TERRAIN-Dependent SPEED AUTOMATION

Abstract

A system comprises: one or more processors; and a memory that stores instructions executable by the one or more processors. When executed by the one or more processors, the instructions configure the one or more processors to: receive data indicating an upcoming ride quality problem at a location where a working machine is performing an ongoing operation; identify a ride quality problem at the upcoming location at the location based on the data; and control the working machine, at least based on the ride quality problem at the upcoming location at the location.

Description

[0001] This description concerns the activities of working machines. More specifically, it concerns perception sensor systems used in the control of working machines, such as agricultural machinery. BACKGROUND

[0002] There is a wide variety of different types of work machines. These include, but are not limited to, agricultural machinery such as harvesting vehicles (e.g., combine harvesters, etc.). As the terrain changes, the ride quality of a work machine can vary. Poor ride quality can negatively impact both machine operation and operator comfort.

[0003] The foregoing explanation is provided for general background information only and is not intended to be used as an aid in determining the scope of protection of the claimed subject matter. SUMMARY

[0004] A system comprises: one or more processors; and a memory that stores instructions executable by the one or more processors. When executed by the one or more processors, the instructions configure the one or more processors to: receive data indicating an upcoming ride quality problem at a location where a working machine is performing an ongoing operation; identify a ride quality problem at the upcoming location at the location based on the data; and control the working machine, at least based on the ride quality problem at the upcoming location at the location.

[0005] This summary is provided to present a selection of concepts in simplified form, which are further described in detail below. This summary is not intended to identify key features or essential characteristics of the claimed subject matter, nor is it intended to serve as an aid in determining the scope of protection of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS Fig. Figure 1 is a partly pictorial, partly schematic representation of an exemplary working machine in the form of an agricultural harvesting vehicle. Fig. Figure 2 is a block diagram of an example of a system architecture. Fig. Figure 3 is a block diagram illustrating some examples of system architecture components, including a monitoring system with dynamic perception. Fig. Figure 4 is a visual representation that shows an example of how the system architecture works. Fig. Figure 5 shows a flowchart illustrating an example operation of the system architecture when implementing proactive ride quality control. Fig. Figure 6 is a block diagram showing an example of elements of a system architecture in communication with a remote server architecture. Fig. 7, Fig. 8 and Fig. Figure 9 shows examples of mobile devices that can be used in a system architecture. Fig. Figure 10 is a block diagram showing an example of a computing environment that can be used in a system architecture. DETAILED DESCRIPTION

[0006] For a better understanding of the principles of this disclosure, reference is now made to the examples illustrated in the drawings, which are described in specific language. It is understood, however, that these are not to be interpreted as limiting the scope of the disclosure. Any modifications and further adaptations to the described devices, systems, and methods, and any further application of the principles of this disclosure that are normally apparent to a person skilled in the art in the field to which the disclosure relates, are all included. In particular, it is fully taken into consideration that the features, components, and / or steps described with reference to one example may be combined with the features, components, and / or steps described with reference to other examples in this disclosure.

[0007] As described above, the ride comfort of a work machine can vary depending on the terrain. For example, ride comfort may be reduced when driving over uneven terrain. Furthermore, there is generally a desire to complete work at a job site quickly. The speed at which a work machine performs a task at a job site is often directly related to the machine's travel speed. To complete a task more quickly, it may therefore be desirable to drive the work machine faster. Higher speeds and varying terrain conditions can further reduce ride comfort. For example, ride comfort may be more significantly affected at higher speeds than at lower speeds when driving over rough terrain.

[0008] Driving comfort generally correlates with the rocking motion of the machine (rocking along the vertical axis (up and down rocking), rocking along the lateral axis (rocking / swaying), and rocking along the longitudinal axis (forward and backward swaying). A change in the characteristics of the terrain (also referred to as terrain variation or change) over which a machine travels can influence the degree of machine rocking and thus the ride quality (increase or decrease it). Reduced ride quality can negatively affect operator comfort or the performance of the machine (e.g., machine components (such as attachments) may deviate from a desired position, etc.).

[0009] In some examples, sensors on board the work machine, such as observation sensors (e.g., cameras, lidar, radar, ultrasound, etc.), can be used to detect terrain features (e.g., terrain features such as rocks, ruts, washouts, holes, etc.), terrain profiles such as topography (e.g., elevation, slope, changes in gradient and elevation), surface roughness, etc.), and the detected terrain features can be used to control the work machine and improve driving quality. However, it can be difficult to detect certain terrain features while working at the job site, especially in some environments, such as with agricultural machinery, where vegetation (e.g., crops and weeds) can obstruct the view.Furthermore, given the speed of some construction machines, it can be difficult to recognize features in time and react to them in order to proactively control the machine.

[0010] This document describes systems and methods that enable the proactive control of a working machine, such as an agricultural machine, to improve ride quality in the face of terrain variability. These systems and methods allow the acquisition of data indicating ride quality problems ahead of the machine (e.g., relative to the machine's direction of travel or route) and enable proactive control of the machine based on this data to improve ride quality (e.g., by reducing the impact of terrain changes on ride quality). Data indicating ride quality problems can include aerial photographs or maps of the work site showing terrain characteristics, sensor data (e.g., ride quality sensor data) from sensors on board the machine (e.g.,...Sensor data generated while the machine travels over a nearby area of ​​the work site (e.g., an adjacent crossing, etc.) or sensor data from sensors on board another work machine that has previously worked at the work site. The system and the procedures described herein thus enable the determination and execution of control settings for a work machine at a specific location within a work site before the work machine reaches that location, in order to improve driving quality.

[0011] It is understood that, although some examples presented here relate to agricultural machinery such as harvesters, the systems and methods disclosed herein are applicable to and can be used with a wide variety of other types of machinery, including a wide variety of other types of agricultural machinery. Other types of agricultural machinery may include, for example, seed drills, sprayers, tillage equipment, and various other agricultural machinery.

[0012] Fig. Figure 1 is a partly pictorial, partly schematic representation of an exemplary work machine 100 in the form of an agricultural work machine, specifically an agricultural harvesting vehicle 100-1. The example of Fig. Figure 1 shows the agricultural harvesting vehicle 100-1 in the form of a combine harvester. As in Fig. As shown in Figure 1, the harvesting vehicle 100-1 comprises ground-contacting traction elements (wheels or tracks) 144 and 145, which may be driven by a drive system (e.g., internal combustion engine, electric motors, hydrostatic drive, and other drivetrain elements such as a gearbox) to propel the harvesting vehicle 100 across a work area 10 (e.g., a field). The harvesting vehicle 100-1 includes an operator compartment or cab 119, which incorporates a variety of different operator interface mechanisms (e.g., 218 shown in Figure 1). Fig. 2) for controlling the harvesting vehicle 100-1 and for presenting (e.g., displays, etc.) various information. The harvesting vehicle 100-1 comprises an inclined conveyor 106, a feed accelerator 108, and a threshing unit generally designated 110. The inclined conveyor 106 and the feed accelerator 108 are part of a material handling subsystem 125. The harvesting header 104 is pivotally coupled to a frame 103 of the harvesting vehicle 100-1 along a pivot axis 105. One or more actuators 107 drive the movement of the harvesting header 104 about the axis 105 in the direction generally indicated by the arrow 109. Thus, the vertical position of the harvesting header 104 (the harvesting header height) above the ground of the operating area 10, over which the harvesting header 104 travels, can be controlled by actuating the actuator 107. Although in Fig. Not shown in Figure 1, the agricultural harvesting vehicle 100-1 may also include one or more actuators that apply an angle of inclination, a tilt angle or both to the harvesting header 104 or sections of the harvesting header 104.

[0013] The material handling subsystem 125 further comprises a threshing unit 110 with a threshing rotor 112 and a series of threshing concaves 114. The material handling subsystem 125 also includes a separator 116. The agricultural harvesting vehicle 100-1 further comprises a cleaning subsystem or sieve box (collectively referred to as the cleaning subsystem 118), which includes one or more cleaning blowers 120, a chaff sieve 122, and a fine sieve 124. The material handling subsystem 125 further comprises an unloading drum 126, a return elevator 128, and a clean grain elevator 130. The clean grain elevator conveys clean grain into a material hopper (or clean grain tank) 132.

[0014] The combine harvester 100-1 also includes a material transfer system comprising a conveying mechanism 134 and an unloading auger 135. The unloading auger 135 includes a discharge spout 136. In some examples, the discharge spout 136 can be movably coupled to the unloading auger 135, allowing the discharge spout 136 to be rotated in a controlled manner to change its orientation. The conveying mechanism 134 can be of various different types, such as an auger, a blower, or a belt conveyor. The conveying mechanism 134 is connected to the clean grain tank 132 and is driven (e.g., by an actuator such as an electric motor or an internal combustion engine) to convey material from the grain tank 132 through the unloading auger 135 and the discharge spout 136. The unloading pipe 135 can be extended from a stowed position via a series of positions (in Fig. (1 shown) away from the agricultural harvesting vehicle 100-1, the discharge spout 136 can be rotated into a variety of fold-out positions to align it with a material intake of a material receiving machine designed to receive the material in the grain tank 132. In some examples, the discharge spout 136 is also rotatable by an actuator to adjust the direction of the material flow exiting the discharge spout 136.

[0015] The harvesting vehicle 100-1 also includes a residue separation system 138, which can include a chopper 140 and a spreader 142.

[0016] In some examples, a harvesting vehicle within the scope of this disclosure may have more than one of the aforementioned subsystems. In some examples, the harvesting vehicle 100-1 may have a left and a right cleaning subsystem, a left and a right separator, etc., which are arranged in Fig. 1 are not shown.

