Method for rider-specific and / or bicycle-specific determination of at least one bicycle function algorithm

Abstract

The invention relates to a method for the rider-specific and / or bicycle-specific determination of at least one bicycle function algorithm, said method comprising the following method steps: - acquiring sensor data from a plurality of cyclists and / or bicycles; - acquiring metadata that can be assigned to the sensor data; - determining a rider type based on the sensor data and the metadata; - determining a rider-specific bicycle function algorithm based on the sensor data and metadata that can be assigned to the rider type; - providing open-loop or closed-loop control of the bicycle and / or a bicycle component based on the rider-specific bicycle function algorithm.

Description

[0001] Description

[0002] title

[0003] Method for driver-specific and / or bicycle-specific determination of at least one bicycle function algorithm

[0004] State of the art

[0005] EP 3 531 071 A1 describes a method for supporting a shared driving experience for drivers of a plurality of mobile units.

[0006] Disclosure of the invention

[0007] The invention relates to a method for the rider-specific and / or bicycle-specific determination of at least one bicycle function algorithm, comprising the following method steps:

[0008] - Collection of sensor data from a large number of cyclists and / or bicycles;

[0009] - Collecting metadata that can be associated with the sensor data;

[0010] - Determination of a driver type based on the sensor data and metadata;

[0011] - Determination of a rider-specific bicycle function algorithm based on the sensor data and metadata attributable to the rider type;

[0012] - Controlling or regulating the bicycle and / or a bicycle component based on the rider-specific bicycle function algorithm. This can advantageously optimize the control of the bicycle and / or bicycle component.

[0013] The bicycle function algorithm can be stored locally, for example in a storage unit of the bicycle or in a storage unit of the bicycle component. It is also conceivable for the bicycle function algorithm to be stored decentrally, for example in a cloud. The method for determining the bicycle function algorithm preferably takes place in a cloud or another type of computing network or on a server. The sensor data can be recorded directly from the bicycles and / or indirectly from bicycle components. The metadata is preferably information that cannot be recorded via sensors of the bicycle or sensors of the bicycle component. The metadata can, for example, be queried by the bicycle and / or the bicycle component and recorded via user input. It is also conceivable for the metadata to be provided via a database.

[0014] The driver types can be, for example, a sporty driver type, a safety-conscious driver type, a risk-loving driver type, a comfort-conscious driver type, a fitness-oriented driver type, a speed-oriented driver type, an acceleration-oriented driver type, etc. in preferably different gradations.

[0015] The bicycle can in particular be designed as an electric bicycle. In the context of this application, an electric bicycle is to be understood in particular as a bicycle that has a drive unit for assisting the rider. The electric bicycle is preferably designed as an e-bike, a pedelec, a cargo bike, a folding bicycle or the like. The drive unit has a motor, which can be designed, for example, as a mid-engine or as a hub motor. The motor is preferably designed as an electric motor. The drive unit is connected to an energy storage device for supplying the drive unit with energy. The energy supply unit is preferably designed as a battery pack and has a battery housing, which is preferably detachably connected to a frame of the bicycle. The electric bicycle comprises electronics with a control unit for controlling or regulating the electric bicycle.The electronics preferably comprise a sensor unit, which may include, for example, motion sensors, torque sensors, speed sensors, a GNSS receiver, magnetic sensors, or the like. Furthermore, the electronics comprise a communication interface for wirelessly connecting the electric bicycle to an external device, such as a smartphone, and / or a server.

[0016] The bicycle component can be designed, for example, as a bicycle light, a suspension system, an anti-lock braking system, a seat post, a gearshift, or the like. It is also conceivable for the bicycle component to be designed as an external device, in particular a mobile device, such as a smartphone, wherein the smartphone is preferably connectable to the bicycle during use.

