A dynamic gated power supply method and device adaptive to a riding state

Abstract

This invention discloses a dynamic gating power supply method and device for adaptive cycling state, relating to the field of power supply control technology. It involves real-time acquisition of triaxial acceleration data from wheel speed sensors during bicycle riding, followed by filtering and noise reduction to obtain effective acceleration data. Feature extraction is performed on the effective acceleration data to obtain an acceleration feature set. This feature set is then input into a preset finite state machine model to obtain the state to be determined. The preset finite state machine model includes a stationary state, a constant speed riding state, and an emergency braking state. Anti-shake analysis is performed based on the acceleration feature set, the state to be determined, and a preset state timer to obtain the riding condition. The operation of functional modules in the wheel speed sensor signal chain is controlled according to the riding condition. This method significantly reduces the overall energy consumption of the equipment while ensuring accurate condition recognition, avoiding power loss caused by continuous high-load operation throughout the entire chain.

Description

Technical Field

[0001] This invention belongs to the field of power supply control technology, specifically relating to a dynamic gating power supply method and device that adapts to riding conditions. Background Technology

[0002] Traditional speed control and switching technologies mainly rely on fixed control logic and preset parameters. When switching between different operating conditions, loads, or operating modes, the control strategy is relatively simple and it is difficult to adaptively adjust according to real-time operating status. As equipment operating scenarios become increasingly complex, the requirements for the smoothness, response speed, and stability of speed switching are constantly increasing. Existing conventional control methods are gradually showing limitations in terms of dynamic adaptability and multi-condition collaborative optimization, making it difficult to meet the requirements of high-precision and high-reliability speed switching. Therefore, further improvements and enhancements to speed control switching methods are needed.

[0003] Patent application CN114523987A discloses a method for transitioning vehicle speed control from an ADAS or AD system to a driver, supported by a deviation assessment system (1) of the vehicle (2). The deviation assessment system (1) derives (1001) the current system activation value of a system parameter affecting speed associated with speed control performed by the ADAS or AD system (21). The deviation assessment system (1) also derives (1002) the value of a corresponding intervention parameter affecting speed associated with driver-activated interventions in the speed control. The deviation assessment system (1) also presents (1003) a graphical representation (4) on the vehicle display (23) indicating the difference between the system activation value and the driver activation value. This application also relates to the deviation assessment system described above, a vehicle including such a deviation assessment system, and corresponding computer program products and non-volatile computer-readable storage media.

[0004] However, this solution relies on IMU data fusion technology to achieve state determination and control, which not only generates high hardware power consumption, but also cannot adapt to the core logic of data fusion and command issuance that requires an external MCU. This not only increases the cost and complexity of hardware deployment, but also causes significant delays in state response, making it difficult to achieve high-speed real-time determination of conditions such as emergency braking. Summary of the Invention

[0005] The purpose of this invention is to solve the problem of high power consumption caused by relying on IMU data fusion to achieve state determination, and to propose a dynamic gating power supply method and device that is adaptive to riding state.

[0006] In a first aspect of this invention, a dynamic gating power supply method adaptive to riding state is first proposed, the method comprising: The triaxial acceleration data of the wheel speed sensor is collected in real time during the bicycle riding process, and the triaxial acceleration data is filtered and noise-reduced to obtain effective acceleration data; An acceleration feature set is obtained by extracting features from the effective acceleration data; the acceleration features include the composite acceleration, the variance of the composite acceleration, and the rate of change of acceleration. The acceleration feature set is input into a preset finite state machine model to obtain the state to be determined; the preset finite state machine model includes a stationary state, a constant speed riding state, and an emergency braking state. The riding condition is obtained by anti-shake analysis based on the acceleration feature set, the state to be determined and the preset state timer; the preset state timer includes a stationary state timer, a constant speed riding state timer and an emergency braking state timer. The operation of the functional modules in the wheel speed sensor signal chain is controlled according to the riding conditions.

[0007] Optionally, inputting the acceleration feature set into a preset finite state machine model to obtain the state to be determined includes: Obtain the current stable historical state in the finite state machine model and the duration of the corresponding historical state; the duration of the state is recorded as the number of sampling periods in which the historical state is continuously maintained. Calculate the distance metric between the acceleration feature set and the state feature space corresponding to the stationary state, the constant speed riding state, and the emergency braking state, respectively; pass Calculate the original confidence levels for the stationary state, the constant speed riding state, and the emergency braking state; where, This is the distance penalty coefficient. For state The corresponding distance metric; The modified state confidence is obtained by attenuating and compensating the original confidence based on the distance metric corresponding to the historical state. The corrected state confidence and the original confidence of the other two states are used as the final confidence of each state at the current sampling time, and the state with the highest final confidence is selected as the state to be determined.

