Riding gear switching reminding method and system based on electronic speed change

Abstract

The invention provides a riding gear switching reminding method and system based on electronic speed change, electronic equipment and a storage medium. Real-time vehicle states are represented by pedaling frequency, vehicle speed and gears. Environment information taking the current road gradient and a predicted gradient sequence generated based on the front path as the core; and the user personalized parameters comprise the optimal pedaling frequency interval. According to the current state evaluation step, real-time vehicle data and the current gradient are combined to estimate the riding load, collaborative analysis is conducted on the riding load and the optimal pedaling frequency interval preset by a user, and therefore whether the current gear is matched with the riding state or not is judged. In the future trend prediction step, the real-time data, the current gradient and the front predicted gradient sequence are utilized to simulate the riding state change in a future period of time under the condition that the current gear is kept unchanged, and whether two core indexes of the pedaling frequency and the load deviate from an ideal range or not is emphatically pre-judged. And in the decision instruction output step, intelligent response is made according to the preorder evaluation and prediction result.

Description

Technical Field

[0001] This invention relates to the field of cycling assistance technology, and in particular to a cycling gear shifting reminder method and system based on electronic gear shifting. Background Technology

[0002] With increasing health and environmental awareness, cycling continues to gain popularity worldwide. As a core technology in cycling equipment, the gear system has undergone a transformation from mechanical cable-operated to electronically controlled systems. Electronic gear systems control the derailleur via electronic signals, achieving more precise and faster gear shifting, greatly improving ease of operation and smoothness of shifting. They are widely used in mid-to-high-end road bikes, mountain bikes, and electric-assist bicycles. However, current electronic gear systems are essentially still passive tools, with shift commands entirely dependent on the rider's manual operation. Riders need to judge and trigger gear shifts based on road conditions, their own physical sensations, and data such as speed and cadence displayed on the dashboard. This "human judgment, human operation" model demands a high level of experience from the rider. Novices often struggle to master the optimal shifting timing, leading to inefficiency or equipment damage, while even experienced riders may experience delays or errors in judgment under complex road conditions or when fatigued.

[0003] To address these issues, some products with shift reminder functions have emerged on the market, such as cycling computers or cycling apps that integrate simple algorithms. The reminder logic of these existing technologies is typically quite simplistic, for example, setting only a fixed cadence threshold (e.g., prompting downshifting when cadence is below 60 rpm and upshifting when cadence is above 90 rpm), or simply mapping based on current speed. These methods fail to fully consider the complex and ever-changing cycling environment, such as sudden changes in gradient, headwinds, and differences in rolling resistance on different road surfaces. Furthermore, these reminder functions are usually one-way information prompts, unable to form a closed-loop linkage with the electronic shifting system; riders still need to operate manually, failing to fundamentally liberate riders and allow them to focus more on road conditions and the ride itself. Therefore, existing technological solutions have significant shortcomings in terms of intelligence, personalization, forward-thinking capabilities, and deep integration with vehicles, making it difficult to meet the comprehensive needs of users with different physical conditions and cycling goals (such as leisure, racing, and training) for efficient, safe, and comfortable riding. Summary of the Invention

[0004] This invention provides a cycling gear shifting reminder method, system, electronic device, and storage medium based on electronic gear shifting, to solve the technical problems in the prior art where cycling gear shifting relies on human experience, the reminder method is singular and rigid, and it cannot combine multi-dimensional real-time and predictive data to achieve forward-looking intelligent decision-making.

[0005] In a first aspect, embodiments of the present invention provide a cycling gear shifting reminder method based on electronic transmission, comprising: The system acquires real-time vehicle data, environmental data, and user-personalized parameters for the cycling equipment. The real-time vehicle data includes at least real-time cadence, real-time speed, and current gear. The environmental data includes at least the current road gradient and a predicted gradient sequence generated based on the path data ahead.

[0006] The cycling load is estimated in real time based on the real-time vehicle data and the current road gradient, and the optimal cadence range in the user's personalized parameters is used to evaluate whether the current gear is suitable for the current cycling state.

[0007] Based on the real-time vehicle data, the current road gradient, and the predicted gradient sequence, while keeping the current gear unchanged, the predicted riding state within a future preset time window is simulated. The predicted riding state includes at least the predicted cadence and the predicted riding load.

[0008] When the evaluation result indicates that the current gear is not suitable, or when either the predicted cadence or the predicted cycling load within the future preset time window exceeds the corresponding preset threshold range, a corresponding gear switching reminder instruction is generated and output.

[0009] Preferably, the user-personalized parameters include at least the user's weight, drag coefficient, rolling friction coefficient, the user's preset optimal cadence range, and training power target; the rolling friction coefficient is set according to the type of road tire or mountain tire.

[0010] Preferably, the future preset time window is dynamically adjusted according to the real-time vehicle speed.

[0011] Simulate predicted riding conditions within a future preset time window, including: Based on the real-time cadence, the real-time speed, the current road gradient, and the predicted gradient sequence, a linear recursive algorithm is used to simulate the trend of cadence change with gradient while keeping the current gear constant, and to generate the predicted cadence within the future preset time window.

[0012] Based on the predicted cadence, the predicted gradient sequence, and the user-personalized parameters, a corresponding predicted cycling load is calculated and generated.

[0013] Preferably, the assessment of whether the current gear is suitable for the current riding state specifically includes: The real-time cadence is compared with the optimal cadence range, and a joint determination is made based on whether the cycling load is within the user's preset load comfort range.

