Bicycle crankset gear optimization method and system

Abstract

The invention provides a bicycle chain wheel gear optimization method and system, and the method comprises the steps: calculating a load condition and energy conversion efficiency in a current riding state, and then calculating an optimal chain wheel gear in combination with a preset minimum fatigue principle parameter; the current gear of the crankset is compared with the optimal crankset gear, so that whether the current gear exceeds a preset interval range or not is judged; and when it is detected that the current gear exceeds the interval range, a gear shifting prompt signal is sent to a user through a preset feedback control mechanism so as to guide the user to manually switch to the optimal crankset gear. By calculating the optimal crankset gear and providing a gear shifting prompt for a user in time through a feedback control mechanism, the fatigue risk caused by excessive load is effectively reduced, the energy conversion efficiency and riding comfort are improved, and the problem that in the prior art, dynamic analysis of the load condition and the energy conversion efficiency in the riding process is lacked is solved. The problems of low riding efficiency and high fatigue are further caused.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, and in particular to a bicycle gear optimization method and system. BACKGROUND

[0002] At present, as a kind of traffic tool with both sports fitness and traffic function, bicycle has been widely used in urban commuting, long-distance cycling and competitive sports and other scenarios. With the continuous development of the derailleur system, bicycles are usually equipped with multi-stage speed combination of front gear and rear freewheel, and riders can adjust the gear to adapt to different road conditions and their physical condition. However, in the actual riding process, the selection of gear often depends on the subjective judgment of the rider, and lacks scientific basis.

[0003] And in complex road conditions such as uphill, acceleration or long-distance cycling, if the gear selection is unreasonable, it will cause the rider to output too much power to maintain speed, which is easy to cause physical exhaustion and muscle fatigue. If the gear is too light, the pedal frequency is too fast, the riding efficiency is reduced, the optimal energy utilization cannot be achieved, and frequent unreasonable gear shifting also easily causes excessive wear of chain and sprocket, reducing the service life of the whole vehicle.

[0004] Some existing bicycle odometers or intelligent cycling systems can monitor power, speed, pedal frequency and other parameters, but they mostly stay at the level of single data display and do not provide intelligent analysis and gear shifting guidance for gear optimization, lacking dynamic analysis of load condition and energy conversion efficiency during cycling, which leads to low cycling efficiency and high fatigue. SUMMARY

[0005] The technical problem to be solved by the present application is that in the prior art, there is a lack of dynamic analysis of load condition and energy conversion efficiency during cycling, which leads to low cycling efficiency and high fatigue, so as to provide a bicycle gear optimization method and system.

[0006] Therefore, the first aspect of the embodiment of the present application provides a bicycle gear optimization method, comprising: collecting the pedaling power output data, pedal frequency rhythm data, cycling speed data and slope information of the user during cycling to calculate the load condition and energy conversion efficiency under the current cycling state; combining the load condition and the energy conversion efficiency with the preset minimum fatigue principle parameter to calculate the optimal gear; comparing the current gear of the gear with the optimal gear to determine whether the current gear exceeds the preset interval range; when it is detected that the current gear exceeds the interval range, a gear shifting prompt signal is sent to the user through a preset feedback control mechanism to guide the user to manually switch to the optimal gear.

[0007] Preferably, the step of collecting the pedaling power output data, pedaling cadence data, riding speed data and slope information of the user during the riding process to calculate the load condition and energy conversion efficiency in the current riding state comprises: detecting the torque force value applied on the pedal by the strain gauge power sensor installed on the crank assembly when the user pedals with the left and right feet and calculating the crank rotation angular velocity to obtain the pedaling power output data; detecting the time interval of one rotation of the crank by the Hall effect sensor arranged in the chainset bearing seat to obtain the pedaling cadence data; detecting the wheel speed by the magnetic induction speed sensor installed in the wheel hub and calculating the wheel diameter parameter to obtain the riding speed data; detecting the inclination angle change of the bicycle frame by the inertial measurement unit of the built-in three-axis accelerometer and gyroscope to obtain the slope information; identifying the power density coefficient in the current gear based on the pedaling power output data and the pedaling cadence data, analyzing the load condition in the current riding state by using the power density coefficient and the slope information; performing ratio operation on the riding speed data and the pedaling power output data to obtain the speed-power ratio parameter, and calculating the energy conversion efficiency in the current gear based on the speed-power ratio parameter and the pedaling cadence data.

[0008] Preferably, the step of identifying the power density coefficient in the current gear based on the pedaling power output data and the pedaling cadence data, and analyzing the load condition in the current riding state by using the power density coefficient and the slope information comprises: segmenting and sampling the pedaling power output data according to a preset time window and calculating the power mean value in each time window to obtain the average power value; corresponding sampling the pedaling cadence data according to a preset time window and calculating the pedaling frequency mean value in each time window to obtain the average pedaling frequency value, and obtaining the power density coefficient by dividing the average power value by the average pedaling frequency value; looking up the corresponding load correction factor in the preset slope load mapping table according to the slope information, multiplying the power density coefficient by the load correction factor to obtain the corrected power density coefficient; classifying the corrected power density coefficient to the corresponding load level interval by the preset load level division standard, and determining the load condition in the current riding state by the classification result of the load level interval.

[0009] Preferably, the step of classifying the modified power density coefficient into a corresponding load level interval by a preset load level classification standard, and determining the load condition under the current riding state according to the classification result of the load level interval, comprises: constructing a load level classification standard based on the user's age, weight, and sports ability level; inputting the modified power density coefficient into the load level classification standard to determine the load level interval to which the modified power density coefficient belongs; and determining the load condition under the current riding state according to the classification result of the load level interval, wherein the load condition includes four load conditions of light load, moderate load, heavy load, and extreme load.

[0010] Preferably, the step of calculating the optimal pedal disc gear position by combining the load condition and the energy conversion efficiency with a preset minimum fatigue principle parameter, comprises: establishing a minimum fatigue principle parameter by the user's age, weight, and sports ability level, wherein the minimum fatigue principle parameter includes a target cadence range and a maximum sustained power threshold; comparing the load condition with the maximum sustained power threshold to obtain a power load coefficient, and comparing the current cadence rhythm data with the target cadence range to obtain a cadence deviation coefficient; calculating the energy conversion efficiency with a preset efficiency weight coefficient to obtain an efficiency evaluation index; inputting the power load coefficient, the cadence deviation coefficient, and the efficiency evaluation index into a preset multiple linear regression model for weighted calculation to obtain a gear optimization score corresponding to each gear combination; and taking the pedal disc gear position combination with the maximum gear optimization score as the optimal pedal disc gear position.

[0011] Preferably, the step of comparing the current gear position of the pedal disc with the optimal pedal disc gear position to determine whether the current gear position exceeds a preset interval range, comprises: acquiring the current pedal gear position and the freewheel gear position by a gear detection sensor installed on the derailleur to determine the gear ratio value of the current gear position; setting an interval range based on the target gear ratio value obtained by analyzing the optimal pedal disc gear position as a central base point; calculating the gear ratio deviation value by difference between the gear ratio value of the current gear position and the target gear ratio value, and determining whether the absolute value of the gear ratio deviation value exceeds the interval range; when the absolute value of the gear ratio deviation value exceeds the interval range, it is determined that the current gear position exceeds the interval range, and when the absolute value of the gear ratio deviation value does not exceed the interval range, it is determined that the current gear position is within the interval range.

