Controller, learning method of learning model, computer program, and storage medium

Abstract

To provide a controller of a component that optimally and automatically controls a component including a stretching / contracting mechanism, a learning method of a learning model, a computer program, and a storage medium.SOLUTION: A controller includes a control unit that controls a stretching / contracting mechanism on the basis of output information concerning control of the stretching / contracting mechanism outputted from a learning model according to input information which includes at least one of travel information on a human powered vehicle relating to travel of the human powered vehicle and travel environment information on the human powered vehicle, and an evaluation unit that evaluates the output information outputted from the learning model. The evaluation unit is an evaluation button that accepts evaluation on control based on the output information sent from a user of the human powered vehicle, and updates the learning model on the basis of the evaluation by the evaluation unit.SELECTED DRAWING: Figure 11

Description

[Technical field]

[0001] The present invention relates to a control device for controlling components of a human-powered vehicle, a learning method for a learning model, a computer program, and a storage medium. [Background technology]

[0002] There are human-powered vehicles that use human power at least partially, such as bicycles, electrically assisted bicycles, and electric bicycles known as E-bikes. Human-powered vehicles include multiple components such as a transmission, a brake device, a seat post, and a suspension. A method has been proposed for transmitting operation commands by wireless communication signals to not only the transmission but also each of the multiple components (Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2018-089989 A Summary of the Invention [Problem to be solved by the invention]

[0004] Among the multiple components, the seat post, including the telescopic mechanism, and the suspension are involved in vibrations that are closely related to the ride comfort of a human-powered vehicle. By suppressing vibrations or controlling the spring constant to an appropriate coefficient in the seat post and suspension, the ride comfort of a human-powered vehicle can be improved and efficient driving can be achieved with less effort. A rider needs skill to manually control the seat post and suspension appropriately while driving. It is expected that optimal automatic control will be realized according to the driving conditions, road conditions, and purpose of the human-powered vehicle, in line with the user's preferences.

[0005] An object of the present invention is to provide a component control device that automatically and optimally controls a component including an extension / retraction mechanism, a learning method for a learning model, a computer program, and a storage medium. [Means for solving the problem]

[0006] (1) A control device according to a first aspect of the present invention includes a control unit that controls a telescopic mechanism based on output information related to control of the telescopic mechanism that is output by a learning model in response to input information related to traveling of a human-powered vehicle. This makes it possible to realize automatic control of the telescopic mechanism according to various situations or driving environments based on a variety of input information related to the driving of the human-powered vehicle.

[0007] (2) In the control device according to a second aspect of the present invention, the telescopic mechanism is a suspension of the human-powered vehicle, and the output information includes at least one of a stroke length, a lockout state, a spring force, and a damping rate. This makes it possible to realize automatic control of the suspension of the human-powered vehicle in accordance with various situations or driving environments based on a variety of input information related to the driving of the human-powered vehicle.

[0008] (3) In a control device according to a third aspect of the present invention, the suspension includes a first member and a second member movably mounted relative to the first member, and the input information includes a first acceleration output by a first acceleration sensor attached to the first member and a second acceleration output by a second acceleration sensor attached to the second member. This allows automatic suspension control of the human-powered vehicle according to road conditions based on vibration information obtained from an acceleration sensor attached to the movable member.

[0009] (4) In a control device according to a fourth aspect of the present invention, the learning model outputs the output information so that a difference between the first acceleration and the second acceleration falls within a predetermined range. This allows automatic suspension control for human-powered vehicles to be achieved that adjusts to road conditions while absorbing vibrations within an appropriate range.

[0010] (5) In a control device according to a fifth aspect of the present invention, the telescopic mechanism is a seat post of the human-powered vehicle, and the output information includes at least one of seat height, seat inclination, fore-aft position, and left-right position. This makes it possible to realize automatic control of the seat post of the human-powered vehicle in accordance with various situations or driving environments based on a variety of input information related to the driving of the human-powered vehicle.

[0011] (6) In a control device according to a sixth aspect of the present invention, the input information includes at least one of driving information of the human-powered vehicle and driving environment information of the human-powered vehicle. This allows for appropriate automatic control of the telescopic mechanism in accordance with the driving environment for the human-powered vehicle.

[0012] (7) In a control device according to a seventh aspect of the present invention, the driving information includes at least one of the driving speed of the human-powered vehicle, the acceleration of the human-powered vehicle, the attitude of the human-powered vehicle, the crank cadence of the human-powered vehicle, the driving force of the human-powered vehicle, the load balance of the human-powered vehicle, the gear ratio of the human-powered vehicle, the amount of brake operation of the human-powered vehicle, and physical information of the user. This makes it possible to realize automatic control of the telescopic mechanism according to various situations or driving environments based on a variety of input information related to the driving of the human-powered vehicle.

[0013] (8) In a control device according to an eighth aspect of the present invention, the traveling environment information of the human-powered vehicle includes at least one of a wheel contact state, a road surface condition, position information, and weather. This makes it possible to realize automatic control of the telescopic mechanism according to various situations or driving environments based on a variety of input information related to the driving of the human-powered vehicle.

[0014] (9) In a control device according to a ninth aspect of the present invention, the control unit outputs the control content of the telescopic mechanism to a notification unit that notifies a user. This allows the user to recognize the control content of the extension mechanism that is tailored to various situations or driving environments based on a variety of input information regarding the driving of the human-powered vehicle, thereby realizing automatic control that does not cause any discomfort to the user.

[0015] (10) A control device according to a tenth aspect of the present invention includes an evaluation unit that evaluates the output information, and updates the learning model based on the evaluation by the evaluation unit. This makes it possible to realize automatic control of the telescopic mechanism that matches the driving characteristics and preferences of the user who rides the human-powered vehicle.

[0016] (11) In a control device according to an eleventh aspect of the present invention, the evaluation unit evaluates the output information based on post-control input information which is the input information after the control unit controls the extension / contraction mechanism. This makes it possible to realize automatic control of the telescopic mechanism that matches the driving characteristics and preferences of the user who rides the human-powered vehicle.

[0017] (12) A control device according to a twelfth aspect of the present invention includes an operation unit that accepts a designation operation regarding the output information, and evaluates the output information based on a comparison between post-control output information output from the learning model based on the post-control input information and the designation operation. This makes it possible to realize automatic control of the telescopic mechanism that matches the operation content based on the driving characteristics and preferences of the user who rides the human-powered vehicle.

[0018] (13) In the control device according to the thirteenth aspect of the present invention, the learning model is prepared for each driving course on which the human-powered vehicle is to travel, and a selection of a driving course is accepted, and the control unit controls the extension mechanism based on the output information output by the learning model corresponding to the selected driving course in response to the input information. This makes it possible to realize automatic control of the telescopic mechanism in response to various situations or driving environments based on a variety of input information suited to the driving course of the human-powered vehicle.

[0019] (14) A method for generating a learning model according to a fourteenth aspect of the present invention uses a neural network that outputs output information related to control of a telescopic mechanism, one of the components of a human-powered vehicle, when input information related to the running of the human-powered vehicle is input, acquires the input information related to the running of the human-powered vehicle, provides the acquired input information to the neural network to identify output information output, and, when the telescopic mechanism is controlled based on the identified output information, learns parameters in an intermediate layer of the neural network by a computer so as to improve the evaluation of the running state of the human-powered vehicle. For this reason, a learning model is generated so that output information for controlling the telescopic mechanism based on various input information related to the driving of the human-powered vehicle is output in accordance with various situations or driving environments. By using the output information output from the learning model, appropriate automatic control can be realized according to the situation or driving environment.

[0020] (15) A learning model according to the fifteenth aspect of the present invention comprises an input layer which inputs input information relating to the driving of a human-powered vehicle, an output layer which outputs output information relating to the control of a part of a component of the human-powered vehicle having an extension mechanism, and an intermediate layer having parameters learned to improve the evaluation of the driving state of the human-powered vehicle when the part is controlled based on the output information output from the output layer, and when the input information is input to the input layer, a calculation is performed in the intermediate layer, and output information relating to the control of the part is output from the output layer. As a result, appropriate automatic control can be achieved according to the situation or driving environment using a trained learning model that outputs output information for controlling the extension mechanism based on various input information regarding the driving of the human-powered vehicle.