[0014] If the real-time cadence is lower than the lower limit of the optimal cadence range and exceeds the first deviation threshold, and the cycling load exceeds the upper limit of the load comfort range, then the current gear is determined to be too high.

[0015] If the real-time cadence is higher than the upper limit of the optimal cadence range and exceeds the second deviation threshold, and the cycling load is lower than the lower limit of the load comfort range, then the current gear is determined to be too low.

[0016] If the real-time cadence is within the optimal cadence range and the cycling load is within the load comfort range, then the current gear is determined to be suitable.

[0017] Preferably, the gear shifting reminder instruction includes a first-level reminder instruction, which is used to control the output device to provide gear shifting suggestions to the rider in one or more ways, such as visual cues, sound cues, or vibration feedback; wherein, the visual cues include highlighted icons displayed on the computer screen, graphical gear suggestions, or color changes.

[0018] Preferably, the gear shifting reminder instruction further includes a second-level control instruction; the second-level control instruction is generated when it is detected that the user has authorized the automatic shifting function and the preset forced shifting trigger conditions are met, and is used to send a shifting instruction to the controller of the electronic transmission system through a wireless communication protocol, so that the controller drives the actuator to perform gear shifting.

[0019] Preferably, the preset forced shift trigger conditions include: The assessment determines that the current gear is unsuitable, and the real-time cadence deviates from the optimal cadence range by more than a first mandatory threshold; or, If either the predicted cadence or the predicted cycling load exceeds the corresponding preset threshold range by more than the second mandatory threshold, and the expected duration exceeds the preset duration, then the prediction is true.

[0020] Secondly, embodiments of the present invention provide a cycling gear shifting reminder system based on electronic transmission, comprising: A data acquisition module, installed on the cycling equipment, is used to acquire real-time vehicle data, environmental data, and user-personalized parameters of the cycling equipment; the real-time vehicle data includes at least real-time cadence, real-time speed, and current gear; the environmental data includes at least the current road gradient and a predicted gradient sequence generated based on the forward path data. The intelligent decision-making module, communicatively connected to the data acquisition module, is used to estimate the cycling load in real time based on the real-time vehicle data and the current road gradient, and, in conjunction with the optimal cadence range in the user's personalized parameters, evaluate whether the current gear is suitable for the current cycling state; and, Based on the real-time vehicle data, the current road gradient, and the predicted gradient sequence, while keeping the current gear unchanged, the predicted riding state within a future preset time window is simulated. The predicted riding state includes at least the predicted cadence and the predicted riding load. When the evaluation result indicates that the current gear is not suitable, or when either the predicted cadence or the predicted cycling load within the future preset time window exceeds the corresponding preset threshold range, a corresponding gear switching reminder instruction is generated and output. The instruction output module is communicatively connected to the intelligent decision module and is used to receive and output the gear shifting reminder instruction.

[0021] Thirdly, embodiments of the present invention provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the electronic gear shifting reminder method for riding as described in the first aspect of the present invention.

[0022] Fourthly, embodiments of the present invention provide a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the electronic gear shifting reminder method for riding as described in the first aspect of the present invention.

[0023] This invention provides a method, system, electronic device, and storage medium for electronic gear shifting reminders. Through a data acquisition step, it obtains three types of key information: real-time vehicle status represented by cadence, speed, and gear; environmental information centered on the current road gradient and a predicted gradient sequence generated based on the path ahead; and personalized user parameters including the optimal cadence range. This data foundation breaks through the limitations of traditional solutions that rely solely on single real-time data. Based on this, a current status assessment step combines real-time vehicle data with the current gradient to estimate the riding load and performs collaborative analysis with the user-preset optimal cadence range to determine whether the current gear matches the riding state. Subsequently, a future trend prediction step uses real-time data, the current gradient, and the predicted gradient sequence ahead to simulate changes in the riding state over a future period while maintaining the current gear, focusing on predicting whether the two core indicators, cadence and load, will deviate from the ideal range. Finally, a decision command output step provides an intelligent response based on the preceding assessment and prediction results: if the current gear is no longer suitable, or if the prediction indicates that the future state will exceed the allowable range, the system generates a corresponding gear shift reminder command.

[0024] Based on the above technical approach, this embodiment achieves significant technical effects: First, by introducing gradient prediction based on the forward path, the system possesses "foresight," enabling it to anticipate and issue alerts before substantial changes in riding resistance occur, giving riders ample preparation time and making gear shifting smoother and more efficient. Second, integrating personalized parameters allows the alert strategy to be tailored to individual needs, adapting to different fitness levels, riding styles, and training goals, achieving true intelligent assistance. Third, by combining current state assessment with future trend prediction, the system effectively solves the problem of misjudgment or delays in traditional single-threshold judgments, improving the accuracy and robustness of decision-making. Ultimately, this solution significantly reduces riders' distraction caused by frequent gear selection, allowing them to focus more on road conditions and improving riding safety; simultaneously, by guiding riders to consistently ride within a reasonable cadence and load range, it helps reduce mechanical wear on the transmission system, extends component lifespan, and optimizes the overall riding experience and efficiency. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a cycling gear shifting reminder method based on electronic transmission according to an embodiment of the present invention; Figure 2 This is a structural block diagram of a cycling gear shifting reminder system based on electronic transmission according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the physical structure according to an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. 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.

[0028] In the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] This invention provides a method for reminding riders of gear shifting based on electronic transmission, such as... Figure 1 As shown, the method includes: S1. Obtain real-time vehicle data, environmental data, and user-personalized parameters of the cycling device; the real-time vehicle data includes at least real-time cadence, real-time speed, and current gear; the environmental data includes at least the current road gradient and a predicted gradient sequence generated based on the forward path data.

