Control method, device and equipment of moped and medium

Abstract

The embodiment of the invention discloses a moped control method, device and equipment and a medium, the method comprises the steps that current riding data and current body data of a user are obtained, and the current riding data and the current body data represent the riding strength and the body state of the user in the riding process respectively; according to the current riding data and the current body data, the ideal pedaling frequency of the user is determined from a preset pedaling frequency library, and the ideal pedaling frequency in the preset pedaling frequency library is determined through historical motion data and historical body data; and according to the current pedaling frequency and the ideal pedaling frequency of the user, the power assisting strength of the moped is updated. According to the technical scheme provided by the invention, the exercise safety of the user can be ensured, and the power assisting mode switching efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of electric bicycles, and more particularly to a control method, device, equipment, and medium for electric bicycles. Background Technology

[0002] As an important tool for modern transportation and exercise, one of the core functions of e-bikes is to help users maintain an efficient and comfortable cadence during riding through appropriate gear adjustments. If the cadence is too low, the legs need to exert a lot of force, which can easily lead to muscle strain; if the cadence is too high, breathing and heart rate cannot be matched, resulting in shortness of breath and rapid energy depletion. Therefore, it is necessary to adjust the assist level and assist intensity appropriately.

[0003] However, currently, adjusting the power assist level and intensity mainly relies on the user's personal experience and feeling of movement, and then manually adjusting the power assist level. However, adjusting the power assist intensity based on the user's own experience has a lag, and by then the body may have already experienced discomfort, thus affecting the user's health. At the same time, manually adjusting the power assist level is inefficient. Summary of the Invention

[0004] The technical solution of the present invention provides a control method, device, equipment and medium for electric bicycles. Through the solution of the embodiments of the present invention, the safety of the user's exercise process can be guaranteed and the efficiency of assist intensity adjustment can be improved.

[0005] In a first aspect, embodiments of the present invention provide a control method for an electric bicycle, comprising:

[0006] The system acquires the user's current cycling data and current physical data, wherein the current cycling data and current physical data respectively represent the user's cycling intensity and physical state during the cycling process;

[0007] Based on the current cycling data and current body data, the user's ideal cadence is determined from a preset cadence library, wherein the ideal cadence in the preset cadence library is determined through historical exercise data and historical body data;

[0008] The assist level of the electric bicycle is updated based on the user's current cadence and ideal cadence.

[0009] Secondly, embodiments of the present invention provide a control device for an electric bicycle, comprising:

[0010] The acquisition module is used to acquire the user's current cycling data and current body data, wherein the current cycling data and current body data respectively represent the user's cycling intensity and physical state during the cycling process;

[0011] The ideal cadence determination module is used to determine the user's ideal cadence from a preset cadence library based on the current cycling data and current body data. The ideal cadence in the preset cadence library is determined through historical exercise data and historical body data.

[0012] The update module is used to update the assist level of the electric bicycle based on the user's current cadence and ideal cadence.

[0013] Thirdly, embodiments of the present invention provide an electronic device, the electronic device comprising:

[0014] At least one processor; and,

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the control method for the electric bicycle as described in any one of the embodiments of the present invention.

[0017] Fourthly, embodiments of the present invention provide a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions, which are used to cause a processor to execute and implement the control method of the electric bicycle described in any one of the embodiments of the present invention.

[0018] This invention provides a control method, device, equipment, and medium for an electric bicycle. The method includes: acquiring a user's current riding data and current body data, wherein the current riding data and current body data respectively represent the user's riding intensity and physical state during riding; determining the user's ideal cadence from a preset cadence library based on the current riding data and current body data, wherein the ideal cadence in the preset cadence library is determined through historical exercise data and historical body data; and updating the assist intensity of the electric bicycle based on the user's current cadence and ideal cadence. Specifically, the user's ideal cadence can be determined from the preset cadence library based on the current riding data and current body data, and then the assist intensity can be adjusted to keep the user's current cadence consistent with the ideal cadence, ensuring the user's exercise safety, while simultaneously achieving automatic adjustment of the assist intensity and improving the efficiency of assist adjustment. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This invention provides a control method for an electric bicycle according to Embodiment 1.

