Method and system for estimating work equipment connected to a work vehicle.

Abstract

To more accurately estimate the type of a work machine connected to a work vehicle.SOLUTION: A method is disclosed which is implemented by one or more computers communicating with a work vehicle which drives a connected work machine to implement work. The method includes the steps of: repeatedly acquiring 10 or more signals each of which indicates a different internal state of the work vehicle, from the work vehicle; generating input data on the basis of the 10 or more signals; inputting the input data into one or more learned models for estimating the type of the work machine on the basis of the input data to estimate the type of the work machine; and generating and outputting output data including information indicating the estimated type of the work machine.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a method and a system for estimating a work implement connected to a work vehicle. [Background Art]

[0002] There is a growing need for predictive maintenance that remotely monitors the state of a vehicle, identifies or predicts failures or malfunctions, and performs maintenance such as part replacement or repair. To implement predictive maintenance, it is required to acquire various data indicating the internal state of the vehicle and estimate the operating state of the vehicle based on such data.

[0003] Patent Documents 1 and 2 disclose a technique for automatically estimating the type of work performed by a work implement attached to an agricultural tractor. In the techniques disclosed in Patent Documents 1 and 2, a management server estimates the type of work performed by the work implement based on time-based position information and time-based operation information transmitted from the tractor and a pre-trained machine learning model. Examples of the operation information include vehicle speed, engine on / off information, engine speed, PTO clutch on / off information, PTO clutch rotation speed, and engine load factor. [Prior Art Literature] [Patent Literature]

[0004] [Patent Document 1] Japanese Unexamined Patent Publication No. 2021-089477 [Patent Document 2] Japanese Unexamined Patent Publication No. 2021-087361 [Summary of the Invention] [Problem to be Solved by the Invention]

[0005] For dealerships and other businesses that provide maintenance and repair services for agricultural tractors and other work vehicles, as well as for manufacturers of these vehicles, it is important to understand how these vehicles are typically used. In particular, since a wide variety of implements can be attached to these vehicles, understanding what implements are used and what tasks are performed is crucial for improving the quality of services or products.

[0006] While the conventional technology described above can determine the type of work performed, it cannot accurately identify the diverse range of work machines.

[0007] This disclosure provides a technique for more accurately estimating the type of work equipment connected to a work vehicle. [Means for solving the problem]

[0008] A method according to one aspect of the present disclosure is performed by one or more computers communicating with a work vehicle that drives a connected work implement to perform work. The method includes the steps of: repeatedly acquiring 10 or more signals from the work vehicle, each indicating a different internal state of the work vehicle; generating input data based on the 10 or more signals; inputting the input data into one or more trained models for estimating the type of work implement based on the input data to estimate the type of work implement; and generating and outputting output data that includes information indicating the estimated type of work implement.

[0009] The comprehensive or specific embodiments of this disclosure may be implemented by apparatus, systems, methods, integrated circuits, computer programs, or computer-readable non-temporary storage media, or any combination thereof. Computer-readable storage media may include volatile storage media or non-volatile storage media. Apparatus may consist of multiple devices. If apparatus consists of two or more devices, these two or more devices may be located in a single device or in two or more separate devices. [Effects of the Invention]

[0010] According to embodiments of this disclosure, it becomes possible to more accurately estimate the type of work equipment connected to a work vehicle. [Brief explanation of the drawing]

[0011] [Figure 1] Figure 1 is a schematic diagram showing an example of the system configuration according to an exemplary embodiment of the present disclosure. [Figure 2] Figure 2 is a side view showing an example of a work vehicle and a work machine attached to the work vehicle. [Figure 3] Figure 3 is a block diagram showing an example configuration of a work vehicle and server. [Figure 4] Figure 4 shows an example of a group of control switches installed inside the cabin. [Figure 5] Figure 5 is a block diagram showing examples of hardware configurations for dealer terminals and manufacturer computers. [Figure 6] Figure 6 is a flowchart illustrating an example of data transmission operation by a communication device on a work vehicle. [Figure 7] Figure 7 is a flowchart illustrating an example of server operation. [Figure 8] Figure 8 is a schematic diagram illustrating an example of time-series data. [Figure 9] Figure 9 is a flowchart showing a more specific example of the process in step S230 in Figure 7. [Figure 10] FIG. 10 is a diagram illustrating an example of a decision tree, which is an example of a trained model used for the determination processing in the first step (step S231). [Figure 11] FIG. 11 is a diagram showing a decision tree, which is an example of a trained model used for the determination processing in the second step (step S233). [Figure 12] FIG. 12 is a diagram showing an example of a signal waveform when the working implement is a plow. [Figure 13] FIG. 13 is a diagram showing an example of a signal waveform when the working implement is a slurry tanker. [Figure 14] FIG. 14 is a diagram showing an example of a signal waveform when the working implement is a seed drill. [Figure 15] FIG. 15 is a flowchart showing an example of an operation executed by a processing circuit of a dealer terminal. [Figure 16] FIG. 16 is a diagram showing an example of an image displayed on a display. MODE FOR CARRYING OUT THE INVENTION

[0012] Hereinafter, embodiments of the present disclosure will be described. However, an unnecessarily detailed description may be omitted. For example, detailed descriptions of already well-known matters and redundant descriptions of substantially the same configuration may be omitted. This is to prevent the following description from being unnecessarily redundant and to facilitate understanding by those skilled in the art. The inventor provides the accompanying drawings and the following description to allow those skilled in the art to fully understand the present disclosure, and does not intend to limit the subject matter recited in the claims. In the following description, constituent elements having the same or similar functions are assigned the same reference numerals.

[0013] The following embodiments are illustrative, and the technology of the present disclosure is not limited to the following embodiments. For example, numerical values, shapes, steps, the order of steps, and the layout of a display screen shown in the following embodiments are merely examples, and various modifications are possible as long as no technical contradiction arises. Further, one embodiment and another embodiment can be combined as long as no technical contradiction arises.

[0014] <Example of System Configuration> FIG. 1 is a diagram schematically showing a configuration example of a system according to an exemplary embodiment of the present disclosure. This system is a system for remotely monitoring the operating status of one or more work vehicles 100.

[0015] The system shown in FIG. 1 includes a plurality of work vehicles 100, a server computer 600 (hereinafter referred to as "server 600"), a dealer terminal 400 provided at a dealer of the work vehicles 100, and a manufacturer computer 500 managed by the manufacturer of the work vehicles 100. The server 600 may be, for example, a cloud server installed in a data center. Each of the work vehicle 100, the dealer terminal 400, and the manufacturer computer 500 is configured to be able to communicate with the server 600 via a network 80. In the example of FIG. 1, a plurality of work vehicles 100 are included in the system, but the number of work vehicles 100 may be one.

[0016] The work vehicle 100 and the dealer terminal 400 may be used, for example, in the same region or country. Each of the manufacturer computer 500 and the server 600 may be provided in a region or country different from that of the work vehicle 100 and the dealer terminal 400. Each of the manufacturer computer 500 and the server 600 may be an aggregate of a plurality of computers. The server 600 may be a cloud server managed by a provider that provides machine learning services. In this case, the server 600 may be installed in a country different from any of the work vehicle 100, the dealer terminal 400, and the manufacturer computer 500. The server 600 may be an aggregate of a plurality of computers distributed and arranged in different regions or countries.

