Predictive 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] Various conditions in the field can have several adverse effects on harvesting operations. Therefore, when encountering these conditions during harvesting, operators may try to modify the harvester 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 4A This is a block diagram illustrating an example of a prediction model generator and a prediction graph generator.

[0011] Figure 4B This is a block diagram illustrating an example of a prediction model generator and a prediction graph generator.

[0012] Figure 5 This is a flowchart illustrating an example of the operation of an agricultural harvester in receiving maps, detecting characteristics, and generating functional prediction maps used to control the agricultural harvester during harvesting operations.

[0013] Figure 6A This is a block diagram illustrating an example of a prediction model generator and a prediction graph generator.

[0014] Figure 6B This is a block diagram showing some examples of field sensors.

[0015] Figure 7 The flowchart illustrates an example of the operation of an agricultural harvester, including generating a functional prediction map using prior information maps and field sensor inputs.

[0016] Figure 8 This is a block diagram illustrating an example of a control area generator.

[0017] Figure 9 It is a diagram. Figure 8 The flowchart shows an example of the operation of the control area generator.

[0018] Figure 10 The diagram illustrates an example of how a control system operates when selecting a target setpoint to control an agricultural harvester.

[0019] Figure 11 This is a block diagram illustrating an example of an operator interface controller.

[0020] Figure 12 This is a flowchart illustrating an example of an operator interface controller.

[0021] Figure 13 This is an illustrative diagram showing an example of an operator interface display.

[0022] Figure 14 This is a block diagram illustrating an example of an agricultural harvester communicating with a remote server environment.

[0023] Figures 15 to 17 An example of a mobile device that can be used in agricultural harvesters is shown.

[0024] Figure 18 This is a block diagram illustrating an example of a computing environment that can be used for agricultural harvesters. Detailed Implementation

[0025] 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.

[0026] In some examples, this specification relates to generating predictive maps by combining them with prior data and field data acquired concurrently with agricultural operations, and more specifically, generating predictive speed maps. In some examples, predictive speed maps can be used to control agricultural machinery (e.g., combine harvesters). As described above, predictive speed maps can improve the performance of combine harvesters to control their speed as they encounter different conditions in the field. For example, if the crop is mature, the weeds may still be green, resulting in increased moisture content in the biomass encountered by the combine harvester. This problem is exacerbated when weed patches are wet (e.g., shortly after rainfall or when weed patches contain dew) and may be exacerbated before the weeds have a chance to dry. Therefore, when a combine harvester encounters an area of ​​increased biomass, the operator can slow down the harvester to maintain a constant feed rate of material through it. Maintaining a constant feed rate helps maintain the performance of the combine harvester.

[0027] The performance of agricultural harvesters can be adversely affected by a variety of different criteria. These criteria can include variations in biomass, crop condition, topography, soil properties, sowing characteristics, or other conditions. Therefore, it can also be useful to control the harvester speed based on other conditions that may exist in the field. For example, by controlling the harvester speed based on the biomass encountered by the harvester, the crop condition of the crop being harvested, the topography of the field being harvested, the soil properties of the soil in the field being harvested, the sowing characteristics of the field being harvested, the yield of the field being harvested, or other conditions present in the field, the harvester's performance can be maintained at an acceptable level.

[0028] Some current systems provide vegetation index maps. Vegetation index maps illustratively map vegetation index values ​​(which can indicate vegetation growth) at different geographic locations within a field of interest. An example of a vegetation index includes the normalized difference vegetation index (NDVI). Many other vegetation indices also exist within the scope of this disclosure. In some examples, vegetation indices can be derived from sensor readings of one or more electromagnetic radiation bands reflected by the plants. Without limitation, these electromagnetic radiation bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

[0029] Vegetation index maps can be used to identify the presence and location of vegetation. In some examples, these maps enable the identification and georeferencing of vegetation in the presence of bare soil, crop residues, or other vegetation (including crops or other weeds).

[0030] In some examples, biomass maps are provided. Biomass maps illustratively map measurements of biomass at different locations within a field being harvested. Biomass maps can be generated from vegetation index values, historically measured or estimated biomass levels, images or other sensor readings acquired during previous or prior operations in the field, or otherwise. In some examples, biomass can be modulated by a factor representing a portion of the total biomass passing through the agricultural harvester. For maize, this factor is typically around 50%. For moisture content in the harvested crop material, this factor is typically 10%–30%. In some examples, this factor may represent a portion of weed material or weed seeds. In some examples, this factor may represent a portion of one crop in an intercropping mixture.

[0031] In some examples, crop state maps are provided. Crop state can define whether the crop is lodged, standing, partially lodged, or the orientation of lodged or partially lodged crops relative to the land surface or relative to a compass direction. Crop state maps illustratively map the crop state at different locations within a field being harvested. Crop state maps can be generated from aerial or other imagery of the field, from images or other sensor readings acquired in the field during prior or previous operations, or otherwise prior to harvest.

[0032] In some examples, a seeding map is provided. A seeding map maps seeding characteristics (such as seed location, seed type, or seed population) to different locations in the field. Seeding maps can be generated during previous seeding operations in the field. Seeding maps can be derived from control signals used by the seeder when planting seeds, or from sensors on the seeder confirming that seeds have been metered or planted. The seeder may also include geolocation sensors to geolocate seeding characteristics in the field.

[0033] In some examples, soil property maps are provided. A soil property map illustratively maps measurements of one or more soil properties at different locations within a field being harvested, such as soil type, soil chemical composition, soil structure, residue cover, tillage history, or soil moisture. Soil property maps can be generated from vegetation index values, from historically measured or estimated soil properties, from images or other sensor readings acquired during prior or previous operations in the field, or otherwise.

[0034] In some examples, additional prior information maps are provided. Such prior information maps may include topographic maps of fields being harvested, predicted yield maps of fields being harvested, or other prior information maps.

[0035] In some examples, this description relates to using field data acquired concurrently with agricultural operations, combined with data from a map, to generate a predictive map, and more specifically, a predictive cut height characteristic map. In some examples, the predictive cut height characteristic map can be used to control agricultural machinery (e.g., agricultural harvesters). The predictive cut height characteristic map may include georegistered, predicted cut height values ​​or values ​​for cut height variability. In such examples, this discussion also includes receiving, in addition to receiving a prior information map (e.g., a prior topographic map), a predictive map that predicts characteristics based on the prior information map and its relationship with field sensor outputs. In one example, the predictive map is a predictive velocity map. In one example, the predictive velocity map is the functional predictive velocity map described herein. In other examples, the predictive velocity map may be created based on other prior information maps or may be generated in other ways.

[0036] Therefore, this discussion pertains to a system that receives one or more maps of a field, or maps generated during previous or a priori operations, and also uses field sensors to detect variables indicating one or more characteristics. The system generates a model that models the relationship between values ​​on the received one or more maps and output values ​​from the field sensors. This model is used to generate a functional prediction map that predicts, for example, cut height or cut height variability at different locations in the field. The functional prediction map generated during harvesting operations can be presented to the operator or other users, and / or used to automatically control agricultural harvesters during harvesting operations.

[0037] Agricultural harvesters are typically equipped with a header that is movable relative to the ground or other features (e.g., vertical movement and rotatable movement about one or more axes, such as pitching (e.g., tilting forward and backward)) or tumbling (e.g., tilting left and right across the width of the header). For example, one or more hydraulic actuators (or other actuators) are coupled between the feeder housing and the harvester frame, although they may also be coupled in other locations. One or more hydraulic actuators can actuate the movement of the header, such as raising and lowering it. In some cases, operating the harvester requires maintaining a desired relationship between the header and features such as the surface of the field or a portion of the harvester. For example, one or more hydraulic actuators can be used to maintain the header at a selected distance from the field surface, at a height above the field surface, at an angle relative to the field surface, etc. The harvester operator can set an initial height setting to establish a height above the field surface, where the operator wishes to maintain the header at that height during operation. Aside from other position settings (e.g., roll, pitch, etc.), the height of the header above the field determines the cutting height. The cutting height is the height at which the vegetation in the field will be cut. The control system (or, in some examples, the operator) detects variables indicating the header height or cutting height and controls actuators that actuate the movement of the header to move it to maintain the desired height. These variables can be detected using one or more sensors.

[0038] Fields may exhibit variations in terrain characteristics, such as elevation changes, necessitating adjustments to the header of agricultural harvesters to maintain the desired cutting height as the harvester moves across the field. These terrain changes can occur too rapidly on the harvester, hindering effective header movement. For example, the harvester may be traveling at speeds that do not allow for timely header adjustments. This inability to make timely header adjustments at specific harvester speeds can stem from factors such as the responsiveness of machine actuators (e.g., response speed), sensor capabilities, or human operator skill. Consequently, the header may slam into the ground, cut vegetation undesirably, and cause other harmful effects.

[0039] Therefore, this description is made for a system that receives maps such as topographic maps, predicted velocity maps, or both. The system includes field sensors capable of detecting header height, cutting height, or both. The system further includes a model generator that identifies the relationship between values ​​(e.g., topographic feature values ​​and velocity values) in the received map and features (e.g., cutting height, cutting height variability, etc.) detected by the field sensors. The model generator generates a predicted cutting height feature model. A predicted cutting height feature model is used by a predicted map generator to generate a functional predicted cutting height feature map that maps the predicted cutting height feature values. The functional predicted cutting height feature map generated during harvesting operations can be presented to the operator or other users and / or used to automatically control the agricultural harvester during harvesting operations.

[0040] 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.

[0041] 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.

[0042] The threshing machine 110 exemplarily includes a threshing drum 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.

[0043] 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 gathers 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. Operator commands are commands issued by the operator. 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 selected sensitivity level. If the sensitivity level is set at a higher 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.

[0044] 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 roller 112 that rotates against a concave plate 114. In a separator 116, a separator roller moves the threshed crop, while a discharge agitator 126 moves a portion of the residue toward the residue subsystem 138. That portion of the residue conveyed to the residue subsystem 138 is shredded by the 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 remover (not shown), such as a seed bagger or other seed collector, or a seed shredder or other seed crusher.

[0045] The grain falls into the grain cleaning subsystem 118. A husk sieve 122 separates larger pieces of grain, while 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.

[0046] 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.

[0047] Figure 1 It is also shown that, in one example, the agricultural harvester 100 includes a machine 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.

[0048] Machine speed sensor 146 senses the travel speed of the agricultural harvester 100 on the ground. Machine speed sensor 146 can sense the travel speed of the agricultural 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 Area Reconnaissance and Ranging) system, or various other systems or sensors that provide an indication of travel speed.

[0049] 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.

[0050] 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.

[0051] 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 cutting height sensor that senses the height at which vegetation on the field is cut; 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 drum 112 and the concave plate 114; and a threshing drum speed sensor. The harvester 100 includes a roller speed sensor for sensing the roller speed of roller 112; a husk sieve gap sensor for sensing the opening size in husk sieve 122; a sieve mesh gap sensor for sensing the opening size in sieve 124; a material other than grain (MOG) moisture sensor for sensing the moisture level of 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 for sensing the orientation of the harvester 100; and a crop property sensor for sensing 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 can 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.

[0052] The header height sensor can take many different forms. For example, it can be a potentiometer or angle encoder that senses the rotation of the header or the angular position of the header relative to the frame of the harvester 100. Knowing the dimensions of the header 102 and the harvester 100, the height of the header 102 above the field (e.g., ground 111) can be determined using the sensor data. The header height sensor can also be a sensor that directly senses the height of the header 102 above the field. Examples of sensors that can directly sense the height of the header above the surface of the field include, but are not limited to: radar, lidar, ultrasonic sensors, laser sensors, or mechanical sensors.