[0017] The determination of the rider type and the determination of the bicycle function algorithm can be based on a machine learning system. In the context of this application, a machine learning system is understood to mean, in particular, algorithms that build a statistical model using training data. The statistical model can, for example, be used to determine parameters and attributes that go beyond the scope of the training data. The algorithms of the machine learning system can be supervised learning, unsupervised learning, or reinforcement learning. The machine learning system can, for example, be implemented as a neural network.

[0018] The training data, in particular the sensor data and the metadata, are preferably collected by the bicycle and provided for training the machine learning system. However, it is also conceivable that the training data is collected and / or provided partially or completely by an external device. The machine learning system is preferably trained on a server. It is also conceivable that the machine learning system is also trained locally on the vehicle or the external device, and then the multiple trained machine learning systems are consolidated on the server.

[0019] The method according to the invention can have a single machine learning system for determining the bicycle function algorithm or two machine learning systems for determining the bicycle function algorithm and the rider type. The training data for the two machine learning systems are at least partially identical, preferably essentially completely identical. The machine learning systems can be designed the same or differently. Furthermore, it is proposed that the machine learning system is designed to carry out a classification method, in particular by means of a logistic regression, a decision tree, a KNN approach, or a Naive Bayes classification. Furthermore, it is proposed that the machine learning system is designed to carry out a regression method, in particular by means of a linear regression, a decision tree, a Gaussian bell regression, or an KNN approach.Advantageously, the choice of classification or regression method allows the training to be optimally adapted to the subjective driver condition parameter and / or the specific application. The machine learning system can also be implemented as a neural network, particularly as a neural network designed to process time series, such as an RNN neural network, an LSTM neural network, or a TCN neural network.

[0020] Furthermore, it is proposed that the driver type be determined based on a cluster analysis. Preferably, the sensor data and the metadata are correlated prior to the cluster analysis. This can be done, for example, using a hierarchical cluster analysis, a partitioning clustering method, in particular the K-Means algorithm, a density-based clustering method, in particular maximum margin clustering, etc. In particular, the cluster analysis is performed using a machine learning system.

[0021] It is also proposed that the sensor data and metadata be standardized before cluster analysis. This can advantageously ensure optimal determination of driver types even without correlation. Standardization can be achieved, for example, using a z-transformation or another standardization method known to those skilled in the art.

[0022] It is further proposed that the sensor data be acquired by a sensor arranged on the bicycle and / or the rider. The sensor is, in particular, part of a sensor unit. The sensor can be designed, for example, as an acceleration sensor, a gyro sensor, a magnetic sensor, a torque sensor, a speed sensor, a power sensor, a brake pressure sensor, a cadence sensor, a cardiac data sensor, in particular a heart rate monitor, a brightness sensor, an optical sensor, in particular in the form of a camera, a microphone, etc. One sensor or several different sensors can be provided to acquire the sensor data.

[0023] In addition, it is proposed that the sensor data be in the form of heart data, in particular a pulse, a driver performance, a speed, an acceleration, a brake pressure, a cadence and / or a torque.

[0024] Furthermore, it is proposed that the metadata be configured as a perceived effort, a driving experience, driving comfort, and / or a driving style. Alternatively or additionally, the metadata can be configured as route information, environmental information, and / or gradient information.

[0025] It is further proposed that a riding experience be determined based on the bicycle function algorithm and sensor data from a bicycle acquired during a ride, with the riding experience being displayed on a display unit of the bicycle. The display unit can be detachably mounted on the bicycle or permanently integrated into the bicycle. The display unit can be designed, for example, as an on-board computer, a screen, or a smartphone. The riding experience can be displayed during the ride and / or at the end of the ride based on the recorded sensor data.

[0026] It is also proposed that shift information be determined based on the bicycle function algorithm and sensor data from a bicycle captured during a ride, with a shifting unit of the bicycle being controlled based on the shift information. The shifting unit can, for example, be an electronically controlled, preferably automatic, gearshift. This advantageously allows for a shifting process that is optimally adapted to the rider.