[0017] In this embodiment, the work vehicle 100 is a tractor capable of performing agricultural work while driving in a field. The work vehicle 100 is not limited to a tractor; it may be other types of agricultural work vehicles, such as a transplanter or a harvester. Alternatively, the work vehicle 100 may be a vehicle used for purposes other than agriculture, such as a construction vehicle.

[0018] The work vehicle 100 can have an implement attached to its front or rear. The work vehicle 100 can drive the implement and perform agricultural work according to the implement. There are various types of implements, and by changing the implement, different types of agricultural work can be performed.

[0019] In this embodiment, the work vehicle 100 is equipped with a communication device (also called a "direct communication unit") for communicating with the server 600. The communication device is configured to repeatedly transmit a plurality of signals to the server 600, each indicating a different internal state of the work vehicle 100. The types of signals are diverse, and for example, 10 or more, 20 or more, 50 or more, 100 or more, or 200 or more signals are transmitted from the communication device of the work vehicle 100 to the server 600. These signals may be generated based on signals transmitted between a plurality of sensors and a plurality of electronic control units (ECUs) within the work vehicle 100. For example, each signal may be a signal flowing through a bus such as a CAN (Control Area Network) in the work vehicle 100, or a signal processed based on signals flowing through the bus. In this embodiment, the plurality of signals transmitted from the work vehicle 100 to the server 600 may include not only signals indicating vehicle speed, engine on / off information, engine speed, PTO clutch on / off information, PTO clutch speed, and engine load percentage, as disclosed in, for example, Patent Documents 1 and 2, but also many more signals. The transmitted signal may include one or more of the following signals, for example: • A signal indicating the measured value of the draft sensor that measures the load associated with towing the work equipment. • A signal indicating the speed (vehicle speed) of the work vehicle. • A signal indicating the rotational speed of the power take-off (PTO) shaft that drives the work implement. • A signal indicating the height position of the coupling device (e.g., a three-point hitch) used to connect the work equipment. • A signal indicating the rotational speed of the prime mover (engine). • Signal indicating the position of the hand accelerator • A signal indicating the torque acting on the engine. • Signals indicating the direction of travel for work vehicles • Signals indicating the location of work vehicles • Signal indicating the gear shift status

[0020] The transmitted signals may include signals that show basic statistics such as the mean, standard deviation, or variance of such signals over a predetermined time period (e.g., 10 seconds, 30 seconds, 60 seconds, etc.). Each signal may be transmitted in association with a vehicle ID, which is an identifier for the work vehicle 100, and time information (timestamp) indicating the time the signal was generated. The communication device of the work vehicle 100 may be configured to repeatedly transmit multiple signals to the server 600 at predetermined time intervals (e.g., 0.5 seconds, 1 second, or 2 seconds). This time interval may be set to a value of, for example, 5 seconds or less, more preferably 2 seconds or less. The transmission frequency may differ depending on the signal. For example, signals whose values ​​do not change frequently, such as a signal indicating the height position of a three-point hitch, may be transmitted only when the value changes.

[0021] Server 600 functions as a processing unit that acquires multiple signals from the work vehicle 100 and estimates the type of work equipment connected to the work vehicle 100 based on those signals. Server 600 generates multiple time-series data corresponding to each of the acquired signals by performing preprocessing on the acquired signals. Server 600 generates time-series data having the value of each signal at regular intervals (e.g., 0.5 seconds, 1 second, 2 seconds, etc.) and generates input data based on this time-series data. Preprocessing may include, for example, resampling the values ​​of the transmitted signals at regular intervals (e.g., 1 second), imputing missing data, and correcting outliers. Based on the time-series data containing the value of each signal at each time after preprocessing, Server 600 generates input data showing the time-dependent changes of multiple features. Features can be obtained by calculating, for example, the moving average, rate of change, or moving standard deviation of the signal values. Examples of features that can be generated include the moving average of vehicle speed, the moving standard deviation of acceleration, the moving average of draft sensor values, the rate of change of engine speed, and the moving standard deviation of hitch position. In the following explanation, the features contained in the input data may also be referred to as "signals."

[0022] The storage device of server 600 stores one or more pre-trained machine learning models (trained models). These models are used to estimate the type of work machine based on input data. These models are generated by commands from manufacturer computer 500 and trained using training data transmitted from manufacturer computer 500 as needed. Server 600 estimates the type of work machine 300 by inputting input data into the trained models.

[0023] As a machine learning algorithm, for example, a decision tree-based algorithm may be used. By using a decision tree, it is possible to understand the criteria used to estimate the type of workpiece, making it easier to improve the model compared to other black-box models where the criteria are unknown. Examples of decision tree-based algorithms that can be used include ID3, C4.5, CART, CHAID, or Random Forest. Not limited to algorithms using decision trees, other machine learning algorithms such as neural networks or support vector machines may also be used.

[0024] As will be explained in more detail later, the trained model may include a first model for determining whether the work vehicle 100 is in operation or in another state, and a second model for identifying the type of work implement connected to the work vehicle 100. In that case, the server 600 may be configured to perform work implement estimation using the second model only if the estimation using the first model determines that the work vehicle 100 is in operation. Performing such a two-step estimation allows for a more accurate estimation of the type of work implement 300 compared to estimating the work implement in one step.

[0025] When the server 600 estimates the type of work implement, it generates output data containing information indicating the estimated type of work implement. The generation of output data may occur continuously (for example, every 0.5 seconds, 1 second, or 2 seconds) while the work vehicle 100 is in operation. The output data may include not only the estimated type of work implement but also information indicating the operating status of the work vehicle 100. Information indicating the operating status may include, for example, whether the work vehicle is working, driving without working (referred to as "non-working driving"), turning at a headland (referred to as "turning at a headland"), or idling. Information indicating the operating status may also include other information such as vehicle speed, fuel consumption, or total operating time from a certain point in time. The server 600 transmits the output data to another computer, such as the dealer terminal 400.

[0026] The dealer terminal 400 is a terminal device used by a monitor (e.g., a dealer's representative) to monitor the condition, perform maintenance, or diagnose faults of the work vehicle 100. The dealer terminal 400 shown in Figure 1 is a laptop PC (Personal Computer) with a built-in display. The dealer terminal 400 may also be a desktop PC with an external display. Alternatively, the dealer terminal 400 may be another type of computer, such as a tablet computer or a smartphone. Based on output data transmitted from the server 600, the dealer terminal 400 displays information on the display indicating the time-series operating status of the work vehicle 100. This time-series operating status information may include, for example, information indicating whether work was performed, the type of work, or the type of equipment used for each day and time period. Based on the displayed information, the monitor can understand in real time how the work vehicle 100 is being used.

[0027] A dealer of work vehicle 100 sells or leases work vehicle 100 to users or agricultural business operators, and also performs maintenance and repairs on work vehicle 100. In order to perform repairs and maintenance, it is important to understand how the work vehicle 100 is normally used. In particular, it is important to understand what kind of work equipment users attach to work vehicle 100 for their work. However, with conventional technology, it was not possible to accurately understand what kind of work equipment was attached to work vehicle 100, and maintenance and other tasks could not be performed efficiently. Especially when the dealer's service area was large, it was necessary to visit multiple users scattered across a wide area and conduct interviews to check the condition of each work vehicle. According to this embodiment, the dealer can use the dealer terminal 400 to remotely understand the type of work equipment attached to each work vehicle 100 and the operating status of work vehicle 100. As a result, the frequency of visits can be reduced and the efficiency of maintenance and other tasks can be greatly improved.

