Predictive machine characteristic map generation and control system

Abstract

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural property values at different geographic locations of a field. An on-board sensor on the agricultural work machine senses an agricultural property as the agricultural work machine moves through the field. A prediction map generator generates a prediction map based on a relationship between the values in the one or more information maps and the agricultural property sensed by the on-board sensor, the prediction map predicting a predicted agricultural property at different locations in the field. The prediction map can be output and used for automated machine control.

Description

Technical Field

[0001] This manual covers agricultural machinery, forestry machinery, construction machinery, and lawn management machinery. Background Technology

[0002] There are various types of agricultural machinery. Some agricultural machinery includes harvesters, such as combine harvesters, sugarcane harvesters, cotton harvesters, self-propelled forage harvesters, and reapers. Some harvesters can also be equipped with different types of headers to harvest different types of crops.

[0003] Terrain characteristics can have several detrimental effects on harvesting operations. For example, when a harvester passes over a slope, the harvester's pitch or roll may impair its performance. Therefore, when encountering a slope during harvesting operations, the operator may try modifying the harvester's controls.

[0004] The above discussion is provided only as general background information and is not intended to help determine the scope of the subject matter for which protection is sought. Summary of the Invention

[0005] One or more information maps are obtained through agricultural machinery. These maps map one or more agricultural characteristic values ​​to different geographical locations within the field. As the agricultural machinery moves across the field, field sensors on the machinery detect the agricultural characteristics. A prediction map generator generates prediction maps of the predicted agricultural characteristics at different locations within the field based on the relationships between the values ​​in the one or more information maps and the agricultural characteristics sensed by the field sensors. These prediction maps can be output and used for automated machine control.

[0006] The present invention is provided to introduce selected concepts in a simplified form, which are further described in the detailed embodiments below. The present invention is not intended to identify key or essential features of the claimed subject matter, nor is it intended to help determine the scope of the claimed subject matter. The claimed subject matter is not limited to examples addressing any or all of the shortcomings pointed out in the background art. Attached Figure Description

[0007] Figure 1 This is a partial schematic diagram of an example of a combine harvester.

[0008] Figure 2 This is a block diagram showing some parts of an agricultural harvester in more detail, based on some examples of this disclosure.

[0009] Figures 3A to 3B A flowchart illustrating an example of the operation of an agricultural harvester when generating a diagram is shown.

[0010] Figure 4This is a block diagram illustrating an example of a prediction model generator and a prediction graph generator.

[0011] Figure 5 This is a flowchart illustrating an example of how an agricultural harvester receives topographic maps, detects machine characteristics, and generates functional predictive maps that are displayed and used to control the agricultural harvester during harvesting operations.

[0012] Figure 6A This is a block diagram illustrating an example of a prediction model generator and a prediction graph generator.

[0013] Figure 6B This is a block diagram showing some examples of field sensors.

[0014] Figure 7 The flowchart illustrates an example of the operation of an agricultural harvester, including the generation of functional predictive maps using infographics and field sensor inputs.

[0015] Figure 8 This is a block diagram illustrating an example of a control area generator.

[0016] Figure 9 It is a diagram. Figure 8 The flowchart shows an example of the operation of the control area generator.

[0017] Figure 10 The diagram illustrates an example of how a control system operates when selecting a target setpoint to control an agricultural harvester.

[0018] Figure 11 This is a block diagram illustrating an example of an operator interface controller.

[0019] Figure 12 This is a flowchart illustrating an example of an operator interface controller.

[0020] Figure 13 This is an illustrative diagram showing an example of an operator interface display.

[0021] Figure 14 This is a block diagram illustrating an example of an agricultural harvester communicating with a remote server environment.

[0022] Figures 15 to 17 An example of a mobile device that can be used in agricultural harvesters is shown.

[0023] Figure 18 This is a block diagram illustrating an example of a computing environment that can be used for agricultural harvesters. Detailed Implementation

[0024] To facilitate understanding of the principles of this disclosure, reference will now be made to the examples shown in the accompanying drawings, and they will be described using specific language. However, it will be understood that this is not intended to limit the scope of this disclosure. Any changes and further modifications to the described apparatus, systems, and methods, as well as any further application of the principles of this disclosure, are fully contemplated, as would normally occur to those skilled in the art to which this disclosure pertains. Specifically, it is fully contemplated that features, components, and / or steps described with respect to one example may be combined with features, components, and / or steps described with respect to other examples of this disclosure.

[0025] This specification relates to generating predictive maps, and more specifically, predictive machine characteristic maps, using field data acquired concurrently with agricultural operations in combination with prior data. In some examples, predictive machine characteristic maps can be used to control agricultural machinery (e.g., agricultural harvesters). As mentioned above, the performance of agricultural harvesters can deteriorate when combined with terrain features such as slopes. For example, if the harvester is going uphill, power demand increases and machine performance may decrease. This problem can be exacerbated when the soil is wet (e.g., shortly after rainfall) and tires or tracks face increased slippage. Additionally, the performance of harvesters (or other agricultural machinery) can be adversely affected by the terrain of the field. For example, when traversing a slope, the terrain can cause the machine to roll a certain amount. Not limitingly, machine pitch or roll can affect machine stability, internal material distribution, spray pressure on sprayers, etc. For example, grain loss can be affected by terrain features that cause the harvester to pitch or roll 100 degrees. Increased pitch causes grain to exit the rear more quickly, while reduced pitch retains grain within the machine, and tumbling elements can overload the sides of the clearing system and cause more grain loss on those sides. Similarly, grain quality can be affected by both pitch and tumbling, and similar to grain loss, the material remaining in or leaving the machine, other than grain, can affect quality output based on pitch or tumbling reactions. In another example, terrain characteristics affecting pitch will influence the amount of waste entering the waste system, thus affecting the waste sensor output. Pitch considerations and the time spent at that level can relate to how much waste volume increases and can be useful for estimations when it is necessary to predict and adjust for control levels.

[0026] Topographic maps graphically depict ground elevation across different geographic locations within a field of interest. Since ground slope indicates changes in elevation, two or more elevation values ​​allow for the calculation of slope across areas with known elevation values. Greater granularity of slope can be achieved by using more areas with known elevation values. As an agricultural harvester travels across the terrain in a known direction, the harvester's pitch and roll can be determined based on the ground slope (i.e., the area of ​​elevation change). Topographic characteristics mentioned below may include (but are not limited to) elevation, slope (e.g., including machine orientation relative to the slope), and ground profile (e.g., roughness).

[0027] Therefore, this discussion focuses on a system that receives a topographic map of the field during harvesting operations and also uses field sensors to detect values ​​indicating one or more of the following: internal material distribution, power characteristics, ground speed, grain loss, impurities, grain quality, or another machine characteristic. The system generates a model that models the relationship between the topographic characteristics derived from the topographic map and the output values ​​from the field sensors. This model is used to generate a functional predictive machine characteristic map that predicts, for example, the power used at different locations in the field. The functional predictive machine characteristic map generated during harvesting operations can be used to automatically control the harvester during the harvesting operation. In some cases, the functional predictive machine characteristic map is used to generate tasks or path planning for agricultural harvesters operating in the field to, for example, improve power utilization, speed, or uniformity of internal material distribution throughout the operation. Of course, internal material distribution, power characteristics, ground speed, grain loss, impurities, and grain quality are only examples of machine characteristics that can be predicted based on topographic characteristics, and other machine characteristics can also be predicted and used to control the machine.

[0028] Figure 1 This is a partial schematic diagram of a self-propelled agricultural harvester 100. In the example shown, the agricultural harvester 100 is a combine harvester. Furthermore, although combine harvesters are provided as examples throughout this disclosure, it will be understood that this specification also applies to other types of harvesters, such as cotton harvesters, sugarcane harvesters, self-propelled hay harvesters, slashers, or other agricultural operating machines. Therefore, this disclosure is intended to cover the various types of harvesters described, and is therefore not limited to combine harvesters. In addition, this disclosure relates to other types of operating machines, such as agricultural seeders and sprayers to which predictive mapping can be applied, construction equipment, forestry equipment, and turf management equipment. Therefore, this disclosure is intended to cover these various types of harvesters and other operating machines, and is therefore not limited to combine harvesters.

[0029] like Figure 1As shown, the agricultural harvester 100 exemplarily includes an operator's cab 101, which may have various different operator interface mechanisms for controlling the agricultural harvester 100. The agricultural harvester 100 includes front-end equipment, such as a header 102 and a cutter generally indicated by 104. The agricultural harvester 100 also includes a feeder housing 106, a feed accelerator 108, and a thresher generally indicated by 110. The feeder housing 106 and the feed accelerator 108 form part of a material handling subsystem 125. The header 102 is pivotally coupled to the frame 103 of the agricultural harvester 100 along a pivot axis 105. One or more actuators 107 drive the header 102 to move about the axis 105 in a direction generally indicated by arrow 109. Therefore, the vertical position (cutting height) of the cutting table 102 above the ground 111 (where the cutting table 102 travels) can be controlled by actuating the actuator 107. Although Figure 1 As not shown, the agricultural harvester 100 may also include one or more actuators operable to apply a tilt angle, a tumble angle, or both to the header 102 or a portion thereof. Tilt refers to the angle at which the cutter 104 engages with the crop. For example, the tilt angle is increased by controlling the header 102 to point the distal edge 113 of the cutter 104 more towards the ground. The tilt angle is decreased by controlling the header 102 to point the distal edge 113 of the cutter 104 further away from the ground. Tumble refers to the orientation of the header 102 about the longitudinal axis of the agricultural harvester 100.

[0030] The threshing machine 110 exemplarily includes a threshing rotor 112 and a set of concave plates 114. Additionally, the agricultural harvester 100 includes a separator 116. The agricultural harvester 100 also includes a cleaning subsystem or cleaning chamber 118 (collectively referred to as cleaning subsystem 118), which includes a cleaning fan 120, a chaff screen 122, and a screen 124. The material handling subsystem 125 also includes a discharge agitator 126, a waste lifter 128, a clean grain lifter 130, and an unloading screw conveyor 134 and a nozzle 136. The clean grain lifter moves clean grain into a clean grain bin 132. The agricultural harvester 100 also includes a residue subsystem 138, which may include a shredder 140 and a spreader 142. The agricultural harvester 100 also includes a propulsion subsystem, which includes an engine driving a ground engagement assembly 144 (e.g., wheels or tracks). In some examples, the combine harvester within the scope of this disclosure may have more than one of any of the above subsystems. In some examples, the agricultural harvester 100 may have Figure 1 The left and right grain cleaning subsystems and separators are not shown in the diagram.

[0031] In operation, as an overview, the combine harvester 100 exemplarily moves across the field in the direction indicated by arrow 147. As the combine harvester 100 moves, the header 102 (and associated reel 164) engages the crop to be harvested and collects the crop toward the cutter 104. The operator of the combine harvester 100 can be a local human operator, a remote human operator, or an automated system. The operator of the combine harvester 100 can determine one or more of the header 102's height setting, tilt angle setting, or tumble angle setting. For example, the operator inputs one or more settings to the control system (described in more detail below) that controls the actuator 107. The control system can also receive settings from the operator for establishing the tilt angle and tumble angle of the header 102, and implements the input settings by controlling the associated actuators (not shown) to change the tilt angle and tumble angle of the header 102. Actuator 107 maintains the header 102 at a height above ground 111 based on a height setting, and, where applicable, at a desired tilt and yaw angle. Each of the height setting, tumble setting, and tilt setting can be implemented independently of the others. The control system responds to header errors (e.g., the difference between the height setting and the measured height of the header 104 above ground 111, and in some cases, tilt and tumble angle errors) with a responsiveness determined based on a sensitivity level. If the sensitivity level is set at a higher sensitivity level, the control system responds to smaller header position errors and attempts to reduce the detected error faster than when the sensitivity level is lower.

[0032] Returning to the description of the operation of the agricultural harvester 100, after the crop is cut by the cutter 104, the cut crop material is conveyed in the feeder housing 106 towards the feed accelerator 108, which accelerates the crop material into the thresher 110. The crop is threshed by a rotor 112 that rotates the crop material against a concave plate 114. In a separator 116, a separator rotor moves the threshed crop, while a discharge agitator 126 moves a portion of the residue toward the residue subsystem 138. The portion of residue conveyed to the residue subsystem 138 is shredded by a residue shredder 140 and spread across the field by a spreader 142. In other configurations, the residue is discharged from the agricultural harvester 100 in piles. In other examples, the residue subsystem 138 may include a seed eliminator (not shown), such as a seed bagger or other seed collector, or a seed shredder or other seed crusher.

[0033] The grain falls into the grain cleaning subsystem 118. A husk sieve 122 separates larger pieces of grain, and a screen 124 separates smaller pieces from the clean grain. The clean grain falls onto a screw conveyor that moves the grain to the inlet of a clean grain elevator 130, which then moves the clean grain upwards, causing it to settle in a clean grain bin 132. Airflow generated by a cleaning fan 120 removes residue from the grain cleaning subsystem 118. The cleaning fan 120 directs air upwards along an airflow path through the screen and husk sieve. The airflow then transports the residue backwards within the agricultural harvester 100 toward the residue handling subsystem 138.

[0034] The waste elevator 128 returns the waste to the threshing machine 110, where it is re-threshed. Alternatively, the waste may also be conveyed by the waste elevator or another conveying device to a separate re-threshing mechanism, where it is also re-threshed.

[0035] Figure 1 It is also shown that, in one example, the agricultural harvester 100 includes a ground speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward-view image capture mechanism 151 (which may be in the form of a stereo camera or a monocular camera), and one or more loss sensors 152 disposed in the grain cleaning subsystem 118.

[0036] Ground speed sensor 146 senses the travel speed of the harvester 100 on the ground. Ground speed sensor 146 can sense the travel speed of the harvester 100 by sensing the rotational speed of ground-engaging components (e.g., wheels or tracks), drive shafts, axles, or other components. In some cases, a positioning system may be used to sense the travel speed, such as a Global Positioning System (GPS), dead reckoning system, LoRAN (Local Remote Navigation System), Doppler velocity sensor, or various other systems or sensors that provide an indication of travel speed. Ground speed sensor 146 may also include, in combination with speed, a compass, magnetometer, gravity sensor, gyroscope, GPS-derived orientation sensor to determine the direction of travel in two or three dimensions. Thus, when the harvester 100 is on a slope, the orientation of the harvester 100 relative to the slope is known. For example, the orientation of the harvester 100 may include uphill, downhill, or traversing a slope. As mentioned in this disclosure, machine speed or ground speed may also include a two-dimensional or three-dimensional direction of travel.

[0037] Loss sensor 152 exemplarily provides an output signal indicating the amount of grain loss occurring on both the right and left sides of the grain cleaning subsystem 118. In some examples, sensor 152 is an impact sensor that counts grain impacts per unit time or per unit distance traveled to provide an indication of grain loss occurring at the grain cleaning subsystem 118. The impact sensors on the right and left sides of the grain cleaning subsystem 118 can provide separate signals or combined or aggregated signals. In some examples, instead of providing separate sensors for each grain cleaning subsystem 118, sensor 152 may include a single sensor.

[0038] Separator loss sensor 148 provides indication of the left and right separators ( Figure 1 (Not shown separately) The separator loss sensor 148 can be associated with the left and right separators and can provide individual grain loss signals or combined or aggregated signals. In some cases, various types of sensors may also be used to sense grain loss in the separator.

[0039] The agricultural harvester 100 may also include other sensors and measuring mechanisms. For example, the agricultural harvester 100 may include one or more of the following sensors: a header height sensor that senses the height of the header 102 above the ground 111; a stability sensor that senses the oscillation or jumping (and amplitude) of the agricultural harvester 100; a residue setting sensor configured to sense whether the agricultural harvester 100 is configured to chop residue, pile it, etc.; a cleaning chamber fan speed sensor that senses the speed of the fan 120; a concave plate gap sensor that senses the gap between the rotor 112 and the concave plate 114; and a threshing rotor speed sensor that senses the rotor speed of the rotor 112. The harvester 100 includes: a speed sensor; a husk sieve gap sensor that senses the opening size in the husk sieve 122; a sieve mesh gap sensor that senses the opening size in the sieve mesh 124; a material other than grain (MOG) moisture sensor that senses the moisture level of the MOG passing through the harvester 100; one or more machine setting sensors configured to sense various configurable settings of the harvester 100; a machine orientation sensor that senses the orientation of the harvester 100; and a crop property sensor that senses various types of crop properties, such as crop type, crop moisture, and other crop properties. While the harvester 100 is processing crop material, the crop property sensor can also be configured to sense the characteristics of the cut crop material. For example, in some cases, the crop property sensor may sense: grain quality, such as broken grain, MOG level; grain composition, such as starch and protein; and the grain feed rate as the grain passes through the feeder housing 106, the clean grain elevator 130, or elsewhere in the harvester 100. The crop property sensor can also sense the feed rate of biomass through the feeder housing 106, separator 116, or elsewhere in the harvester 100. The crop property sensor can also sense the feed rate as the mass flow rate of grain through the elevator 130 or other parts of the harvester 100, or provide other output signals indicating other sensed variables.

[0040] Examples of sensors used to detect or sense power characteristics include (but are not limited to) voltage sensors, current sensors, torque sensors, hydraulic sensors, hydraulic flow sensors, force sensors, bearing load sensors, and rotation sensors. Power characteristics can be measured at a varying granularity level. For example, power usage can be sensed across the machine, within a subsystem, or by individual components of a subsystem.

[0041] Examples of sensors used to detect the distribution of internal materials include (but are not limited to) one or more cameras, capacitive sensors, electromagnetic or ultrasonic time-of-flight reflective sensors, signal attenuation sensors, weight or mass sensors, material flow sensors, etc. These sensors may be placed at one or more locations within the agricultural harvester 100 to sense the distribution of materials within the agricultural harvester 100 during operation of the agricultural harvester 100.

[0042] Examples of sensors used to detect or sense the pitch or roll of the agricultural harvester 100 include accelerometers, gyroscopes, inertial measurement units, gravity sensors, magnetometers, etc. These sensors can also indicate the slope of the terrain where the agricultural harvester 100 is currently located.