[0021] Figure 2 A schematic diagram of the control method for an electric bicycle provided in an embodiment of the present invention;

[0022] Figure 3 This is a flowchart of a preset cadence library generation method provided in Embodiment 2 of the present invention;

[0023] Figure 4 This is a schematic diagram of the structure of a control device for a power-assisted bicycle provided in Embodiment 3 of the present invention;

[0024] Figure 5 This is a schematic diagram of the structure of the electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] It should be noted that the collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0028] Example 1

[0029] Figure 1The present invention provides a control method, device, equipment and medium for a power-assisted bicycle. The method is specifically applicable to situations where a power-assisted bicycle is used for movement, and the power assist level or power assist intensity is adjusted. The method can be composed of software and / or hardware and is configured in the control system of the power-assisted bicycle.

[0030] like Figure 1 As shown, it includes:

[0031] Step 110: Obtain the user's current cycling data and current body data, wherein the current cycling data and current body data respectively represent the user's cycling intensity and physical condition during the cycling process.

[0032] Among them, the e-bike can be a fitness e-bike, and the current riding data is used to describe the user's riding intensity and riding status during the riding process, such as riding speed, acceleration and riding time; the current body data is the user's physical status data during the riding process, such as the user's heart rate, blood pressure and blood oxygen level.

[0033] It should be noted that the user's current physical data can be obtained through wearable devices that communicate with the e-bike, such as wristbands and watches. Specifically, wristbands and watches can detect the user's current physical data in real time and transmit it to the connected e-bike. It should also be noted that the e-bike and wristband are accessory devices; the collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information related to the physical data are all authorized by the user, comply with relevant laws and regulations, and do not violate public order and good morals.

[0034] Furthermore, current physical data can reflect the user's physical condition. If the user's physical condition becomes abnormal, the intensity of the assistance needs to be adjusted immediately to avoid accidents. For example, if an abnormally high heart rate is detected, it means that the current exercise intensity is too high, so the intensity of assistance needs to be changed to reduce the user's exercise burden and ensure the user's safety.

[0035] Step 120: Determine the user's ideal cadence from the preset cadence library based on the current cycling data and current body data, wherein the ideal cadence in the preset cadence library is determined through historical exercise data and historical body data.

[0036] The preset cadence database contains historical cycling data, historical body data, and historical cadence recorded by the user during past workouts, along with the corresponding relationships between these three. Specifically, by analyzing the correlation between cycling intensity indicators (such as speed and acceleration) and physiological indicators (such as heart rate and blood pressure) in historical workout data, the assist intensity can be dynamically adjusted. If cycling data, body data, and cadence maintain a specific proportional relationship over a long period, it indicates that the user is accustomed to or prefers this exercise intensity; if these three indicators suddenly fluctuate significantly, it suggests that the user may be experiencing a physical abnormality, requiring immediate adjustment of the assist intensity to ensure the user's safety.

[0037] Specifically, the current cycling data and current body data can be used as query indexes to determine the historical cadences that correspond to the current cycling data and current body data from the preset cadence library, and the historical cadences can be used as the ideal cadences.

[0038] Optionally, the preset cadence library includes multiple sub-libraries, each corresponding to a user identifier.

[0039] The step of determining the user's ideal cadence from a preset cadence library based on the current cycling data and current body data includes:

[0040] Based on the user's user identifier, determine the sub-database corresponding to the user identifier;

[0041] Based on the current cycling data and current body data, the user's ideal cadence is determined from each key-value pair in the sub-library.

[0042] Specifically, due to significant differences in individual physiological characteristics (such as cardiopulmonary function and muscle endurance) and exercise habits among different users, the correlation patterns between their heart rate, speed, and cadence often exhibit unique individual characteristics. Therefore, the preset cadence database adopts a multi-sub-database structure. Each sub-database is associated with a specific user through user identification (such as user ID and device-linked account), specifically storing that user's historical exercise data, historical body data, and corresponding historical cadence values. Based on this data, a user-specific "cycling data-body data-cadence" mapping relationship is established. This sub-database design effectively avoids data confusion between different users, thereby ensuring that the calculated ideal cadence results better match the user's actual needs and significantly improve individual adaptability.

