Electronic speed change method and system for mountain bike, terminal and storage medium

Abstract

The invention relates to an electronic speed change method and system for a mountain bike, a terminal and a storage medium, and the method comprises the steps: obtaining real-time riding data which comprises the speed, pedaling frequency, gradient, riding resistance, the output power of a rider and environmental conditions; inputting the real-time riding data into a pre-trained variable speed control model for analysis to obtain a variable speed demand, the variable speed demand comprising a variable speed time point and a gear adjustment strategy; the gear of the transmission is adjusted according to the speed change requirement, so that the riding efficiency and comfort are ensured; when gear shifting of the transmission is completed, current gear data are obtained, the current gear data are matched with the gear numerical value in the gear adjusting strategy, and a matching result is obtained; and if the matching result is that the current gear data is not matched with the gear numerical value, generating an alarm signal, and transmitting the alarm signal to a display end. The method has the effect of improving the riding experience of a rider.

Description

Technical Field

[0001] This application relates to the technical field of motor control, and in particular, to an electronic shifting method, system, terminal, and storage medium for mountain bikes. Background Art

[0002] Currently, with the popularity of mountain bike sports, users have higher and higher requirements for the bike shifting system. Most mountain bikes on the market currently use mechanical shifting systems, which require users to manually operate for shifting. The operation is complex and the response speed is slow, unable to meet the needs of complex and changeable mountain riding environments. Although existing electronic shifting systems can achieve automatic shifting, they cannot achieve accurate and fast shifting in complex and changeable mountain riding environments.

[0003] The above-mentioned existing technical solutions have the following defects: The existing electronic shifting systems still need to be improved in terms of reaction speed and shifting accuracy. Especially in the complex riding environment of mountain bikes, accurate automatic shifting cannot be achieved, so there is room for improvement. Summary of the Invention

[0004] In order to improve the riding experience of riders, this application provides an electronic shifting method, system, terminal, and storage medium for mountain bikes.

[0005] The first invention object of this application is achieved through the following technical solutions: An electronic shifting method for a mountain bike, the electronic shifting method for a mountain bike includes: Obtain real-time riding data, the real-time riding data includes vehicle speed, cadence, slope, riding resistance, the output power of the rider, and environmental conditions; Input the real-time riding data into a pre-trained shifting control model for analysis to obtain a shifting requirement, the shifting requirement includes a shifting time point and a gear adjustment strategy; Adjust the gear of the transmission according to the shifting requirement to ensure riding efficiency and comfort; When the gear shifting of the transmission is completed, obtain the current gear data, match the current gear data with the gear value in the gear adjustment strategy to obtain a matching result; If the matching result is that the current gear data does not match the gear value, generate an alarm signal and transmit the alarm signal to the display end.

[0006] By adopting the above technical solution, real-time cycling data is obtained. The real-time cycling data includes vehicle speed, cadence, slope, cycling resistance, the output power of the cyclist, and environmental conditions. These data provide comprehensive cycling environment and cyclist status information for the bicycle gear shifting system, laying a data foundation for realizing intelligent gear shifting. The real-time cycling data is input into a pre-trained gear shifting control model for analysis to obtain gear shifting requirements. The gear shifting requirements include the gear shifting time point and the gear adjustment strategy. The obtained gear shifting requirements can guide the bicycle on when and how to adjust the gear of the transmission to adapt to the changes in cycling conditions, improving cycling efficiency and comfort. According to the gear shifting requirements, the gear of the transmission is adjusted to ensure cycling efficiency and comfort. By adjusting the gear, the bicycle can better adapt to different cycling conditions and ensure that the cyclist can maintain the best cycling state under various road conditions. When the gear shifting of the transmission is completed, the current gear data is obtained, and the current gear data is matched with the gear value in the gear adjustment strategy to obtain a matching result. If the matching result shows that the current gear data does not match the gear value, an alarm signal is generated and transmitted to the display end. The alarm signal is used to remind the cyclist or the system operator to check and adjust to ensure that the transmission is in the correct gear and avoid potential cycling risks.

[0007] In a preferred example of this application, it can be further configured that: the obtaining of the real-time cycling data includes: The data during the cycling process is detected by a sensor module to obtain sensor data. The sensor module includes a speed sensor, a gyroscope, a cadence sensor, a slope sensor, a power meter, and an environmental sensor. The environmental conditions include temperature, humidity, and altitude. The sensor data is transmitted to the vehicle-mounted electronic control unit in real time through wireless transmission to ensure the real-time performance and accuracy of data transmission.

