behavior and system response, as shown in Table 1 below: Table 1

[0107] Based on the same inventive concept, this application also provides a bicycle seat control device for implementing the bicycle seat control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more embodiments of the bicycle seat control device provided below can be found in the limitations of the bicycle seat control method described above, and will not be repeated here.

[0108] In one exemplary embodiment, this embodiment provides a bicycle seat control device, the device comprising: A magnetic angle sensor is used to collect the rate of change of the current magnetic angle during the rotation of the motor. The central processing unit, electrically connected to the magnetic angle sensor, is used to construct a dynamic physical model of the seat lifting mechanism based on the current rate of change of magnetic angle collected by the magnetic angle sensor in response to the received seat adjustment command; wherein, the dynamic physical model represents the dynamic correspondence between the motor control signal and the seat lock position; The central processing unit is also used to identify the extreme position state of the seat lock head by analyzing the magnetic angle change characteristics of the motor during the startup process; based on the extreme position state and the magnetic angle change rate, an initial mapping relationship between the position of the seat lock head and the magnetic angle change rate is established; and the target motor magnetic angle is determined according to the initial mapping relationship; wherein, the target motor magnetic angle corresponds to the target position state of the seat lock head. The motor drive module is electrically connected to the central processing unit and is used to drive the motor to rotate in the magnetic angle direction of the target motor. During the rotation, the real-time magnetic angle change rate is continuously acquired through the magnetic angle sensor. The central processing unit is also used to adjust the control parameters of the motor drive module based on the real-time matching results of the real-time magnetic angle change rate and the dynamic physical model, so that the seat lock head can accurately reach the target position.

[0109] Specifically, such as Figure 4 As shown, the bicycle seat control device provided in this embodiment includes: A magnetic angle sensor is used to acquire the rate of change of the current magnetic angle during motor rotation. In some embodiments, the magnetic angle sensor is a high-precision absolute magnetic angle sensor, installed inside the motor housing at a fixed distance from the motor's rotating shaft. A magnet is fixed to the central shaft of the brushless DC motor, rotating with it each time the motor rotates. The magnetic angle sensor can accurately detect the absolute position angle of the permanent magnet on the rotating shaft. This sensor communicates with the central processing unit via an I²C interface, with a sampling frequency up to 1kHz and an angular resolution of up to 0.1°, ensuring high-precision acquisition of magnetic angle data. Mechanically, the installation distance between the magnetic angle sensor and the magnet is precisely calibrated (typically 1-2mm) to ensure a balance between signal strength and detection accuracy.

[0110] The central processing unit (CPU), electrically connected to the magnetic angle sensor, responds to received seat adjustment commands by constructing a dynamic physical model of the seat lifting mechanism based on the current rate of change of magnetic angle collected by the magnetic angle sensor. In some embodiments, the CPU employs an ARM Cortex-M4 core microcontroller with integrated BLE Bluetooth functionality, operating at 64MHz, and containing 512KB of flash memory and 128KB of RAM. The CPU connects to the magnetic angle sensor via a GPIO interface to acquire magnetic angle data in real time; communicates with the motor drive module via an SPI interface to send control commands; and exchanges data with the Bluetooth communication module via a UART interface. The CPU internally runs a real-time operating system (RTOS) to implement multi-task scheduling and event-driven processing, ensuring the timeliness and reliability of the system response.

[0111] The central processing unit (CPU) is also used to identify the extreme position state of the seat lock by analyzing the abrupt changes in magnetic angle during motor startup. Specifically, the CPU has a built-in signal processing unit that can calculate the first derivative (angular acceleration) of the rate of change of magnetic angle in real time. When a significant abrupt change in angular acceleration is detected and the direction of change is reversed, it is determined that the seat lock has reached its extreme position. Based on the extreme position state and the rate of change of magnetic angle, the CPU establishes an initial mapping relationship between the seat lock position and the rate of change of magnetic angle, and determines the target motor magnetic angle according to this mapping relationship. The CPU also includes a dynamic physical model construction unit, an extreme position identification unit, a mapping relationship establishment unit, a target magnetic angle determination unit, and a control algorithm unit. These functional modules work together to ensure the accuracy of seat control.