[0053] In one example, the header height sensor may be a mechanical sensor assembly including means coupled to the header and configured to contact the surface of the field and generate a sensor signal indicating the height of the header relative to the surface of the field. Various other header height sensors are also considered herein. The header height output of the header height sensor can be processed (e.g., through aggregation or various other processing, such as statistical summarization) to provide an indication of header height variability for a given area of ​​the field. Header height variability can indicate changes in header height relative to a given area. Header height variability can be expressed in various ways, such as average header height for a specific area, average header height deviation for a specific area, or variation for a specific area, and many other expressions.

[0054] The agricultural harvester 100 may also include a cut height sensor in the form of an optical sensor (e.g., a camera) and various other sensors such as radar, lidar, ultrasonic, or laser sensing devices. The cut height sensor is configured to detect characteristics indicating the cut height (i.e., the height at which the cutter cuts vegetation). For example, the cut height sensor may detect the height of remaining crop residue extending above the field surface in a field area surrounding the agricultural harvester 100 (e.g., behind the agricultural harvester relative to its direction of travel, or behind a component of the agricultural harvester (e.g., the cutter head 102)) as an indication of the cut height. Furthermore, the cut height output of the cut height sensor may be processed (e.g., through aggregation or various other processes, such as statistical summarization) to provide an indication of cut height variability in a specific area of ​​the field (e.g., the area behind the agricultural harvester 100). Cut height variability can indicate changes in the cut height for a specific area of ​​the field. Variations in cutting height can be expressed in a variety of ways, such as the average cutting height for a specific area, the deviation or variation of the average cutting height for a specific area, and many other expressions.

[0055] In some examples, the output of the header height sensor can be used as an indication of the cutting height. For instance, by knowing the height of the header relative to the field surface, the height of the vegetation cut can also be known or estimated.

[0056] Before describing how the agricultural harvester 100 generates a functional predictive speed map and uses that 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 plot describes receiving a general type of prior information map and combining information from the prior information map with georegistered sensor signals generated by field sensors, where the sensor signals indicate characteristics of the field, such as characteristics of the field itself, crop characteristics of crops or grains present in the field, or characteristics of agricultural harvesters. Field characteristics may include (but are not limited to): field characteristics such as slope, weed density, weed type, soil moisture, and surface quality; crop characteristics such as crop height, crop moisture, crop density, and crop condition; grain characteristics such as grain moisture, grain size, and grain test weight; and machine operating characteristics such as machine speed, outputs from different controllers, and machine performance such as loss level, work quality, fuel consumption, and power utilization. Relationships are identified between characteristic values ​​obtained from or derived from the field sensor signals and values ​​in the prior information map, 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, such as controlling one or more subsystems of an agricultural harvester. In some cases, functional prediction maps may be presented to users, such as operators of agricultural machinery (e.g., combine harvesters). Functional prediction maps can be presented to users visually (e.g., via a display), tactilely, or audibly. Users can interact with the functional prediction map to perform editing operations and other user interface actions. In some cases, functional prediction maps may be used to control agricultural machinery (e.g., combine harvesters), presented to operators or other users, or presented to operators or users to facilitate operator or user interaction, among one or more of these methods.

[0057] In reference Figure 2 , Figure 3A and Figure 3B After describing the general method, refer to Figure 4 and... Figure 5 More 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.

[0058] 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.

[0059] Figure 2 The agricultural harvester 100 is also shown to receive a priori information map 258. As described below, for example, the priori information map 258 includes vegetation index maps, biomass maps, crop status maps, topographic maps, soil property maps, sowing maps, or maps from prior or previous operations. However, the priori information map 258 may also encompass other types of data obtained prior to the harvesting operation or maps from prior or previous operations. Figure 2The 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.

[0060] Using communication system 206 or other methods, prior information map 258 can be downloaded 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.

[0061] 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.

[0062] The field sensor 208 can be referenced above. Figure 1 Any sensors described. Field sensors 208 include onboard sensors 222 mounted on the agricultural harvester 100. For example, these sensors may include information about... Figure 1Any of the sensors discussed, sensing sensors (e.g., forward-looking monocular or stereo camera systems and image processing systems), and image sensors inside the agricultural harvester 100 (e.g., a grain cleaning camera, or a camera installed to identify material leaving the agricultural harvester 100 through or from the residue subsystem). Field sensor 208 also includes a remote field sensor 224 for capturing 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.

[0063] Predictive model generator 210 generates a model indicating the relationship between values ​​sensed by field sensors 208 and metrics mapped to the field via prior information map 258. For example, if prior information map 258 establishes a mapping between vegetation index values ​​and different locations in the field, and field sensors 208 are sensing values ​​indicating machine speed, then prior information variable to field variable model generator 228 generates a predictive speed model that models the relationship between vegetation index values ​​and machine speed values. Predictive speed models can also be generated based on vegetation index values ​​from prior information map 258 and multiple field data values ​​generated by field sensors 208. Then, predictive map generator 212 uses the predictive speed model generated by predictive model generator 210 to generate a functional predictive speed map that predicts the target machine speed sensed by field sensors 208 at different locations in the field based on prior information map 258.

[0064] 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.

[0065] In the example where prior information map 258 is a vegetation index map and field sensor 208 senses values ​​indicating machine speed, prediction map generator 212 can use the vegetation index values ​​in prior information map 258 and the model generated by prediction model generator 210 to generate a functional prediction map 263 predicting the target machine speed at different locations in the field. Prediction map generator 212 then outputs prediction map 264.

[0066] 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 vegetation index values ​​and machine speed, then given vegetation index values ​​at different locations across the field, prediction map generator 212 generates prediction map 264 predicting target machine speed values ​​at different locations across the field. Prediction map 264 is generated using vegetation index values ​​at those locations obtained from the vegetation index map and the relationship between vegetation index values ​​and machine speed obtained from the prediction model.

[0067] The following will now 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.

[0068] In some examples, the data type in the prior infographic 258 differs from the data type sensed by the field sensor 208, while the data type in the prediction infographic 264 is the same as that sensed by the field sensor 208. For example, the prior infographic 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be yield. The prediction infographic 264 could then be a predicted yield map mapping the predicted yield values ​​to different geographic locations in the field. In another example, the prior infographic 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be crop height. Therefore, the prediction infographic 264 could be a predicted crop height map mapping the predicted crop height values ​​to different geographic locations in the field.

[0069] 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 may be a vegetation index map, and the variable sensed by the field sensor 208 may be crop height. Therefore, the prediction map 264 may be a predicted biomass map mapping predicted biomass values ​​to different geographic locations in the field. In another example, the prior information map 258 may be a vegetation index map, and the variable sensed by the field sensor 208 may be yield. Therefore, the prediction map 264 may be a predicted speed map mapping predicted harvester speed values ​​to different geographic locations in the field.

[0070] In some examples, the prior information map 258 is derived from prior or previous traversals of the field during a prior or previous operation, and the 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.

[0071] In some examples, the prior information map 258 is derived from prior or previous traversals of the field during a prior or previous operation, and the data type is the same as that sensed by the field sensor 208. Similarly, 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. Therefore, the prediction map 264 could be a predicted yield map that maps predicted yield values ​​to different geographic locations within 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 harvest operation. The prediction map generator 210 then uses the prediction model to generate the predicted yield map.

[0072] In another example, the prior information map 258 could be a weed density map generated during a previous or prior operation, such as from a sprayer, and the variable sensed by the field sensor 208 could be weed density. Thus, the prediction map 264 could be a predicted weed density map mapping the predicted weed density values ​​to different geographic locations in the field. In such an example, the weed density map at spraying time is recorded in a georegistration manner and provided to the agricultural harvester 100 as the prior information map 258 for weed density. The field sensor 208 can detect the weed density at geographic locations in the field, and then the prediction model generator 210 can build a prediction model that models the relationship between the weed density at harvest and the weed density at spraying time. This is because the sprayer affects weed density at spraying time, but weeds may still regrow in similar areas at harvest time. However, the weed area at harvest time may have different densities based on factors such as harvest time, weather, and weed type.

[0073] In some examples, prediction map 264 may be provided to control zone generator 213. Control zone generator 213 groups adjacent portions of a region into one or more control zones based on data values ​​associated with adjacent portions of the region in prediction map 264. A control zone may include two or more consecutive portions of a region (e.g., a field), for which control parameters corresponding to the control zone for controlling the controllable subsystem are constant. For example, changing the response time of the controllable subsystem 216 settings may not satisfactorily respond to changes in values ​​contained in a map such as prediction map 264. In this case, control zone generator 213 parses the map and identifies control zones with defined dimensions adapted to the response time of the controllable subsystem 216. In another example, control zones may be sized to reduce wear caused by excessive actuator movement due to continuous adjustment. In some examples, different groups of control zones may exist for individual controllable subsystems 216 or groups of controllable subsystems 216. Control zones may be added to prediction map 264 to obtain prediction control zone map 265. Therefore, except that Figure 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 a control area. 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 a control area (e.g., predicted map 264). In another example, the functional predicted map 263 does include a control area (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 predicted map 264 and predicted control area map 265 accordingly.

[0074] 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 displaying only the generated control regions. In some examples, the control regions can be used to control and / or calibrate the agricultural harvester 100. In other examples, the control regions can be presented to the operator 260 for controlling or calibrating the agricultural harvester 100, and in still other examples, the control regions can be presented to the operator 260 or another user, or stored for later use.

[0075] 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.

[0076] 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 type of weeds displayed on the map based on the operator's observation. The setting controller 232 can generate control signals to control 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, concave plate clearance, drum settings, grain 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 receive various inputs indicating the feed rate of material through the harvester 100 and can control various subsystems such as the propulsion subsystem 250 and the machine actuator 248 to control the feed rate based on prediction diagram 264 or prediction control area diagram 265, or both. For example, as the harvester 100 approaches a weed patch with a density value higher than a selected threshold, the feed rate controller 236 can generate a control signal to control the propulsion subsystem 250 to reduce the speed of the harvester 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 diagram 264, prediction control area diagram 265, or both to control the belt conveyor or other belt conveyor functions.The cover plate 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 plate included on the harvester. 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, based on the different types of seeds and weeds passing through the agricultural harvester 100, a specific type of machine cleaning operation or the frequency of performing cleaning operations can be controlled. Other controllers included on the agricultural harvester 100 can also control other subsystems based on prediction diagram 264 or prediction control area diagram 265, or both.

[0077] 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.

[0078] 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 vegetation index map generated from aerial imagery. Another priori information map may be a map generated during a previous pass through the field, which may be performed by different machines (e.g., sprayers, planting machines, seeding machines, or unmanned aerial vehicles (UAVs) or other machines) performing priors or prior operations in the field. The process of selecting one or more prior 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 can be collected based on aerial images obtained in the previous year, earlier in the current growing season, or at other times. The data can be based on data detected in ways other than using aerial images. For example, the agricultural harvester 100 can be equipped with sensors (e.g., internal optical sensors) that identify weed seeds or other types of material leaving the agricultural harvester 100. Weed seed or other data detected by the sensors during the previous year's harvest can be used as data for generating the prior information map 258. The sensed weed data or other data can be combined with other data to generate the prior information map 258. For example, based on the quantity of weed seeds leaving the agricultural harvester 100 at different locations and based on other factors (e.g., whether the seeds are broadcast or fall in heaps; weather conditions, such as wind, at the time of seed fall or dispersal; drainage conditions around the field that may cause seed movement; or other information), the location of those weed seeds can be predicted, such that the prior information map 258 maps the predicted seed locations in the field. The communication system 206 can be used to send data for the prior information map 258 to the agricultural harvester 100 and store it in the data storage device 202. The communication system 206 can also be used to provide the data for the prior information map 258 to the agricultural harvester 100 in other ways, as indicated by box 286 in the flowchart of Figure 3. In some examples, the prior information map 258 can be received by the communication system 206.