[0021] (16) A computer program according to a sixteenth aspect of the present invention causes a computer to execute a process of acquiring input information regarding the traveling of a human-powered vehicle using a neural network that outputs output information regarding the control of a part of a component of the human-powered vehicle that has an extension mechanism when input information regarding the traveling of the human-powered vehicle is input, acquiring input information regarding the traveling of the human-powered vehicle by providing the acquired input information to the neural network to identify output information outputted, and learning parameters in an intermediate layer of the neural network so as to improve an evaluation of comfort for occupants of the human-powered vehicle when the part is controlled based on the identified output information. For this reason, a learning model is generated so that the computer outputs output information for controlling the telescopic mechanism based on various input information related to the driving of the human-powered vehicle in accordance with various situations or driving environments. By using the output information output from the learning model, appropriate automatic control can be realized according to the situation or driving environment.

[0022] (17) A storage medium according to a seventeenth aspect of the present invention stores the computer program according to (16) above. Therefore, based on a computer program read by the computer from a storage medium, the computer can generate a learning model that outputs output information for controlling the extension mechanism based on various input information regarding the driving of the human-powered vehicle, in accordance with various situations or driving environments. Effect of the Invention

[0023] According to the data control device for the control of a human-powered vehicle of the present invention, it is possible to realize automatic control of the telescopic mechanism according to various situations or driving environments based on various input information related to the driving of the human-powered vehicle. [Brief description of the drawings]

[0024] [Figure 1] FIG. 2 is a side view of a human-powered vehicle to which the control device is applied. [Diagram 2]FIG. 2 is a block diagram showing the internal configuration of a control unit. [Diagram 3] 1 is a flowchart illustrating an example of a method for generating a learning model. [Figure 4] 1 is a flowchart showing a first example method for generating a learning model. [Diagram 5] FIG. 1 is a diagram illustrating an overview of a first example learning model. [Figure 6] 13 is a flowchart showing a second example method for generating a learning model. [Figure 7] FIG. 13 is a diagram illustrating an overview of a learning model of a second example. [Figure 8] 13 is a flowchart showing an example of a control procedure using a learning model by a control unit. [Figure 9] FIG. 11 is a block diagram showing the configuration of a control system according to a second embodiment. [Figure 10] FIG. 11 is a diagram showing an example of a screen displayed based on an application program. [Figure 11] FIG. 11 is a diagram showing an example of a screen displayed based on an application program. [Figure 12] 10 is a flowchart showing an example of a re-learning procedure of a control unit in the second embodiment. [Figure 13] FIG. 13 is a block diagram showing the configuration of a control system according to a third embodiment. [Figure 14] 13 is a flowchart showing an example of a control procedure using a learning model by a terminal device 2 in the third embodiment. [Figure 15] FIG. 13 is a diagram showing an example of the contents of a selection screen. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] The following description of each embodiment is merely an example of a form that the control device according to the present invention may take, and is not intended to limit the form. The control device, learning model generation method, learning model, computer program, and storage medium according to the present invention may take forms different from the respective embodiments, such as a modified version of each embodiment, or a combination of at least two mutually non-contradictory modified versions.

[0026] In the following description of each embodiment, terms expressing directions such as front, rear, forward, backward, left, right, side, up, and down are used based on the directions when a user is seated in the saddle of the human-powered vehicle.

[0027] (First embodiment) FIG. 1 is a side view of a human-powered vehicle A to which the control device of the first embodiment is applied. The human-powered vehicle A is a road bike that includes an assist mechanism C that uses electric energy to assist the propulsion of the human-powered vehicle A. The configuration of the human-powered vehicle A can be changed as desired. In a first example, the human-powered vehicle A does not include the assist mechanism C. In a second example, the type of human-powered vehicle A is a city cycle, a mountain bike, or a cross bike. In a third example, the human-powered vehicle A includes the features of the first and second examples.

[0028] The human-powered vehicle A includes a main body A1, a handlebar A2, a front wheel A3, a rear wheel A4, a front fork A5, a seat A6, and a derailleur hanger A7. The human-powered vehicle A includes a drive mechanism B, an assist mechanism C, an operating device D, a transmission E, a seat post F, a suspension G, a battery unit H, and a control unit 100. The human-powered vehicle A includes a speed sensor S1, a cadence sensor S2, a torque sensor S3, an angle sensor S4, an acceleration sensor S5, an air pressure sensor S6, and an image sensor S7. The main body A1 includes a frame A12.

[0029] The drive mechanism B transmits the human-powered driving force to the rear wheel A4 by a chain drive, a belt drive, or a shaft drive. Figure 1 illustrates a chain drive drive mechanism B. The drive mechanism B includes a crank B1, a first sprocket assembly B2, a second sprocket assembly B3, a chain B4, and a pair of pedals B5.

[0030] The crank B1 includes a crankshaft B11, a right crank B12, and a left crank B13. The crankshaft B11 is rotatably supported by an assist mechanism C provided on the frame A12. The right crank B12 and the left crank B13 are each connected to the crankshaft B11. One of a pair of pedals B5 is rotatably supported by the right crank B12. The other of the pair of pedals B5 is rotatably supported by the left crank B13.

[0031] The first sprocket assembly B2 has a first rotational axis and is connected to the crankshaft B11 so as to be rotatable together with the crankshaft B11. The first sprocket assembly B2 includes one or more sprockets B22. The rotational axis of the crankshaft B11 and the rotational axis of the first sprocket assembly B2 are coaxial.

[0032] The second sprocket assembly B3 has a second rotational center axis and is rotatably supported on a hub (not shown) of the rear wheel A4. The second sprocket assembly B3 includes one or more sprockets B31.

[0033] The chain B4 is wound around one of the sprockets B22 of the first sprocket assembly B2 and one of the sprockets B31 of the second sprocket assembly B3. When the crank B1 rotates forward due to a manual driving force applied to a pair of pedals B5, the first sprocket assembly B2 rotates forward together with the crank B1, and the rotation of the first sprocket assembly B2 is transmitted to the second sprocket assembly B3 via the chain B4, causing the rear wheel A4 to rotate forward.

[0034] The assist mechanism C assists in the propulsion of the human-powered vehicle A. In one example, the assist mechanism C assists in the propulsion of the human-powered vehicle A by transmitting torque to the first sprocket assembly B2. The assist mechanism C includes an electric motor. The assist mechanism C may include a reducer. The assist mechanism C runs a chain B4 that transmits driving force to the rear wheel A4 of the human-powered vehicle A. The assist mechanism C is part of a component that can be controlled by a signal control to assist the running of the chain B4.

[0035] The operation device D includes an operation unit D1 operated by a user. An example of the operation unit D1 is one or more buttons. The operation device D accepts, through the operation unit D1, a designation operation for controlling various components, such as designation of the operation of the electric seat post F and designation of the operation of the electric suspension G. Another example of the operation unit D1 may accept switching of the mode (energy saving mode, high power mode, etc.) of the assist mechanism C. The operation unit D1 is a brake lever. The operation device D outputs the operation amount of the brake lever to the control unit 100. The number of gears or the gear ratio of the transmission E can be changed every time the brake levers provided on the left and right handlebars are tilted to the left or right. The operation device D is communicatively connected to the control unit 100 or each component so as to transmit and receive signals according to the operation of the operation unit D1. In a first example, the operation device D is communicatively connected to the components by a communication line or an electric wire capable of PLC (Power Line Communication). In a second example, the operation device D is communicatively connected to the components by a wireless communication unit capable of wireless communication.

[0036] The transmission E may take various forms. In a first example, the transmission E is an external transmission that changes the state of connection between the second sprocket assembly B3 and the chain B4. In a second example, the transmission E is an external transmission that changes the state of connection between the first sprocket assembly B2 and the chain B4. In a third example, the transmission E is an internal transmission. In the third example, the moving part of the transmission E includes at least one of a sleeve and a pawl of the internal transmission. In a fourth example, the transmission E is a continuously variable transmission. In the fourth example, the moving part of the transmission E includes a ball planetary (planetary rolling element) of the continuously variable transmission. The transmission E is part of a component that can be controlled by a signal control to change the number of gears.