[0017] During operation, the harvesting vehicle 100-1 moves, for example, across a work site (e.g., a field) 10 in the direction indicated by arrow 147. While the harvesting vehicle 100-1 is moving, the harvesting header 104 engages the crop plants to be harvested and cuts them with a cutter bar 111 on the harvesting header 104 to produce cut crop material.

[0018] The cut crop material is captured by a transverse conveyor (e.g., auger, conveyor belts, etc.) 113, which conveys the separated crop material to the center of the header 104. There, the separated crop material is transported through an opening to a conveying device in the inclined conveyor 106, leading to the feed accelerator 108, which accelerates the separated crop material into the threshing unit 110. The separated crop material is threshed by the rotor 112, which rotates the crop material against threshing concaves 114. The threshed crop material is moved by a separator rotor in the separator 116, with a portion of the residue being moved by the discharge drum 126 to the residue separator 138. The portion of the residues that is transferred to the residue processing system 138 is chopped by the residue chopper 140 and distributed on the field by the spreader 142.In other configurations, the residue is spread in a swath by the agricultural harvesting vehicle 100-1.

[0019] Grain falls into the cleaning subsystem 118. The chaff screen 122 separates some larger pieces of corn cob from the grain, and the fine screen 124 separates some finer pieces of corn cob from the grain. The grain then falls onto a conveyor (e.g., screw conveyor, etc.) that transports the grain to an inlet end of the grain elevator 130, and the grain elevator 130 conveys the grain upwards and deposits it in the grain tank 132. Residue is removed from the cleaning subsystem 118 by an airflow generated by one or more cleaning blowers 120. The cleaning blowers 120 direct air along an airflow path upwards through the fine screens and the chaff screens. The airflow transports the residue in the harvester 100-1 to the rear of the residue handling subsystem 138.

[0020] The return elevator 128 directs the returned material back to the threshing unit 110, where it is threshed again. Alternatively, the returned material can also be conveyed by a return elevator or other transport device to a separate re-threshing mechanism, where it is also threshed again.

[0021] The 100-1 harvesting vehicle can include various sensors, some of which are in Fig. 1 shown, such as a locomotion speed sensor 146, driving quality sensors 148 and observation sensors 150.

[0022] The ground speed sensor 146 detects the ground speed of the harvesting vehicle 100-1. The ground speed sensor 146 can detect the ground speed of the harvesting vehicle 100-1 by measuring the rotational speed of the ground-contacting traction elements 144 and / or 145, a drive shaft, an axle, or other components. In some cases, the ground speed can be measured using a positioning system, such as a global positioning system (GPS), a dead reckoning system, a long-range navigation system (LORAN system), a Doppler velocity sensor, or a wide variety of other systems or sensors that provide a ground speed reading.The ground speed sensors 146 can also include direction sensors such as a compass, magnetometer, gravimetric sensor, gyroscope, or GPS to determine the direction of travel in two or three dimensions in combination with the speed. If the harvester 100-1 is on a slope, its orientation relative to the slope is thus known. An orientation of the harvester 100-1 could be, for example, uphill, downhill, or perpendicular to the slope.

[0023] The ride quality sensors 148 detect, for example, a rocking motion of the harvesting vehicle 100-1, including rocking motion along multiple axes (e.g., three axes), such as a vertical axis, a longitudinal axis, and a transverse axis. Ride quality sensors can detect movements or accelerations along these axes. Some examples of ride quality sensors 148 are accelerometers and inertial measurement units (IMUs). As in Fig. As shown in Figure 1, a working machine 100, such as the harvesting vehicle 100-1, can include a variety of ride quality sensors 148 mounted at different locations. For example, as shown, a working machine 100, such as the combine harvester 100-1, can include a ride quality sensor 148 mounted on an implement (e.g., the header 104), a ride quality sensor 148 attached to a frame / chassis (e.g., 103), and a ride quality sensor 148 mounted in a driver's cab (e.g., 119). These are just a few examples of mounting locations. It is understood that the ride quality sensors 148 can additionally or alternatively be mounted at a variety of other locations.

[0024] The harvesting vehicle 100-1 also includes one or more observation sensors. Some examples of observation sensors include image acquisition mechanisms such as cameras, lidar sensors, radar sensors, and ultrasonic sensors. The observation sensors 150 can detect features such as terrain characteristics of the operating location 10 (e.g., terrain features (e.g., rocks, ruts, washouts, holes, etc.)), terrain profiles (e.g., topography (e.g., elevation, slope, changes in slope and elevation), surface roughness, etc.), and various other terrain features. The observation sensors 150 can detect a working machine 100, such as the harvesting vehicle 100-1, relative to a direction of travel 147 or route.

[0025] Even if in Fig. Figure 1 shows an example of an attachment point for an observation sensor 150; alternatively or additionally, the observation sensors 150 can be attached to a variety of other locations on a working machine 100, e.g., the harvesting vehicle 100-1.

[0026] A work machine 100, such as the harvester 100-1, as well as other work machines, can include various other sensors, some of which are in Fig. 2 will be described. A working machine 100, such as the harvesting vehicle 100-1, as well as other working machines, can include various other elements, some of which are described in Fig. 2 will be described.

[0027] Fig. Figure 2 is a block diagram showing an example system architecture 500 (here also referred to as System 500). If the work machine 100 is an agricultural work machine (e.g., the harvester 100-1, etc.), System 500 can also be referred to as an agricultural system architecture (or agricultural system). System 500 comprises one or more work machines 100. System 500 also comprises one or more remote computing systems 300, one or more networks 359, one or more interface mechanisms for remote users 364, and may also include a variety of other elements 202.

[0028] Each working machine 100 itself includes, for example, one or more processors or servers 201, one or more data storage devices 204, a communication system 206, one or more sensors 208, a control system 214, one or more controllable subsystems 216, one or more operator interface mechanisms 218, and various other elements and functionalities 219.

[0029] The remote computing systems 300 comprise, as illustrated, one or more processors or servers 301, one or more data storage devices 304, a communication system 306, and may include various other elements and functionalities 319.

[0030] Data stores 204 and 304 each store a variety of data (generally referred to as data 205 and data 305, respectively), some of which are described in more detail here. For example, data 205 or data 305, or a combination thereof, may include, but are not limited to, near-field data, far-field data, data of past operations, sensor data, threshold data, data relating to a planned route, operator control adaptation data, predicted velocity data, and various other data, including but not limited to various other data described herein. Some examples of the various data are given in Fig. 3 described in more detail. Additionally, the data 205 may include computer-executable instructions that can be executed by one or more processors or servers 201 to implement other elements or functionalities of the system 500, including other elements or functionalities of the work machines 100. Additionally, the data 305 may include computer-executable instructions that can be executed by one or more processors or servers 301 to implement other elements or functionalities of the system 500, including other elements of the remote computing systems 300. It is understood that the data storage 204 and data storage 304 may include various forms of data storage, for example, both volatile data storage (e.g., random access memory (RAM)) and non-volatile data storage (e.g., read-only memory (ROM), hard disks, solid-state drives, etc.).

[0031] The sensors 208 can include one or more heading / speed sensors 225, one or more geoposition sensors 203, one or more observation sensors 226, one or more ride quality sensors 228, and various other sensors 229. The sensor data generated by the sensors 208 can be transmitted to remote computing systems 300 and to other working machines 100.

[0032] The control system 214 itself can include one or more controllers 235 for controlling various other elements of a working machine 100, the ride quality system 215, and also other elements 237. The controllable subsystems 216 can include the drive subsystem 250, the steering subsystem 252, the actuators 254, and various other subsystems 258.

[0033] The heading / speed sensors 225 detect a heading property (e.g., direction of travel) and / or speed property (e.g., travel speed, change in travel speed, etc.) of a working machine 100. This can include sensors that detect the movement (e.g., rotational movement) of ground-contacting traction elements (e.g., wheels or tracks) or the movement of components coupled to the ground-contacting elements or other elements (e.g., axles), or that can utilize received signals from other sources, such as geoposition sensors. Thus, while the heading / speed sensors 225 described here are shown separately from geoposition sensors 203, in some examples the direction / speed of travel of the machine is derived from signals coming from geoposition sensors 203 and subsequent processing.In other examples, the 225 course / speed sensors are separate sensors and do not use signals received from other sources. An example of 225 course / speed sensors are those in [reference to relevant document]. Fig. 1 of the sensors shown 146.

[0034] The geoposition sensors 203 detect or recognize, for example, the geoposition or geographic location of a work machine 100. The geoposition sensors 203 may include, among other things, a receiver of a global navigation satellite system (GNSS) that receives signals from a GNSS satellite transmitter. The geoposition sensors 203 may also include a real-time kinematic (RTK) component configured to improve the accuracy of position data derived from the GNSS signal. The geoposition sensors 203 may include a dead reckoning navigation system, a cellular triangulation system, or any of a variety of other geoposition sensors.

[0035] The observation sensors 226 can detect site characteristics, such as terrain features, even at points in front of (relative to a direction of travel or route) a work machine 100. Some examples of observation sensors 226 include image acquisition mechanisms such as cameras (e.g., monocular cameras, stereo cameras, color cameras (e.g., RGB cameras), multispectral cameras, thermal imaging cameras, infrared cameras, etc.), lidar sensors, radar sensors, and ultrasonic sensors. The observation sensors 226 generate sensor data (e.g., images, sensor signals, etc.) that indicate the detected characteristics (e.g., terrain features). The sensor data generated by the observation sensors 226 can be used to control a work machine 100. An example of observation sensors 226 is those described in Fig. 150 observation sensors shown.