[0079] At the start of the harvesting operation, field sensor 208 generates sensor signals indicating one or more field data values, which indicate characteristics such as velocity characteristics, as indicated in box 288. Examples of field sensor 288 are discussed with reference to boxes 222, 290, and 226. As described above, field sensor 208 includes: an airborne 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 velocity data from geolocation sensor 204 is used to georeference the data from the airborne sensor.

[0080] 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.

[0081] 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.

[0082] 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 layer may represent a data type different from that of another layer, or the layers may have the same data type acquired at different times. The individual maps in the two or more different maps, or the individual layers in the two or more different layers of a map, map 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 various types of variables mapped by the prior information map 258 and the various types of variables sensed by the field sensors 208. The prediction map generator 212 can use the prediction model and the various maps or layers in the prior information map 258 to generate a functional prediction map 263 that predicts the value of each sensed characteristic (or characteristic related to the sensed characteristic) sensed by the field sensor 208 at different locations in the harvesting field.

[0083] 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 are described with reference to boxes 296, 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.

[0084] Control zone generator 213 can divide prediction map 264 into control zones based on values ​​on prediction map 264. Geographically contiguous values ​​within each other's thresholds 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 in box 295. Prediction map generator 212 configures prediction map 264 for presentation to an operator or other user. Control zone generator 213 can configure prediction control zone map 265 for presentation to an operator or other user. This is indicated in 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 and authorization system may be provided to implement the verification and authorization process. For example, there may be a hierarchy of individuals authorized to view and modify the map and other presented information. As an example, an onboard display device may display the map locally on the machine in approximately real-time, or the map may be generated at one or more remote locations, or both. In some examples, each physical display device at each location may 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 (e.g., a supervisor at a remote location) may be able to see prediction graph 264 on the display but be prevented from making any changes. A manager at a separate remote location may be able to see all elements on prediction graph 264 and also be able to change prediction graph 264. In some cases, prediction graph 264, which can be accessed and changed by a manager at a remote location, can be 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.

[0085] In box 298, the control system receives input from the geolocation sensor 204 and other field sensors 208. Specifically, in box 300, the control system 214 detects and identifies the geolocation of the harvester 100 from the geolocation sensor 204. 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.

[0086] 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.

[0087] As an example, the generated prediction map 264, in the form of a predicted velocity map, can be used to control one or more subsystems 216. For example, the predicted velocity map may include velocity values ​​georeferenced to locations within a field being harvested. Velocity values ​​from the predicted velocity map can be extracted and used to control the propulsion subsystem 250. By controlling the propulsion subsystem 250, the feed rate of material moving through the agricultural harvester 100 can be controlled. Similarly, the header height can be controlled to draw in more or less material, and therefore the header height can also be controlled to control the feed rate of material through the agricultural harvester 100. In other examples, if the prediction map 264 maps weed height with respect to locations in the field, control of the header height can be implemented. For example, if values ​​present in the predicted weed map indicate that one or more areas have a weed height of a first height, the header and reel controller 238 can control the header height such that the header, during the harvesting operation, is positioned above the first height of weeds in the one or more areas having weeds at the first height. Therefore, the header and reel controller 238 can be controlled using georegistered values ​​present in the predicted weed map to position the header at a height higher than the predicted height value of the weeds obtained from the predicted weed map. Furthermore, when the agricultural harvester 100 travels through the field, the header and reel controller 238 can automatically change the header height using georegistered values ​​obtained from the predicted weed map. The foregoing examples of using predicted weed maps concerning weed height and density are provided only as examples. Therefore, values ​​obtained from predicted weed maps 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] In other examples, the learning trigger criteria may be based on how much the field sensor data from field sensor 208 has changed over time or compared to previous or prior values. For example, if the change within the field sensor data (or the relationship between the field sensor data and the information in the prior information map 258) is within a selected 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 is outside the selected range, greater than a defined amount, or above a threshold, 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 the amount of data exceeding a selected range, or the magnitude of the change in the relationship between the field sensor data and the information in the prior information map 258) can be used as triggers to induce the generation of new prediction models and prediction maps. Continuing with the example described above, the threshold, range, and limited quantity can be set to default values, set by an operator or user through a user interface, set by an automation system, or set in other ways.

[0092] 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.

[0093] In some cases, operator 260 may also edit prediction graph 264 or prediction control area graph 265, or both. This editing may change the values ​​on prediction graph 264, and / or change the size, shape, position, or presence of control areas on prediction control area graph 265. Box 321 shows that the edited information can be used as a learning trigger criterion.

[0094] 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 the operator's expectation that the controllable subsystem will operate in a manner different from that commanded by control system 214. Therefore, operator 260's manual change of settings may cause one or more of the following to occur based on the adjustments made by operator 260 (as shown in box 322): causing predictive model generator 210 to relearn the model, causing predictive graph generator 212 to regenerate graph 264, causing control area generator 213 to regenerate one or more control areas on predictive control area graph 265, and causing control system 214 to relearn its control algorithm or perform machine learning on one or more components of controller components 232 to 246 in control system 214. Box 324 indicates the use of other triggered learning criteria.

[0095] In other examples, relearning can be performed periodically or intermittently based on, for example, selected time intervals (e.g., discrete or variable time intervals), as indicated in box 326.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] Figure 4A Is Figure 1 A block diagram of a portion of an agricultural harvester 100 is shown. Specifically, among other things, Figure 4A Examples of the prediction model generator 210 and the prediction graph generator 212 are shown in more detail. Figure 4A The information flow between the various components is also illustrated. The predictive model generator 210 receives a priori information map 258, which may be a vegetation index map 332, a predicted yield map 333, a biomass map 335, a crop status map 337, a topographic map 339, a soil property map 341, or a sowing map 343 as a priori information map. In other examples, the predictive model generator 210 may receive various other maps 401, such as other prior information maps or other predictive maps. The predictive model generator 210 also receives a geographic location 334 or an indication of a geographic location from the geographic location sensor 204. The field sensor 208 illustratively includes a machine speed sensor 146, or a sensor 336 sensing the output from the feed rate controller 236, and a processing system 338. The processing system 338 processes sensor data from the machine speed sensor 146, or from the sensor 336, or from both, to generate processed data, some examples of which are described below.

[0100] In some examples, sensor 336 may be a sensor that generates a signal indicating the control output from feed rate controller 236. The control signal may be a speed control signal, or other control signals applied to controllable subsystem 216 to control the feed rate of material through agricultural harvester 100. Processing system 338 processes the signals obtained via sensor 336 to generate processed data 340 identifying the speed of agricultural harvester 100.

[0101] In some examples, raw or processed data from (one or more) field sensors 208 can be presented to operator 260 via operator interface mechanism 218. Operator 260 can be on the agricultural harvester 100 or at a remote location.

[0102] This discussion is based on the example where the field sensor 208 is the machine speed sensor 146. It should be understood that this is only one example, and other examples of the aforementioned sensors from which machine speed can be derived, such as the field sensor 208, are also considered in this document. Figure 4A As shown, the example prediction model generator 210 includes one or more of the following: a vegetation index (VI) value-to-vegetation model generator 342, a biomass-to-vegetation model generator 344, a topography-to-vegetation model generator 345, a yield-to-vegetation model generator 347, a crop state-to-vegetation model generator 349, a soil property-to-vegetation model generator 351, and a sowing property-to-vegetation model generator 346. In other examples, the prediction model generator 210 may include more than Figure 4A The examples show more, fewer, or different components. Therefore, in some examples, the predictive model generator 210 may also include other items 348, which may include other types of predictive model generators to generate other types of models.

[0103] Model generator 342 identifies the relationship between the detected machine speed in the processed data 340 at a geographic location corresponding to the location where the processed data 340 was obtained and one or more vegetation index values ​​from vegetation index map 332 corresponding to the same location in the field where the speed characteristic was detected. Based on this relationship established by model generator 342, model generator 342 generates a predicted speed model. Speed ​​map generator 352 uses this predicted speed model to predict target or expected machine speed values ​​at said different locations in the field based on the georegistered vegetation index values ​​included in vegetation index map 332 at said different locations in the field.

[0104] Model generator 344 identifies the relationship between machine speed at a geographic location corresponding to processed data 340 and biomass values ​​at the same geographic location, as represented in processed data 340. Similarly, biomass values ​​are georegistered values ​​included in biomass map 335. Model generator 344 then generates a predicted speed model, which speed map generator 352 uses to predict the target machine speed at a location in the field based on the biomass value at that location.

[0105] Model generator 345 identifies the relationship between machine speeds represented in processed data 340 at geographic locations corresponding to processed data 340 and terrain feature values ​​at the same geographic locations. Similarly, the terrain feature values ​​are georegistered values ​​included in topographic map 339. Model generator 345 then generates a predicted speed model, which speed map generator 352 uses to predict the target or expected machine speed at a location in the field based on the terrain feature values ​​at that location.

[0106] Model generator 346 identifies the relationship between the machine speed at a specific location in the field, as identified by processed data 340, and the seeding characteristic value at that same location from seeding characteristic map 343. Model generator 346 generates a predicted speed model, which speed map generator 352 uses to predict the target or expected machine speed at that specific location in the field based on the seeding characteristic value at that specific location.

[0107] Model generator 347 identifies the relationship between machine speed at a geographic location corresponding to processed data 340 and yield values ​​at the same geographic location, as represented in processed data 340. Similarly, the yield values ​​are georegistered values ​​included in the predicted yield map 333. Model generator 347 then generates a predicted speed model, which speed map generator 352 uses to predict the target or expected machine speed at a location in the field based on the yield values ​​at that location.

[0108] Model generator 348 identifies the relationship between machine speeds represented in processed data 340 at geographic locations corresponding to processed data 340 and crop state values ​​at the same geographic locations. Similarly, crop state values ​​are georegistered values ​​included in crop state map 337. Model generator 348 then generates a predicted speed model, which speed map generator 352 uses to predict the target or expected machine speed at a location in the field based on the crop state values ​​at that location.

[0109] Model generator 351 identifies the relationship between machine speeds at geographic locations corresponding to processed data 340 and soil property values ​​at the same geographic location, as represented in processed data 340. Similarly, the soil property values ​​are georegistered values ​​included in soil property map 341. Model generator 351 then generates a predicted speed model, which speed map generator 352 uses to predict the target or expected machine speed at a location in the field based on soil property values ​​at that location.

[0110] In light of the foregoing, the prediction model generator 210 is operable to generate multiple prediction velocity models, such as one or more prediction velocity models generated by model generators 342, 344, 345, 346, 347, 348, and 351. In another example, two or more of the aforementioned prediction velocity models can be combined into a single prediction velocity model that predicts the target machine speed based on vegetation index values, biomass values, topography, yield, sowing characteristics, crop status, or soil properties at different locations in the field. Any one of these velocity models or a combination thereof in... Figure 4A The values ​​are jointly represented by prediction model 350.

[0111] The prediction model 350 is provided to the prediction map generator 212. Figure 4A In one example, the prediction map generator 212 includes a velocity map generator 352. In other examples, the prediction map generator 212 may include more, fewer, or different map generators. Therefore, in some examples, the prediction map generator 212 may include additional items 358, which may include other types of map generators to generate velocity maps. The velocity map generator 352 receives a prediction model 350 and one or more prior information maps 258, the prediction model 350 predicting the target machine velocity based on values ​​from the one or more prior information maps 258, and the velocity map generator 352 generates prediction maps that predict the target machine velocity at different locations in the field.