[0008] By adopting the above technical solution, the data during the cycling process is detected by a sensor module to obtain sensor data. The sensor module includes a speed sensor, a gyroscope, a cadence sensor, a slope sensor, a power meter, and an environmental sensor. The environmental conditions include temperature, humidity, and altitude. These data provide detailed cycling information for the intelligent control system of the bicycle, which can be used to analyze cycling performance, adjust cycling strategies, or provide feedback to the cyclist. The environmental data is very important for evaluating cycling conditions and the comfort of the cyclist and can also be used to adjust certain performance parameters of the bicycle. The sensor data is transmitted to the vehicle-mounted electronic control unit in real time through wireless transmission to ensure the real-time performance and accuracy of data transmission. The wireless transmission ensures the real-time performance and accuracy of data transmission, eliminates the need for physical connection, and improves the flexibility of the system and the user experience.

[0009] In a preferred embodiment, the present application can be further configured as follows: when inputting the real-time riding data into a pre-trained shift control model for analysis to obtain a shift requirement, the method for electronically shifting a mountain bike further includes: Collect historical riding data, preprocess and label the historical riding data to obtain a training set; Use the training set to perform forward propagation and backpropagation training on a shift control model constructed based on a multi-layer neural network, and optimize the shift control model after forward propagation and backpropagation training by combining a genetic algorithm to obtain the pre-trained shift control model.

[0010] By adopting the above technical solution, historical data during bicycle riding is collected, preprocessed and labeled to obtain a training set. These data provide a basis for training the shift control model, helping the model learn shift strategies under different riding conditions. The preprocessed and labeled data is more suitable for training machine learning models, which helps improve the accuracy and generalization ability of the model; the training set is used to train a shift control model based on a multi-layer neural network. Through training, the model learns how to predict the best shift strategy according to the input riding data. After training, it can predict and recommend the best shift strategy according to real-time riding data, helping riders improve riding efficiency and performance.

[0011] In a preferred embodiment, the present application can be further configured as follows: the step of inputting the real-time riding data into a pre-trained shift control model for analysis to obtain a shift requirement includes: Extract features from the real-time riding data to obtain a feature vector of the real-time riding data; Analyze and process the feature vector based on a multi-layer neural network, predict the riding state at the next moment, and generate the shift requirement according to the riding state at the next moment.

[0012] By adopting the above technical solution, features are extracted from the real-time riding data to obtain a feature vector of the real-time riding data. By extracting key features that can represent the riding state from these data to form a feature vector, these feature vectors capture important information about the riding environment and rider performance; the obtained feature vector is analyzed and processed based on a multi-layer neural network, the riding state at the next moment is predicted, and the shift requirement is generated according to the riding state at the next moment. Through the analysis and processing of the multi-layer neural network, the model can predict the riding state of the rider at the next moment to improve riding efficiency and comfort, reduce the physical consumption of the rider, and provide a more personalized riding experience.

[0013] In a preferred example, the present application can be further configured as follows: Adjusting the transmission gear according to the speed change requirement includes: Obtaining the speed change priority from the gear adjustment strategy, and determining the execution order according to the speed change priority; Determining the operating speeds of the motor and the gear set according to the speed change smoothness in the gear adjustment strategy; Adjusting the gear of the transmission according to the speed change time point, the execution order, and the operating speeds of the motor and the gear set.

[0014] By adopting the above technical solution, by determining the priority of speed change, the system can arrange the order of gear adjustment according to importance and urgency, ensuring that the most critical speed change requirements are met first. The system arranges the specific execution order of gear adjustment according to the speed change priority, ensuring that the gear adjustment is executed in the predetermined priority order, optimizing the riding experience and performance; by controlling the operating speeds of the motor and the gear set, smooth gear changes can be achieved, avoiding sudden impacts or discomfort felt by the rider; by precisely controlling the time and speed of gear adjustment, the system can ensure that the transmission responds quickly and accurately to changes in riding conditions, improving riding efficiency and comfort.

[0015] In a preferred example, the present application can be further configured as follows: The electronic speed change method for a mountain bike further includes: Setting a riding mode, adaptively adjusting the speed change requirement according to the riding mode to obtain a corresponding adaptive speed change strategy, and adjusting the gear of the transmission according to the corresponding adaptive speed change strategy, where the riding mode includes a climbing mode, a flat mode, and a downhill mode.

[0016] Recording the riding data and the speed change operation history after each ride, and updating the pre-trained speed change control model through an adaptive learning algorithm to make it more accurate and efficient in subsequent rides.