[0043] Before describing how the agricultural harvester 100 generates a functional predictive machine characteristic map and uses this map for control, a brief description of some items on the agricultural harvester 100 and their operation will be provided first. Figure 2 , Figure 3A and Figure 3B The drawing describes receiving a general type of prior information map and combining information from the prior information map with georeferenced sensor signals generated by field sensors, where the sensor signals indicate characteristics of the field, such as the characteristics of crops or weeds present in the field. Characteristics of the "field" may include (but are not limited to): field characteristics such as slope, weed density, weed type, soil moisture, and surface quality; crop properties such as crop height, crop moisture, crop density, and crop condition; grain properties such as grain moisture, grain size, and grain test weight; and machine performance characteristics such as loss level, working quality, fuel consumption, and power utilization. Relationships between characteristic values ​​obtained from field sensor signals and prior information map values ​​are identified, and these relationships are used to generate new functional prediction maps. The functional prediction maps predict values ​​at different geographic locations in the field, and one or more of those values ​​can be used to control the machine. In some cases, the functional prediction maps may be presented to users, such as operators of agricultural machinery (e.g., agricultural harvesters). The functional prediction maps may be presented to users visually (e.g., via a display), tactilely, or audibly. Users can interact with functional prediction graphs to perform editing operations and other user interface operations. In some cases, functional prediction graphs can be used to control agricultural machinery (e.g., agricultural harvesters), presented to operators or other users, and presented to operators or users to facilitate operator or user interaction.

[0044] In reference Figure 2 , Figure 3A and Figure 3B After describing the general method, refer to Figure 4 and Figure 5More specific methods are described for generating functional predictive maps that can be presented to an operator or user or used to control an agricultural harvester 100 or both. Similarly, although this discussion is directed toward agricultural harvesters (specifically, combine harvesters), the scope of this disclosure extends to other types of agricultural harvesters or other agricultural operating machines.

[0045] Figure 2 This is a block diagram showing some parts of an example agricultural harvester 100. Figure 2 The agricultural harvester 100, as illustrated, includes one or more processors or servers 201, a data storage device 202, a geolocation sensor 204, a communication system 206, and one or more field sensors 208 that sense one or more agricultural characteristics of the field simultaneously with the harvesting operation. Agricultural characteristics may include any characteristics that can affect the harvesting operation. Some examples of agricultural characteristics include characteristics of the harvesting machine, the field, the plants on the field, and the weather. Other types of agricultural characteristics are also included. The field sensors 208 generate values ​​corresponding to the sensed characteristics. The agricultural harvester 100 also includes a prediction model or relation generator (collectively referred to as "prediction model generator 210"), a prediction map generator 212, a control area generator 213, a control system 214, one or more controllable subsystems 216, and an operator interface mechanism 218. The agricultural harvester 100 may also include various other agricultural harvester functions 220. For example, field sensors 208 include airborne sensors 222, remote sensors 224, and other sensors 226 that sense field characteristics during agricultural operations. Predictive model generator 210 exemplarily includes a prior information variable-to-field variable model generator 228, and may include other items 230. Control system 214 includes a communication system controller 229, an operator interface controller 231, a setting controller 232, a path planning controller 234, a feed rate controller 236, a header and reel controller 238, a belt conveyor controller 240, a cover plate position controller 242, a residue system controller 244, a machine cleaning controller 245, a zone controller 247, and may include other items 246. The controllable subsystem 216 includes machine and header actuator 248, propulsion subsystem 250, steering subsystem 252, residue subsystem 138, machine grain cleaning subsystem 254, and subsystem 216 may include various other subsystems 256.

[0046] Figure 2The diagram also shows that the agricultural harvester 100 can receive prior information map 258. As described below, for example, prior map information map 258 includes topographic maps from prior or previous operations in the field (e.g., range scanning operations performed by unmanned aerial vehicles from known altitudes), aircraft-sensed topographic maps, satellite-sensed topographic maps, topographic maps sensed by ground vehicles (e.g., GPS-equipped seeders), etc. However, prior map information may also encompass other types of data acquired prior to the harvesting operation or maps from prior or previous operations. For example, topographic maps can be retrieved from remote sources such as the United States Geological Survey (USGS). Figure 2 The diagram also shows an operator 260 capable of operating an agricultural harvester 100. The operator 260 interacts with an operator interface mechanism 218. In some examples, the operator interface mechanism 218 may include a joystick, joystick, steering wheel, linkage, pedals, buttons, dials, keypad, user-actuable elements on a user interface display (e.g., icons, buttons, etc.), microphone and speaker (where speech recognition and speech synthesis are provided), and various other types of control devices. Where a touch-sensitive display system is provided, the operator 260 may interact with the operator interface mechanism 218 using touch gestures. The examples provided above are exemplary and not intended to limit the scope of this disclosure. Therefore, other types of operator interface mechanisms 218 may also be used and are within the scope of this disclosure.

[0047] Using communication system 206 or other means, prior information diagram 258 can be transmitted to agricultural harvester 100 and stored in data storage device 202. In some examples, communication system 206 may be a cellular communication system, a system communicating via a wide area network or local area network, a system communicating via a near field communication network, or a communication system configured to communicate via any or a combination of various other networks. Communication system 206 may also include a system for facilitating the download or transfer of information to and from a Secure Digital (SD) card or a Universal Serial Bus (USB) card, or both.

[0048] The geolocation sensor 204 exemplarily senses or detects the geolocation or orientation of the agricultural harvester 100. The geolocation sensor 204 may include (but is not limited to) a Global Navigation Satellite System (GNSS) receiver that receives signals from a GNSS satellite transmitter. The geolocation sensor 204 may also include a Real-Time Kinematic (RTK) component configured to enhance the accuracy of position data derived from GNSS signals. The geolocation sensor 204 may include any of a dead reckoning system, a cellular triangulation system, or a variety of other geolocation sensors.

[0049] The field sensor 208 can be referenced above. Figure 1Any sensors described. Field sensors 208 include onboard sensors 222 mounted on the agricultural harvester 100. These sensors may include, for example, speed sensors (e.g., GPS, speedometer, or compass), image sensors inside the agricultural harvester 100 (e.g., a clean grain camera, or a camera mounted to identify material distribution within the agricultural harvester 100, such as in a residue subsystem or grain cleaning system), grain loss sensors, impurity characteristic sensors, and grain quality sensors. Field sensors 208 also include remote field sensors 224 that capture field information. Field data includes data acquired from sensors on the harvester or, where data is detected during harvesting operations, data acquired by any sensor.

[0050] Predictive model generator 210 generates a model indicating the relationship between values ​​sensed by field sensors 208 and characteristics mapped to the field via prior information map 258. For example, if prior information map 258 establishes a mapping between terrain characteristics and different locations in the field, and field sensors 208 are sensing values ​​indicating power usage, then prior information variable to field variable model generator 228 generates a predictive machine model that models the relationship between terrain characteristics and power usage. Predictive machine models can also be generated based on terrain characteristics from prior information map 258 and multiple field data values ​​generated by field sensors 208. Then, predictive map generator 212 uses the predictive machine model generated by predictive model generator 210 to generate a functional predictive machine characteristic map that predicts values ​​of machine characteristics (e.g., internal material distribution) sensed by field sensors 208 at different locations in the field based on prior information map 258.

[0051] In some examples, the type of values ​​in functional prediction graph 263 may be the same as the type of field data sensed by field sensor 208. In some cases, the type of values ​​in functional prediction graph 263 may have a different unit than the data sensed by field sensor 208. In some examples, the type of values ​​in functional prediction graph 263 may be different from the type of data sensed by field sensor 208, but related to the type of data sensed by field sensor 208. For example, in some examples, the type of data sensed by field sensor 208 may indicate the type of values ​​in functional prediction graph 263. In some examples, the type of data in functional prediction graph 263 may be different from the type of data in prior information graph 258. In some cases, the type of data in functional prediction graph 263 may have a different unit than the data in prior information graph 258. In some examples, the type of data in functional prediction graph 263 may be different from the type of data in prior information graph 258, but related to the type of data in prior information graph 258. For example, in some examples, the data type in the prior information graph 258 may indicate the type of data in the functional prediction graph 263. In some examples, the type of data in the functional prediction graph 263 differs from one or both of the field data type sensed by the field sensor 208 and the data type in the prior information graph 258. In some examples, the type of data in the functional prediction graph 263 is the same as one or both of the field data type sensed by the field sensor 208 and the data type in the prior information graph 258. In some examples, the type of data in the functional prediction graph 263 is the same as one of the field data type sensed by the field sensor 208 or the data type in the prior information graph 258, and different from the other.

[0052] The prediction map generator 212 can use the terrain features in the prior information map 258 and the model generated by the prediction model generator 210 to generate a functional prediction map 263 that predicts machine characteristics at different locations in the field. The prediction map generator 212 then outputs a prediction map 264.

[0053] like Figure 2As shown, prediction map 264 is based on prior information values ​​at those locations in prior information map 258 and uses a prediction model to predict the values ​​of sensed characteristics (sensed by field sensors 208) or characteristics related to sensed characteristics at multiple locations across the field. For example, if prediction model generator 210 has generated a prediction model indicating the relationship between terrain characteristics and power usage, then given terrain characteristics at different locations across the field, prediction map generator 212 generates prediction map 264 predicting the values ​​of power usage at different locations across the field. Prediction map 264 is generated using the terrain characteristics at those locations obtained from the topographic map and the relationship between terrain characteristics and machine characteristics obtained from the prediction model. The control system can use the predicted power usage to adjust, for example, the power distribution between engine throttle valves or various subsystems to meet the predicted power usage requirements.

[0054] The following will describe some changes to the data types mapped in prior information graph 258, the data types sensed by field sensor 208, and the data types predicted in prediction graph 264.

[0055] In some examples, the data type in the prior information map 258 differs from the data type sensed by the field sensor 208, while the data type in the prediction map 264 is the same as the data type sensed by the field sensor 208. For example, the prior information map 258 could be a topographic map, and the variable sensed by the field sensor 208 could be a machine characteristic. Then, the prediction map 264 could be a prediction machine characteristic map that maps the predicted machine characteristic values ​​to different geographical locations in the field.

[0056] Furthermore, in some examples, the data type in the prior information map 258 differs from the data type sensed by the field sensor 208, and the data type in the prediction map 264 differs from both the data type in the prior information map 258 and the data type sensed by the field sensor 208. For example, the prior information map 258 could be a topographic map, and the variable sensed by the field sensor 208 could be machine pitch / roll. Therefore, the prediction map 264 could be a predicted internal distribution map that maps the predicted internal distribution values ​​to different geographical locations in the field.

[0057] In some examples, the prior information map 258 is derived from prior or previous operations across the field, and its data type differs from that sensed by the field sensor 208, while the data type in the prediction map 264 is the same as that sensed by the field sensor 208. For example, the prior information map 258 could be a seed population map generated during planting, and the variable sensed by the field sensor 208 could be stem size. Thus, the prediction map 264 could be a predicted stem size map mapping predicted stem size values ​​to different geographic locations in the field. In another example, the prior information map 258 could be a seed mix map, and the variable sensed by the field sensor 208 could be crop state, such as upright or lodged crops. Thus, the prediction map 264 could be a predicted crop state map mapping predicted crop state values ​​to different geographic locations in the field.

[0058] In some examples, the prior information map 258 comes from prior or previous operations across the field, and its data type is the same as that sensed by the field sensor 208, and the data type in the prediction map 264 is also the same as that sensed by the field sensor 208. For example, the prior information map 258 could be a yield map generated in the previous year, and the variable sensed by the field sensor 208 could be yield. Thus, the prediction map 264 could be a predicted yield map that maps predicted yield values ​​to different geographic locations in the field. In this example, the prediction model generator 210 can use the relative yield differences from the georeferenced prior information map 258 from the previous year to generate a prediction model that models the relationship between the relative yield differences on the prior information map 258 and the yield values ​​sensed by the field sensor 208 during the current harvesting operation. The prediction map generator 210 then uses the prediction model to generate the predicted yield map.

[0059] In some examples, prediction map 264 may be provided to control region generator 213. Control region generator 213 groups consecutive individual point data values ​​on prediction map 264 into control regions. A control region may include two or more consecutive portions of a region (e.g., a field), for which the control parameters for controlling the controllable subsystem corresponding to the control region are constant. For example, changing the response time of the settings of controllable subsystem 216 may not be satisfactory in responding to changes in values ​​contained in a map such as prediction map 264. In this case, control region generator 213 parses the map and identifies control regions with defined dimensions to accommodate the response time of controllable subsystem 216. In another example, control regions may be sized to reduce wear caused by excessive actuator movement due to continuous adjustment. In some examples, different groups of control regions may exist for individual controllable subsystems 216 or groups of controllable subsystems 216. Control regions may be added to prediction map 264 to obtain prediction control region map 265. Therefore, except that the predicted control area map 265 includes control area information defining the control area, the predicted control area map 265 may be similar to the predicted map 264. Thus, as described herein, the functional predicted map 263 may or may not include control areas. Both predicted map 264 and predicted control area map 265 are functional predicted maps 263. In one example, the functional predicted map 263 does not include control areas (e.g., predicted map 264). In another example, the functional predicted map 263 does include control areas (e.g., predicted control area map 265). In some examples, if an intercropping production system is implemented, multiple crops may coexist in the field. In this case, the predicted map generator 212 and the control area generator 213 are able to identify the location and characteristics of two or more crops and then generate the predicted map 264 and the predicted control area map 265 accordingly.

[0060] It will also be understood that the control region generator 213 can cluster values ​​to generate control regions, and these control regions can be added to the predicted control region map 265 or to a separate map that only displays the generated control regions. In some examples, the control regions may be used solely for controlling and / or calibrating the agricultural harvester 100. In other examples, the control regions may be presented to the operator 260 for controlling or calibrating the agricultural harvester 100, and in still other examples, the control regions may be presented solely to the operator 260 or another user, or stored for later use.

[0061] Predictive map 264 or predictive control area map 265, or both, are provided to control system 214, which generates control signals based on predictive map 264 or predictive control area map 265, or both. In some examples, communication system controller 229 controls communication system 206 to communicate predictive map 264 or predictive control area map 265, or control signals based on predictive map 264 or predictive control area map 265, to other agricultural harvesters harvesting in the same field. In some examples, communication system controller 229 controls communication system 206 to transmit predictive map 264, predictive control area map 265, or both, to other remote systems.

[0062] In some examples, prediction map 264 may be provided to route / task generator 267. Route / task generator 267 uses prediction map 264 to plot the travel path of harvester 100 during harvesting operations. The travel path may also include machine control settings corresponding to positions along the travel path. For example, if the travel path is uphill, the travel path may include controls at points before the ascent, indicating that power should be directed to the propulsion system to maintain the speed or feed rate of harvester 100. In some examples, route / task generator 267 analyzes different orientations of harvester 100 for multiple different travel paths and the predicted machine characteristics generated based on the prediction map 264, and selects a route with desirable outcomes (e.g., fast harvesting time or desired power utilization or material distribution uniformity).

[0063] The operator interface controller 231 is operable to generate control signals to control the operator interface mechanism 218. The operator interface controller 231 is also operable to present the predictive map 264 or the predictive control area map 265, or other information derived from or based on the predictive map 264, the predictive control area map 265, or both, to the operator 260. The operator 260 can be a local operator or a remote operator. As an example, the controller 231 generates control signals to control the display mechanism to display one or both of the predictive map 264 and the predictive control area map 265 to the operator 260. The controller 231 can generate an operator-actuable mechanism that is displayed and can be actuated by the operator to interact with the displayed map. The operator can edit the map by, for example, correcting the power utilization rate displayed on the map based on the operator's observation. The setting controller 232 can generate control signals for various settings on the agricultural harvester 100 based on the predictive map 264, the predictive control area map 265, or both. For example, the setting controller 232 can generate control signals to control the machine and header actuator 248. In response to the generated control signals, the machine and header actuator 248 operate to control, for example, screen and chaff screen settings, thresher clearance, rotor settings, clearing fan speed settings, header height, header function, reel speed, reel position, belt conveyor function (where the harvester 100 is coupled to a belt conveyor header), grain header function, internal distribution control, and other actuators 248 affecting other functions of the harvester 100. The path planning controller 234 exemplarily generates control signals to control the steering subsystem 252 to turn the harvester 100 according to a desired path. The path planning controller 234 can control the path planning system to generate a route for the harvester 100 and can control the propulsion subsystem 250 and steering subsystem 252 to turn the harvester 100 along that route. The feed rate controller 236 can control various subsystems, such as the propulsion subsystem 250 and the machine actuator 248, to control the feed rate based on prediction map 264 or prediction control area map 265, or both. For example, as the harvester 100 approaches descending terrain with an estimated speed value higher than a selected threshold, the feed rate controller 236 can reduce the speed of the machine 100 to maintain a constant feed rate of biomass through the harvester 100. The header and reel controller 238 can generate control signals to control the header or reel or other header functions. The belt conveyor controller 240 can generate control signals based on prediction map 264, prediction control area map 265, or both, to control the belt conveyor belt or other belt conveyor functions. For example, as the harvester 100 approaches descending terrain with an estimated speed value higher than a selected threshold, the belt conveyor controller 240 can increase the speed of the belt conveyor to prevent material from clogging the belt.The cover position controller 242 can generate control signals based on prediction diagram 264 or prediction control area diagram 265, or both, to control the position of the cover included on the header, and the residue system controller 244 can generate control signals based on prediction diagram 264 or prediction control area diagram 265, or both, to control the residue subsystem 138. The machine cleaning controller 245 can generate control signals to control the machine cleaning subsystem 254. For example, when the combine harvester 100 is traversing a slope (where it is estimated that the internal material distribution will be disproportionately on one side of the cleaning subsystem 254), the machine cleaning controller 245 can adjust the cleaning subsystem 254 to account for or correct for the disproportionate material. Other controllers included on the combine harvester 100 can also control other subsystems based on prediction diagram 264 or prediction control area diagram 265, or both. For example, one or more subsystems can be controlled to adjust the internal material distribution.

[0064] Figure 3A and Figure 3B A flowchart is shown, illustrating an example of the operation of an agricultural harvester 100 in generating a prediction map 264 and a prediction control area map 265 based on prior information map 258.

[0065] At box 280, the agricultural harvester 100 receives a priori information map 258. Examples of priori information map 258 or receiving priori information map 258 are discussed with reference to boxes 281, 282, 284, and 286. As described above, priori information map 258 maps the values ​​of variables corresponding to a first characteristic to different locations in the field, as indicated by box 282. As indicated by box 281, receiving priori information map 258 may involve selecting one or more of a plurality of possible priori information maps available. For example, one priori information map may be a topographic profile map generated from an aerial phase profile measurement image. Another priori information map may be a map generated during a previous pass through the field, which may be performed by a different machine (e.g., a sprayer or other machine) performing a priori or priori operation in the field. The process of selecting one or more priori information maps may be manual, semi-automatic, or automatic. Priori information map 258 is based on data collected prior to the current harvesting operation. This is indicated by box 284. For example, the data may be collected by a GPS receiver mounted on the device during prior or previous field operations. For example, the data may be collected during lidar range scanning operations in the previous year, earlier in the current growing season, or at other times. The data may be based on data detected or received in ways other than lidar range scanning. For example, a drone equipped with a fringe projection profilometry system may detect the contours or height of the terrain. Or, for example, some terrain characteristics, such as ruts formed due to erosion or clump disintegration in freeze-thaw cycles, may be estimated based on weather patterns. In some examples, a priori information map 258 may be created by combining data from several sources such as those listed above. Or, for example, data from the priori information map 258 (e.g., a topographic map) may be transmitted to the agricultural harvester 100 using communication system 206 and stored in data storage device 202. The data from the priori information map 258 may also be provided to the agricultural harvester 100 in other ways using communication system 206, which is provided by… Figure 3A Box 286 in the flowchart indicates this. In some examples, prior information diagram 258 may be received by communication system 206.