[0112] Prediction map generator 212 outputs one or more functional predicted speed maps 360 that predict the speeds of one or more target machines. The functional predicted speed maps 360 predict the speeds of the target machines at different locations in the field. The functional predicted speed maps 360 can be provided to control zone generator 213, control system 214, or both. Control zone generator 213 generates control zones and incorporates these control zones into the functional predicted maps (i.e., prediction maps 360) to produce a predicted control zone map 265. One or both of prediction maps 264 and predicted control zone maps 265 can be provided to control system 214, which generates control signals based on prediction maps 264, predicted control zone maps 265, or both, to control one or more controllable subsystems 216 (e.g., propulsion subsystem 250).

[0113] Figure 4B Is Figure 1 A block diagram of a portion of an agricultural harvester 100 is shown. Specifically, among other things, Figure 4B Examples of the prediction model generator 210 and the prediction graph generator 212 are shown in more detail. Figure 4BThe diagram also illustrates the information flow between the various components shown. Predictive model generator 210 receives topographic map 339 as a priori information map. Topographic map 332 includes geo-registered topographic feature values. Predictive model generator 210 also receives a predicted velocity map (e.g., a functional predicted velocity map 360). Functional predicted velocity map 360 includes geo-registered predicted velocity values. In other examples, predictive model generator 210 may receive other maps 401, such as other prior information maps or other predicted maps, for example, predicted velocity maps generated in a different manner than those generated for example, functional predicted velocity map 360.

[0114] Generator 210 also receives a geographic location indicator 334 from geographic location sensor 204. Field sensors 208 illustratively include a cut height sensor (e.g., cut height sensor 1336), a header height sensor (e.g., header height sensor 1337), and a processing system 1338. Cut height sensor 1336 detects a characteristic indicating the height at which vegetation has been cut in a field. In some examples, cut height sensor 1336 is an optical sensor that detects the cut vegetation material and generates sensor data indicating the height at which the vegetation material has been cut. Header height sensor 1337 can sense a variety of different characteristics (e.g., a variety of different characteristics indicating header height or header position (such as pitch or roll)) and generate a sensor signal indicating header height. In some examples, the output of header height sensor 1337 can also indicate the cut height. In some cases, cut height sensor 1336 or header height sensor 1337, or both, may be located on agricultural harvester 100. The processing system 1338 processes sensor data generated from the cutting height sensor 1336 or the header height sensor 1337 or both to generate processed sensor data 1340, such as processed sensor data indicating cutting height, cutting height variability, header height, or header height variability, some examples of which are described below.

[0115] This discussion is based on an example in which the cut height sensor 1336 senses the cut height characteristic, and the header height sensor 1337 senses the height of the header on the agricultural harvester 100 above the field surface. Figure 4B As shown, the example prediction model generator 210 includes one or more of the following: a velocity-to-cut-height model generator 1342, a velocity-to-cut-height model generator 1343, a velocity-to-cut-height variability model generator 1345, a velocity-to-cut-height variability model generator 1344, a terrain-to-cut-height model generator, and a terrain-to-cut-height variability model generator 1347. In other examples, the prediction model generator 210 may include more than Figure 4BThe examples shown may include more, fewer, or different components. Therefore, in some examples, the prediction model generator 210 may also include other items 348, which may include other types of prediction model generators to generate other types of cut height characteristic models. For example, the prediction model generator 210 may include specific terrain characteristic model generators, such as a slope-to-cut height model generator or a slope-to-cut height variability model generator. Slope may also include slope variability.

[0116] The velocity and terrain characteristics model generator 1342 identifies a relationship between the cut height at a geographic location (corresponding to the location where the cut height sensor 1336 senses a cut height characteristic (which indicates the height at which vegetation is cut in the field) and / or the location where the ridge height sensor 1337 senses a ridge height) and the velocity value from the predicted velocity map 360 and the terrain characteristic value (e.g., one or more slope values) from the topographic map 339 at the same location corresponding to the detected cut height in the field. Based on this relationship established by the velocity and terrain characteristics model generator 1342, the velocity and terrain characteristics model generator 1342 generates a predicted cut height characteristic model. The prediction map generator 212 uses this predicted cut height characteristic model to predict the cut height at different locations in the field based on the georegistered velocity values ​​and georegistered terrain characteristic values ​​(e.g., slope values) contained in the predicted velocity map 360 and the topographic map 339, respectively, at the same location in the field. In some examples, velocity values ​​can be derived from another graph (e.g., another graph 401) that is different from the predicted velocity graph 360, which may include other predicted velocity graphs or other prior information graphs.

[0117] The velocity and terrain characteristics modifier for cut height variation model 1344 identifies the relationship between the change in cut height at a geographic location (corresponding to the location where the cut height sensor 1336 senses the change in cut height or the location where the header height sensor 1337 senses the change in header height) and velocity values ​​from the predicted velocity map 360 and terrain characteristic values ​​(e.g., one or more slope values) from the topographic map 339 corresponding to the detected change in header height and / or the detected change in cut height at one or more of the same locations. Based on this relationship established by the velocity and terrain characteristics modifier for cut height variation model 1344, the velocity and terrain characteristics modifier for cut height variation model 1344 generates a predicted cut height characteristic model. The prediction map generator 212 uses the prediction cut height characteristic model 1350 to predict cut height variability at different locations in the field based on georegistered velocity values ​​contained in the prediction velocity map at the same location in the field and georegistered topographic characteristic values ​​(e.g., slope values) contained in the topographic map 339. In some examples, velocity values ​​may be derived from another map (e.g., another map 401) different from the prediction velocity map 360, which may include other prediction velocity maps or other prior information maps.

[0118] The velocity-cut height model generator 1343 identifies the relationship between the cut height at the geographic location corresponding to the processed data 1340, as represented in the processed data 1340, and the velocity value at the same geographic location. The velocity value is a georegistered value included in the predicted velocity map 360. The model generator 1343 then generates a predicted cut height characteristic model, which the predicted cut height map generator 1352 uses to predict the cut height at a location in the field based on the velocity value at that location.

[0119] The velocity-cut height variability model generator 1345 identifies the relationship between the cut height variability at the geographic location corresponding to the processed data 1340, as represented in the processed data 1340, and the velocity value at the same geographic location. The velocity value is a georegistered value included in the predicted velocity map 360. The model generator 1345 then generates a predicted cut height characteristic model, which the predicted cut height variability map generator 1354 uses to predict the cut height variability at a location in the field based on the velocity value at that location.

[0120] The terrain features, as represented in the cut height model generator 1346, indicate the relationship between the cut height at the geographic location corresponding to the processed data 1340 and the terrain feature values ​​at the same geographic location. The terrain feature values ​​are georegistered values ​​included in the topographic map 339. The model generator 1346 then generates a predicted cut height feature model, which the predicted cut height map generator 1352 uses to predict the cut height at a location in the field based on the terrain feature values ​​at that location.

[0121] The terrain feature model generator 1347 identifies the relationship between the cut height variability, as represented in the processed data 1340, at the geographic location corresponding to the processed data 1340, and the terrain feature values ​​at the same geographic location. The terrain feature values ​​are georegistered values ​​included in the topographic map 339. The model generator 1347 then generates a predicted cut height feature model, which the predicted cut height variability map generator 1354 uses to predict the cut height variability at a location in the field based on the terrain feature values ​​at that location.

[0122] In view of the foregoing, the prediction model generator 210 is operable to generate multiple prediction cut height characteristic models, such as one or more prediction cut height characteristic models generated by model generators 1342, 1343, 1344, 1345, 1346, 1347, and 1348. In another example, two or more of the above-mentioned prediction cut height characteristic models can be combined into a single prediction cut height characteristic model, which predicts the cut height and cut height variability at different locations in the field based on different values ​​(such as detected cut height, detected cut height variability, detected header height, and detected header height variability). Any one of these cut height characteristic models or combinations thereof in Figure 4B The characteristics of the cutting height are represented by model 1350.

[0123] The predicted cutting height characteristic model 1350 is provided to the prediction map generator 212. Figure 4B In one example, the prediction graph generator 212 includes a cut height graph generator 1352 and a cut height variation graph generator 1354. 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 other items 1358, such as other types of graph generators for generating cut height graphs. For example, the prediction graph generator 212 may include a cutter height graph generator or a cutter height variation graph generator, or both.

[0124] Cut height map generator 1352 receives a predicted cut height characteristic model 1350, which predicts the cut height based on velocity values ​​and / or terrain characteristic values, and on field sensor data indicating the cut height (e.g., sensor data from cut height sensor 1336 or header height sensor 1337, or both). Using the predicted cut height characteristic model 1350 and one or more received maps, cut height map generator 1352 generates predicted maps mapping the predicted cut heights at different locations in the field.

[0125] Cut height variability map generator 1354 receives a predicted cut height characteristic model 1350, which predicts cut height variability based on velocity values ​​and / or terrain characteristic values, and on field sensor data indicating cut height variability (e.g., sensor data from cut height sensor 1336 or header height sensor 1337, or both). Using the predicted cut height characteristic model 1350 and one or more received maps, cut height variability map generator 1354 generates predicted maps mapping the predicted cut height variability at different locations in the field.

[0126] The graph generator 212 outputs one or more functional predicted cut height characteristic graphs 1360, representing one or more of the predicted cut heights or cut height variability. Each of the predicted cut height characteristic graphs 1360 predicts the cut height or cut height variability at different locations in the field. Each generated predicted cut height characteristic graph 1360 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 graphs (i.e., functional prediction graphs 1360) to provide a functional prediction graph 1360 with control zones. The functional prediction graphs 1360 (with or without control zones) can be provided to the control system 214, which generates control signals based on the functional prediction graphs 1360 (with or without control zones) to control one or more of the controllable subsystems 216.

[0127] Figure 5This is a flowchart illustrating an example of the operation of the prediction model generator 210 and the prediction map generator 212 in generating the predicted cut height characteristic model 1350 and the functional predicted cut height characteristic map 1360. At block 362, the prediction model generator 210 and the prediction map generator 212 receive one or more of the following maps: a predicted velocity map (e.g., the functional predicted velocity map 360), a topographic map (e.g., topographic map 339), or some other map 401. At block 364, the processing system 338 receives one or more sensor signals from field sensors 208 (e.g., the cut height sensor 1336, or the kerf height sensor 1337, or both). In other examples, the field sensor 208 may be another type of sensor, as indicated by block 370. For example, the field sensor 208 may be another type of sensor that provides an indication of cut height, cut height variability, kerf height, or kerf height variability.

[0128] At box 372, processing system 1338 processes the received one or more sensor signals to generate data indicating cutting height characteristics. As shown in box 374, the cutting height characteristic can be the cutting height itself. As shown in box 376, the cutting height characteristic can be cutting height variability. As shown in box 380, the sensor data can indicate other characteristics, such as header height or header height variability. As discussed earlier herein, header height and header height variability can indicate cutting height and cutting height variability.

[0129] At box 382, ​​the prediction model generator 210 also obtains the geographic location corresponding to the sensor data. For example, the prediction model generator 210 can obtain the geographic location from the geographic location sensor 204 and determine the precise geographic location or region of the captured or derived sensor data 340 based on machine latency, machine speed, etc.

[0130] At box 384, prediction model generator 210 generates one or more prediction models, such as one or more cutting height characteristic models generated by model generators 1342, 1343, 1344, 1345, 1346, 1347 or 1348, which are collectively represented by cutting height characteristic model 1350.

[0131] At box 386, a prediction model, such as prediction cut height characteristic model 1350, is provided to prediction map generator 212. Prediction map generator 212 generates a prediction cut height characteristic map 1360 based on a prediction velocity map (e.g., prediction velocity map 360) or topographic map 339 or both, and prediction cut height characteristic model 1350, which maps the predicted cut height characteristics. For example, in some examples, prediction cut height characteristic map 1360 maps the predicted cut height or predicted cut height variability across multiple different locations in the field. Furthermore, prediction cut height characteristic map 1360 can be generated during agricultural operations. Therefore, prediction cut height characteristic map 1360 is generated while an agricultural harvester moves across the field to perform an agricultural operation.