[0031] Among them, real-time vehicle data refers to the real-time operating status data of the electronic gear cycling device during the ride; real-time cadence is the number of crank rotations per minute during the ride, which is a core indicator reflecting the rider's power output and directly reflects the rationality of power output in road and mountain biking; real-time speed is the current speed of the cycling device, which can be obtained through speed sensors or GPS; current gear is the current gear of the electronic gear system, which determines the rider's power ratio; environmental data is external data related to the riding road conditions, current road gradient is the degree of road inclination at the current riding position, calculated by the IMU inertial measurement unit built into the bike computer and combined with GPS altitude data and then filtered and optimized, which is a key factor affecting riding resistance; the predicted gradient sequence is the road gradient change data generated based on the three-level path data of 50 / 100 / 200m ahead, used to predict changes in road conditions in advance; user-personalized parameters are relevant data preset by the rider according to their own physical fitness and training needs, such as the optimal cadence range, weight, training power target, etc., to adapt to individual riding characteristics.

[0032] This embodiment integrates a cadence / speed sensor, electronic shifting system, IMU (Inertial Measurement Unit), GPS module, and user-preset input into the cycling device's computer to achieve multi-dimensional data fusion. It acquires traditional real-time vehicle operation data, calculates accurate current road gradient data, generates a three-level predicted gradient sequence, and incorporates user-preset personalized parameters. Addressing the shortcomings of existing technologies that rely solely on speed or cadence data for gear shifting decisions, have a single data source dimension, and lack road condition prediction data and personalized adaptation criteria, this embodiment solves the technical problems of insufficient decision-making basis for gear shifting and inability to adapt to complex cycling environments and personalized cycling needs. It provides a comprehensive, accurate, and time-dimensional information foundation for subsequent gear adaptation evaluation and cycling status trend projection, breaking the limitations of relying solely on single data for gear shifting decisions and making subsequent gear judgments more aligned with the actual cycling environment and the individual cyclist's situation.

[0033] S2. Estimate the cycling load in real time based on the real-time vehicle data and the current road gradient, and evaluate whether the current gear is suitable for the current cycling state by combining the optimal cadence range in the user's personalized parameters.

[0034] Among them, cycling load is the real-time cycling power calculated by comprehensively considering the three core resistances of climbing resistance, rolling resistance, and wind resistance. It is an indicator that quantitatively represents the rider's current exertion load. In actual cycling, excessive load can easily lead to rapid fatigue for the rider, while excessive load will result in wasted effort. The optimal cadence range is the optimal range of crank rotations per minute preset by the rider based on their own physical fitness and training goals. The optimal cadence range varies from person to person. Gear matching refers to the degree to which the current gear of the electronic shifting system matches the rider's exertion state and the current cycling road conditions. The current cycling state is the comprehensive cycling state formed by the rider combining their own exertion, the real-time operation of the vehicle, and the current road conditions.

[0035] This embodiment is based on the multi-dimensional data collected by S1, and adopts a cycling resistance superposition model. It combines real-time vehicle data and current road slope to accurately estimate the real-time cycling load, characterized by cycling power. Then, it compares the real-time cadence with the user's preset optimal cadence range, and considers whether the cycling load is within the user's preset comfort range. It comprehensively evaluates the matching degree between the current gear and the current cycling state. This addresses the shortcomings of existing technologies that rely solely on a single cadence or speed indicator for gear matching assessment, do not quantify the cycling load, do not combine load for collaborative judgment, and lack scientific rigor in gear matching. It solves the technical problems of existing technologies that rely on a single basis for gear matching assessment, lack quantitative load reference, and produce inaccurate judgment results. It achieves a scientific and accurate assessment of gear matching, avoids misjudging the gear based on a single indicator, and makes the gear matching judgment more consistent with the rider's actual exertion state and current road conditions, providing an accurate real-time judgment basis for subsequent gear switching decisions.

[0036] S3. Based on the real-time vehicle data, the current road gradient, and the predicted gradient sequence, while keeping the current gear unchanged, simulate the predicted riding state within a future preset time window. The predicted riding state includes at least the predicted cadence and the predicted riding load.

[0037] The preset time window is a 1-3 second future riding time range dynamically adjusted based on real-time vehicle speed. It is the time dimension for predicting riding status. In actual riding, a faster speed corresponds to a longer riding distance ahead, and a slower speed corresponds to a shorter distance ahead. The predicted riding status is the combined state of the rider's force and load within the preset time window while maintaining the current gear. The predicted cadence is the number of crank rotations per minute within the preset time window simulated by an algorithm. The predicted riding load is the future riding power calculated based on the predicted cadence, predicted gradient sequence, and user-personalized parameters. The linear recursive algorithm is the core algorithm that extrapolates future riding status changes based on current riding data and the predicted gradient sequence ahead, and is used to achieve forward-looking prediction of gear trends.