[0066] At the start of the harvesting operation, field sensor 208 generates sensor signals indicating one or more field data values, which indicate machine characteristics such as power consumption, machine speed, internal material distribution, grain loss, impurities, or grain quality. Examples of field sensor 288 are discussed with reference to boxes 222, 290, and 226. As described above, field sensor 208 includes: an onboard sensor 222; a remote field sensor 224, such as a UAV-based sensor that flies once to collect field data (shown in box 290); or other types of field sensors specified by field sensor 226. In some examples, position, heading, or speed data from geolocation sensor 204 is georeferenced to data from the onboard sensor.

[0067] Predictive model generator 210 controls prior information variables to pair with field variable model generator 228 to generate a model that models the relationship between the values ​​mapped in prior information graph 258 and the field values ​​sensed by field sensor 208, as indicated by box 292. The characteristics or data types represented by the values ​​mapped in prior information graph 258 and the field values ​​sensed by field sensor 208 can be the same or different characteristics or data types.

[0068] The relation or model generated by the prediction model generator 210 is provided to the prediction map generator 212. The prediction map generator 212 uses the prediction model and prior information map 258 to generate a prediction map 264, which predicts the values ​​of different characteristics sensed by the field sensor 208 or related to the characteristics sensed by the field sensor 208 at different geographical locations in the harvesting field, as indicated by box 294.

[0069] It should be noted that in some examples, the prior information map 258 may include two or more different maps, or two or more different layers of a single map. Each map in the two or more different maps, or each layer in the two or more different layers of a single map, maps different types of variables to geographical locations in the field. In this example, the predictive model generator 210 generates a predictive model that models the relationship between field data and the different variables mapped by the two or more different maps or two or more different layers. Similarly, the field sensors 208 may include two or more sensors, each sensing different types of variables. Therefore, the predictive model generator 210 generates a predictive model that models the relationship between the different types of variables mapped by the prior information map 258 and the different types of variables sensed by the field sensors 208. The predictive map generator 212 can use the predictive model and the different maps or layers in the prior information map 258 to generate a functional predictive map 263 that predicts the value of each sensed characteristic (or characteristic associated with the sensed characteristic) sensed by the field sensors 208 at different locations in a harvesting field.

[0070] Prediction map generator 212 configures prediction map 264 such that prediction map 264 can be manipulated (or used) by control system 214. Prediction map generator 212 may provide prediction map 264 to control system 214 or control area generator 213 or both. Some examples of different ways in which prediction map 264 can be configured or output will be described with reference to boxes 296, 293, 295, 299 and 297. For example, prediction map generator 212 configures prediction map 264 such that prediction map 264 includes values ​​that can be read by control system 214 and used as the basis for generating control signals for one or more different controllable subsystems of agricultural harvester 100, as indicated in box 296.

[0071] Route / task generator 267 plots the travel path of harvester 100 during harvesting operations based on prediction map 204, as indicated by box 293. Control zone generator 213 can divide prediction map 264 into control zones based on values ​​on prediction map 264. Geographically contiguous values ​​within a threshold range can be grouped into a control zone. This threshold can be a default threshold, or it can be set based on operator input, input from the automation system, or other criteria. The size of the zones can be based on the responsiveness of control system 214, controllable subsystem 216, wear considerations, or other criteria, as indicated by box 295. Prediction map generator 212 configures prediction map 264 for presentation to the operator or other user. Control zone generator 213 can configure prediction control zone map 265 for presentation to the operator or other user. This is indicated by box 299. When presented to an operator or other user, the presentation of prediction map 264 or prediction control area map 265, or both, may include geographic location-related predicted values ​​on prediction map 264, geographic location-related control areas on prediction control area map 265, and one or more setpoints or control parameters used based on the predicted values ​​on map 264 or the areas on prediction control area map 265. In another example, the presentation may include more abstract or more detailed information. The presentation may also include a confidence level indicating the accuracy with which the predicted values ​​on prediction map 264 or the areas on prediction control area map 265 conform to measurements that can be measured by sensors on the agricultural harvester 100 as the harvester 100 moves through the field. Furthermore, where information is presented to more than one location, a verification or authorization system may be provided to enable verification and authorization processes. For example, a hierarchy of individuals may exist who are authorized to view and modify the map and other presented information. As an example, an onboard display device may display the map locally, approximately in real time, only on the machine, or the map may be generated at one or more remote locations. In some examples, each physical display device at each location can be associated with a person or user permission level. The user permission level can be used to determine which display markers are visible on the physical display device and which values ​​the corresponding person can change. As an example, the local operator of machine 100 may not be able to see the information corresponding to prediction graph 264 or make any changes to the machine's operation. However, a supervisor at a remote location may be able to see prediction graph 264 on the display but cannot make changes. A manager at a separate remote location may be able to see all elements on prediction graph 264 and also change prediction graph 264 used for machine control. This is an example of an achievable authorization hierarchy. Prediction graph 264 or prediction control area graph 265, or both, may also be configured in other ways, as indicated by box 297.

[0072] In box 298, the control system receives input from geolocation sensor 204 and other field sensors 208. Box 300 indicates that the control system 214 receives input from geolocation sensor 204 identifying the geolocation of the harvester 100. Box 302 indicates that the control system 214 receives sensor input indicating the trajectory or heading of the harvester 100, and box 304 indicates that the control system 214 receives the speed of the harvester 100. Box 306 indicates that the control system 214 receives other information from various field sensors 208.

[0073] At block 308, control system 214 generates control signals to control controllable subsystem 216 based on prediction map 264 or prediction control area map 265, or both, and inputs from geographic location sensor 204 and any other field sensors 208. At block 310, control system 214 applies the control signals to the controllable subsystem. It will be understood that the specific control signals generated and the specific controllable subsystem 216 being controlled can vary based on one or more different things. For example, the generated control signals and the controllable subsystem 216 being controlled can be based on the type of prediction map 264 or prediction control area map 265, or both, being used. Similarly, the timing of the generated control signals, the controllable subsystem 216 being controlled, and the timing of the control signals can be based on various delays in the crop flow through agricultural harvester 100 and the responsiveness of controllable subsystem 216.

[0074] As an example, the generated prediction map 264, in the form of a predicted machine characteristic map, can be used to control one or more subsystems 216. For example, the predicted machine characteristic map may include machine speed values ​​georeferenced to locations within a harvesting field. Machine speed values ​​from the predicted machine characteristic map can be extracted and used to control the header and feeder housing speeds to ensure that the header 104 and feeder housing 106 can handle the increase in material engaged as the harvester 100 moves more rapidly across the field. The foregoing example of using a predicted machine characteristic map to relate to machine speed is provided by way of example only. Therefore, values ​​obtained from the predicted machine characteristic map or other types of prediction maps can be used to generate a variety of other control signals to control one or more controllable subsystems 216.

[0075] At box 312, it is determined whether the harvesting operation has been completed. If the harvesting is not completed, the process proceeds to box 314, where field sensor data from geolocation sensor 204 and field sensor 208 (and possibly other sensors) are continuously read.

[0076] In some examples, at box 316, the agricultural harvester 100 may also detect learning trigger criteria to perform machine learning on one or more of the following: the prediction graph 264, the prediction control area graph 265, the model generated by the prediction model generator 210, the area generated by the control area generator 213, one or more control algorithms implemented by the controller in the control system 214, and other triggered learning.

[0077] Learning triggering criteria can include any of a variety of different criteria. Some examples of triggering criteria detection are discussed with reference to boxes 318, 320, 321, 322, and 324. For example, in some examples, triggered learning may involve recreating the relationships used to generate a predictive model when a threshold amount of field sensor data is received from field sensor 208. In these examples, receiving a threshold amount of field sensor data from field sensor 208 triggers or causes predictive model generator 210 to generate a new predictive model used by predictive map generator 212. Thus, as the agricultural harvester 100 continues its harvesting operation, receiving a threshold amount of field sensor data from field sensor 208 triggers the creation of a new relationship represented by the predictive model generated by predictive model generator 210. Furthermore, a new predictive map 264, predictive control area map 265, or both can be regenerated using the new predictive model. Box 318 indicates detecting a threshold amount of field sensor data used to trigger the creation of a new predictive model.

[0078] In other examples, the learning trigger criterion may be based on how much the field sensor data from field sensor 208 has changed from a previous or prior value or threshold. For example, if the change within the field sensor data (or the relationship between the field sensor data and the information in prior information map 258) is within a range, less than a defined amount, or below a threshold, then the prediction model generator 210 does not generate a new prediction model. As a result, the prediction map generator 212 does not generate a new prediction map 264 and / or prediction control area map 265. However, for example, if the change within the field sensor data exceeds that range or exceeds a predetermined amount or threshold, or for example, if the relationship between the field sensor data and the information in prior information map 258 changes by a defined amount, then the prediction model generator 210 uses all or part of the newly received field sensor data used by the prediction map generator 212 to generate a new prediction map 264 to generate a new prediction model. At box 320, changes in the field sensor data (e.g., the magnitude of a quantity exceeding a selected range, or the magnitude of a change in the relationship between the field sensor data and information in prior information graph 258) can be used as triggers to induce the generation of new predictive models and predictive graphs. The thresholds, ranges, and defined quantities can be set to default values, or set by an operator or user through a user interface, or by an automation system, or otherwise.

[0079] Other learning trigger criteria can also be used. For example, if the predictive model generator 210 switches to a different prior infographic (different from the initially selected prior infographic 258), switching to a different prior infographic can trigger the predictive model generator 210, the predictive map generator 212, the control area generator 213, the control system 214, or other items to relearn. In another example, the agricultural harvester 100 changing to a different terrain or a different control area can also be used as a learning trigger criterion.

[0080] In some cases, operator 260 may also edit prediction graph 264 or prediction control area graph 265, or both. This editing may change values ​​on prediction graph 264, or change the size, shape, position, or presence of the control area and / or change values ​​on prediction control area graph 265. Box 321 shows that the edited information can be used as a learning trigger criterion.

[0081] In some cases, operator 260 may also observe that the automatic control of the controllable subsystem is not as desired by the operator. In these cases, operator 260 may provide manual adjustments to the controllable subsystem, reflecting that operator 260 expects the controllable subsystem to operate in a manner different from that commanded by control system 214. Therefore, operator 260's manual change of settings may cause predictive model generator 210 to relearn the model based on the adjustments made by operator 260 (as shown in box 322), predictive graph generator 212 to regenerate graph 264, control area generator 213 to regenerate the control area on predictive control area graph 265, and control system 214 to relearn its control algorithm or perform machine learning on one of the controller components 232-246 in control system 214. Box 324 indicates the use of other triggered learning criteria.

[0082] In other examples, relearning can be performed periodically or intermittently based on, for example, selected time intervals (e.g., discrete or variable time intervals). This is indicated by box 326.

[0083] As indicated in box 326, if relearning is triggered (whether based on a learning trigger criterion or on a past time interval), one or more of the predictive model generator 210, predictive graph generator 212, control area generator 213, and control system 214 perform machine learning to generate a new predictive model, a new predictive graph, a new control area, and a new control algorithm, respectively, based on the learning trigger criterion. Any additional data collected since the last learning operation is performed is used to generate the new predictive model, new predictive graph, and new control algorithm. The execution of relearning is indicated in box 328.

[0084] If the harvesting operation is complete, the operation moves from box 312 to box 330, where one or more of the prediction map 264, the prediction control area map 265, and the prediction model generated by the prediction model generator 210 are stored. The prediction map 264, the prediction control area map 265, and the prediction model may be stored locally on the data storage device 202 or sent to a remote system using the communication system 206 for subsequent use.

[0085] It should be noted that while some examples in this paper describe the predictive model generator 210 and the predictive graph generator 212 receiving prior information graphs when generating predictive models and functional predictive graphs, respectively, in other examples, the predictive model generator 210 and the predictive graph generator 212 may receive other types of graphs, including predictive graphs, such as functional predictive graphs generated during harvesting operations, when generating predictive models and functional predictive graphs, respectively.

[0086] Figure 4 yes Figure 1 A block diagram of a portion of an agricultural harvester 100. Specifically, among other things, Figure 4 Examples of the prediction model generator 210 and the prediction graph generator 212 are shown in more detail. Figure 4 The information flow between the various components is also illustrated. Predictive model generator 210 receives topographic map 332 as a priori information map. Predictive model generator 210 also receives geographic location 334 or an indication of geographic location from geographic location sensor 204. Field sensor 208 exemplarily includes machine sensors (e.g., machine sensor 336) and processing system 338. In some cases, machine sensor 336 may be located on agricultural harvester 100. Processing system 338 processes sensor data generated from onboard machine sensor 336 to generate processed data, some examples of which are described below.

[0087] In some examples, machine sensor 336 may generate electronic signals indicating characteristics sensed by machine sensor 336. Processing system 338 processes one or more sensor signals obtained via machine sensor 336 to generate processed data identifying one or more machine characteristics. The machine characteristics identified by processing system 338 may include internal material distribution, power consumption, power utilization rate, machine speed, wheel slippage, etc.

[0088] The field sensor 208 may be or include an optical sensor, such as a camera (hereinafter referred to as a "process camera") located in the agricultural harvester 100, which views the interior of the agricultural harvester 100 that processes grain agricultural material. Thus, in some examples, the processing system 338 is capable of operating to detect the internal distribution of agricultural material passing through the agricultural harvester 100 based on images captured by the machine sensor 208. For example, whether the agricultural material is unevenly distributed across the grain cleaning system, such uneven distribution might be due to machine tumbling or pitching.

[0089] In other examples, the field sensor 208 may be or include a GPS device for sensing the machine's position. In this case, the processing system 338 may also derive speed and direction from the sensor signals. In another example, the field sensor 208 may include one or more power sensors that detect individual or aggregated power characteristics of one or more subsystems on the agricultural harvester 100. In this case, the processing system 338 may aggregate or separate the power characteristics by subsystem or machine component.

[0090] Other machine properties and sensors may also be used. In some examples, raw or processed data from machine sensor 336 may be presented to operator 260 via operator interface mechanism 218. Operator 260 may be on the agricultural harvester 100 or at a remote location.

[0091] like Figure 4 As shown, the example prediction model generator 210 includes one or more of the following: a power characteristic versus terrain characteristic model generator 342, a machine speed versus terrain characteristic model generator 344, a material distribution versus terrain characteristic model generator 345, a grain loss versus terrain characteristic model generator 346, a waste versus terrain characteristic model generator 347, and a grain quality versus terrain characteristic model 348. In other examples, the prediction model generator 210 may include... Figure 4 The examples show more, fewer, or different components. Therefore, in some examples, the predictive model generator 210 may also include other items 349, which may include other types of predictive model generators to generate other types of machine characteristic models.

[0092] This discussion focuses on the example of machine sensor 336 being a power characteristic sensor (e.g., a hydraulic sensor, a voltage sensor, etc.). It will be understood that these are merely examples, and other examples of the aforementioned sensors may be conceived herein as machine sensor 336. Model generator 342 identifies the relationship between the power characteristics at the geographic location corresponding to the processed data 340 and the terrain characteristic values ​​at the same geographic location. The terrain characteristic values ​​are georegistered values ​​included in topographic map 332. Model generator 342 then generates a predictive machine model 350, which is used by power characteristic map generator 352 to predict the power characteristics at that location in the field based on the terrain characteristics of the location. For example, field sensor 208 senses power usage, and predictive map generator 352 outputs estimated power usage requirements at multiple locations in the field.

[0093] This discussion is based on the example of machine sensor 336 being a machine speed sensor (e.g., a GPS device, speedometer, compass, etc.). It will be understood that these are merely examples, and other examples of the aforementioned sensors may be conceived herein as machine sensor 336. Model generator 344 identifies and processes the relationship between the machine speed at the geographic location corresponding to the processed sensor data 340 and the terrain characteristic values ​​at the same geographic location. Again, the terrain characteristic values ​​are geo-registered values ​​included in topographic map 332. Model generator 344 then generates a predictive machine model 350, which is used by machine speed map generator 354 to predict the machine speed at that location in the field based on the terrain characteristic values ​​of the location. For example, machine speed and direction are sensed by field sensor 208, and prediction map generator 354 outputs estimated machine speed and direction at multiple locations in the field.

[0094] This discussion is made with the example of machine sensor 336 being an image sensor such as a camera. It will be understood that this is merely one example, and other examples of the aforementioned sensors may be conceived herein as machine sensor 336. Model generator 345 identifies a relationship between the material distribution detected in processed data 340 at a geographic location corresponding to the location where the image was acquired (e.g., the material distribution in agricultural harvester 100 can be identified based on an image captured by a camera) and the topographic features of a topographic map 332 corresponding to the same location in the field where the material distribution was detected. Based on this relationship established by model generator 345, model generator 345 generates a predictive machine model 350. Material distribution map generator 355 uses predictive machine model 350 to predict material distribution at different locations in the field based on the georegistration topographic features contained in the topographic map 332 at the same location in the field.

[0095] This discussion is based on the example of machine sensor 336 being a grain loss sensor. It will be understood that this is merely one example, and other examples of the aforementioned sensor may be conceived herein as machine sensor 336. Model generator 346 identifies a relationship between grain loss detected in processed data 340 at a geographic location corresponding to the location where the sensor data is geolocated, and topographic features of topographic map 332 corresponding to the same location in the field (where the grain loss is geolocated). Based on this relationship established by model generator 346, model generator 346 generates a predictive machine model 350. Grain loss map generator 356 uses predictive machine model 350 to predict grain loss at different locations in the field based on the georegistered topographic features contained in topographic map 332 at the same location in the field.

[0096] This discussion is based on the example of machine sensor 336 being a redundant sensor. It will be understood that this is merely an example, and other examples of the aforementioned sensor may be conceived herein as machine sensor 336. Model generator 347 identifies a relationship between the redundant features detected in processed data 340 at a geographic location corresponding to the geolocated sensor data and the topographic features of topographic map 332 corresponding to the same location in the field (where the redundant features are geolocated). Based on this relationship established by model generator 347, model generator 347 generates a predictive machine model 350. Redundant map generator 357 uses predictive machine model 350 to predict redundant features at different locations in the field based on the georeferenced topographic features contained in topographic map 332 at the same location in the field.