[0017] By adopting the above technical solution, the system automatically adjusts the speed change requirement according to the selected riding mode, providing a dynamic gear adjustment plan that can be updated in real time according to changes in riding conditions. The adaptive adjustment ensures that the gear of the transmission is consistent with the riding conditions and the rider's needs, enabling the bicycle to better adapt to different riding environments and improving riding efficiency and comfort; recording the riding data and the speed change operation history after each ride, and updating the pre-trained speed change control model through an adaptive learning algorithm. The updated model can better understand the rider's habits and preferences, as well as the best speed change strategy under different riding conditions. Through continuous learning and updating, the performance of the speed change control model is improved, making the subsequent riding experience smoother and more personalized.

[0018] The second above-mentioned invention object of the present application is achieved by the following technical solutions: A mountain bike electronic shifting system, the mountain bike electronic shifting system includes: A riding data acquisition module for acquiring real-time riding data, the real-time riding data including vehicle speed, pedal frequency, slope, riding resistance, the output power of the rider, and environmental conditions; A model analysis module for inputting the real-time riding data into a pre-trained shifting control model for analysis to obtain a shifting requirement, the shifting requirement including a shifting time point and a gear adjustment strategy; An adjustment module for adjusting the gear of the transmission according to the shifting requirement to ensure riding efficiency and comfort; A detection module for, when the gear shifting of the transmission is completed, acquiring the current gear data and matching the current gear data with the gear value in the gear adjustment strategy to obtain a matching result; An alarm module for generating an alarm signal and transmitting the alarm signal to a display end if the matching result is that the current gear data does not match the gear value.

[0019] By adopting the above technical solutions, real-time riding data is acquired. The real-time riding data includes vehicle speed, pedal frequency, slope, riding resistance, the output power of the rider, and environmental conditions. These data provide comprehensive riding environment and rider state information for the bicycle shifting system, providing a data basis for realizing intelligent shifting; the real-time riding data is input into a pre-trained shifting control model for analysis to obtain a shifting requirement. The shifting requirement includes a shifting time point and a gear adjustment strategy. The obtained shifting requirement can guide the bicycle on when and how to adjust the gear of the transmission to adapt to changes in riding conditions, improving riding efficiency and comfort; according to the shifting requirement, the gear of the transmission is adjusted to ensure riding efficiency and comfort. By adjusting the gear, the bicycle can better adapt to different riding conditions, ensuring that the rider can maintain the best riding state under various road conditions; when the gear shifting of the transmission is completed, the current gear data is acquired and the current gear data is matched with the gear value in the gear adjustment strategy to obtain a matching result; if the matching result is that the current gear data does not match the gear value, an alarm signal is generated and the alarm signal is transmitted to the display end. The rider or the system operator is reminded by the alarm signal to check and adjust to ensure that the transmission is in the correct gear, avoiding potential riding risks.

[0020] The third above-mentioned object of the present application is achieved by the following technical solutions: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned electronic shifting method for a mountain bike are implemented.

[0021] The above-mentioned fourth object of the present application is achieved by the following technical solutions: A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned electronic shifting method for a mountain bike are implemented.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: 1. By obtaining real-time riding data, the real-time riding data includes vehicle speed, cadence, slope, riding resistance, the output power of the rider, and environmental conditions. These data provide comprehensive riding environment and rider status information for the bicycle shifting system, providing a data basis for realizing intelligent shifting; 2. Input the real-time riding data into a pre-trained shifting control model for analysis to obtain shifting requirements. The shifting requirements include the shifting time point and the gear adjustment strategy. The obtained shifting requirements can guide the bicycle on when and how to adjust the transmission gear to adapt to changes in riding conditions, improving riding efficiency and comfort; According to the shifting requirements, adjust the transmission gear to ensure riding efficiency and comfort. By adjusting the gear, the bicycle can better adapt to different riding conditions, ensuring that the rider can maintain the best riding state under various road conditions; 3. When the transmission gear shift is completed, obtain the current gear data, match the current gear data with the gear value in the gear adjustment strategy to obtain a matching result; If the matching result is that the current gear data does not match the gear value, generate an alarm signal and transmit the alarm signal to the display end. Remind the rider or system operator to check and adjust through the alarm signal to ensure that the transmission is in the correct gear and avoid potential riding risks. Description of the Drawings

[0023] Figure 1 is a flowchart of an electronic shifting method for a mountain bike in an embodiment of the present application; Figure 2 is an implementation flowchart of step S10 in an electronic shifting method for a mountain bike in an embodiment of the present application; Figure 3 is an implementation flowchart of step S20 in an electronic shifting method for a mountain bike in an embodiment of the present application; Figure 4 is an implementation flowchart of step S20 in an electronic shifting method for a mountain bike in an embodiment of the present application; Figure 5 It is a flowchart of implementing step S30 in a method for electronic shifting of a mountain bike according to an embodiment of the present application; Figure 6 It is a flowchart of implementing a method for electronic shifting of a mountain bike according to an embodiment of the present application; Figure 7 It is a schematic block diagram of a mountain bike electronic shifting system according to an embodiment of the present application; Figure 8 It is a schematic diagram of a device according to an embodiment of the present application. Detailed implementation manners

[0024] The present application will be further described in detail below with reference to the accompanying drawings.