[0043] Step 130: Update the assist intensity of the electric bicycle based on the user's current cadence and ideal cadence.

[0044] Here, cadence refers to the number of pedal rotations per minute during cycling; ideal cadence is a historical cadence stored in a preset cadence library, associated with specific current cycling data and current body data. This historical cadence is a cadence value that the user has maintained over a long period of cycling and formed a habit or preference. Based on this, by comparing the current cadence with the ideal cadence, it can be determined whether the user's current exercise intensity is appropriate; furthermore, based on the difference between the two, the assist intensity of the e-bike can be dynamically adjusted.

[0045] Optionally, step 130 includes:

[0046] Determine the difference between the current cadence and the ideal cadence; update the assist intensity of the electric bicycle based on the difference.

[0047] Specifically, the difference between the current cadence and the ideal cadence represents the degree of difference between the current cadence and the ideal cadence. By adjusting the assist intensity, the user's actual pedaling force can be affected, thereby changing the user's cadence and ultimately achieving the purpose of adjusting the user's exercise intensity.

[0048] Specifically, updating the assist level of the electric bicycle based on the difference includes:

[0049] The direction of the assist intensity adjustment is determined based on the difference; the assist intensity is updated according to the preset adjustment range based on the adjustment direction; the user's current cadence after the assist intensity update is obtained; if the duration of the current cadence exceeds a preset duration threshold, and the difference between the current cadence and the ideal cadence is less than a preset cadence threshold, the assist intensity update is stopped.

[0050] Specifically, when the current cadence is higher than the ideal cadence, it means the user needs to pedal harder to maintain the current speed, which may lead to muscle fatigue or abnormal heart rate. In this case, the assist intensity needs to be increased so that the user can maintain riding without excessive effort, thus naturally reducing the cadence and bringing it back to the ideal value. Preset adjustment ranges prevent sudden changes in assist intensity due to excessive adjustments in a single step, improving both the user experience and riding safety.

[0051] The preset duration threshold is used to determine whether the user has entered a stable cycling state, and the preset cadence threshold is used to determine whether the difference between the current cadence and the ideal cadence is acceptable. If the current cadence consistently meets the condition that the difference between it and the ideal cadence is less than the preset cadence threshold within the preset duration threshold, it indicates that the user is in a comfortable and stable exercise state, and there is no need to continue adjusting the assist intensity.

[0052] For example, Figure 2This is a schematic diagram of the control method for an electric bicycle provided in an embodiment of the present invention. Specifically, it first collects three types of data in real time: cadence, cycling heart rate, and cycling speed. A filtering algorithm is then used to eliminate sensor noise and environmental interference to ensure data accuracy. Next, it determines whether the three types of data remain stable over a period of time. If unstable, it directly enters the next round of cyclic monitoring; if stable, the stable data is recorded in the learning library (a preset cadence library), and then the cycle monitoring phase returns. It should be noted that data stability can be judged by the fluctuation amplitude and duration of the data over a period of time. If the fluctuation amplitude is less than a fluctuation threshold and the duration is greater than a duration threshold, it indicates that the currently collected data is stable. By recording the stable data of each movement in the learning library and using it for subsequent determination of the ideal cadence, the self-updating of the learning library and the accuracy of the ideal cadence determination can be achieved. While the user is cycling steadily, the system searches the learning library for historical records that match the current cycling data and current physical data. If no similar records are found, it returns to loop monitoring. If similar records are found, the system determines the ideal cadence based on the correspondence between cycling speed, cycling heart rate, and historical cadence in the preset cadence library, according to the current cycling data (cycling speed) and current physical data (cycling heart rate). Finally, it checks whether the error between the current real-time cadence and the ideal cadence is within a preset range. If it is within the range, it returns to loop monitoring. If it is not, it switches the assist level, completes the parameter adjustment, and then re-enters loop monitoring. It should be noted that due to changes in the user's physical condition, the correspondence between cadence, speed, and heart rate may deviate at different time periods (months / ages) for the same user. Therefore, the learning library may record multiple sets of cadence-speed-heart rate correspondences. Thus, the system can be set to use the correspondence closest to the current time point as the basis for determining the ideal cadence.