[0036] The ride quality sensors 228 are capable of detecting rocking of the work machine 100, including rocking in multiple axes (e.g., three axes), such as a vertical axis, a longitudinal axis, and a transverse axis of the work machine 100. The ride quality sensors 228 can detect movements or accelerations in these axes. Some examples of ride quality sensors 228 are accelerometers and inertial measurement units (IMUs). A work machine 100 can include one or more ride quality sensors 228. A work machine 100 can include a plurality of ride quality sensors 228, such as a plurality of ride quality sensors 228, with each ride quality sensor 228 of the plurality being located at a different location on the work machine 100. The ride quality sensors 228 generate sensor data that indicate a ride quality of the work machine, e.g., sensor data indicating rocking of the work machine 100.The sensor data generated by the driving quality data 228 can be used to control a working machine 100. An example of driving quality sensors 228 are those in . Fig. 1 shown driving quality sensors 148.

[0037] The sensors 208 can also include various other types of sensors 229.

[0038] The control system 214 can include one or more controllers 235 (e.g., electronic control units that include or can be implemented by one or more processors, such as one or more processors 201) that generate control signals for controlling one or more components of a working machine 100 and / or components of the system 500. For example, but not exclusively, the controllers 235 can be a communication system controller for controlling the communication system 206, an interface controller for controlling one or more interface mechanisms (e.g.,218 or 364 or both), a drive control for controlling the drive subsystem 250 to control a travel speed of a working machine 100, a path planning control for controlling the steering subsystem 252 to control a route or course of a working machine 100, and one or more actuator controls for controlling the operation of the actuators 254. In other examples, a central control 235 may be used to generate control signals to control a variety of controllable subsystems 216, and in some examples, other elements of a working machine 100 or a system 500.

[0039] The drive subsystem 250 comprises one or more controllable actuators (e.g. internal combustion engine, motors, pumps, gearboxes, etc.) that drive the ground-gripping traction elements (e.g. wheels or tracks) of a working machine 100 in order to vary the travel speed of a working machine 100.

[0040] The steering subsystem 252 comprises one or more controllable actuators (e.g. electric actuators, hydraulic actuators, etc.) which can be controlled to operate the steering and thus the course of a working machine 100.

[0041] The actuators 254 encompass a wide variety of actuator types that control operating parameters (e.g., position (e.g., height, position, orientation), speed, distance, etc.) of one or more components of a machine 100. The actuators 254 can include actuators that control the position (e.g., height, depth, or distance from another component of the machine or from the operating point) or orientation (e.g., pitch, roll, yaw, etc.) of components of a machine 100, as well as actuators that control the speed of movement (e.g., rotational speed, lifting speed, etc.) of components of a machine 100. Actuators 254 can include, among others, electric motors, valves, pumps, hydraulic actuators (e.g., hydraulic cylinders, etc.), pneumatic actuators (e.g., pneumatic cylinders, etc.), electrical actuators (e.g., linear actuators, etc.), and various other types of actuators.Insofar as the working machine 100 is an agricultural harvesting vehicle 100-1, the actuators can comprise 254 actuators which can be controlled in such a way that they control operating parameters of one or more in . Fig. 1 of the components described. Some example actuators 254 include actuators that adjust a position (e.g., height, depth, and / or orientation) of an attachment or working tool of a machine relative to the surface of the work site. Some example actuators 254 include actuators that adjust or provide a preload force (e.g., lifting force or downward force) that preloads an attachment toward or away from the work site surface.

[0042] Fig. Figure 2 also shows that the control system 214 can include a driving quality system 215. The driving quality system 215 is capable of detecting driving quality problems and generating action outputs that can be used for the proactive control of a working machine to improve driving quality. The driving quality system 215 is used in Fig. 3 explained in more detail.

[0043] The communication system 206 is used to provide communication between components of a work machine 100 or with other elements of the system 500, such as remote computing systems 300, or other work machines 100 or user interface mechanisms 364, or a combination thereof. The communication system 306 is used for communication between the components of a remote computing system 300 or with other elements of the system 500, such as work machines 100, other remote computing systems 300, or user interface mechanisms 364, or a combination thereof.

[0044] Communication systems 206 and 306 can each include wired and / or wireless communication circuits, as well as wired and wireless communication components. In some examples, communication systems 206 and 306 can each be a system for communication over the Internet, a cellular communication system, a system for communication over a wide area network or local area network, a system for communication over a controller area network (CAN), such as a CAN bus, a system for communication over a CAN-FD (controller area network flexible data-rate), such as a CAN-FD bus, a system for communication over a near-field communication network, a system for communication over Ethernet, or a communication system designed for communication over a variety of other networks.Communication Systems 206 and 306 can also include a system that enables the downloading or transfer of information to and from an SD (Secure Digital) card or a USB (Universal Serial Bus) card, or both. Both Communication Systems 206 and 306 can utilize Network 359. Network 359 can be any network from a variety of different types, such as the Internet, a cellular network, a wide area network (WAN), a local area network (LAN), a CAN (controller area network), a CAN-FD (controller area network flexible data-rate), a near-field communication network, Ethernet, or one from a wide variety of other networks or communication systems.

[0045] Fig. Figure 2 shows that one or more operators 361 can operate the work machines 100. The operators 361 interact with operator interface mechanisms 218. The operator interface mechanisms 218 may, in some examples, include, among a wide variety of other types of control devices, joysticks, levers, a steering wheel, couplings, pedals, buttons, wireless devices (e.g., mobile computing devices, etc.), rotary controls, keypads, a display device (including a display screen), user-operated elements (such as icons, buttons, etc.) on a display device, a microphone, and a loudspeaker (where speech recognition and speech synthesis are provided). Where a touch-sensitive display system is provided, the operators 361 can interact with the operator interface mechanisms 218 via touch gestures. In addition, at least some of the operator interface mechanisms 218 may be used for presentation (e.g.,Display, acoustic representation, haptic representation, etc.) of various information may be used. The examples described above are provided as illustrative examples and are not intended to limit the scope of protection of this disclosure. Accordingly, other types of user interface mechanisms 218 may be used and are within the scope of protection of this disclosure.

[0046] Fig. Figure 2 also shows remote users 366 who communicate with work machines 100 and remote computing systems 300 via networks 359 using user interface mechanisms 364. The user interface mechanisms 364 may, in some examples, include, among a wide variety of other types of control devices, joysticks, levers, a steering wheel, couplings, pedals, buttons, wireless devices (e.g., mobile computing devices, etc.), rotary knobs, keypads, a display device (including a display screen), user-operable elements (such as icons, buttons, etc.) on a display device, a microphone, and a speaker (where speech recognition and speech synthesis are provided). Where a touch-sensitive display system is provided, users 366 may interact with the user interface mechanisms 364 using touch gestures. Furthermore, at least some of the user interface mechanisms 364 may be used for presentation (e.g.,Display, acoustic representation, haptic representation, etc.) of various information may be used. The examples described above are provided as illustrative examples and are not intended to limit the scope of protection of this disclosure. Accordingly, other types of user interface mechanisms may be used and are within the scope of protection of this disclosure.

[0047] The remote computing systems 300 can be a wide variety of different types of systems or combinations thereof. For example, the remote computing systems 300 may be located in a remote server environment. Furthermore, the remote computing systems 300 may be remote computing systems such as mobile devices, a remote network, an agricultural management system, a supplier system, or a wide variety of other remote systems. In one example, farm machinery 100 may be remotely controlled by remote computing systems 300 and / or remote users 366. In some examples, operators 361 are on board (e.g., in an operator compartment such as a cabin) the farm machinery 100. In some examples, the operators 361 are remote from the farm machinery 100 and control the farm machinery 100 via one or more interface mechanisms (e.g.,one or more of 218), which are remote from the machines but operationally coupled to the machines (e.g. communicatively coupled, such as via networks 359).

[0048] It is understood that in some examples the elements in System 500 may be distributed in different ways, including those determined by the one in Fig. The example shown in point 2 may differ. For example, the ride quality system 215, which is in Fig. 2, as shown arranged on the working machines 100, may also be located elsewhere, e.g. in one or more remote computing systems 300. In further examples, the driving quality system 215 can be distributed across a working machine 100 and a remote computing system 300.

[0049] Fig. Figure 3 is a block diagram that illustrates examples of some of the components of System 500 as well as the information flow between the components in more detail.

[0050] As in Fig. As shown in Figure 3, the data stores 204 or 304, or a combination thereof, can contain data (205 or 305) including near data 501, far data 502, data of previous operations 503, sensor data 504, threshold data 505, data relating to a planned route 506, operator control adaptation data 507, predicted speed data 508, and various other data 510, including, but not limited to, other data described elsewhere herein. In some examples, the location of the data may depend on the location of the ride quality system 215 (also referred to as System 215).

[0051] As in Fig. As shown in Figure 3, the system 215 comprises a data selector system 330, one or more data processing systems 332, a ride quality problem identification system 334, a threshold identification system 336, a proximity identification system 338, a control action identification system 340, a map generator 342, a presentation generator 334, and various other elements and functions 359. As will be described in more detail below, the system 215 can be operated to produce one or more outputs 360. The ride quality problem identification system 334 comprises an identification system for emerging ride quality problems 350, an identification system for impending ride quality problems 351, a confidence zone generator 352, comparison logic 353, and may include various other elements and functions 354.The control action identification system 340 itself can include a setting logic 355, a control action instruction generator 356 and various other elements and functions 357.