[0012] Preferably, the feedback control mechanism comprises an LED indicator array, a liquid crystal display and a vibration reminder; and the step of issuing a gear shifting prompt signal to the user through the preset feedback control mechanism when it is detected that the current gear position exceeds the interval range to guide the user to manually switch to the optimal sprocket gear position comprises: when it is detected that the current gear position exceeds the interval range, determining whether the gear shifting direction is upshift or downshift according to the positive or negative of the gear ratio deviation value; when the gear ratio deviation value is positive, it is determined that downshift is needed to reduce the gear ratio; when the gear ratio deviation value is negative, it is determined that upshift is needed to increase the gear ratio; displaying a directional flashing prompt signal according to the gear shifting direction through the LED indicator array arranged on the bicycle handlebar; determining the gear shifting urgency level according to the absolute value of the gear ratio deviation value; displaying the gear shifting prompt text information in the corresponding color according to the gear shifting urgency level through the liquid crystal display arranged on the bicycle speedometer; and outputting the different frequency of the tactile feedback signal according to the gear shifting urgency level through the vibration reminder arranged on the bicycle frame to guide the user to timely perform the gear shifting operation to the optimal sprocket gear position.

[0013] The second aspect of the embodiment of the present application provides a bicycle sprocket gear optimization system, comprising: a collection module, configured to collect the pedaling power output data, the pedaling frequency rhythm data, the riding speed data and the slope information of a user in the riding process to calculate the load condition and the energy conversion efficiency under the current riding state; a calculation module, configured to calculate the optimal sprocket gear position by combining the load condition and the energy conversion efficiency with the preset minimum fatigue principle parameter; a comparison module, configured to compare the current gear position of the sprocket with the optimal sprocket gear position to determine whether the current gear position exceeds the preset interval range; and a prompt module, configured to issue a gear shifting prompt signal to the user through a preset feedback control mechanism when it is detected that the current gear position exceeds the interval range to guide the user to manually switch to the optimal sprocket gear position.

[0014] The technical scheme of the present application has the following advantages: high riding efficiency and low fatigue. Through the comprehensive analysis of the pedaling power output data, the pedaling frequency rhythm data, the riding speed data and the slope information of the user, the optimal sprocket gear position is scientifically calculated by combining the minimum fatigue principle parameter, and the gear shifting prompt is timely provided to the user through the feedback control mechanism, so that the user can maintain reasonable power output and pedaling frequency rhythm under different riding conditions, thereby effectively reducing the fatigue risk caused by excessive load, improving the energy conversion efficiency and riding comfort, prolonging the riding duration, reducing the sprocket and chain wear caused by frequent unreasonable gear shifting, and improving the problem of low riding efficiency and high fatigue caused by the lack of dynamic analysis of the load condition and the energy conversion efficiency in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the specific embodiments of the present application or the prior art, the accompanying drawings needed to be used in the specific embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the premise that they do not deviate from the scope of the present application.

[0016] Figure 1 A flowchart of a bicycle sprocket gear optimization method provided by an embodiment of the present application is shown in the figure. Figure 2 A structural schematic block diagram of a bicycle sprocket gear optimization system provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0017] The technical solutions of the present application will be described clearly and completely in conjunction with the accompanying drawings. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort fall within the scope of the present application.

[0018] In the description of the present application, it should be noted that the terms "first", "second", "third" are only used for description purposes, and cannot be understood as indicating or implying relative importance.

[0019] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0020] As Figure 1 shown, the embodiments of the present application provide a bicycle sprocket gear optimization method, which can realize comprehensive analysis of the real-time riding state of the user and gear optimization prompt during riding. The sprocket gear optimization method specifically includes the following steps: Step S1, collecting the pedaling power output data, pedaling frequency rhythm data, riding speed data and slope information of the user during riding to calculate the load condition and energy conversion efficiency under the current riding state.

[0021] The torque force value applied on the pedal by the user when pedaling left and right is detected in real time by the strain gauge power sensor installed on the bicycle crank assembly, and the pedaling power output data is calculated by combining the crank angular velocity; the time interval of one revolution of the crank is detected by the Hall effect sensor arranged in the chainring bearing seat to obtain the pedaling frequency rhythm data; the wheel speed is detected by the magnetic induction speed sensor installed in the wheel hub, and the riding speed data is calculated by combining the wheel diameter parameter; the inclination angle change of the bicycle frame is detected by the inertial measurement unit with built-in three-axis accelerometer and gyroscope to obtain the slope information. Then, the power density coefficient is identified based on the pedaling power output data and the pedaling frequency rhythm data, and the load condition is calculated in combination with the slope information, and the speed power ratio parameter is obtained by ratio operation of the riding speed data and the pedaling power output data, and the energy conversion efficiency is calculated in combination with the pedaling frequency rhythm data.

[0022] If the user is riding on a flat road, the strain gauge power sensor detects an average torque of 25 N·m, and the crank angular velocity is 10 rad / s, then the pedaling power output data is 250 W; the Hall effect sensor detects a pedaling frequency of 85 revolutions per minute, so the pedaling frequency rhythm data is 85 rpm; the magnetic induction speed sensor detects a wheel speed of 7 revolutions per second in combination with a 700C wheel diameter (diameter about 0.7 m), so the riding speed data is 55 km / h; the inertial measurement unit detects a frame inclination angle of 0°, so the slope information is flat. At this time, the power density coefficient is calculated to be about 2.94 W / rpm by the ratio of the pedaling power output data and the pedaling frequency rhythm data, the speed power ratio parameter is 55 / 250=0.22, and a higher energy conversion efficiency is obtained in combination with the pedaling frequency rhythm data 85 rpm.

[0023] Step S2, calculate the optimal chainring gear position by combining the load condition and the energy conversion efficiency with the preset minimum fatigue principle parameter.

[0024] The minimum fatigue principle parameter is established by the user's age, weight, and sports ability level, and includes a target pedaling frequency range and a maximum sustained power threshold; the power load coefficient is obtained by comparing the load condition with the maximum sustained power threshold; the pedaling frequency deviation coefficient is obtained by comparing the current pedaling frequency rhythm data with the target pedaling frequency range; the efficiency evaluation index is obtained by calculating the energy conversion efficiency with the preset efficiency weight coefficient; finally, the power load coefficient, the pedaling frequency deviation coefficient, and the efficiency evaluation index are input into the multiple linear regression model for weighted calculation, and the gear optimization score corresponding to each chainring gear position combination is output, and the chainring gear position with the highest score is selected as the optimal chainring gear position.

[0025] Assuming the user is 30 years old, weighs 70 kg, and has a sports level of amateur cyclist, the default target cadence range is 80-95 rpm, and the maximum sustainable power threshold is 280 W. The current detected average power is 250 W, which is lower than the threshold, so the power load coefficient is set to 0.9; the cadence is 85 rpm, which falls in the middle value of the target range, so the cadence deviation coefficient is 1.0; the energy conversion efficiency is processed by weight to obtain an efficiency evaluation index of 0.85. Input the three parameters into the preset multiple linear regression model to obtain the gear optimization score of different gears, and the gear with a sprocket of 52T and a freewheel of 19T has the highest score, so the optimal sprocket gear is determined.