[0053] This invention provides a control method for an electric bicycle. The method includes: acquiring a user's current riding data and current body data, wherein the current riding data and current body data respectively represent the user's riding intensity and physical state during riding; determining the user's ideal cadence from a preset cadence library based on the current riding data and current body data, wherein the ideal cadence in the preset cadence library is determined through historical exercise data and historical body data; and updating the assist intensity of the electric bicycle based on the user's current cadence and ideal cadence. Specifically, the user's ideal cadence can be determined from the preset cadence library based on the current riding data and current body data, and then the assist intensity can be adjusted to keep the user's current cadence consistent with the ideal cadence, ensuring the user's exercise safety and simultaneously achieving automatic adjustment of the assist intensity, thus improving the efficiency of assist adjustment.

[0054] Example 2

[0055] Figure 3This is a flowchart of a preset cadence library generation method provided in Embodiment 2 of the present invention, as follows: Figure 2 As shown, it includes:

[0056] like Figure 3 As shown, it includes:

[0057] Step 210: Obtain the historical cadence, cycling speed, and cycling heart rate at each historical moment of the user's historical cycling process.

[0058] Specifically, the first step is to obtain the number of pedal rotations per minute (cadence), the actual riding speed, and the real-time heart rate at each historical moment during the user's historical riding process.

[0059] Step 220: Generate corresponding cadence-time curves, speed-time curves, and heart rate-time curves based on the historical cadence, cycling speed, and cycling heart rate at each historical moment.

[0060] Specifically, based on historical cadence, cycling speed, and heart rate at various historical moments, cadence-time curves, speed-time curves, and heart rate-time curves can be generated. The cadence-time curve is plotted with time on the horizontal axis and historical cadence on the vertical axis, reflecting the trend of cadence changes over time through continuous data points. The speed-time curve is plotted with time on the horizontal axis and cycling speed on the vertical axis, reflecting the trend of speed changes over time through continuous data points. The heart rate-time curve is plotted with time on the horizontal axis and cycling heart rate on the vertical axis, reflecting the trend of heart rate changes over time through continuous data points.

[0061] Step 230: Based on the cadence-time curve, speed-time curve, and heart rate-time curve, determine whether there is a corresponding relationship between the historical cadence, cycling speed, and cycling heart rate within the historical time period.

[0062] Specifically, by analyzing the trends of cadence-time curves, speed-time curves, and heart rate-time curves, it can be determined whether there is a corresponding relationship between cadence, speed, and heart rate.

[0063] Step 230 includes:

[0064] Determine the fluctuation range of historical cadence, cycling speed, and cycling heart rate over historical periods;

[0065] Within the historical time period, a target time period is determined based on the intersection of time periods where the fluctuation amplitude is less than a preset fluctuation threshold; if the target time period is greater than a preset duration threshold, then it is determined that there is a corresponding relationship between the historical cadence, cycling speed, and cycling heart rate within the historical time period.

[0066] Among them, the fluctuation range refers to the difference between the maximum and minimum values ​​of data within a historical period, and the fluctuation threshold is a preset allowable fluctuation range, specifically including: cadence fluctuation threshold (e.g., ±5RPM, meaning the historical cadence fluctuation range must be ≤5RPM), speed fluctuation threshold (e.g., ±2km / h, meaning the cycling speed fluctuation range must be ≤2km / h), and heart rate fluctuation threshold (e.g., ±5 beats / minute, meaning the cycling heart rate fluctuation range must be ≤5 beats / minute).

[0067] Specifically, the target time period is the period in which the fluctuation range of historical cadence, cycling speed, and cycling heart rate is all less than the corresponding preset fluctuation threshold.