[0097] This discussion is based on the example of machine sensor 336 being a grain quality sensor. It will be understood that this is merely an example, and other examples of the aforementioned sensor may be conceived herein as machine sensor 336. Model generator 348 identifies a relationship between the grain quality detected in processed data 340 at a geographic location corresponding to the geolocated sensor data and the topographic features of a topographic map 332 corresponding to the same location in the field (where the grain quality is geolocated). Based on this relationship established by model generator 348, model generator 348 generates a predictive machine model 350. Grain quality map generator 358 uses predictive machine model 350 to predict grain quality at different locations in the field based on the georeferenced topographic features contained in the topographic map 332 at the same location in the field.

[0098] Predictive model generator 210 is operable to generate multiple predictive machine models, such as one or more predictive machine models generated by model generators 342, 344, and 345. In another example, two or more of the aforementioned predictive machine models 342, 344, and 345 can be combined into a single predictive machine model that predicts two or more machine characteristics, such as material distribution, power characteristics, and machine speed, based on topographic characteristics at different locations in the field. Any one or a combination of these machine models is generated by... Figure 4 The machine model 350 is represented uniformly in the model.

[0099] The prediction machine model 350 is provided to the prediction graph generator 212. Figure 4 In one example, the prediction graph generator 212 includes a power characteristic graph generator 352, a machine speed graph generator 354, a material distribution graph generator 355, a grain loss graph generator 356, a waste graph generator 357, and a grain quality graph generator 358. In other examples, the prediction graph generator 212 may include more, fewer, or different graph generators. Therefore, in some examples, the prediction graph generator 212 may include additional items 359, which may include other types of graph generators to generate machine characteristic graphs of other types of machine characteristics.

[0100] The power characteristic map generator 352 receives a prediction machine model 350 that predicts power characteristics based on terrain characteristics from a topographic map 332, and generates prediction maps of power characteristics at different locations in the field. For example, the predicted power characteristics may include the predicted desired power.

[0101] The machine speed map generator 354 generates a prediction map that predicts the machine speed at different locations in the field based on the machine speed values ​​at different locations in the field and the prediction machine model 350.

[0102] Material distribution map generator 355 exemplarily generates material distribution map 360, which predicts the material distribution at those locations in the field based on the topographic characteristics of different locations in the field and prediction machine model 350.

[0103] The grain loss map generator 356 generates a grain loss map 360, which predicts the grain loss at those locations in the field based on the topographic features of different locations in the field and the prediction machine model 350.

[0104] The redundant map generator 357 generates a redundant map 360, which predicts the redundant characteristics of those locations in the field based on the topographic features of different locations in the field and the prediction machine model 350.

[0105] The grain quality map generator 358 exemplarily generates a grain quality map 360, which predicts the characteristics indicating grain quality at those locations in the field based on the topographic features of different locations in the field and the prediction machine model 350.

[0106] The prediction map generator 212 outputs one or more predicted machine characteristic maps 360 as predictions of machine characteristics. Each predicted machine characteristic map 360 predicts the corresponding machine characteristics at different locations in the field. Each generated predicted machine characteristic map 360 can be provided to a control zone generator 213, a control system 214, or both. The control zone generator 213 generates control zones and incorporates those control zones into the functional prediction maps 360. One or more functional prediction maps can be provided to the control system 214, which generates control signals based on the one or more functional prediction maps to control one or more controllable subsystems 216.

[0107] Figure 5 This is a flowchart illustrating an example of the operation of the prediction model generator 210 and the prediction map generator 212 in generating the prediction machine model 350 and the prediction machine characteristic map 360. At block 362, the prediction model generator 210 and the prediction map generator 212 receive a priori topographic map 332. At block 364, the processing system 338 receives one or more sensor signals from the machine sensor 336. As described above, the machine sensor 336 can be a power sensor 366, a speed sensor 368, a material distribution sensor 370, or other types of sensors 371.

[0108] At box 372, processing system 338 processes the received one or more sensor signals to generate data indicative of the machine's characteristics. In some cases, as shown in box 374, the sensor data may indicate power characteristics. In some cases, as shown in box 378, the sensor data may indicate the harvester's speed. In some cases, as shown in box 379, the sensor data (e.g., one or more images) may indicate the material distribution within the harvester. The sensor data may also include other data, as shown in box 380.

[0109] At box 382, ​​the predictive model generator 210 also obtains the geographic location corresponding to the sensor data. For example, the predictive model generator 210 can obtain the geographic location from the geographic location sensor 204 and determine the precise geographic location of the captured or derived sensor data 340 based on machine latency, machine speed, etc. Furthermore, at box 382, ​​the orientation of the agricultural harvester 100 relative to terrain features can be determined. For example, the orientation of the agricultural harvester 100 is obtained because a machine located on a slope may exhibit different machine characteristics based on its orientation relative to the slope.

[0110] At box 384, the prediction model generator 210 generates one or more prediction machine models, such as machine model 350, that model the relationship between terrain features obtained from a prior information map (e.g., prior information map 258) and machine features or related features being sensed by the field sensor 208. For example, the prediction model generator 210 may generate a prediction machine model that models the relationship between terrain features and sensed machine features indicated by sensor data 340 obtained from the field sensor 208.

[0111] At box 386, a predictive machine model, such as predictive machine model 350, is provided to predictive map generator 212, which generates a predictive machine characteristic map 360 based on the topographic map and the predicted machine characteristics mapped from predictive machine model 350. In some examples, predictive machine characteristic map 360 predicts power characteristics, as shown in box 388. In some examples, predictive machine characteristic map 360 predicts machine speed, as shown in box 390. In some examples, predictive machine characteristic map 360 predicts material distribution in a harvester, as shown in box 392. In other examples, predictive map 360 predicts other items or combinations of the above items, as shown in box 393.

[0112] A predictive machine characteristic map 360 can be generated during agricultural operations. Therefore, as an agricultural harvester moves across the field to perform an agricultural operation, a predictive machine characteristic map 360 is generated while that operation is being performed.

[0113] At block 394, the prediction map generator 212 outputs a predictive machine characteristic map 360. At block 391, the predictive machine characteristic map generator 212 outputs a predictive machine characteristic map to be presented to operator 260 for possible interaction. At block 393, the prediction map generator 212 can configure the map for use by the control system 214. At block 395, the prediction map generator 212 can also provide map 360 to the control area generator 213 to generate a control area. At block 397, the prediction map generator 212 also configures map 360 in other ways. The predictive machine characteristic map 360 (with or without a control area) is provided to the control system 214. At block 396, the control system 214 generates control signals based on the predictive machine characteristic map 360 to control the controllable subsystem 216.

[0114] As can be seen, this system employs a priori information maps, which map characteristics such as terrain features to different locations in the field. The system also uses one or more field sensors to sense data indicating machine characteristics (e.g., power consumption, machine speed, or material distribution) and generates a model that models the relationship between the machine characteristics or related characteristics sensed by the field sensors and the characteristics mapped in the priori information map. Therefore, the system uses the model, field data, and priori information map to generate a functional prediction map, which can be configured for use by the control system or presented to a local operator, remote operator, or other user. For example, the control system can use this map to control one or more systems of an agricultural harvester.

[0115] Figure 6A yes Figure 1 A block diagram of an example portion of an agricultural harvester 100 is shown. Specifically, among other things, Figure 6A Examples of predictive model generator 210 and predictive map generator 212 are shown. In the illustrated example, infographic 258 is one or more of topographic map 332, predictive machine characteristic map 360, or different prior operation maps 400. The values ​​in the prior operation map 400 may be values ​​collected during previous or prior operations (e.g., previous or prior operations performed by a tiller, sprayer, or UAV).

[0116] In addition, Figure 6A In the example shown, the field sensor 208 may include one or more of a power sensor 402, a feed rate sensor 403, an operator input sensor 404, and a processing system 406. The field sensor 208 may also include other sensors 408. For example, Figure 6B An additional example of the field sensor 208 is shown.

[0117] Power sensor 402 senses variables that indicate the power characteristics of the agricultural harvester 100. Examples of power sensors 402 include (but are not limited to) voltage sensors, current sensors, torque sensors, hydraulic pressure sensors, hydraulic flow sensors, force sensors, bearing load sensors, and rotation sensors. Power characteristics can be measured at varying granular levels. For example, power consumption can be sensed across the machine, across a subsystem, or by individual components of a subsystem.

[0118] The feed rate sensor 403 senses a variable indicating the feed rate through one or more parts of the agricultural harvester 100. The feed rate sensor 403 may include a rotor drive force sensor, a forward-looking optical sensor that observes the material being collected by the agricultural harvester, a force plate sensor that senses the grain feed rate, a capacitive sensor in the feeder housing, etc.

[0119] Operator input sensor 404 exemplarily senses various operator inputs. These inputs may be setting inputs or other control inputs, such as steering inputs and other inputs, for controlling settings on the agricultural harvester 100. Therefore, when the operator 260 changes settings or provides command inputs through the operator interface mechanism 218, such inputs are detected by operator input sensor 404, which provides a sensor signal indicating the sensed operator input. Processing system 406 may receive sensor signals from biomass sensor 402 or operator input sensor 404, or both, and generate an output indicating the sensed variable. For example, processing system 406 may receive sensor inputs from optical sensor 410 or rotor pressure sensor 412 and generate an output indicating biomass. Processing system 406 may also receive inputs from operator input sensor 404 and generate an output indicating the sensed operator input.

[0120] Predictive model generator 210 may include a terrain characteristic to power model generator 410, a terrain characteristic to feed rate model generator 412, a terrain characteristic to sensor data model generator 414, a terrain characteristic to operator command model generator 416, a machine characteristic to power model generator 418, a machine characteristic to feed rate model generator 420, a machine characteristic to sensor data model generator 422, and a machine characteristic to operator command model generator 424. In other examples, predictive model generator 210 may include more, fewer, or other model generators 425. Predictive model generator 210 may receive a geolocation indicator 334 from geolocation sensor 204 and generate a predictive model 426 that models the relationship between one or more of the power characteristics sensed by power sensor 402, the feed rate sensed by feed rate sensor 403, and the operator input commands sensed by operator input sensor 404, and information in one or more of the information graphs.

[0121] For example, the terrain feature to power model generator 410 generates a relationship between terrain feature values ​​(which may be on terrain map 332 or on prior operation map 400) and power feature values ​​sensed by power sensor 402. The terrain feature to feed rate model generator 412 exemplarily generates a model representing the relationship between terrain features and variables indicating the feed rate sensed by feed rate sensor 403. The terrain feature to sensor data model generator 414 exemplarily generates a model representing the relationship between terrain features and variables sensed by one or more field sensors 208. The terrain feature to operator command model generator 416 generates a model that models the relationship between terrain features, such as those reflected on terrain map 332, prior operation map 400, or both, and operator input commands sensed by operator input sensor 404.

[0122] The machine characteristic to power model generator 418 generates a relationship between machine characteristic values ​​(which may be on the predicted machine characteristic map 360 or the prior operation map 400) and power characteristic values ​​sensed by the power sensor 402. The machine characteristic to feed rate model generator 420 exemplarily generates a model representing the relationship between machine characteristics and variables indicating the feed rate sensed by the feed rate sensor 403. The machine characteristic to sensor data model generator 422 exemplarily generates a model representing the relationship between machine characteristics and variables sensed by one or more field sensors 208. The machine characteristic to operator command model generator 424 generates a model that models the relationship between machine characteristics, such as those reflected in the predicted machine characteristic map 360, the prior operation map 400, or both, and operator input commands sensed by the operator input sensor 404.

[0123] The prediction model 426 generated by prediction model generator 210 may include one or more prediction models generated by terrain characteristics to power model generator 410, terrain characteristics to feed rate model generator 412, terrain characteristics to sensor data model generator 414, terrain characteristics to operator command model generator 416, machine characteristics to power model generator 418, machine characteristics to feed rate model generator 420, machine characteristics to sensor data model generator 422, and machine characteristics to operator command model generator 424, as well as other model generators that may be included as part of other items 425.

[0124] exist Figure 6A In one example, the prediction graph generator 212 includes a prediction power graph generator 428, a prediction feed rate graph generator 429, a prediction sensor data graph generator 430, and a prediction operator command graph generator 432. In other examples, the prediction graph generator 212 may include more, fewer, or other graph generators 434.

[0125] The predictive power map generator 428 receives a predictive model 426 (e.g., a predictive model generated by the terrain feature to power model generator 410 or the machine feature to power model generator 418) that models the relationship between terrain features or machine features and power features, and one or more of the aforementioned information maps.

[0126] The predictive feed rate map generator 429 generates a functional predictive feed rate map 437, which predicts a target feed rate at these different locations in the field based on one or more terrain features or machine features at different locations in the field from one or more of the infographics and based on a prediction model 426 (e.g., a prediction model generated by the terrain feature versus feed rate model generator 412 or the machine feature versus feed rate model generator 420). The target feed rate is the volume, mass, or other quantity of material delivered through a portion of the harvester within a given time, satisfying constraints and other criteria. The harvester is controlled to achieve the target feed rate. The target feed rate may be constrained by machine throughput limits, minimum productivity levels (e.g., machine speed through the work area), maximum financial costs, and other factors. Unrestricted, additional constraints and criteria may be based on grain loss through the front or rear of the harvester, total operating costs, labor costs, fuel costs, grain damage, machine wear, and harvest time.

[0127] The predictive sensor data map generator 438 receives a predictive model 426 (e.g., a predictive model generated by the terrain-to-sensor data model generator 414 or the machine characteristic-to-sensor data model generator 422) that models the relationship between terrain or machine characteristics and sensor data, and one or more of the aforementioned information maps to generate a predictive sensor data map 438 that maps the predicted values ​​of the characteristics sensed by the field sensor 208.

[0128] The predictive operator command map generator 432 receives a predictive model 426 (e.g., a predictive model generated by the terrain feature to command model generator 416 or the machine feature to command model generator 424) that models the relationship between terrain features or machine features and operator command inputs detected by the operator input sensor 404, and generates a functional predictive operator command map 440 that predicts operator command inputs at different locations in the field based on terrain feature or machine feature values ​​from the terrain map 432 or machine feature values ​​from the predictive machine feature map 360 and the predictive model 426.

[0129] Prediction graph generator 212 outputs one or more functional prediction graphs 436, 437, 438, and 440. Each of the functional prediction graphs 436, 437, 438, and 440 can be provided to control region generator 213, control system 214, or both. Control region generator 213 generates control regions to provide a predictive control region graph 265 corresponding to each prediction graph 436, 437, 438, and 440 received by control region generator 213. Any or all of the functional prediction graphs 436, 437, 438, or 440 and the corresponding graph 265 can be provided to control system 214, which generates control signals based on one or all of the functional prediction graphs 436, 437, 438, and 430 or based on the corresponding graph 265 including the control region to control one or more controllable subsystems 216. Predicting any one or all of Figures 436, 437, 438, or 440, or the corresponding Figure 265, can be presented to operator 260 or another user.

[0130] Figure 6B This is a block diagram showing some examples of the real-time (field) sensor 208. Figure 6B Some of the sensors shown, or different combinations thereof, may simultaneously have sensor 402 and processing system 406, while other sensors may be used as references. Figure 6A and Figure 7 The described sensor 402, in Figure 6A and Figure 7 The processing system 406 is either separate or independent. Figure 6B Some of the possible field sensors 208 shown are illustrated and described relative to the previous figures and are similarly numbered. Figure 6B The field sensors 208 shown may include operator input sensors 480, machine sensors 482, harvested material property sensors 484, field and soil property sensors 485, environmental property sensors 487, and may include a variety of other sensors 226. Operator input sensors 480 may be sensors that sense operator input via operator interface mechanisms 218. Therefore, operator input sensors 480 can sense user movements of linkages, joysticks, steering wheels, buttons, dials, or pedals. Operator input sensors 480 can also sense user interactions with other operator input mechanisms, such as interactions with a touchscreen, a microphone utilizing voice recognition, or any of the various other operator input mechanisms.

[0131] Machine sensor 482 can sense various characteristics of the agricultural harvester 100. For example, as discussed above, machine sensor 482 may include machine speed sensor 146, separator loss sensor 148, clean grain camera 150, forward-view image capture mechanism 151, loss sensor 152, or geolocation sensor 204, examples of which are described above. Machine sensor 482 may also include machine setting sensor 491 for sensing machine settings. (See above references) Figure 1Some examples of machine settings are described. A front-end device (e.g., header) position sensor 493 can sense the position of the header 102, reel 164, cutter 104, or other front-end devices relative to the frame of the harvester 100. For example, sensor 493 can sense the height of the header 102 above the ground. Machine sensor 482 may also include a front-end device (e.g., header) orientation sensor 495. Sensor 495 can sense the orientation of the header 102 relative to the harvester 100 or relative to the ground. Machine sensor 482 may include a stability sensor 497. Stability sensor 497 senses vibrational or bouncing movements (and amplitude) of the harvester 100. Machine sensor 482 may also include a residue setting sensor 499, configured to sense whether the harvester 100 is configured to shred residue, create a stockpile, or otherwise process residue. Machine sensor 482 may include a cleaning chamber fan speed sensor 551 that senses the speed of the cleaning fan 120. Machine sensor 482 may include a concave plate gap sensor 553 that senses the gap between the rotor 112 and the concave plate 114 on the agricultural harvester 100. Machine sensor 482 may include a husk sieve gap sensor 555 that senses the size of the openings in the husk sieve 122. Machine sensor 482 may include a threshing rotor speed sensor 557 that senses the rotor speed of the rotor 112. Machine sensor 482 may include a rotor pressure sensor 559 that senses the pressure used to drive the rotor 112. Machine sensor 482 may include a screen gap sensor 561 that senses the size of the openings in the screen 124. Machine sensor 482 may include a MOG humidity sensor 563 that senses the humidity level of the MOG passing through the harvester 100. Machine sensor 482 may include a machine orientation sensor 565 that senses the orientation of the harvester 100. Machine sensor 482 may include a material feed rate sensor 567 that senses the rate at which material is fed as it travels through the feeder housing 106, the clean grain elevator 130, or other locations within the harvester 100. Machine sensor 482 may include a biomass sensor 569 that senses the biomass traveling through the feeder housing 106, the separator 116, or other locations within the harvester 100. Machine sensor 482 may include a fuel consumption sensor 571 that senses the rate at which the harvester 100 consumes fuel over time.Machine sensor 482 may include a power utilization sensor 573 that senses power utilization in the harvester 100 (such as which subsystems are using power), or the rate at which subsystems are using power, or the power distribution among the subsystems in the harvester 100. Machine sensor 482 may include a tire pressure sensor 577 that senses the inflation pressure in the tires 144 of the harvester 100. Machine sensor 482 may include a variety of other machine performance sensors or machine characteristic sensors (as shown in box 475). The machine performance sensors and machine characteristic sensor 575 can sense the machine performance or characteristics of the harvester 100.