[0052] Near-location data 501 includes data indicating driving quality problems at locations near an upcoming location for which proactive control is to be performed. For example, near-location data 501 may include data indicating driving quality problems at a first location at the work site (currently being traversed or already traversed by a work machine 100 in an ongoing operation (e.g., harvested area, etc.)) that is near a second location (not yet traversed by the work machine 100 in the current operation (e.g., unharvested area)) for which proactive control is to be performed (i.e., the work machine 100 should be proactive before it reaches the second location).Proximity, as used here in relation to nearby data (or locations close to preceding locations), refers to a spatial relationship between locations such that a driving quality problem at a first location is likely to persist (or already exists) at a second location due to the proximity. The first and second locations may be in the same pass (e.g., harvest track) or in adjacent passes. An adjacent pass may be immediately adjacent to the current pass or separated from it by one or more intervening passes. A pass generally corresponds to a working width of the machine (e.g., a width of the implement or tool, such as the header 104) and generally represents a path from one end of the field to the opposite end of the field (e.g.,between opposite headlands of an agricultural field). Near-field data 501 can include ride quality sensor data generated by ride quality sensors 228, indicating a rocking motion of a work machine 100, or speed sensor data generated by heading / speed sensors 225, indicating a travel speed of the work machine 100 (or a change in its speed, such as acceleration or deceleration), or both, corresponding to locations near an upcoming location. For example, a ride quality problem detected at a first location can be used to predict a ride quality problem at an upcoming second location. Furthermore, a change in the travel speed of the work machine (e.g., a deceleration) detected at a first location can be used to predict a ride quality problem at an upcoming second location.The approximate data 501 can include observation sensor data generated by the observation sensors 226, indicating terrain features of locations near an upcoming location. For example, a terrain feature detected at an initial location can be used to predict a ride quality problem at an upcoming location.

[0053] Remote 502 data comprises data indicating terrain characteristics of a work site (where a work machine is to operate or is operating) that have been collected by systems located remotely from the work site, such as satellites or overhead machines (e.g., drones, aircraft, etc.). Remote 502 data may include aerial imagery or other sensor data generated by sensors in remote systems. Remote 502 data may be in the form of maps of the work site, showing terrain characteristics at various locations within the work site.

[0054] Previous Operation Data 503 includes data generated or derived during a previous operation at a work site (where a work machine is to be deployed or is being deployed), such as sensor data indicating ride quality problems generated by sensors on a work machine 100 during a previous operation at the work site, or sensor data indicating terrain characteristics of the work site generated by sensors on a work machine during a previous operation at the work site. In some examples, the Previous Operation Data 503 may be in the form of a map of the work site showing terrain characteristics or ride quality problems at various locations within the work site that were detected during the previous operation. In some examples, the Previous Operation Data may have been generated by a different machine or by the same machine as the work machine 100 of a current operation.For a current or upcoming operation, for example, a first work machine 100 may be in operation or about to be put into operation at the place of use, and the data of previous operations 503 may have been generated by the first work machine 100 during a previous operation at the place of use.

[0055] In another example, for a current or upcoming operation, a first machine 100 may currently be operating or about to be put into operation at the site, and the data of previous operations 503 may have been generated by a second machine 100, which is different from the first machine 100, during a previous operation at the site. The second machine may be of the same type as the first machine (e.g., both are combine harvesters (e.g., 100-1)). The second machine may be of a different type than the first machine (e.g., the second machine 100 is a seed drill, sprayer, tillage machine, or harvester, and the first machine 100 is another machine selected from a seed drill, sprayer, tillage machine, or harvester).In such examples (second working machine of a different machine type), the second working machine 100 also performs a different type of work than the first working machine 100.

[0056] It is understood that the data from previous operations may include 503 data from several previous operations at one deployment site from a variety of different work machines.

[0057] The sensor data 504 comprises sensor data generated by sensors 208 and is distinct from ride quality sensor data generated by ride quality sensors 228, observation sensor data generated by observation sensors 226, and speed sensor data generated by heading / speed sensors 225. Thus, the sensor data 504 includes geoposition sensor data generated by the geoposition sensors 203, heading sensor data generated by the heading / speed sensors 225, and sensor data generated by other sensors 229.

[0058] The threshold data 505 includes thresholds such as ride quality (or rocking) thresholds, machine speed (e.g., travel speed, travel speed change (e.g., deceleration), etc.) thresholds, and terrain feature thresholds. It is understood that in some examples, the thresholds may be a range of values. Thresholds may be provided by an operator or user (e.g., by inputting them into an interface mechanism), they may be provided by a manufacturer or other third party (e.g., a service provider), they may be programmed into a working machine 100, or they may be generated by the system 215, as described below. A ride quality (or rocking) value (and thus a ride quality (or rocking) threshold) is a value that represents an acceleration (or rocking) of the working machine in one axis (e.g.,a vertical, transverse, or longitudinal axis of the working machine 100) and can be detected by ride quality sensors 228. A machine speed value (and thus a machine speed threshold value) is a value that describes a travel speed (e.g., miles per hour, kilometers per hour, meters per second, etc.) or a change in travel speed (e.g., deceleration) of the working machine 100. A terrain feature value (and thus a terrain property value) is a value that corresponds to a terrain feature. In some examples, terrain feature values ​​can be binary values ​​(e.g., yes / no, 0 / 1, etc.) that indicate the presence of a terrain feature, such as a terrain feature. In some examples, terrain feature values ​​can be categorical to specify a type (or subtype) of terrain features, such as unique values ​​for each of a multitude of different terrain features.In some examples, the terrain property values ​​may be numerical values ​​that describe a terrain profile, such as numerical values ​​that describe a topography (e.g. elevation, slope, change in slope, change in elevation) or a surface roughness.

[0059] It is understood that the term "exceed" in relation to a value exceeding a threshold, as used here, does not in every example mean that the value is greater than the threshold. Rather, "exceeding" means that the value does not meet the threshold, which in some examples may mean that the value is less than the threshold, or in others that the value is greater than the threshold. In some examples, a threshold may also be a range of values, so that "exceeding" means that the value does not fall within the threshold range (e.g., it is outside the range, regardless of whether it is higher or lower).

[0060] The data relating to a planned route 506 includes data indicating a planned route for a work machine 100 for a current or upcoming operation. Data relating to a planned route 506 can be provided by an operator or user, or by other means (e.g., as output from the control system 214).

[0061] Operator control adaptation data 507 comprises data indicating operator adjustments to machine settings. This may include data indicating interaction with one or more interface mechanisms 218 (e.g., movement, input of setting values, etc.) used by the operator to adjust machine settings. Such interaction can be detected in some examples by sensors (e.g., 229) that detect movement of the interface mechanisms 218 or values ​​entered into the interface mechanisms 218.

[0062] Predictive speed data 508 includes data indicating anticipated (e.g., planned, prescribed, predicted) speeds (e.g., travel speed) of the work machine 100 at various locations within the work site, including upcoming locations. In some examples, the predictive speeds may be based on a plan or regulation (e.g., a speed control map for the work site, a setting, etc.). In some examples, the predictive speeds may be generated by the system 500 based on a travel speed during the operation and other characteristics of the work site.

[0063] The data selector system 330 can select data to be used by the system 215 in identifying ride quality problems and implementing proactive control to improve ride quality.

[0064] For example, if the remote data 502 comprises separate data sets, the data selector system 330 can select one of the data sets based on criteria. The data selector system 332 can, for instance, be configured to select the remote data set 502 whose generation is closest in time to the current or upcoming operation, or it can favor remote data set generated at a time when the site was essentially bare (e.g., after tillage and before vegetation emerges after planting). In another example, the remote data 502 may comprise separate data sets from different remote systems, and the data selector 332 can be configured to favor (and thus select) remote data set generated by one type of remote system over remote data set generated by another type of remote system.

[0065] In other examples where the remote data 503 comprises separate data, the data selection system 330 can select one of the data based on criteria. For example, the data selector system 330 can be configured to select the data of previous operations 503 of an operation that is closest in time to the current or upcoming operation. In another example, the data selector system 330 can be configured to select the data of previous operations 503 based on the machine type of the machine that performed the previous operations. For example, the data selector system 330 can be configured to favor (and thus select) data of previous operations 503 that were generated by the same machine or a machine of the same type as the worker machine 100 that is performing a current operation.If the machine types differ from the work machine 100 that is performing the current operation, the data selector system 330 can be configured to favor (and thus select) data from previous operations 503 generated by one particular other machine type over data from yet another machine type.

[0066] The Data Processing Systems 332 process data 205 / 305 (including data selected by the Data Selector System 330) to extract or generate computer-readable values ​​that can be used by other elements of System 215 (and System 500). The Data Processing Systems 332 may include various processors or processing functionalities, including image processing functionality, sensor signal processing functionality, filter processing functionality, categorization processing functionality, normalization processing functionality, aggregation processing functionality, color extraction processing functionality, analog-to-digital conversion processing functionality, other conversion processing functionality (e.g., reference tables, equations, mathematical functions, models, etc.), and various other data processing functionalities.

[0067] It is understood, therefore, that the 332 data processing systems can, for example, convert analog signals into readable digital signals (or digital values). It is understood that the 332 data processing systems can, for example, process images, sensor signals, maps, tables, input values, and various other data that display values ​​in order to extract values ​​and further convert the extracted values. It is understood that the 332 data processing systems can perform preprocessing and postprocessing. It is understood that the 332 data processing systems can perform various forms of aggregation of the extracted or converted values.

[0068] The 334 ride quality problem identification system is capable of identifying ride quality problems relevant to the operating location. A ride quality problem can be identified by the 334 ride quality problem identification system based on a comparison of a data value indicating a ride quality problem with a corresponding threshold. A data value indicating a ride quality problem can be a ride quality value (e.g., a ride quality value detected by the 228 ride quality sensors (e.g., provided in the near data 501) or provided in the data of previous operations 503). A data value indicating a ride quality problem can be a machine speed value (e.g., a machine speed value detected by the 225 heading / speed sensors (e.g., provided in the near data 501) or provided in the data of previous operations 502).A data value indicating a driving quality problem can be a terrain feature value (e.g., a terrain feature value detected by the observation sensors 226 (e.g., provided in the near data 501), or in the far data 502, or in the data from previous operations 503). The comparison logic 353 compares a data value indicating a driving quality problem with a corresponding threshold and produces an output showing the comparison.