[0025] In one embodiment, as Figure 1 shown, the present application discloses a method for electronic shifting of a mountain bike, which specifically includes the following steps: S10: Obtain real-time riding data, where the real-time riding data includes vehicle speed, cadence, slope, riding resistance, rider's output power, and environmental conditions.

[0026] Specifically, various sensors are used to collect real-time data during the bicycle ride, including vehicle speed, cadence, slope, riding resistance, rider's output power, and environmental conditions. These data provide comprehensive information about the riding environment and the rider's state for the bicycle shifting system and are the basis for realizing intelligent shifting.

[0027] S20: Input the real-time riding data into a pre-trained shifting control model for analysis to obtain shifting requirements, where the shifting requirements include shifting time points and gear adjustment strategies.

[0028] Specifically, the collected real-time riding data is input into a pre-trained shifting control model. This model may be based on machine learning algorithms and can perform intelligent analysis according to riding conditions and the rider's needs. After analyzing the data, the model can generate shifting requirements, including shifting time points and gear adjustment strategies. The shifting requirements guide the bicycle on when and how to adjust the transmission gears to adapt to changes in riding conditions and improve riding efficiency and comfort.

[0029] S30: Adjust the transmission gears according to the shifting requirements to ensure riding efficiency and comfort.

[0030] Specifically, according to the shifting requirements, the transmission gears of the bicycle are automatically or manually adjusted. By adjusting the gears, the bicycle can better adapt to different riding conditions, such as uphill, downhill, or flat road, ensuring that the rider can maintain the best riding state under various road conditions.

[0031] S40: When the gear shift of the transmission is completed, obtain the current gear data, match the current gear data with the gear values in the gear adjustment strategy, and obtain a matching result.

[0032] Specifically, after the gear shift is completed, the system obtains the current gear data and matches it with the gear values in the shifting requirements. The matching process ensures that the transmission gear is correctly adjusted to meet the shifting requirements, guaranteeing smooth and efficient cycling, and finally obtaining a matching result.

[0033] S50: If the matching result shows that the current gear data does not match the gear value, generate an alarm signal and transmit the alarm signal to the display end.

[0034] Specifically, if the current gear data does not match the preset gear value, the system will generate an alarm signal and send a warning to the cyclist through the display end, such as the display screen on the bicycle or the user's mobile application. The alarm signal reminds the cyclist or the system operator to check and adjust to ensure that the transmission is in the correct gear and avoid potential cycling risks.

[0035] By adopting the above technical solution, real-time cycling data is obtained. The real-time cycling data includes vehicle speed, cadence, slope, cycling resistance, the output power of the cyclist, and environmental conditions. These data provide comprehensive cycling environment and cyclist status information for the bicycle transmission system, providing a data basis for realizing intelligent shifting; inputting the real-time cycling data into a pre-trained shifting control model for analysis to obtain shifting requirements, which include the shifting time point and the gear adjustment strategy. The obtained shifting requirements can guide the bicycle on when and how to adjust the transmission gear to adapt to changes in cycling conditions, improving cycling efficiency and comfort; adjusting the transmission gear according to the shifting requirements to ensure cycling efficiency and comfort. By adjusting the gear, the bicycle can better adapt to different cycling conditions and ensure that the cyclist can maintain the best cycling state under various road conditions; when the gear shift of the transmission is completed, obtain the current gear data, match the current gear data with the gear values in the gear adjustment strategy, and obtain a matching result; if the matching result shows that the current gear data does not match the gear value, generate an alarm signal and transmit the alarm signal to the display end, and remind the cyclist or the system operator to check and adjust through the alarm signal to ensure that the transmission is in the correct gear and avoid potential cycling risks.

[0036] In one embodiment, as Figure 2 shown, in step S10, that is, obtaining real-time cycling data, specifically includes: S11: Detect data during the cycling process through the sensor module to obtain sensor data. The sensor module includes a speed sensor, a gyroscope, a cadence sensor, a slope sensor, a power meter, and an environmental sensor. The environmental conditions include temperature, humidity, and altitude.