[0028] The manufacturer computer 500 is a computer that instructs the server 600 to generate and train a machine learning model. The manufacturer computer 500 can be any computer, such as a server computer, personal computer (PC), tablet computer, or smartphone. The manufacturer computer 500 may be used, for example, by a manufacturer's employee. The manufacturer computer 500 sends training data to the server 600 in response to the employee's operation or according to a set schedule. The server 600 trains (learns) the machine learning model based on the transmitted training data. Model training based on training data may be performed, for example, on a daily basis. This allows for iterative improvement of the trained model, thereby improving the accuracy of estimations.

[0029] The server 600 may also send output data to the manufacturer's computer 500, which may include information such as the type of work equipment connected to each work vehicle 100. In this case, the manufacturer can understand how each work vehicle 100 is being used. This makes it easier to identify the cause of any malfunctions or failures that occur in, for example, a work vehicle 100. The manufacturer can use the information on the operating status of each work vehicle 100 obtained from the server 600 to help with future product development.

[0030] The configuration and operation of each component will be explained in more detail below.

[0031] <Outline configuration of work vehicles> Figure 2 is a side view showing an example of a work vehicle 100 and a work machine 300 connected to the work vehicle 100. The work vehicle 100 comprises a vehicle body 101, a prime mover 102, and a transmission 103. The vehicle body 101 is provided with wheels 104 with tires and a cabin 105. The wheels 104 include a pair of front wheels 104F and a pair of rear wheels 104R. Inside the cabin 105 are a driver's seat 107, a steering system 106, and a group of switches for operation. One or both of the front wheels 104F and the rear wheels 104R may be replaced with multiple wheels fitted with tracks (crawlers) instead of wheels with tires.

[0032] The work vehicle 100 is further equipped with a GNSS unit 120. GNSS (Global Navigation Satellite System) is a general term for satellite positioning devices such as GPS (Global Positioning System), QZSS (Quasi-Zenith Satellite System, e.g., Michibiki), GLONASS, Galileo, and BeiDou. The GNSS unit 120 includes an antenna that receives signals from GNSS satellites and a processor that determines the position of the work vehicle 100 based on the signals received by the antenna. The GNSS unit 120 receives GNSS signals transmitted from multiple GNSS satellites and performs positioning based on the GNSS signals. In this embodiment, the GNSS unit 120 is located on top of the cabin 105, but it may be located in other positions.

[0033] The prime mover 102 may be, for example, a diesel engine. An electric motor may be used instead of a diesel engine. The transmission 103 can change the propulsion force and travel speed of the work vehicle 100 by shifting gears. The transmission 103 can also switch the work vehicle 100 between forward and reverse.

[0034] The steering system 106 includes a steering wheel, a steering shaft connected to the steering wheel, and a power steering system that assists steering by the steering wheel. The front wheels 104F are steering wheels, and the direction of travel of the work vehicle 100 can be changed by changing their steering angle (also referred to as the "steering angle"). The steering angle of the front wheels 104F can be changed by operating the steering wheel. The power steering system includes a hydraulic system or electric motor that supplies auxiliary force to change the steering angle of the front wheels 104F. When automatic steering is performed, the steering angle is automatically adjusted by the force of the hydraulic system or electric motor under control from a control device located inside the work vehicle 100.

[0035] A coupling device 108 is provided at the rear of the vehicle body 101. The coupling device 108 includes, for example, a three-point support device (also referred to as a "three-point hitch"), a PTO (Power Take Off) shaft, a universal joint, and a communication cable. The coupling device 108 allows the work implement 300 to be attached to and detached from the work vehicle 100. The coupling device 108 can control the position or orientation of the work implement 300 by raising and lowering the three-point hitch, for example, by a hydraulic system. Power can also be supplied from the work vehicle 100 to the work implement 300 via the universal joint. The work vehicle 100 can pull the work implement 300 and have the work implement 300 perform a predetermined task. The coupling device may also be provided at the front of the vehicle body 101. In that case, the work implement can be connected to the front of the work vehicle 100.

[0036] The implement 300 shown in Figure 2 is a rotary tiller, but the implement 300 is not limited to a rotary tiller. For example, any implement such as a mower, seeder, spreader, rake, baler, harvester, sprayer, or harrow can be connected to the work vehicle 100 and used.

[0037] <Example configuration of work vehicles and servers> Figure 3 is a block diagram showing an example configuration of a work vehicle 100 and a server 600. In the example in Figure 3, the work vehicle 100 includes a GNSS unit 120, a group of operation switches 130, a drive unit 140, a group of sensors 150, a storage device 170, a control device 180, a communication device 190, and the group of operation switches 130. These components can be connected to each other via a bus so as to be able to communicate with one another.

[0038] The drive system 140 includes various devices necessary for the movement of the work vehicle 100 and the driving of the work equipment 300, such as the prime mover 102, transmission 103, steering system 106, and coupling device 108. The prime mover 102 may be an internal combustion engine, such as a diesel engine. The drive system 140 may also be equipped with an electric motor for traction, either in place of or in conjunction with the internal combustion engine.

[0039] The sensor group 150 includes various sensors such as an engine rotation sensor 151, an axle rotation sensor 152, a PTO rotation sensor 153, a hitch position sensor 154, and a draft sensor 155. The engine rotation sensor 151 measures the rotational speed of the engine, i.e., the number of rotations per unit time (e.g., 1 minute). The axle rotation sensor 152 measures the rotational speed of the axle connected to the wheel 104. The PTO rotation sensor 153 measures the rotational speed of the PTO shaft. The hitch position sensor 154 measures the height position of the three-point hitch. The draft sensor 155 measures the load associated with towing the implement 300. The sensor group 150 may also include various other sensors such as a temperature sensor, a fuel sensor, a water temperature sensor, an oil level gauge, a shuttle sensor, a hand accelerator sensor, an accelerator pedal sensor, a main transmission lever sensor, a sub-transmission lever sensor, an acceleration sensor, and an angular velocity sensor. The signals output from these sensors can be sent via the bus to the control unit 180 and transmitted to the server 600 by the communication device 190.

[0040] The control unit 180 is a collection of multiple ECUs. The control unit 180 includes, for example, an ECU 181 for communication control, an ECU 182 for engine control, and an ECU 183 for gear shift control. The control unit 180 may also include various other ECUs, such as an ECU for steering control, an ECU for PTO control, and an ECU for hydraulic control. The communication control ECU 181 controls the transmission of signals by the communication device 190. The operation of the multiple ECUs included in the control unit 180 enables the movement of the work vehicle 100, the operation of the work machine 300, and communication with the server 600. These ECUs can communicate with each other according to vehicle bus standards such as CAN. Each ECU may include one or more processors and one or more memories.