[0069] The driving quality problem identification system 350 is capable of identifying driving quality problems occurring at a work site for a work machine 100. For example, the driving quality problem identification system 350 can identify driving quality problems of a work machine based on a driving quality value (which is detected, for example, by driving quality sensors 228). This can be based, for instance, on a comparison of a driving quality value with a corresponding driving quality threshold, which can be executed and output by the comparison logic 353.

[0070] The Identification System for Imminent Driving Quality Problems 351 identifies driving quality problems for imminent (not yet driven on during ongoing operation (e.g., not harvested, etc.)) locations at a deployment site based on one or more data elements 205 / 305 and / or outputs of other elements of the system 215.

[0071] The Identification System for Upcoming Ride Quality Problems 351 can identify ride quality problems at upcoming locations based on values ​​(e.g., terrain feature values) provided in the remote data 502 at those upcoming locations. In some examples, this might involve comparing the values ​​(e.g., terrain feature values) with appropriate thresholds (e.g., terrain feature thresholds), which is performed and output by the Comparison Logic 353. For example, if the remote data 502 indicates a value (e.g., a terrain feature value) of a certain level (e.g., a terrain feature value exceeding an appropriate threshold) at an upcoming location, the Identification System for Upcoming Ride Quality Problems 351 can identify a ride quality problem at that upcoming location.

[0072] The Identification System for Upcoming Ride Quality Problems 351 can identify ride quality problems at upcoming locations based on values ​​(e.g., terrain feature values, ride quality values, machine speed values) provided in the data from previous operations 503 at those upcoming locations. In some examples, this might involve comparing the values ​​(e.g., terrain feature values, ride quality values, machine speed values) with appropriate thresholds (e.g., terrain feature thresholds, ride quality thresholds, machine speed thresholds), which is executed and output by the Comparison Logic 353. For example, if data from previous operations 503 contains a value (e.g., a terrain feature value, a ride quality value, a machine speed value) of a certain level (e.g.,If a terrain feature value exceeding a corresponding threshold, a ride quality value exceeding a corresponding threshold, or a machine speed value exceeding a corresponding threshold is indicated at an upcoming location, the Upcoming Ride Quality Problem Identification System 351 can identify a ride quality problem at that upcoming location.

[0073] The Upcoming Ride Quality Problem Identification System 351 can identify ride quality problems at upcoming locations based on terrain feature values ​​(e.g., terrain feature values ​​provided by Near Data 501, Far Data 502, or Past Operation Data 503) and predicted machine speed values ​​corresponding to the upcoming locations, provided by Predicted Speed ​​Data 508. For example, the Ride Quality Problem Identification System 351 can identify a ride quality problem at the upcoming location based on a terrain feature value (corresponding to an upcoming location or a location near the upcoming location) and a predicted machine speed value corresponding to the upcoming location.

[0074] The Identification System for Upcoming Ride Quality Problems 351 can identify ride quality problems at upcoming locations based on values ​​(e.g., terrain feature values, ride quality values, machine speed values) provided in the Near Data 501 at locations of the deployment site close to the upcoming locations. As mentioned earlier, the term "near," as used here with reference to Near Data or to locations close to upcoming locations, refers to a spatial relationship between locations. That is, a spatial relationship between a second location (an upcoming location) and a first location (the current or already traversed location) such that a ride quality problem at the first location is likely to also be relevant to the second location (e.g., that the ride quality problem at the first location is likely to persist or exist at the second location).The reason for this is that a terrain feature that causes a ride quality problem at one location may also be present at a second location. As previously explained, the first and second locations may be in the same pass or in adjacent passes.

[0075] In some examples, this can involve a comparison of values ​​(e.g., terrain property values, ride quality values, machine speed values) at nearby locations with corresponding threshold values ​​(e.g., terrain property thresholds, ride quality thresholds, machine speed thresholds), which is executed and output by the comparison logic 353. For example, if the nearby data 503 contains a value (e.g., a terrain property value, a ride quality value, a machine speed value) of a certain level (e.g.,If a terrain feature value exceeding a corresponding threshold, a ride quality value exceeding a corresponding threshold, a machine speed value exceeding a corresponding threshold) is displayed at a first location, the Identification System for Imminent Ride Quality Problems 351 can identify a ride quality problem at a second, imminent location that is close to the first location (has a spatial relationship to it).

[0076] In some examples, the Identification System for Probable Ride Quality Issues 351 projects (or predicts) a path of a terrain feature or at least predicts that a terrain feature present at a first location will also be present at a second location. Terrain features may not extend uniformly across the deployment area, for example, in a uniform footprint or direction. Therefore, it is not sufficient to simply transfer a terrain feature from a first location to a second location along a straight line, such as along the same latitude or longitude. For example, the second position of a terrain feature may differ from the first position in several directions. The Identification System for Probable Ride Quality Issues 351 can use sensor data, such as...The system uses sensor data generated by the observation sensors 226 to identify and predict a terrain feature trajectory, if available. The Identification System for Imminent Ride Quality Problems 351 can use a combination of different data 205 / 305 to identify and predict a terrain feature trajectory, such as a combination of near-field data 501, far-field data 502, data from previous operations 503, and sensor data 504. However, the system 215 can also establish confidence zones (or bands) to account for errors, as described below.

[0077] The Confidence Zone Generator 352 can identify confidence zones (or bands) around impending locations. The confidence zones (or bands) indicate a larger area of ​​the deployment location where an impending ride quality problem might exist, thus accounting for errors in predicting impending ride quality problems. For example, it is understood that a terrain feature may not have a straight or uniform path across a deployment location, and if the impending ride quality problem identification system 351 predicts a location of an impending ride quality problem (e.g., by projecting a terrain feature's path from a nearby location), the Confidence Zone Generator 352 can identify confidence zones (or bands) around the predicted location of the impending ride quality problem.In some examples, the working machine 100 may be controlled relative to a confidence zone, and not just relative to the predicted location of the impending ride quality problem. The decision to create a confidence zone, as well as the size of the confidence zone, can depend on confidence criteria such as the quality of the data indicating the ride quality problem, machine characteristics (e.g., machine type, operating mode, machine settings (e.g., travel speed)), and the nature of the terrain features (e.g., projecting the path of a rut may be more reliable than projecting a washout). An example of a confidence zone is shown in [reference]. Fig. 4 shown.

[0078] The threshold identification system 336 is able to identify new or adjusted thresholds based on one or more data elements 205 / 305, such as, but not limited to, operator control adjustment data 507. For example, if an operator 361 of a work machine 100 changes settings of the work machine 100 (e.g., change in travel speed, change in lifting capacity, or position settings for the attachment / implement, etc.), the threshold identification system 336 can determine that a new or adjusted threshold is required (i.e., that the current threshold did not result in an automatic control adjustment and the operator had to make a control adjustment instead).In such cases, for example, a ride quality threshold, a machine speed threshold, or a terrain characteristic threshold may need to be adjusted to ensure that the machine setting is adjusted (as requested by the operator) without requiring manual adjustment by the operator.

[0079] The proximity identification system 338 is capable of identifying the approach angle of a work machine 100 relative to a terrain feature at an upcoming location at the work site, for example, based on a planned route data 506 or on heading sensor data generated by heading / speed sensors 225 (and provided in the sensor data 504). The angle at which a work machine 100 approaches a terrain feature can influence the resulting control to account for the terrain feature. For example, the work machine 100 may be able to traverse an upcoming location (with the terrain feature) by approaching from a first approach angle compared to approaching from a second approach angle.Furthermore, the planned route or course of the machine 100 may need to be changed when approaching from a first approach angle, whereas it may not need to be changed when approaching from a second approach angle. Accordingly, the approach angle can affect the resulting control of the actuators 254, for example, how and whether a position setting or a lifting force setting for an attachment (e.g., the harvester header 104) is affected based on the approach angle of the machine relative to an approaching point (with the terrain property).

[0080] The Map Generator 342 is capable of generating maps of the work site that include information identified or detected during an ongoing operation, such as terrain characteristics, ride quality values, ride quality problems, machine speed values, confidence zones, and various other data. This information can be incorporated into the map so that it is located at a geographic location that corresponds to the geographic location at the work site where the information was detected or identified.

[0081] The presentation generator system 344 can be operated to generate one or more presentations (e.g., visual, audible, haptic, etc.) for presentation (e.g., display, acoustic presentation, haptic presentation, etc.) on one or more interface mechanisms (e.g., 218 and / or 364). The presentations can, for example, warn an operator of an impending ride quality problem, provide an operator with a recommendation for adjusting the machine control to improve ride quality, present a map generated by the map generator 342, and provide various other information. In one example, the presentation generator 344 is capable of generating a presentation that prompts an operator or user for feedback (e.g., approval or rejection) regarding an output, such as...Approval or rejection of a threshold identified by the Threshold Identification System 336, or approval or rejection of an issue of the Tax Action Identification System 340.

[0082] The control action identification system 340 is capable of identifying machine settings and issuing corresponding control action instructions to use the identified machine settings to improve ride quality (e.g., to anticipate ride quality problems at upcoming locations). The setting logic 355 is capable of identifying one or more machine settings to improve ride quality based on upcoming ride quality problems identified by the upcoming ride quality problem identification system 351 and one or more data elements 205 / 305.

[0083] In some examples, setting logic 355 can identify a machine setting that matches a machine setting from data 205 / 305. For example, data from previous operations 503 can provide machine settings that were used in a previous operation at the location, and setting logic 355 can identify matching machine settings for the current operation at the location (the upcoming location). For example, if a worker 100 slowed down to 2 miles per hour in the previous operation, setting logic 355 can identify a travel speed of 2 miles per hour as the machine setting for the current operation. A matching machine setting is particularly applicable if the data from the previous operation 503 comes from the same worker 100 used in the current operation, or if the worker 100 in the previous operation is of the same type (e.g.,Harvesting vehicle 100-1 etc.) like the working machine 100 in the current process.