[0037] Specifically, use a module integrated with multiple sensors to continuously monitor various data during the cycling process. Measure the cycling speed of the bicycle through the speed sensor, detect the tilt angle and rotational speed of the bicycle through the gyroscope, which helps to understand the cycling state and balance. Record the frequency of the cyclist's pedaling, i.e., the cadence per minute, through the cadence sensor. Measure the tilt angle of the bicycle when climbing or descending through the slope sensor. Calculate the power output by the cyclist, usually combining cadence and torque, through the power meter. Measure the ambient air pressure through the environmental sensor, which helps to understand altitude changes and weather conditions. The data collected by each sensor is converted into an electronic signal to form sensor data, providing detailed cycling information for the system, which can be used to analyze cycling performance, adjust cycling strategies, or provide feedback to the cyclist.

[0038] S12: Transmit the sensor data to the vehicle-mounted electronic control unit in real time through wireless transmission to ensure the real-time performance and accuracy of data transmission.

[0039] Specifically, the sensor data is sent to the vehicle-mounted electronic control unit on the bicycle in real time through wireless transmission technologies such as Bluetooth, ANT+, or WiFi. Wireless transmission ensures the real-time performance and accuracy of data transmission, without physical connection, improving the flexibility of the system and the user experience.

[0040] In one embodiment, as Figure 3 shown, before step S20, that is, before inputting the real-time cycling data into the pre-trained shift control model for analysis to obtain the shift requirement, this electronic shift method for a mountain bike further includes: S201: Collect historical cycling data, preprocess and label the historical cycling data to obtain a training set.

[0041] Specifically, collect historical data during the cycling process of the bicycle. This data may include speed, cadence, slope, cycling resistance, power output, etc. Perform preprocessing operations such as cleaning, formatting, and standardizing on the collected historical data, and perform labeling. The labeling may include determining the ideal shift timing, gear selection, etc. The preprocessed and labeled data is more suitable for training machine learning models, which helps to improve the accuracy and generalization ability of the models. Organize the preprocessed and labeled data into a training set, which will be used to train the machine learning model.

[0042] S202: Use the training set to perform forward propagation and backpropagation training on the variable-speed control model constructed based on a multi-layer neural network, and optimize the variable-speed control model after forward propagation and backpropagation training by combining with a genetic algorithm to obtain a pre-trained variable-speed control model.

[0043] Specifically, use the training set to train the variable-speed control model based on a multi-layer neural network. The training process includes forward propagation and backpropagation, that is, calculate the prediction result and adjust the model parameters according to the difference between the prediction result and the actual result, and use a genetic algorithm to perform hyperparameter optimization on the trained variable-speed control model, such as adjusting the learning rate, the number of network layers, the number of neurons, etc. The genetic algorithm finds the optimal combination of model parameters by simulating the process of natural selection, further improving the performance of the model. After training and optimization, a variable-speed control model with good performance is obtained. This model can predict and recommend the best variable-speed strategy according to real-time riding data, helping riders improve riding efficiency and performance.

[0044] In one embodiment, as Figure 4 shown, in step S20, input the real-time riding data into the pre-trained variable-speed control model for analysis to obtain the variable-speed demand, specifically including: S21: Extract features from the real-time riding data to obtain the feature vector of the real-time riding data.

[0045] Specifically, use a feature extraction algorithm to process the real-time riding data. These data include vehicle speed, cadence, slope, riding resistance, the output power of the rider, and environmental conditions such as temperature, humidity, and altitude. Extract key features that can represent the riding state from these data to form a feature vector. These feature vectors capture important information about the riding environment and the rider's performance.

[0046] S22: Analyze and process the feature vector based on a multi-layer neural network, predict the riding state at the next moment, and generate a variable-speed demand according to the riding state at the next moment.

[0047] Specifically, input the extracted feature vector into a variable-speed control model based on a multi-layer neural network. Through the analysis and processing of the multi-layer neural network, the model can predict the riding state of the rider at the next moment, providing decision support for variable speed. Based on the analysis results of the neural network, the system predicts the riding conditions that the rider may encounter at the next moment, such as uphill, downhill, or flat road, and generates a variable-speed demand accordingly. The variable-speed demand includes the recommended variable-speed time point and gear adjustment strategy. This information guides the bicycle transmission to make timely adjustments to ensure riding efficiency and comfort.