[0132] At block 394, the prediction graph generator 212 outputs a predicted cutting height characteristic graph 1360. At block 391, the predicted cutting height characteristic graph generator 212 outputs the predicted cutting height characteristic graph 1360 to be presented to the operator 260 for possible interaction. At block 393, the prediction graph generator 212 can configure the predicted cutting height characteristic graph 1360 for use by the control system 214. At block 395, the prediction graph generator 212 can also provide the predicted cutting height characteristic graph 1360 to the control area generator 213 to generate and incorporate a control area. At block 397, the prediction graph generator 212 further configures the predicted cutting height characteristic graph 1360 in other ways. The predicted cutting height characteristic graph 1360 (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 functional predicted cutting height characteristic graph 1360 to control the controllable subsystem 216.

[0133] In an example where the control system 214 receives a functional prediction map or a functional prediction map with added control areas, the path planning controller 234 controls the steering subsystem 252 to turn the harvester 100. In another example where the control system 214 receives a functional prediction map or a functional prediction map with added control areas, the residue system controller 244 controls the residue subsystem 138. In another example where the control system 214 receives a functional prediction map or a functional prediction map with added control areas, the setting controller 232 controls the threshing settings of the threshing machine 110. In another example where the control system 214 receives a functional prediction map or a functional prediction map with added control areas, the setting controller 232 or another controller 246 controls the material handling subsystem 125. In another example where the control system 214 receives a functional prediction map or a functional prediction map with added control areas, the setting controller 232 controls the crop cleaning subsystem 118. In another example where the control system 214 receives a functional prediction diagram or a functional prediction diagram with added control areas, the machine clearing controller 245 controls the machine clearing subsystem 254 on the harvester 100. In another example where the control system 214 receives a functional prediction diagram or a functional prediction diagram with added control areas, the communication system controller 229 controls the communication system 206. In another example where the control system 214 receives a functional prediction diagram or a functional prediction diagram with added control areas, the operator interface controller 231 controls the operator interface mechanism 218 on the harvester 100. In another example where the control system 214 receives a functional prediction diagram or a functional prediction diagram with added control areas, the cover plate position controller 242 controls the machine / header actuator 248 to control the cover plate on the harvester 100. In another example where the control system 214 receives a functional prediction diagram or a functional prediction diagram with added control areas, the belt conveyor controller 240 controls the machine / header actuator 248 to control the belt conveyor belt on the harvester 100. In another example where the control system 214 receives a functional prediction map or adds a functional prediction map with a control area, other controllers 246 control other controllable subsystems 256 on the agricultural harvester 100.

[0134] In one example, control system 214 may receive a functional prediction map or a functional prediction map with added control zones, and header / reel controller 238 may control header or other machine actuator 248 based on the functional prediction map (with or without control zones) to control the height, tumble, or tilt of header 102. For example, header / reel controller 238 may control header or other machine actuator 248 to adjust the height of header 102 above the field surface. In another example, header / reel controller 238 may control header or other machine actuator 248 to adjust the tilt of header, such as tilting header 102 from front to back (tilt may also be referred to as pitch). In yet another example, header / reel controller 238 may control header or other machine actuator 248 to adjust the tumble of header, such as edge-to-edge tumble of header 102, i.e., tumble across the width of header.

[0135] In one example, the control system 214 or the operator of the agricultural harvester may receive a functional prediction map or a functional prediction map with added control areas, and adjust one or more header settings based on the functional prediction map or the functional prediction map with added control areas, such as header position settings (e.g., header height settings, header pitch settings, or header roll settings), header sensitivity settings that control the responsiveness of the agricultural harvester to header height errors, or ground pressure settings that control the amount of floating force applied to the header by one or more actuators (e.g., hydraulic cylinders).

[0136] This demonstrates that the system receives maps that map characteristic values, such as terrain features or predicted velocity values, to different locations in the field. The system also uses one or more field sensors to sense data indicating cut height characteristics (e.g., cut height or cut height variability) and generates a model that models the relationship between the characteristics or related characteristics sensed by the field sensors and the characteristics mapped in the received maps. Therefore, the system uses the model, field data, and maps to generate a functional prediction map, and the generated functional prediction map can be configured for use by the control system and / or presented to a local operator, a remote operator, or other users. For example, the control system can use this map to control one or more systems of a combine harvester.

[0137] Figure 6A yes Figure 1 A block diagram of an example portion of an agricultural harvester 100 is shown. Specifically, among other things, Figure 6AExamples of a prediction model generator 210 and a prediction map generator 212 are shown. In the illustrated example, the prior information map 258 is a prior or previous operation map 400. The prior or previous operation map 400 may include cut height characteristic values ​​from prior or previous operations in the field at different locations. For example, the prior or previous operation map 400 may be a historical cut height characteristic map generated during harvesting operations in previous harvesting seasons, which includes cut height characteristic values ​​at multiple different locations and may include background information such as header settings, operating speed, and various other machine settings used during the prior or previous operations. Figure 6A It is also shown that the predictive model generator 210 and the predictive map generator 212 can replace receiving the prior information map 258 or, in addition to receiving the prior information map 258, receive a functional predictive cut height characteristic map (e.g., functional predictive cut height characteristic map 1360). The functional predictive cut height characteristic map 1360 can be used similarly to the prior information map 258 because the model generator 210 models the relationship between the information provided by the functional predictive cut height characteristic map 1360 and the characteristics sensed by the field sensor 208. The map generator 212 can therefore use this model to generate a functional predictive map based on one or more values ​​in the functional predictive cut height characteristic map 1360 at different locations in the field and based on the predictive model. This functional predictive map predicts the characteristics sensed by the field sensor 208 or characteristics related to the sensed characteristics at different locations in the field. Figure 6A As shown, the prediction model generator 210 and the prediction map generator 212 can also receive other maps 401, such as other prior information maps or other prediction maps, such as other prediction cut height characteristic maps generated in a different manner than the functional prediction cut height characteristic map 1360.

[0138] In addition, Figure 6A In the example shown, the field sensor 208 may include one or more of the agricultural characteristic sensor 402, the operator input sensor 404, and the processing system 406. The field sensor 208 may also include other sensors 408.

[0139] Agricultural characteristic sensor 402 senses values ​​indicating agricultural characteristics. Operator input sensor 404 senses various operator inputs. These inputs may be setting inputs or other control inputs for controlling settings on the agricultural harvester 100, such as steering inputs and other inputs. Thus, when the operator 260 changes settings or provides command inputs through the operator interface mechanism 218, such inputs are detected by the operator input sensor 404, which provides a sensor signal indicating the sensed operator input. In one example, the input may be a header setting input (or other setting inputs related to header control), such as a header sensitivity setting input, a header position input (e.g., a header height setting input, a header pitch setting input, or a header roll setting input), a header ground pressure setting input, and a variety of other header setting inputs.

[0140] The processing system 406 can receive sensor signals from one or more of the agricultural characteristic sensor 402 and the operator input sensor 404, and generate an output indicating the sensed variables. For example, the processing system 406 can receive sensor output from the agricultural characteristic sensor 402 and generate an output indicating the agricultural characteristics. The processing system 406 can also receive input from the operator input sensor 404 and generate an output indicating the sensed operator input.

[0141] Predictive model generator 210 may include a cut height characteristic to agricultural characteristic model generator 410 and a cut height characteristic to command model generator 414. In other examples, predictive model generator 210 may include more, fewer, or other model generators 415. For example, predictive model generator 210 may include specific cut height characteristic model generators, such as a cut height to agricultural characteristic model generator, a cut height variability to agricultural characteristic model generator, a cut height to command model generator, or a cut height variability to command model generator. Predictive model generator 210 may receive a geographic location indicator 334 from geographic location sensor 204 and generate a predictive model 426 that models the relationship between information in one or more prior information graphs 258 or functionally predicted cut height characteristic graphs 1360 and one or more agricultural characteristics sensed by agricultural characteristic sensor 402 and operator input commands sensed by operator input sensor 404.

[0142] The cut height characteristic generates a relationship between cut height characteristic values ​​(e.g., cut height characteristics provided on the predicted cut height characteristic diagram 1360, the prior operation diagram 400, or other diagram 401) and agricultural characteristics sensed by the agricultural characteristic sensor 402 in the agricultural characteristic model generator 410. The cut height characteristic generates a prediction model 426 corresponding to this relationship in the agricultural characteristic model generator 410.

[0143] The operator command model generator 414 generates a model that models the relationship between the cutting height characteristics reflected in the predicted cutting height characteristic diagram 1360, the prior operation diagram 400, or other diagram 401, and the operator input command sensed by the operator input sensor 404. The operator command model generator 414 generates a prediction model 426 corresponding to this relationship.

[0144] Other model generators 415 may include, for example, specific cut height characteristic model generators, such as cut height versus agricultural characteristic model generators, cut height variability versus agricultural characteristic model generators, cut height versus command model generators, or cut height variability versus command model generators.

[0145] The prediction model 426 generated by the prediction model generator 210 may include one or more prediction models generated by the cut height characteristic to agricultural characteristic model generator 410 and the cut height characteristic to operator command model generator 414, as well as other model generators that may be included as part of other items 415.

[0146] exist Figure 6A In one example, the prediction graph generator 212 includes a prediction agricultural characteristic graph generator 416 and a prediction operator command graph generator 422. In other examples, the prediction graph generator 212 may include more, fewer, or other graph generators 424.

[0147] The predictive agricultural characteristic map generator 416 receives a predictive model 426 (e.g., a predictive model generated by the cut height characteristic to agricultural characteristic model generator 410) that models the relationship between cut height characteristics and agricultural characteristics sensed by agricultural characteristic sensor 402, and receives one or more of the prior information map 258 or the functional predictive cut height characteristic map 1360 or other maps 401. The predictive agricultural characteristic map generator 416 generates a functional predictive agricultural characteristic map 427 based on one or more cut height characteristic values ​​at different locations in the field in one or more of the prior information map 258 or the functional predictive cut height characteristic map 1360 or other maps 401, and based on the predictive model 426. This functional predictive agricultural characteristic map 427 predicts agricultural characteristic values ​​(or the agricultural characteristics indicated by these values) at different locations in the field.

[0148] The predictive operator command graph generator 422 receives a predictive model 426 (e.g., a predictive model generated by the cut height characteristic to command model generator 414) that models the relationship between cut height characteristics and operator command inputs detected by operator input sensor 404, and receives one or more of the prior information graph 258 or the functional predictive cut height characteristic graph 1360 or other graphs 401. The predictive operator command graph generator 422 generates a functional predictive operator command graph 440 based on one or more cut height characteristic values ​​at different locations in the field from the prior information graph 258 or the functional predictive cut height characteristic graph 1360 or other graphs 401, and based on the predictive model 426. This functional predictive operator command graph 440 predicts operator command inputs at different locations in the field.

[0149] Prediction graph generator 212 outputs one or more functional prediction graphs 427 and 440. Each of the functional prediction graphs 427 and 440 can be provided to control area generator 213, control system 214, or both. Control area generator 213 generates a control area and combines the control area to provide functional prediction graphs 427 and 440 with control areas. Any or all of functional prediction graph 427 (with or without control areas) and functional prediction graph 440 (with or without control areas) can be provided to control system 214, which generates control signals based on one or all of functional prediction graphs 427 (with or without control areas) and functional prediction graph 440 (with or without control areas) to control one or more of the controllable subsystems 216. Any or all of graphs 427 and 440 (with or without control areas) can be presented to operator 260 or another user.