[0132] While crop material is being processed by the agricultural harvester 100, the harvest material property sensor 484 can sense the characteristics of the cut crop material. Crop properties may include things such as crop type, crop moisture content, grain quality (e.g., broken grain), MOG level, grain composition (e.g., starch and protein), MOG moisture content, and other crop material properties. Other sensors can sense straw “toughness,” corn and ear adhesion, and other properties that can be beneficially used to control processing for better grain capture, reduced grain damage, lower power consumption, reduced grain loss, and so on.

[0133] The field and soil property sensor 485 can sense the characteristics of fields and soils. Field and soil properties may include soil moisture, soil compaction, presence and location of waterlogging, soil type, and other soil and field characteristics.

[0134] The environmental characteristic sensor 487 can sense one or more environmental characteristics. Environmental characteristics may include things such as wind direction and speed, precipitation, fog, dust level, or other obstacles or other environmental features.

[0135] Figure 7A flowchart illustrating an example of the operation of the prediction model generator 210 and the prediction map generator 212 in generating one or more prediction models 426 and one or more functional prediction maps 436, 437, 438, and 440 is shown. At box 442, the prediction model generator 210 and the prediction map generator 212 receive an information map. The information map may be a topographic map 332, a prediction machine characteristic map 360, or a priori operation map 400 created using data obtained during previous or prior operations in the field. At box 444, the prediction model generator 210 receives sensor signals containing sensor data from field sensors 208. The field sensors may be one or more of a power sensor 402, a feed rate sensor 403, or another sensor 408. Box 446 indicates that the sensor signals received by the prediction model generator 210 include data indicating the type of power characteristics. Box 448 indicates that the sensor signal data may indicate the orientation of the agricultural harvester 100, such as pitch, roll, and heading. Box 449 indicates that sensor signal data may indicate the feed rate through one or more sections of the agricultural harvester 100. Box 450 indicates that the sensor signals received by the prediction model generator 210 may be data of the type indicating operator command input, such as sensor signals sensed by operator input sensor 404. The prediction model generator 210 may also receive other field sensor inputs (as shown in box 452).

[0136] At box 454, processing system 406 processes data contained in one or more sensor signals received from one or more field sensors 208 to obtain processed data 409, such as... Figure 6A As shown. Data contained in one or more sensor signals may be in its raw format, which is processed to receive processed data 409. For example, a temperature sensor signal includes resistance data, which can be processed into temperature data. In other examples, processing may include digitizing, encoding, formatting, scaling, filtering, or classifying the data. Processed data 409 may indicate one or more of the power, feed rate, operator input commands, or other agricultural characteristics sensed by field sensor 208. Processed data 409 is provided to predictive model generator 210.

[0137] Back Figure 7 At box 456, the prediction model generator 210 also receives geolocation 334 from geolocation sensor 204, such as Figure 6AAs shown. Geographic location 334 can be associated with the geographic location of one or more sensed variables sensed by field sensor 208. For example, predictive model generator 210 can obtain geographic location 334 from geographic location sensor 204 and determine the precise geographic location based on machine latency, machine speed, etc., from which processed data 409 is derived.

[0138] At box 458, the prediction model generator 210 generates one or more prediction models 426 that model the relationship between the mapping values ​​in the infographic and the characteristics represented in the processed data 409. For example, in some cases, the mapping values ​​in the infographic can be terrain features or machine features, which can be one or more of the terrain feature values ​​in the terrain map 332, the machine feature values ​​in the functional prediction machine feature map 360, or different values ​​in the prior operation map 400; and the prediction model generator 210 uses the mapping values ​​of the infographic and features sensed by the field sensor 208 (such as those represented in the processed data 490) or related features (such as features related to features sensed by the field sensor 208) to generate the prediction models.

[0139] For example, at box 460, prediction model generator 210 can generate prediction model 426 that models the relationship between terrain or machine characteristic values ​​obtained from one or more infographics and power characteristic data obtained from field sensors. In another example, at box 462, prediction model generator 210 can generate prediction model 426 that models the relationship between terrain or machine characteristic values ​​obtained from one or more infographics and the feed rate of agricultural harvester 100 obtained from field sensors. In yet another example, at box 463, prediction model generator 210 generates prediction model 426 that models the relationship between terrain or machine characteristics and operator command input. In yet another example, at box 464, prediction model generator 210 generates prediction model 426 that models the relationship between terrain or machine characteristics and one or more field sensor signals from one or more field sensors 208.

[0140] One or more prediction models 426 are provided to the prediction map generator 212. At box 466, the prediction map generator 212 generates one or more functional prediction maps. The functional prediction maps can be a functional prediction power map 436, a functional prediction feed rate map 437, a functional prediction sensor data map 438, a functional prediction operator command map 440, or any combination of these maps. The functional prediction power map 436 predicts the power characteristics of the combine harvester 100 at different locations in the field. The functional prediction feed rate map 437 predicts the desired machine feed rate of the combine harvester 100 at different locations in the field. The functional prediction sensor data map 438 predicts sensor data values ​​that will be detected by the field sensors 208 at different locations in the field. The functional prediction operator command map 440 predicts possible operator command inputs at different locations in the field. Furthermore, one or more of the functional prediction maps 436, 437, 438, and 440 can be generated during agricultural operations. Therefore, when the agricultural harvester 100 moves across the field to perform agricultural operations, one or more prediction maps 436, 437, 438 and 440 are generated during the performance of the agricultural operations.

[0141] At box 468, the prediction graph generator 212 outputs one or more functional prediction graphs 436, 437, 438, and 440. At box 470, the prediction graph generator 212 can configure the graphs to be presented to operator 260 or other users and for possible interactions with operator 260 or other users. At box 472, the prediction graph generator 212 can configure the graphs for use by control system 214. At box 474, the prediction graph generator 212 can provide one or more prediction graphs 436, 437, 438, and 440 to control area generator 213 for generating control areas. At box 476, the prediction graph generator 212 otherwise configures one or more prediction graphs 436, 437, 438, and 440. In an example where one or more functional prediction diagrams 436, 437, 438, and 440 are provided to the control area generator 213, the one or more functional prediction diagrams 436, 437, 438, and 440, which include the control area represented by the corresponding diagram 265 described above, may be presented to the operator 260 or another user, or also provided to the control system 214.

[0142] At box 478, the control system 214 then generates control signals to control the controllable subsystem based on the one or more functional prediction maps 360, 436, 437, 438, and 440 (or functional prediction maps 360, 436, 437, 438, and 440 with control areas) and input from the geolocation sensor 204. For example, when map 360 is used as a functional prediction map, the controllable subsystem can be controlled to improve power characteristics, improve internal material distribution, reduce grain loss, improve grain quality, or controllable impurity-based characteristics.

[0143] For example, when control system 214 receives a functional prediction map, path planning controller 234 controls steering subsystem 252 to turn harvester 100. In another example where control system 214 receives a functional prediction map, residue system controller 244 controls residue subsystem 138. In another example where control system 214 receives a functional prediction map, setting controller 232 controls threshing settings of thresher 110. In another example where control system 214 receives a functional prediction map, setting controller 232 or another controller 246 controls material handling subsystem 125. In another example where control system 214 receives a functional prediction map, setting controller 232 controls crop cleaning subsystem. In another example where control system 214 receives a functional prediction map, machine cleaning controller 245 controls machine cleaning subsystem 254 on harvester 100. In another example where control system 214 receives a functional prediction map, communication system controller 229 controls communication system 206. In another example where the control system 214 receives the functional prediction diagram, the operator interface controller 231 controls the operator interface mechanism 218 on the harvester 100. In another example where the control system 214 receives the functional prediction diagram, the cover plate position controller 242 controls the machine / header actuator to control the cover plate on the harvester 100. In another example where the control system 214 receives the functional prediction diagram, the belt conveyor controller 240 controls the machine / header actuator to control the belt conveyor belt on the harvester 100. In another example where the control system 214 receives the functional prediction diagram, other controllers 246 control other controllable subsystems 256 on the harvester 100.

[0144] In some examples, the control system 214 may generate one or more control signals to control the setting (e.g., position, orientation, etc.) of adjustable material engagement elements disposed within the material flow path inside the agricultural harvester, thereby controlling or compensating for the internal material distribution within the agricultural harvester 100. For example, one or more control signals may control actuators to actuate the movement of the adjustable material engagement elements, thereby changing the position or orientation of the adjustable material engagement elements to guide at least a portion of the material flow to the right or left relative to the flow direction. In some examples, this direction may be from a region with a greater material depth to a region with a smaller material depth in a lateral or longitudinal direction relative to the material flow direction.

[0145] Figure 8 A block diagram illustrating an example of a control zone generator 213 is shown. The control zone generator 213 includes a work machine actuator (WMA) selector 486, a control zone generation system 488, and a regime zone generation system 490. The control zone generator 213 may also include other items 492. The control zone generation system 488 includes a control zone standard identifier component 494, a control zone boundary definition component 496, a target setting identifier component 498, and other items 520. The regime zone generation system 490 includes a regime zone standard identifier component 522, a regime zone boundary definition component 524, a settings resolver identifier component 526, and other items 528. Before describing the overall operation of the control zone generator 213 in more detail, a brief description of some of the items in the control zone generator 213 and their corresponding operations will be provided first.

[0146] The agricultural harvester 100 or other operating machine may have various types of controllable actuators that perform different functions. The controllable actuators on the agricultural harvester 100 or other operating machine are collectively referred to as operating machine actuators (WMAs). Each WMA can be controlled independently based on values ​​on the functional prediction map, or WMAs can be controlled in groups based on one or more values ​​on the functional prediction map. Therefore, the control area generator 213 can generate control areas corresponding to each individual controllable WMA, or corresponding to groups of WMAs that are controlled in a coordinated manner.

[0147] WMA selector 486 selects the WMA or WMA group for which a corresponding control region is to be generated. Control region generation system 488 then generates a control region for the selected WMA or WMA group. For each WMA or WMA group, different criteria can be used to identify the control region. For example, for a WMA, the WMA response time can be used as a criterion for defining the boundaries of the control region. In another example, wear characteristics (e.g., the degree of wear of a particular actuator or mechanism due to its movement) can be used as a criterion for defining the boundaries of the control region. Control region criterion identifier component 494 identifies the specific criterion that will be used to define the control region for the selected WMA or WMA group. Control region boundary definition component 496 processes the values ​​on the functional prediction map in the analysis to define the boundaries of the control region on the functional prediction map based on the values ​​on the functional prediction map in the analysis and based on the control region criteria of the selected WMA or WMA group.

[0148] The target setting identifier component 498 sets the value of the target setting that will be used to control the WMA or WMA group in different control areas. For example, if the selected WMA is the propulsion system 250 and the functional prediction map in the analysis is the functional prediction speed map 438, then the target setting in each control area can be a target speed setting based on the speed values ​​contained in the functional prediction speed map 238 within the identified control area.

[0149] In some examples, when the harvester 100 is controlled based on its current or future position, multiple target settings are possible for the WMA at a given position. In this case, the target settings may have different values ​​and may compete with each other. Therefore, it is necessary to resolve the target settings so that only a single target setting is used to control the WMA. For example, in the case where the WMA is an actuator controlled in the propulsion system 250 to control the speed of the harvester 100, there may be multiple different competing sets of criteria, which are considered by the control zone generation system 488 when identifying the control zone and the target setting of the WMA selected in the control zone. For example, different target settings for controlling machine speed may be generated based on, for example, detected or predicted feed rate values, detected or predicted fuel efficiency values, detected or predicted grain loss values, or combinations of these values. However, at any given time, the harvester 100 cannot travel on the ground at multiple speeds simultaneously. Instead, at any given time, the harvester 100 travels at a single speed. Therefore, one of the competing objectives is selected to control the speed of the agricultural harvester 100.

[0150] Therefore, in some examples, the dynamic zone generation system 490 generates dynamic zones to resolve multiple different competing objective settings. The dynamic zone criterion identification component 522 identifies the criteria used to establish dynamic zones on the selected WMA or WMA group on the functional prediction map in the analysis. Some criteria that can be used to identify or define dynamic zones include, for example, terrain slope, terrain roughness, soil type, crop type or crop species based on the planting map, or another source of crop type or crop species, weed type, weed density, or crop state (such as whether the crop is lodged, partially lodged, or upright). Just as each WMA or WMA group may have a corresponding control zone, different WMAs or WMA groups may also have corresponding dynamic zones. The dynamic zone boundary definition component 524 identifies the boundaries of the dynamic zones on the functional prediction map in the analysis based on the dynamic zone criteria identified by the dynamic zone criterion identification component 522.

[0151] In some examples, dynamic zones may overlap. For instance, a crop type dynamic zone may partially or completely overlap with a terrain roughness dynamic zone. In such examples, different dynamic zones can be assigned priority levels such that, in the case of two or more overlapping dynamic zones, the dynamic zone assigned a higher priority level position or importance takes precedence over the dynamic zone with a lower priority level position or importance. The priority levels of dynamic zones can be set manually or automatically using rule-based systems, model-based systems, or other systems. As an example, in the case of an overlap between a terrain roughness dynamic zone and a crop type dynamic zone, the terrain roughness dynamic zone can be assigned greater importance in the priority level than the crop type dynamic zone, thus giving priority to the terrain roughness dynamic zone.

[0152] Furthermore, for a given WMA or WMA group, each dynamic region may have a unique setting resolver. The setting resolver identifier component 526 identifies a specific setting resolver for each dynamic region identified on the functional prediction graph in the analysis, and identifies a specific setting resolver for the selected WMA or WMA group.

[0153] Once a setting resolver is identified for a specific dynamic zone, it can be used to resolve competing target settings, where more than one target setting is identified based on the control zone. Different types of setting resolvers can take different forms. For example, a setting resolver for each dynamic zone may include a manually selected resolver, in which the competing target settings are presented to the operator or other user for resolution. In another example, the setting resolver may include a neural network or other artificial intelligence or machine learning system. In this case, the setting resolver can resolve competing target settings based on predicted or historical quality metrics corresponding to each of the different target settings. As an example, an increased vehicle speed setting may reduce harvesting time and corresponding time-based labor and equipment costs, but may increase grain loss. A decreased vehicle speed setting may increase harvesting time and corresponding time-based labor and equipment costs, but may reduce grain loss. When grain loss or harvesting time is selected as a quality metric, given two competing vehicle speed setting values, the predicted or historical value for the selected quality metric can be used to resolve the speed setting. In some cases, the parser can be a set of threshold rules that can be used to replace or supplement the dynamic region. Examples of threshold rules can be expressed as follows:

[0154] If the predicted biomass value is greater than x kg (where x is the selected or predetermined value) within 20 feet of the header of the agricultural harvester 100, the target setpoint selected based on the feed rate rather than other competing targets is used; otherwise, the target setpoint based on grain loss rather than other competing targets is used.

[0155] A target set parser can be a logical component that executes logical rules when identifying a target set. For example, a target set parser can parse a target set while attempting to minimize harvest time, minimize total harvest cost, or maximize harvested grain, or other variables calculated as a function of different candidate target sets. Harvesting time can be minimized when the amount of harvested grain is reduced to or below a selected threshold. Total harvest cost can be minimized when the total harvest cost is reduced to or below a selected threshold. Harvested grain can be maximized when the amount of harvested grain is increased to or above a selected threshold.

[0156] Figure 9 This is a flowchart illustrating an example of the operation of the control region generator 213 when generating control regions and dynamic regions for a graph (e.g., a graph in analysis) received by the control region generator 213 for region processing.

[0157] At box 530, control area generator 213 receives the graph in the analysis for processing. In one example, as shown in box 532, the graph in the analysis is a functional prediction graph. For example, the graph in the analysis could be one of functional prediction graphs 436, 437, 438, or 440. Box 534 indicates that the graph in the analysis could also be other graphs.

[0158] At box 536, WMA selector 486 selects the WMA or WMA group for which a control zone is to be generated on the graph in the analysis. At box 538, control zone criterion identification component 494 obtains the control zone defining criteria for the selected WMA or WMA group. Box 540 indicates an example where the control zone criteria are or include the wear characteristics of the selected WMA or WMA group. Box 542 indicates an example where the control zone defining criteria are or include the magnitude and variation of input source data, such as the magnitude and variation of values ​​on the graph in the analysis or the magnitude and variation of inputs from various field sensors 208. Box 544 indicates an example where the control zone defining criteria are or include physical machine characteristics, such as the physical dimensions of the machine, the speed of operation of different subsystems, or other physical machine characteristics. Box 546 indicates an example where the control zone defining criteria are or include the responsiveness of the selected WMA or WMA group when a setpoint for a new command is reached. Box 548 indicates an example where the control zone defining criteria are or include machine performance metrics. Box 550 indicates an example where the control zone defining criterion is or includes operator preference. Box 552 indicates an example where the control zone defining criterion is also or includes other items. Box 549 indicates an example where the control zone defining criterion is time-based, meaning that the harvester 100 will not cross the boundary of the control zone until a selected amount of time has elapsed since the harvester 100 entered the specific control zone. In some cases, the selected amount of time may be a minimum amount of time. Therefore, in some cases, the control zone defining criterion can prevent the harvester 100 from crossing the boundary of the control zone until at least the selected amount of time has elapsed. Box 551 indicates an example where the control zone defining criterion is based on a selected size value. For example, a control zone defining criterion based on a selected size value can exclude the definition of control zones smaller than the selected size. In some cases, the selected size may be a minimum size.

[0159] At box 554, the dynamic zone criterion identification component 522 obtains the dynamic zone defining criteria for the selected WMA or WMA group. Box 556 indicates an example where the dynamic zone defining criteria are based on manual input from operator 260 or another user. Box 558 shows an example where the dynamic zone defining criteria are based on topographic characteristics such as slope. Box 560 shows an example where the dynamic zone defining criteria are based on topographic characteristics such as roughness. Limiting box 564 indicates an example where the dynamic zone defining criteria are also or include other criteria. For example, the dynamic zone defining criteria may be based on soil type, crop type or crop variety, weed type, weed density, or crop status.

[0160] At box 566, the control area boundary defining component 496 generates the boundary of the control area on the graph in the analysis based on the control area criteria. The dynamic area boundary defining component 524 generates the boundary of the dynamic area on the graph in the analysis based on the dynamic area criteria. Box 568 indicates an example where the boundaries of the control area and the dynamic area are identified. Box 570 shows that the target setting identifier component 498 identifies the target setting for each of the control areas. The control area and the dynamic area can also be generated in other ways, and this is indicated by box 572.

[0161] At box 574, the set parser identifier component 526 identifies the set parser for the selected WMA in each dynamic region defined by the dynamic region boundary defining component 524. As discussed above, the dynamic region parser can be a human parser 576, an artificial intelligence or machine learning system parser 578, a parser 580 based on the predicted quality or historical quality of each competing objective setting, a rule-based parser 582, a performance criterion-based parser 584, or another parser 586.