[0008] This solution combines historical states and their durations to first calculate the distance metric between the acceleration feature set and the feature space of each riding condition to obtain the original confidence level. Then, based on the historical states, the confidence level is attenuated to obtain the corrected confidence level. Finally, the state with the highest confidence level is selected as the state to be judged. This approach achieves a comprehensive consideration of current motion characteristics and historical states, and balances the weights of new state characteristics and historical states through distance penalty and duration compensation mechanisms. This effectively improves the robustness and accuracy of the finite state machine model in judging riding states, avoids misjudgments caused by data fluctuations at a single moment or outdated state information, and lays a reliable foundation for subsequent accurate condition anti-shake analysis and power consumption control.

[0009] Optionally, the corrected state confidence is obtained by attenuating the historical state confidence based on the distance metric corresponding to the historical state, including: pass Calculate the corrected state confidence; where, The original confidence level. The maximum duration compensation coefficient, The decay rate coefficient, To preset the minimum duration, The duration of the historical state.

[0010] This solution uses a distance metric based on historical states and introduces an exponential decay formula with duration compensation to correct state confidence. This not only assigns higher confidence to stable states whose duration exceeds a preset threshold, but also gradually weakens the influence of past states over time. This achieves dynamic balance and precise calibration of historical state confidence, effectively improving the stability and accuracy of the finite state machine model in determining riding conditions. It avoids interference from instantaneous abnormal data or outdated state information on the identification of current conditions, providing a more reliable basis for state confidence in subsequent anti-shake analysis of riding conditions.

[0011] Optionally, the cycling conditions obtained by performing anti-shake analysis based on the acceleration feature set, the state to be determined, and the preset state timer include: When the state to be determined is stationary, it is determined whether the variance of the composite acceleration is greater than a first preset stationary threshold and whether the deviation between the composite acceleration and the gravitational acceleration is greater than a second preset stationary threshold. If both are greater, it is determined whether the stationary state timer is greater than a first preset duration. If it is greater, then stationary is taken as the riding condition; otherwise, constant speed is taken as the riding condition. If neither is greater, then stationary is taken as the riding condition. When the state to be compared is constant speed, it is determined whether the acceleration rate is less than a preset braking acceleration rate threshold and whether the combined acceleration is less than a preset braking acceleration threshold; if both are less than the threshold, emergency braking is taken as the riding condition; if not both are less than the threshold, constant speed is taken as the riding condition. When the state to be compared is emergency braking, it is determined whether the current count value of the emergency braking state timer has reached the second preset duration; if the current count value of the emergency braking state timer is less than the second preset duration, then emergency braking is taken as the riding condition; otherwise, it is determined whether the variance of the composite acceleration is less than the first preset static threshold and whether the deviation between the composite acceleration and the gravitational acceleration is less than the second preset static threshold; if both are less, then static riding is taken as the riding condition; if not both are less, then constant speed riding is taken as the riding condition.

[0012] This solution combines acceleration feature sets, the state to be determined, and a preset state timer for anti-jitter analysis. Based on the initial state machine determination, it introduces multi-dimensional feature verification and duration timing mechanisms, effectively avoiding misjudgments of operating conditions caused by single data fluctuations or instantaneous interference. This ensures the accuracy and stability of identifying three types of operating conditions: stationary, constant speed riding, and emergency braking. Furthermore, it enables smooth state transitions through continuous counting of the timer, improving the robustness and reliability of riding condition determination and laying a solid foundation for subsequent precise power consumption gating control.

[0013] Optionally, controlling the operation of functional modules in the wheel speed sensor signal chain according to the riding conditions includes: When the riding condition is stationary, the control wheel speed sensor signal chain only keeps the Hall chip bias circuit in working state, and shuts down the giant magnetoresistive channel, analog-to-digital converter, digital filter and communication module; When the riding condition is constant speed riding, the wheel speed sensor signal chain is controlled to enable the giant magnetoresistive channel and start the analog-to-digital converter to collect data at the first preset sampling frequency, while the high-speed mode of the on-chip finite impulse response filter and the communication module is turned off. When the riding condition is emergency braking, the control wheel speed sensor signal chain is activated, including the Hall chip bias circuit and the giant magnetoresistive channel working simultaneously. The analog-to-digital converter is started to perform high-speed data acquisition at a second preset sampling frequency. The on-chip finite impulse response filter is enabled to perform real-time filtering processing on the acquired signal, and the high-speed transmission mode of the communication module is activated.