[0027] Furthermore, it is proposed that braking information be determined based on the bicycle function algorithm and sensor data from a bicycle acquired during a ride, with a brake assist unit of the bicycle being controlled based on the braking information. The brake assist unit is advantageously designed to be electronically controllable and / or adjustable. The brake assist unit is preferably designed as an anti-lock braking system. This advantageously allows the braking process to be optimally adapted to the rider.

[0028] Furthermore, the invention relates to a bicycle, in particular an electric bicycle, with a

[0029] A control unit for controlling or regulating a bicycle, comprising a sensor unit for acquiring sensor data, wherein the bicycle and / or an external device connected to the bicycle is controlled based on the previously described bicycle function algorithm. This advantageously allows the bicycle functions to be optimized.

[0030] Drawings

[0031] Further advantages emerge from the following drawing description. The drawings, the description, and the claims contain numerous features in combination. Those skilled in the art will also expediently consider the features individually and combine them into further meaningful combinations.

[0032] They show:

[0033] Fig. 1 is a schematic view of a system for collecting training data for a machine learning system;

[0034] Fig. 2 is a flowchart showing a method for determining a first bicycle function algorithm;

[0035] Fig. 3 is a flowchart showing a method for determining a second bicycle function algorithm;

[0036] Fig. 4 is a flowchart showing a method for determining a third bicycle function algorithm;

[0037] Fig. 5 is a flowchart showing a method for controlling an electric bicycle based on the bicycle function algorithms.

[0038] Description of the embodiments

[0039] Fig. 1 schematically illustrates a system 10 for acquiring sensor data and metadata and for determining a bicycle function algorithm. The system 10 comprises a server 12 in the form of a web server and a motor-assisted vehicle 14. The motor-assisted vehicle 14 is embodied, for example, as a bicycle, in particular as an electric bicycle 16. The electric bicycle 16 can be embodied, for example, as a pedelec or as an e-bike.

[0040] The electric bicycle 16 has a housing in the form of a frame 20 or a bicycle frame. Two wheels 22 are connected to the frame 20. The electric bicycle 16 also has an energy storage device 24 in the form of a battery pack. The electric bicycle 16 also has a drive unit 26 that includes an electric motor or auxiliary motor. The electric motor is preferably designed as a permanent magnet-excited, brushless DC motor. The electric motor is designed, for example, as a mid-motor, although a hub motor or the like is also conceivable. The electric bicycle 16, in particular the drive unit 26 of the electric bicycle 16, is supplied with energy via the energy storage device 24. The energy storage device 24 can be fastened to the frame 20 from the outside or can be integrated into the frame 20.

[0041] The electric bicycle 16 comprises a control unit (not shown) designed to control or regulate the electric bicycle 16, in particular the electric motor. The electric bicycle 16 has a pedal crank 28. The pedal crank 28 has a pedal crankshaft (not shown). The control unit of the electric bicycle 16 is connected to a sensor unit (not shown). The sensor unit of the electric bicycle 16 comprises, for example, several sensor elements, such as a torque sensor, an acceleration sensor, a speed sensor, a brightness sensor, and a magnetic sensor. The sensors can be arranged in different positions depending on the intended use. The magnetic sensor, the acceleration sensor, and the torque sensor are arranged, for example, in the area of ​​the drive unit 26. The brightness sensor is, for example, integrated into a lighting system 31 of the electric bicycle 16.In addition, the electric bicycle 16 has an electronic switching unit (not shown in detail), which is connected to the control unit in such a way that the electronic switching unit can be controlled via a switching signal from the control unit. The electronic switching unit preferably has an electrical actuator.

[0042] The control unit and the drive unit 26 with the electric motor and the pedal crankshaft are arranged in a drive housing 29 connected to the frame 20. The drive movement of the electric motor is preferably transmitted to the pedal crankshaft via a gear (not shown), wherein the level of assistance provided by the drive unit 26 is controlled or regulated by the control unit. The control unit is designed to control the drive unit 26 in such a way that the rider of the electric bicycle 16 is assisted when pedaling. The control unit is preferably designed to be operable by the rider, so that the rider can adjust the level of assistance.