[0041] The communication device 190 is a wireless communication device, such as a TCU (Telematics Control Unit). The communication device 190 communicates with the server 600 via the network 80. The network 80 may include, for example, a cellular mobile communication network such as 3G, 4G, or 5G, a wireless communication network such as Wi-Fi (Wireless Fidelity, registered trademark), or LPWA (Low Power Wide Area), and the Internet. The communication device 190 communicates with the server 600 via multiple network devices such as routers and switches included in the network 80.

[0042] The communication device 190 transmits data to the server 600 containing multiple signals indicating different internal states of the work vehicle 100, in accordance with instructions from the communication control ECU 181 in the control device 180. The communication device 190 includes a processing circuit 192 that generates data containing multiple signals and a communication circuit 194 that transmits the data wirelessly. The communication device 190 transmits various signals flowing through the CAN bus to the server 600 at predetermined time intervals defined for each signal. The communication device 190 may be directly connected to the communication control ECU 181 in the control device 180 instead of being connected to the bus. The types of signals transmitted by the communication device 190 can be set by the communication control ECU 181. The function of generating the transmission data by the processing circuit 192 may be implemented in the communication control ECU 181 in the control device 180. In that case, the combination of the communication control ECU 181 and the communication device 190 can be called the "communication device".

[0043] The communication device 190 can be manufactured and sold independently of the work vehicle 100. For example, the communication function in this embodiment may be realized by later attaching the communication device 190 to a work vehicle 100 that does not have one.

[0044] The storage device 170 includes one or more storage media, such as flash memory or magnetic disks. The storage device 170 stores signals output from each sensor and various data generated by the control device 180. The storage device 170 may also be configured to store computer programs that cause each ECU in the control device 180 to perform various operations. Such computer programs may be provided to the work vehicle 100 via a storage medium (e.g., semiconductor memory or optical disk) or a telecommunications line (e.g., the Internet). Such computer programs may be sold as commercial software.

[0045] Figure 4 shows an example of an operation switch group 130 located inside the cabin 105. Inside the cabin 105 is an operation switch group 130, which includes multiple switches (including buttons, levers, and pedals) that can be operated by the user. The operation switch group 130 may include, for example, a switch for switching the main gear (e.g., a button), a switch for switching the sub-gear (e.g., a shift lever), a switch for switching between forward and reverse (e.g., a shuttle lever), and a switch for raising and lowering the work equipment 300. Pedals such as a clutch pedal, accelerator pedal, and brake pedal are also included in the operation switch group 130. Each switch is provided with a sensor to detect the state of the switch. These sensors are also included in the sensor group 150.

[0046] Next, we will describe the hardware configuration of Server 600.

[0047] As shown in Figure 3, the server 600 includes a storage device 650, a processing circuit 660, a ROM (Read Only Memory) 670, a RAM (Random Access Memory) 680, and a communication circuit 690. These components are connected to each other via a bus so that they can communicate with one another.

[0048] The communication circuit 690 is a communication module for communicating with external devices such as a work vehicle 100 via the network 80. The communication circuit 690 can perform wired communication compliant with communication standards such as IEEE 1394 (registered trademark) or Ethernet (registered trademark). The communication circuit 690 may also perform wireless communication compliant with Bluetooth (registered trademark) or Wi-Fi standards, or cellular mobile communication such as 3G, 4G, or 5G.

[0049] The storage device 650 may be, for example, a magnetic storage device or a semiconductor storage device. An example of a magnetic storage device is a hard disk drive (HDD). An example of a semiconductor storage device is a solid-state drive (SSD). The storage device 650 may be a device independent of the server 600. For example, the storage device 650 may be a storage device connected to the server 600 via the network 80, such as cloud storage. The storage device 650 may store data transmitted from the work vehicle 100 and one or more trained models for estimating the type of work machine 300 connected to the work vehicle 100.

[0050] The processing circuit 660 may be, for example, a semiconductor integrated circuit including a central processing unit (CPU). The processing circuit 660 may be implemented by a microprocessing circuit or microcontroller. Alternatively, the processing circuit 660 may also be implemented by an FPGA (Field Programmable Gate Array) equipped with a CPU, a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), an ASSP (Application Specific Standard Product), or a combination of two or more circuits selected from these. The processing circuit 660 sequentially executes a computer program stored in the ROM 670 that describes a set of instructions for performing at least one process, thereby achieving the desired process. For example, the processing circuit 660 estimates the work machine 300 connected to the work vehicle 100 based on data transmitted from the communication device 190 of the work vehicle 100 and a learned model stored in the storage device 650. The processing circuit 660 transmits output data including the estimation result to the dealer terminal 400 via the communication circuit 690. The processing circuit 660 also generates and trains (learns) machine learning models based on instructions from the manufacturer computer 500.

[0051] ROM670 is, for example, writable memory (e.g., PROM), rewritable memory (e.g., flash memory), or read-only memory. ROM670 stores a program that controls the operation of the processing circuit 660. ROM670 does not have to be a single storage medium; it may be a collection of multiple storage media. Some of the collection of multiple storage media may be removable memory.

[0052] RAM680 provides a workspace for temporarily unpacking the control program stored in ROM670 during boot-up. RAM680 does not need to be a single storage medium; it may be a collection of multiple storage media.

[0053] <Example configuration of dealer terminal and manufacturer computer> Figure 5 is a block diagram showing an example of the hardware configuration of the dealer terminal 400 and the manufacturer computer 500.

[0054] The dealer terminal 400 comprises an input device 420, a display 430, a storage device 450, a processing circuit 460, a ROM 470, a RAM 480, and a communication circuit 490. These components are connected to each other via a bus for communication. The input device 420 is a device for converting user instructions into data and inputting it to the processing circuit 460. The input device 420 may include, for example, a keyboard, mouse, or touch panel. The display 430 may be any display, such as a liquid crystal display or an organic EL display. The hardware configurations of the processing circuit 460, ROM 470, RAM 480, storage device 450, and communication circuit 490 are similar to the hardware configurations of the corresponding devices in the server 600. The dealer terminal 400 is used to request output data from the server 600 indicating the operating status of the work vehicle 100 and to display an image based on the output data.

[0055] The maker computer 500 comprises a processing circuit 560, a ROM 570, a RAM 580, a storage device 550, and a communication circuit 590. The hardware configurations of the processing circuit 560, ROM 570, RAM 580, storage device 550, and communication circuit 590 are the same as the hardware configurations of the corresponding devices in the server 600. The maker computer 500 may be used in conjunction with input devices such as a keyboard and mouse, and a display such as a liquid crystal display or an organic EL display. Alternatively, the maker computer 500 may have input devices and a display. The maker computer 500 is used to instruct the server 600 to generate and train machine learning models.

[0056] <Example of communication operation> Figure 6 is a flowchart illustrating an example of data transmission operation by the communication device 190 of the work vehicle 100. The communication device 190 sequentially transmits multiple signals indicating the internal state of the work vehicle 100 to the server 600 by repeating the operations from steps S110 to S130. This operation can be performed continuously while the work vehicle 100 is in operation (for example, while the engine is on).