[0014] This solution implements differentiated and hierarchical control of the wheel speed sensor signal chain functional modules for three riding conditions: stationary, constant speed riding, and emergency braking. In the stationary state, only the minimum necessary power supply is retained to maximize power saving. In the constant speed state, performance and power consumption are balanced to achieve regular data acquisition. In the emergency braking state, the entire link is activated to ensure high-speed sampling and real-time filtering. This achieves precise matching of power consumption and performance under different conditions, avoids the power waste caused by continuous operation of the entire module, and can quickly respond and provide high-quality sensor data under critical conditions, effectively improving the device's endurance and the real-time performance and reliability of adapting to different operating conditions.

[0015] In a second aspect of the invention, a dynamic gating power supply device that adapts to riding conditions is provided, comprising: The acquisition module is used to acquire triaxial acceleration data from the wheel speed sensor of the bicycle in real time during riding, and to filter and reduce noise from the triaxial acceleration data to obtain effective acceleration data. The feature extraction module is used to extract features from the effective acceleration data to obtain an acceleration feature set; the acceleration features include composite acceleration, composite acceleration variance, and acceleration rate of change. The state generation module is used to input the acceleration feature set into a preset finite state machine model to obtain the state to be determined; the preset finite state machine model includes a stationary state, a constant speed riding state, and an emergency braking state. The anti-shake analysis module is used to perform anti-shake analysis based on the acceleration feature set, the state to be determined, and the preset state timer to obtain the riding condition; the preset state timer includes a stationary state timer, a constant speed riding state timer, and an emergency braking state timer. The control module is used to control the operation of the functional modules in the wheel speed sensor signal chain according to the riding conditions.

[0016] Optionally, the state generation module includes: The acquisition module is used to acquire the currently stable historical state in the finite state machine model and the duration of the corresponding historical state; the duration of the state is recorded as the number of sampling periods in which the historical state is continuously maintained. The distance metric calculation module is used to calculate the distance metric between the acceleration feature set and the state feature space corresponding to the stationary state, the constant speed riding state, and the emergency braking state, respectively. The original confidence calculation module is used to calculate the confidence level by... Calculate the original confidence levels for the stationary state, the constant speed riding state, and the emergency braking state; where, This is the distance penalty coefficient. For state The corresponding distance metric; The attenuation compensation module is used to attenuate and compensate the original confidence based on the distance metric corresponding to the historical state to obtain the corrected state confidence. The filtering module is used to take the confidence of the corrected state and the original confidence of the other two states as the final confidence of each state at the current sampling time, and select the state with the highest final confidence as the state to be determined.

[0017] 8. The dynamic gating power supply method for adaptive cycling state according to claim 7, characterized in that the attenuation compensation module comprises: pass Calculate the corrected state confidence; where, The original confidence level. The maximum duration compensation coefficient, The decay rate coefficient, To preset the minimum duration, The duration of the historical state.

[0018] Optionally, the image stabilization analysis module includes: The first condition module is used to determine whether the variance of the composite acceleration is greater than a first preset static threshold and whether the deviation between the composite acceleration and the gravitational acceleration is greater than a second preset static threshold when the state to be determined is stationary; if both are greater, it determines whether the stationary state timer is greater than a first preset duration; if it is greater, then stationary is taken as the riding condition, otherwise constant speed is taken as the riding condition; if neither is greater, then stationary is taken as the riding condition. The second condition module is used to determine whether the acceleration rate is less than a preset braking acceleration rate threshold and whether the combined acceleration is less than a preset braking acceleration threshold when the state to be compared is constant speed; if both are less than the threshold, then emergency braking is taken as the riding condition; if not both are less than the threshold, then constant speed is taken as the riding condition. The third condition module is used to determine whether the current count value of the emergency braking state timer has reached a second preset duration when the state to be compared is emergency braking; if the current count value of the emergency braking state timer is less than the second preset duration, then emergency braking is taken as the riding condition; otherwise, it is determined whether the variance of the composite acceleration is less than a first preset static threshold and whether the deviation between the composite acceleration and the gravitational acceleration is less than a second preset static threshold; if both are less than, then static riding is taken as the riding condition; if not both are less than, then constant speed riding is taken as the riding condition.

[0019] Optionally, the control module includes: The first operating module is used to control the wheel speed sensor signal chain to maintain only the Hall chip bias circuit in working state when the riding condition is stationary, and to shut down the giant magnetoresistive channel, analog-to-digital converter, digital filter and communication module. The second operating module is used to control the wheel speed sensor signal chain to enable the giant magnetoresistive channel and start the analog-to-digital converter to collect data at a first preset sampling frequency when the riding condition is constant speed riding, and to turn off the high-speed mode of the on-chip finite impulse response filter and the communication module. The third operating module is used to control the wheel speed sensor signal chain to activate the Hall chip bias circuit and the giant magnetoresistive channel to work simultaneously when the riding condition is emergency braking, start the analog-to-digital converter to perform high-speed data acquisition at a second preset sampling frequency, enable the on-chip finite impulse response filter to perform real-time filtering processing on the acquired signal, and activate the high-speed transmission mode of the communication module when the riding condition is emergency braking.

