Predictive map generation and control system

Abstract

One or more information maps are obtained by an agricultural machinery. These maps map one or more agricultural characteristic values ​​to different geographical locations within the field. Field sensors on the agricultural machinery sense the agricultural characteristics as the machinery moves through the field. A prediction map generator generates a prediction map based on the relationship between the values ​​in the one or more information maps and the agricultural characteristics sensed by the field sensors. This prediction map predicts the agricultural characteristics at different locations within 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, the operator 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 4 This is a block diagram illustrating an example of a prediction model generator and a prediction metric graph generator.

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

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

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

[0014] Figure 7 The flowchart illustrates an example of the operation of an agricultural harvester, including generating a functional prediction map using prior information maps and field sensor inputs.

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

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

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

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

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

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

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

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

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

[0024] To facilitate understanding of the principles of this disclosure, reference will now be made to the examples shown in the accompanying drawings, and they will be described using specific language. However, it will be understood that this is not intended to limit the scope of this disclosure. Any changes and further modifications to the described apparatus, systems, and methods, as well as any further applications 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, steps, or combinations thereof described with respect to one example may be combined with features, components, steps, or combinations thereof described with respect to other examples of this disclosure.

[0025] 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 header characteristic maps. In some examples, the predictive header characteristic maps can be used to control agricultural machinery (e.g., agricultural harvesters). The operation and control of the header on an agricultural harvester may be influenced by one or more topographic features of the field (e.g., slope).

[0026] The performance of a harvester can be adversely affected by a variety of different criteria. For example, the topography of a field can alter the characteristics of the harvester's header. For instance, as the topography changes, the distance between the header and the field surface may be adversely affected, causing the header height or tilt to no longer be at the optimal setting for ideally engaging the crop in that field.

[0027] Topographic maps graphically depict the ground elevation across different geographic locations within a field of interest. Since ground slope indicates changes in elevation, two or more elevation values ​​allow for the calculation of slope across areas with known elevation values. Greater granularity of slope can be achieved by using more areas with known elevation values. As an agricultural harvester traverses the terrain in a known direction, the harvester's pitch and roll can be determined based on the ground slope (i.e., the area of ​​elevation change). The terrain features mentioned below may include (but are not limited to) elevation, slope (e.g., including machine orientation relative to the slope), ground profile (e.g., roughness), and a variety of other terrain features.

[0028] Therefore, this discussion is based on an example in which the system receives a topographic map during harvesting operations and also uses field sensors to detect variables indicating header settings, such as operator input indicating header height, tumble, or tilt settings. The system generates a model that models the relationship between topographic feature values ​​from the topographic map and field data from the field sensors. This model is used to generate a functional predictive header setting map that predicts the expected header characteristics of the combine harvester in the field, such as expected header height, tumble, or tilt. The functional predictive header setting map generated during harvesting operations can be presented to the operator or other users and / or used to automatically control the combine harvester during harvesting operations.

[0029] 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.

[0030] like Figure 1 As 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 1As 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.

[0031] 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.

[0032] In operation, as an overview, the agricultural harvester 100 exemplarily moves across the field in the direction indicated by arrow 147. As the agricultural harvester 100 moves, the header 102 (and associated reel 164) engages the crop to be harvested and collects the crop toward the cutter 104. The operator of the agricultural harvester 100 can be a local human operator, a remote human operator, or an automated system. Operator commands are commands from the operator. The operator of the agricultural 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 implement 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] Figure 1 It is also shown that, in one example, the agricultural harvester 100 includes a ground speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward-view image capture mechanism 151 (which may be in the form of a stereo camera or a monocular camera), and one or more loss sensors 152 disposed in the grain cleaning subsystem 118.

[0037] Ground speed sensor 146 senses the travel speed of the agricultural harvester 100 on the ground. Ground 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 can be used to sense the travel speed, such as a Global Positioning System (GPS), dead reckoning system, LoRAN (Local Remote Navigation System), or various other systems or sensors that provide an indication of travel speed.

[0038] 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.

[0039] Separator loss sensor 148 provides indication of the left and right separators ( Figure 1(Not shown separately) The grain loss signal in the separator. 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.