[0037] The seat post F is one of the telescopic mechanisms included in the human-powered vehicle A. The seat post F includes a post body F1 movably provided with respect to the frame A12, and a yawl F2 provided at the upper end of the post body F1. The seat post F is a part of a component that can be controlled by setting the seat height as an operating parameter. The seat post F includes an actuator F3 for the post body F1. In the first example, the seat post F is an electric seat post. The actuator F3 of the seat post F in the first example is an electric motor. The seat height setting value is one or more. The seat post F raises and lowers the post body F1 relative to the frame A12 according to the seat height setting value corresponding to a signal according to an operation transmitted from the operating device D or a signal automatically transmitted from the control unit 100. The seat height of the seat A6 is adjusted by raising and lowering the post body F1. The seat post F uses a sensor that detects the protruding length of the post body F1 from the frame A12, and adjusts the protruding length to a value corresponding to the seat height setting value. The seat post F may be adjusted so that the amount of relative linear movement between the first cylinder and the second cylinder of the post F body F1 corresponds to the set value of the seat height, using a sensor that detects the amount of relative linear movement between the first cylinder and the second cylinder of the post F body F1. In the second example, the seat post F is a mechanical seat post. The seat post F of the second example includes a hydraulic seat post or a hydraulic and pneumatic seat post. The seat post F of the second example extends by at least one of the force of a spring and air, and retracts by applying human force. In the seat post F of the second example, the actuator F3 is an electromagnetic valve that opens and closes the flow path of oil or air. The seat post F of the second example opens the electromagnetic valve when it receives a signal corresponding to opening from the operating device D or the control unit 100. When the electromagnetic valve is open, the seat post F tries to extend by at least one of the force of a spring and air. When the electromagnetic valve is closed, the projection length or linear movement amount of the post body F1 from the frame A12 does not change. The seat post F of the second example opens the solenoid valve for a predetermined period of time when a signal corresponding to opening is received, or opens the solenoid valve until the next operating command is received.The seat post F of the second example may be configured to use a sensor that detects the projection length or linear movement amount of the post body F1 from the frame A12 and open the solenoid valve until the projection length or linear movement amount reaches a value corresponding to the set value of the seat height. In a third example, the seat post F may be a seat post equipped with an opening and closing mechanism that combines an electric motor with a hydraulic valve or a pneumatic valve.

[0038] The seat A6 is attached to the yawl F2 of the seat post F. The seat A6 includes a seat surface A61 and a rail A62 along the front-rear direction. The rail A62 of the seat A6 is fitted into a groove along the front-rear direction provided in the yawl F2. The front-rear position of the seat A6 relative to the post body F2 is adjusted depending on the position of the yawl F2 relative to the rail A62. The seat post F includes an actuator F4. In the first example, the actuator F4 is an electric motor that rotates a gear provided on the yawl F2 so as to engage with the rail A62 as a rack rail. The seat post F is a part of a component that can be controlled by setting the front-rear position of the seat A6 as an operating parameter. The seat post F adjusts the position of the yawl F2 relative to the rail A62 of the seat A6 to a position corresponding to the set front-rear position. The seat post F may be configured to be able to control the left-right position by providing a groove in the yawl F2 in the left-right direction and providing a rail along the left-right direction on the seat A6.

[0039] The seatpost F's yoke F2 is attached to the post body F1 in a state in which the angle can be adjusted in the front-back or left-right direction. The seatpost F includes an actuator F5. The actuator F5 is an electric motor. The seatpost F is a component that can control, as an operating parameter, the inclination of the seating surface A61 of the seat A6 relative to the post body F1, i.e., the frame A12. The seatpost F adjusts the inclination of the seating surface A61 of the seat A6 relative to the post body F1 to a set angle.

[0040] The adjustment of the front-rear position, left-right position, and inclination of the seat A6 may be realized by other configurations. A configuration may be used in which the rail A62 of the seat A6 is sandwiched between the yaw frame F2 and a clamp rotatably provided on the yaw frame F2 and fixed with bolts and nuts. In this configuration, the actuator F4 or the actuator F5 is an electric motor that controls the rotation angle and left-right or front-rear position of the clamp, and an electric motor that tightens and loosens the bolt.

[0041] The suspension G is one of the telescopic mechanisms included in the human-powered vehicle A. The suspension G can take various forms. In a first example, the suspension G is a front suspension provided on the front fork A5 and attenuates an impact applied to the front wheel A3. The suspension G includes a first member G1 attached to the front wheel A3 and a second member G2 provided movably relative to the first member G1 via a spring or an air spring. The second member G2 is fixed to the main body A1 or the rider part of the handlebar A2. The suspension G includes an actuator G3. The suspension G is a part of a component that can be controlled by setting stroke length, lockout state, spring force, and damping rate as operating parameters. The operating parameters of the suspension G can be changed by driving the actuator G3, which is, for example, an electric motor. The suspension G may be a rear suspension that attenuates an impact applied to the rear wheel A4. In the case of a rear suspension, the suspension G includes a first member attached to the rear wheel A4 and fixed to the seat stay or chain stay, and a second member fixed to the seat tube, top tube or down tube of the frame A12. The second member G2 is movable relative to the first member via a spring or an air spring. The suspension G may be provided on the seat post F. The suspension G may be a front suspension and a rear suspension. The suspension G may be either a hydraulic suspension or a hybrid suspension of pneumatic and hydraulic pressure. The actuator G3 may be a valve that opens and closes an oil or air flow path. The suspension G switches between a locked state and a released state in response to a signal according to an operation transmitted from the operating device D or a signal automatically transmitted from the control unit 100. The suspension G may include a pump and a valve that adjusts the supply of air from the pump to the air spring, and may be capable of changing the stroke length to a plurality of stages in response to the signal. To change the stroke length, the actuator G3 is an electromagnetic valve that opens and closes a valve.The suspension G may be capable of changing the spring force or damping rate in a plurality of stages in response to a signal by driving the actuator G3.

[0042] The spring force here refers to the strength of the suspension G as a spring, and is, for example, spring characteristics such as a spring constant. If the suspension G is hydraulic or pneumatic, it may have nonlinear spring characteristics according to the hydraulic pressure or pneumatic pressure.

[0043] The battery unit H includes a battery H1 and a battery holder H2. The battery H1 is a storage battery including one or more battery cells. The battery holder H2 is fixed, for example, to the frame A12 of the human-powered vehicle A. The battery holder H2 may be fixed to a bicycle part other than the frame A12. The battery H1 is detachable from the battery holder H2. When the battery H1 is attached to the battery holder H2, it is electrically connected to the assist mechanism C, the gear shift E, the actuators F3, F4, and F5 of the seat post F, the actuator G1 of the suspension G, and the control unit 100.

[0044] The speed sensor S1 is fixed to the front fork A5. The speed sensor S1 is a sensor that outputs a signal indicating the traveling speed of the human-powered vehicle A. The speed sensor S1 includes, for example, a magnet provided on the front wheel A3 and a main body provided on the front fork A5 for detecting the magnet, and measures the rotation speed.

[0045] The cadence sensor S2 is provided to measure the cadence of either the right crank B12 or the left crank B13. The cadence sensor S2 outputs a signal indicative of the measured cadence. The torque sensor S3 is provided to measure the torque applied to the right crank B12 and the left crank B13, respectively. The torque sensor S3 outputs a signal indicative of the torque measured in at least one of the right crank B12 and the left crank B13.

[0046] The angle sensor S4 is fixed to the frame A12. The angle sensor S4 is a sensor that outputs signals indicating the yaw, roll, and pitch of the human-powered vehicle A. The angle sensor S4 may output at least one of the three axes, not all of them. In the first example, the angle sensor S4 is a gyro sensor. In the second example, the angle sensor S4 is a direction sensor that outputs a rotation angle.

[0047] The acceleration sensor S5 includes a first acceleration sensor S51 attached to a first member G1 of the suspension G and a second acceleration sensor S52 attached to a second member G2. The first acceleration sensor S51 and the second acceleration sensor S52 output signals indicative of acceleration.

[0048] The tire pressure sensor S6 is provided on the front wheel A3 or the rear wheel A4, and outputs a signal indicating a value corresponding to the tire air pressure.

[0049] The image sensor S7 is mounted on the frame A12 facing forward. In a first example, it is mounted on the front fork A5 together with a light facing forward. In a second example, it is mounted on the handlebar A2. The image sensor S7 outputs an image corresponding to the user's field of vision using a camera module. The image sensor S7 outputs an image signal capturing an image of an object present in the traveling direction. The image sensor S7 may be integrated with an image recognition unit that distinguishes and recognizes roads, buildings, and other traveling vehicles from the image, and may be a module that outputs the recognition result.

[0050] Fig. 2 is a block diagram showing the internal configuration of the control unit 100. The control unit 100 includes a control unit 10, a storage unit 12, and an input / output unit 14. The control unit 100 is installed at any location on the frame A12. The control unit 100 may be provided between the handlebar A2 and the frame A12. In a first example, as shown in Fig. 1, the control unit 100 is provided between the first sprocket assembly B2 and the frame A12. In a second example, the control unit 100 is provided in the battery holder H2.