[0150] 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 6BThe field sensors 208 shown may include an operator input sensor 980, a machine sensor 982, a harvested material property sensor 984, a field and soil property sensor 985, an environmental property sensor 987, and may include a variety of other sensors 226. The operator input sensor 980 may be a sensor that senses operator input via an operator interface mechanism 218. Therefore, the operator input sensor 980 can sense user movements of linkages, joysticks, steering wheels, buttons, dials, or pedals. The operator input sensor 980 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.

[0151] Machine sensor 982 can sense various characteristics of the agricultural harvester 100. For example, as discussed above, machine sensor 982 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 982 may also include machine setting sensor 991 for sensing machine settings. (See above references) Figure 1Examples of machine settings are described. A front-end device (e.g., header) position sensor 993 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 993 can sense the height of the header 102 above the ground. Machine sensor 982 may also include a front-end device (e.g., header) orientation sensor 995. Sensor 995 can sense the orientation of the header 102 relative to the harvester 100 or relative to the ground. Machine sensor 982 may include a stability sensor 997. Stability sensor 997 senses vibrational or bouncing movements (and amplitude) of the harvester 100. Machine sensor 982 may also include a residue setting sensor 999, configured to sense whether the harvester 100 is configured to shred residue, create a stockpile, or otherwise process residue. Machine sensor 982 may include a cleaning chamber fan speed sensor 951 that senses the speed of the cleaning fan 120. Machine sensor 982 may include a concave plate gap sensor 953 that senses the gap between the roller 112 and the concave plate 114 on the agricultural harvester 100. Machine sensor 982 may include a husk sieve gap sensor 955 that senses the size of the openings in the husk sieve 122. Machine sensor 982 may include a threshing drum speed sensor 957 that senses the drum speed of the roller 112. Machine sensor 982 may include a drum pressure sensor 959 that senses the pressure used to drive the roller 112. Machine sensor 982 may include a screen gap sensor 961 that senses the size of the openings in the screen 124. Machine sensor 982 may include a MOG humidity sensor 963 that senses the humidity level of the MOG passing through the harvester 100. Machine sensor 982 may include a machine orientation sensor 965 that senses the orientation of the harvester 100. Machine sensor 982 may include a material feed rate sensor 967 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 982 may include a biomass sensor 969 that senses the biomass traveling through the feeder housing 106, the separator 116, or other locations within the harvester 100. Machine sensor 982 may include a fuel consumption sensor 971 that senses the rate at which the harvester 100 consumes fuel over time.Machine sensor 982 may include a power utilization sensor 973 that senses power utilization in the harvester 100 (such as which subsystems are using power), the rate at which subsystems are using power, or the power distribution among the subsystems in the harvester 100. Machine sensor 982 may include a tire pressure sensor 977 that senses the inflation pressure in the tires 144 of the harvester 100. Machine sensor 982 may include a variety of other machine performance sensors or machine characteristic sensors (as shown in block 975). The machine performance sensors and machine characteristic sensors 975 can sense the machine performance or characteristics of the harvester 100.

[0152] While crop material is being processed by the agricultural harvester 100, the harvest material property sensor 984 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 characteristics that can be beneficially used to control processing to achieve better grain capture, reduced grain damage, lower power consumption, reduced grain loss, etc.

[0153] The 985 field and soil property sensor can sense the characteristics of fields and soils. Field and soil properties may include soil moisture, soil density, presence and location of waterlogging, soil type, and other soil and field characteristics.

[0154] The environmental characteristic sensor 987 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.

[0155] 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 427 and 440 is shown. At box 442, the prediction model generator 210 and the prediction map generator 212 receive maps. The map received by the prediction model generator 210 or the prediction map generator 212 in generating one or more prediction models 426 and one or more functional prediction maps 427 and 440 may be a prior information map 258, such as a prior or prior operation map 400 created using prior or prior data obtained during previous operations in the field. The map received by the prediction model generator 210 or the prediction map generator 212 in generating one or more prediction models 426 and one or more functional prediction maps 427 and 440 may be a functional prediction cut height characteristic map 1360. Other maps may also be received, as indicated by box 401, such as other prior information maps or other prediction maps, such as other prediction cut height characteristic maps.

[0156] At box 444, the predictive model generator 210 receives sensor signals containing sensor data from field sensors 208. The field sensors may be one or more of agricultural characteristic sensors 402 and operator input sensors 404. Agricultural characteristic sensors 402 sense agricultural characteristics. Operator input sensors 404 sense operator input commands. The predictive model generator 210 may also receive other field sensor inputs, as indicated by box 408.

[0157] 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. The processed data 409 may indicate one or more agricultural characteristics or operator input commands. The processed data 409 is provided to the predictive model generator 210.

[0158] 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.

[0159] At box 458, prediction model generator 210 generates one or more prediction models 426 that model the relationship between the mapping values ​​in the received graph and the characteristics represented in the processed data 409. For example, in some cases, the mapping values ​​in the received graph may be cut height characteristics (such as cut height or cut height variability), and prediction model generator 210 uses the mapping values ​​of the received graph and characteristics sensed by field sensor 208 (as represented in the processed data 409) or related characteristics (such as characteristics related to the characteristics sensed by field sensor 208) to generate prediction models.

[0160] 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 agricultural characteristic map 427 and a functional prediction operator command map 440, or any combination of these maps. The functional prediction agricultural characteristic map 427 predicts agricultural characteristic values ​​(or agricultural characteristics indicated by said values) at different locations in the field. The functional prediction operator command map 440 predicts desired or possible operator command inputs at different locations in the field. Furthermore, one or more functional prediction maps 427 and 440 can be generated during agricultural operations. Thus, when the agricultural harvester 100 moves across the field to perform agricultural operations, one or more prediction maps 427 and 440 are generated during the performance of those operations.

[0161] At box 468, the prediction graph generator 212 outputs one or more functional prediction graphs 427 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 interaction 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 427 and 440 to control area generator 213 for generating control areas. At box 476, the prediction graph generator 212 configures one or more prediction graphs 427 and 440 in other ways. In the example where one or more functional prediction graphs 427 and 440 are provided to control area generator 213, the one or more functional prediction graphs 427 and 440 represented by the corresponding graph 265 described above, which includes the control area, can be presented to operator 260 or another user, or also provided to control system 214.

[0162] 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, 427 and 440 (or functional prediction maps 360, 427 and 440 with control areas) and inputs from the geolocation sensor 204.

[0163] In an example where the control system 214 receives a functional prediction map or a functional prediction map with added control areas, the path planning controller 234 controls the steering subsystem 252 to turn the harvester 100. In another example where the control system 214 receives a functional prediction map or a functional prediction map with added control areas, the residue system controller 244 controls the residue subsystem 138. In another example where the control system 214 receives a functional prediction map or a functional prediction map with added control areas, the setting controller 232 controls the threshing settings of the threshing machine 110. In another example where the control system 214 receives a functional prediction map or a functional prediction map with added control areas, the setting controller 232 or another controller 246 controls the material handling subsystem 125. In another example where the control system 214 receives a functional prediction map or a functional prediction map with added control areas, the setting controller 232 controls the crop cleaning subsystem 118. In another example where the control system 214 receives a functional prediction diagram or a functional prediction diagram with added control areas, the machine clearing controller 245 controls the machine clearing subsystem 254 on the harvester 100. In another example where the control system 214 receives a functional prediction diagram or a functional prediction diagram with added control areas, the communication system controller 229 controls the communication system 206. In another example where the control system 214 receives a functional prediction diagram or a functional prediction diagram with added control areas, the operator interface controller 231 controls the operator interface mechanism 218 on the harvester 100. In another example where the control system 214 receives a functional prediction diagram or a functional prediction diagram with added control areas, 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 a functional prediction diagram or a functional prediction diagram with added control areas, 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 a functional prediction map or a functional prediction map with added control areas, other controllers 246 control other controllable subsystems 256 on the agricultural harvester 100.

[0164] In one example, control system 214 may receive a functional prediction map or a functional prediction map with added control zones, and header / reel controller 238 may control header or other machine actuator 248 based on the functional prediction map (with or without control zones) to control the height, tumble, or tilt of header 102. For example, header / reel controller 238 may control header or other machine actuator 248 to adjust the height of header 102 above the field surface. In another example, header / reel controller 238 may control header or other machine actuator 248 to adjust the tilt of header, such as tilting header 102 from front to back (tilt may also be referred to as pitch). In yet another example, header / reel controller 238 may control header or other machine actuator 248 to adjust the tumble of header, such as edge-to-edge tumble of header 102, i.e., tumble across the width of header.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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 zones. For example, if the selected WMA is a cutting table or other machine actuator 248, and the functional prediction graph in the analysis is a functional prediction cut height characteristic graph 1360, then the target setting in each control zone can be a target cutting table height, pitch, or roll setting based on the cut height characteristic values ​​contained in the functional prediction cut height characteristic graph 1360.

[0169] In some examples, when controlling the harvester 100 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 area generation system 488 when identifying the control area and the target setting of the WMA selected in the control area. For example, different target settings for controlling header height, tilt, or tumble can be generated based on, for example, detected or predicted cut height characteristic values ​​(such as cut height or cut height variability), detected or predicted agricultural characteristic values, detected or predicted speed values, detected or predicted terrain characteristic values ​​(e.g., slope values), detected or predicted feed rate values, detected or predicted fuel efficiency values, detected or predicted grain loss values, or combinations of these values. It should be noted that these are merely examples, and multiple different WMA target settings can be based on multiple different other values ​​or combinations of values. However, at any given time, the agricultural harvester 100 cannot simultaneously travel on the ground with multiple header heights, multiple header tilts, or multiple header tumbles. Instead, at any given time, the agricultural harvester 100 has a single header height, a single header tilt, and a single header tumble. Therefore, one of the competing target settings is selected to control the header height, tilt, or tumble of the agricultural harvester 100.

[0170] 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, cut height characteristics (such as cut height or cut height variability), agricultural characteristics, topographic characteristics (e.g., slope), velocity characteristics (e.g., predicted velocity values), operator command input, crop type or crop variety (e.g., based on a planting map, or another source of crop type or crop variety), weed type, weed density, or crop state (e.g., whether the crop is lodged, partially lodged, or upright). These are just some examples of criteria that can be used to identify or define dynamic zones. 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.

[0171] In some examples, dynamic zones may overlap. For instance, a crop type dynamic zone may partially or completely overlap with a crop state 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 or importance takes precedence over the dynamic zone with a lower priority level or importance. The priority levels of dynamic zones can be set manually or automatically using rule-based, model-based, or other systems. As an example, in the case of an overlap between a lodging crop dynamic zone and a crop type dynamic zone, the lodging crop dynamic zone can be assigned greater importance in the priority level than the crop type dynamic zone, thus giving priority to the lodging crop dynamic zone.

[0172] 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.

[0173] 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 a predicted or historical quality metric 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:

[0174] 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 target setpoints is used; otherwise, the target setpoint based on grain loss rather than other competing target setpoints is used.

[0175] A target parser can be a logical component that executes logical rules when identifying a target target. For example, a target parser can parse a target target 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 targets. 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.

[0176] 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.

[0177] 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 1360, 427, or 440. Box 534 indicates that the graph in the analysis could also be other graphs.

[0178] At box 536, WMA selector 486 selects the WMA or WMA group for which a control area will be generated on the graph in the analysis. At box 538, control area criterion identification component 494 obtains the control area defining criteria for the selected WMA or WMA group. Box 540 indicates an example where the control area criteria are or include the wear characteristics of the selected WMA or WMA group. Box 542 indicates an example where the control area 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 area 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 area 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 area 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.

[0179] 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 terrain characteristics (e.g., slope). Box 559 shows an example where the dynamic zone defining criteria are based on velocity characteristics (e.g., velocity values ​​provided by the functional predicted velocity map 360). Box 560 shows an example where the dynamic zone defining criteria are based on cut height characteristics (such as cut height or cut height variability). Box 564 indicates an example where the dynamic zone defining criteria are also or include other criteria.