[0040] The agricultural harvester 100 may also include other sensors and measuring mechanisms. For example, the agricultural harvester 100 may include one or more of the following sensors: a header height sensor that senses the height of the header 102 above the ground 111; a stability sensor that senses the oscillation or jumping (and amplitude) of the agricultural harvester 100; a residue setting sensor configured to sense whether the agricultural harvester 100 is configured to chop residue, pile it, etc.; a cleaning chamber fan speed sensor that senses the speed of the fan 120; a concave plate gap sensor that senses the gap between the drum 112 and the concave plate 114; and a threshing drum speed sensor that senses the speed of the drum 112. The harvester 100 includes: a speed sensor; a husk sieve gap sensor that senses the opening size in the husk sieve 122; a sieve mesh gap sensor that senses the opening size in the sieve mesh 124; a material other than grain (MOG) moisture sensor that senses the moisture level of the MOG passing through the harvester 100; one or more machine setting sensors configured to sense various configurable settings of the harvester 100; a machine orientation sensor that senses the orientation of the harvester 100; and a crop property sensor that senses various types of crop properties, such as crop type, crop moisture, and other crop properties. While the harvester 100 is processing crop material, the crop property sensor can also be configured to sense the characteristics of the cut crop material. For example, in some cases, the crop property sensor may sense: grain quality, such as broken grain, MOG level; grain composition, such as starch and protein; and the grain feed rate as the grain passes through the feeder housing 106, the clean grain elevator 130, or elsewhere in the harvester 100. The crop property sensor can also sense the feed rate of biomass through the feeder housing 106, separator 116, or elsewhere in the harvester 100. The crop property sensor can also sense the feed rate as the mass flow rate of grain through the elevator 130 or other parts of the harvester 100, or provide other output signals indicating other sensed variables.

[0041] The agricultural harvester 100 may also include an operator input sensor. The operator input sensor exemplarily senses a variety of different 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. For example, the input sensed by the operator input sensor may be a setting input for controlling settings on the header 102 on the agricultural harvester 100 (such as header height setting, header tumble setting, or header tilt setting).

[0042] Before describing how the agricultural harvester 100 generates a functional predictive header setting diagram and uses this functional predictive header setting diagram for control, a brief description of some items on the agricultural harvester 100 and their operation will be given first. Figure 2 , Figure 3A and Figure 3B The diagram 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 the power characteristics of an agricultural harvester. Field characteristics may include (but are not limited to): field properties such as slope, weed density, weed type, soil moisture, and surface quality; crop properties such as crop height, crop moisture, crop density, and crop condition; grain properties such as grain moisture, grain size, and grain test weight; and machine performance properties such as loss level, work quality, fuel consumption, and power utilization. Relationships between characteristic values ​​obtained from field sensor signals and prior information map values ​​are identified, and these relationships are used to generate new functional prediction maps. The functional prediction maps predict values ​​at different geographic locations in the field, and one or more of those values ​​can be used to control the machine, such as controlling one or more subsystems of an agricultural harvester. In some cases, the functional prediction maps may be presented to users, such as operators of agricultural machinery (which could be agricultural 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 maps to perform editing operations and other user interface operations. In some cases, functional prediction maps can be used to control agricultural machinery (e.g., agricultural harvesters), presented to operators or other users, and presented to operators or users to facilitate operator or user interaction, or one or more of these functions.

[0043] In reference Figure 2 , Figure 3A and Figure 3B After describing the general method, refer to Figure 4 and Figure 5A more specific method is described for generating a functional predictive header setting diagram 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.

[0044] 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 simultaneously sense one or more agricultural characteristics of the field during harvesting operations. Agricultural characteristics may include any characteristics that can affect the harvesting operations. 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 agricultural harvester 100 also includes a prediction model or relation generator (hereinafter 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 onboard sensors 222, remote sensors 224, and other sensors 226 that sense the characteristics of the field during agricultural operations. The predictive model generator 210 exemplarily includes a prior information variable versus field variable model generator 228, and the predictive model generator 210 may include other items 230. The 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 the system 214 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.

[0045] Figure 2The agricultural harvester 100 is also shown to receive a priori information map 258. As described below, for example, the priori information map 258 includes, for example, a topographic map. However, the priori information map 258 may also encompass other types of data acquired prior to the harvesting operation or maps from prior or previous operations. Figure 2 The diagram also shows an operator 260 capable of operating an agricultural harvester 100. The operator 260 interacts with an operator interface mechanism 218. In some examples, the operator interface mechanism 218 may include a joystick, joystick, steering wheel, linkage, pedals, buttons, dials, keypad, user-actuable elements on a user interface display (e.g., icons, buttons, etc.), microphone and speaker (where speech recognition and speech synthesis are provided), and various other types of control devices. Where a touch-sensitive display system is provided, the operator 260 may interact with the operator interface mechanism 218 using touch gestures. The examples provided above are exemplary and not intended to limit the scope of this disclosure. Therefore, other types of operator interface mechanisms 218 may also be used and are within the scope of this disclosure.

[0046] 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.

[0047] 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.