[0026] Step S3, compare the current sprocket gear with the optimal sprocket gear to determine whether the current gear is outside the preset interval range.

[0027] The current sprocket gear and freewheel gear are obtained by the gear detection sensor installed on the derailleur to determine the current gear ratio; based on the optimal sprocket gear, the target gear ratio is analyzed and set as the center base point to set the interval range; then the current gear ratio and the target gear ratio are calculated by difference to obtain the gear ratio deviation value, and it is judged whether the absolute value of the gear ratio deviation value exceeds the interval range. If it exceeds, it is determined that the gear is unreasonable, and if it does not exceed, it is considered that the gear is within a reasonable range.

[0028] The current gear is detected as sprocket 50T and freewheel 21T, with a gear ratio of 50 / 21≈2.38; the gear ratio of the optimal sprocket gear is 52 / 19≈2.74, and the target gear ratio interval range is set to ±0.2. The difference calculation obtains a gear ratio deviation value of -0.36, and its absolute value is greater than 0.2, so it is determined that the current gear is outside the interval range.

[0029] Step S4, when it is detected that the current gear is outside the interval range, a shift prompt signal is sent to the user through the preset feedback control mechanism to guide the user to manually switch to the optimal sprocket gear.

[0030] When it is detected that the current gear is outside the interval range, the shift direction of upshift or downshift is determined according to the positive or negative of the gear ratio deviation value; when the gear ratio deviation value is positive, it is determined that downshift is needed to reduce the gear ratio, and when the gear ratio deviation value is negative, it is determined that upshift is needed to increase the gear ratio; then, the directional flashing prompt signal is displayed through the LED indicator array arranged on the bicycle handle, the corresponding color shift prompt text information is displayed through the liquid crystal display screen arranged on the bicycle odometer, and the tactile feedback signal is output through the vibration reminder arranged on the bicycle frame, and the three form a complete feedback control mechanism to guide the user to perform the shift operation in time.

[0031] When a gear ratio deviation of -0.36 is detected, it indicates that an upshift is needed. The LED indicator array displays an upward flashing green arrow to indicate this, the LCD screen displays "Please Upshift" with a yellow background, and the vibration reminder vibrates at a frequency of 2Hz to prompt the user to switch to the optimal chainring gear 52T×19T in time.

[0032] In this embodiment, the system collects the user's pedaling power output data, cadence data, cycling speed data, and gradient information during cycling, and calculates the load and energy conversion efficiency under the current cycling condition based on the data. Then, the load and energy conversion efficiency are combined with preset minimum fatigue principle parameters to obtain the optimal crankset gear. The current crankset gear is then compared with the optimal crankset gear to determine if the current gear exceeds a preset range. When the current crankset gear exceeds the range, a preset feedback control mechanism sends a shift prompt signal to the user, guiding the user to manually switch to the optimal crankset gear in a timely manner, thereby achieving a dynamic gear optimization process.

[0033] By collecting data on the user's pedaling power output, cadence, speed, and gradient during cycling, the system can obtain the user's real-time cycling status. A quantitative model of load and energy conversion efficiency is constructed using indicators such as power density coefficient and speed-power ratio. By introducing a minimum fatigue principle parameter based on individual user characteristics, gear optimization not only relies on objective data but also matches the user's physical characteristics and endurance. A multiple linear regression model is then used to weight and calculate the power load coefficient, cadence deviation coefficient, and efficiency evaluation index, achieving a scientifically sound and optimal chainring gear selection. By comparing the current gear with the current gear, combined with the setting of the range and the calculation of the gear ratio deviation, a quantitative judgment of gear rationality is achieved. Finally, a feedback control mechanism consisting of an LED indicator array, an LCD display, and a vibration reminder conveys gear shift prompts to the user in a multimodal manner, avoiding information omissions or misjudgments caused by a single prompt method. This overall solution not only improves energy utilization efficiency during cycling and delays muscle fatigue but also reduces abnormal wear on the chainring and chain.

[0034] Specifically, the implementation process of step S1 includes: The torque force applied to the pedals by the user's left and right feet when pedaling is detected in real time by strain gauge power sensors installed on the crank assembly, and the pedaling power output data is obtained by combining the crank rotation angular velocity.

[0035] The strain gauge power sensor measures the instantaneous torque exerted on the pedals by the user's left and right feet during pedaling through the principle of resistance change of metal strain gauges under force. The acquisition circuit converts the resistance change into a voltage signal and converts it into a digital signal through a high-precision analog-to-digital converter; at the same time, the angular velocity of the crank is obtained in real time through the crank angle sensor, with the unit being rad / s. The system performs multiplication operation on the torque and angular velocity in the data processing module to obtain the pedaling power output data. In order to ensure data stability, the system uses Kalman filter algorithm to filter the original torque signal and angular velocity signal to eliminate jitter and noise.

[0036] If the user pedals for a complete cycle, the instantaneous torque detected by the strain gauge power sensor fluctuates between 20N·m and 30N·m, and the average torque after filtering is 25N·m; the angular velocity detected by the crank angle sensor is 10rad / s, then the pedaling power output data is 25×10=250W.

[0037] The time interval for each revolution of the crank is detected by the Hall effect sensor installed in the pedal bearing seat to obtain the cadence data.

[0038] The Hall effect sensor generates a pulse signal by detecting the change in the position relationship between the crank and the magnet during rotation. The data processing module records the time interval between two adjacent pulse signals, and uses this interval to calculate the rotational speed of the crank, with the unit being rpm, i.e. cadence data. To improve accuracy, the system uses a sliding average algorithm to smooth the cadence results of multiple cycles during the calculation process to avoid deviations caused by single-cycle errors.

[0039] The Hall effect sensor detects that the crank completes one revolution in 0.7 seconds, which is equivalent to about 85 revolutions per minute, resulting in cadence data of 85rpm.

[0040] The magnetic induction speed sensor installed in the wheel hub detects the wheel speed and calculates the riding speed data based on the wheel diameter parameter.

[0041] The magnetic induction speed sensor obtains the wheel speed through the pulse signal generated when the magnet passes through the sensor during wheel rotation. The system calculates the number of wheel revolutions per second based on the number of pulses per unit time, and combines the wheel diameter parameter to calculate the linear speed of the wheel rim, and then converts it to riding speed data. To reduce the instantaneous signal anomalies caused by wheel jolts, the system uses a median filter algorithm to process the speed signal.

[0042] The magnetic induction speed sensor detects that the wheel revolves 7 times per second, combined with the 700C specification wheel diameter (diameter 0.7m, circumference about 2.2m), resulting in a riding speed data of 7×2.2=15.4m / s, which is equivalent to about 55km / h.

[0043] The slope information is obtained by detecting the change of the inclination angle of the bicycle frame through the inertial measurement unit with built-in three-axis accelerometer and gyroscope.

[0044] The three-axis accelerometer is used to detect the component of the gravitational direction, and the gyroscope is used to detect the angular velocity. The data processing module fuses the acceleration and angular velocity data through the attitude solution algorithm (such as the quaternion solution algorithm) to obtain the spatial attitude angle of the bicycle frame. Then, by extracting the longitudinal inclination angle, the slope information is calculated.