[0057] In step S110, the processing circuit 192 of the communication device 190 generates transmission data that includes a plurality of signals, each indicating the internal state of the work vehicle 100. The number of signal types is 10 or more, and is typically 100 or more, or even 200 or more. These signals may be generated based on signals output from the sensor group 150. The transmission data may include not only the signals output from the sensor group 150 themselves, but also signals indicating values ​​such as the average or standard deviation of the signals over a predetermined time (e.g., 10 seconds). The processing circuit 192 generates data that associates each signal with the vehicle ID and a timestamp indicating the generation time of that signal.

[0058] In step S120, the processing circuit 192 transmits the generated data to the server 600 via the communication circuit 194.

[0059] In step S130, the processing circuit 192 determines whether the operation of the work vehicle 100 has stopped. The processing circuit 192 determines that the operation of the work vehicle 100 has stopped, for example, when the engine is turned off. If the operation of the work vehicle 100 has stopped, the process ends. If the operation of the work vehicle 100 has not stopped, the process returns to step S110.

[0060] While the work vehicle 100 is in operation, the operations from steps S110 to S130 are repeated. The generation and transmission of data for transmission may be repeated at predetermined time intervals (e.g., 0.5 seconds, 1 second, 2 seconds, etc.).

[0061] The combination of signals included in the data to be transmitted may be the same each time, or it may differ with each transmission. For example, signals whose values ​​change frequently, such as vehicle speed and engine RPM, may be transmitted each time, while signals whose values ​​do not change frequently, such as the height of a three-point hitch, may be transmitted only when their values ​​change. Which signals are transmitted and the frequency of transmission for each signal may be specified in a configuration file created, for example, by the ECU 181 for communication control. The communication device 190 may be configured to transmit specific signals according to the configuration file.

[0062] Figure 7 is a flowchart illustrating an example of the operation of the server 600. The server 600 sequentially estimates the type of work equipment 300 connected to the work vehicle 100 by repeating the process from steps S210 to S260. This operation can be performed continuously while the work vehicle 100 is in operation.

[0063] In step S210, the processing circuit 660 of the server 600 determines whether or not data has been acquired from the work vehicle 100. As described above, data containing multiple signals may be transmitted from the work vehicle 100 at predetermined time intervals. When the data is received by the communication circuit 590, the process proceeds to step S220.

[0064] In step S220, the processing circuit 660 performs preprocessing based on the acquired data and generates input data for input to the trained model. The transmitted data includes the values ​​of multiple signals and the time information of each signal. Based on the values ​​and time information of each signal included in the continuously acquired transmitted data, the processing circuit 660 generates multiple time-series data corresponding to the multiple signals. Based on the time-series data, the processing circuit 660 generates input data for input to the trained model.

[0065] Figure 8 schematically illustrates an example of time-series data. The time-series data shown in Figure 8 associates date and time (e.g., in seconds) with the values ​​of multiple signals. In the example in Figure 7, the values ​​of signals S1, S2, S3, S4, ... are associated every second. Each of signals S1, S2, S3, S4, ... represents, for example, vehicle speed, engine rotation speed, PTO shaft rotation speed, three-point hitch height position, and draft sensor measurement. Although four signals are illustrated in Figure 8, the number of signal types can be more than 10, and in some examples, it can be more than 50, more than 100, and in yet another, more than 200. The time-series data may span a period of several seconds to tens of seconds, or even hundreds of seconds.

[0066] The signals transmitted from the work vehicle 100 may include signals transmitted at time intervals longer than one second. For example, there may be signals transmitted every 10 seconds, or signals transmitted irregularly only when their value changes. For such signals, the processing circuit 660 may generate time-series data by interpolating values ​​for times when there are no values.

[0067] The processing circuit 660 generates input data containing multiple features for input to the trained model based on such time-series data. Each feature may be the signal value itself, or it may be a value obtained by averaging the signal value over a certain time period (e.g., 10 seconds, 30 seconds, 60 seconds, etc.) or calculating the standard deviation. Alternatively, each feature may be a value calculated by combining two or more signals in the time-series data. The input data may be a combination of 10 or more, 20 or more, 50 or more, 100 or more, or 200 or more features.

[0068] In step S230 shown in Figure 7, the processing circuit 660 inputs the generated input data into one or more trained models to estimate the type of work equipment 300 connected to the work vehicle 100. The trained models can be generated and trained by machine learning algorithms such as decision trees, as described above.

[0069] Figure 9 is a flowchart showing a more specific example of the process in step S230. In the example in Figure 9, step S230 includes the processes in steps S231, S232, and S233. In this example, the input data includes first input data and second input data, and the trained models include first model and second model. The first input data and second input data may be the same or different. The first model is a model for determining whether the work vehicle 100 is working or in another state based on the first input data. The second model is a model for estimating the type of work machine 300 based on the second input data. Each of the first and second models is a model based on a machine learning algorithm, such as a decision tree.

[0070] In step S231, the processing circuit 660 inputs the first input data to the first model to determine whether the work vehicle 100 is working or in another state. The first model may be, for example, a model for determining whether the work vehicle 100 is working, driving non-working, turning at a headland, or idling, based on the first input data. In that case, the processing circuit 660 can determine whether the work vehicle 100 is working, driving non-working, turning at a headland, or idling by inputting the first input data to the first model. Here, "working" refers to the state in which the implement is being driven. "Driving non-working" refers to the state in which the vehicle is driving in a location other than a headland without driving the implement. "Turning at a headland" refers to the state in which the vehicle is turning at a headland outside the work area in order to move back and forth within the field. "Idling" refers to the state in which the vehicle is stopped with the engine running.

[0071] In step S232, the processing circuit 660 determines whether the work vehicle 100 is in operation based on the determination result in step S231. If the work vehicle 100 is in operation, the process proceeds to step S233. If the work vehicle 100 is in a state other than operation, the process ends in step S230 and proceeds to step S240.

[0072] In step S233, the processing circuit 660 inputs the second input data to the second model to estimate the type of work equipment 300 connected to the work vehicle 100. After step S233, the process proceeds to step S240.

[0073] Thus, in the example in Figure 9, step S230 for estimating the type of implement 300 includes a first step (step S231) in which first input data is input to the first model to determine whether the work vehicle 100 is working or in another state, and a second step (step S233) in which, if it is determined that the work vehicle 100 is working, second input data is input to the second model to estimate the type of implement 300. More specifically, the first step includes determining whether the work vehicle 100 is working, driving non-working, turning on a headland, or idling. By performing such a two-step process, a more accurate estimation becomes possible compared to estimating the type of implement 300 in one step.

[0074] Figure 10 illustrates a decision tree, which is an example of a trained model used in the decision processing of the first step (step S231). In this decision tree, the state in which the work vehicle 100 is working, driving non-working, turning at a headland, or other (e.g., idling) is determined based on the signals or features shown in Table 1 below that are included in the input data.

[0075] [Table 1]

[0076] In the example shown in Figure 10, c1, c2, c3, c4, and c5 represent certain constants, which are determined based on a decision tree algorithm. Software implementing the decision tree algorithm can display the discrimination results, as exemplified in Figure 10, on a screen. Such a display allows, for example, a user of the manufacturer's computer 500 to understand the basis for the estimation.

[0077] The signals or features described above are just examples, and the state of the work vehicle 100 may be determined based on different signals or features. In the example in Figure 10, the four types of features shown in Table 1 are used, but estimation based on a deeper level of decision trees may be performed using more features. The order of decisions based on each feature is not limited to the order shown in the diagram and may be changed depending on the algorithm or learning content used.