[0020] The beneficial effects of this invention are as follows: This invention proposes a dynamic gating power supply method that adapts to cycling conditions. It collects triaxial acceleration data from wheel speed sensors in real time during bicycle riding. After filtering, noise reduction, and feature extraction to obtain an acceleration feature set, this set is input into a preset finite state machine model to obtain the state to be determined. Then, combining the acceleration feature set, the state to be determined, and a preset state timer, anti-shake analysis is performed to accurately identify three cycling conditions: stationary, constant speed riding, and emergency braking. Finally, the operation of the wheel speed sensor signal chain functional modules is dynamically controlled according to the cycling conditions. This method ensures accurate identification of cycling conditions while implementing hierarchical dynamic gating of the sensor signal chain functional modules based on the three typical conditions of stationary, constant speed riding, and emergency braking, thereby significantly reducing the overall energy consumption of the equipment. Attached Figure Description

[0021] The present invention will now be further described with reference to the accompanying drawings.

[0022] Figure 1 A flowchart of a dynamic gating power supply method for adaptive cycling state provided in an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0024] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] This invention provides a dynamic gating power supply method that adapts to riding conditions. See also... Figure 1, Figure 1 A flowchart illustrating a dynamic gating power supply method for adaptive cycling state provided in an embodiment of the present invention. The method includes the following steps: S101 collects triaxial acceleration data from the wheel speed sensor during bicycle riding in real time, and performs filtering and noise reduction processing on the triaxial acceleration data to obtain effective acceleration data; S102, Perform feature extraction on the effective acceleration data to obtain an acceleration feature set; S103, Input the acceleration feature set into the preset finite state machine model to obtain the state to be determined; S104, based on the acceleration feature set, the state to be determined and the preset state timer, performs anti-shake analysis to obtain the riding conditions; S105 controls the operation of functional modules in the wheel speed sensor signal chain according to the riding conditions; Among them, the acceleration characteristics include the composite acceleration, the variance of the composite acceleration, and the rate of change of acceleration; The preset finite state machine model includes a stationary state, a constant speed riding state, and an emergency braking state. The preset state timers include a stationary state timer, a constant speed riding state timer, and an emergency braking state timer.

[0026] This invention provides a dynamic gating power supply method for adaptive cycling conditions. It collects triaxial acceleration data from wheel speed sensors during real-time cycling, performs filtering and noise reduction, and extracts features to construct an acceleration feature set. This feature set is then input into a preset finite state machine model to initially determine the state to be judged. The method combines the feature set, the state to be judged, and a preset state timer to complete anti-shake verification, accurately identifying three core cycling conditions: stationary, constant speed cycling, and emergency braking. Based on the condition type, it implements hierarchical dynamic gating of the functional modules in the wheel speed sensor signal chain. This method significantly reduces overall device energy consumption while ensuring accurate condition recognition, avoiding power loss caused by continuous high-load operation across the entire chain.

[0027] In one implementation, through Calculate the resultant acceleration; where a x (t), a y (t), a z (t) represents the effective acceleration along the X, Y, and Z axes at the current moment; through Calculate the variance of the combined acceleration; where, This represents the number of sampling points at the current moment. Let be the composite acceleration at time i. The average of the composite accelerations up to the current moment; through Calculate the rate of change of acceleration; where, The composite acceleration at the current moment, The composite acceleration of the previous moment. This represents the time interval between two consecutive data collections.

[0028] In one embodiment, inputting the acceleration feature set into a preset finite state machine model to obtain the state to be determined includes: Obtain the current stable historical state and the duration of the corresponding historical state in the finite state machine model; the duration of the state is recorded as the number of sampling periods in which the historical state is continuously maintained. Calculate the distance metric between the acceleration feature set and the state feature space corresponding to the stationary state, the constant speed riding state, and the emergency braking state, respectively. pass Calculate the original confidence levels for the stationary state, the constant speed riding state, and the emergency braking state; where, This is the distance penalty coefficient. For state The corresponding distance metric; The modified state confidence is obtained by attenuating and compensating the original confidence based on the distance metric corresponding to the historical state. The corrected state confidence and the original confidence of the other two states are used as the final confidence of each state at the current sampling time, and the state with the highest final confidence is selected as the state to be determined.

[0029] In one implementation, the composite acceleration, the variance of the composite acceleration, and the spatial distance metric of the acceleration change rate relative to the state features of the stationary state, the constant speed riding state, and the emergency braking state are calculated using Euclidean distance.