[0084] In some examples, setting logic 355 can identify a scaled machine setting based on a machine setting from data 205 / 305. For example, data from previous operations 503 can provide machine settings used in a previous operation at the location, and setting logic 355 can identify scaled machine settings for the current operation at the location (the upcoming location). A scaled machine setting is particularly applicable when the data from the previous operation 503 comes from a worker 100 that is of a different machine type than the worker 100 in the current operation. For example, seed drills and sprayers generally operate at higher speeds than harvesters. Therefore, a speed setting for a seed drill or sprayer might not be appropriate for a harvester. For example, ifIf a seed drill or sprayer reduces its speed from 10 mph to 5 mph, adjusting the harvester's speed to 5 mph would not be appropriate, as harvesters generally operate at 3 mph. Instead, the machine setting logic 355 can identify a scaled machine setting. Continuing with the previous example, since the seed drill or sprayer has halved its speed, the scaled machine setting can cause the harvester to reduce its speed by half (i.e., from 3 mph to 1.5 mph) or by another proportion (i.e., from 3 mph to 2 mph).

[0085] If no machine setting values ​​from previous operations are available, the setting logic 355 can instead be programmed to identify machine settings by other means, such as preset adjustment values ​​or based on learning functions. For example, the setting logic 355 can identify a machine setting value for an upcoming job and then monitor the ride quality (detected, for example, by the ride quality sensors 228) or other operator adjustments (provided by the operator control adjustment data 507) corresponding to the job in order to learn the effectiveness of the identified machine setting value. This continuous learning can be used in identifying future machine settings.

[0086] The control action instruction generator 356 is capable of generating control action instructions based on machine settings identified by the setting logic 355. The control action instructions define the machine setting as well as other information (e.g., a time) and are provided to controllers 235. These controllers can then use the controllers to control one or more corresponding controllable subsystems 216 to apply the machine setting (at the correct time). The control action instruction generator 356 can also generate the control instructions based on various other data 205 / 305, e.g., geoposition sensor data (from sensors 203) indicating the geoposition of the machine 100, speed data (e.g., speed sensor data (from sensors 225) indicating the current speed of the machine 100), predicted speed data from forecast data 508, etc.) and course sensor data (from sensors 225) or data relating to a planned route 503 indicating a course or route of the working machine 100 to direct a suitable time for the control action (i.e., so that the machine setting is adjusted as desired at the correct time when the working machine 100 arrives at the approaching location).

[0087] It is evident that the system 215 is capable of generating one or more driving quality outputs 360 (hereinafter also referred to as output / outputs 360). An output 360 can be one or more impending driving quality problems identified by the impending driving quality problem identification system 351, control action instructions generated by the control action instruction generator 356, machine setting values ​​identified by the setting logic 355, thresholds identified by the threshold identification system 336, maps generated by the map generator 342, presentations generated by the presentation generator 344, or values ​​displayed by the data 205 / 305 or extracted by the data processing system 332 (e.g., driving quality values, terrain feature values, machine speed values, etc.).), as well as other information derived from data 205 / 305 or generated or identified by system 215. An output 360 can be used to control a working machine 100. For example, an output 360 can be obtained (e.g., retrieved or received) from one or more control systems 214 to control a working machine 100, such as by controlling one or more controllable subsystems 216 and / or one or more interface mechanisms 218 (e.g., for displaying information about or based on the output 360). Additionally or alternatively, an output 360 can be obtained (e.g., retrieved or received) by various other elements and used in various other ways. For example, but not limited to, a harvest logistics output 360 can be obtained by one or more other elements 362 (e.g.,(retrieved or received) as through one or more interface mechanisms 364 (e.g. to present information of the (or based on) output 360 (e.g. to display etc.)).

[0088] Fig. Figure 4 is a pictorial representation showing an example of the operation of System 500. Fig. Figure 4 shows a field 600, an emerging ride quality problem 602 corresponding to a first digit 604, an impending ride quality problem 606 corresponding to a second digit 608, and a confidence zone (or confidence band) 612. The field 600 comprises a plurality of passes 610.

[0089] As can be seen, in the example shown, an emerging ride quality problem 602 was identified, corresponding to a first location 604 in a first pass 610-1 at the deployment site. System 215 (e.g., the emerging ride quality problem identification system 350) identified the emerging ride quality problem based on sensor data generated by a work machine during the first pass 610-1 (e.g., specified in the near data 501). Based on the identified emerging ride quality problem 602 (or the data indicating it), System 214 (e.g., the impending ride quality problem identification system 351) identifies an impending ride quality problem 606 at a second location 608 in a second (adjacent) pass 610-2. The second pass 610-2 borders, as shown in Fig. As shown in Figure 4, the first pass 610-1 is directly adjacent to the first pass. However, this need not be the case; instead, the system 215 could identify an impending ride quality problem in another pass (based on data corresponding to the first pass 610-1), for example, in pass 610-3, which is not directly adjacent to the first pass 610-1. As can be seen, the predicted location of the impending ride quality problem 606 is offset from the occurring ride quality problem 602 in both a first direction 614 and a second direction 616. Additionally, the system 215 (e.g., the confidence zone generator 352) generates a confidence zone 612, which indicates a region of the deployment location where the system 215 considers it likely (above a certain threshold) that a ride quality problem could exist, in order to account for errors in predicting the location of the impending ride quality problems 606.

[0090] Fig. Figure 5 shows a flowchart illustrating an example operation 700 of system 500 (e.g., of the ride quality system 215) during the implementation of proactive ride quality control.

[0091] In Block 702, one or more data elements indicating an upcoming driving quality problem at a deployment location are obtained (e.g., retrieved or received) from System 500 (e.g., the driving quality system 215). The obtained data may include near-field data 501, as indicated by Block 704. The obtained data may include far-field data 502, as indicated by Block 706. The obtained data may include data from previous operations 503, as indicated by Block 708. The obtained data may include predicted speed data 508, as specified by Block 710. The obtained data may include various other data indicating an upcoming driving quality problem at a deployment location, as indicated by Block 711. Furthermore, it is understood that one or more of the data elements can be continuously obtained (or updated) via Process 700.

[0092] In block 712, one or more other data elements are obtained (e.g., retrieved or received) by system 500 (e.g., the ride quality system 215). The obtained data may include sensor data 504, as indicated by block 714. The obtained data may include threshold data 505, as indicated by block 716. The obtained data may include data relating to a planned route 506, as indicated by block 718. The obtained data may include operator control adaptation data 507, as indicated by block 720. The obtained data may include various other data 510, as indicated by block 722.

[0093] In block 724, system 215 (e.g., the Identification System for Imminent Ride Quality Problems 351) identifies an imminent ride quality problem at the deployment location based on the received data. Some examples of system 215 identifying an imminent ride quality problem at the deployment location are shown in Fig. 3 described. In one example, System 215 identifies a ride quality problem at an upcoming location based on data corresponding to a nearby location at the work site (e.g., Near Data 501), as shown in Block 726. In another example, System 215 identifies a ride quality problem at an upcoming location based on data corresponding to the upcoming location (e.g., Far Data 502 or Past Operation Data 503), as shown by Block 728. In some examples, System 215 identifies a ride quality problem based on a combination of data, as shown in Block 729. For example, a combination of terrain feature values ​​(e.g., from Near Data 501, Far Data 502, or Past Operation Data 503) and predicted machine speed values ​​from predicted machine speed data 508.In some examples, identifying a ride quality problem at an upcoming location may involve system 215 (e.g., comparison logic 353) comparing the data (or a value from it) to a threshold (or limit value), as specified in block 730. As already mentioned in . Fig. As described in section 3, in some examples a threshold is provided to System 215, and in some other examples a threshold is identified by System 215 (e.g. by the Threshold Identification System 336).

[0094] As stated in Block 731, in some examples, System 215 (e.g., the Confidence Zone Generator 352) generates a confidence zone corresponding to the identified ride quality problem at the upcoming location. Some examples of confidence zone generation are given with reference to Fig. 3 described.

[0095] In block 732, system 500 (e.g., control system 214) controls the working machine at least based on the identified driving quality problem at the upcoming location. In some examples, system 500 (e.g., control system 214) in block 732 further controls the working machine based on the confidence zone(s) corresponding to the upcoming location.

[0096] In some examples, controlling the working machine may involve System 215 (e.g., the Control Action Identification System 340) identifying one or more machine settings for controlling the working machine relative to the identified ride quality problem at the upcoming location, as specified in Block 734. Some examples of System 215 identifying one or more machine settings for controlling the working machine relative to an identified ride quality problem at an upcoming location are given with reference to Fig. 3 described. In some examples, identifying the one or more machine settings may involve matching machine setting(s) from the obtained data, as specified in Block 736. In some examples, identifying the one or more machine settings may involve scaling the machine setting(s) from the obtained data, as specified in Block 738. In some examples, identifying the one or more machine settings involves implementing learning functions, as specified in Block 740. The one or more machine settings may be identified in various other ways, as specified in Block 742.

[0097] Controlling the working machine 100 may include controlling one or more controllable subsystems 216, as specified in Block 744, such as controlling one or more controllable subsystems based on the one or more machine settings identified in Block 734. Controlling the working machine may additionally or alternatively include controlling one or more interface mechanisms 218, as specified in Block 746. The working machine 100 may also be controlled in other ways, as specified by Block 748.

[0098] Block 750 determines whether the operation of machine 100 is complete. If the operation of machine 100 is not complete, processing returns to block 702. If the operation of machine 100 is complete in block 750, processing is terminated.