[0078] Figure 11 shows a decision tree, which is an example of a trained model used in the decision processing of the second step (step S233). This decision tree determines whether the type of implement 300 connected to the work vehicle 100 is a baler, seed drill, harrow, shredder, stubble couch, plow, tedder, or front loader. The type of implement 300 is determined based on the signals or features shown in Table 2 below, which are included in the input data.

[0079] [Table 2]

[0080] In the example shown in Figure 11, d1, d2, d3, d4, d5, d6, and d7 represent certain constants, which are determined based on a decision tree algorithm. Software implementing the decision tree algorithm can display the discrimination results, as exemplified in Figure 11, on a screen. Such a display allows, for example, a user of the manufacturer's computer 500 to understand the basis for the estimation.

[0081] The signals or features described above are just examples, and the type of work implement 300 may be determined based on different signals or features. Furthermore, models capable of distinguishing other types of work implements, not limited to balers, seed drills, harrows, shredders, stubble couches, plows, tedders, and front loaders, may be used. In the example in Figure 11, the six features shown in Table 2 are used, but estimation based on a deeper decision tree using more features may be performed. The order of decisions based on each feature is not limited to the order shown and can be changed depending on the algorithm or training content used.

[0082] Here, with reference to Figures 12 to 14, examples of different signal waveforms depending on the type of work machine 300 will be explained.

[0083] Figure 12 shows an example of a signal waveform in one implement 300 (hereinafter referred to as "implement 1"). Figure 13 shows an example of a signal waveform in another implement 300 (hereinafter referred to as "implement 2"). Figure 14 shows an example of a signal waveform in yet another implement 300 (hereinafter referred to as "implement 3"). Implements 1, 2, and 3 are different types of implements, and their uses are different. Figures 12, 13, and 14 illustrate the waveforms showing the time variation of four signals (signal 1, signal 2, signal 3, and signal 4). Signals 1 to 4 represent signals such as the height of the three-point hitch, the measurement value of the draft sensor, the vehicle speed, and the value of the PTO indicator lamp. In reality, more signals may be used, but Figures 12 to 14 show the waveforms of four signals as examples. As can be seen from these figures, the waveforms of each signal differ significantly depending on the type of implement 300. In this embodiment, by using a machine learning model that estimates the type of work implement 300 based on the differences in these signal waveforms, the type of work implement 300 connected to the work vehicle 100 can be estimated with high accuracy.

[0084] Refer to Figure 7 again. Once the processing in step S230 is complete, proceed to step S240.

[0085] In step S240, the processing circuit 660 generates output data that includes information about the state of the work vehicle 100 estimated in step S230 (e.g., working, non-working, turning at a headland, idling) and the type of work equipment 300 connected to the work vehicle 100. The output data may be, for example, data in which the date, time (e.g., in seconds), vehicle ID, estimated state of the work vehicle 100, and estimated type of work equipment 300 are correlated. The output data may also include the position of the work vehicle 100 measured by the GNSS unit 120 and the measured values ​​of one or more sensors included in the sensor group 150 (e.g., vehicle speed, fuel level, fuel consumption, total operating time).

[0086] In step S250, the processing circuit 660 transmits the output data to the dealer terminal 400 via the communication circuit 690. The dealer terminal 400 records the transmitted output data in the storage device 450. In response to an operation by a user (e.g., a dealer's representative), the dealer terminal 400 can generate and display an image showing the operating status of the work vehicle 100 based on the output data.

[0087] In step S260, the processing circuit 660 determines whether or not to terminate the operation. For example, it determines to terminate the operation when an instruction to terminate the operation is given from an external computer, such as the manufacturer computer 500. An instruction to terminate the operation may be given, for example, when stopping the estimation operation in order to further train the trained model using new training data. The processing from steps S210 to S260 is repeated until an instruction to terminate the operation is given.

[0088] In the example shown in Figure 7, after step S230, the generation of output data in step S240 and the transmission of output data in step S250 occur consecutively. In this case, the generation and transmission of output data are performed at the same frequency as the frequency at which data is received from the work vehicle 100 (for example, every second). Alternatively, the generation and transmission of output data in steps S240 and S250 may be performed after multiple estimation processes have been completed. For example, the generation and transmission of output data may be performed in batches at predetermined intervals, such as once every 10 seconds or once every 100 seconds. Or, the processing circuit 660 may generate and transmit output data to the dealer terminal 400 only when requested by the dealer terminal 400.

[0089] Next, we will explain the operation of the dealer terminal 400 to display the operating status of the work vehicle 100.

[0090] Figure 15 is a flowchart showing an example of the operation performed by the processing circuit 460 of the dealer terminal 400.

[0091] In step S310, the processing circuit 460 determines whether an operation to display the operational information of the work vehicle 100 has been performed via the input device 420. This operation may be performed, for example, by a maintenance person for the work vehicle 100 at a dealership. If this operation has been performed, the process proceeds to step S320.

[0092] In step S320, the processing circuit 460 acquires the output data transmitted from the server 600 by reading it from the storage device 450. The server 600 may also be configured to transmit output data to the dealer terminal 400 in response to a request from the dealer terminal 400. In that case, the processing circuit 460 acquires the output data by requesting it from the server 600 via the communication circuit 490.

[0093] In step S340, the processing circuit 460 generates a display image containing information indicating the time-series operating status of the work vehicle 100 based on the output data and displays it on the display 430.

[0094] Figure 16 shows an example of an image displayed on the display 430. The displayed image in this example includes multiple display areas 431, 432, 433, and 434. Display area 431 shows when the work vehicle 100 performed work. Display area 432 shows the utilization rate of each work implement during a certain period. Display area 433 shows the fuel consumption per hour (average fuel consumption), operating rate, operating time, idling time, and fuel consumption during that period. Display area 434 shows detailed operating information of the work vehicle 100 during that period. Display area 434 displays information such as time, fuel consumption, average fuel consumption, average engine speed (RPM), and average PTO speed (RPM) for each of the following states: when the work vehicle 100 is stationary (idling), performing work, turning at a headland, and driving without performing work. For the state in which work is being performed, this information is displayed for each type of work implement used.

[0095] By displaying this information, dealership staff can understand how the work vehicle 100 is being used. This allows them to detect malfunctions, defects, or signs of malfunctions in the work vehicle 100 early, enabling efficient maintenance such as parts replacement and repairs.

[0096] The display screen shown in Figure 16 is merely an example, and the layout of the display screen is not limited to what is illustrated. What is displayed based on the data transmitted from the server 600 will be determined as appropriate according to the purpose.

[0097] In this embodiment, the dealer terminal 400 generates and displays a display image based on output data transmitted from the server 600, but the manufacturer computer 500 may perform a similar operation. For example, the server 600 may transmit output data including the estimated results of the work machine to the manufacturer computer 500 in response to a request from the manufacturer computer 500. Based on this output data, the manufacturer computer 500 may display a display image on a display screen as shown in Figure 16. Such a display allows the manufacturer's personnel to gain insights into how the work vehicle 100 is actually used, and this insight can be used for future product development or services.