[0030] In one implementation, the process utilizes historical states and their durations to characterize the continuity of system motion, and performs attenuation compensation on the original confidence score calculated based on the feature spatial distance. This enhances the confidence score when historical states are maintained and suppresses erroneous transitions during abrupt state changes through the attenuation mechanism. This design solves the problem of frequent state transitions caused by sensor noise or instantaneous disturbances when relying solely on current-moment features for condition identification, while preserving the system's rapid response capability to changes in real-world conditions, thus achieving an effective balance between real-time performance and stability in identification.

[0031] In one embodiment, the process of attenuating and compensating for historical state confidence based on the distance metric corresponding to the historical state to obtain corrected state confidence includes: pass Calculate the corrected state confidence; where, The original confidence level. The maximum duration compensation coefficient, The decay rate coefficient, To preset the minimum duration, The duration of the historical state.

[0032] In one implementation, the maximum duration compensation coefficient The setting is based on the confidence enhancement required after exceeding a preset minimum duration, according to historical data. The larger the value, the stronger the influence of historical states on the current judgment; decay rate coefficient The settings are based on how quickly the confidence compensation for historical states decays with increasing duration. The larger the value, the faster the compensation effect decays, and the influence of historical state on the current judgment weakens rapidly with the increase of duration; in practical applications, it is specifically determined through statistical analysis of historical experimental data.

[0033] In one implementation, the formula nonlinearly compensates for the original confidence level by introducing the duration of historical states. The compensation mechanism is activated only when the duration of a historical state exceeds a preset minimum duration threshold. The compensation strength is determined by multiplying the maximum compensation coefficient by an exponential decay factor, which gradually decreases as the duration of the state increases. This means that the strongest confidence enhancement is obtained when the state just exceeds the minimum duration threshold, and the compensation effect gradually slows down as the state continues. When the duration of a historical state does not reach the minimum duration threshold, the compensation term is inactive, ensuring that short-term states do not receive excessive compensation. This design reflects the accumulation of trust in stable states while avoiding the historical state lock-in effect caused by excessively long durations, thus achieving a dynamic balance between the inertia of historical states and the rapid response to new states.

[0034] In one embodiment, the cycling conditions obtained through anti-shake analysis based on the acceleration feature set, the state to be determined, and a preset state timer include: When the state to be determined is stationary, it is determined whether the variance of the composite acceleration is greater than the first preset stationary threshold and whether the deviation between the composite acceleration and the gravitational acceleration is greater than the second preset stationary threshold. If both are greater, it is determined whether the stationary state timer is greater than the first preset duration. If it is greater, then stationary is taken as the riding condition; otherwise, constant speed is taken as the riding condition. If neither is greater, then stationary is taken as the riding condition. When the state to be compared is constant speed, it is determined whether the acceleration rate is less than the preset braking acceleration rate threshold and whether the combined acceleration is less than the preset braking acceleration threshold. If both are less than the threshold, emergency braking is taken as the riding condition. If neither is less than the threshold, constant speed is taken as the riding condition. When the state to be compared is emergency braking, it is determined whether the current count value of the emergency braking state timer has reached the second preset duration. If the current count value of the emergency braking state timer is less than the second preset duration, then emergency braking is taken as the riding condition. Otherwise, it is determined whether the variance of the composite acceleration is less than the first preset static threshold and whether the deviation between the composite acceleration and the gravitational acceleration is less than the second preset static threshold. If both are less than the threshold, then static riding is taken as the riding condition. If neither is less than the threshold, then constant speed riding is taken as the riding condition.

[0035] In one implementation, the first preset static threshold, the second preset static threshold, the first preset duration, the second preset duration, the preset braking acceleration rate threshold, and the preset braking acceleration threshold are set by a technician; specifically, they can all be set to 0.1 m / s². 2 1m / s 2 1s, 3s, 8m / s 2 and 7.8m / s 2 .

[0036] In one implementation, when the state to be determined is stationary but the real-time acceleration feature has shown a deviation from the stationary feature space, the system does not immediately exit the stationary state. Instead, a stationary state timer is introduced as the basis for decision-making. If the stationary state has lasted for a sufficiently long time, the stationary determination is maintained even if the current feature deviates, reflecting the accumulation of trust in the long-term stable state. If the stationary state has not lasted long enough, a switch to a uniform state is allowed to ensure a rapid response to real motion changes. This design solves the problem that it is impossible to distinguish between instantaneous disturbances and real state changes based solely on the current feature. It enables the system to make differentiated processing based on historical stability when facing feature fluctuations, thereby achieving a balance between robustness and sensitivity in recognition.