[0048] In one embodiment, as Figure 5As shown, in step S30, the transmission gear is adjusted according to the speed change requirement, which specifically includes: S31: Obtaining a speed change priority from the gear adjustment strategy, and determining an execution order according to the speed change priority.

[0049] Specifically, in the gear shift adjustment strategy, it is defined which gear adjustments are prioritized. This may be based on changes in riding conditions, such as a sudden increase or decrease in slope, or a significant change in the rider's power output. By determining the priority of the gear shift, the system can arrange the order of gear adjustments according to importance and urgency, ensuring that the most critical gear shift needs are met first. The system arranges the specific execution order of gear adjustments according to the gear shift priority. For example, if going uphill requires an urgent gear reduction to increase torque, this may be given the highest priority, ensuring that gear adjustments are executed in the predetermined priority order to optimize the riding experience and performance.

[0050] S32: Determine the operating speed of the motor and the gear set according to the speed change smoothness in the gear adjustment strategy.

[0051] Specifically, the speed adjustment strategy will include the requirement for speed shifting smoothness, that is, the smoothness of gear changes. The system needs to adjust the operating speed of the motor and gear set according to this requirement to achieve smooth speed shifting. By controlling the operating speed of the motor and gear set, smooth gear changes can be achieved to avoid sudden impact or discomfort to the rider.

[0052] S33: adjusting the gear position of the transmission according to the speed change timing point, the execution sequence and the operating speed of the motor and the gear set.

[0053] Specifically, the system accurately adjusts the gear of the transmission based on the shifting timing, execution order and operating speed. This involves real-time monitoring of the current riding status and executing the predetermined gear adjustment at the appropriate time. By precisely controlling the timing and speed of the gear adjustment, the system can ensure that the transmission responds quickly and accurately to changes in riding conditions, thereby improving riding efficiency and comfort.

[0054] In one embodiment, if Figure 6 As shown, the electronic speed change method for a mountain bicycle also includes: S60: Setting the riding mode, adaptively adjusting the speed change demand according to the riding mode, obtaining the corresponding adaptive speed change strategy, and adjusting the transmission gear according to the corresponding adaptive speed change strategy. The riding modes include climbing mode, flat mode and downhill mode.

[0055] Specifically, the system allows users to select different riding modes according to riding conditions, such as climbing mode, flat mode, and downhill mode. Each mode is optimized for specific riding conditions to provide the best riding experience and performance. The system automatically adjusts the shifting requirements according to the selected riding mode. For example, in climbing mode, it may lower the gear in advance to increase torque, and in downhill mode, it may raise the gear to increase speed. Finally, the system generates an adaptive shifting strategy based on the current riding mode and real-time riding data. The system executes the gear adjustment instructions in the adaptive shifting strategy and operates the transmission to make actual gear changes. The adaptive adjustment ensures that the transmission gear is consistent with the riding conditions and the rider's needs, improving riding efficiency and comfort.

[0056] S70: Record the riding data and shifting operation history after each ride, and update the pre-trained shifting control model through an adaptive learning algorithm to make it more accurate and efficient in subsequent rides.

[0057] Specifically, the system collects data during the ride after the ride ends, including speed, cadence, slope, gear changes, etc. It uses an adaptive learning algorithm, such as online learning or incremental learning, to update the shifting control model based on the collected riding data and shifting operation history. The updated model can better understand the rider's habits and preferences, as well as the best shifting strategies under different riding conditions. Through continuous learning and updating, the performance of the shifting control model is improved, making the subsequent riding experience smoother and more personalized.

[0058] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution is prior or posterior. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0059] In one embodiment, a mountain bike electronic shifting system is provided, and this mountain bike electronic shifting system corresponds one-to-one with the mountain bike electronic shifting method in the above embodiment. As Figure 7 shown, this mountain bike electronic shifting system includes a riding data acquisition module, a model analysis module, an adjustment module, a detection module, and an alarm module. The detailed description of each functional module is as follows: The riding data acquisition module is used to acquire real-time riding data, and the real-time riding data includes vehicle speed, cadence, slope, riding resistance, the rider's output power, and environmental conditions; The model analysis module is used to input the real-time riding data into a pre-trained shifting control model for analysis to obtain shifting requirements, and the shifting requirements include the shifting time point and the gear adjustment strategy; The adjustment module is used to adjust the transmission gear according to the shifting requirements to ensure riding efficiency and comfort; A detection module, configured to obtain current gear data when the gear shifting of the transmission is completed, match the current gear data with the gear values in the gear adjustment strategy, and obtain a matching result; An alarm module, configured to generate an alarm signal and transmit the alarm signal to a display end if the matching result shows that the current gear data does not match the gear value.