[0098] A terminal device used by the user or owner of the work vehicle 100 (e.g., an agricultural business owner) may perform operations similar to those of the dealer terminal 400. For example, the server 600 may send output data, including the estimated results of the work equipment, to the terminal device in response to a request from the terminal device. Based on the output data, the terminal device may display a display image on its screen, as shown in Figure 16. Such a display allows, for example, an agricultural business owner to see how the work vehicle 100 is being used and use this information to improve future agricultural operations. For example, if it is found that the work vehicle 100 used by an employee is idling too much, the employee can be instructed to reduce idling, thereby reducing fuel consumption. Also, if it is found that there are too many headland turns, the user can decide to introduce a wider work equipment to reduce the number of turns or to introduce a larger work vehicle.

[0099] Similar assessments can also be made by dealer representatives. By looking at a display screen like the one shown in Figure 16, a dealer representative can, for example, identify excessive headland turns and propose the introduction of wider work equipment or larger work vehicles. This can lead not only to increased efficiency in maintenance operations but also to improved sales activities.

[0100] In the above embodiment, the system includes one or more work vehicles 100, a server 600, a dealer terminal 400, and a manufacturer computer 500, but this is merely an example. For example, the system may include only some of these devices. Also, the server 600, the dealer terminal 400, and the manufacturer computer 500 may each be other types of computers. For example, the server 600 and the manufacturer computer 500 may both be computers managed by the manufacturer of the work vehicle 100. Furthermore, a company different from the manufacturer of the work vehicle 100 may sell or operate communication devices installed in the work vehicle 100, or computers that collect signals from the communication devices and perform the estimation processing according to this embodiment. Such a company may also use terminal devices similar to the dealer terminal 400 to monitor the operating status of the work vehicle 100 and provide services such as maintenance.

[0101] Computer programs defining the methods by which each device in the above embodiments is executed can be manufactured and sold independently of the devices. These computer programs may be provided, for example, by being stored in a computer-readable, non-temporary storage medium. They may also be provided by download via telecommunications lines (e.g., the Internet).

[0102] As described above, this disclosure includes methods, processing devices, programs, communication devices, work vehicles, terminal devices, and systems related to the following items.

[0103] [Item 1] A method performed by one or more computers communicating with a work vehicle that drives connected work equipment to perform work, The steps include repeatedly acquiring 10 or more signals from the work vehicle, each indicating a different internal state of the work vehicle, A step of generating input data based on the 10 or more signals, The steps include: inputting the input data into one or more trained models for estimating the type of work implement based on the input data to estimate the type of work implement; A step of generating and outputting output data that includes information indicating the estimated type of the work machine, A method that includes this.

[0104] [Item 2] The method according to item 1, wherein the step of generating the input data includes generating 10 or more time-series data corresponding to each of the 10 or more signals, and generating the input data based on the time-series data.

[0105] [Item 3] The aforementioned input data includes first input data and second input data. The one or more trained models include a first model for determining whether the work vehicle is working or in another state based on the first input data, and a second model for estimating the type of work machine based on the second input data. The aforementioned estimation step is, A first step involves inputting the first input data to the first model to determine whether the work vehicle is in operation or in another state, If it is determined that the work vehicle is in operation, the second step is to input the second input data to the second model to estimate the type of work machine, The method described in item 1 or 2, including the method described in item 1 or 2.

[0106] [Item 4] The first model is a model for determining, based on the first input data, whether the work vehicle is in operation, non-operational driving, turning at a headland, or idling. The first step includes inputting the first input data into the first model to determine whether the work vehicle is in operation, non-operational driving, turning at a headland, or idling. The method described in item 3.

[0107] [Item 5] The method described in item 3 or 4, wherein each of the first and second models is a decision tree-based model.

[0108] [Item 6] The method according to any one of items 1 to 5, wherein the 10 or more signals are 20 or more signals.

[0109] [Item 7] The method according to any one of items 1 to 6, wherein the 10 or more signals are 50 or more signals.

[0110] [Item 8] The method according to any one of items 1 to 7, wherein the 10 or more signals include signals indicating the measured value of a draft sensor that measures the load associated with the towing of the work machine.

[0111] [Item 9] The method according to any one of items 1 to 8, wherein the 10 or more signals include a signal indicating the travel speed of the work vehicle, a signal indicating the rotational speed of the power take-off (PTO) shaft that drives the work implement, a signal indicating the height position of the coupling device that connects the work implement, and a signal indicating the rotational speed of the prime mover.

[0112] [Item 10] Each of the 10 or more signals is generated based on signals flowing through the CAN (Control Area Network) bus in the work vehicle, according to the method described in any one of items 1 to 9.

[0113] [Item 11] The method according to any one of items 1 to 10, wherein the output step includes transmitting the output data to another computer.

[0114] [Item 12] The other computer is a terminal device used by a monitor who performs condition monitoring, maintenance, or fault diagnosis of the work vehicle. The output data is used to display the time-series operating status of the work vehicle on the display of the terminal device. The method described in item 11.

[0115] [Item 13] The method according to any one of items 1 to 12, wherein the work vehicle is an agricultural tractor, and the work implement is driven to perform agricultural work.

[0116] [Item 14] A processing device that communicates with a work vehicle that drives a connected work machine to perform work, A communication circuit that repeatedly receives 10 or more signals from the aforementioned work vehicle, each indicating a different internal state of the work vehicle, A processing circuit that generates input data based on the 10 or more signals, inputs the input data to one or more trained models for estimating the type of work implement based on the input data to estimate the type of work implement, and generates and outputs output data including information indicating the estimated type of work implement, A processing device equipped with the following features.

[0117] [Item 15] A computer program executed by a computer that communicates with a work vehicle that drives connected work equipment to perform work, The steps include repeatedly receiving 10 or more signals from the work vehicle, each indicating a different internal state of the work vehicle, A step of generating input data based on the 10 or more signals, The steps include: inputting the input data into one or more trained models for estimating the type of work implement based on the input data to estimate the type of work implement; A step of generating and outputting output data that includes information indicating the estimated type of the work machine, A computer program that causes the aforementioned computer to execute.

[0118] [Item 16] A communication device mounted on a work vehicle that drives connected work equipment to perform work, and which communicates with the processing device described in item 14, A processing circuit that generates data including 10 or more signals, each indicating a different internal state of the aforementioned work vehicle, A communication circuit that transmits the aforementioned data to the processing device, A communication device equipped with the following features.

[0119] [Item 17] A work vehicle equipped with the communication device described in item 16.

[0120] [Item 18] A terminal device that communicates with the processing device described in item 14, A communication circuit that receives the output data from the processing unit, A processing circuit that displays an image on a display showing the time-series operating status of the work vehicle based on the output data, A terminal device equipped with the following features.

[0121] [Item 19] The processing apparatus described in item 14, The communication device described in item 16, A system equipped with these features.

[0122] [Item 20] The processing apparatus described in item 14, The work vehicles listed in item 15, A system equipped with these features.

[0123] [Item 21] The system described in item 19, further comprising the terminal device described in item 18.