[0043] The control unit and the sensor unit are assigned to an electronics unit (not shown) of the electric bicycle 16. The electronics unit comprises, for example, a circuit board on which a computing unit in the form of a CPU, a memory unit, and the sensor unit are arranged. The electronics unit is, for example, arranged entirely in the drive housing 29 of the drive unit 26. However, it is also conceivable for the electronics unit to be only partially arranged in the drive housing, with components of the electronics unit being arranged in other areas of the electric bicycle 16. Furthermore, an arrangement of the electronics unit outside the drive housing 29 is also conceivable.

[0044] The electric bicycle 16 also includes, for example, an on-board computer 30, which is arranged on a handlebar 32 of the electric bicycle 16. The on-board computer 30 is designed to be partially detachable from the electric bicycle 16. The on-board computer 30 includes a display unit 34 designed to display information. The on-board computer 30 also includes a control element via which the user or rider can control the on-board computer 30 and / or the electric bicycle 16. The control element is designed, for example, as a touch-sensitive screen; however, it is also conceivable for the control element to be formed from buttons or keys. The on-board computer 30 is connected to the control unit of the electric bicycle 16 such that information can be exchanged.For example, a speed determined by the control unit, a set level of assistance of the electric motor, route information from a navigation unit and a charge state of the energy storage device 24 can be displayed via the display unit 34.

[0045] The connection between the on-board computer 30 and the electronics of the electric bicycle 16 can be established via a wireless communication interface (not shown) of the electric bicycle 16 or via a wired connection. The wireless communication interface of the electric bicycle 16 is embodied, for example, as a short-range communication interface in the form of a BLE (Bluetooth Low Energy) interface. Alternatively, other short-range communication interfaces are also conceivable. Alternatively or additionally, it is also conceivable for the electric bicycle 16 to have a long-range communication interface, for example a mobile radio interface, in particular an LTE interface, which is designed to connect the electric bicycle 16 to a server, for example a web server.

[0046] The system optionally comprises an external device 100 in the form of a smartphone 102. The external device 100 has a wireless communication interface designed to connect the external device 100 to the electric bicycle 16 and to the server 12. The wireless communication interface of the external device 100 comprises, for example, a BLE interface for connecting to the electric bicycle 16 and an LTE interface for connecting to the server 12. Thus, the electric bicycle 16 can exchange data with the server 12 using the external device 100. In the case of an electric bicycle 16 with a long-range communication interface, a direct exchange of data between the electric bicycle 16 and the server 12 would also be possible.

[0047] Figure 2 shows a flowchart with a first exemplary method for determining a first bicycle function algorithm. The bicycle function is designed to determine the riding experience.

[0048] In a first step 200, sensor data is recorded by the sensor unit of the respective electric bicycles while a plurality of different electric bicycles 16 are riding with different riders. The sensor data includes, for example, first sensor data in the form of rider performance, in particular the power the rider has contributed during the ride. Furthermore, the sensor data includes, for example, second sensor data in the form of an average speed. Alternatively or additionally, a maximum speed would also be conceivable, for example. Furthermore, the sensor data includes third sensor data in the form of an acceleration parameter, wherein the acceleration parameter can be positive during acceleration or negative during deceleration, for example during braking.Optionally, the sensor data may include fourth sensor data in the form of pulse data, which is recorded, for example, by a chest strap worn by the driver while driving.