[0048] The field sensor 208 can be referenced above. Figure 1Any sensors described. Field sensors 208 include onboard sensors 222 mounted on the agricultural harvester 100. These sensors may include, for example, perception sensors (e.g., forward-looking monocular or stereo camera systems and image processing systems) and image sensors inside the agricultural harvester 100 (e.g., grain-cleaning cameras, or cameras mounted to identify weed seeds leaving the agricultural harvester 100 through or from the grain-cleaning subsystem). Field sensors 208 also include remote field sensors 224 that capture field information. Field data includes data acquired from sensors on the harvester or, in the case of data detected during harvesting operations, data acquired by any sensor. Figure 6B Some other examples of the field sensor 208 are shown below.

[0049] Predictive model generator 210 generates a model indicating the relationship between values ​​sensed by field sensors 208 and measurements mapped to the field via prior information map 258. For example, if prior information map 258 establishes a mapping between topographic feature values ​​and different locations in the field, and field sensors 208 are sensing values ​​indicating header settings, then prior information variable-to-field variable model generator 228 generates a predictive header setting model that models the relationship between topographic feature values ​​and header setting values. A predictive header setting model can also be generated based on topographic feature values ​​from prior information map 258 and multiple field data values ​​generated by field sensors 208. Then, predictive map generator 212 uses the predictive header setting model generated by predictive model generator 210 to generate a functional predictive header setting map, which predicts, based on prior information map 258, the values ​​of header settings (e.g., header height setting, header pitch (forward and backward tilt), or header roll setting) sensed by field sensors 208 at different locations in the field.

[0050] 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.

[0051] Continuing the previous example, where prior information map 258 is a topographic map and field sensor 208 senses values ​​indicating cutter settings, prediction map generator 212 can use the topographic feature values ​​in prior information map 258 and the model generated by prediction model generator 210 to generate a functional prediction map 263, which predicts cutter settings at different locations in the field. Prediction map generator 212 then outputs prediction map 264.

[0052] like Figure 2As shown, prediction map 264 predicts the values ​​of sensed features (sensed by field sensors 208) or features related to sensed features at multiple locations across the field, based on prior information values ​​at those locations in prior information map 258 and a prediction model. For example, if prediction model generator 210 has generated a prediction model indicating the relationship between terrain feature values ​​and cutter settings, then given terrain feature values ​​at different locations across the field, prediction map generator 212 generates prediction map 264 predicting the values ​​of cutter settings at those different locations across the field. Prediction map 264 is generated using terrain feature values ​​at those locations obtained from the topographic map and the relationship between terrain features and cutter settings obtained from the prediction model.

[0053] The following will describe some changes to the data types mapped in prior information graph 258, the data types sensed by field sensor 208, and the data types predicted in prediction graph 264.

[0054] In some examples, the data type in the prior information map 258 is different from the data type sensed by the field sensor 208, while the data type in the prediction map 264 is the same as the data type sensed by the field sensor 208. For example, the prior information map 258 may be a topographic map, and the variable sensed by the field sensor 208 may be a header setting. Then, the prediction map 264 may be a predicted header setting map that maps the predicted header setting values ​​to different geographical locations in the field.

[0055] 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 topographic map, and the variable sensed by the field sensor 208 may be a cutter height setting. Therefore, the prediction map 264 may be a predicted cutter height map that maps the predicted cutter height values ​​to different geographical locations in the field.

[0056] In some examples, the prior information map 258 is derived from prior or previous traversal of the field during a prior or previous operation, and the data type is different from the data type sensed by the field sensor 208, while the data type in the prediction map 264 is the same as the data type sensed by the field sensor 208. For example, the prior information map 258 may be a topographic map generated during planting or spraying operations on the field, and the variable sensed by the field sensor 208 may be the header tilt setting. Thus, the prediction map 264 may be a predicted header tilt setting map that maps the predicted header tilt setting value to different geographical locations in the field.

[0057] In some examples, the prior infographic 258 is derived from passage through the field during a prior or previous operation, and the data type is the same as that sensed by the field sensor 208, and the data type in the prediction infographic 264 is also the same as that sensed by the field sensor 208. For example, the prior infographic 258 could be a header setting map generated in the previous year, and the variable sensed by the field sensor 208 could be the header setting. Thus, the prediction infographic 264 could be a predicted header setting map that maps predicted header setting values ​​to different geographic locations in the field. In this example, the prediction model generator 210 can use the relative header setting differences from the georeferenced prior infographic 258 from the previous year to generate a prediction model that models the relationship between the relative header setting differences on the prior infographic 258 and the header setting values ​​sensed by the field sensor 208 during the current harvesting operation. The prediction infographic generator 210 then uses the prediction model to generate the prediction header setting map.