[0124] [Item 22] The steps include: providing one or more computers with 10 or more signals, each indicating a different internal state of the work vehicle; On the aforementioned one or more computers, The process involves generating input data based on the aforementioned 10 or more signals, The input data is input into one or more trained models for estimating the type of work equipment connected to the work vehicle based on the input data, and the type of work equipment is estimated. Outputting the estimated result of the type of the aforementioned work machine, Steps to execute, A method that includes this.

[0125] [Item 23] A method performed by a communication device mounted on the work vehicle that communicates with the computer performing the method described in item 1, The steps include generating the 10 or more signals based on signals output from multiple devices mounted on the work vehicle, The step of transmitting the 10 or more signals to the computer, A method that includes this.

[0126] [Item 24] A method performed by another computer connected via a network to a computer performing the method described in item 1, The steps include sending training data for generating the aforementioned trained model to the computer, The steps include causing the computer to generate the trained model based on the trained model, A method that includes this.

[0127] [Item 25] A method performed by a terminal device connected via a network to a computer performing the method described in item 1, The steps include requesting the computer to transmit the output data, The steps include receiving the output data transmitted from the computer, The steps include displaying an image on a display showing the estimated type of work machine based on the output data, A method that includes this. [Industrial applicability]

[0128] The technology disclosed herein can be applied, for example, to work vehicles such as tractors, transplanters, harvesters, construction vehicles, or snowplows, to communication devices mounted on such work vehicles, and to processing devices that communicate with such work vehicles. [Explanation of Symbols]

[0129] 80...Network, 100...Work vehicle, 101...Vehicle body, 102...Motor, 103...Transmission, 104...Wheels, 105...Cabin, 106...Steering system, 107...Driver's seat, 108...Coupling device, 120...GNSS unit, 140...Drive system, 150...Sensor group, 170...Storage device, 180...Control device, 190...Communication device, 300...Work machine, 400...Dealer terminal, 420...Input Power device, 430...Display, 450...Storage device, 460...Processing circuit, 470...ROM, 480...RAM, 490...Communication circuit, 500...Manufacturer computer, 550...Storage device, 560...Processing circuit, 570...ROM, 580...RAM, 590...Communication circuit, 600...Server computer, 650...Storage device, 660...Processing circuit, 670...ROM, 680...RAM, 690...Communication circuit

Claims

1. A method performed by one or more computers communicating with a work vehicle that drives connected work equipment to perform work, The steps include repeatedly acquiring 10 or more signals from the work vehicle, each indicating a different internal state of the work vehicle, A step of generating input data based on the 10 or more signals, The steps include: inputting the input data into one or more trained models for estimating the type of work implement based on the input data to estimate the type of work implement; A step of generating and outputting output data that includes information indicating the estimated type of the work machine, Includes, The method wherein the 10 or more signals include a signal indicating a measurement value from a draft sensor that measures the load associated with towing the work machine, a signal indicating the travel speed of the work vehicle, a signal indicating the rotational speed of the power take-off (PTO) shaft that drives the work machine, a signal indicating the height position of the coupling device that connects the work machine, and a signal indicating the rotational speed of the prime mover.

2. The method according to claim 1, wherein the step of generating the input data includes generating 10 or more time-series data corresponding to each of the 10 or more signals, and generating the input data based on the time-series data.

3. The aforementioned input data includes first input data and second input data. The one or more trained models include a first model for determining whether the work vehicle is working or in another state based on the first input data, and a second model for estimating the type of work machine based on the second input data. The aforementioned estimation step is, A first step involves inputting the first input data to the first model to determine whether the work vehicle is in operation or in another state, If it is determined that the work vehicle is in operation, the second step is to input the second input data to the second model to estimate the type of work machine, The method according to claim 1, including the method described in claim 1.

4. The first model is a model for determining, based on the first input data, whether the work vehicle is in operation, non-operational driving, turning at a headland, or idling. The first step includes inputting the first input data to the first model to determine whether the work vehicle is in operation, non-operational driving, turning at a headland, or idling. The method according to claim 3.

5. The method according to claim 3 or 4, wherein each of the first model and the second model is a decision tree-based model.

6. The method according to any one of claims 1 to 4, wherein the 10 or more signals are 20 or more signals.

7. The method according to any one of claims 1 to 4, wherein the 10 or more signals are 50 or more signals.

8. The method according to any one of claims 1 to 4, wherein each of the 10 or more signals is generated based on signals flowing through a CAN (Control Area Network) bus in the work vehicle.

9. The method according to any one of claims 1 to 4, wherein the output step includes transmitting the output data to another computer.

10. The other computer is a terminal device used by a monitor who performs condition monitoring, maintenance, or fault diagnosis of the work vehicle. The output data is used to display the time-series operating status of the work vehicle on the display of the terminal device. The method according to claim 9.

11. The method according to any one of claims 1 to 4, wherein the work vehicle is an agricultural tractor, and the work implement is driven to perform agricultural work.

12. A processing device that communicates with a work vehicle that drives a connected work machine to perform work, A communication circuit that repeatedly receives 10 or more signals from the aforementioned work vehicle, each indicating a different internal state of the work vehicle, A processing circuit that generates input data based on the 10 or more signals, inputs the input data to one or more trained models for estimating the type of work implement based on the input data to estimate the type of work implement, generates output data including information indicating the estimated type of work implement, and outputs it; Equipped with, The processing device includes, for the 10 or more signals, a signal indicating the measured value of a draft sensor that measures the load associated with the towing of the work machine, a signal indicating the travel speed of the work vehicle, a signal indicating the rotational speed of the power take-off (PTO) shaft that drives the work machine, a signal indicating the height position of the coupling device that connects the work machine, and a signal indicating the rotational speed of the prime mover.

13. A computer program executed by a computer that communicates with a work vehicle that drives connected work equipment to perform work, The steps include repeatedly receiving 10 or more signals from the work vehicle, each indicating a different internal state of the work vehicle, A step of generating input data based on the 10 or more signals, The steps include: inputting the input data into one or more trained models for estimating the type of work implement based on the input data to estimate the type of work implement; A step of generating and outputting output data that includes information indicating the estimated type of the work machine, The computer is made to execute the above, The computer program includes, for the 10 or more signals, a signal indicating the measured value of a draft sensor that measures the load associated with towing the work machine, a signal indicating the travel speed of the work vehicle, a signal indicating the rotational speed of the power take-off (PTO) shaft that drives the work machine, a signal indicating the height position of the coupling device that connects the work machine, and a signal indicating the rotational speed of the prime mover.

14. A communication device mounted on a work vehicle that drives a connected work machine to perform work, and which communicates with the processing device described in claim 12, A processing circuit that generates data including 10 or more signals, each indicating a different internal state of the aforementioned work vehicle, A communication circuit that transmits the aforementioned data to the processing device, A communication device equipped with the following features.

15. A work vehicle equipped with the communication device described in claim 14.

16. A terminal device that communicates with the processing device described in claim 12, A communication circuit that receives the output data from the processing unit, A processing circuit that displays an image on a display showing the time-series operating status of the work vehicle based on the output data, A terminal device equipped with the following features.

17. The processing apparatus according to claim 12, A communication device according to claim 14, A system equipped with these features.

18. The processing apparatus according to claim 12, The work vehicle according to claim 15, A system equipped with these features.

19. The system according to claim 17, further comprising the terminal device according to claim 16.

Images