[0037] In one implementation, when the state to be determined is uniform speed, the system monitors two key features in real time: the rate of change of acceleration and the resultant acceleration. If both are simultaneously below the corresponding braking threshold, it indicates that the vehicle is undergoing a significant deceleration process, which meets the dynamic characteristics of emergency braking. At this point, regardless of how long the uniform speed state has lasted, the condition is immediately determined to be emergency braking, ensuring timely identification of safety events. If the two conditions are not simultaneously met, it means that the current motion characteristics are still within the feature space of the uniform speed state, and the uniform speed determination is maintained. This design solves the problem of needing instantaneous identification of safety-critical operating conditions without relying on historical state delays by setting the highest response priority for emergency braking. While ensuring the stability of daily operating condition identification, it also ensures zero-delay response to sudden dangerous events.

[0038] In one implementation, when the state to be determined is emergency braking, the system first checks whether the emergency braking state timer has reached a second preset duration. If the timer has not reached this duration, it indicates that the emergency braking event is still ongoing or has just ended. At this time, regardless of how the real-time acceleration characteristics change, the emergency braking determination is forcibly maintained to ensure that this safe operating condition is not prematurely interrupted by brief characteristic fluctuations. When the timer reaches the second preset duration, the system begins to evaluate whether the current motion characteristics are tending towards stillness or uniform speed recovery. By judging whether the variance of the composite acceleration and the deviation between the composite acceleration and the gravitational acceleration are simultaneously lower than the corresponding stillness threshold, if both are lower, the operating condition is restored to stillness, indicating that the vehicle has completely stopped; if not, the operating condition is restored to uniform speed riding, indicating that the vehicle has resumed normal driving. This design solves the problem of determining the timing of state recovery after emergency braking through a two-stage mechanism of first forcibly maintaining and then restoring the condition, ensuring both the complete recording of the safety event and achieving a smooth transition to normal operating conditions.

[0039] In one embodiment, controlling the operation of functional modules in the wheel speed sensor signal chain according to riding conditions includes: When the riding condition is stationary, the control wheel speed sensor signal chain only keeps the Hall chip bias circuit in working state, and shuts down the giant magnetoresistive channel, analog-to-digital converter, digital filter and communication module; When the riding condition is constant speed riding, the control wheel speed sensor signal chain enables the giant magnetoresistive channel and starts the analog-to-digital converter to collect data at the first preset sampling frequency, while turning off the high-speed mode of the on-chip finite impulse response filter and the communication module. When the riding condition is emergency braking, the control wheel speed sensor signal chain is activated, including the Hall chip bias circuit and the giant magnetoresistive channel working simultaneously. The analog-to-digital converter is started to perform high-speed data acquisition at the second preset sampling frequency. The on-chip finite impulse response filter is enabled to perform real-time filtering processing on the acquired signal, and the high-speed transmission mode of the communication module is activated.

[0040] In one implementation, the first preset sampling frequency is 200 Hz and the second preset sampling frequency is 8000 Hz.

[0041] In one implementation, when the operating condition is identified as stationary, the system control signal chain enters a minimum power consumption mode, maintaining only the Hall chip bias circuit in operation to preserve basic wake-up capability, with a power consumption of only 32 microwatts. All non-essential functional units, such as the giant magnetoresistive channel, analog-to-digital converter, digital filter, and communication module, are disabled. When the operating condition is identified as constant-speed cycling, based on the smooth motion characteristics of this condition, the system activates the giant magnetoresistive channel and starts the analog-to-digital converter to acquire data at a sampling frequency of 200 Hz, with a total power consumption of 180 microwatts. Simultaneously, the on-chip finite impulse response filter and the high-speed mode of the communication module are disabled to further conserve energy. When the operating condition is identified as emergency braking, the system recognizes a critical safety event and immediately activates... In the full-channel operating mode, the Hall chip bias circuit and the giant magnetoresistive channel operate simultaneously. The analog-to-digital converter performs high-speed data acquisition at a sampling frequency of 8 kHz. The on-chip finite impulse response filter is enabled to perform real-time filtering of the acquired signal, and the high-speed transmission mode of the communication module is activated to ensure real-time data reporting. The total power consumption is 1.2 milliwatts. This dynamic switching strategy of allocating system resources on demand solves the contradiction between power consumption and performance of the sensor during continuous operation. It ensures the necessary data acquisition quality in non-stationary states, sacrifices power consumption for the highest acquisition accuracy and real-time performance in safety-critical conditions, and reduces power consumption to an extremely low level in stationary states. Thus, it achieves a balance between system endurance and reliability of condition identification.

[0042] The foregoing has described one embodiment of the present invention in detail, but this content is merely a preferred embodiment and should not be considered as limiting the scope of the present invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the scope of the claims of this invention.