[0051] The control unit 10 is a processor using a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit), and executes processing by using built-in memories such as a ROM (Read Only Memory) and a RAM (Random Access Memory) to control a learning algorithm (to be described later) and components provided in the human-powered vehicle A. The control unit 10 acquires time information at any timing using a built-in clock.

[0052] The storage unit 12 includes a non-volatile memory such as a flash memory. The storage unit 12 stores a learning program 1P and a control program 2P. The learning program 1P may be included in the control program 2P. The storage unit 12 stores a learning model 1M created by processing of the control unit 10. The learning program 1P may be obtained by reading out a learning program 8P stored in a recording medium 18 and copying it to the storage unit 12. The control program 2P may be obtained by reading out a control program 9P stored in a storage medium 19 and copying it to the storage unit 12.

[0053] The input / output unit 14 is connected to the group of sensors S1-S7 provided on the human-powered vehicle A, the operating device D, and the actuators F3, F4, and F5 of the seat post F to be controlled. The input / output unit 14 is also connected to the actuator G3 of the suspension G to be controlled. The control unit 10 obtains a signal indicating the speed from the speed sensor S1 through the input / output unit 14. The control unit 10 obtains a signal indicating the cadence from the cadence sensor S2 and a signal indicating the torque from the torque sensor S3 through the input / output unit 14. The control unit 10 inputs a signal indicating the attitude of the human-powered vehicle A, specifically, the yaw, roll, or pitch, from the angle sensor S4 through the input / output unit 14. The control unit 10 obtains signals indicating the acceleration from the first acceleration sensor S51 and the second acceleration sensor 52 through the input / output unit 14. The control unit 10 obtains a signal indicating a value corresponding to the tire air pressure from the air pressure sensor S6 through the input / output unit 14. The control unit 10 obtains a video signal or a recognition result based on the video signal from the image sensor S7 through the input / output unit 14. The control unit 10 processes the information obtained from the group of sensors S1-S7 as input information. The control unit 10 inputs a signal from the operation device D via the input / output unit 14. In the block diagram of Fig. 2, the input / output unit 14 is connected to the actuators F3, F4, F5, and actuator G3, but does not need to be connected to actuators that are not to be controlled.

[0054] The control unit 10 provides input information regarding the driving of the human-powered vehicle acquired by the input / output unit 14 to the learning model 1M, and controls the telescopic mechanism based on output information regarding the control of the telescopic mechanism such as the seat post F or suspension G output by the learning model 1M.

[0055] The learning model 1M stored in the storage unit 12 is generated in advance in a running test environment of the human-powered vehicle A as follows. In the first stage, the learning model 1M may be generated by executing a simulation on a model of the human-powered vehicle A, rather than actually running the human-powered vehicle A. The learning model 1M may be generated by collecting running information and operation results of the human-powered vehicle A when the human-powered vehicle A, in which the user can operate the seat post F and suspension G, is actually run, and learning may be performed based on the collected information. The learning model 1M may be generated separately for the seat post F and suspension G, which are the objects to be controlled, or may be generated so as to output control information for both.

[0056] 3 is a flowchart showing an example of a method for generating a learning model 1M. The control unit 10 executes the method for generating a learning model 1M shown below based on a learning program 1P. In this embodiment, the learning algorithm is reinforcement learning (deep Q learning) using a neural network (hereinafter referred to as NN: Neural Network). The learning method may be a supervised learning algorithm or a recurrent neural network using time series data.

[0057] The control unit 10 acquires input information related to the traveling of the human-powered vehicle A through the input / output unit 14 (step S101).

[0058] The control unit 10 provides the input information acquired in step S101 to the NN (step S103), and identifies the output information output from the NN (step S105).

[0059] The control unit 10 calculates an evaluation value for the identified output information (step S111). In a first example, the evaluation value of the driving state is derived based on post-control input information that can be acquired after control based on the output information identified in step S105. In a second example, the evaluation value of the driving state is derived based on a comparison between the output information identified in step S105 and the user's actual designated operation on the operating device D. Step S111 corresponds to the "evaluation unit" in the first embodiment.

[0060] The control unit 10 determines whether or not the evaluation value calculated in step S111 satisfies the condition (step S113), and if it is determined that the evaluation value does not satisfy the condition (S113: NO), the process proceeds to the next step S115.

[0061] The control unit 10 uses the evaluation value calculated in step S111 as, for example, a reward, updates parameters in the intermediate layer of the NN so as to improve the evaluation of the driving state based on the output information on the control, and proceeds with learning (step S115). As for the details of the learning method, it is advisable to select and use an appropriate method such as a known method in reinforcement learning, double deep Q learning, AC (Actor-Critic), A3C, etc. The control unit 10 returns the process to step S101 and continues learning.

[0062] If it is determined in step S113 that the evaluation value satisfies the condition (S113: YES), the control unit 10 ends the learning process.

[0063] When generating the learning model 1M using a supervised learning algorithm, the control unit 10 acquires input information obtainable from the sensor group S1-S7 when a model user of the human-powered vehicle A drives the vehicle, and the operation details of the model user on the operation device D, and uses these as teacher data. The control unit 10 updates the parameters in the NN so that the output information output when the input information is given to the NN matches the actual operation details by the model user, and proceeds with the learning.

[0064] A method for generating the learning model 1M shown in the flowchart of Fig. 3 will be specifically described. Fig. 4 is a flowchart showing a first example of a method for generating the learning model 1M. In the first example, the control target is a suspension G. The processing procedure shown in the flowchart of Fig. 4 is a specific procedure of the processing procedure shown in the flowchart of Fig. 3 for the purpose of controlling the suspension G.

[0065] The control unit 10 acquires at least the first acceleration from the first acceleration sensor S51 and the second acceleration from the second acceleration sensor S52 from the information that can be acquired from the sensor group S1-S7 by the input / output unit 14 (step S121). In step S121, the control unit 10 may acquire the traveling speed, cadence, power obtained from the cadence and torque, and attitude for each yaw, pitch, and roll that can be acquired from the sensor group S1-S7. In step S121, the control unit 10 may acquire the gear ratio obtained from the transmission E or the operating device D of the human-powered vehicle A. In step S121, the control unit 10 may acquire the operation amount of the brake lever of the human-powered vehicle A obtained from the operating device D.

[0066] The control unit 10 provides input information including at least the first acceleration and the second acceleration to the NN (step S123), and identifies output information including at least one of the stroke length, lockout state, spring force, and damping rate of the suspension G from the NN (step S125).

[0067] In the first example, the control unit 10 outputs a control signal based on the output information specified in step S125 to the actuator G3 of the suspension G to control it (step S127), and acquires post-control input information including the first acceleration and the second acceleration after control (step S129). The control unit 10 calculates an evaluation value based on the post-control input information (step S131). In step S131, the control unit 10 calculates the difference between the first acceleration and the second acceleration acquired as the post-control input information as the evaluation value. The smaller the difference, the higher the evaluation. Since it is difficult to make the difference small, it is sufficient that the difference is within a predetermined range. In step S131, the control unit 10 may calculate the evaluation value so that a transfer function with the first acceleration as input and the second acceleration as output is minimized. In step S131, the control unit 10 may calculate the amplitude or power value of the second acceleration as the evaluation value. The smaller the amplitude or power value, the higher the evaluation. In some cases, such as when the road surface is unstable, the larger the amplitude, the higher the evaluation. The evaluation value may be calculated based on whether or not the difference between the first acceleration and the second acceleration and the transfer function are within a predetermined range.

[0068] The control unit 10 determines whether or not the condition that the difference calculated as the evaluation value is within a predetermined range is satisfied (step S133). In step S133, the control unit 10 determines whether or not the condition that the amplitude or power value of the second acceleration on the frame A12 side is less than a predetermined value is satisfied.

[0069] If it is determined in step S133 that the condition is not satisfied (S133: NO), the control unit 10 uses the evaluation value calculated in step S131 as a reward, updates the parameters in the intermediate layer of the NN so as to improve the evaluation of the running state, and proceeds with learning (step S135). In step S135, the control unit 10 updates the parameters so as to reduce the amplitude of the second acceleration, for example.

[0070] If it is determined in step S133 that the condition is satisfied (S133: YES), the control unit 10 ends the learning process.

[0071] By following the procedure shown in the flowchart of FIG. 4, it is possible to generate a learning model 1M that learns to control the suspension G so as to absorb vibrations of the frame A12 and provide comfort.