[0068] Specifically, the fluctuation ranges of historical cadence, cycling speed, and cycling heart rate within the historical time period must be less than their respective fluctuation thresholds to ensure that the three remain in a stable fluctuation state within the time period, excluding data fluctuations caused by sudden events. Furthermore, the duration of this stable fluctuation state of historical cadence, cycling speed, and cycling heart rate must be greater than or equal to a preset duration threshold (e.g., 10 consecutive minutes) to ensure that this stable state is not a brief, accidental phenomenon, but rather a continuous performance of the user during the current ride. Through the combined judgment of the above two conditions of "low fluctuation range + long duration," the intrinsic correlation between historical cadence, cycling speed, and cycling heart rate can be effectively established, providing a reliable basis for subsequently generating a preset cadence database that accurately reflects the user's cycling habits.

[0069] For example, the cadence fluctuation threshold is ±5 RPM (i.e., the historical cadence fluctuation amplitude must be ≤5 RPM).

[0070] Speed ​​fluctuation threshold: ±2km / h (i.e., the fluctuation range of cycling speed must be ≤2km / h);

[0071] Heart rate fluctuation threshold: ±5 beats / minute (i.e., the heart rate fluctuation range during cycling must be ≤5 beats / minute);

[0072] Duration threshold: 10 minutes (i.e., a stable fluctuation state must last for ≥10 minutes).

[0073] If, during a historical period, the cadence is consistently between 88-90 RPM, the speed is consistently between 18-20 km / h, and the heart rate is consistently between 122-124 beats / minute, and this lasts for 11 minutes, then there is a corresponding relationship between (88-90 RPM, 18-20 km / h, and 122-124 beats / minute). These three parameters can be stored in a preset cadence library as parameter groups or key-value pairs. Then, the ideal cadence can be determined by using the current cycling data (speed) and current body data (heart rate) as query indexes.

[0074] Step 240: Generate a preset cadence library based on historical cadence, cycling speed and cycling heart rate that have corresponding relationships.

[0075] Specifically, the historical cadences that correspond to the cycling speed and heart rate are stored in the preset cadence library as key-value pairs; wherein the key is the combination of the cycling speed and the cycling heart rate, and the value is the historical cadence corresponding to the combination. The preset cadence library can then be constructed based on these key-value pairs.

[0076] Optionally, if the target time period is longer than a preset time threshold, then the target cadence, target cycling speed, and target cycling heart rate are determined based on the historical cadence, cycling speed, and cycling heart rate within the target time period; and a preset cadence library is generated based on the target cadence, target cycling speed, and target cycling heart rate.

[0077] Specifically, a target time period can be determined based on the fluctuation amplitudes. If the target time period is greater than a preset time threshold, it indicates that cycling has entered a stable period. Further, target cadence, target speed, and target heart rate can be determined based on the historical cadence, cycling speed, and heart rate within the target time period; a preset cadence library is then generated based on the target cadence, target speed, and target heart rate. The target cadence, target speed, and target heart rate can be the average of the historical cadence, speed, and heart rate within the target time period.

[0078] This invention provides a method for generating a preset cadence library. By using the preset cadence library, the ideal cadence corresponding to the user's current cycling state (such as real-time speed and heart rate) can be quickly matched. Then, by adjusting the assist intensity, the user's current cadence and ideal cadence can be kept consistent, ensuring the user's exercise safety. At the same time, the assist intensity can be automatically adjusted, improving the efficiency of assist adjustment.

[0079] Example 3

[0080] Figure 4 This is a schematic diagram of the control device for a power-assisted bicycle provided in Embodiment 3 of the present invention. Figure 4 As shown, the device includes:

[0081] The acquisition module 310 is used to acquire the user's current cycling data and current body data, wherein the current cycling data and current body data respectively represent the user's cycling intensity and physical state during the cycling process;

[0082] The ideal cadence determination module 320 is used to determine the user's ideal cadence from a preset cadence library based on the current cycling data and current body data, wherein the ideal cadence in the preset cadence library is determined through historical exercise data and historical body data;

[0083] The update module 330 is used to update the assist intensity of the electric bicycle based on the user's current cadence and ideal cadence.