[0099] This discussion mentions processors and servers. In some examples, the processors and servers include computer processors with associated memory and timing circuitry, not shown separately. They are functional parts of the systems or devices to which they belong and are activated or enabled by the other components or elements in those systems.

[0100] Furthermore, various user interface displays were discussed. These displays can take a wide variety of forms and can incorporate a wide variety of user-operated interface mechanisms. For example, text fields, checkboxes, toggle buttons, links, drop-down menus, search fields, and so on can be among the user-activated interface mechanisms. These user-activated interface mechanisms can also be operated in a variety of other ways. For example, they can be operated using interface mechanisms such as a point-and-click device like a trackball or mouse, hardware buttons, switches, a joystick or keyboard, thumb switches or thumbpads, a virtual keyboard, or other virtual actuators.If the screen displaying the user interface mechanisms is a touchscreen, the user interface mechanisms can also be operated using touch gestures. Furthermore, the user interface mechanisms can be operated using voice commands via speech recognition functionality. Speech recognition can be implemented using a speech recognition device, such as a microphone, and software that recognizes the detected speech and executes commands based on the received speech.

[0101] Furthermore, various data storage devices were discussed. It should be noted that each data storage device can be subdivided into multiple data stores. In some examples, one or more of the data stores may be located locally to the systems accessing the data stores, one or more of the data stores may all be located remotely from a system using the data storage device, or one or more of the data stores may be located locally while others are located remotely. All of these configurations are considered by the present disclosure.

[0102] Furthermore, the figures depict a number of blocks, each assigned a specific functionality. It should be noted that fewer blocks can be used to illustrate that the functionality assigned to several different blocks is performed by fewer components. Conversely, more blocks can be used to illustrate that the functionality can be distributed across more components. In other examples, functions can be added and some removed.

[0103] It should be noted that the above discussion has described a variety of different systems, logics, generators, controllers, components, and interactions. It is understood that any or all of such systems, logics, generators, controllers, components, and interactions can be implemented by hardware elements, such as one or more processors, one or more processors that execute computer-executable instructions stored in memory, memory, or other processing components, some of which are described below, that perform the functions associated with these systems, generators, logics, controllers, components, or interactions.Furthermore, some or all of the systems, logics, generators, control devices, components, and interactions can be implemented by software that is loaded into memory and subsequently executed by one or more processors, servers, or other computing component(s), as described below. Any or all of the systems, logics, generators, control devices, components, and interactions can also be implemented by various combinations of hardware, software, firmware, etc., some examples of which are described below. These are merely some examples of different structures that can be used to implement any or all of the systems, logics, generators, control devices, components, and interactions described above. Other structures may also be used.

[0104] Fig. Figure 6 is a block diagram of a remote server architecture 1000. Fig. Figure 6 also shows one or more worker machines 100, one or more remote computing systems 300, and one or more remote user interface mechanisms 364 communicating with the remote server environment. The worker machines 100, the remote computing systems 300, and the remote user interface mechanisms 364 communicate with elements of a remote server architecture 1000. In some examples, the remote server architecture 1000 provides computing, software, data access, and storage services that do not require the end user to have any knowledge of the physical location or configuration of the system providing the services. In other examples, remote servers can provide the services over a wide area network, such as the Internet, using appropriate protocols.For example, remote servers can provide applications over a wide area network that can be accessed via a web browser or any other computing device. The software or components shown in previous figures, as well as the associated data, can be stored on servers at a remote location. The computing resources in a remote server environment can be concentrated in a remote data center location, or they can be distributed across multiple remote data centers. Remote server infrastructures can deliver services across shared data centers, although the services appear as a single access point to the user. Thus, the components and functions described here can be provided by a remote server at a remote location using a remote server architecture.Alternatively, the components and functions can be provided by a server, or the components and functions can be installed directly, or in some other way, on client devices.

[0105] In the Fig. In the example shown in Figure 6, some elements resemble those shown in previous figures, and these elements are similarly numbered. Fig. Figure 6 shows in particular that the driving quality system 215, the data storage 204 or data storage 304, or a combination thereof, can be located at a server location 1002, which is remote from the work machines 100, the remote computing systems 300, and the interface mechanisms for remote users 364. Therefore, in the Fig. In the example shown, the work machines 100, the remote computing systems 300, and the interface mechanisms for remote users 364 access the systems via the remote server location 1002. In other examples, various other elements may also be located at server location 1002, such as various other elements of the system architecture 500.

[0106] Fig. Figure 6 also shows another example of a remote server architecture. Fig. Figure 6 shows that some elements from previous figures may be located at a remote server location 1002, while others may be located elsewhere. For example, one or more of the data stores 204 or 304 may be located at a location separate from location 1002, and storage access may be via the remote server at location 1002. Similarly, the ride quality system 215 may be located at a location separate from location 1002, and access may be via the remote server at location 1002. Regardless of where the elements are located, access to the elements may be directly by work machines 100, remote computing systems 300, and remote user interface mechanisms 364 via a network, such as...a wide area network or a local area network; the elements can be hosted at a remote location by a service; or the elements can be provided as a service or be accessible through a connectivity service located at a remote location. Furthermore, data can be stored at any location, and the stored data can be accessible to or routed to operators, users, or systems. For example, physical carriers can be used instead of, or in addition to, carriers of electromagnetic waves. In some examples where wireless telecommunications service coverage is poor or nonexistent, another machine, such as a tanker truck or other mobile machine or vehicle, can have an automated, semi-automated, or manual information gathering system. If a mobile machine (e.g.,When the mobile machine (e.g., the mobile machine 100) approaches the machine containing the information collection system, such as a tanker truck before refueling, the information collection system gathers information from the mobile machine (e.g., the mobile machine 100) via any type of ad-hoc wireless connection. The gathered information can then be forwarded to another network when the machine containing the received information reaches a location where wireless telecommunications service or other wireless connectivity is available. For example, a tanker truck can enter an area with wireless communication coverage when traveling to a site to refuel other machines or when it is at a main fuel storage site. Other mobile machines or vehicles can enter an area with wireless communication coverage when traveling to other sites or when they are at a different location.All of these architectures are considered here. The information can also be stored on a mobile machine (e.g., on work machine 100) until the mobile machine enters an area with wireless communication coverage. The mobile machine (e.g., work machine 100) itself can then send the information to another network.

[0107] It should also be noted that the elements of previous figures, or parts thereof, may be arranged on a wide variety of different devices. One or more of these devices may include an onboard computer, an electronic control unit, a display unit, a server, a desktop computer, a laptop computer, a tablet computer, or other mobile device, such as a palmtop computer, a mobile phone, a smartphone, a multimedia player, a personal digital assistant, etc.

[0108] In some examples, the remote server architecture can incorporate 1000 cybersecurity measures. Without limitation, these measures can include encrypting data on storage devices, encrypting data transmitted between network nodes, authenticating individuals or processes accessing data, and using ledgers to record metadata, data, data transfers, data access, and data transformations. In some examples, the ledgers can be distributed and immutable (e.g., implemented as a blockchain).

[0109] Fig. Figure 7 is a simplified block diagram of an illustrative example of a portable or mobile computing device that can be used as a portable device 16 by a user or client and in which the present system (or parts thereof) can be deployed. A mobile device can, for example, be deployed in the operator compartment of a mobile machine (e.g., work machine 100) or can be coupled to a mobile machine (e.g., work machine 100) via communication technology for use in generating, processing, or displaying the outputs discussed above (e.g., 360). Fig. 8 and Fig. 9 are examples of handheld or mobile devices.

[0110] Fig. Figure 7 shows a general block diagram of the components of a client device 16, which can execute some of the components shown in the preceding figures, interact with them, or both. The device 16 provides a communication link 13 that enables the handheld device to communicate with other computing devices and, in some examples, provides a channel for automatically receiving information, such as by scanning. Examples of the communication link 13 include enabling communication via one or more communication protocols, such as wireless services used to provide cellular access to a network, and protocols that provide local wireless connections to networks.

[0111] In other examples, applications can be received on a removable Secure Digital (SD) card connected to a user interface 15. The interface 15 and communication links 13 communicate with a processor 17 (which can also represent processors or servers from other figures) along a bus 19, which is also connected to memory 21 and input / output (I / O) components 23, as well as a clock 25 and a location system 27.

[0112] The I / O components 23 are provided in an example to enable input and output operations. The I / O components 23 for various examples of the device 16 can include input components such as buttons, touch sensors, optical sensors, microphones, touchscreens, proximity sensors, accelerometers, orientation sensors, and output components such as a display device, a speaker, and / or a printer port. Other I / O components 23 can also be used.

[0113] Clock 25 includes, for example, a real-time clock component that outputs a time and date. This can also, for example, provide timing functions for processor 17.

[0114] The tracking system 27 includes, for example, a component that outputs a current geographic location of the device 16. This can be, for example, a GPS (Global Positioning System) receiver, a LORAN system, a dead reckoning navigation system, a cellular triangulation system, or another positioning system. The tracking system 27 can also include, for example, mapping software or navigation software that generates desired maps, navigation routes, and other geographic functions.

[0115] Memory 21 stores an operating system 29, network settings 31, applications 33, application configuration settings 35, a client system 24, a data store 37, communication drivers 39, and communication configuration settings 41. Memory 21 can include all types of physical volatile and non-volatile computer-readable storage devices. Memory 21 can also include computer storage media (described below). Memory 21 stores computer-readable instructions which, when executed by the processor 17, cause the processor to perform computer-implemented steps or functions according to the instructions. The processor 17 can be activated by other components to enable their functionality as well.

[0116] Fig. Figure 8 shows an example where the device 16 is a tablet computer 1100. Fig. Figure 8 shows the Computer 1100 with a user interface display screen 1102. The screen 1102 can be a touchscreen or a pen-enabled user interface that receives input from a pen or stylus. The Tablet Computer 1100 can also use a virtual keyboard on the screen. Naturally, the Computer 1100 can also be attached to a keyboard or other user input device via a suitable mounting mechanism, such as a wireless link or a USB connection. The Computer 1100 can also receive voice input, for example.