[0072] In step S121, the control unit 10 may derive driving environment information including any one of the ground contact state of the wheels, the road surface state, and the weather from the video signal obtained from the image sensor S7 by the input / output unit 14, and may use the derived driving environment information as input information. The road surface state is, for example, a result of classifying whether the road surface is smooth such as asphalt or a road surface with large bumps such as a mountain road based on the video signal. Alternatively, the road surface state may be a result of determining whether the road surface is slippery such as wet or frozen. The control unit 10 may obtain the types of the front wheels A3 and the rear wheels A4, and may specify the ground contact state of the wheels based on the air pressure obtained from the air pressure sensor S6 and use the specified information as input information. Here, the types of the front wheels A3 and the rear wheels A4 are types (product numbers) that differ depending on the size, material, thickness, and groove pattern. The types may be model numbers. Based on the correspondence between the air pressure and the ground contact state that is stored in advance for each type of the front wheels A3 or the rear wheels A4, the control unit 10 specifies the ground contact state for each type of the front wheels A3 or the rear wheels A4. For example, the control unit 10 can determine the ground contact state differently for a thick, stiff front wheel A3 of a mountain bike and a thin front wheel A3 for racing, even if the air pressures are similar.

[0073] FIG. 5 is a diagram showing an overview of the learning model 1M of the first example. In the specific example referring to FIG. 5, the learning model 1M that outputs output information related to the control of the suspension G will be described. The learning model 1M includes an input layer 31 that inputs input information related to the traveling of the human-powered vehicle A, an output layer 32 that outputs output information related to the control of the suspension G having a telescopic mechanism among the components of the human-powered vehicle A, and an intermediate layer 33 that has parameters learned to improve the evaluation of the traveling state of the human-powered vehicle A when the suspension G is controlled based on the output information output from the output layer 32. When input information related to traveling is input to the input layer 31, a calculation is performed in the intermediate layer 33 using the learned parameters, and output information related to the control of the suspension G is output from the output layer 32.

[0074] 5, at least a first acceleration obtained from a first acceleration sensor S51 and a second acceleration obtained from a second acceleration sensor S52 are provided to the input layer 31. A cadence obtained from a cadence sensor S2 may be provided to the input layer 31. A power obtained by a calculation using the torque and the cadence may be provided to the input layer 31.

[0075] Additionally, attitude data of the human-powered vehicle A obtained from the angle sensor S4 may be input to the input layer 31. The attitude data is information indicating the inclination of the human-powered vehicle A. The inclination is represented by a yaw component whose axis is the vertical direction, a roll component whose axis is the fore-and-aft direction of the human-powered vehicle A, and a pitch component whose axis is the left-right direction.

[0076] The input layer 31 may be provided with the load balance of the human-powered vehicle A. The load balance may be a value calculated by the control unit 10 from the ratio of the pressures of the front wheels A3 and the rear wheels A4 obtained from the air pressure sensor S6. The load balance may be calculated using a vector sensor or a strain gauge, or may be calculated by combining different types of sensors depending on the design. The load balance may also be the result of arranging piezoelectric sensors on the front wheels A3 and the rear wheels A4, as well as the seat A6 and the handlebars A2 of the human-powered vehicle A under a running test environment to determine whether the load balance is toward the front or rear.

[0077] A signal indicating the ground contact state of the front wheel A3 or the rear wheel A4 on the road surface obtained from the air pressure sensor S6 may be input to the input layer 31. The signal indicating the ground contact state is, in other words, driving environment information. The signal indicating the ground contact state corresponds to a numerical value indicating the size of the ground contact area, which is smaller as the air pressure is higher. The signal indicating the ground contact state may be a signal indicating the state of the ground contact area, which is large, medium, or small. As another driving environment information, a signal indicating the road surface condition obtained by image processing of the video signal obtained from the image sensor S7 may be input to the input layer 31. The signal indicating the road surface condition is, for example, a signal indicating the road surface condition such as the result of determining whether the road is off-road or paved, the result of determining whether the road is up or down, and the presence or absence of slippage.

[0078] The output layer 32 outputs the spring force of the suspension G. Specifically, the output layer 32 outputs the action value of each of the spring forces classified into three levels, strong, medium, and weak, i.e., information indicating what kind of spring force will improve the ride comfort. Based on the action value of the output information, the control unit 10 can select the spring force with the highest action value and output it to the suspension G. The spring force output from the output layer 32 that produces smaller vibrations measured at the second acceleration is evaluated as being higher.

[0079] As the reinforcement learning progresses, control information for the suspension G to improve the ride comfort of the human-powered vehicle A according to the driving conditions, road surface conditions, etc., is obtained by the learning model 1M.

[0080] Fig. 6 is a flowchart showing a method for generating a learning model 1M of a second example. In the second example, the control target is a seat post F. The processing procedure shown in the flowchart of Fig. 6 is a specific procedure of the processing procedure shown in the flowchart of Fig. 3, with the control of the seat post F as the target.

[0081] The control unit 10 obtains at least the running speed, cadence, and driving force obtained from the cadence and torque from the speed sensor S1 among the information obtainable from the sensor group S1-S7 by the input / output unit 14 (step 141). In step S141, the control unit 10 may obtain the attitude of the human-powered vehicle A for each yaw pitch roll. In step S141, the control unit 10 may obtain the gear ratio obtained from the transmission E or the operating device D of the human-powered vehicle A. In step S141, the control unit 10 may obtain the operation amount of the brake lever of the human-powered vehicle A obtained from the operating device D.

[0082] The control unit 10 provides input information including at least the riding speed, and the cadence or driving force to the NN (step S143), and identifies output information including at least one of the seat height of the seat A6 on the seat post F, the inclination of the seat surface A61, the fore-aft position, and the left-right position of the seat A6 (step S145).

[0083] In the second example, the control unit 10 also outputs control signals based on the output information identified in step S145 to the actuators F3, F4, and F5 of the seat post F to control them (step S147), and after the control, acquires post-control input information including, for example, torque (step S149). The control unit 10 calculates an evaluation value based on the post-control input information (step S151). In step S151, the control unit 10 acquires torque as the post-control input information, and calculates the torque or driving force as the evaluation value. The smaller the torque or driving force, the easier the user is pedaling, and the higher the evaluation.

[0084] The control unit 10 determines whether or not the condition that the torque or driving force calculated in step S151 is less than a predetermined value is satisfied (step S153).

[0085] If it is determined in step S153 that the condition is not satisfied (S153: NO), the control unit 10 uses the evaluation value calculated in step S151 as a reward, updates the parameters in the intermediate layer of the NN so as to improve the evaluation of the driving state, and proceeds with learning (step S155). In step S155, the control unit 10 updates the parameters so as to reduce, for example, the torque or the driving force.

[0086] If it is determined in step S153 that the condition is satisfied (S153: NO), the control unit 10 ends the learning process.

[0087] In the second example, in step S151, the control unit 10 may omit steps S147 and S149, and may calculate the degree of deviation as an evaluation value based on a comparison between the output information identified in step S145 and the user's actual designated operation on the operation device D. For example, when the user actually operates the operation device D to adjust the seat height, front-rear position, and left-right position of the seat post F, learning may be advanced so that the output information output from the NN matches the actual control content. In this case, the operation content of the operation device D is teacher data, and learning is advanced by supervised learning.

[0088] In the second example, as learning progresses, learning model 1M obtains control information for seat post F to make pedaling easier for human-powered vehicle A. Through reinforcement learning based on information such as driving speed, image information, inclination, and power, the saddle position is automatically adjusted to the optimum position for the situation.

[0089] In the second example, the control unit 10 may also derive driving environment information including any one of the ground contact state of the wheels, the road surface condition, and the weather from the video signal obtained from the image sensor S7 by the input / output unit 14 in step S141, and use the derived driving environment information as input information. The road surface condition is, for example, a result of classifying, based on the video signal, whether the road surface is smooth such as asphalt, or whether the road surface is highly uneven such as a mountain road. Alternatively, the road surface condition may be a result of determining whether the road surface is slippery such as wet or frozen. The control unit 10 may obtain the types of the front wheels A3 and rear wheels A4, and may specify the ground contact state of the wheels based on the air pressure obtained from the air pressure sensor S6, and use the specified information as input information.

[0090] FIG. 7 is a diagram showing an overview of a learning model 1M of a second example. In the specific example referring to FIG. 7, a learning model 1M that outputs output information related to the control of a seat post F will be described. The learning model 1M includes an input layer 31 that inputs input information related to the traveling of the human-powered vehicle A, an output layer 32 that outputs output information related to the control of the seat post F having a telescopic mechanism among the components of the human-powered vehicle A, and an intermediate layer 33 that has parameters learned to improve the evaluation of the traveling state of the human-powered vehicle A when the seat post F is controlled based on the output information output from the output layer 32. When input information related to traveling is input to the input layer 31, a calculation is performed in the intermediate layer 33 using the learned parameters, and output information related to the control of the seat post F is output from the output layer 32.