[0060] Optionally, this electronic gear shifting system for a mountain bike further includes: A setting mode module, configured to set a riding mode, adaptively adjust the gear shifting requirements according to the riding mode to obtain a corresponding adaptive gear shifting strategy, and adjust the transmission gear according to the corresponding adaptive gear shifting strategy. The riding mode includes a climbing mode, a flat mode, and a downhill mode.

[0061] A model optimization module, configured to record riding data and the history of gear shifting operations after each ride, and update a pre-trained gear shifting control model through an adaptive learning algorithm to make it more accurate and efficient in subsequent rides; A historical data collection module, configured to collect historical riding data, preprocess and annotate the historical riding data to obtain a training set; A model training module, configured to perform forward propagation and backward propagation training on a gear shifting control model constructed based on a multi-layer neural network using the training set, and optimize the gear shifting control model after forward propagation and backward propagation training in combination with a genetic algorithm to obtain a pre-trained gear shifting control model.

[0062] Optionally, the riding data acquisition module includes: A sensing detection sub-module, configured to detect data during the riding process through a sensor module to obtain sensor data. The sensor module includes a speed sensor, a gyroscope, a cadence sensor, a slope sensor, a power meter, and an environmental sensor. The environmental conditions include temperature, humidity, and altitude; A data transmission sub-module, configured to transmit the sensor data to an in-vehicle electronic control unit in real time through wireless transmission to ensure the real-time performance and accuracy of data transmission.

[0063] Optionally, the model analysis module includes: A feature extraction sub-module, configured to extract features from real-time riding data to obtain a feature vector of the real-time riding data; A data analysis sub-module, configured to analyze and process the feature vector based on a multi-layer neural network, predict the riding state at the next moment, and generate a gear shifting requirement according to the riding state at the next moment.

[0064] Optionally, the adjustment module includes: A determining execution order sub-module, configured to obtain a shifting priority from a gear shifting strategy and determine an execution order according to the shifting priority; A determining operation speed sub-module, configured to determine the operation speeds of a motor and a gear set according to a shifting smoothness in the gear shifting strategy; A adjusting gear position sub-module, configured to adjust the gear position of a transmission according to a shifting time point, an execution order, and the operation speeds of the motor and the gear set.

[0065] For the specific limitations of an electronic gear shifting system for a mountain bike, reference can be made to the limitations of an electronic gear shifting method for a mountain bike in the foregoing text, which will not be elaborated herein. Each module in the above-mentioned electronic gear shifting system for a mountain bike can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in a processor in a computer device in hardware form or independent of the processor, or stored in a memory in the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to the above-mentioned modules.

[0066] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as Figure 8 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements an electronic gear shifting method for a mountain bike.

[0067] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented: Obtain real-time riding data, where the real-time riding data includes vehicle speed, pedal frequency, slope, riding resistance, output power of the rider, and environmental conditions; Input the real-time riding data into a pre-trained gear shifting control model for analysis to obtain a gear shifting demand, where the gear shifting demand includes a shifting time point and a gear shifting strategy; Adjust the gear position of the transmission according to the gear shifting demand to ensure riding efficiency and comfort; When the gear position switching of the transmission is completed, obtain the current gear position data, and match the current gear position data with the gear position value in the gear shifting strategy to obtain a matching result; When the matching result indicates that the current gear data does not match the gear value, an alarm signal is generated and transmitted to the display end.

[0068] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: Obtain real-time riding data, which includes vehicle speed, cadence, slope, riding resistance, the output power of the rider, and environmental conditions; Input the real-time riding data into a pre-trained shift control model for analysis to obtain a shift requirement, which includes a shift time point and a gear adjustment strategy; Adjust the gear of the transmission according to the shift requirement to ensure riding efficiency and comfort; When the gear shift of the transmission is completed, obtain the current gear data, match the current gear data with the gear value in the gear adjustment strategy to obtain a matching result; When the matching result indicates that the current gear data does not match the gear value, an alarm signal is generated and transmitted to the display end.

[0069] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0070] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above.

[0071] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements 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 application, and should all be included in the protection scope of the present application.

Claims

1. An electronic shifting method for a mountain bike, characterized in that, The described electronic shifting method for a mountain bike includes: Obtaining real-time riding data, where the real-time riding data includes vehicle speed, cadence, slope, riding resistance, the output power of the rider, and environmental conditions; Inputting the real-time riding data into a pre-trained shifting control model for analysis to obtain a shifting requirement, where the shifting requirement includes a shifting time point and a gear adjustment strategy; Adjusting the gear of the transmission according to the shifting requirement to ensure riding efficiency and comfort; When the gear shift of the transmission is completed, obtaining the current gear data, and matching the current gear data with the gear value in the gear adjustment strategy to obtain a matching result; If the matching result is that the current gear data does not match the gear value, generating an alarm signal and transmitting the alarm signal to the display end.