[0038] This embodiment relies on real-time vehicle data collected by S1, accurate current road gradient, and a three-level predicted gradient sequence ahead. Using a preset time window of 1-3 seconds for dynamic adjustment, a linear recursive algorithm is employed to simulate the trend of cadence changes with the forward gradient while maintaining the current gear. This generates a predicted cadence, which is then combined with user-personalized parameters and a resistance superposition model to calculate the corresponding predicted riding load. This comprehensively constructs the predicted riding state within the preset time window. Addressing the shortcomings of existing technologies that lack a gear trend prediction step and only assess the current riding state, failing to anticipate changes in riding state due to road conditions ahead and prone to delayed gear shifts, this embodiment solves the technical problems of existing technologies that only focus on the current state, lack forward-looking prediction, and cannot cope with sudden changes in road conditions ahead. It achieves forward-looking trend prediction of riding state, enabling advance prediction of cadence and load changes under changing road conditions ahead. This makes gear shift reminders more proactive, avoiding problems such as abnormal cadence and overload caused by sudden changes in road conditions, and providing riders with ample preparation time for gear shifting.

[0039] S4. When the evaluation result indicates that the current gear is not suitable, or when either the predicted cadence or the predicted cycling load within the future preset time window exceeds the corresponding preset threshold range, generate and output the corresponding gear switching reminder command.

[0040] The gear shift reminder command is generated based on the gear adaptation evaluation results and the predicted riding state deduction results. It is used to prompt gear shifting or drive automatic gear shifting and is the core basis for subsequent hierarchical reminders and automatic gear shifting. The preset threshold range is a reasonable range set for predicted cadence and predicted riding load. It is the judgment standard for triggering the gear shift reminder. In actual application, the threshold for predicted cadence is a deviation of more than ±15 rpm from the optimal cadence range, and the threshold for predicted riding load is a deviation of more than ±20% from the comfort range. Both must exceed the threshold simultaneously to trigger the warning.

[0041] This embodiment uses the gear matching evaluation result of S2 and the predicted riding state deduction result of S3 as dual judgment criteria. When the current gear is evaluated as unsuitable, or when both the predicted cadence and predicted riding load within a future preset time window exceed the corresponding preset threshold range, a corresponding gear shifting reminder instruction is generated and output based on the specific judgment result. This addresses the shortcomings of existing technologies, such as simple and rigid shifting reminder trigger conditions, fixed thresholds based on a single indicator, and the lack of dual judgment criteria, which result in reminders being triggered either too early or too late, and reminder instructions lacking specificity. This embodiment solves the technical problems of existing technologies, such as single shifting reminder trigger conditions, lack of scientific dual judgment criteria, and inaccurate triggering timing. It achieves accurate generation and output of gear shifting reminder instructions, and the dual judgment criteria make the reminder triggering timing more in line with actual riding needs, avoiding invalid or untimely reminders. This provides accurate instruction support for subsequent hierarchical human-computer interaction reminders and user-authorized automatic gear shifting, effectively improving the timeliness and rationality of gear shifting.

[0042] Based on the above embodiments, as a preferred implementation, the user-personalized parameters include at least the user's weight, drag coefficient, rolling friction coefficient, the user's preset optimal cadence range, and training power target; the rolling friction coefficient is set according to the type of road tire or mountain tire.

[0043] Among them, the user-personalized parameters are core data sets preset by cyclists based on their physical fitness, training needs, and cycling equipment configuration, providing an individualized basis for calculating cycling load and gear selection. These parameters can be flexibly adjusted in different scenarios such as road cycling and mountain biking. User weight is the total weight of the cyclist including their cycling equipment. It is a core basic parameter for calculating climbing resistance and rolling resistance, directly affecting the quantitative results of cycling load. The drag coefficient is a coefficient that characterizes the magnitude of air resistance during cycling. The default value is 0.5 and can be customized by the user. This coefficient can be reduced when the cyclist is leaning forward during road cycling, while it is relatively higher during mountain biking due to factors such as the bike body and posture. The rolling friction coefficient is a coefficient that characterizes the magnitude of rolling resistance between the cycling equipment tires and the road surface, with a value range of 0.005-0. 012 is an important parameter for calculating cycling load; the optimal cadence range is the range of crank rotations per minute preset by the cyclist based on their own physical fitness and cycling habits, and is the core reference standard for gear matching assessment. There are significant individual differences in the optimal cadence range among different cyclists; the training power target is the cycling power threshold preset by the cyclist based on their own training needs, such as different power targets for aerobic and anaerobic training, used to determine whether the cycling load matches the training needs; road tire type and mountain bike tire type are two core tire configuration types for cycling equipment. Road tires are smoother and have lower rolling resistance, while mountain bike tires have deeper treads and higher rolling resistance; the matching setting refers to the setting method of matching the rolling friction coefficient with the corresponding value according to the actual tire type of the cycling equipment, so that the parameters match the equipment configuration.

[0044] It should be noted that this embodiment clearly defines the core categories of user-personalized parameters, incorporating user weight, drag coefficient, rolling friction coefficient, optimal cadence range, and training power target into the user-personalized parameter system. Furthermore, it specifically adapts the rolling friction coefficient to the different configurations of road tires and mountain bike tires, ensuring that the personalized parameters cover both the individual cyclist's physical fitness and training needs while also fitting the actual configuration of the cycling equipment. In contrast, some existing shift reminder schemes that consider personalized parameters only include a single cadence range parameter, failing to cover core parameters affecting cycling load calculation such as weight, drag coefficient, and rolling friction coefficient. Moreover, they do not adapt parameters to the tire type of the cycling equipment, leading to significant deviations in cycling load calculation and shift strategies that cannot simultaneously adapt to the cyclist's needs. This technology addresses the shortcomings of individual characteristics and equipment configuration, resolving existing technical issues such as insufficient coverage of personalized user parameters and lack of parameter adaptation based on cycling equipment tire type. These issues lead to low accuracy in calculating cycling load and a lack of dual adaptability of shifting strategies to both individual and equipment. The resulting improvements allow for more accurate quantitative calculations of cycling load, effectively eliminating calculation biases caused by missing individual parameters and mismatches between equipment configuration and parameters. This results in more precise subsequent gear adaptation assessments and cycling status trend projections. Furthermore, the shifting strategy takes into account the rider's physical condition, training goals, and the characteristics of the cycling equipment's tire configuration, further enhancing the personalization, accuracy, and scenario adaptability of shifting reminders. This ensures that riders with different physical abilities, training needs, and tire configurations can receive appropriate shifting suggestions.