Claims

1. A dynamic gating power supply method that adapts to riding conditions, characterized in that, The method includes: The triaxial acceleration data of the wheel speed sensor is collected in real time during the bicycle riding process, and the triaxial acceleration data is filtered and noise-reduced to obtain effective acceleration data; An acceleration feature set is obtained by extracting features from the effective acceleration data; the acceleration features include the composite acceleration, the variance of the composite acceleration, and the rate of change of acceleration. The acceleration feature set is input into a preset finite state machine model to obtain the state to be determined; the preset finite state machine model includes a stationary state, a constant speed riding state, and an emergency braking state. The riding condition is obtained by anti-shake analysis based on the acceleration feature set, the state to be determined and the preset state timer; the preset state timer includes a stationary state timer, a constant speed riding state timer and an emergency braking state timer. The operation of the functional modules in the wheel speed sensor signal chain is controlled according to the riding conditions.

2. The dynamic gating power supply method for adaptive cycling state according to claim 1, characterized in that, The acceleration feature set is input into a preset finite state machine model to obtain the state to be determined, including: Obtain the current stable historical state in the finite state machine model and the duration of the corresponding historical state; the duration of the state is recorded as the number of sampling periods in which the historical state is continuously maintained. Calculate the distance metric between the acceleration feature set and the state feature space corresponding to the stationary state, the constant speed riding state, and the emergency braking state, respectively; pass Calculate the original confidence levels for the stationary state, the constant speed riding state, and the emergency braking state; where, This is the distance penalty coefficient. For state The corresponding distance metric; The modified state confidence is obtained by attenuating and compensating the original confidence based on the distance metric corresponding to the historical state. The corrected state confidence and the original confidence of the other two states are used as the final confidence of each state at the current sampling time, and the state with the highest final confidence is selected as the state to be determined.

3. The adaptive dynamic gating power supply method for riding state according to claim 2, characterized in that, The corrected state confidence is obtained by attenuating and compensating the historical state confidence based on the distance metric corresponding to the historical state, including: pass Calculate the corrected state confidence; where, The original confidence level. The maximum duration compensation coefficient, The decay rate coefficient, To preset the minimum duration, The duration of the historical state.

4. The adaptive dynamic gating power supply method for riding state according to claim 1, characterized in that, Based on the acceleration feature set, the state to be determined, and the preset state timer, the following riding conditions are obtained through anti-shake analysis: When the state to be determined is stationary, it is determined whether the variance of the composite acceleration is greater than a first preset stationary threshold and whether the deviation between the composite acceleration and the gravitational acceleration is greater than a second preset stationary threshold. If both are greater, it is determined whether the stationary state timer is greater than a first preset duration. If it is greater, then stationary is taken as the riding condition; otherwise, constant speed is taken as the riding condition. If neither is greater, then stationary is taken as the riding condition. When the state to be compared is constant speed, it is determined whether the acceleration rate is less than a preset braking acceleration rate threshold and whether the combined acceleration is less than a preset braking acceleration threshold; if both are less than the threshold, emergency braking is taken as the riding condition; if not both are less than the threshold, constant speed is taken as the riding condition. When the state to be compared is emergency braking, it is determined whether the current count value of the emergency braking state timer has reached the second preset duration; if the current count value of the emergency braking state timer is less than the second preset duration, then emergency braking is taken as the riding condition; otherwise, it is determined whether the variance of the composite acceleration is less than the first preset static threshold and whether the deviation between the composite acceleration and the gravitational acceleration is less than the second preset static threshold; if both are less, then static riding is taken as the riding condition; if not both are less, then constant speed riding is taken as the riding condition.

5. The dynamic gating power supply method for adaptive cycling state according to claim 1, characterized in that, Controlling the operation of functional modules in the wheel speed sensor signal chain according to the riding conditions includes: When the riding condition is stationary, the control wheel speed sensor signal chain only keeps the Hall chip bias circuit in working state, and shuts down the giant magnetoresistive channel, analog-to-digital converter, digital filter and communication module; When the riding condition is constant speed riding, the wheel speed sensor signal chain is controlled to enable the giant magnetoresistive channel and start the analog-to-digital converter to collect data at the first preset sampling frequency, while the high-speed mode of the on-chip finite impulse response filter and the communication module is turned off. When the riding condition is emergency braking, the control wheel speed sensor signal chain is activated, including the Hall chip bias circuit and the giant magnetoresistive channel working simultaneously. The analog-to-digital converter is started to perform high-speed data acquisition at a second preset sampling frequency. The on-chip finite impulse response filter is enabled to perform real-time filtering processing on the acquired signal, and the high-speed transmission mode of the communication module is activated.