[0091] 7, a cadence obtained from a cadence sensor S2 and a torque obtained from a torque sensor S3 are provided to the input layer 31. The input layer 31 may also be provided with power obtained by calculation using the torque and cadence.

[0092] The input layer 31 may be provided with a gear ratio obtained from the transmission E or the operating device D.

[0093] Data on the attitude of the human-powered vehicle A obtained from the angle sensor S4 may be input to the input layer 31. The attitude data is information indicating the inclination of the human-powered vehicle A. The inclination is represented by a yaw component whose axis is the vertical direction, a roll component whose axis is the front-to-rear direction of the human-powered vehicle A, and a pitch component whose axis is the left-to-right direction. By inputting the attitude of the human-powered vehicle A, it is expected that it will be possible to realize control of the seat post F, such as lowering the seat A6 on downhill slopes and raising the seat A6 on uphill slopes.

[0094] The input layer 31 may receive the amount of brake operation obtained from an operating device D including a brake lever as an operating part D1. When the amount of brake operation is large, the seat A6 is automatically lowered to make pedaling easier and to allow the rider to stop safely.

[0095] A video signal obtained from the image sensor S7 may be input to the input layer 31. Instead of inputting the video signal as it is, a signal indicating the road surface condition obtained by image processing of the video signal may be input to the input layer 31. The signal indicating the road surface condition is, in other words, driving environment information. The signal indicating the road surface condition is, for example, a signal indicating the road surface condition such as the result of discrimination of whether the road is off-road or paved, the result of discrimination of ups and downs, the presence or absence of slippage, etc. By classifying the ups and downs of the road surface to be traveled from the video signal corresponding to the scenery seen in the travelling direction, it becomes possible to determine whether the seat A6 should be raised or lowered and control the seat post F.

[0096] In addition, a signal indicating the traveling speed of the human-powered vehicle A obtained from the speed sensor S1 may be input to the input layer 31. When the traveling speed is below a predetermined speed and the vehicle is stopped, it becomes possible to control the seat post F so that the seat A6 automatically becomes lower.

[0097] The output layer 32 outputs the height of the seat A6 in the seat post F. Specifically, the output layer 32 outputs the height of the seat A6 divided into three stages, high, medium, and low, that is, the action value of each of the projection length or linear movement amount of the seat post F, that is, information indicating what height makes the ride comfortable. The height of the seat post F may not only be divided into three stages, high, medium, and low, but may also be controlled by the projection length or linear movement amount itself that is steplessly adjustable. It may also be a two-stage adjustment product. Based on the action value of the output information, the height with the highest action value can be selected, and the projection length or linear movement amount corresponding to the selected height can be output to the seat post F. The height of the seat A6 output from the output layer 32 is highly evaluated when the torque or power is smaller and no unnecessary force is applied. Based on the correspondence between the seat height and the projection length, as well as the correspondence between the seat height and the linear movement amount, the linear movement amount corresponding to the selected height may be output to the seat post F.

[0098] In another example, the output layer 32 may output an action value for each of a plurality of set positions for the front-rear or left-right positions of the seat A6 on the seat post F. The output layer 32 may also output an action value for each inclination of the seat surface A61 of the seat A6.

[0099] As the reinforcement learning progresses, control information for seat post F to improve the riding comfort of human-powered vehicle A according to the driving conditions, road surface conditions, etc. can be obtained by learning model 1M.

[0100] In the processing procedure shown in the flowcharts of Figs. 3, 4, and 6, a reward is given every control period, and learning is advanced by immediate reward. However, the present invention is not limited to this, and a reward may be given using the power, torque, or cadence of the second acceleration in a predetermined period. The information input to the learning model 1M or the NN before learning is a numerical value or information obtained from the sensor group S1-S7 every control period. However, a numerical value or information obtained from the sensor group S1-S7 over a predetermined period may be provided to the input layer 31. In this case, an image in which the output from the sensor group S1-S7 is graphed may be provided to the input layer 31.

[0101] The control unit 10 of the control unit 100 controls the components based on the control information output from the learning model 1M created as described above. Fig. 8 is a flowchart showing an example of a control procedure using the learning model 1M by the control unit 100. The control unit 10 repeatedly executes the processing procedure shown in the flowchart of Fig. 8. The processing may be executed at a predetermined control period (e.g., 30 milliseconds).

[0102] The control unit 10 acquires input information related to the traveling of the human-powered vehicle A through the input / output unit 14 (step S201). In step S201, the control unit 10 refers to the signal levels from the group of sensors S1-S7 input through the input / output unit 14 for each control period, and temporarily stores the signal levels in the internal memory of the control unit 10 or in a memory built into the input / output unit 14.

[0103] The control unit 10 provides the input information acquired in step S201 to the input layer 31 of the learning model 1M (step S203). In step S203, the control unit 10 selects and provides input information for the suspension G, and selects and provides input information for the seat post F.

[0104] The control unit 10 identifies output information related to the control of the component to be controlled, which is output from the learning model 1M (step S205). For example, in step S205, the control unit 10 identifies the height of the seat A6 related to the seat post F, which is output from the learning model 1M, as the output information. In another example, the control unit 10 identifies the strength of the spring force of the suspension G, which is output from the learning model 1M, as the output information.

[0105] The control unit 10 refers to the state of the control target based on the specified output information (step S207). The control unit 10 determines whether or not it is necessary to output a control signal based on the relationship between the control content indicated by the specified output information and the referred state (step S209).

[0106] If it is determined in step S209 that the control is necessary (S209: YES), the control unit 10 outputs a control signal to the component to be controlled based on the output information related to the identified control (step S211), and ends the control processing for one control cycle.

[0107] If it is determined in step S209 that the control is unnecessary (S209: NO), the control unit 10 ends the process without outputting a control signal based on the output information related to the specified control. The determination processes in steps S207 and S209 are not essential.

[0108] The control unit 10 may execute the process shown in the flowchart of FIG. 8 for each of the seat post F and the suspension G, or may execute step S201 in common and select and provide different information for each component in step S203.

[0109] When the suspension G or seat post F is automatically controlled, the control unit 100 may use a speaker as a notification unit to notify the user, the rider, of the control content, and the control unit 101 may output a buzzer sound or voice guidance to the speaker. In order to notify the user of the control content, the control unit 100 may provide a cycle computer having a display as a notification unit on the handlebar A2 and output the control content to the cycle computer. By notifying the user of the control content, the user can be made aware of the control content of the components related to the running of the human-powered vehicle A, and automatic control that does not cause discomfort can be achieved.

[0110] Second embodiment In the second embodiment, instead of the control unit 100, a learning model 1M is created in a user's terminal device, and a process of outputting control information of the component is executed. FIG. 9 is a block diagram showing the configuration of a control system 200 of the second embodiment. The control system 200 includes a terminal device 2 and a control unit 100. The control unit 100 in the second embodiment includes a control unit 11, a storage unit 12, an input / output unit 14, and a communication unit 16. Among the components of the control unit 100 in the second embodiment, components common to the control unit 100 in the first embodiment are assigned the same reference numerals and detailed description thereof will be omitted.

[0111] The control unit 11 of the control unit 100 in the second embodiment is a processor using a CPU, and uses built-in memories such as ROM and RAM to control each component and execute processing. The control unit 11 does not execute the learning process performed by the control unit 10 of the control unit 100 in the first embodiment. The control unit 11 inputs signals output from the sensor group S1-S7 provided in the human-powered vehicle A by the input / output unit 14, and transmits them to the terminal device 2 via the communication unit 16. The control unit 11 refers to the control state of the operating device D and the operation signal output from the operating device D, and transmits them to the terminal device 2 via the communication unit 16. The control unit 11 gives a control signal to the seat post F or suspension G to be controlled, based on the operation signal output from the operating device D or based on an instruction from the terminal device 2.

[0112] The terminal device 2 is a small, portable communication terminal device used by a user. In a first example, the terminal device 2 is a cycle computer. In a second example, the terminal device 2 may be a communication terminal device known as a junction box that is connected to components of the human-powered vehicle A by wire or wirelessly. In a third example, the terminal device 2 is a smartphone. In a fourth example, the terminal device 2 is a wearable device such as a smart watch. If the terminal device is a cycle computer or smartphone, a holder for the cycle computer or smartphone may be attached to the handlebar A2 of the human-powered vehicle A, and the cycle computer or smartphone may be fitted into this holder (see FIG. 10).