[0045] Based on the above embodiments, as a preferred implementation, the future preset time window is dynamically adjusted according to the real-time vehicle speed.

[0046] Simulate predicted riding conditions within a future preset time window, including: Based on the real-time cadence, real-time speed, current road gradient, and predicted gradient sequence, a linear recursive algorithm is used to simulate the trend of cadence variation with gradient while maintaining the current gear, generating the predicted cadence within the preset future time window. The linear recursive algorithm is the core algorithm for deriving the trend of cadence variation with gradient, and its core formula is as follows: N t +△ t = N t ×cos i t / cos( i t +△ t )( N t This is the current real-time cadence. i t fort The current slope at any given moment (Δt is the predicted time) and the predicted cadence is the number of crank rotations per minute simulated while maintaining the current gear within a future preset time window.

[0047] Furthermore, for the current slope, the core calculation formula is:

[0048] Among them, △ H GPS △ represents the change in elevation per unit displacement. L GPS The slope is calculated as a unit horizontal displacement with an accuracy of ±0.3%. Kalman filtering is used to eliminate slope errors caused by road bumps and vehicle tilt. The slope data is updated every 200ms to ensure that the slope data is accurate and reliable.

[0049] This embodiment uses real-time vehicle and road condition data and a three-level predicted gradient sequence as its basis. While maintaining the current gear, it substitutes the core formula of a linear recursive algorithm and combines it with the dynamic trend of gradient changes to simulate and calculate the cadence as the gradient changes, ultimately generating a predicted cadence within a preset future time window. This addresses the shortcomings of existing technologies that do not employ scientific algorithms to simulate cadence trends based on dynamic gradient changes, relying solely on experience or single data points to estimate cadence. These methods result in large simulation errors and fail to accurately reflect cadence changes under forward road conditions. This embodiment solves the technical problems of existing technologies lacking scientific algorithm support, having low accuracy in predicting cadence trends, and failing to truly reflect cadence changes when the gear remains constant. By using a linear recursive algorithm combined with gradient changes to accurately simulate cadence trends, and relying on the algorithm formula to make the calculation process more scientific, the generated predicted cadence data has high accuracy and can truly reflect cadence changes while maintaining the current gear within a preset future time window. This provides accurate basic data support for subsequent calculations of predicted cycling load.

[0050] Based on the predicted cadence, the predicted gradient sequence, and the user-personalized parameters, a corresponding predicted cycling load is calculated and generated.

[0051] Specifically, real-time accurate estimation of cycling load: A cycling resistance superposition model is used, integrating the three core resistances of climbing resistance, rolling resistance, and wind resistance, combined with the current slope. i Speed v User weight m drag coefficient f The cycling load is quantified in real time using a power load calculation formula: P = k ·( mg ·sin i + mg cos i · m +½ frv ²)·v.

[0052] in, k The transmission efficiency coefficient (values ​​range from 0.92 to 0.98, adaptive based on the wheel set type). m User weight (including equipment, unit: kg). g The acceleration due to gravity is 9.8 m / s². m The rolling friction coefficient (values ​​range from 0.005 to 0.012, suitable for road / mountain tires). r The density of air is taken as 1.225 kg / m³ at normal temperature and pressure. f The drag coefficient is calculated (default 0.5, can be customized); the result is the real-time riding power (in W), which determines whether the load is within the user's preset comfort range.

[0053] This embodiment uses the generated predicted cadence as a basis, combined with the predicted gradient sequence ahead and user-preset personalized parameters such as weight and drag coefficient, and substitutes them into the core power calculation formula of the cycling resistance superposition model. It integrates the three core resistances of climbing resistance, rolling resistance, and wind resistance to calculate the predicted cycling load corresponding to the future preset time window. It addresses the shortcomings of existing technologies that only calculate the current cycling load, cannot combine future cadence and forward gradient to predict future load changes, and do not incorporate user personalized parameters, resulting in a mismatch between the load calculation and the individual characteristics of the cyclist. It solves the technical problems of existing technologies that cannot accurately predict future cycling load and lack individual adaptability in load calculation, failing to reflect the cyclist's force state when the gear remains unchanged. By combining the resistance superposition model formula with predicted cadence, forward gradient, and personalized parameters to calculate the predicted cycling load, the calculation results not only fit the resistance changes of future road conditions, but also highly adapt to the individual physical fitness and cycling equipment configuration of the cyclist. It accurately quantifies the force load of future cycling and, together with the predicted cadence, forms a complete predicted cycling state within the future preset time window, providing comprehensive and accurate predictive data for the accurate generation of subsequent gear shift reminder instructions.