[0117] Fig. 9 resembles Fig. 8 except that the device is a smartphone 71. The smartphone 71 has a touch-sensitive display 73 that shows icons or tiles or other user input mechanisms 75. The mechanisms 75 can be used by a user to run applications, make calls, perform data transfer operations, etc. In general, the smartphone 71 is built on a mobile operating system and offers more advanced computing capabilities and connectivity than a feature phone.

[0118] It should be noted that other forms of devices 16 are possible.

[0119] Fig. Figure 10 is an example of a computational environment in which elements from previously described figures can be used. With reference to Fig. Figure 10 comprises an example system for implementing some embodiments, comprising a computing device in the form of a computer 1210 programmed to operate as described above. The components of the computer 1210 may, but are not limited to, include a processing unit 1220 (which may include processors or servers from previous figures), a system memory 1230, and a system bus 1221 connecting various system components, including the system memory, to the processing unit 1220. The system bus 1221 may be one of several types of bus structure, including a memory bus or memory controller, a peripheral bus, and a local bus, employing any variety of bus architectures. The memory and programs described with reference to the preceding figures herein may be represented in corresponding portions of Fig. 10 will be used.

[0120] The Computer 1210 typically incorporates a variety of computer-readable media. Computer-readable media can be any available media that the Computer 1210 can access, including both volatile and non-volatile, removable and non-removable media. By way of example, and without limitation, computer-readable media can include computer storage media and communication media. Computer storage media are distinct from and do not include a modulated data signal or carrier wave. Computer-readable media include hardware storage media, including both volatile and non-volatile, removable and non-removable media, implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data.Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other storage technology, CD-ROM, DVD (Digital Versatile Discs) or other optical disc storage, magnetic cartridges, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that the Computer 1210 can access. Communication media can embody computer-readable instructions, data structures, program modules, or other data in a transport mechanism and include any information delivery media. The term "modulated data signal" means a signal that has one or more of its characteristics set or modified in such a way that information is encoded in the signal.

[0121] System memory 1230 comprises computer storage media in the form of volatile and / or non-volatile memory, or both, such as ROM (read-only memory) 1231 and RAM (random access memory) 1232. A BIOS (basic input / output system) 1233, which contains the basic routines that assist in transferring information between elements within the computer 1210, such as during startup, is typically stored in ROM 1231. RAM 1232 typically contains data and / or program modules, or both, that are immediately accessible and / or are currently being processed by the processing unit 1220. This is illustrated by way of example and without limitation. Fig. 10 an operating system 1234, application programs 1235, other program modules 1236 and program data 1237.

[0122] The Computer 1210 may also include other removable / non-removable volatile / non-volatile computer storage media. These are merely examples. Fig. 10 a hard disk drive 1241, which reads from or writes to the non-removable non-volatile magnetic media, an optical disk drive 1255 and a non-volatile optical disk 1256. The hard disk drive 1241 is typically connected to the system bus 1221 via a non-removable storage interface, such as the 1240 interface, and the optical disk drive 1255 is typically connected to the system bus 1221 via a removable storage interface, such as the 1250 interface.

[0123] Alternatively or additionally, the functionality described here can be implemented, at least partially, by one or more hardware logic components. Examples of suitable hardware logic components include, but are not limited to, freely programmable gate arrays (FPGAs), application-specific integrated circuits (e.g., ASICs), application-specific standard products (e.g., ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), quantum computers, etc.

[0124] The drives and their aforementioned and in Fig. The 10 illustrated associated computer storage media provide storage for computer-readable instructions, data structures, program modules, and other data for the Computer 1210. Fig. Figure 10, for example, illustrates the hard disk drive 1241 as storing the operating system 1244, the application programs 1245, other program modules 1246, and the program data 1247. It should be noted that these components can either be the same as the operating system 1234, the application programs 1235, the other program modules 1236, and the program data 1237, or they can be different from them.

[0125] A user can input commands and information into the computer 1210 via input devices such as a keyboard 1262, a microphone 1263, and a pointing device 1261, such as a mouse, trackball, or touchpad. Other input devices (not shown) may include a joystick, gamepad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 1220 via a user input interface 1260 coupled to the system bus, but may also be connected via other interface and bus structures. A visual display 1291 or other type of display device is also connected to the system bus 1221 via an interface such as a video interface 1290.In addition to the monitor, computers may also include other peripheral output devices, such as loudspeakers 1297 and a printer 1296, which may be connected via an output peripheral interface 1295.

[0126] The computer 1210 is operated in a networked environment via logical connections (e.g. Controller Area Network - CAN, Local Area Network - LAN or Wide Area Network - WAN) with one or more remote computers, such as a remote computer 1280.

[0127] When used in a LAN network environment, the computer 1210 is connected to the LAN 1271 via a network interface or adapter 1270. When used in a WAN networking environment, the computer 1210 typically includes a modem 1272 or other means of establishing communications over the WAN 1273, such as the Internet. In a networked environment, program modules can be stored in a remote storage device. Fig. For example, 10 shows that remote application programs 1285 can be located on the remote computer 1280.

[0128] It should also be noted that the various examples described here can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of this is taken into consideration here.

[0129] Even if the subject matter is described in a language specific to structural features and / or methodological actions, it is understood that the subject matter defined in the accompanying claims is not necessarily limited to the specific features or actions described above. Instead, the specific features and actions described above are disclosed as examples of the claims.

Claims

[1] System (500) comprising: one or more processors (201, 301); and a memory (204, 304) that stores instructions (205, 305) that can be executed by the one or more processors and which, when executed by the one or more processors, cause the one or more processors to: Data (501, 502, 503) is received by (702) indicating a ride quality problem at an upcoming location at a work site where a work machine (100) is performing an ongoing operation; Based on the data, a ride quality problem was identified at the upcoming location at the deployment site (724); and the work machine controls at least on the basis of the driving quality problem at the upcoming location at the place of use (732). [2] System according to claim 1, wherein the data comprise one of the following: (i) near data (501) corresponding to a location at the deployment site other than the upcoming location; (ii) previous operation data (503) generated during a previous operation at the deployment site; or (iii) remote data (502) generated by a system located remote from the deployment site. [3] System according to claim 2, wherein the working machine comprises a first working machine with a first machine type and wherein the earlier operation is carried out by a second working machine with a second machine type which differs from the first machine type. [4] System according to claim 2, wherein the data indicates one or more of the following: (i) machine speed, (ii) a rocking of the machine; or (iii) a terrain feature. [5] System according to claim 1, wherein the data are generated by one or more sensors (208, 228) of the working machine and correspond to a different location at the place of use than the upcoming location. [6] System according to claim 1, wherein the instructions, when executed by the one or more processors, further configure the one or more processors such that they: compare the data with a threshold value (730); and Identify the ride quality problem at the upcoming location at the deployment site based on a comparison of the data with the threshold (724). [7] System according to claim 6, wherein the threshold during the current operation is identified on the basis of operator control adaptation data corresponding to a location at the deployment site that differs from the upcoming location, wherein the operator control adaptation data indicates an operator adaptation on the machine being worked. [8] System according to claim 1, wherein the data comprise a first machine setting value and wherein, when executed by the one or more processors, the instructions further configure the one or more processors such that they: Identify a second machine setting value based on the first machine setting value, wherein the second machine setting value is the same as the first machine setting value (736); and control the machine at least on the basis of the second machine setting value. [9] System according to claim 1, wherein the data comprise a first machine setting value and wherein, when executed by the one or more processors, the instructions further configure the one or more processors such that they: scale the first machine setting value (738) to identify a second machine setting value; and control the machine at least on the basis of the second machine setting value. [10] System according to claim 1, wherein the instructions, when executed by the one or more processors, further configure the one or more processors to control the working machine by controlling one or more of the following: (i) an interface mechanism (218) of the working machine to generate a presentation; (ii) a drive subsystem (250) of the working machine to change a speed of the working machine; (iii) an actuator (254) of the working machine to adjust a position of an attachment of the working machine; or (iv) an actuator (254) of the working machine to adjust a preload force applied to an attachment of the working machine. [11] Computer-implemented method for controlling an agricultural work machine (100), comprising: Received (702) of data indicating a ride quality problem at an upcoming location at a work site where the work machine is performing an ongoing operation; Identify (724) a ride quality problem corresponding to the upcoming location at the deployment site, based on the data; Control (732) the working machine at least on the basis of the driving quality problem at the upcoming location at the place of use. [12] Computer-implemented method according to claim 11, wherein obtaining the data comprises obtaining one or more of the following: (i) near data (501) corresponding to a location at the deployment site other than the upcoming location; (ii) previous operation data (503) generated during a previous operation at the deployment site; or (iii) remote data (502) generated by a system located remote from the deployment site. [13] Computer-implemented method according to claim 11, wherein obtaining the data comprises obtaining data indicating one or more of the following: (i) machine speed, (ii) a rocking of the machine; or (iii) a terrain feature. [14] Computer-implemented method according to claim 13, wherein obtaining the data comprises obtaining sensor data corresponding to a location other than the upcoming location and generated by one or more sensors of the working machine. [15] Computer-implemented method according to claim 11, wherein controlling the working machine comprises one or more of the following: (i) controlling an interface mechanism (218) of the working machine to generate a presentation; (ii) controlling a drive subsystem (250) of the working machine to change a speed of the working machine; (iii) controlling an actuator (254) of the working machine to adjust a position of an attachment of the working machine; or (iv) controlling an actuator (254) of the working machine to adjust a preload force applied to an attachment of the working machine.

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