[0162] At box 588, WMA selector 486 determines if there are more WMAs or WMA groups to process. If there are additional WMAs or WMA groups to process, processing returns to box 436, where the next WMA or WMA group to define the control area and dynamic area for is selected. When no additional WMAs or WMA groups remain to generate control areas or dynamic areas for, processing moves to box 590, where control area generator 213 generates a graph of control area, target setting, dynamic area, and setting resolver for each output in each WMA or WMA group. As discussed above, the output graph can be presented to operator 260 or another user; the output graph can be provided to control system 214; or the output graph can be output in other ways.

[0163] Figure 10An example is shown of a control system 214 controlling the operation of an agricultural harvester 100 based on a map output by a control zone generator 213. Thus, at box 592, the control system 214 receives a map of the work site. In some cases, this map may be a functional predictive map that includes control zones and dynamic zones (as shown in box 594). In some cases, the received map may be a functional predictive map that excludes control zones and dynamic zones. Box 596 indicates an example where the received work site map may be an information map having control zones and dynamic zones identified on it. Box 598 indicates an example where the received map may include multiple different maps or multiple different layers. Box 610 indicates an example where the received map may also take other forms.

[0164] At box 612, control system 214 receives sensor signals from geolocation sensor 204. Sensor signals from geolocation sensor 204 may include data indicating the geolocation 614 of harvester 100, the speed 616 of harvester 100, the heading 618 of harvester 100, or other information 620. At box 622, area controller 247 selects a dynamic area, and at box 624, area controller 247 selects a control area on the map based on the geolocation sensor signals. At box 626, area controller 247 selects a WMA or WMA group to be controlled. At box 628, area controller 247 obtains one or more target settings for the selected WMA or WMA group. The target settings obtained for the selected WMA or WMA group can come from various different sources. For example, box 630 shows an example where one or more target settings for the selected WMA or WMA group are based on inputs from a control area on a map from the work site. Box 632 illustrates an example where one or more target settings are obtained from manual input by operator 260 or another user. Box 634 illustrates an example where target settings are obtained from field sensors 208. Box 636 illustrates an example where one or more target settings are obtained from sensors on other machines operating simultaneously with agricultural harvester 100 in the same field, or from sensors on machines that have previously operated in the same field. Box 638 illustrates an example where target settings are also obtained from other sources.

[0165] At box 640, the zone controller 247 accesses the setpoint resolver of the selected dynamic zone and controls the setpoint resolver to resolve competing target settings into a resolved target setting. As discussed above, in some cases, the setpoint resolver may be a manual resolver, in which case the zone controller 247 controls the operator interface mechanism 218 to present competing target settings to the operator 260 or another user for resolution. In some cases, the setpoint resolver may be a neural network or other artificial intelligence or machine learning system, and the zone controller 247 submits competing target settings to the neural network, artificial intelligence, or machine learning system for selection. In some cases, the setpoint resolver may be based on predicted or historical quality metrics, threshold rules, or logical components. In any of these later examples, the zone controller 247 executes the setpoint resolver to obtain a resolved target setting based on predicted or historical quality metrics, threshold rules, or, when using logical components.

[0166] At block 642, if the zone controller 247 has identified the resolved target setting, it provides the resolved target setting to other controllers in the control system 214. These controllers generate control signals based on the resolved target setting and apply these control signals to the selected WMA or WMA group. For example, if the selected WMA is a machine or header actuator 248, the zone controller 247 provides the resolved target setting to the setting controller 232 or the header / actual controller 238, or both, to generate control signals based on the resolved target setting, and those generated control signals are applied to the machine or header actuator 248. At block 644, if an additional WMA or additional WMA group is to be controlled at the current geographic location of the harvester 100 (as detected in block 612), the process returns to block 626, where the next WMA or WMA group is selected. The process represented by blocks 626 through 644 continues until all WMAs or WMA groups to be controlled at the current geographic location of the harvester 100 have been resolved. If no additional WMA or WMA group remains to be controlled at the current geographic location of the harvester 100, the process proceeds to box 646, where the area controller 247 determines whether an additional control area to be considered exists in the selected dynamic area. If an additional control area to be considered exists, the process returns to box 624, where the next control area is selected. If no additional control area needs to be considered, the process proceeds to box 648, where it is determined whether any additional dynamic areas remain to be considered. The area controller 247 determines whether any additional dynamic areas remain to be considered. If any additional dynamic areas remain to be considered, the process returns to box 622, where the next dynamic area is selected.

[0167] At box 650, the zone controller 247 determines whether the operation being performed by the harvester 100 has been completed. If not, the zone controller 247 determines whether control zone criteria have been met to continue processing, as shown in box 652. For example, as mentioned above, control zone defining criteria may include criteria that define when the harvester 100 can cross the control zone boundary. For example, whether the harvester 100 can cross the control zone boundary may be defined by a selected time period, meaning that the harvester 100 is prevented from crossing the zone boundary until the selected amount of time has elapsed. In this case, at box 652, the zone controller 247 determines whether the selected time period has elapsed. Additionally, the zone controller 247 can perform processing continuously. Therefore, the zone controller 247 does not wait for any specific time period before continuing to determine whether the operation of the harvester 100 has been completed. At box 652, if the zone controller 247 determines that it is time to continue processing, then processing continues at box 612, where the zone controller 247 again receives input from the geolocation sensor 204. It should also be understood that the zone controller 247 can use a multiple-input multiple-output controller to control the WMA and WMA group simultaneously, rather than controlling the WMA and WMA group sequentially.

[0168] Figure 11 This is a block diagram illustrating an example of an operator interface controller 231. In the example shown, the operator interface controller 231 includes an operator input command processing system 654, other controller interaction systems 656, a voice processing system 658, and a motion signal generator 660. The operator input command processing system 654 includes a voice management system 662, a touch gesture management system 664, and other items 666. The other controller interaction system 656 includes a controller input processing system 668 and a controller output generator 670. The voice processing system 658 includes a trigger detector 672, a recognition component 674, a synthesis component 676, a natural language understanding system 678, a dialogue management system 680, and other items 682. The motion signal generator 660 includes a visual control signal generator 684, an audio control signal generator 686, a tactile control signal generator 688, and other items 690. Figure 11 Before managing various operator interface actions, the example operator interface controller 231 shown in the figure first provides a brief description of some items in the operator interface controller 231 and their associated operations.

[0169] The operator input command processing system 654 detects operator input on the operator interface mechanism 218 and processes these command inputs. The voice management system 662 detects voice input and manages interaction with the voice processing system 658 to process voice command inputs. The touch gesture management system 664 detects touch gestures on touch-sensitive elements in the operator interface mechanism 218 and processes these command inputs.

[0170] Other controller interaction system 656 manages the interaction with other controllers in control system 214. Controller input processing system 668 detects and processes inputs from other controllers in control system 214, and controller output generator 670 generates outputs and provides these outputs to the other controllers in control system 214. Voice processing system 658 recognizes voice inputs, determines the meaning of these inputs, and provides outputs indicating the meaning of the voice inputs. For example, voice processing system 658 can recognize voice input from operator 260 as a setting change command in which operator 260 is instructing control system 214 to change the setting of controllable subsystem 216. In such an example, voice processing system 658 recognizes the content of the voice command, identifies the meaning of the command as a setting change command, and returns the meaning of the input to voice management system 662. Voice management system 662 then interacts with controller output generator 670 to provide command outputs to the appropriate controllers in control system 214 to complete the voice setting change command.

[0171] The voice processing system 658 can be invoked in various ways. For example, in one example, the voice management system 662 continuously provides input from a microphone (as part of the operator interface mechanism 218) to the voice processing system 658. The microphone detects speech from the operator 260, and the voice management system 662 provides the detected speech to the voice processing system 658. A trigger detector 672 detects a trigger indicating that the voice processing system 658 has been invoked. In some cases, when the voice processing system 658 receives continuous voice input from the voice management system 662, the voice recognition component 674 performs continuous speech recognition on all speech uttered by the operator 260. In some cases, the voice processing system 658 is configured to be invoked using a wake-up word. That is, in some cases, the operation of the voice processing system 658 can be initiated based on the recognition of a selected speech word (referred to as a wake-up word). In such an example, when the recognition component 674 recognizes the wake-up word, the recognition component 674 provides an indication to the trigger detector 672 that the wake-up word has been recognized. Trigger detector 672 detects that voice processing system 658 has been invoked or triggered by a wake-up word. In another example, voice processing system 658 may be invoked by operator 260 actuating an actuator on the user interface mechanism, such as by touching an actuator on a touch-sensitive display, by pressing a button, or by providing another trigger input. In such an example, trigger detector 672 can detect that voice processing system 658 has been invoked when a trigger input via the user interface mechanism is detected. Trigger detector 672 may also detect that voice processing system 658 has been invoked in other ways.

[0172] Once the speech processing system 658 is invoked, speech input from operator 260 is provided to speech recognition component 674. Speech recognition component 674 recognizes linguistic elements in the speech input, such as words, phrases, or other linguistic units. Natural language understanding system 678 identifies the meaning of the recognized speech. This meaning can be any of the following: natural language output, command output identifying a command reflected in the recognized speech, value output identifying a value in the recognized speech, or a variety of other outputs reflecting an understanding of the recognized speech. For example, more generally, natural language understanding system 678 and speech processing system 568 can understand the meaning of speech recognized in the environment of agricultural harvester 100.

[0173] In some examples, the speech processing system 658 can also generate output that guides the operator 260 through a voice-based user experience. For example, the dialogue management system 680 can generate and manage dialogues with the user to identify what the user wants to do. This dialogue can disambiguate user commands, identify one or more specific values ​​required to execute the user command, or obtain or provide additional information from the user, or both. The synthesis component 676 can generate speech synthesis that can be presented to the user through an audio operator interface mechanism such as a speaker. Therefore, dialogues managed by the dialogue management system 680 can be exclusively verbal dialogues, or a combination of visual and verbal dialogues.

[0174] Motion signal generator 660 generates motion signals to control operator interface mechanism 218 based on outputs from one or more of the operator input command processing system 654, other controller interaction system 656, and voice processing system 658. Visual control signal generator 684 generates control signals to control visual items in operator interface mechanism 218. Visual items may be lights, displays, warning indicators, or other visual items. Audio control signal generator 686 generates outputs to control audio elements of operator interface mechanism 218. Audio elements include speakers, audible alarm mechanisms, horns, or other audible elements. Tactile control signal generator 688 generates control signals that are output to control tactile elements of operator interface mechanism 218. Tactile elements include vibratory elements that can be used to make vibrations, such as an operator's seat, steering wheel, pedals, or joystick used by the operator. Tactile elements may include tactile feedback or force feedback elements that provide tactile feedback or force feedback to the operator through the operator interface mechanism. Tactile elements may also include a wide variety of other tactile elements.

[0175] Figure 12 This is a flowchart illustrating an example of the operation of the operator interface controller 231 when generating an operator interface display unit on an operator interface mechanism 218 that may include a touch-sensitive display screen. Figure 12 An example of how the operator interface controller 231 can detect and process operator interactions with the touch-sensitive display is also shown.

[0176] At box 692, operator interface controller 231 receives a graph. Box 694 indicates that the graph is an example of a functional prediction graph, while box 696 indicates that the graph is an example of another type of graph. At box 698, operator interface controller 231 receives input from geolocation sensor 204 identifying the geolocation of harvester 100. As shown in box 700, the input from geolocation sensor 204 may include the heading and position of harvester 100. Box 702 indicates that the input from geolocation sensor 204 includes an example of the speed of harvester 100, and box 704 indicates that the input from geolocation sensor 204 includes an example of other items.

[0177] At box 706, the vision control signal generator 684 in the operator interface controller 231 controls the touch-sensitive display in the operator interface mechanism 218 to generate a display showing all or part of the field represented by the received map. Box 708 indicates that the displayed field may include a current position marker indicating the current position of the harvester 100 relative to the field. Box 710 indicates an example where the displayed field includes a next work unit marker that identifies the next work unit (or area on the field) in which the harvester 100 will operate. Box 712 indicates an example where the displayed field includes an upcoming area display showing areas not yet processed by the harvester 100, and box 714 indicates an example where the displayed field includes a previously visited display representing areas of the field that the harvester 100 has already processed. Box 716 indicates an example where the field shown displays multiple characteristics of the field with a georeferenced location on the map. For example, if the received map is a predictive machine characteristic map, the displayed field could show different predicted internal material distributions at different locations within the field. The mapped characteristics could be shown in previously visited areas (as shown in box 714), upcoming areas (as shown in box 712), and the next work unit (as shown in box 710). Box 718 indicates that the field shown therein also includes examples of other items.

[0178] Figure 13 This illustration shows one example of a user interface display 720 that can be generated on a touch-sensitive display screen. In other embodiments, the user interface display 720 can be generated on other types of displays. The touch-sensitive display screen can be installed in the operator's compartment of the agricultural harvester 100, or on mobile devices, or elsewhere. (Continuing the description...) Figure 12 Before the flowchart shown, the user interface display unit 720 will be described.

[0179] exist Figure 13 In the example shown, the user interface display 720 illustrates a touch-sensitive display including display features for operating a microphone 722 and a speaker 724. Therefore, the touch-sensitive display can be communicatively connected to the microphone 722 and the speaker 724. Box 726 indicates that the touch-sensitive display may include various user interface control actuators, such as buttons, keypads, softkeys, links, icons, switches, etc. The operator 260 can actuate the user interface control actuators to perform various functions.

[0180] exist Figure 13 In the example shown, the user interface display 720 includes a field display portion 728 that displays at least a portion of the field in which a harvester 100 is operating. The field display portion 728 is shown with a current position marker 708 corresponding to the current position of the harvester 100 within the portion of the field shown in the field display portion 728. In one example, the operator can control a touch-sensitive display to zoom in on a portion of the field display portion 728, or pan or scroll the field display portion 728 to display different portions of the field. The next work unit 730 is shown as the area of ​​the field directly in front of the current position marker 708 of the harvester 100. The current position marker 708 can also be configured to identify the direction of travel of the harvester 100, the speed of travel of the harvester 100, or both. Figure 13 In the image, the shape of the current position marker 708 provides an indication of the orientation of the agricultural harvester 100 in the field, which can be used as an indication of the direction of travel of the agricultural harvester 100.

[0181] The size of the next work unit 730, marked on the field display section 728, can vary based on a variety of different criteria. For example, the size of the next work unit 730 can vary based on the travel speed of the harvester 100. Therefore, when the harvester 100 travels faster, the area of ​​the next work unit 730 can be larger than if the harvester 100 travels slower. In another example, the size of the next work unit 730 can vary based on the size of the harvester 100 (including equipment on the harvester 100, such as the header 102). For example, the width of the next work unit 730 can vary based on the width of the header 102. The field display section 728 is also shown displaying previously visited areas 714 and upcoming areas 712. The previously visited area 714 represents an area that has already been harvested, while the upcoming area 712 represents an area that still needs to be harvested. The field display section 728 is also shown displaying different characteristics of the field. Figure 13In the example shown, the plot being displayed is a predicted loss plot, such as a functional predicted loss plot 420. Therefore, multiple different loss level markers are displayed on the field display section 728. A set of loss level display markers 732 is shown in the already visited area 714. A set of loss level display markers 732 is also shown in the upcoming area 712, and a set of loss level display markers 732 is shown in the next work unit 730. Figure 13 The loss level display marker 732 is shown to consist of different symbols indicating areas with similar loss levels. Figure 13 In the example shown, the "!" symbol indicates an area with a high loss level; the "*" symbol indicates an area with a medium loss level; and the "#" symbol indicates an area with a low loss level. Therefore, the field display section 728 displays different measured or predicted values ​​(or characteristics indicated by said values) located in different areas of the field, and uses various display markers 732 to represent those measured or predicted values ​​(or characteristics indicated by said values). As shown, the field display section 728 includes display markers at specific locations associated with specific locations on the field being displayed, in particular... Figure 13 The example shown includes a loss level display marker 732. In some cases, each location of the field may have a display marker associated with that location. Therefore, in some cases, a display marker may be provided at each location of the field display section 728 to identify an attribute that maps a characteristic to each particular location of the field. Thus, this disclosure includes providing, for example, a loss level display marker 732 (as shown in the example) at one or more locations on the field display section 728. Figure 13 The display markers (in the context of this example) identify the attributes, degree, etc., of the feature being displayed, thereby identifying the feature at the corresponding location in the field being displayed. As previously mentioned, the display markers 732 can consist of different symbols, and as described below, these symbols can be any display feature, such as different colors, shapes, patterns, intensities, text, icons, or other display features.

[0182] In other examples, the graph being displayed can be one or more of the graphs described herein, including infographics, information maps, functional predictive graphs such as predictive graphs or predictive control area graphs, or combinations thereof. Therefore, the labels and characteristics being displayed will be associated with the information, data, characteristics, and values ​​provided by the one or more graphs being displayed.

[0183] exist Figure 13 In the example, the user interface display 720 also has a control display section 738. The control display section 738 allows the operator to view information and interact with the user interface display 720 in various ways.

[0184] The actuators and display markers in display section 738 can be displayed as, for example, individual items, fixed lists, scrollable lists, drop-down menus, or drop-down lists. Figure 13 In the example shown, display portion 738 displays information corresponding to three different loss levels for the three symbols mentioned above. Display portion 738 also includes a set of touch-sensitive actuators that operator 260 can interact with by touch. For example, operator 260 can touch the touch-sensitive actuator with a finger to activate the corresponding touch-sensitive actuator.

[0185] like Figure 13 As shown, display portion 738 includes an interactive sign display portion indicated approximately at 741. Interactive sign display portion 741 includes a sign bar 739 that displays signs that have been set automatically or manually. Sign actuator 740 allows operator 260 to mark locations (e.g., the current location of the harvester, or another location on the field specified by the operator) and add information indicating the level of loss found at the current location. For example, when operator 260 actuates sign actuator 740 by touching it, touch gesture management system 664 in operator interface controller 231 identifies the current location as a location where harvester 100 has encountered a high level of loss. When operator 260 touches button 742, touch gesture management system 664 identifies the current location as a location where harvester 100 has encountered a medium level of loss. When operator 260 touches button 744, touch gesture management system 664 identifies the current location as a location where harvester 100 has encountered a low level of loss. When one of the sign actuators 740, 742, or 744 is actuated, the touch gesture management system 664 can control the visual control signal generator 684 to add a symbol corresponding to the identified loss level on the field display portion 728 at the user-identified location. In this way, areas of the field where predicted values ​​cannot accurately identify actual values ​​can be marked for later analysis or for machine learning. In other examples, an operator can specify an area in front of or around the harvester 100 by actuating one of the sign actuators 740, 742, or 744, allowing control of the harvester 100 based on the values ​​specified by the operator 260.