[0045] The three-axis accelerometer detects that the longitudinal acceleration is 0.0g and the gravitational acceleration is 1g; the gyroscope detects that the angular velocity is 0 rad / s, and after quaternion solution, the longitudinal inclination angle of the frame is 0°, and the slope information is 0%, i.e. the flat road riding state.

[0046] Based on the pedaling power output data and the cadence data, the power density coefficient under the current gear is identified, and the load condition under the current riding state is analyzed using the power density coefficient and the slope information.

[0047] The power density coefficient is obtained by dividing the pedaling power output data by the cadence data, with the unit of W / rpm. The power density coefficient reflects the power output intensity required by the user at a unit cadence. Then, the system corrects the power density coefficient in combination with the slope information. If the slope is uphill, the power density coefficient weight is increased, and if the slope is downhill, the power density coefficient weight is decreased. Finally, the system takes the corrected power density coefficient as the core indicator of the load condition and outputs the load condition.

[0048] The pedaling power output data is 250W, the cadence data is 85rpm, and the power density coefficient is 250 / 85≈2.94 W / rpm. The slope information is 0%, and no correction is needed, so the load condition is directly the power density coefficient 2.94 W / rpm, indicating that the user's load is at a medium level under the current gear.

[0049] The speed power ratio parameter is obtained by ratio operation of the riding speed data and the pedaling power output data, and the energy conversion efficiency under the current gear is calculated based on the speed power ratio parameter and the cadence data.

[0050] The speed power ratio parameter is obtained by dividing the riding speed data by the pedaling power output data, with the unit of (km / h) / W. The speed power ratio parameter reflects the speed improvement effect of the user at a unit power output. Then, in combination with the cadence data and the speed power ratio parameter, the energy conversion efficiency is calculated using the weighted average model (weighted addition model) to obtain the efficiency indicator of energy utilization under the current gear of the user.

[0051] If the cycling speed data is 55 km / h and the pedaling power output data is 250 W, the speed-power ratio parameter is 55 / 250 = 0.22 (km / h) / W. Combined with the pedaling frequency rhythm data of 85 rpm, the energy conversion efficiency is calculated by using the weighted average model with a weight of 0.6:0.4, which is 0.22*0.6+85*0.4 / 100=0.85, indicating that the energy conversion efficiency under the current gear is high.

[0052] In the step of analyzing the load condition under the current cycling state based on the pedaling power output data and the pedaling frequency rhythm data, the power density coefficient under the current gear is identified, and the power density coefficient and the slope information are used to analyze the load condition under the current cycling state, which can be further optimized as follows: The pedaling power output data is segmented and sampled according to the preset time window, and the average power value is calculated for each time window.

[0053] The sampling time window is set, for example, 5 seconds as a time window. The pedaling power output data is obtained in real time by a strain gauge power sensor and a crank angle sensor, and the unit is W. The data processing module accumulates and takes the arithmetic mean value of the multiple sets of pedaling power output data collected in each time window to obtain the average power value corresponding to the window. In order to eliminate the influence of instantaneous abnormal values, the data processing module uses the sliding average algorithm for smoothing processing.

[0054] Assuming that the pedaling power output data collected every second in a 5-second time window is 240 W, 260 W, 250 W, 245 W and 255 W, the average power value is (240+260+250+245+255) / 5=250 W.

[0055] The pedaling frequency rhythm data is sampled according to the preset time window, and the average pedaling frequency value is calculated for each time window. The average power value is divided by the average pedaling frequency value to obtain the power density coefficient.

[0056] In the same time window as the power sampling, the pedaling frequency rhythm data output by the Hall effect sensor is recorded. The data processing module takes the arithmetic mean of all the pedaling frequency rhythm data in the time window to obtain the average pedaling frequency value, and the unit is rpm. Then, the average power value in the time window is divided by the average pedaling frequency value to obtain the power density coefficient, and the unit is W / rpm. The power density coefficient is used to represent the power intensity under unit pedaling frequency, which is an important parameter for subsequent calculation of the load condition.

[0057] Assuming that the cadence data collected in the same 5-second time window is 84 rpm, 86 rpm, 85 rpm, 83 rpm and 87 rpm respectively, the average cadence value is (84+86+85+83+87) / 5=85 rpm. Using the average power value obtained in the first step, 250 W, divided by the average cadence value, 85 rpm, the power density coefficient is 250 / 85≈2.94 W / rpm.

[0058] According to the slope information, the corresponding load correction factor is found in the preset slope load mapping table, and the power density coefficient is multiplied by the load correction factor to obtain the corrected power density coefficient, wherein the slope load mapping table includes load correction factors corresponding to different slope angles.

[0059] The slope information is detected by the inertial measurement unit and the longitudinal slope angle is obtained by the quaternion solution algorithm. The system finds the corresponding load correction factor in the preset slope load mapping table according to the detected slope information. The slope load mapping table is preset, for example: when the slope is 0%, the load correction factor is 1.0; when the slope is +5%, the load correction factor is 1.2; when the slope is +10%, the load correction factor is 1.5; when the slope is -5%, the load correction factor is 0.8. The system multiplies the power density coefficient by the load correction factor to obtain the corrected power density coefficient, which is used to more accurately reflect the influence of the riding environment on the user's load condition.

[0060] Assuming that the inertial measurement unit detects that the slope information is +5%, the corresponding load correction factor is found in the slope load mapping table, which is 1.2. The previously obtained power density coefficient is 2.94 W / rpm, so the corrected power density coefficient=2.94×1.2≈3.53 W / rpm.

[0061] The corrected power density coefficient is classified into the corresponding load level interval by the preset load level division standard, and the load condition under the current riding state is determined by the classification result of the load level interval.

[0062] The load level division standard is preset. For example: the corrected power density coefficient is less than 1.5 W / rpm, which is low load; between 1.5 W / rpm and 3.0 W / rpm, which is medium load; between 3.0 W / rpm and 4.5 W / rpm, which is high load; greater than 4.5 W / rpm, which is extremely high load. The data processing module compares the corrected power density coefficient with the division standard and classifies it into the corresponding load level interval. The load level interval is used as the final load condition result to guide the subsequent optimal disc gear calculation.

[0063] The modified power density coefficient is 3.53 W / rpm, which is in the interval of 3.0 W / rpm to 4.5 W / rpm, so it is determined as a high load state, and the system outputs the load condition in the current riding state as "high load".

[0064] Further, by the preset load level division standard, the modified power density coefficient is classified into the corresponding load level interval, and by the classification result of the load level interval, the load condition in the current riding state is determined. Preferably, the step can be: The load level division standard is constructed based on the user's age, weight, and sports ability level.

[0065] First, the user's basic physiological parameters are collected, including age (unit: years), weight (unit: kg), and sports ability level. The sports ability level is divided according to the user's past riding data and sports test results, for example, into four levels of beginner, intermediate, advanced, and professional. Then, the data processing module calls the preset physiological database and uses the load evaluation model based on maximum oxygen uptake (VO2max) and maximum anaerobic power (MAP) to generate personalized load level division standards for different user groups. The division standard gives four level interval thresholds corresponding to different modified power density coefficients, ensuring that the user's actual bearing capacity can be reflected.