[0113] The terminal device 2 includes a control unit 201 , a storage unit 203 , a display unit 205 , an operation unit 207 , a voice input / output unit 209 , a GPS receiving unit 211 , a first communication unit 213 and a second communication unit 215 .

[0114] The control unit 201 includes a processor such as a CPU or a GPU, a memory, etc. The control unit 201 may be configured as a single piece of hardware (SoC: System On a Chip) that integrates the processor, the memory, the storage unit 203, the first communication unit 213, and the second communication unit 215. Based on the application program 20P stored in the storage unit 203, the control unit 201 learns output information related to the control of the human-powered vehicle A and executes component control based on the learning.

[0115] The storage unit 203 includes a non-volatile memory such as a flash memory. The storage unit 203 stores a learning program 10P and an application program 20P. The learning program 10P may be included in the application program 20P. The storage unit 203 stores a learning model 2M created by the processing of the control unit 201. The storage unit 203 stores data referenced by the control unit 201. The learning program 10P may be obtained by reading out a learning program 40P stored in the recording medium 4 and copying it to the storage unit 21. The application program 20P may be obtained by reading out an application program 50P stored in the storage medium 5 and copying it to the storage unit 203.

[0116] The memory unit 203 stores map information. The map information includes information on ups and downs, whether the road is off-road or paved, etc. The control unit 201 can refer to the map information stored in the memory unit 203 from the position of the terminal device 2 obtained by the GPS receiving unit 211, i.e., the position of the human-powered vehicle A, and specify whether the road is ups and downs, off-road, etc., as driving environment information for the human-powered vehicle A that corresponds to the map information.

[0117] The display unit 205 includes a display device such as a liquid crystal panel or an organic EL display. The display unit 205 is a notification unit that notifies the user of the control content for the seat post F or the suspension G.

[0118] The operation unit 207 is an interface that accepts user operations, and includes physical buttons and a touch panel device with a built-in display. The operation unit 207 can accept operations on the screen displayed on the display unit 205 using the physical buttons or the touch panel.

[0119] The voice input / output unit 209 includes a speaker, a microphone, and the like. The voice input / output unit 209 includes a voice recognition unit 217, and is capable of recognizing operation contents from input voice via the microphone and accepting the operation. The voice input / output unit 209 is an alarm unit that generates voice or a beep via the speaker and notifies the user of the control contents for the seat post F or the suspension G. The voice input / output unit 209 may have a function of outputting a vibration of a specific pattern and vibrating the entire terminal device 2 or the surface of the display unit 205.

[0120] The GPS receiver 211 is a communication unit that receives a GPS (Global Positioning System) signal. The GPS receiver 211 is provided to obtain position information (longitude and latitude information) of the terminal device 2. The GPS receiver 211 may assist in the calculation of the position information by using the reception strength of radio waves in wireless communication standards such as Wi-Fi and Bluetooth (registered trademark).

[0121] The first communication unit 213 is a communication module corresponding to the communication unit 16 of the control unit 100. In a first example, the first communication unit 213 is a USB communication port. In a second example, the first communication unit 213 is a short-range wireless module.

[0122] The second communication unit 215 is a wireless communication module that realizes transmission and reception of information with other communication devices (not shown) via a public communication network or by a predetermined mobile communication standard. The second communication unit 215 uses a network card, a wireless communication device, or a carrier communication module. The control unit 201 can obtain weather information from other communication devices via the second communication unit 215. The control unit 201 can obtain weather information as driving environment information for human-powered vehicle A based on the position of the terminal device 2 obtained by the GPS receiving unit 211, i.e., the position information of human-powered vehicle A.

[0123] In the second embodiment, the control unit 100 continuously acquires input information obtained from the sensors S1-S7 provided in the human-powered vehicle A by the input / output unit 14, and transmits the input information to the terminal device 2 via the communication unit 16.

[0124] The control unit 201 of the terminal device 2 operates in an automatic control mode using a learning model 2M created in advance, and a re-learning mode in which the learning model 2M is updated for the rider. In the automatic control mode, the control unit 201 controls the seat post F based on information related to the control of the seat post F that is output by providing input information to the learning model 2M for the seat post F. In the automatic control mode, the control unit 201 controls the suspension G based on information related to the control of the suspension G that is output by providing input information to the learning model 2M for the suspension G. The method of creating the learning model 2M created in advance is the same as the method of creating the learning model 1M for the seat post F or the method of creating the learning model 1M for the suspension G described in the first embodiment, and therefore a description thereof will be omitted.

[0125] The processing of the control section 201 in the automatic control mode is similar to the processing contents of the control unit 100 in the first embodiment shown in the flowchart of FIG. 8, and therefore a detailed description thereof will be omitted.

[0126] The following describes the re-learning mode in which the learning model 2M is updated based on the evaluation. FIG. 10 and FIG. 11 are diagrams showing examples of screens displayed based on the application program 20P. In the example shown in FIG. 10, the terminal device 2 is a cycle computer and is attached to the handlebar A2 so that the user can view the display unit 205. FIG. 10 shows a main screen 250 displayed on the display unit 205 based on the application program 20P. The control unit 201 causes the display unit 205 to display the main screen 250 based on the application program 20P. The control unit 201 establishes a communication connection with the control unit 100 based on the application program 20P. The main screen 250 includes selection buttons 252 and 254 for the re-learning mode and the automatic control mode. When the selection button 252 is selected, the control unit 201 starts operating in the re-learning mode. When the selection button 254 is selected, the control unit 201 starts operating in the automatic control mode.

[0127] FIG. 11 shows an example of the contents of a relearning mode screen 256 that is displayed when the selection button 252 for the relearning mode is selected. The relearning mode screen 256 is a notification section for displaying a message indicating the control contents when the control section 201 controls a component having a telescopic mechanism such as the seat post F or the suspension G. The relearning mode screen 256 includes a high evaluation button 258 and a low evaluation button 260. The high evaluation button 258 and the low evaluation button 260 correspond to a first example of an evaluation section. The control section 201 recognizes that the high evaluation button 258 and the low evaluation button 260 have been selected by the operation section 207, and accepts the evaluation. The high evaluation button 258 in the example of FIG. 11 is selected when the control based on the learning model 2M is not comfortable. The control section 201 recognizes which of the high evaluation button 258 and the low evaluation button 260 has been selected, and recognizes the evaluation contents that have been accepted. Only the low evaluation button 260 may be displayed as the evaluation section.

[0128] The evaluation unit in the second example may be a physical button provided on the operation unit D1. A specific evaluation reception button may be provided on the operation unit D1. A button for receiving an evaluation may be provided separately near the operation device D.

[0129] The evaluation unit in the third example is the voice recognition unit 217 of the voice input / output unit 209. The control unit 201 recognizes the user's voice in the voice recognition unit 217 and accepts the evaluation. The control unit 201 determines whether the control was comfortable or not based on the recognized voice. If it is comfortable, it gives a high evaluation.

[0130] The evaluation unit of the fourth example identifies facial expressions from an image of the rider's face obtained from a camera, determines whether or not the control was comfortable based on the facial expressions, and accepts an evaluation based on whether or not the control was comfortable.

[0131] Fig. 12 is a flowchart showing an example of a re-learning processing procedure of the control unit 201 in the second embodiment. When the re-learning mode selection button 252 is selected, the control unit 201 executes the following processing at a predetermined control period (e.g., 30 milliseconds) until the automatic control mode is selected. The flowchart in Fig. 12 shows the procedure in which the seat post F is the control target.

[0132] The control unit 201 acquires input information related to the traveling of the human-powered vehicle A obtained from the control unit 100 through the first communication unit 213 (step S301). In step S301, the control unit 201 acquires information that the control unit 10 of the control unit 100 refers to the signal levels from the sensors S1-S7 for each control period and temporarily stores in the internal memory of the control unit 10 or the memory built into the input / output unit 14.

[0133] The control unit 201 provides the input information acquired in step S301 to the learning model 2M (step S303), and identifies output information related to the control of the component output from the learning model 2M (step S305).

[0134] The control unit 201 outputs a control instruction based on the output information identified in step S305 to the control unit 100 (step S307), and notifies the user of the control content by displaying it on the display unit 205 (step S309).

[0135] The control unit 201 accepts a user evaluation within a predetermined time after the control process (step S311). In step S311, the control unit 201 accepts the evaluation with the high evaluation button 258 and the low evaluation button 260 on the re-learning mode screen 256 as shown in FIG.