[0180] 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 in the control area. The control area and the dynamic area can also be generated in other ways, and this is indicated by box 572.

[0181] 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.

[0182] 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 the 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.

[0183] Figure 10 An 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 prediction map that includes control zones and dynamic zones (as shown in box 594). In some cases, the received map may be a functional prediction map that excludes control zones and dynamic zones. Box 596 indicates an example where the received work site map may be a priori information map with control zones and dynamic zones identified on that map. 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.

[0184] At box 612, the control system 214 receives sensor signals from the geolocation sensor 204. The sensor signals from the geolocation sensor 204 may include data indicating the geolocation 614 of the harvester 100, the speed 616 of the harvester 100, the heading 618 of the harvester 100, or other information 620. At box 622, the area controller 247 selects a dynamic area, and at box 624, the area controller 247 selects a control area on the map based on the geolocation sensor signals. At box 626, the area controller 247 selects a WMA or WMA group to be controlled. At box 628, the 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 a variety of different sources. For example, box 630 shows an example where one or more of the 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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 haptic 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.

[0189] 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.

[0190] Other controller interaction system 656 manages and interacts 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 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.

[0191] 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.

[0192] 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.

[0193] 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 other 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, the dialogue managed by the dialogue management system 680 can be exclusively verbal, or a combination of visual and verbal dialogue.

[0194] 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 variety of other tactile elements.

[0195] 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.

[0196] 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.

[0197] 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 that are georeferenced on the map. For example, if the received map is a cut height characteristic map (e.g., predicted cut height characteristic map 1360), the displayed field may show the different cut height characteristics present in the field that are georeferenced within the displayed field. The mapped characteristics may 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.

[0198] Figure 13 This illustration shows an 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 equipment or elsewhere. (Continuing the description...) Figure 12 Before the flowchart shown, the user interface display unit 720 will be described.

[0199] 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.

[0200] 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.

[0201] 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 cut height characteristic plot, such as a functional predicted cut height characteristic plot 1360. Therefore, multiple cut characteristic markers are displayed on the field display section 728. A set of cut height characteristic display markers 732 is shown in the already visited area 714. A set of cut height characteristic display markers 732 is also shown in the upcoming area 712, and a set of cut height characteristic display markers 732 is shown in the next work unit 730. Figure 13 The cutting height characteristic display mark 732 is shown to consist of different symbols indicating areas with similar header characteristic values. Figure 13 In the example shown, the "!" symbol represents a region with a high cutting height; the "*" symbol represents a region with a medium ideal cutting height; and the "#" symbol represents a region with a low cutting height.

[0202] Therefore, the field display section 728 displays different measured or predicted values ​​(or characteristics indicated by said values) located in different areas within the field, and uses various display markers 732 to represent those measured or predicted values ​​(or characteristics indicated by said values ​​or derived from 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 cut height characteristic display mark 732 is shown in the example. In some cases, each location of the field may have a display mark associated with that location. Therefore, in some cases, a display mark may be provided at each location of the field display portion 728 to identify the attribute of the characteristic mapped to each particular location of the field. Therefore, this disclosure includes providing a cut height characteristic display mark 732 (as shown in the example) at one or more locations on the field display portion 728. Figure 13 The display markers (in the context of this example) are used to identify the attributes, degree, etc., of the characteristic being displayed, thereby identifying the characteristic 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.

[0203] In other examples, the graph being displayed can be one or more of the graphs described herein, including infographics, prior infographics, functional predictive graphs such as predictive graphs or predictive control area graphs, other predictive 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.

[0204] exist Figure 13In 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.

[0205] 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 cutting height categories corresponding to 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 actuator. As shown, display portion 738 also includes multiple interactive tabs, such as a cutting height tab 762, a cutting height variability tab 764, and other tabs 770. Activating one of these tabs can modify which values ​​are displayed in display portions 728 and 738. For example, as shown, cutting height tab 762 is activated, and therefore, the values ​​mapped on portion 728 and displayed in portion 738 correspond to the cutting height values ​​of the agricultural harvester 100. When operator 260 touches tab 764, touch gesture management system 664 updates portions 728 and 738 to display characteristics related to the cutting height variability values ​​of the agricultural harvester 100. When the operator 260 touches the tab 770, the touch gesture management system 664 updates the sections 728 and 738 to display other cutting height characteristics related to the agricultural harvester 100, such as header height or header height variability.

[0206] like Figure 13As 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 a location (e.g., the current location of the harvester, or another location on the field specified by the operator) and add information indicating characteristics found at the current location, such as cutting height characteristics (e.g., cutting height, cutting height variability, etc.). 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 the harvester 100 has a high cutting height. When operator 260 touches button 742, touch gesture management system 664 identifies the current location as a location where the harvester 100 has an ideal cutting height. When operator 260 touches button 744, touch gesture management system 664 identifies the current location as a low cutting height for harvester 100. When one of the marker actuators 740, 742, or 744 is actuated, touch gesture management system 664 can control visual control signal generator 684 to add a symbol corresponding to the identified characteristic on 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, the operator can specify an area in front of or around harvester 100 by actuating one of the marker actuators 740, 742, or 744, allowing control of harvester 100 based on values ​​specified by operator 260.

[0207] 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 13 In the case of a cutting height characteristic, the symbol corresponding to the value or characteristic is shown. Display portion 738 also includes an interactive specifier display portion indicated approximately at 745. Interactive specifier display portion 745 includes a specifier bar 748 that displays the value or characteristic (in...) Figure 13 In the case of a header feature, the designator (which can be a text designator or other designator) is used to identify the category. Without limitation, the symbols in the symbol bar 746 and the designators in the designator bar 748 can include any display features, such as different colors, shapes, patterns, intensities, text, icons, or other display features, and can be customized through interaction with the operator of the agricultural harvester 100.

[0208] 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 cut heights of 18 inches or more are classified as “high cut heights,” predicted or measured cut heights of 12 inches or more are classified as “ideal cut heights,” and predicted or measured cut heights of 6 inches or less are classified as “low cut heights.” In some examples, the selected values ​​may include a range such that predicted or measured values ​​within the selected range are classified under the corresponding designator. For example, “ideal cut height” may include, for example, a range of 11 to 12 inches, such that predicted or measured cut height values ​​falling within the 11 to 12-inch range are classified as “ideal header heights.” The values ​​selected in the value display bar 750 can be adjusted by the operator of the agricultural harvester 100. In one example, the operator 260 may select a specific portion of the field display section 728 to display values ​​in bar 750 for that specific portion. Therefore, the values ​​in bar 750 may correspond to values ​​in display sections 712, 714, or 730.

[0209] 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, satisfies the corresponding action threshold in threshold display bar 752, control system 214 takes one or more actions 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 value selected in value display bar 750, such that predicted or measured characteristic values ​​that reach or fall within that range satisfy the threshold. For example, in the example of header characteristics, a predicted cut height value falling within 2 inches of 18 inches would satisfy the corresponding action threshold (within 2 inches of 18 inches), and control system 214 would take actions such as adjusting the header position setting, adjusting the sensitivity setting, or adjusting the header ground pressure setting of the agricultural harvester. In other examples, the threshold in threshold display bar 752 is separated from the selected value in value display bar 750, such that the value in value display bar 750 defines the classification and display of predicted or measured values, while the action threshold defines when to take action based on the measured or predicted value. For example, while a predicted or measured cut height of 6 inches might be designated as "low cut height" for classification and display purposes, the action threshold could be 8 inches, such that no action will be taken until the predicted or measured cut height satisfies the threshold. In other examples, the threshold in the threshold display bar 752 may include distance or time. For example, in the distance example, the threshold may be a threshold distance from an area of ​​the field 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 5 feet means that action will be taken when the harvester is located 5 feet or less from the area of ​​the field where the measured or predicted value is georeferenced. In the example where the threshold is time, the threshold may be a threshold time for the harvester 100 to reach the area of ​​the field 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 area of ​​the field where the measured or predicted value is georeferenced.In such an example, the current position and speed of the agricultural harvester can be considered.

[0210] Display portion 738 also includes an interactive action display portion indicated approximately at 751. 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 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, adjusting the cutter position setting (such as height, pitch, or roll setting), adjusting the sensitivity setting, and adjusting the ground pressure setting are identified as actions to be taken when the measured or predicted value meets the threshold in bar 752. In some examples, multiple actions can be taken when the threshold is reached. For example, the cutter sensitivity setting can be adjusted (such as raising or lowering), and the ground pressure setting can be adjusted (such as raising or lowering). These are just some examples.

[0211] 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 a speed-changing action that, when executed, changes the speed at which the combine harvester 100 travels through the field. These actions can include setting-changing actions for changing the settings of an internal actuator or another WMA or WMA group, or setting-changing actions for implementing changes to settings such as one or more header settings (e.g., header position setting, header sensitivity setting, or header ground pressure setting). These are merely examples, and a wide variety of other actions are considered herein.

[0212] Items displayed on the user interface display 720 can be visually controlled. Visual control of the interface display 720 can be performed to capture the attention of the operator 260. For example, the items can be controlled to modify the intensity, color, or pattern of the displayed items. Additionally, the items can be controlled to blink. As an example, a description of changes to the visual appearance of the items is provided. Therefore, other aspects of the visual appearance of the items can be changed. Thus, the items 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, including more or fewer specific items, can be included on the user interface display 720.

[0213] 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. Block 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 selected value 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.

[0214] 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.

[0215] At box 790, operator interface controller 231 receives a signal indicating an alarm condition. For example, box 792 indicates a signal that a detected or predicted value, which can be received by controller input processing system 668, satisfies a threshold condition present in column 752. As previously explained, a threshold condition may include a value below a threshold, a value at a threshold, or a value above a threshold. Box 794 shows that action signal generator 660 can, in response to receiving an alarm condition, generate a visual alarm using visual control signal generator 684, an audio alarm using audio control signal generator 686, a tactile alarm using tactile control signal generator 688, or any combination thereof, to alarm operator 260. Similarly, as shown in box 796, controller output generator 670 can generate outputs to other controllers in control system 214, causing these controllers to perform the corresponding actions identified in column 754. Box 798 shows that operator interface controller 231 can also detect and process alarm conditions in other ways.

[0216] 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.

[0217] 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".

[0218] Table 1

[0219] Operator: "Johnny, tell me about the current cut height characteristics."

[0220] Operator interface controller: "Currently, the cutting table height is high."

[0221] Operator: "Johnny, what should I do about this cutting height?"

[0222] Operator interface controller: "Adjust header sensitivity setting".

[0223] 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).

[0224] Table 2

[0225] Operator interface controller: "In the past 10 seconds, the cutting height has changed beyond the ideal cutting height."

[0226] Operator interface controller: "The predicted cut height for the next 1 acre is low."

[0227] Operator interface controller: "Note: Upcoming slope changes, header height increase."

[0228] 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 of cut height characteristics in a field being harvested.

[0229] Table 3

[0230] Human: "Johnny, mark the area with high cut and high variability."

[0231] Operator interface controller: "Areas with high cutting height variability have been marked."

[0232] 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 the cutting height characteristic area.

[0233] Table 4

[0234] Human: "Johnny, begin marking areas with high cut heights."

[0235] Operator interface controller: "Mark areas with high cutting heights".

[0236] Human: "Johnny, stop marking areas with high cut heights."

[0237] Operator interface controller: "Mark stop for areas with high cutting height".

[0238] The example shown in Table 5 illustrates that the motion signal generator 160 can generate signals for marking regions with cutting height characteristics in a manner different from that shown in Tables 3 and 4.