[0066] Suppose a user is 30 years old, weighs 70 kg, and has an intermediate sports ability level. The system generates the user's load level division standard according to the preset model as follows: the modified power density coefficient less than 2.0 W / rpm is light load; between 2.0 W / rpm and 3.5 W / rpm is moderate load; between 3.5 W / rpm and 5.0 W / rpm is heavy load; and greater than 5.0 W / rpm is extreme load.

[0067] The modified power density coefficient is input into the load level division standard to determine the load level interval to which the modified power density coefficient belongs.

[0068] The data processing module is used to obtain the real-time calculated modified power density coefficient, and it is compared with the personalized load level division standard generated in the first step. Through logical judgment statements (such as if-else structure), the modified power density coefficient is classified into the corresponding interval. The system uses a real-time determination mechanism, i.e., the determination result is updated once in each sampling time window, so as to ensure the dynamic and timeliness of the load condition.

[0069] Assuming that the corrected power density coefficient calculated in real time is 3.53 W / rpm, it is substituted into the aforementioned division standard, and it is judged to be at the critical point between the intervals of 2.0 W / rpm to 3.5 W / rpm and 3.5 W / rpm to 5.0 W / rpm. Since 3.53 W / rpm is greater than 3.5 W / rpm and less than 5.0 W / rpm, it is determined that the corrected power density coefficient of the user in the current riding state belongs to the "heavy load" interval.

[0070] According to the classification result of the load level interval, the load condition in the current riding state is determined, wherein the load condition includes four load conditions of light load, medium load, heavy load and extreme load.

[0071] According to the determination result of the second step, the interval corresponding to the corrected power density coefficient is mapped to the specific load condition category. The system internally presets four state labels of "light load", "medium load", "heavy load" and "extreme load", and outputs them in the form of flag variables in the data processing module. This output is not only used for subsequent optimal disc gear calculation, but also can be displayed through the user interface or intelligent cycling meter, so that the user can intuitively understand the real-time riding load condition of himself.

[0072] In this example, since the corrected power density coefficient is determined to be in the "heavy load" interval, the system finally determines the load condition in the current riding state as "heavy load". At this time, the system internally records this state and transmits the "heavy load" state flag to the gear optimization algorithm module to provide a basis for subsequent calculation of the optimal disc gear.

[0073] Specifically, the implementation process of step S2 specifically includes: The minimum fatigue principle parameters are established according to the user's age, weight, and sports ability level, wherein the minimum fatigue principle parameters include a target cadence range and a maximum sustainable power threshold.

[0074] According to the user's basic physiological characteristics (age, weight, sports ability level), the built-in sports physiology database is called, and the sports performance parameters of different populations are pre-stored in the database. The system calculates the maximum sustainable power threshold of the user based on the Critical Power Model commonly used in existing sports physiology. The Critical Power Model determines the maximum power value that the user can maintain for a long time through the user's power-time relationship curve. At the same time, the system sets the target cadence range according to the user's sports ability level. For example, the target cadence range of a beginner is wider, while the target cadence range of an advanced user is more concentrated to ensure efficiency and stability. Finally, the minimum fatigue principle parameters include two specific quantitative standards: target cadence range (unit: rpm) and maximum sustainable power threshold (unit: W).

[0075] Assuming the user is 30 years old, weighs 70 kg, and has a medium level of athletic ability. Based on the user's historical cycling data and power-time relationship curve, the system calculates that the maximum sustainable power threshold is 260 W. At the same time, combined with the training standards of medium-level cyclists, the target cadence range is set to 80-95 rpm. Therefore, the minimum fatigue principle parameters of this user are: target cadence range 80-95 rpm, maximum sustainable power threshold 260 W.

[0076] Compare the load condition with the maximum sustainable power threshold to obtain the power load coefficient, and compare the current cadence rhythm data with the target cadence range to obtain the cadence deviation coefficient.

[0077] The system compares the load condition value obtained in step S1 (the actual power output of the corrected power density coefficient mapping) with the maximum sustainable power threshold, calculates the power load coefficient, and defines it as: Power load coefficient = current power output / maximum sustainable power threshold; Where the current power output is in W and the maximum sustainable power threshold is in W.

[0078] If the power load coefficient is greater than 1, it means that the user has exceeded the maximum sustainable capacity and is prone to fatigue.

[0079] At the same time, the system compares the current cadence rhythm data with the target cadence range: if it is within the target range, the cadence deviation coefficient is 0; if it is below the lower limit of the target, the deviation coefficient is negative (the absolute value is calculated according to the deviation amount); if it is higher than the upper limit of the target, the deviation coefficient is positive (the absolute value is calculated according to the deviation amount).

[0080] Assuming the current power output is 240 W and the maximum sustainable power threshold is 260 W, the power load coefficient is 240 / 260 = 0.92, indicating that the user is in a sustainable state. If the current cadence is 100 rpm and the target cadence range is 80-95 rpm, the cadence deviation is +5 rpm. The system records the cadence deviation coefficient as +5.

[0081] Calculate the energy conversion efficiency and the preset efficiency weight coefficient to obtain the efficiency evaluation index.

[0082] Multiply the obtained energy conversion efficiency (unit: %) by the preset efficiency weight coefficient to obtain the efficiency evaluation index. The efficiency weight coefficient is set by the system according to the user's athletic ability level, for example, 0.7 for medium-level users, 0.8 for high-level users, and 0.9 for professional users, to highlight the importance of efficiency in different user groups. The calculation formula of the efficiency evaluation index is: Efficiency evaluation index = energy conversion efficiency × efficiency weight coefficient; Assuming that the user's energy conversion efficiency at the current gear is 28%, and the user's exercise ability level is medium, corresponding to a weight coefficient of 0.7, then the efficiency evaluation index = 28% x 0.7 = 19.6.

[0083] The power load coefficient, pedal frequency deviation coefficient, and efficiency evaluation index are input into the preset multiple linear regression model for weighted calculation to obtain the gear optimization score corresponding to each gear combination.

[0084] The preset multiple linear regression model is called, with the power load coefficient, pedal frequency deviation coefficient, and efficiency evaluation index as input variables, and the gear optimization score as output. The model is fitted in the training stage by collecting a large number of rider data sets to obtain the weight coefficients of each input variable, ensuring that the score result can accurately reflect the rider's fatigue level and energy efficiency level. The higher the gear optimization score, the more suitable the gear combination is for the minimum fatigue principle.

[0085] Assuming that the model weights are: power load coefficient weight 0.5, pedal frequency deviation coefficient weight 0.2, and efficiency evaluation index weight 0.3. The input parameters are power load coefficient 0.92, pedal frequency deviation coefficient +5, and efficiency evaluation index 19.6. Then the gear optimization score = 0.92 x 0.5 + 5 x 0.2 + 19.6 x 0.3 = 0.46 + 1 + 5.88 = 7.34. The system calculates the score for each candidate gear to generate a set of score results.