2. The method for electronically shifting gears of a mountain bike according to claim 1, wherein, The obtaining of the real-time riding data includes: Detecting data during the riding process through a sensor module to obtain sensor data. The sensor module includes a speed sensor, a gyroscope, a cadence sensor, a slope sensor, a power meter, and an environmental sensor. The environmental conditions include temperature, humidity, and altitude; Transmitting the sensor data to the vehicle-mounted electronic control unit in real time through wireless transmission to ensure the real-time and accuracy of data transmission.

3. A method for electronically shifting gears of a mountain bike according to claim 1, characterized in that, In the step of inputting the real-time riding data into a pre-trained shifting control model for analysis to obtain a shifting requirement, the described electronic shifting method for a mountain bike further includes: Collecting historical riding data, preprocessing and annotating the historical riding data to obtain a training set; Using the training set to perform forward propagation and backward propagation training on a shifting control model constructed based on a multi-layer neural network, and optimizing the shifting control model after forward propagation and backward propagation training by combining a genetic algorithm to obtain the pre-trained shifting control model.

4. A method for electronically shifting gears of a mountain bike according to claim 1, characterized in that, The step of inputting the real-time riding data into a pre-trained shifting control model for analysis to obtain a shifting requirement includes: Extracting features from the real-time riding data to obtain a feature vector of the real-time riding data; Analyzing and processing the feature vector based on a multi-layer neural network, predicting the riding state at the next moment, and generating the shifting requirement according to the riding state at the next moment.

5. A method for electronically shifting gears of a mountain bike according to claim 1, characterized in that, The adjusting of the gear of the transmission according to the shifting requirement includes: Obtaining the shifting priority from the gear adjustment strategy and determining the execution order according to the shifting priority; Determining the operating speed of the motor and the gear set according to the shifting smoothness in the gear adjustment strategy; Adjusting the gear of the transmission according to the shifting time point, the execution order, and the operating speed of the motor and the gear set.

6. The electronic shifting method for a mountain bike according to claim 1, characterized in that The described electronic shifting method for a mountain bike further includes: Setting a riding mode, adaptively adjusting the shifting requirement according to the riding mode to obtain a corresponding adaptive shifting strategy, and adjusting the gear of the transmission according to the corresponding adaptive shifting strategy. The riding mode includes a climbing mode, a flat mode, and a downhill mode; Record the cycling data and the history of gear shifting operations after each ride, and update the pre-trained gear shifting control model through an adaptive learning algorithm to make it more accurate and efficient in subsequent rides.

7. An electronic shifting system for a mountain bike, characterized in that, The electronic gear shifting system for a mountain bike includes: A cycling data acquisition module for acquiring real-time cycling data, where the real-time cycling data includes vehicle speed, cadence, slope, cycling resistance, the output power of the cyclist, and environmental conditions; A model analysis module for inputting the real-time cycling data into the pre-trained gear shifting control model for analysis to obtain a gear shifting requirement, where the gear shifting requirement includes a gear shifting time point and a gear adjustment strategy; An adjustment module for adjusting the gear of the transmission according to the gear shifting requirement to ensure cycling efficiency and comfort; A detection module for acquiring the current gear data when the gear shift of the transmission is completed, and matching the current gear data with the gear value in the gear adjustment strategy to obtain a matching result; An alarm module for generating an alarm signal and transmitting the alarm signal to the display end if the matching result is that the current gear data does not match the gear value.

8. The electronic shift system for a mountain bike according to claim 7, characterized in that, The electronic gear shifting system for a mountain bike further includes: A setting mode module for setting a cycling mode, adaptively adjusting the gear shifting requirement according to the cycling mode to obtain a corresponding adaptive gear shifting strategy, and adjusting the gear of the transmission according to the corresponding adaptive gear shifting strategy. The cycling mode includes a climbing mode, a flat mode, and a downhill mode; A model optimization module for recording the cycling data and the history of gear shifting operations after each ride, and updating the pre-trained gear shifting control model through an adaptive learning algorithm to make it more accurate and efficient in subsequent rides.

9. A computer 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 computer program, it implements the steps of the electronic gear shifting method for a mountain bike according to any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the electronic gear shifting method for a mountain bike according to any one of claims 1 to 6.

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