[0049] In a step 202, metadata is recorded during or after the ride. The metadata is recorded, for example, by the electric bicycle 16. However, it is alternatively or additionally also conceivable for the metadata to be recorded by the mobile device 100. The metadata comprises, for example, first metadata in the form of a riding experience parameter. The riding experience parameter is queried in a rider-specific manner, for example, by the display unit 34 of the electric bicycle 16, by the rider being able to make an input. For example, the rider can subjectively enter their riding experience on a scale of 1 to 5, with 5 being a particularly good riding experience. The entered value is saved as a riding experience. Additionally and optionally, the metadata can also comprise further metadata in the form of a riding comfort parameter, a riding style parameter, or a perceived exertion parameter.

[0050] The metadata is assigned to the sensor data and provided to the web server 12 via the wireless communication interface of the electric bicycle 16 in a step 204.

[0051] In a subsequent step 206, the sensor data and metadata are transformed by a z-transformation:

[0052] Zx n = - X ^ sn (D standardized.

[0053] In a further method step 208, a cluster analysis is performed on the web server 12, which determines bicycle types based on the recorded and standardized sensor data and metadata of a large number of cyclists. The respective sensor data and metadata can be assigned to the bicycle types. The cluster analysis is carried out, for example, using a machine learning system, in particular a hierarchical algorithm. The bicycle types are, for example, designed as a sporty rider type, a safety-conscious rider type, and a balanced rider type.

[0054] In three further method steps 210, 212, 214, a bicycle function algorithm is determined for each rider type using a machine learning system based on the sensor data and metadata assigned to the respective rider type. The determined bicycle function algorithms are thus specific to the rider type. Thus, a different set of training data consisting of the sensor data and metadata is used for each rider type. The machine learning system is preferably designed as a nonlinear and non-parametric learning system, for example, as a Gaussian process regression. For example, a correlation between the sensor data in the form of a speed parameter, an engine performance parameter, or the rider performance parameter and the metadata in the form of the riding experience is used as training data.

[0055] Figure 3 shows a flowchart with a second exemplary method for determining a second bicycle function algorithm. The bicycle function is designed to determine gear shift information.

[0056] In a first step 300, sensor data is collected by the sensor unit of the respective electric bicycles during the ride of a plurality of different electric bicycles 16 with different riders. The sensor data includes, for example, first sensor data in the form of rider performance. Furthermore, the sensor data includes, for example, second sensor data in the form of an average speed. Furthermore, the sensor data includes fifth sensor data in the form of a cadence and sixth sensor data in the form of a torque acting on the pedal crank.

[0057] In a step 302, metadata is recorded during the ride or after the ride. The metadata is recorded, for example, by the electric bicycle 16. The metadata includes, for example, second metadata in the form of ride comfort. The ride comfort is queried on a rider-specific basis, for example, by the display unit 34 of the electric bicycle 16, by which the rider can make an input. For example, the rider can subjectively enter their ride comfort on a scale of 1 to 3, with 3 being particularly good ride comfort. The entered value is saved as ride comfort. By way of example and optionally, additional metadata in the form of route information, environmental information, and gradient information are provided. This additional metadata is provided via the communication interface, for example, by an external server or the web server 12.Route information refers to information about the route traveled. Environmental information can, for example, be weather information, in particular wind strength and / or wind direction during the journey. Gradient information refers, in particular, to information about the gradient of the route sections traveled during the journey and for which sensor data was recorded.

[0058] The metadata is assigned to the sensor data and provided to the web server 12 via the wireless communication interface in a step 304.

[0059] In a step 306, the sensor data and the metadata are correlated with each other.

[0060] In a further method step 308, a cluster analysis is performed on the web server 12, which determines bicycle types based on the recorded and standardized sensor data and metadata of a large number of cyclists. The respective sensor data and metadata can be assigned to the bicycle types. The cluster analysis is carried out, for example, using a machine learning system, in particular a hierarchical algorithm. The bicycle types are configured, for example, as high-frequency rider types (high cadence), normal-frequency rider types, and low-frequency rider types.