[0239] Table 5

[0240] Human: "Johnny, mark the last 100 feet as the low-cut height zone."

[0241] Operator interface controller: "The last 100 feet are marked as the low-cutting-height area."

[0242] Return again Figure 12 Box 906 shows 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 shows that the output can be an audio message. Box 910 shows that the output can be a visual message, and Box 912 shows 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.

[0243] 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.

[0244] Therefore, one or more maps are obtained by an agricultural harvester, showing agricultural characteristic values ​​at different geographical locations in the field being harvested. Field sensors on the harvester sense characteristics with values ​​indicating cutting height as the harvester moves through the field. A prediction map generator generates a prediction map based on the agricultural characteristic values ​​in the maps and the agricultural characteristics sensed by the field sensors. This prediction map predicts control values ​​for different locations in the field. The control system controls the controllable subsystems based on the control values ​​in the prediction map.

[0245] A control value is a value upon which an action can be based. As described herein, a control value can include any value (or a 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 a priori 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.

[0246] 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.

[0247] 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.

[0248] 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 the data storage device; one or more data storage devices may all be located remotely from the system utilizing the 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.

[0249] 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.

[0250] 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.

[0251] 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 2The software or components shown herein, 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 shared data centers, even if the service appears as a single access point for a 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.

[0252] 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.

[0253] Figure 14 Another example of a remote server architecture is also described. Figure 14 It shows Figure 2Some 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.

[0254] It will also be noted that Figure 2 The components or parts 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.

[0255] 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).

[0256] Figure 15This 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.

[0257] 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 shown interact, or both. 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.

[0258] In other examples, applications 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.

[0259] 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.

[0260] 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.

[0261] Positioning system 27 schematically includes components that output the current geographic location of 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.

[0262] 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.

[0263] 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.

[0264] 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 these 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.

[0265] Note that other forms of device 16 are possible.

[0266] Figure 18 It is one of the deployable ones Figure 2 An example of a computing environment for the components. Reference Figure 18 An 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.

[0267] 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.

[0268] 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 18 The operating system 834, application program 835, other program modules 836, and program data 837 are shown.

[0269] Computer 810 may also include other removable / non-removable volatile / non-volatile computer storage media. This is just one example. Figure 18A 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).

[0270] 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.

[0271] The above discussion and Figure 18 The driver and its associated computer storage media shown provide storage for computer-readable instructions, data structures, program modules, and other data for computer 810. 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.

[0272] Users can input commands and information to computer 810 through 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 connected 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.

[0273] 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).

[0274] 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.

[0275] It should also be noted that the different examples described in this paper 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 paper.

[0276] Example 1 is an agricultural operating machine, comprising:

[0277] A communication system that receives a map, the map including values ​​of cut height characteristics corresponding to different locations in a field;

[0278] A geolocation sensor that detects the geolocation of the agricultural machinery;

[0279] A field sensor that detects a value of an agricultural characteristic corresponding to the geographical location;

[0280] A prediction map generator generates a functional predictive agricultural map of the field based on the value of the cut height characteristic in the map and the value of the agricultural characteristic, the functional predictive agricultural map mapping the predicted control values ​​to the different geographical locations in the field;

[0281] Controllable subsystem; and

[0282] 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.

[0283] Example 2 is any or all of the agricultural machinery of the foregoing examples, wherein the graph includes a predicted cut height characteristic graph, the predicted cut height characteristic graph including predicted values ​​of the cut height characteristic corresponding to the different locations in the field as values ​​of the cut height characteristic.

[0284] Example 3 is any or all of the agricultural operating machines of the foregoing examples, wherein the prediction map generator includes:

[0285] A predictive agricultural characteristic map generator generates a functional predictive agricultural characteristic map as the functional predictive agricultural map, which maps the predicted values ​​of the agricultural characteristics as the predicted control values ​​to the different geographical locations in the field.

[0286] Example 4 is any or all of the agricultural operating machines of the foregoing examples, wherein the field sensors detect the values ​​of operator commands that indicate the commanded controlled actions of the agricultural operating machine as values ​​of the agricultural characteristics.

[0287] Example 5 is any or all of the agricultural operating machines of the foregoing examples, wherein the prediction map generator includes:

[0288] A predictive operator command map is generated, which serves as a functional predictive operator command map for the functional predictive agriculture map. The functional predictive operator command map maps the predicted operator command values ​​as predicted control values ​​to the different geographical locations in the field.

[0289] Example 6 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system includes:

[0290] 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 operator command map, and to control the controllable subsystem to execute the operator command based on the operator command control signal.

[0291] Example 7 is any or all of the agricultural machinery of the foregoing examples, wherein the control system generates the control signal to control the controllable subsystem to adjust the settings of the cutting table on the agricultural machinery.

[0292] Example 8 is any or all of the agricultural operating machines of the foregoing examples, further including:

[0293] A predictive model generator generates a predictive agricultural model based on the value of the cut height characteristic in the graph at the geographic location and the value of the agricultural characteristic detected by the field sensor corresponding to the geographic location. The predictive agricultural model models the relationship between the cut height characteristic and the agricultural characteristic. The predictive map generator generates the functional predictive agricultural map based on the value of the cut height characteristic in the graph and the predictive agricultural model.

[0294] Example 9 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system further includes:

[0295] 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.

[0296] 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.

[0297] Example 11 is a computer-implemented method for controlling agricultural machinery, comprising:

[0298] Obtain a map, which includes values ​​of cut height characteristics corresponding to different geographical locations in the field;

[0299] Detect the geographical location of the agricultural machinery;

[0300] The value of an agricultural characteristic corresponding to the geographical location is detected using field sensors;

[0301] A functional predictive agriculture map of the field is generated based on the values ​​of the cutting height characteristic and the agricultural characteristics in the map, and the functional predictive agriculture map maps the predicted control values ​​to the different geographical locations in the field; and

[0302] The controllable subsystem is controlled based on the geographical location of the agricultural machinery and the control values ​​in the functional predictive agriculture map.

[0303] Example 12 is a computer-implemented method of any or all of the foregoing examples, wherein obtaining the graph includes:

[0304] Obtain a predicted cutting height characteristic map, which includes predicted values ​​of the cutting height characteristic corresponding to different locations in the field.

[0305] Example 13 is a computer-implemented method of any or all of the foregoing examples, wherein generating the functional predictive agriculture map includes:

[0306] A functional predictive agricultural characteristic map is generated, wherein the predicted values ​​of the agricultural characteristics are mapped as predicted control values ​​to the different geographical locations in the field.

[0307] Example 14 is a computer-implemented method of any or all of the foregoing examples, wherein detecting the value of the agricultural characteristic using field sensors includes:

[0308] The field sensors detect operator commands that indicate the controlled actions of the agricultural machinery, serving as values ​​representing the agricultural characteristics.

[0309] Example 15 is a computer-implemented method of any or all of the foregoing examples, wherein generating the functional predictive agriculture map includes:

[0310] A functional predictive operator command graph is generated, which maps the predicted operator command values ​​as the predicted control values ​​to the different geographical locations in the field.

[0311] Example 16 is a computer-implemented method of any or all of the foregoing examples, wherein controlling the controllable subsystem includes:

[0312] Based on the detected geographic location and the functional predictive operator command map, an operator command control signal is generated to indicate operator commands; and

[0313] The controllable subsystem is controlled to execute the operator's command based on the operator's command control signal.

[0314] Example 17 is a computer-implemented method of any or all of the foregoing examples, wherein controlling the controllable subsystem includes:

[0315] Control the controllable subsystem to adjust the settings of the cutting table on the agricultural machine.

[0316] Example 18 is a computer-implemented method of any or all of the foregoing examples, further comprising:

[0317] A predictive agriculture model is generated based on the value of the cutting height characteristic in the figure at the geographical location and the value of the agricultural characteristic detected by the field sensor corresponding to the geographical location. The predictive agriculture model models the relationship between the cutting height characteristic and the agricultural characteristic. The generation of the functional predictive agriculture map includes generating the functional predictive agriculture map based on the value of the cutting height characteristic in the figure and based on the predictive agriculture model.

[0318] Example 19 is an agricultural operating machine, comprising:

[0319] A communication system that receives a map, the map including values ​​of cut height characteristics corresponding to different geographical locations in the field;

[0320] A geolocation sensor that detects the geolocation of the agricultural machinery;

[0321] A field sensor that detects a value of an agricultural characteristic corresponding to the geographical location;

[0322] A predictive model generator generates a predictive agricultural model based on the value of the cut height characteristic in the figure at the geographical location and the value of the agricultural characteristic detected by the field sensor corresponding to the geographical location. The predictive agricultural model models the relationship between the cut height characteristic and the agricultural characteristic.

[0323] A predictive map generator generates a functional predictive agriculture map of the field based on the value of the cut height characteristic in the map and based on the predictive agriculture model, the functional predictive agriculture map mapping the predicted control values ​​to the different geographical locations in the field;

[0324] Controllable subsystem; and

[0325] 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.

[0326] Example 20 is any or all of the agricultural operating machines of the foregoing examples, wherein the control signal controls the controllable subsystem to adjust the settings of the cutting platform on the agricultural operating machine based on the detected geographic location and the functional predictive agricultural map.

[0327] 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 graph (258) comprising values ​​of cut height characteristics corresponding to different locations in the field; A geolocation sensor (204) detects the geolocation of the agricultural machinery (100); A field sensor (208) detects a value corresponding to the geographical location that is different from the cutting height characteristic; A predictive model generator generates a predictive agricultural model based on the value of the cut height characteristic in the figure at the geographical location and the value of the agricultural characteristic detected by the field sensor corresponding to the geographical location. The predictive agricultural model models the relationship between the cut height characteristic and the agricultural characteristic. A prediction map generator (212) generates a functional predictive agricultural map of the field based on the value of the cut height characteristic in the map and based on the predictive agricultural model, the functional predictive agricultural map mapping the predicted control values ​​to 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 graph includes a predicted cut height characteristic graph, which includes predicted values ​​of the cut height characteristic corresponding to the different locations in the field.

3. The agricultural machinery according to claim 1, wherein, The prediction map generator includes: A predictive agricultural characteristic map generator generates a functional predictive agricultural characteristic map as the functional predictive agricultural map, which maps the predicted values ​​of the agricultural characteristics as the predicted control values ​​to the different geographical locations in the field.

4. The agricultural machinery according to claim 1, wherein, The field sensors detect the values ​​of operator commands that indicate the command-controlled actions of the agricultural machinery, serving as values ​​of the agricultural characteristics.

5. The agricultural machinery according to claim 4, wherein, The prediction map generator includes: A predictive operator command map is generated, which serves as a functional predictive operator command map for the functional predictive agriculture map. The functional predictive operator command map maps the predicted operator command values ​​as predicted control values ​​to the different geographical locations in the field.

6. The agricultural machinery according to claim 5, wherein, The control system includes: 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 operator command map, and to control the controllable subsystem to execute the operator command based on the operator command control signal.

7. The agricultural machinery according to claim 1, wherein, The control system generates the control signal to control the controllable subsystem to adjust the settings of the cutting platform on the agricultural machine.

8. A computer-implemented method for controlling agricultural machinery (100), comprising: Obtain a map (258) that includes values ​​of cut height characteristics corresponding to different geographical locations in the field; Detect the geographical location of the agricultural machinery (100); The field sensor (208) detects a value corresponding to the geographical location for an agricultural characteristic that is different from the cutting height characteristic; A predictive agriculture model is generated based on the value of the cutting height characteristic in the figure at the geographical location and the value of the agricultural characteristic detected by the field sensor corresponding to the geographical location. The predictive agriculture model models the relationship between the cutting height characteristic and the agricultural characteristic. Based on the value of the cutting height characteristic in the figure (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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