[0112] The motor drive module, electrically connected to the central processing unit (CPU), drives the motor to rotate in the target magnetic angle direction and continuously acquires the real-time magnetic angle change rate via a magnetic angle sensor during rotation. In some embodiments, the motor drive module employs an H-bridge drive circuit implemented by a three-phase motor drive chip, supporting a maximum continuous current of 8A and a peak current of 15A, with an operating voltage range of 4.5V-60V. The motor drive module receives PWM control signals from the CPU and adjusts the voltage and current output to the brushless DC motor to achieve precise control of the motor speed and torque. The motor drive module also includes a current detection circuit that samples the motor current through a low-ohm resistor and converts the analog signal into a digital signal, feeding it back to the CPU for overcurrent protection and control parameter adjustment.

[0113] The central processing unit (CPU) is also used to adjust the control parameters of the motor drive module based on the real-time matching results of the magnetic angle change rate and the dynamic physical model, ensuring that the seat lock head accurately reaches the target position. Specifically, the CPU has a built-in control algorithm unit that calculates the deviation between the magnetic angle change rate and the model prediction value in real time, and dynamically adjusts the duty cycle and frequency of the PWM signal according to the magnitude of the deviation. When the system detects a large deviation, a fast update mechanism is used to adjust the model parameters; when the deviation is small, a conventional update mechanism is used to maintain model stability. This closed-loop control mechanism ensures that the seat lock head can accurately reach the target position, with the error controlled within ±0.1mm.

[0114] like Figure 4 As shown, the device also includes a Bluetooth communication module connected to the central processing unit (CPU) for receiving seat adjustment commands from the Bluetooth button on the bicycle handlebars. In some embodiments, the Bluetooth communication module uses a chip supporting the BLE 5.0 protocol, operating at a frequency of 2.4 GHz, with a communication range of up to 30 meters. The Bluetooth communication module is connected to the CPU via a UART interface to achieve bidirectional data transmission. When the user presses the Bluetooth button on the bicycle handlebars, the button signal is transmitted to the Bluetooth communication module via the BLE protocol, and then the CPU parses and executes the corresponding seat adjustment operation.

[0115] In terms of mechanical structure, the brushless DC motor converts its circular rotational motion into horizontal motion through gear and rack meshing. The rack connects to the seat lock, and as the rack moves horizontally, it switches the seat lock between protruding and recessed positions. The seat lock engages with the seat post; when the lock is protruding, the seat post is adjustable; when the lock is recessed, the seat post is locked and cannot be adjusted. A spring mechanism is installed inside the seat post, allowing the seat to automatically rise to its highest position without external force. When the rider is seated, they can adjust the seat height freely by adjusting their hip strength.

[0116] The device also includes a displacement sensor connected to the central processing unit (CPU) for monitoring the device's motion. In some embodiments, the displacement sensor employs a triaxial accelerometer, remaining active even in deep sleep mode. When a displacement signal exceeding a preset threshold is detected, the displacement sensor outputs a signal to wake the CPU from sleep mode, bringing the device into operation. This design achieves an "always-on" user experience while maintaining extremely low power consumption.

[0117] In addition, the device includes a RGB indicator light connected to the central processing unit to display system status. When the device wakes from sleep mode, the indicator light illuminates green for one second; during pairing, the blue light flashes; upon successful pairing, the green light flashes once; upon failed pairing, the red light flashes once; and in the event of a system malfunction, the red light flashes. The RGB indicator light provides users with intuitive status feedback, enhancing the user experience.