[0061] In three further method steps 310, 312, 314, a bicycle function algorithm is determined for each rider type using a machine learning system based on the sensor data and metadata assigned to the respective rider type. The determined bicycle function algorithms are thus specific to the rider type. Thus, a different set of training data consisting of the sensor data and metadata is used for each rider type. The machine learning system is preferably designed as a nonlinear and nonparametric learning system, for example, as a Gaussian process regression. For example, a correlation between the sensor data in the form of rider cadence and rider torque and the metadata in the form of route information and environmental information is used as training data.

[0062] Figure 4 shows a flowchart with a third exemplary method for determining a third bicycle function algorithm. The bicycle function is designed to determine braking information.

[0063] In a first step 400, sensor data is acquired by the sensor unit of the respective electric bicycles while a plurality of different electric bicycles 16 are traveling with different riders. The sensor data includes, for example, second sensor data in the form of an average speed. Furthermore, the sensor data includes seventh sensor data in the form of a brake pressure of an anti-lock braking system (not shown) of the electric bicycle 16.

[0064] In a step 402, metadata is recorded during the ride or after the ride. The metadata is recorded, for example, by the electric bicycle 16. The metadata includes, for example, second metadata in the form of riding safety. Riding safety is queried on a rider-specific basis, for example, by the display unit 34 of the electric bicycle 16, by the rider being able to make an input. For example, the rider can subjectively enter their riding safety on a scale of 1 to 10, with 10 being particularly good riding safety. The entered value is saved as riding safety. For example and optionally, additional metadata in the form of a riding experience is recorded and provided as route information. This additional metadata is partly provided via the communication interface from an external server or the web server 12.

[0065] The metadata is assigned to the sensor data and provided to the web server 12 via the wireless communication interface in a step 404.

[0066] In a step 406, the sensor data and metadata are correlated. Alternatively, the sensor data and metadata can be standardized as described above.

[0067] In a further method step 408, a cluster analysis is performed on the web server 12, which determines bicycle types based on the recorded and standardized sensor data and metadata of a large number of cyclists. The respective sensor data and metadata can be assigned to the bicycle types. The cluster analysis is performed, for example, using a machine learning system, in particular a hierarchical algorithm. The bicycle types are, for example, trained as a cautious rider type and a sporty rider type.

[0068] In two further method steps 410, 412, a third bicycle function algorithm is determined for each rider type using a machine learning system based on the sensor data and metadata assigned to the respective rider type. The determined bicycle function algorithms are thus specific to the riding type. Thus, a different set of training data consisting of the sensor data and metadata is used for each rider type. The machine learning system is preferably designed as a non-linear and non-parametric learning system, for example as a Gaussian process regression. For example, a correlation between the sensor data in the form of brake pressure and speed and the metadata in the form of riding safety is used as training data. It is also conceivable to take additional metadata into account when determining the bicycle function algorithm, for example the type of route traveled.

[0069] Figure 5 shows a method for controlling the electric bicycle 16 in a flow chart.

[0070] In a first step 600, the first bicycle function algorithms, the second bicycle function algorithms, and the third bicycle function algorithms are provided to the electric bicycle 16. The provision can be performed, for example, by the web server 12 via the wireless communication interface of the electric bicycle 16.

[0071] In a second step 602, the bicycle function algorithms are stored on the electric bicycle 16, for example, in a memory unit of the electronics. Alternatively, storage on the mobile device 100 connected to the electric bicycle 16 is also conceivable.

[0072] In a third step 604, the rider type is selected. The rider type can be selected by user input from the rider via the on-board computer 30 or the mobile device 100. Alternatively, it is also conceivable for the electric bicycle 16 to automatically assign the rider to a rider type based on sensor data, without any input or action from the rider. For example, only one riding type is queried or determined. However, it is also conceivable for one riding type to be queried or determined for each bicycle function algorithm.

[0073] In a fourth step 606, the electric bicycle 16 is controlled based on the provided bicycle function algorithms, whereby for the respective rider only the bicycle function algorithms that correspond to the rider type are used.