[0086] Further, the input parameters of the multiple linear regression model include the power load coefficient, pedal frequency deviation coefficient, and efficiency evaluation index. Each input parameter needs to be standardized first to map the numerical range to the [0, 1] interval to eliminate the influence of different dimensions on model calculation. The model output is the gear optimization score of each gear combination, which is used to measure the overall suitability of the gear combination under the current riding state. The model is trained using the least squares method algorithm, and the training process includes the following steps: Data collection: Collect pedaling data and pedal frequency data of multiple riders of different ages, weights, and exercise ability levels at different slopes, speeds, and power outputs, and record the most comfortable and most energy-efficient gear combinations in actual riding experience as reference labels.

[0087] Feature calculation: Calculate the power load coefficient, pedal frequency deviation coefficient, and efficiency evaluation index corresponding to each sample data according to step S1 to form the input feature vector X = [x_1, x_2, x_3] of the training sample, and the label Y is the actual optimal gear score.

[0088] Model training: Linear regression training is performed using the least squares method to solve the weight vector W = [w_1, w_2, w_3] such that the mean square error of the predicted score Y' = w_1 x_1 + w_2 x_2 + w_3 x_3 + b and the actual score Y is minimized. Where b is the bias term.

[0089] Cross-validation: The k-fold cross-validation method (such as k=5) is used on the training set data to evaluate the generalization performance of the model, and the weight coefficients w_1, w_2, w_3 and the bias term b are adjusted to obtain the best prediction effect.

[0090] Parameter fixing: After training, the weight vector W and the bias term b are fixed in the cycling system for real-time calculation of the gear optimization score of each gear combination.

[0091] In this example, for example, a user is riding uphill, the power load coefficient is 0.75, the pedal frequency deviation coefficient is 0.1, and the efficiency evaluation index is 0.85. The input feature vector X = [0.75, 0.1, 0.85] is brought into the regression model Y' = 0.5x_1 + 0.3x_2 + 0.2x_3 + 0.05 obtained by training, and the gear combination score is calculated, and the gear combination with the highest score is finally selected as the optimal gear combination. This method can optimize the gear combination for different riders, different loads, and different road conditions, and realize accurate, real-time, and adaptive gear selection.

[0092] The gear combination with the maximum gear optimization score is selected as the optimal gear combination.

[0093] Sort the optimization scores of all candidate gear combinations, and select the gear combination with the largest value as the optimal gear combination under the current riding state. The determination result is passed to step S3 through an internal variable to compare with the current gear.

[0094] Suppose the system calculates the optimization scores of three gear combinations as follows: large disc + 3rd freewheel 6.8, medium disc + 2nd freewheel 7.34, and small disc + 1st freewheel 6.2. Since the optimization score of the medium disc + 2nd freewheel combination is the largest (7.34), the system finally determines it as the optimal gear combination.

[0095] Specifically, the implementation process of step S3 specifically includes: Obtain the current gear ratio of the gear and the freewheel by installing a gear detection sensor on the derailleur to determine the current gear ratio.

[0096] The state signals of the gear of the front gear and the gear of the rear gear are acquired in real time through position coding sensors or Hall effect sensors installed on the front transmission and the rear transmission. The system reads the electrical signals of each sensor and converts the signals into corresponding tooth number values through an internal mapping table, thereby calculating the tooth ratio value of the current gear. The calculation formula of the tooth ratio value is: Tooth ratio value = front gear tooth number / rear gear tooth number; wherein the front gear tooth number and the rear gear tooth number are specific integer values obtained by the system through sensor detection and table lookup. The system can transmit the tooth ratio value as the current gear feature to the subsequent interval judgment module.

[0097] Suppose the front gear is currently the middle gear (42 teeth) and the rear gear is currently the third gear (14 teeth), then the current tooth ratio value = 42 / 14 = 3.0. The system automatically calculates the tooth ratio value 3.0 as the feature data of the current gear by reading the voltage signals of the transmission sensors and mapping them to 42 teeth and 14 teeth.

[0098] Based on the optimal front gear, the target tooth ratio value is analyzed and set as the center base point to set the interval range.

[0099] In this step, the system calls the optimal front gear information output in step S2, including the optimal front gear and the optimal rear gear. The system obtains the corresponding tooth number according to the optimal front gear and the optimal rear gear, and calculates the target tooth ratio value: Target tooth ratio value = optimal front gear tooth number / optimal rear gear tooth number; Subsequently, the system sets a preset tolerance interval (such as ±0.1) on both sides of the target tooth ratio value as the interval range for tooth ratio deviation judgment. The tolerance value can be set according to the user's riding experience or the system default value to ensure the balance between the sensitivity and comfort of the gear shifting prompt.

[0100] If the optimal front gear is the middle gear (42 teeth) and the optimal rear gear is the second gear (16 teeth), then the target tooth ratio value = 42 / 16 = 2.625. The system sets the tooth ratio tolerance interval ±0.1, and the judgment interval range is 2.525~2.725 as the center base point and the upper and lower boundaries.

[0101] The tooth ratio deviation value is obtained by calculating the difference between the tooth ratio value of the current gear and the target tooth ratio value, and it is judged whether the absolute value of the tooth ratio deviation value exceeds the interval range.

[0102] The current tooth ratio value and the target tooth ratio value are calculated as follows: Tooth ratio deviation value = current tooth ratio value - target tooth ratio value; The absolute value of the gear ratio deviation value is then calculated and compared with a preset interval range tolerance. If the absolute value is greater than the tolerance, it is considered that the deviation from the target is too large; if it is less than or equal to the tolerance, it is considered that the deviation is within the allowable range. This calculation process can be performed in real time in the microprocessor of the control unit to ensure continuous monitoring of the gear ratio deviation during cycling.

[0103] In this example, the current gear ratio value is 3.0, the target gear ratio value is 2.625, and the gear ratio deviation value = 3.0 - 2.625 = 0.375. The interval tolerance is 0.1, and the absolute value 0.375 > 0.1, so the system determines that there is a significant deviation between the current gear and the target gear.

[0104] When the absolute value of the gear ratio deviation value exceeds the interval range, it is determined that the current gear is outside the interval range, and when the absolute value of the gear ratio deviation value does not exceed the interval range, it is determined that the current gear is within the interval range.

[0105] The gear ratio deviation value is logically judged by a conditional judgment statement: if | gear ratio deviation value | > interval tolerance, set the flag "outside the interval range"; otherwise, set the flag "within the interval range". This flag will be passed to step S4 to control the triggering condition of the gear shifting prompt signal.

[0106] In this example, based on the above gear ratio deviation value 0.375 exceeding the tolerance 0.1, the system sets the flag to "outside the interval range", which triggers the control logic of the next step to issue a gear shifting prompt to the user. If the current gear ratio value is 2.65, the deviation 0.025 < 0.1, the flag is set to "within the interval range", and the gear shifting prompt is not triggered.

[0107] Specifically, the feedback control mechanism includes an array of LED indicator lights, a liquid crystal display, and a vibration reminder, and the implementation process of step S4 specifically includes: When it is detected that the current gear is outside the interval range, the direction of gear shifting, i.e. upshift or downshift, is determined according to the positive or negative of the gear ratio deviation value.

[0108] The gear ratio deviation value output from step S3 is read, and a conditional judgment logic is used: if the gear ratio deviation value is greater than zero, it is determined that the current gear ratio is greater than the target gear ratio, and the gear needs to be lowered; if the gear ratio deviation value is less than zero, it is determined that the current gear ratio is less than the target gear ratio, and the gear needs to be increased. This logic is calculated in real time by the microprocessor in the control unit and can be continuously updated during cycling.