[0136] The control unit 201 acquires actual operation contents using the operation unit D1 of the operation device D from the control unit 100 (step S313). The control unit 201 calculates an evaluation value based on a comparison between information on control of the component corresponding to the control contents acquired in step S313 and the output information identified in step S305 (step S315). The control unit 201 calculates a higher evaluation value as the difference between the information acquired in step S311 and the output information identified in step S305 becomes smaller.

[0137] The control unit 201 uses the evaluation content received in step S311 and the evaluation value calculated in step S315 as a reward for the output information identified in step S305, updates the parameters in the intermediate layer of the learning model 2M so as to improve the evaluation from the user (step S317), and terminates the processing.

[0138] The processes in steps S313 and S315 may be performed only when the evaluation content received in step S311 is a low evaluation.

[0139] Control unit 201 ends the relearning mode when the user selects automatic control mode selection button 254, when the evaluations received in step S311 are high at a predetermined rate or more, or when the evaluation value calculated in step S315 falls within a preset range. Control unit 201 may execute the procedure shown in the flowchart of Fig. 12 for a predetermined time after relearning mode selection button 252 is selected.

[0140] Through the re-learning process described in the second embodiment, the processing of the terminal device 2 and the control unit 100 realizes automatic control of the telescopic mechanism suited to the driving characteristics and preferences of the user who is the rider of human-powered vehicle A.

[0141] Third embodiment The configuration of the control system 200 in the third embodiment is similar to that in the second embodiment, except that a learning model 2M is prepared for each traveling course on which the human-powered vehicle A travels. Among the configurations of the control system 200 in the third embodiment, the configurations common to the configurations in the first and second embodiments are given the same reference numerals and detailed explanations are omitted.

[0142] FIG. 13 is a block diagram showing the configuration of the control system 200 of the third embodiment. In the third embodiment, as shown in FIG. 13, a plurality of learning models 21M, 22M are stored in the storage unit 203 of the terminal device 2. The plurality of learning models 21M, 22M are models created and prepared by the processing procedure shown in the flowchart of FIG. 3, FIG. 4, or FIG. 5 of the first embodiment under different driving test environments. In the first example, the different driving test environments are driving courses on which the human-powered vehicle A travels. That is, the learning models 21M, 22M are prepared for different driving courses on which the human-powered vehicle A travels, and are stored in the storage unit 203. The configuration may be such that models corresponding to different driving environments, not limited to driving courses, can be selected. The user can use the terminal device 2 to select any one of the learning models 2M from an external server, download it to the terminal device 2, and store it in the storage unit 203. The control unit 201 may be able to accept an operation from the user and accept the setting of a name corresponding to the stored learning model 2M.

[0143] The automatic control in which the learning models 21M, 22M for different driving courses are appropriately selected will be described below. Fig. 14 is a flowchart showing an example of a control procedure using the learning models 21M, 22M by the terminal device 2 in the third embodiment.

[0144] The control unit 201 displays a selection screen for the driving course on the display unit 205 (step S401) and accepts the selection of the driving course on the selection screen (step S403). FIG. 15 is a diagram showing an example of the contents of the selection screen. The selection screen 262 includes a plurality of selection buttons 264, 266 for the driving courses as shown in FIG. 15. The control unit 201 can recognize which of the selection buttons 264, 266 has been selected by the operation unit 207. The control unit 201 selects a learning model corresponding to the driving course selected in step S403 as shown in FIG. 15 from a plurality of learning models 21M, 22M stored in the storage unit 203 (step S405).

[0145] The control section 201 acquires information relating to the traveling of the human-powered vehicle A obtained from the control unit 100 through the first communication section 213 (step S407).

[0146] The control unit 201 provides the input information acquired in step S407 to the learning model 21M or learning model 22M selected in step S405 (step S409), and identifies output information regarding the control of the component output from the selected learning model 21M or learning model 22M (step S411).

[0147] The control unit 201 acquires the state of the controlled object based on the identified output information from the control unit 100 (step S413). The control unit 201 determines whether or not it is necessary to output a control signal based on the relationship between the control content indicated by the identified output information and the referenced state (step S415).

[0148] If it is determined in step S415 that the control is necessary (S415: YES), the control unit 201 transmits a control instruction based on the output information identified in step S411 to the control unit 100 (step S417). The control unit 201 displays the control content on the display unit 205 to notify the user (step S419), and ends the process.

[0149] If it is determined in step S415 that the control instruction is not required (S415: NO), the control unit 201 ends the process without transmitting a control instruction based on the output information related to the specified control. The determination processes in steps S413 and S415 are not essential.

[0150] In this way, by learning the learning models 21M, 22M for different driving environments, automatic control of the telescopic mechanism according to various situations or driving environments based on various input information suited to the driving course is realized.

[0151] The terminal device 2 in the third embodiment may re-learn the learning models 21M and 22M as described in the second embodiment.

[0152] In this way, by using learning models 1M, 2M or 21M, 22M, control unit 100 does not need to make decisions based on comparing the values ​​of the driving information obtained by the multiple sensors S1-S7 with threshold values. Control unit 100 is able to perform appropriate automatic control conforming to human perception, which comprehensively controls input information related to the driving of human-powered vehicle A obtained by the multiple sensors S1-S7 in consideration of nature. [Explanation of symbols]

[0153] 100...control unit (control device), 10...control unit, 12...memory unit, 1M...learning model, 1P...learning program, 2P...control program, 2...terminal device (control device), 201...control unit, 203...memory unit, 2M, 21M, 22M...learning model, 10P...learning program, 20P...application program, A12...frame, A2...handlebar, A5...front fork, A6...seat, A61...seat surface, F...seat post, F1...post body, F2...seatpost, F3, F4, F5...actuator, G...suspension, G1...first member, G2...second member, G3...actuator, S1...speed sensor, S2...cadence sensor, S3...torque sensor, S4...angle sensor, S5...acceleration sensor, S51...first acceleration sensor, S52...second acceleration sensor, S6...air pressure sensor, S7...image sensor.

Claims

1. a control unit that controls the telescopic mechanism based on output information related to control of the telescopic mechanism that is output by a learning model in response to input information including at least one of driving information of the human-powered vehicle related to the driving of the human-powered vehicle and driving environment information of the human-powered vehicle; and An evaluation unit that evaluates the output information output by the learning model, the evaluation unit is an evaluation button that receives an evaluation from a user of the human-powered vehicle regarding control based on the output information, A control device that updates the learning model based on the evaluation by the evaluation unit.

2. The control device according to claim 1 , wherein the evaluation unit includes a like button that is selected by a user of the human-powered vehicle when the control based on the output information is comfortable.

3. The control device according to claim 1 , wherein the evaluation unit includes a low evaluation button that is selected by a user of the human-powered vehicle when the control based on the output information is not comfortable.

4. an operation unit that receives a designation operation related to control of the telescopic mechanism from a user of the human-powered vehicle, The control device according to claim 1 , wherein the evaluation button is provided on the operation unit.

5. The control device according to claim 4 , wherein the operation unit includes a button displayed on a display unit provided for notifying the user of a control content of the extension / contraction mechanism.

6. a neural network that outputs output information related to control of a telescopic mechanism among components of the human-powered vehicle when input information including at least one of driving information of the human-powered vehicle related to the driving of the human-powered vehicle and driving environment information of the human-powered vehicle is input; Acquire input information regarding the traveling of the human-powered vehicle; Identifying output information outputted by providing the acquired input information to the neural network; receiving an evaluation of a running state of the human-powered vehicle after controlling the telescopic mechanism based on the specified output information from a user of the human-powered vehicle by pressing an evaluation button; A learning method for a learning model, the learning method comprising: learning parameters in an intermediate layer of the neural network by a computer so as to improve the evaluation received by the evaluation button.

7. a neural network that outputs output information relating to control of a portion of a component of the human-powered vehicle having an extension mechanism when input information including at least one of driving information of the human-powered vehicle relating to the driving of the human-powered vehicle and driving environment information of the human-powered vehicle is input, Acquire input information regarding the traveling of the human-powered vehicle; Identifying output information outputted by providing the acquired input information to the neural network; receiving an evaluation of a running state of the human-powered vehicle after controlling the telescopic mechanism based on the specified output information from a user of the human-powered vehicle by pressing an evaluation button; A computer program that causes a computer to execute a process of learning parameters in an intermediate layer of the neural network so as to improve the evaluation received by the evaluation button.

8. A storage medium on which the computer program according to claim 7 is stored.

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