[0074] Alternatively, it is also conceivable that the rider type is determined before the bicycle function algorithms are provided by the web server 12 and that only the bicycle function algorithms that can be assigned to the rider are provided to the electric bicycle 16.

[0075] Alternatively or additionally, the determination of further bicycle function algorithms is conceivable.

[0076] For example, it is conceivable to determine a fourth bicycle function based on which the range of the electric bicycle 16 can be determined. This can advantageously optimize the range determination. The bicycle function is determined in particular based on sensor data in the form of energy consumption and rider behavior, which can vary depending on the route. The rider-specific fourth bicycle function is designed in particular to determine range information based on the motor power, rider power, route information, and / or speed.

[0077] Furthermore, it is also conceivable to determine a fifth bicycle function, based on which accident information can be determined, which is designed to control the electric bicycle 15 in the event of an accident. The electric bicycle 16 controls the electric bicycle 16 based on the accident information, wherein the control can be implemented, for example, in the form of a reduction in motor power or an increase in brake pressure. Furthermore, the control of the electric bicycle 16 can be implemented based on the accident information in the form of transmitting a warning signal via the communication interface of the electric bicycle 16. The threshold for transmitting the signal can be adjusted by the fifth bicycle function algorithm.

Claims

Claims 1 . Method for the rider-specific and / or bicycle-specific determination of at least one bicycle function algorithm, comprising the following method steps: - Collection of sensor data from a large number of cyclists and / or bicycles; - Collecting metadata that can be associated with the sensor data; - Determination of a driver type based on the sensor data and metadata; - Determination of a rider-specific bicycle function algorithm based on the sensor data and metadata attributable to the rider type; - Control or regulation of the bicycle and / or a bicycle component based on the rider-specific bicycle function algorithm.

2. Method according to claim 1, characterized in that the determination of the driver type is based on a cluster analysis.

3. Method according to claim 2, characterized in that the sensor data and the metadata are correlated before the cluster analysis.

4. Method according to one of claims 2 or 3, characterized in that the sensor data and the metadata are standardized before the cluster analysis.

5. Method according to one of claims 2 to 4, characterized in that the cluster analysis is carried out by means of a machine learning model.

6. Method according to one of the preceding claims, characterized in that the sensor data are recorded by a sensor arranged on the bicycle and / or on the rider.

7. Method according to one of the preceding claims, characterized in that the sensor data is designed as heart data, in particular as pulse information, as driver performance, as speed, as acceleration, as brake pressure, as cadence, as yaw rate, as roll rate, as steering angle and / or as torque 8. Method according to one of the preceding claims, characterized in that the metadata is designed as a feeling of exertion, as a driving experience, as driving comfort and / or as a driving style.

9. Method according to one of the preceding claims, characterized in that the metadata is designed as route information, as environmental information and / or as gradient information 10. Method according to one of the preceding claims, characterized in that the bicycle function algorithm is stored locally.

11. Method according to one of the preceding claims, characterized in that a determination of a riding experience is carried out based on the bicycle function algorithm and by sensor data of a bicycle recorded during a ride, wherein the riding experience is displayed on a display unit (34) of the bicycle.

12. Method according to one of the preceding claims, characterized in that a determination of switching information is carried out based on the bicycle function algorithm and by sensor data of a bicycle recorded during a ride, wherein a switching unit of the bicycle is controlled based on the switching information.

13. Method according to one of the preceding claims, characterized in that a determination of braking information is carried out based on the bicycle function algorithm and by sensor data of a bicycle recorded during a ride, wherein a brake assistance unit of the bicycle is controlled based on the braking information.

14. Bicycle, in particular an electric bicycle (16), with a control unit for controlling or regulating the bicycle, with a sensor unit for recording sensor data, wherein the bicycle and / or an external device (100) connected to the bicycle is controlled based on the bicycle function algorithm according to claim 1.

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