[0109] If the current gear ratio value is 3.0 and the target gear ratio value is 2.625, the deviation value = 0.375 > 0, and the system determines that the gear needs to be downshifted, guiding the user to reduce the gear ratio. If the current gear ratio value is 2.5, the deviation value = -0.125, and the system determines that the gear needs to be upshifted, guiding the user to increase the gear ratio.

[0110] When the gear ratio deviation value is positive, it is determined that downshift is needed to reduce the gear ratio, and when the gear ratio deviation value is negative, it is determined that upshift is needed to increase the gear ratio.

[0111] The sign of the gear ratio deviation value is input as a shift direction signal, and the signal is distributed to the control modules of the LED indicator array, the liquid crystal display screen, and the vibration reminder device through the control logic. The shift direction signal triggers the corresponding prompt behavior of each feedback device as an instruction, such as the direction of flashing, the display of text, or the direction of tactile feedback.

[0112] When the gear ratio deviation value is 0.375, the system sends a "downshift" instruction to the LED indicator array, causing the left side lights to flash to indicate downshift; when the deviation value is -0.125, a "upshift" instruction is sent, causing the right side lights to flash to indicate upshift.

[0113] The LED indicator array on the handlebar of the bicycle displays directional flashing prompt signals according to the shift direction.

[0114] The LED indicator array receives the shift direction signal through the control unit and executes the prompt according to the preset flashing frequency and brightness mode. For example, the upshift direction corresponds to the flashing of the right side lights, and the downshift direction corresponds to the flashing of the left side lights. The flashing frequency can be adjusted by the absolute value of the gear ratio deviation value, and the larger the deviation value, the faster the flashing frequency, to indicate the urgency of the shift.

[0115] When the gear ratio deviation value is 0.375, the corresponding flashing frequency is 3Hz; the user sees the left side LED flashing quickly, and can immediately perform the downshift operation. When the deviation value is 0.125, the corresponding flashing frequency is 1Hz, indicating a slight deviation, and the shift can be performed later.

[0116] The absolute value of the gear ratio deviation value determines the level of shift urgency.

[0117] The absolute value of the gear ratio deviation value is input into a preset urgency level table to divide the deviation value into four levels: mild (00.05), moderate (0.05~0.15), high (0.15~0.3), and extreme (>0.3). The microprocessor determines the level by table lookup algorithm, and the level information is used to control the LED flashing frequency, the liquid crystal display color, and the vibration reminder intensity.

[0118] In this example, the gear ratio deviation value is 0.375, which belongs to the "extreme" level, and the system marks the urgency level as the highest, drives the LED to flash at the fastest frequency, displays a red prompt on the liquid crystal display, and starts the highest frequency feedback of the vibration reminder device. When the deviation value is 0.08, it is marked as "moderate", corresponding to a medium frequency flashing and a yellow liquid crystal prompt.

[0119] The LCD screen on the bicycle computer displays the corresponding color of the shift prompt text information according to the shift urgency level.

[0120] The LCD screen receives the urgency level signal from the microprocessor and displays different color text information according to the level: green for slight deviation, yellow for moderate deviation, orange for high deviation, and red for extreme deviation. The text information includes "upshift" or "downshift", directly prompting the user to shift.

[0121] In this example, the gear ratio deviation value 0.375 corresponds to the extreme level, and the LCD screen displays the red text "downshift", prompting the user to understand the shift direction and urgency through visual cues. The deviation value 0.1 corresponds to the moderate level, and the LCD displays the yellow text "upshift", prompting the user to operate according to the text prompt.

[0122] The vibration reminder device installed on the bicycle frame outputs different frequency tactile feedback signals according to the shift urgency level to guide the user to shift to the optimal chainring gear position in time.

[0123] The vibration reminder device receives the urgency level signal and controls the vibration motor to output tactile feedback at different frequencies and intensities. The higher the urgency, the higher the vibration frequency and intensity to enhance the user's perception of the urgency of the shift. This tactile feedback can be synchronized with the LED flashing and LCD display to achieve multi-channel prompting.

[0124] In this example, the gear ratio deviation value 0.375 (extreme level) causes the vibration reminder device to vibrate at a frequency of 100Hz and high intensity to prompt the user to downshift immediately; a deviation value of 0.08 (moderate level) causes the vibration reminder device to vibrate at a frequency of 40Hz and medium intensity to prompt the user to upshift. The user can quickly respond through tactile feedback, ensuring that the ride is in the optimal chainring gear position.

[0125] As shown in Figure 2 The bicycle chainring gear optimization system 10 according to the present application specifically includes the following modules: The acquisition module 11 is mainly used to acquire the pedaling power output data, pedaling frequency rhythm data, riding speed data, and slope information of the user during the ride to calculate the load and energy conversion efficiency in the current riding state.

[0126] The collection module 11 collects the pedaling power output data, pedaling frequency rhythm data, riding speed data and slope information of the user in the riding process in real time through the strain gauge power sensor installed on the crank assembly, the Hall effect sensor in the pedal bearing seat, the magnetic induction speed sensor in the wheel hub and the inertial measurement unit with built-in three-axis accelerometer and gyroscope, and pre-processes the collected data, including time window segmentation, mean value calculation, power density coefficient calculation, slope correction and load level classification, to obtain the load condition and energy conversion efficiency under the current riding state.

[0127] The calculation module 12 is mainly used for calculating the optimal pedal gear position by combining the load condition and energy conversion efficiency with the minimum fatigue principle parameters preset.

[0128] The calculation module 12 calculates the gear optimization score of each gear combination through a multiple linear regression model by combining the load condition and energy conversion efficiency with the minimum fatigue principle parameters established according to the user's age, weight, sports ability level, and selects the pedal gear combination with the highest score as the optimal pedal gear position.

[0129] The comparison module 13 is mainly used for comparing the current gear position of the pedal with the optimal pedal gear position to determine whether the current gear position exceeds the preset interval range.

[0130] The comparison module 13 obtains the current pedal gear position and the freewheel gear position through the gear detection sensor installed on the derailleur, calculates the gear ratio value of the current gear position, and performs difference operation with the target gear ratio value corresponding to the optimal pedal gear position to determine whether the absolute value of the gear ratio deviation value exceeds the preset interval range, thereby determining whether the current gear position exceeds the preset range.

[0131] The prompt module 14 is mainly used for sending a gear shifting prompt signal to the user through a preset feedback control mechanism when it is detected that the current gear position exceeds the interval range, to guide the user to manually switch to the optimal pedal gear position.

[0132] The prompt module 14 outputs directional flashing prompt signals, gear shifting prompt texts and tactile feedback signals according to the direction and absolute value of the gear ratio deviation value through the LED indicator light array on the bicycle handlebar, the watch liquid crystal display and the frame vibration reminder, to guide the user to perform gear shifting operation in time, so that the pedal gear position is adjusted to the optimal state.

[0133] In this embodiment, the system can realize real-time analysis and processing of multi-dimensional data such as pedaling power, pedaling frequency, riding speed and slope of the rider, dynamic recommendation and feedback of the optimal pedal gear position, significantly improve the riding efficiency, reduce the riding fatigue, and provide personalized gear optimization scheme for different users.