[0084] An embodiment of the invention provides a control device for an electric bicycle. This device acquires the user's current riding data and current body data, wherein the current riding data and current body data respectively represent the user's riding intensity and physical state during riding; determines the user's ideal cadence from a preset cadence library based on the current riding data and current body data, wherein the ideal cadence in the preset cadence library is determined through historical exercise data and historical body data; and updates the assist intensity of the electric bicycle based on the user's current cadence and ideal cadence. Specifically, the ideal cadence of the user can be determined from the preset cadence library based on the current riding data and current body data, and then the assist intensity can be adjusted to keep the user's current cadence consistent with the ideal cadence, ensuring the user's exercise safety, while simultaneously achieving automatic adjustment of the assist intensity and improving the efficiency of assist adjustment.

[0085] Optionally, the control device for the electric bicycle also includes: a module for generating a preset cadence library, including:

[0086] The acquisition unit is used to acquire the user's historical cadence, cycling speed, and cycling heart rate at each historical moment during the user's historical cycling process.

[0087] The curve generation unit is used to generate corresponding cadence-time curves, speed-time curves, and heart rate-time curves based on the historical cadence, cycling speed, and cycling heart rate at each historical moment.

[0088] The correspondence determination unit is used to determine whether there is a correspondence between historical cadence, cycling speed and cycling heart rate within a historical time period based on the cadence-time curve, speed-time curve and heart rate-time curve.

[0089] The library generation unit is used to generate a preset cadence library based on historical cadence, cycling speed, and cycling heart rate that have corresponding relationships.

[0090] Optional, correspondence determination unit: includes:

[0091] The amplitude determination subunit is used to determine the fluctuation amplitude of historical cadence, cycling speed, and cycling heart rate over a historical period.

[0092] Determine sub-units for use

[0093] Within the historical time period, a target time period is determined based on the intersection of time periods where the fluctuation amplitude is less than a preset fluctuation threshold; if the target time period is greater than a preset duration threshold, then a corresponding relationship is determined between the historical cadence, cycling speed, and cycling heart rate within the historical time period.

[0094] Optionally, the library generation unit is specifically used to: if the target time period is longer than a preset time threshold, determine the target cadence, target cycling speed, and target cycling heart rate based on the historical cadence, cycling speed, and cycling heart rate within the target time period; and generate a preset cadence library based on the target cadence, target cycling speed, and target cycling heart rate.

[0095] Optionally, the historical cadence, cycling speed, and cycling heart rate are stored in the preset cadence library in key-value pairs; wherein the key is a combination of cycling speed and cycling heart rate, and the value is the historical cadence corresponding to the combination.

[0096] Optionally, the preset cadence library includes multiple sub-libraries, each corresponding to a user identifier. The ideal cadence determination module 320 includes:

[0097] The sub-library determination unit is used to determine the sub-library corresponding to the user identifier based on the user identifier of the user;

[0098] The retrieval unit is used to determine the user's ideal cadence from each key-value pair in the sub-database based on the current cycling data and current body data.

[0099] Optionally, update module 330 includes:

[0100] A difference determination unit is used to determine the difference between the current cadence and the ideal cadence;

[0101] The assist strength determination unit is used to update the assist strength of the electric bicycle based on the difference.

[0102] Optionally, the assist strength determination unit includes:

[0103] The adjustment method determines the sub-unit, which is used to determine the adjustment direction of the assist intensity based on the difference;

[0104] An update subunit is used to update the assist strength based on the adjustment direction and a preset intensity adjustment range;

[0105] Get sub-unit, used to obtain the user's current cadence after the assist intensity update;

[0106] The body mass subunit is used to stop updating the assist intensity if the duration of the current cadence exceeds a preset duration threshold and the difference between the current cadence and the ideal cadence is less than the preset cadence threshold.

[0107] The control device for the electric bicycle provided in this embodiment of the invention can execute the control method for the electric bicycle provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0108] Example 4

[0109] Figure 5 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0110] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0111] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0112] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the control methods for an electric bicycle.

[0113] In some embodiments, the control method for the electric bicycle can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the control method for the electric bicycle described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the control method for the electric bicycle by any other suitable means (e.g., by means of firmware).