[0054] Furthermore, based on the predicted gradient data of 50 / 100 / 200m ahead, a linear recursive algorithm is used to simulate the riding state when the gear remains unchanged. The core derivation formula is:

[0055] in, N t This is the current real-time cadence. v t Current vehicle speed i t for tThe current slope at any given moment, △t is the prediction time (1-3s); if the predicted cadence deviates from the optimal range by more than ±15rpm or the load exceeds the threshold by ±20%, a gear adjustment warning is immediately triggered, accurately generating upshift / downshift suggestions and the optimal execution time to avoid abnormal cadence and overload problems in advance.

[0056] Based on the above embodiments, as a preferred implementation, the step of evaluating whether the current gear is suitable for the current riding state specifically includes: The real-time cadence is compared with the optimal cadence range, and a joint determination is made based on whether the cycling load is within the user's preset load comfort range.

[0057] If the real-time cadence is lower than the lower limit of the optimal cadence range and exceeds the first deviation threshold, while the cycling load exceeds the upper limit of the load comfort range, then the current gear is determined to be too high. The first deviation threshold is a preset threshold value for determining if the cadence is lower than the lower limit of the optimal cadence range, uniformly set to 10 rpm in cycling scenarios. It serves as a standard for quantifying the degree of cadence deviation. Too high a gear means that the force ratio of the current gear in the electronic shifting system is too high, requiring the rider to exert more force to pedal. This state is prone to occur in climbing, headwinds, and other cycling scenarios. This embodiment, based on the coordinated determination of cadence and load, first checks whether the real-time cadence value is lower than the lower limit of the optimal cadence range and the difference exceeds 10 rpm. Then, it confirms whether the cycling load calculated by the resistance superposition model exceeds the upper limit of the user's preset load comfort range. When both conditions are met simultaneously, the current gear is directly determined to be too high. By clearly defining the first deviation threshold of 10 rpm and the dual conditions of cadence and load, the judgment of excessively high gear has a clear and quantifiable standard, completely avoiding misjudgment caused by a single cadence index, accurately identifying problems such as low cadence and overload caused by excessively high gear, and closely matching actual cycling scenarios that require downshifting, such as climbing hills and headwinds.

[0058] If the real-time cadence is higher than the upper limit of the optimal cadence range and exceeds the second deviation threshold, while the cycling load is lower than the lower limit of the load comfort range, then the current gear is determined to be too low. The second deviation threshold is a preset threshold value for determining if the cadence is higher than the upper limit of the optimal cadence range, uniformly set to 10 rpm in cycling scenarios. It serves as a standard for quantifying the degree of cadence being too high. Too low a gear means that the power ratio of the current gear in the electronic shifting system is too small, making it easy for the rider to experience wheel spin during pedaling, a condition that is more likely to occur in flat roads and downhill riding scenarios. Based on the collaborative determination of cadence and load as dual indicators, the system first checks whether the real-time cadence value is higher than the upper limit of the optimal cadence range and the difference exceeds 10 rpm. Then, it verifies whether the cycling load calculated by the resistance superposition model is lower than the user's preset lower limit of the load comfort range. When both conditions are met simultaneously, the current gear is directly determined to be too low. This solves the technical problems in existing technologies where the determination of too low a gear lacks a clearly quantified deviation threshold and does not incorporate load status into the determination conditions, leading to inconsistent judgment standards and a tendency for misjudgments.

[0059] If the real-time cadence is within the optimal cadence range and the cycling load is within the comfortable load range, then the current gear is considered suitable. Gear suitability refers to the electronic shifting system's current gear ratio being highly compatible with the rider's real-time effort and current road conditions, ensuring the rider pedals effortlessly without idle spinning. This is the ideal gear position for all cycling scenarios, including roads, mountains, hills, and flat roads. To address the shortcomings of existing technologies that lack clear dual-indicator criteria for gear matching, relying solely on a vague assessment of cadence without considering load conditions, thus failing to accurately identify the true gear matching state, this paper establishes a gear matching criterion that requires both cadence and load to meet certain standards. This ensures that the gear matching result accurately reflects the rider's actual effort and current road conditions, guaranteeing that the rider is in an optimal effort state when determining gear matching. This avoids both overload and wasted effort, providing a scientific and accurate basis for preventing subsequent gear shift reminders from being triggered.

[0060] Based on the above embodiments, as a preferred implementation, the gear shifting reminder instruction includes a first-level reminder instruction, which is used to control the output device to provide gear shifting suggestions to the rider in one or more ways, such as visual cues, sound cues, or vibration feedback; wherein, the visual cues include highlighted icons displayed on the computer screen, graphical gear suggestions, or color changes.

[0061] Among them, the gear shift reminder command is a shift-related command generated based on the gear adaptation assessment and riding status prediction results. The first-level reminder command is a non-mandatory command that only provides shift suggestions and is the basic level of the gear shift reminder command. The output device in the riding scenario mainly refers to the cycling computer, which is the core device for riding data display and interaction. Visual cues are a feedback method that conveys shift information through visual means, adapted to scenarios where the rider's line of sight can focus on the cycling computer, such as flat roads and gentle slopes. Sound cues are a feedback method that conveys shift information through sound. Vibration feedback is a feedback method that conveys shift information through cycling computer vibration. The latter two are adapted to scenarios where the rider cannot focus on the cycling computer, such as mountain bumps, curves, and high-speed riding. Shift suggestions are precise upshift or downshift guidance. The cycling computer screen is the core presentation carrier of visual cues. Highlighted icons are shift-related icons on the cycling computer presented in a highlighted / flashing form. Graphical gear suggestions use concrete graphics such as arrows and gear numbers to show upshift / downshift needs. Color changes are used to distinguish shift types through different colors on the cycling computer screen. All three are visual presentation forms that are easy to quickly identify in the riding scenario.