[0186] Display section 738 also includes an interactive marker display section indicated approximately at 743. Interactive marker display section 743 includes a symbol bar 746 that displays the value or characteristic of each category tracked on field display section 728 (in...). Figure 13In the case of loss level, the symbol is the symbol corresponding to the loss level. Display section 738 also includes an interactive specifier display section indicated approximately at 745. Interactive specifier display section 745 includes a specifier bar 748 that displays the symbol corresponding to the value or property (in...). Figure 13 In the case of loss level, the designator is a category for identification (which can be a text designator or other designator). Without limitation, the symbols in the symbol bar 746 and the designators in the designator bar 748 may include any display features, such as different colors, shapes, patterns, intensities, text, icons or other display features, and can be customized through the interaction of the operator of the agricultural harvester 100.

[0187] Display section 738 also includes an interactive value display section indicated approximately at 747. Interactive value display section 747 includes a value display bar 750 displaying the selected value. The selected value corresponds to a characteristic or value, or both, being tracked or displayed on field display section 728. The selected value can be selected by the operator of the agricultural harvester 100. The selected value in value display bar 750 defines a range of values ​​or, by virtue of, categorizes other values ​​(e.g., predicted values). Therefore, in Figure 13 In the examples, predicted or measured loss levels of 1.5 bushels per acre or higher are classified as "high loss level," while predicted or measured loss levels of 0.5 bushels per acre or lower are classified as "low loss level." In some examples, the selected values ​​may include a range, such that predicted or measured values ​​within the selected range will be classified under the corresponding designator. For example... Figure 13 As shown, "moderate loss level" includes a range of 0.51 bushels / acre to 1.49 bushels / acre, such that predicted or measured loss levels falling within the range of 0.51 to 1.49 bushels / acre are classified as "moderate loss level". The value selected in the value display column 750 can be adjusted by the operator of the agricultural harvester 100. In one example, the operator 260 can select a specific portion of the field display section 728, and the value displayed in column 750 will be for that specific portion. Therefore, the value in column 750 can correspond to the value in display sections 712, 714, or 730.

[0188] Display section 738 also includes an interactive threshold display section indicated approximately at 749. Interactive threshold display section 749 includes a threshold display bar 752 that displays action thresholds. The action threshold in bar 752 can be a threshold corresponding to a selected value in value display bar 750. If the predicted or measured value of the characteristic being tracked or displayed, or both, meets the corresponding action threshold in threshold display bar 752, the control system 214 takes the action identified in bar 754. In some cases, the measured or predicted value can satisfy the corresponding action threshold by reaching or exceeding it. In one example, operator 260 can select a threshold, for example, to change the threshold by touching it in threshold display bar 752. Once selected, operator 260 can change the threshold. The threshold in bar 752 can be configured such that a specified action is performed when the measured or predicted value of the characteristic exceeds, is equal to, or is less than the threshold. In some cases, a threshold may represent a range of values, or a range of deviations from the values ​​selected in the value display bar 750, such that predicted or measured characteristic values ​​that reach or fall within that range satisfy the threshold. For example, in Figure 13 In one example, a predicted value falling within 10% of 1.5 bushels per acre would satisfy the corresponding action threshold (within 10% of 1.5 bushels per acre), and the control system 214 would take an action such as reducing the speed of the clearing fan. In other examples, the threshold in the threshold display bar 752 is separated from the selected value in the value display bar 750, such that the value in the value display bar 750 defines the classification and display of the predicted or measured value, while the action threshold defines when to take an action based on that measured or predicted value. For example, while a predicted or measured loss value of 1.0 bushels per acre might be designated as “moderate loss level” for classification and display purposes, the action threshold could be 1.2 bushels per acre, such that no action will be taken until the loss value meets that threshold. In other examples, the threshold in the threshold display bar 752 could include distance or time. For example, in the example of distance, the threshold could be a threshold distance from the field to an area where the measured or predicted value is georeferenced, such that the harvester 100 must be in that area before taking action. For example, a threshold distance value of 10 feet means that action will be taken when the harvester is located 10 feet or less from the field to the area where the measured or predicted value is georeferenced. In the example where the threshold is time, the threshold could be a threshold time for the harvester 100 to reach the field to the area where the measured or predicted value is georeferenced. For example, a threshold of 5 seconds means that action will be taken when the harvester 100 is 5 seconds away from the field to the area where the measured or predicted value is georeferenced. In such examples, the harvester's current position and travel speed can be considered.

[0189] Display portion 738 also includes an interactive action display portion indicated approximately at 751. The interactive action display portion 751 includes an action display bar 754 displaying action identifiers that indicate the action to be taken when a predicted or measured value meets an action threshold in a threshold display bar 752. Operator 260 can touch the action identifier in said bar 754 to change the action to be taken. An action can be taken when the threshold is met. For example, at the bottom of bar 754, if the measured value in bar 750 meets the threshold in bar 752, actions to increase the grain cleaning fan speed and actions to decrease the grain cleaning fan speed are identified as actions to be taken. In some examples, multiple actions can be taken when the threshold is reached. For example, in response to the threshold being met, the grain cleaning fan speed can be adjusted, the threshing rotor speed can be adjusted, and the concave plate gap can be adjusted.

[0190] The actions that can be set in column 754 can be any of a variety of different types of actions. For example, these actions can include a prohibition action that, when executed, prevents the combine harvester 100 from harvesting further in an area. These actions can include speed-changing actions that, when executed, change the speed at which the combine harvester 100 travels through the field. These actions can include setting-changing actions for changing the settings of internal actuators or another WMA or WMA group, or setting-changing actions for implementing settings that change the threshing rotor speed, the cleaning fan speed, the position of the header (e.g., tilt, height, tumble, etc.), and a variety of other settings. These are merely examples, and a wide variety of other actions are considered herein.

[0191] The items displayed on the user interface display 720 can be controlled visually. Visual control of the interface display 720 can be performed to capture the attention of the operator 260. For example, the intensity, color, or pattern of the displayed markers can be modified. Additionally, the display markers can be controlled to blink. As an example, a description of changes to the visual appearance of the display markers is provided. Therefore, other aspects of the visual appearance of the display markers can be changed. Thus, the display markers can be modified in a desired manner in various situations to, for example, capture the attention of the operator 260. Furthermore, while a specific number of items are displayed on the user interface display 720, this is not necessary. In other examples, more or fewer items, or more or fewer specific items, can be included on the user interface display 720.

[0192] Now back Figure 12The flowchart continues to describe the operation of the operator interface controller 231. At block 760, the operator interface controller 231 detects an input setting a sign and controls the touch-sensitive user interface display 720 to display the sign on the field display section 728. The detected input can be operator input (as shown at 762) or input from another controller (as shown at 764). At block 766, the operator interface controller 231 detects a field sensor input indicating a measured characteristic of the field from one of the field sensors 208. At block 768, the vision control signal generator 684 generates a control signal to control the user interface display 720 to display actuators for modifying the user interface display 720 and for modifying machine control. For example, block 770 indicates that one or more actuators for setting or modifying values ​​in columns 739, 746, and 748 can be displayed. Therefore, the user can set signs and modify the characteristics of these signs. For example, the user can modify the machine orientation and designator corresponding to the sign. Box 772 indicates that the action threshold in column 752 is displayed. Box 776 indicates that the action in column 754 is displayed, and box 778 indicates that the measured field data in column 750 is displayed. Box 780 indicates that various other information and actuators can also be displayed on the user interface display unit 720.

[0193] At box 782, the operator input command processing system 654 detects and processes operator input corresponding to the interaction with the user interface display unit 720 performed by the operator 260. If the user interface mechanism displayed on the user interface display unit 720 is a touch-sensitive display screen, the interactive input performed by the operator 260 with the touch-sensitive display screen can be a touch gesture 784. In some cases, the operator interactive input can be input using a clicking device 786 or other operator interactive input device 788.

[0194] At box 790, the operator interface controller 231 receives a signal indicating an alarm condition. For example, box 792 indicates a signal that can be received by the controller input processing system 668, indicating that the detected value in indicator bar 750 satisfies a threshold condition present in bar 752. As previously explained, a threshold condition can include a value below a threshold, a value at a threshold, or a value above a threshold. Box 794 shows that the action signal generator 660 can, in response to receiving an alarm condition, generate a visual alarm using the visual control signal generator 684, an audio alarm using the audio control signal generator 686, a tactile alarm using the tactile control signal generator 688, or any combination thereof, to alert the operator 260. Similarly, as shown in box 796, the controller output generator 670 can generate outputs to other controllers in the control system 214, causing these controllers to perform the corresponding actions identified in bar 754. Box 798 shows that the operator interface controller 231 can also detect and process alarm conditions in other ways.

[0195] Box 900 illustrates that the voice management system 662 can detect and process input that invokes the voice processing system 658. Box 902 illustrates that performing voice processing may include using the dialogue management system 680 to converse with the operator 260. Box 904 illustrates that voice processing may include providing signals to the controller output generator 670 to automatically perform control operations based on voice input.

[0196] Table 1 below shows an example of a dialogue between the operator interface controller 231 and the operator 260. In Table 1, the operator 260 invokes the speech processing system 658 using a trigger word or wake-up word detected by the trigger detector 672. In the example shown in Table 1, the wake-up word is "Johnny".

[0197] Table 1

[0198] Operator: "Johnny, tell me about the current machine orientation."

[0199] Operator interface controller: "Current pitch is 5% forward, with a threshold of 10%. Current roll is 2% right, with a threshold of 8%."

[0200] Table 2 illustrates such an example where the speech synthesis component 676 provides output to the audio control signal generator 686 to provide audio updates intermittently or periodically. The interval between updates can be based on time (such as every five minutes), or on coverage or distance (such as every five acres), or on anomalies (such as when a measured value exceeds a threshold).

[0201] Table 2

[0202] Operator interface controller: "In the past 10 minutes, the harvester has made a 60-foot elevation gain and a 15-foot elevation loss."

[0203] Operator interface controller: "In the next 10 minutes, the harvester is predicted to experience an 80-foot drop in altitude with no rise."

[0204] The examples shown in Table 3 illustrate some actuators or user input mechanisms on the touch-sensitive display 720 that can be supplemented by voice dialogue. The examples in Table 3 also show that the motion signal generator 660 can generate motion signals to automatically mark areas in a field being harvested where crops have been cut too low.

[0205] Table 3

[0206] Human: "Johnny, mark the cut too low."

[0207] Operator interface controller: "The low-cut area has been marked."

[0208] The example shown in Table 4 illustrates that the motion signal generator 660 can communicate with the operator 260 to start and end the marking of low-cut areas.

[0209] Table 4

[0210] Human: "Johnny, start marking the low-cut areas."

[0211] Operator interface controller: "Mark low-cut areas".

[0212] Human: "Johnny, stop marking low-cut areas."

[0213] Operator interface controller: "Mark stop for low-cut areas".

[0214] The example shown in Table 5 illustrates that the motion signal generator 160 can generate signals for marking lateral tilt areas in a manner different from that shown in Tables 3 and 4.

[0215] Table 5

[0216] Human: "Johnny, mark the next 100 feet as the low-cut area."

[0217] Operator interface controller: "The next 100 feet is marked as a low-cut area."

[0218] Return again Figure 12Box 906 illustrates that the operator interface controller 231 can also detect and process situations for outputting messages or other information in other ways. For example, the other controller interaction system 656 can detect input from other controllers indicating alarms or output messages that should be presented to the operator 260. Box 908 illustrates that the output can be an audio message. Box 910 illustrates that the output can be a visual message, and Box 912 illustrates that the output can be a tactile message. Until the operator interface controller 231 determines that the current harvesting operation is complete (as shown in Box 914), the processing returns to Box 698, where the geographical location of the harvester 100 is updated, and the processing continues as described above to update the user interface display 720.

[0219] Once the operation is complete, any desired values ​​displayed or already displayed on the user interface display unit 720 can be saved. These values ​​can also be used in machine learning to improve different parts of the predictive model generator 210, predictive map generator 212, control area generator 213, control algorithm, or other projects. The saved desired values ​​are indicated by box 916. These values ​​can be saved locally on the agricultural harvester 100, or they can be saved at a remote server location or sent to another remote system.

[0220] Therefore, the information index map is obtained by the agricultural harvester and includes terrain characteristic values ​​at different geographical locations in the field being harvested. Field sensors on the harvester sense characteristics with values ​​indicating agricultural properties as the harvester moves through the field. A prediction map generator produces a prediction map that predicts control values ​​for different locations in the field based on the terrain characteristic values ​​in the information map and the agricultural properties sensed by the field sensors. The control system controls the controllable subsystems based on the control values ​​in the prediction map.

[0221] A control value is a value upon which an action can be based. As described herein, a control value can include any value (or characteristic indicated by or derived from that value) that can be used to control the agricultural harvester 100. A control value can be any value indicating an agricultural characteristic. A control value can be a predicted value, a measured value, or a detected value. A control value can include any value provided by a graph (such as any of the graphs described herein), for example, a control value can be a value provided by an infographic, a value provided by an infographic, or a value provided by a predictive graph (e.g., a functional predictive graph). A control value can also include any characteristic indicated by a value detected by any of the sensors described herein, or any characteristic derived from a detected value. In other examples, control values ​​can be provided by the operator of the agricultural machine, such as commands entered by the operator of the agricultural machine.

[0222] Processors and servers have been mentioned in this discussion. In some examples, processors and servers include computer processors with associated memory and timing circuitry (not shown separately). Processors and servers are functional parts of the system or device to which they belong, and are activated by and facilitate the function of other components or items in these systems.

[0223] Furthermore, numerous user interface displays have been discussed. These displays can take various forms and can have various user-actuable operator interface mechanisms mounted on them. For example, user-actuable operator interface mechanisms can be text boxes, checkboxes, icons, links, drop-down menus, search boxes, etc. User-actuable operator interface mechanisms can also be actuated in various ways. For example, they can be actuated using operator interface mechanisms such as click devices (e.g., trackballs or mice, hardware buttons, switches, joysticks or keyboards, thumb switches or thumb pads, etc.), virtual keyboards, or other virtual actuators. Furthermore, if the screen displaying the user-actuable operator interface mechanism is a touch-sensitive screen, touch gestures can be used to actuate the mechanism. Moreover, voice recognition functionality can be used to actuate the mechanism using voice commands. Voice recognition can be implemented using voice detection devices (e.g., microphones) and software for recognizing the detected voice and executing commands based on the received voice.

[0224] Many data storage devices are also discussed. It should be noted that each data storage device can be divided into multiple data storage devices. In some examples, one or more data storage devices may be local to the system accessing that data storage device; one or more data storage devices may all be located remotely from the system utilizing that data storage device; or one or more data storage devices may be local while the others are remote. This disclosure considers all of these configurations.

[0225] Furthermore, the accompanying diagram shows multiple boxes, with functionality belonging to each box. It should be noted that fewer boxes can be used to illustrate that functionality attributed to multiple different boxes is performed by fewer components. Moreover, more boxes can be used to show that the functionality can be distributed across more components. In different examples, some functionality can be added, and some functionality can be removed.

[0226] It should be noted that the foregoing discussion has described various different systems, components, logic, and interactions. It should be understood that any or all of such systems, components, logic, and interactions can be implemented by hardware projects, such as processors, memory, or other processing components, including (but not limited to) artificial intelligence components, such as neural networks, that perform functions associated with those systems, components, logic, or interactions, some of which are described below. Furthermore, any or all of the systems, components, logic, and interactions can be implemented by software loaded into memory and subsequently executed by a processor, server, or other computing component, as described below. Any or all of the systems, components, logic, and interactions can also be implemented by different combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that can be used to implement any or all of the systems, components, logic, and interactions described above. Other structures may also be used.

[0227] Figure 14 This is a block diagram of an agricultural harvester 600, which can be similar to... Figure 2 The agricultural harvester 100 is shown in the diagram. The agricultural harvester 600 communicates with components in a remote server architecture 500. In some examples, the remote server architecture 500 can provide computing, software, data access, and storage services that do not require the end user to know the physical location or configuration of the system delivering the services. In various examples, the remote server can deliver the services over a wide area network (such as the Internet) using appropriate protocols. For example, the remote server can deliver applications over a wide area network and can be accessed through a web browser or any other computing component. Figure 2 The software or components shown, along with associated data, can be stored on servers at remote locations. Computing resources in a remote server environment can be consolidated at a remote data center location, or they can be distributed across multiple remote data centers. Remote server infrastructure can deliver services through a shared data center, even if the service appears as a single access point for the user. Therefore, the components and functionalities described herein can be provided from remote servers at remote locations using a remote server architecture. Alternatively, the components and functionalities can be provided from a server, or they can be installed directly or otherwise on client devices.

[0228] exist Figure 14 In the examples shown, some items are similar to Figure 2 The items shown are numbered similarly. Figure 14 Specifically, a prediction model generator 210 or a prediction graph generator 212, or both, can be located at a server location 502, away from the agricultural harvester 600. Therefore, in Figure 14 In the example shown, the agricultural harvester 600 accesses the system via a remote server location 502.

[0229] Figure 14 Another example of a remote server architecture is also described. Figure 14 It shows Figure 2 Some components can be located at a remote server location 502, while others can be located elsewhere. As an example, data storage device 202 can be located at a separate location from location 502 and accessed via a remote server at location 502. Regardless of their location, these components can be directly accessed by the agricultural harvester 600 via a network (such as a wide area network or local area network); these components can be hosted as a service at a remote site; or they can be provided as a service or accessed by a connection service residing at a remote location. Furthermore, data can be stored anywhere, and the stored data can be accessed or forwarded to an operator, user, or system. For example, a physical carrier wave can be used instead of an electromagnetic carrier wave, or a physical carrier wave can be used in addition to an electromagnetic carrier wave. In some examples, where wireless telecommunications service coverage is poor or nonexistent, another machine (such as a fuel truck or other mobile machine or vehicle) can have an automatic, semi-automatic, or manual information collection system. When the combine harvester 600 approaches a machine (such as a fuel truck) containing the information collection system before refueling, the information collection system collects information from the combine harvester 600 using any type of temporary dedicated wireless connection. The collected information can then be forwarded to another network when the machine containing the received information reaches a location with wireless telecommunications service coverage or other available wireless coverage. For example, when the fuel truck travels to a location to refuel other machines or at a main fuel storage location, the fuel truck can enter an area with wireless communication coverage. All these architectures are considered in this paper. Furthermore, information can be stored on the combine harvester 600 until it enters an area with wireless communication coverage. The combine harvester 600 itself can transmit the information to another network.

[0230] It will also be noted that Figure 2 The components or portions thereof can be arranged on a variety of different devices. One or more of these devices may include an airborne computer, electronic control unit, display unit, server, desktop computer, laptop computer, tablet computer, or other mobile devices such as PDAs, cellular phones, smartphones, multimedia players, personal digital assistants, etc.

[0231] In some examples, the remote server architecture 500 may include network security measures. These measures, without limitation, include encryption of data on storage devices, encryption of data transmitted between network nodes, authentication of personnel or processes accessing data, and the use of a ledger to record metadata, data, data transfer, data access, and data transformation. In some examples, the ledger may be distributed and immutable (e.g., implemented as a blockchain).