6. A dynamic gating power supply device that adapts to riding conditions, characterized in that, The device includes: The acquisition module is used to acquire triaxial acceleration data from the wheel speed sensor of the bicycle in real time during riding, and to filter and reduce noise from the triaxial acceleration data to obtain effective acceleration data. The feature extraction module is used to extract features from the effective acceleration data to obtain an acceleration feature set; the acceleration features include composite acceleration, composite acceleration variance, and acceleration rate of change. The state generation module is used to input the acceleration feature set into a preset finite state machine model to obtain the state to be determined; the preset finite state machine model includes a stationary state, a constant speed riding state, and an emergency braking state. The anti-shake analysis module is used to perform anti-shake analysis based on the acceleration feature set, the state to be determined, and the preset state timer to obtain the riding condition; the preset state timer includes a stationary state timer, a constant speed riding state timer, and an emergency braking state timer. The control module is used to control the operation of the functional modules in the wheel speed sensor signal chain according to the riding conditions.

7. A dynamic gating power supply device for adaptive riding state according to claim 6, characterized in that, The state generation module includes: The acquisition module is used to acquire the currently stable historical state in the finite state machine model and the duration of the corresponding historical state; the duration of the state is recorded as the number of sampling periods in which the historical state is continuously maintained. The distance metric calculation module is used to calculate the distance metric between the acceleration feature set and the state feature space corresponding to the stationary state, the constant speed riding state, and the emergency braking state, respectively. The original confidence calculation module is used to calculate the confidence level by... Calculate the original confidence levels for the stationary state, the constant speed riding state, and the emergency braking state; where, This is the distance penalty coefficient. For state The corresponding distance metric; The attenuation compensation module is used to attenuate and compensate the original confidence based on the distance metric corresponding to the historical state to obtain the corrected state confidence. The filtering module is used to take the confidence of the corrected state and the original confidence of the other two states as the final confidence of each state at the current sampling time, and select the state with the highest final confidence as the state to be determined.

8. The adaptive dynamic gating power supply method for riding state according to claim 7, characterized in that, The attenuation compensation module includes: pass Calculate the corrected state confidence; where, The original confidence level. The maximum duration compensation coefficient, The decay rate coefficient, To preset the minimum duration, The duration of the historical state.

9. The adaptive dynamic gating power supply method for riding state according to claim 6, characterized in that, The image stabilization analysis module includes: The first condition module is used to determine whether the variance of the composite acceleration is greater than a first preset static threshold and whether the deviation between the composite acceleration and the gravitational acceleration is greater than a second preset static threshold when the state to be determined is stationary; if both are greater, it determines whether the stationary state timer is greater than a first preset duration; if it is greater, then stationary is taken as the riding condition, otherwise constant speed is taken as the riding condition; if neither is greater, then stationary is taken as the riding condition. The second condition module is used to determine whether the acceleration rate is less than a preset braking acceleration rate threshold and whether the combined acceleration is less than a preset braking acceleration threshold when the state to be compared is constant speed; if both are less than the threshold, then emergency braking is taken as the riding condition; if not both are less than the threshold, then constant speed is taken as the riding condition. The third condition module is used to determine whether the current count value of the emergency braking state timer has reached a second preset duration when the state to be compared is emergency braking; if the current count value of the emergency braking state timer is less than the second preset duration, then emergency braking is taken as the riding condition; otherwise, it is determined whether the variance of the composite acceleration is less than a first preset static threshold and whether the deviation between the composite acceleration and the gravitational acceleration is less than a second preset static threshold; if both are less than, then static riding is taken as the riding condition; if not both are less than, then constant speed riding is taken as the riding condition.

10. The dynamic gating power supply method for adaptive cycling state according to claim 6, characterized in that, The control module includes: The first operating module is used to control the wheel speed sensor signal chain to maintain only the Hall chip bias circuit in working state when the riding condition is stationary, and to shut down the giant magnetoresistive channel, analog-to-digital converter, digital filter and communication module. The second operating module is used to control the wheel speed sensor signal chain to enable the giant magnetoresistive channel and start the analog-to-digital converter to collect data at a first preset sampling frequency when the riding condition is constant speed riding, and to turn off the high-speed mode of the on-chip finite impulse response filter and the communication module. The third operating module is used to control the wheel speed sensor signal chain to activate the Hall chip bias circuit and the giant magnetoresistive channel to work simultaneously when the riding condition is emergency braking, start the analog-to-digital converter to perform high-speed data acquisition at a second preset sampling frequency, enable the on-chip finite impulse response filter to perform real-time filtering processing on the acquired signal, and activate the high-speed transmission mode of the communication module when the riding condition is emergency braking.

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