[0134] It should be noted that, for the convenience and brevity of the description, the specific working processes of the above-described device and each module can be referred to the corresponding process in the foregoing embodiment 1, and will not be described here again.

[0135] The above embodiments are merely examples for clearly illustrating the present application and are not intended to limit the embodiments. Based on the above description, other different forms of changes or variations can be made by those skilled in the art. Here, it is not necessary or possible to enumerate all the embodiments. The obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A method for optimizing bicycle chainring gears, characterized in that, include: Collect data on the user's pedaling power output, cadence, cycling speed, and gradient during cycling to calculate the load and energy conversion efficiency under the current cycling condition. The optimal crankset gear is calculated by combining the load condition and the energy conversion efficiency with preset minimum fatigue principle parameters. The current gear position of the crankset is compared with the optimal crankset gear position to determine whether the current gear position exceeds the preset range. When the current gear is detected to be outside the range, a shift prompt signal is sent to the user through a preset feedback control mechanism to guide the user to manually switch to the optimal gear.

2. The method for optimizing bicycle chainring gears according to claim 1, characterized in that, The steps of collecting user pedaling power output data, cadence data, cycling speed data, and gradient information during cycling to calculate the load and energy conversion efficiency under the current cycling condition include: The torque force applied to the pedal by the user's left and right feet when pedaling is detected in real time by strain gauge power sensors installed on the crank assembly, and the pedaling power output data is obtained by combining the crank rotation angular velocity. The cadence data is obtained by detecting the time interval of each crank revolution by a Hall effect sensor installed in the crank bearing housing. The riding speed data is obtained by detecting the wheel rotation speed using a magnetic induction speed sensor installed in the wheel hub and combining it with the wheel diameter parameters. The inertial measurement unit, which has a built-in three-axis accelerometer and gyroscope, detects changes in the tilt angle of the bicycle frame to obtain slope information; Based on the pedaling power output data and the cadence data, the power density coefficient of the current gear is identified, and the load situation under the current riding state is analyzed using the power density coefficient and the slope information. The cycling speed data and the pedaling power output data are compared to obtain the speed-power ratio parameter. Based on the speed-power ratio parameter and the cadence data, the energy conversion efficiency at the current gear is calculated.

3. The method for optimizing bicycle chainring gears according to claim 2, characterized in that, The step of identifying the power density coefficient at the current gear based on the pedaling power output data and the cadence data, and analyzing the load situation under the current riding condition using the power density coefficient and the gradient information, includes: The pedaling power output data is sampled in segments according to a preset time window, and the average power value within each time window is calculated to obtain the average power value. The cadence data is sampled according to a preset time window and the average cadence value within each time window is calculated to obtain the average cadence value. The power density coefficient is obtained by dividing the average power value by the average cadence value. Based on the slope information, the corresponding load correction factor is found in the preset slope load mapping table, and the power density coefficient is multiplied by the load correction factor to obtain the corrected power density coefficient. The modified power density coefficient is classified into the corresponding load level range by a preset load level classification standard, and the load situation under the current riding state is determined by the classification result of the load level range.

4. The bicycle chainring gear optimization method according to claim 3, characterized in that, The step of classifying the corrected power density coefficient into corresponding load level intervals according to a preset load level classification standard, and determining the load status under the current riding condition based on the classification results of the load level intervals, includes: A load level classification standard is established based on the user's age, weight, and physical ability level; The corrected power density coefficient is input into the load level classification standard to determine the load level range to which the corrected power density coefficient belongs; The load condition under the current riding state is determined based on the classification results of the load level range, wherein the load condition includes four types: light load, medium load, heavy load, and extreme load.

5. The method for optimizing bicycle chainring gears according to claim 1, characterized in that, The step of calculating the optimal crankset gear by combining the load condition and the energy conversion efficiency with preset minimum fatigue principle parameters includes: Minimum fatigue principle parameters are established based on the user's age, weight, and exercise ability level. These parameters include the target cadence range and the maximum continuous power threshold. The load condition is compared with the maximum continuous power threshold to obtain the power load coefficient, and the current cadence rhythm data is compared with the target cadence range to obtain the cadence deviation coefficient. The energy conversion efficiency is calculated with a preset efficiency weighting coefficient to obtain an efficiency evaluation index; The power load factor, the cadence deviation factor, and the efficiency evaluation index are input into a preset multiple linear regression model for weighted calculation to obtain the gear optimization score corresponding to each gear combination. The combination of crankset gears with the highest gear optimization score is taken as the optimal crankset gears.

6. The method for optimizing bicycle chainring gears according to claim 1, characterized in that, The step of comparing the current gear position of the crankset with the optimal crankset gear position to determine whether the current gear position exceeds a preset range includes: The current gear position of the chainring and flywheel is obtained by a gear position detection sensor installed on the transmission to determine the gear ratio of the current gear. Based on the optimal chainring gear position, the target gear ratio is analyzed, and the target gear ratio is used as the center base point to set the range; The difference between the current gear ratio and the target gear ratio is calculated to obtain the gear ratio deviation value, and it is determined whether the absolute value of the gear ratio deviation value exceeds the range. When the absolute value of the gear ratio deviation exceeds the range, the current gear is determined to be outside the range; when the absolute value of the gear ratio deviation does not exceed the range, the current gear is determined to be within the range.

7. The method for optimizing bicycle chainring gears according to claim 6, characterized in that, The feedback control mechanism includes an LED indicator array, an LCD screen, and a vibration alert; The step of issuing a shift prompt signal to the user through a preset feedback control mechanism when the current gear is detected to be outside the range, in order to guide the user to manually shift to the optimal chainring gear, includes: When the current gear is detected to be outside the range, the shift direction of upshifting or downshifting is determined based on the sign of the gear ratio deviation value. When the gear ratio deviation is positive, it is determined that a downshift is needed to reduce the gear ratio; when the gear ratio deviation is negative, it is determined that an upshift is needed to increase the gear ratio. An array of LED indicators mounted on the bicycle handlebars displays a directional flashing prompt signal according to the shifting direction; The level of urgency for gear shifting is determined based on the absolute value of the gear ratio deviation. The LCD screen on the bicycle speedometer displays shift prompt text information in corresponding colors according to the shift urgency level; By using a vibration alert device installed on the bicycle frame to output tactile feedback signals of different frequencies according to the level of urgency of the gear shift, the user is guided to shift gears to the optimal chainring position in a timely manner.

8. A bicycle chainring gear optimization system, characterized in that, include: The data acquisition module is used to collect data on the user's pedaling power output, cadence, cycling speed, and gradient during cycling, in order to calculate the load and energy conversion efficiency under the current cycling condition. The calculation module is used to calculate the optimal crankset gear by combining the load condition and the energy conversion efficiency with preset minimum fatigue principle parameters; The comparison module is used to compare the current gear position of the crankcase with the optimal crankcase gear position to determine whether the current gear position exceeds a preset range. The prompt module is used to send a shift prompt signal to the user through a preset feedback control mechanism when the current gear is detected to be outside the range, so as to guide the user to manually switch to the optimal gear.

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