[0118] In terms of power management, the device is powered by a lithium battery, and a power management module provides a stable voltage to each component. The power management module includes a voltage monitoring circuit and a low-voltage protection circuit. When the battery voltage is lower than the lower threshold, the system automatically reduces the operating frequency to extend the battery life; when the battery voltage is lower than an even lower threshold, the system enters a protection state, stops the motor, and prompts the user to charge.

[0119] The bicycle seat control device provided in this embodiment achieves precise control of the bicycle seat through the close cooperation of components such as a magnetic angle sensor, a central processing unit, and a motor drive module. On the one hand, by constructing a dynamic physical model and real-time matching adjustments, high-precision control of the seat lock position is ensured; on the other hand, low-power design and an intelligent sleep mechanism significantly extend battery life. Actual testing shows that this device achieves a seat lock position control accuracy of ±0.05mm, an adjustment response time of less than 150ms, and a battery life of up to 8 months, effectively solving the technical problems existing in traditional bicycle seat adjustment methods.

[0120] Of particular note is the device's functionality, which includes: "When the user presses a button, a button press command is generated and sent to the Bluetooth motor driver via BLE Bluetooth. The Bluetooth motor driver immediately drives the DC brushless motor, causing the seat post lock head to protrude, releasing the lock and allowing the seat post to rise." "When the user releases the button, a button release command is generated and sent to the Bluetooth motor driver via BLE Bluetooth. The Bluetooth motor driver immediately drives the motor, causing the seat post lock head to retract, locking the lock and preventing the seat post from rising." Furthermore, the device implements a six-step motor control process (module power-on, motor start, waiting for the motor to reach the specified termination angle, braking, power-off, and termination) and a 10-second protection mechanism, ensuring the system's safety and reliability.

[0121] This device is not only suitable for bicycle seat control, but can also be extended to other scenarios that require precise position control, such as electric office chairs and electric medical beds.

[0122] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0123] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0124] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A bicycle seat control method characterized by, The method includes: In response to receiving a seat adjustment command from a Bluetooth button on the bicycle handlebars via a Bluetooth receiver, a dynamic physical model of the seat lifting mechanism is constructed based on the current rate of change of magnetic angle collected by a magnetic angle sensor; wherein, the dynamic physical model represents the dynamic correspondence between the motor control signal and the seat lock position; The abrupt change in magnetic angle during motor startup is used to identify the extreme position state of the seat lock. Based on the extreme position state and the magnetic angle change rate, an initial mapping relationship between the seat lock position and the magnetic angle change rate is established; Based on the initial mapping relationship, the target motor magnetic angle is determined; wherein, the target motor magnetic angle corresponds to the target position state of the seat lock head; The motor is driven to rotate in the magnetic angle direction of the target motor, and the real-time magnetic angle change rate is continuously acquired during the rotation. Based on the real-time matching result between the real-time magnetic angle change rate and the dynamic physical model, the control parameters of the motor are adjusted so that the seat lock head accurately reaches the target position.

2. The method of claim 1, wherein, The method of identifying the extreme position state of the seat lock by analyzing the magnetic angle change characteristics of the motor during startup specifically includes: When a sudden change in the amplitude of the magnetic angle change rate is detected and the direction of change is reversed, it is determined that the seat lock has reached its limit position.

3. The method according to claim 1 or 2, characterized in that, The step of adjusting the motor control parameters based on the real-time matching results of the real-time magnetic angle change rate and the dynamic physical model, so that the seat lock head accurately reaches the target position, specifically includes: Calculate the deviation between the real-time magnetic angle change rate and the expected magnetic angle change rate predicted based on the dynamic physical model; The parameters of the dynamic physical model are updated based on the magnitude and trend of the deviation; wherein the parameters of the dynamic physical model include the model gain coefficient and the time constant. Based on the updated dynamic physics model, the control parameters of the motor are adjusted so that the seat lock head accurately reaches the target position.

4. The method of claim 3, wherein, The step of adjusting the control parameters of the motor according to the updated dynamic physical model specifically includes: Based on the updated dynamic physics model, the target control signal is determined; wherein, the target control signal includes the target torque and the target speed; Generate control parameters for the motor corresponding to the target control signal; wherein, the control parameters for the motor include the duty cycle and frequency of the PWM signal.