[0232] Figure 15 This is a simplified block diagram illustrating a schematic example of a handheld computing device or mobile computing device 16 that can be used as a user's or customer's handheld device, in which the system (or a portion thereof) can be deployed. For example, a mobile device could be deployed in the operator's compartment of an agricultural harvester 100 for use in generating, processing, or displaying the diagrams discussed above. Figures 16 to 17 Examples are handheld or mobile devices.

[0233] Figure 15 A general block diagram of the components of client device 16, which can run... is provided. Figure 2 Some of the components shown in the diagram, the client device 16 can be connected to Figure 2 Some components, or both, are shown interacting. In device 16, a communication link 13 is provided that allows the handheld device to communicate with other computing devices, and in some examples, a channel is provided for automatically receiving information (e.g., by scanning). Examples of communication link 13 include those allowing communication via one or more communication protocols, such as wireless services for providing cellular access to a network and protocols for providing local wireless connectivity to a network.

[0234] In other examples, the application can be received on a removable Secure Digital (SD) card connected to interface 15. Interface 15 and communication link 13 communicate along bus 19 with processor 17 (which may also be represented as a processor or server from other figures), which is also connected to memory 21 and input / output (I / O) components 23, as well as clock 25 and positioning system 27.

[0235] In one example, I / O component 23 is provided to facilitate input and output operations. Various examples of I / O component 23 in device 16 may include input components (such as buttons, touch sensors, optical sensors, microphones, touchscreens, proximity sensors, accelerometers, orientation sensors) and output components (such as display devices, speaker and / or printer ports). Other I / O components 23 may also be used.

[0236] Clock 25 schematically includes a real-time clock component that outputs the time and date. Schematically, clock 25 may also provide timing functionality for processor 17.

[0237] Positioning system 27 schematically includes components that output the current geographic location of the device 16. Positioning system 27 may include, for example, a Global Positioning System (GPS) receiver, a LoRAN system, a dead reckoning system, a cellular triangulation system, or other positioning systems. Positioning system 27 may also include, for example, mapping or navigation software that generates desired maps, navigation routes, and other geographic functions.

[0238] Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data storage device 37, communication driver 39, and communication configuration settings 41. Memory 21 may include all types of tangible volatile and non-volatile computer-readable storage devices. Memory 21 may also include computer storage media (described below). Memory 21 stores computer-readable instructions that, when executed by processor 17, cause the processor to perform computer-implemented steps or functions. Processor 17 may also be activated by other components to facilitate the function of those components.

[0239] Figure 16 The illustration shows an example where device 16 is a tablet computer 600. Figure 16 In the diagram, computer 601 is shown with a user interface display screen 602. Screen 602 may be a touchscreen or a pen-enabled interface that receives input from a pen or stylus. Tablet computer 600 may also use an on-screen virtual keyboard. Of course, computer 601 may also be attached to a keyboard or other user input device, for example, via a suitable attachment mechanism (such as a wireless link or USB port). Computer 601 may also schematically receive voice input.

[0240] Figure 17 Similar to Figure 16 In addition to being a smartphone 71, the smartphone 71 has a touch-sensitive display 73 that shows icons, tiles, or other user input mechanisms 75. Users can use the mechanisms 75 to run applications, make calls, perform data transfer operations, etc. Generally, the smartphone 71 is built on a mobile operating system and offers more advanced computing power and connectivity than feature phones.

[0241] Note that other forms of device 16 are possible.

[0242] Figure 18 It is one of the deployable ones Figure 2 An example of a computing environment for the components. Reference Figure 18An example system for implementing some embodiments includes a computing device in the form of a computer 810 programmed to operate as discussed above. Components of computer 810 may include (but are not limited to) a processing unit 820 (which may include a processor or server from the previous figures), system memory 830, and a system bus 821 that connects various system components, including the system memory, to the processing unit 820. System bus 821 may be any of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of various bus architectures. Regarding... Figure 2 The described memory and program can be deployed in Figure 18 In the corresponding part.

[0243] Computer 810 typically includes a variety of computer-readable media. Computer-readable media can be any available medium accessible to computer 810, and includes volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, computer-readable media can include computer storage media and communication media. Computer storage media are distinct from and do not include modulated data signals or carriers. Computer-readable media include hardware storage media, including volatile and non-volatile, removable and non-removable media implemented in any way or by any technique 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 memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disc storage devices, magnetic tape, magnetic tape, disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to computer 810. Communication media can implement computer-readable instructions, data structures, program modules, or other data in a transmission mechanism, and includes any information delivery medium. The term "modulated data signal" refers to a signal that has one or more characteristics set or changed in a manner that encodes information in the signal.

[0244] System memory 830 includes computer storage media in the form of volatile and / or non-volatile memory or both, such as read-only memory (ROM) 831 and random access memory (RAM) 832. The basic input / output system 833 (BIOS) (which contains basic routines such as those that help transfer information between components within computer 810 during startup) is typically stored in ROM 831. RAM 832 typically contains data and / or program modules, or both, that are readily accessible to and / or currently being operated by processing unit 820. This is by way of example and not limitation. Figure 18The operating system 834, application program 835, other program modules 836, and program data 837 are shown.

[0245] Computer 810 may also include other removable / non-removable volatile / non-volatile computer storage media. This is just one example. Figure 18 A hard disk drive 841 is shown that reads from or writes to a non-removable non-volatile magnetic medium, an optical disk drive 855, and a non-volatile optical disk 856. The hard disk drive 841 is typically connected to the system bus 821 via a non-removable memory interface (such as interface 840), and the optical disk drive 855 is typically connected to the system bus 821 via a removable memory interface (such as interface 850).

[0246] Alternatively or additionally, the functions described herein may be performed at least in part by one or more hardware logic components. For example, but not limited to, illustrative types of hardware logic components that may be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (e.g., ASSPs), systems-on-chips (SoCs), complex programmable logic devices (CPLDs), etc.

[0247] The above discussion and Figure 18 The driver and its associated computer storage medium shown provide the computer 810 with storage for computer-readable instructions, data structures, program modules, and other data. For example, in Figure 18 In this diagram, hard disk drive 841 is shown storing operating system 844, application programs 845, other program modules 846, and program data 847. Note that these components may be the same as or different from operating system 834, application programs 835, other program modules 836, and program data 837.

[0248] Users can input commands and information into computer 810 using input devices such as keyboard 862, microphone 863, and pointing devices 861 (such as mouse, trackball, or touchpad). Other input devices (not shown) may include joysticks, game controllers, satellite dishes, scanners, etc. These and other input devices are typically connected to processing unit 820 via user input interface 860, which is coupled to the system bus, but may also be connected via other interfaces and bus structures. Visual display 891 or other types of display devices are also connected to system bus 821 via an interface such as video interface 890. In addition to the monitor, the computer may also include other peripheral output devices, such as speakers 897 and printers 896, which can be connected via peripheral output interface 895.

[0249] Computer 810 operates in a networked environment using a logical connection (such as a controller local area network (CAN), local area network (LAN), or wide area network (WAN)) of one or more remote computers (such as remote computer 880).

[0250] When used in a LAN networking environment, computer 810 connects to LAN 871 via a network interface or adapter 870. When used in a WAN networking environment, computer 810 typically includes a modem 872 or other devices for establishing communication over a WAN 873 (such as the Internet). In a networking environment, program modules can be stored in remote memory storage devices. For example, Figure 18 This demonstrates that remote application 885 can reside on remote computer 880.

[0251] It should also be noted that the different examples described in this article 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 considered in this article.

[0252] Example 1 is an agricultural operating machine, comprising:

[0253] A communication system that receives a priori information map, the priori information map including values ​​of terrain characteristics corresponding to different geographical locations in the field;

[0254] A geolocation sensor that detects the geolocation of the agricultural machinery;

[0255] A field sensor that detects values ​​of agricultural characteristics corresponding to the geographical location;

[0256] A prediction map generator generates a functional predictive agricultural map of the field based on the values ​​of the terrain characteristics in the prior information map and the values ​​of the agricultural characteristics, the functional predictive agricultural map mapping the predicted control values ​​to the different geographical locations in the field;

[0257] Controllable subsystem; and

[0258] A control system that generates control signals to control the controllable subsystem based on the geographical location of the agricultural machinery and the control values ​​in the functional predictive agriculture map.

[0259] Example 2 is any or all of the agricultural operating machines of the foregoing examples, wherein the prediction map generator includes:

[0260] A predictive power characteristic map generator generates a functional predictive power characteristic map that maps the predicted power characteristics of the agricultural machinery to the different geographical locations in the field.

[0261] Example 3 is any or all of the agricultural operating machines of the foregoing examples, wherein the predicted power characteristic map generator generates a functional predicted power characteristic map that maps the predicted power characteristics of the propulsion system of the agricultural operating machine.

[0262] Example 4 is any or all of the agricultural operating machines of the foregoing examples, wherein the prediction map generator includes:

[0263] A predictive machine characteristic map generator generates a functional predictive machine characteristic map that maps one or more of the predicted machine speed value, internal material distribution value, grain loss value, extraneous characteristic value, and grain quality value to the different geographical locations in the field.

[0264] Example 5 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system includes:

[0265] A setting controller generates a machine setting control signal based on the detected geographical location and the functional predictive machine characteristic map, and controls the controllable subsystem based on the machine setting control signal.

[0266] Example 6 is any or all of the agricultural operating machines of the foregoing examples, wherein the prediction map generator includes:

[0267] A predictive operator command graph generator generates a functional predictive operator command graph that maps predicted operator commands to the different geographical locations in the field.

[0268] Example 7 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system includes:

[0269] A controller is configured to generate an operator command control signal that indicates the predicted operator command based on the detected geographic location and the functional predictive operator command map, and to control the controllable subsystem to execute the operator command based on the operator command control signal.

[0270] Example 8 is any or all of the agricultural operating machines of the foregoing examples, further including:

[0271] A predictive model generator generates a predictive agricultural model based on the values ​​of the terrain features in the prior information map at the geographical location and the values ​​of the agricultural features detected by the field sensors at the geographical location. The predictive agricultural model models the relationship between the terrain features and the agricultural features. The predictive map generator generates the functional predictive agricultural map based on the values ​​of the terrain features in the prior information map and the predictive agricultural model.

[0272] Example 9 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system further includes:

[0273] An operator interface controller generates a user interface diagram representation of the functional predictive agriculture map, the user interface diagram representation including field portions with one or more markers indicating the predicted control values ​​at one or more geographic locations on the field portions.

[0274] Example 10 is any or all of the agricultural operating machines of the foregoing examples, wherein the operator interface controller generates a user interface diagram representation including an interactive display portion that displays: a value display portion indicating a selected value, an interactive threshold display portion indicating an action threshold, and an interactive action display portion indicating a control action to be taken when one of the predicted control values ​​satisfies an action threshold associated with the selected value, and the control system generates the control signal to control the controllable subsystem based on the control action.

[0275] Example 11 is a computer-implemented method for controlling agricultural machinery, comprising:

[0276] Obtain a priori information map, which includes values ​​of topographic features corresponding to different geographical locations in the field;

[0277] Detect the geographical location of the agricultural machinery;

[0278] Use field sensors to detect agricultural characteristics corresponding to geographical locations;

[0279] A functional predictive agricultural map of the field is generated based on the values ​​of the terrain characteristics in the prior information map and the values ​​of the agricultural characteristics. This functional predictive agricultural map maps predicted control values ​​to different geographical locations within the field.

[0280] The controllable subsystem is controlled based on the geographical location of the agricultural machinery and the control values ​​in the functional predictive agriculture map.

[0281] Example 12 is a computer-implemented method of any or all of the foregoing examples, wherein generating a functional prediction graph includes:

[0282] A functional predictive agriculture map is generated, which maps predicted machine characteristic values ​​as control values ​​to the different geographical locations in the field.

[0283] Example 13 is a computer-implemented method of any or all of the foregoing examples, wherein controlling the controllable subsystem includes:

[0284] A speed control signal is generated based on the detected geographic location and the functional predictive agriculture map; and

[0285] The controllable subsystem is controlled based on the speed control signal to control the speed of the agricultural machinery.

[0286] Example 14 is a computer-implemented method of any or all of the foregoing examples, wherein generating a functional prediction graph includes:

[0287] A feed rate control signal is generated based on the detected geographic location and the functional predictive agricultural map; and

[0288] The controllable subsystem is controlled based on the feed rate control signal to control the feed rate of the agricultural machine.

[0289] Example 15 is a computer-implemented method of any or all of the foregoing examples, wherein controlling the controllable subsystem includes:

[0290] Based on the detected geographic location and the functional predictive agriculture map, a set control signal is generated; and

[0291] The grain cleaning subsystem is controlled based on the set control signal.

[0292] Example 16 is a computer-implemented method of any or all of the foregoing examples, wherein generating a functional prediction graph includes:

[0293] Generate a functional predictive operator command map, which maps predicted operator commands to the different geographic locations in the field.

[0294] Example 17 is a computer-implemented method of any or all of the foregoing examples, wherein controlling the controllable subsystem includes:

[0295] Based on the detected geographic location and the functional predictive operator command map, an operator command control signal is generated to indicate the predicted operator commands; and

[0296] The controllable subsystem is controlled to execute the operator's command based on the operator's command control signal.

[0297] Example 18 is a computer-implemented method of any or all of the foregoing examples, and further includes:

[0298] A predictive agriculture model is generated based on the values ​​of the terrain features in the prior information map at the geographical location and the values ​​of the agricultural features detected by the field sensors at the geographical location. The predictive agriculture model models the relationship between the terrain features and the agricultural features. The generation of the functional predictive agriculture map includes generating the functional predictive agriculture map based on the values ​​of the terrain features in the prior information map and based on the predictive agriculture model.

[0299] Example 19 is an agricultural operating machine, comprising:

[0300] A communication system that receives a priori information map, the priori information map including values ​​of a terrain feature corresponding to different geographical locations in the field;

[0301] A geolocation sensor that detects the geolocation of the agricultural machinery;

[0302] A field sensor that detects agricultural characteristics corresponding to geographical locations;

[0303] A predictive model generator generates a predictive agricultural model based on the values ​​of the terrain features in the prior information map at the geographical location and the values ​​of the agricultural features detected by the field sensors at the geographical location. The predictive agricultural model models the relationship between the terrain features and the agricultural features.

[0304] A predictive map generator generates a functional predictive agricultural map of the field based on the values ​​of the terrain features in the prior information map and based on the predictive agriculture model, the functional predictive agricultural map mapping the predicted control values ​​to the different geographical locations in the field;

[0305] Controllable subsystem; and

[0306] A control system that generates control signals to control the controllable subsystem based on the geographical location of the agricultural machinery and the control values ​​in the functional predictive agriculture map.

[0307] Example 20 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system includes at least one of the following controllers:

[0308] A feed rate controller generates a feed rate control signal based on the detected geographic location and the functional predictive agriculture map, and controls the controllable subsystem based on the feed rate control signal to control the feed rate of material through the agricultural machinery.

[0309] A setting controller generates a speed control signal based on the detected geographic location and the functional predictive agriculture map, and controls the controllable subsystem based on the speed control signal to control the speed of the agricultural machinery; and

[0310] A setting controller is configured to generate an operator command control signal that indicates an operator command based on the detected geographic location and the functional predictive agriculture map, and to control the controllable subsystem to execute the operator command based on the operator command control signal.

[0311] Although the subject matter has been described in language specific to structural features or methodological actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are disclosed as examples of the claims.

Claims

1. An agricultural operating machine (100), comprising: A communication system (206) receives a priori information map (258), the priori information map including values ​​of a terrain feature corresponding to different geographical locations in the field; A geolocation sensor (204) detects the geolocation of the agricultural machinery; A field sensor (208) detects agricultural characteristics corresponding to the geographical location; A prediction model generator (210) generates a predictive agricultural model based on the values ​​of the terrain features in the prior information map (258) at the geographic location and the values ​​of the agricultural features detected by the field sensor (208) at the geographic location. The predictive agricultural model models the relationship between the terrain features and the agricultural features. A prediction map generator (212) generates a functional predictive agricultural map of the field based on the values ​​of the terrain characteristics in the prior information map (258) and the predictive agricultural model, the functional predictive agricultural map mapping the predicted control values ​​to the different geographical locations in the field. Controllable subsystem (216); and A control system (214) generates control signals to control the controllable subsystem (216) based on the geographical location of the agricultural machine (100) and the control values ​​in the functional predictive agriculture map.

2. The agricultural machinery according to claim 1, wherein, The prediction map generator includes: A predictive power characteristic map generator generates a functional predictive power characteristic map that maps the predicted power characteristics of the agricultural machinery to the different geographical locations in the field.

3. The agricultural machinery according to claim 2, wherein, The predicted power characteristic map generator generates the functional predicted power characteristic map that maps the predicted power characteristics of the propulsion system of the agricultural machine.

4. The agricultural machinery according to claim 1, wherein, The prediction map generator includes: A predictive machine characteristic map generator generates a functional predictive machine characteristic map that maps one or more of the predicted machine speed value, internal material distribution value, grain loss value, extraneous characteristic value, and grain quality value to the different geographical locations in the field.

5. The agricultural machinery according to claim 4, wherein, The control system includes: A setting controller generates a machine setting control signal based on the detected geographical location and the functional predictive machine characteristic map, and controls the controllable subsystem based on the machine setting control signal.

6. The agricultural machinery according to claim 1, wherein, The prediction map generator includes: A predictive operator command graph generator generates a functional predictive operator command graph that maps predicted operator commands to the different geographical locations in the field.

7. The agricultural machinery according to claim 6, wherein, The control system includes: A setting controller is configured to generate an operator command control signal that indicates the predicted operator command based on the detected geographic location and the functional predictive operator command map, and to control the controllable subsystem to execute the operator command based on the operator command control signal.

8. A computer-implemented method for controlling agricultural machinery (100), comprising: Obtain a priori information map (258), the priori information map including values ​​of a topographic feature corresponding to different geographical locations in the field; Detect the geographical location of the agricultural machinery (100); The values ​​of agricultural characteristics corresponding to geographical location are detected using field sensors (208); A predictive agricultural model is generated based on the values ​​of the terrain features in the prior information map (258) at the geographical location and the values ​​of the agricultural features detected by the field sensor (208) at the geographical location. The predictive agricultural model models the relationship between the terrain features and the agricultural features. Based on the values ​​of the terrain characteristics in the prior information map (258) and based on the predictive agriculture model, a functional predictive agriculture map of the field is generated, which maps the predicted control values ​​to the different geographical locations in the field. and The controllable subsystem (216) is controlled based on the geographical location of the agricultural machine (100) and the control values ​​in the functional predictive agriculture map.

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