[0062] Based on the above embodiments, as a preferred implementation, the gear shifting reminder instruction further includes a second-level control instruction; the second-level control instruction is generated when it is detected that the user has authorized the automatic shifting function and the preset forced shifting trigger condition is met, and is used to send a shifting instruction to the controller of the electronic transmission system through a wireless communication protocol, so that the controller drives the actuator to perform gear shifting.

[0063] The second-level control command is an execution-type shifting command, distinct from the advisory reminder. It is a higher-level shifting reminder command, suitable for riding scenarios requiring rapid shifting, such as starting on steep slopes or sudden road conditions. The automatic shifting function authorization is a function activation operation completed in advance by the rider on the cycling computer. The rider can choose to turn it on or off according to their own riding needs, balancing the automatic shifting needs of beginners and the self-operation needs of experienced riders. The forced shifting trigger condition is a preset judgment condition that requires the automatic shifting to be activated, specifically referring to situations where both cadence and riding load exceed the threshold and reach a certain extent, such as a sudden steep slope causing a sharp drop in cadence and a sharp increase in load. The wireless communication protocol is a transmission protocol adapted to the communication specifications of the electronic shifting system, used to achieve accurate and rapid transmission of shifting commands between the cycling computer and the shift controller without transmission delay. The controller of the electronic shifting system is the core control unit of the electronic shifting system, responsible for receiving shifting commands and issuing action commands to the actuator. The actuator in the riding equipment is the shift drive motor, which is the core component for realizing physical gear switching, driving the chain and flywheel to complete upshifting or downshifting.

[0064] Based on the above embodiments, as a preferred implementation, the preset forced shift triggering conditions include: The assessment determines that the current gear is unsuitable, and the real-time cadence deviates from the optimal cadence range by more than a first mandatory threshold; or, If either the predicted cadence or the predicted cycling load exceeds the corresponding preset threshold range by more than the second mandatory threshold, and the expected duration exceeds the preset duration, then the prediction is true.

[0065] The preset forced shift trigger condition is a quantitative criterion for initiating the second-level control command and triggering automatic shifting. It is the core basis for distinguishing between normal shift suggestions and forced automatic shifting, and is suitable for riding scenarios that require emergency shifting or early shifting, such as starting on steep slopes or long uphill / downhill sections. It is divided into two categories: immediate triggering when the current riding state is abnormal and predictive triggering when the future riding state is abnormal. The first forced threshold is the critical value of the real-time cadence deviating from the optimal cadence range. In riding scenarios, it is set to ±15 rpm and is a quantitative indicator for determining that a forced shift needs to be initiated when the current gear is not suitable. It adapts to scenarios where the current riding state suddenly becomes abnormal, such as a sudden drop in cadence on steep slopes or a sudden increase in cadence on flat roads. The second mandatory threshold is the additional magnitude value of the predicted cadence and predicted riding load exceeding their respective preset threshold ranges. The second mandatory threshold corresponding to the riding load is ±20%, which is a quantitative indicator for determining when a forced shift should be initiated when the future riding state is abnormal. The preset duration is the expected duration after the predicted cadence / load exceeds the corresponding threshold and the magnitude reaches the second mandatory threshold. It is a time judgment standard to avoid the forced shift setting being mistakenly triggered by short-term fluctuations in road conditions, and it adapts to riding scenarios such as short-term road bumps and small-amplitude slope changes.

[0066] Secondly, embodiments of the present invention provide a cycling gear shifting reminder system based on electronic gear shifting, based on the cycling gear shifting reminder methods based on electronic gear shifting in the above embodiments, such as... Figure 2 As shown, the system includes: A data acquisition module, installed on the cycling equipment, is used to acquire real-time vehicle data, environmental data, and user-personalized parameters of the cycling equipment; the real-time vehicle data includes at least real-time cadence, real-time speed, and current gear; the environmental data includes at least the current road gradient and a predicted gradient sequence generated based on the forward path data. The intelligent decision-making module, communicatively connected to the data acquisition module, is used to estimate the cycling load in real time based on the real-time vehicle data and the current road gradient, and, in conjunction with the optimal cadence range in the user's personalized parameters, evaluate whether the current gear is suitable for the current cycling state; and, Based on the real-time vehicle data, the current road gradient, and the predicted gradient sequence, while keeping the current gear unchanged, the predicted riding state within a future preset time window is simulated. The predicted riding state includes at least the predicted cadence and the predicted riding load. When the evaluation result indicates that the current gear is not suitable, or when either the predicted cadence or the predicted cycling load within the future preset time window exceeds the corresponding preset threshold range, a corresponding gear switching reminder instruction is generated and output. The instruction output module is communicatively connected to the intelligent decision module and is used to receive and output the gear shifting reminder instruction.

[0067] Based on the same concept, this invention also provides a schematic diagram of a physical structure, such as... Figure 3 As shown, the server may include a processor 310, a communications interface 320, a memory 330, and a communication bus 340. The processor 310, communications interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions stored in the memory 330 to execute the steps of the electronic gear shifting reminder method for riding bicycles as described in the above embodiments.

[0068] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0069] Based on the same concept, embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program containing at least one piece of code that can be executed by a master control device to control the master control device to implement the steps of the electronic gear shifting reminder method for riding as described in the above embodiments.

[0070] Based on the same technical concept, this application also provides a computer program, which, when executed by a main control device, is used to implement the above-described method embodiments.