[0114] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0115] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0116] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0117] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0118] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0119] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0120] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0121] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A control method for a power-assisted bicycle, characterized in that, include: The system acquires the user's current cycling data and current physical data, wherein the current cycling data and current physical data respectively represent the user's cycling intensity and physical state during the cycling process; Based on the current cycling data and current body data, the user's ideal cadence is determined from a preset cadence library, wherein the ideal cadence in the preset cadence library is determined through historical exercise data and historical body data; The assist level of the electric bicycle is updated based on the user's current cadence and ideal cadence.

2. The method according to claim 1, characterized in that, The historical exercise data includes cycling speed, and the historical physical data includes cycling heart rate; The method for generating the preset cadence library includes: Obtain the user's historical cadence, cycling speed, and cycling heart rate at each historical moment during their cycling journey; Generate corresponding cadence-time curves, speed-time curves, and heart rate-time curves based on historical cadence, cycling speed, and cycling heart rate at each historical moment. Based on the cadence-time curve, speed-time curve, and heart rate-time curve, determine whether there is a corresponding relationship between historical cadence, cycling speed, and cycling heart rate within a historical time period. A preset cadence library is generated based on historical cadence, cycling speed, and cycling heart rate that have corresponding relationships.

3. The method according to claim 2, characterized in that, The step of determining whether there is a corresponding relationship between historical cadence, cycling speed, and cycling heart rate within a historical time period based on the cadence-time curve, speed-time curve, and heart rate-time curve includes: Determine the fluctuation range of historical cadence, cycling speed, and cycling heart rate over historical periods; Within the historical period, the target time period is determined based on the intersection of time periods where the fluctuation amplitude is less than a preset fluctuation threshold; If the target time period is greater than the preset duration threshold, then it is determined that there is a corresponding relationship between the historical cadence, cycling speed and cycling heart rate within the historical time period.

4. The method according to claim 3, characterized in that, The process of generating a preset cadence library based on historical cadence, cycling speed, and cycling heart rate with corresponding relationships includes: If the target time period is longer than a preset time threshold, then the target cadence, target cycling speed and target cycling heart rate are determined based on the historical cadence, cycling speed and cycling heart rate within the target time period. A preset cadence library is generated based on the target cadence, target cycling speed, and target cycling heart rate.

5. The method according to claim 4, characterized in that, The preset cadence library includes multiple sub-libraries, and each sub-library corresponds to a user identifier. The step of determining the user's ideal cadence from a preset cadence library based on the current cycling data and current body data includes: Based on the user's user identifier, determine the sub-database corresponding to the user identifier; Based on the current cycling data and current body data, the user's ideal cadence is determined from each key-value pair in the sub-library.

6. The method according to claim 1, characterized in that, The step of updating the assist level of the electric bicycle based on the user's current cadence and ideal cadence includes: Determine the difference between the current cadence and the ideal cadence; The assist level of the electric bicycle is updated based on the difference.

7. The method according to claim 6, characterized in that, The step of updating the assist intensity of the electric bicycle based on the difference includes: The direction of adjustment for the assist intensity is determined based on the difference. Based on the adjustment direction, the assist strength is updated according to the preset intensity adjustment range; Get the user's current cadence after the power assist intensity update; If the duration of the current cadence exceeds a preset duration threshold, and the difference between the current cadence and the ideal cadence is less than the preset cadence threshold, then the assist intensity will stop being updated.

8. A control device for a power-assisted bicycle, characterized in that, include: The acquisition module is used to acquire the user's current cycling data and current body data, wherein the current cycling data and current body data respectively represent the user's cycling intensity and physical state during the cycling process; The ideal cadence determination module is used to determine the user's ideal cadence from a preset cadence library based on the current cycling data and current body data. The ideal cadence in the preset cadence library is determined through historical exercise data and historical body data. The update module is used to update the assist level of the electric bicycle based on the user's current cadence and ideal cadence.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the control method of the electric bicycle according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the control method for the electric bicycle as described in any one of claims 1-7.

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