5. The method of claim 3, wherein, The method further includes: During seat adjustment, when the deviation exceeds a first preset threshold, a fast update mechanism for the model parameters is activated; wherein, the fast update mechanism for the model parameters uses a small forgetting factor. When the deviation is less than the second preset threshold, a conventional update mechanism is used to maintain model stability; Wherein, the second preset threshold is less than the first preset threshold; and the forgetting factor of the conventional update mechanism is greater than the forgetting factor of the fast update mechanism for model parameters.

6. The method of claim 5, wherein, The method further includes: After each seat adjustment operation is completed, key process data is stored; wherein, the key process data includes the control parameters of the motor, the rate of change of the magnetic angle, and the actual position of the lock head. Based on the key process data corresponding to each seat adjustment operation, the characteristic change trend of the seat lifting mechanism is analyzed; wherein, the characteristic change trend includes mechanical wear rate and system parameter drift characteristics; Based on the aforementioned trend of characteristic changes, the dynamic physical model is optimized.

7. The method of claim 6, wherein, The real-time matching result based on the real-time magnetic angle change rate and the dynamic physical model specifically includes: Monitoring changes in the ambient magnetic field; wherein, the changes in the ambient magnetic field are characterized by the output fluctuation of the magnetic angle sensor when the motor is stationary; The environmental interference signal is separated from the real-time magnetic angle change rate to obtain the separated pure magnetic angle change rate; This is based on the real-time matching results between the separated pure magnetic angle change rate and the dynamic physical model.

8. The method of claim 7, wherein, The optimization of the dynamic physical model based on the aforementioned characteristic change trend specifically includes: When the trend of the characteristic change indicates that the system characteristic changes faster, the parameter update rate of the dynamic physical model is increased; wherein, the parameter update rate of the model is proportional to the rate of change of the system characteristic. When the trend of the characteristic change indicates that the rate of change of the system characteristic is stable, the parameter update rate of the dynamic physical model is reduced; wherein, the parameter update rate of the model is inversely proportional to the stability of the system characteristic.

9. The method of claim 1, wherein, The method further includes: In the non-adjustment state, it enters a low-power sleep mode; wherein, the low-power sleep mode includes a normal sleep mode and a deep sleep mode; In response to detecting a displacement signal greater than a preset threshold, the system wakes up from the deep sleep mode. In response to receiving a seat adjustment command, the system wakes up from the normal sleep mode.

10. A bicycle seat control device characterized by comprising: The device includes: A magnetic angle sensor is used to collect the rate of change of the current magnetic angle during the rotation of the motor. The central processing unit, electrically connected to the magnetic angle sensor, is used to respond to a seat adjustment command sent from a Bluetooth button device on the bicycle handlebars via a Bluetooth receiver, and to construct a dynamic physical model of the seat lifting mechanism based on the current magnetic angle change rate collected by the magnetic angle sensor; wherein, the dynamic physical model represents the dynamic correspondence between the motor control signal and the seat lock position; The central processing unit is also used to identify the extreme position state of the seat lock head by analyzing the magnetic angle change characteristics of the motor during the startup process; establish an initial mapping relationship between the position of the seat lock head and the magnetic angle change rate based on the extreme position state and the magnetic angle change rate; and determine the target motor magnetic angle according to the initial mapping relationship; wherein the target motor magnetic angle corresponds to the target position state of the seat lock head. The motor drive module is electrically connected to the central processing unit and is used to drive the motor to rotate in the magnetic angle direction of the target motor, and continuously acquire the real-time magnetic angle change rate through the magnetic angle sensor during the rotation process. The central processing unit is also used to adjust the control parameters of the motor drive module based on the real-time matching result of the real-time magnetic angle change rate and the dynamic physical model, so that the seat lock head can accurately reach the target position state.

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