[0071] The program may be stored, in whole or in part, on a storage medium packaged with the processor, or in part or in whole on a memory not packaged with the processor.

[0072] Based on the same technical concept, this application also provides a processor for implementing the above-described method embodiments. The processor can be a chip.

[0073] The various embodiments of the present invention can be combined arbitrarily to achieve different technical effects.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for reminding riders of gear shifting based on electronic transmission, characterized in that, The method includes: The system acquires real-time vehicle data, environmental data, and user-personalized parameters of the cycling equipment. The real-time vehicle data includes at least real-time cadence, real-time speed, and current gear. The environmental data includes at least the current road gradient and a predicted gradient sequence generated based on the forward path data. The cycling load is estimated in real time based on the real-time vehicle data and the current road gradient, and the optimal cadence range in the user's personalized parameters is used to evaluate whether the current gear is suitable for the current cycling state. Based on the real-time vehicle data, the current road gradient, and the predicted gradient sequence, while keeping the current gear unchanged, the predicted riding state within a future preset time window is simulated. The predicted riding state includes at least the predicted cadence and the predicted riding load. When the evaluation result indicates that the current gear is not suitable, or when either the predicted cadence or the predicted cycling load within the future preset time window exceeds the corresponding preset threshold range, a corresponding gear switching reminder instruction is generated and output.

2. The cycling gear shifting reminder method based on electronic transmission according to claim 1, characterized in that, The user-personalized parameters include at least the user's weight, drag coefficient, rolling friction coefficient, the user's preset optimal cadence range, and training power target; the rolling friction coefficient is set according to the type of road tire or mountain tire.

3. The cycling gear shifting reminder method based on electronic transmission according to claim 1, characterized in that, The preset future time window is dynamically adjusted based on the real-time vehicle speed. Simulate predicted riding conditions within a future preset time window, including: Based on the real-time cadence, the real-time vehicle speed, the current road gradient, and the predicted gradient sequence, a linear recursive algorithm is used to simulate the trend of cadence change with gradient while keeping the current gear unchanged, and to generate the predicted cadence within the future preset time window. Based on the predicted cadence, the predicted gradient sequence, and the user-personalized parameters, a corresponding predicted cycling load is calculated and generated.

4. The cycling gear shifting reminder method based on electronic transmission according to claim 1, characterized in that, The assessment of whether the current gear is suitable for the current riding state specifically includes: The real-time cadence is compared with the optimal cadence range, and the determination is made in conjunction with whether the cycling load is within the user's preset load comfort range. If the real-time cadence is lower than the lower limit of the optimal cadence range and exceeds the first deviation threshold, and the cycling load exceeds the upper limit of the load comfort range, then the current gear is determined to be too high. If the real-time cadence is higher than the upper limit of the optimal cadence range and exceeds the second deviation threshold, and the cycling load is lower than the lower limit of the load comfort range, then the current gear is determined to be too low. If the real-time cadence is within the optimal cadence range and the cycling load is within the load comfort range, then the current gear is determined to be suitable.

5. The cycling gear shifting reminder method based on electronic transmission according to claim 1, characterized in that, The gear shifting reminder instruction includes a first-level reminder instruction, which is used to control the output device to provide gear shifting suggestions to the rider in one or more ways, such as visual cues, sound cues, or vibration feedback; wherein, the visual cues include highlighted icons displayed on the computer screen, graphical gear suggestions, or color changes.

6. The cycling gear shifting reminder method based on electronic transmission according to claim 5, characterized in that, The gear shifting reminder instruction also includes a second-level control instruction; the second-level control instruction is generated when it is detected that the user has authorized the automatic shifting function and the preset forced shifting trigger conditions are met, and is used to send a shifting instruction to the controller of the electronic transmission system through a wireless communication protocol, so that the controller drives the actuator to perform gear shifting.

7. The cycling gear shifting reminder method based on electronic transmission according to claim 6, characterized in that, The preset forced shift trigger conditions include: The assessment determines that the current gear is unsuitable, and the real-time cadence deviates from the optimal cadence range by more than a first mandatory threshold; or, If either the predicted cadence or the predicted cycling load exceeds the corresponding preset threshold range by more than the second mandatory threshold, and the expected duration exceeds the preset duration, then the prediction is true.

8. A cycling gear shifting reminder system based on electronic transmission, characterized in that, include: The data acquisition module, installed on the cycling equipment, is used to acquire real-time vehicle data, environmental data, and personalized user parameters. The real-time vehicle data includes at least real-time cadence, real-time vehicle speed, and current gear; the environmental data includes at least the current road gradient and a predicted gradient sequence generated based on the forward path data. The intelligent decision-making module is connected in communication with the data acquisition module. It is used to estimate the cycling load in real time based on the real-time vehicle data and the current road slope, and to evaluate whether the current gear is suitable for the current cycling state by combining the optimal cadence range in the user's personalized parameters. as well as, Based on the real-time vehicle data, the current road gradient, and the predicted gradient sequence, while keeping the current gear unchanged, the predicted riding state within a future preset time window is simulated. The predicted riding state includes at least the predicted cadence and the predicted riding load. When the evaluation result indicates that the current gear is not suitable, or when either the predicted cadence or the predicted cycling load within the future preset time window exceeds the corresponding preset threshold range, a corresponding gear switching reminder instruction is generated and output. The instruction output module is communicatively connected to the intelligent decision module and is used to receive and output the gear shifting reminder instruction.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the electronic gear shifting reminder method for riding as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the electronic gear shifting reminder method for riding as described in any one of claims 1 to 7.

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