[0058] 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 the 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 to accommodate 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 the predicted control area map 265 includes control area information defining the control area, the predicted control area map 265 may be similar to the predicted map 264. Thus, as described herein, the functional predicted map 263 may or may not include control areas. Both predicted map 264 and predicted control area map 265 are functional predicted maps 263. In one example, the functional predicted map 263 does not include control areas (e.g., predicted map 264). In another example, the functional predicted map 263 does include control areas (e.g., predicted control area map 265). In some examples, if an intercropping production system is implemented, multiple crops may coexist in the field. In this case, the predicted map generator 212 and the control area generator 213 are able to identify the location and characteristics of two or more crops and then generate the predicted map 264 and the predicted control area map 265 accordingly.

[0059] 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.

[0060] 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.

[0061] The operator interface controller 231 is operable to generate control signals to control the operator interface mechanism 218. The operator interface controller 231 is also operable to present the predictive map 264 or the predictive control area map 265, or other information derived from or based on the predictive map 264, the predictive control area map 265, or both, to the operator 260. The operator 260 can be a local operator or a remote operator. As an example, the controller 231 generates control signals to control the display mechanism to display one or both of the predictive map 264 and the predictive control area map 265 to the operator 260. The controller 231 can generate an operator-actuable mechanism that is displayed and can be actuated by the operator to interact with the displayed map. The operator can edit the map by, for example, correcting the power characteristics displayed on the map based on the operator's observation. The setting controller 232 can generate control signals for various settings on the agricultural harvester 100 based on the predictive map 264, the predictive control area map 265, or both. For example, the setting controller 232 can generate control signals to control the machine and header actuator 248. In response to the generated control signals, the machine and header actuator 248 operate to control, for example, screen and chaff screen settings, 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 control multiple 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 an area with predicted subsystem power consumption above a selected threshold, the feed rate controller 236 can reduce the speed of the harvester 100 to maintain power distribution for the predicted power consumption requirements of one or more subsystems. The header and reel controller 238 can generate control signals to control 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 belt conveyor or other belt conveyor functions.The cover position controller 242 can generate control signals based on prediction diagram 264 or prediction control area diagram 265, or both, to control the position of the cover included on the header, and the residue system controller 244 can generate control signals based on prediction diagram 264 or prediction control area diagram 265, or both, to control the residue subsystem 138. The machine cleaning controller 245 can generate control signals to control the machine cleaning subsystem 254. 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.

[0062] 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.

[0063] At box 280, the agricultural harvester 100 receives a priori information map 258. Examples of priori information map 258 or receiving priori information map 258 are discussed with reference to boxes 281, 282, 284, and 286. As described above, priori information map 258 maps the values ​​of variables corresponding to a first characteristic to different locations in the field, as indicated by box 282. As indicated by box 281, receiving priori information map 258 may involve selecting one or more of a plurality of possible priori information maps available. For example, one priori information map may be a topographic 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 a different machine (e.g., a sprayer or other machine) performing a priori or priori operation in the field. The process of selecting one or more priori information maps may be manual, semi-automatic, or automatic. Priori information map 258 is based on data collected prior to the current harvesting operation. This is indicated by box 284. For example, the data can be collected based on aerial images acquired in the previous year, earlier in the current growing season, or at other times. As indicated by box 285, the prior information map can be a predictive map that predicts characteristics based on the prior information map and its relationship with field sensors. Figure 5 The process of generating the prediction map is presented in the image. This can also be performed using other sensors and other prior maps. Figure 5 The process illustrated here is used to generate, for example, a predicted yield map or a predicted biomass map. These predicted maps can be used as prior maps in other prediction processes, as indicated by box 285. The data can be based on data detected in ways other than using aerial imagery. For example, data for prior information map 258 can be transmitted to agricultural harvester 100 using communication system 206 and stored in data storage device 202. Data for prior information map 258 can also be provided to agricultural harvester 100 in other ways using communication system 206, which is... Figure 3ABox 286 in the flowchart indicates this. In some examples, prior information diagram 258 may be received by communication system 206.

[0064] At the start of the harvesting operation, field sensor 208 generates sensor signals indicating one or more field data values, such as header settings (e.g., header height setting, or header tilt (also known as pitch), or header roll setting), as indicated by 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 speed data from geolocation sensor 204 is used to georeference the data from the airborne sensor.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] Prediction map generator 212 configures prediction map 264 such that prediction map 264 can be manipulated (or used) by control system 214. Prediction map generator 212 may provide prediction map 264 to control system 214 or control area generator 213 or both. Some examples of different ways in which prediction map 264 can be configured or output will be described with reference to boxes 296, 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.

[0069] 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 an agricultural harvester 100 may not be able to see the information corresponding to prediction map 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 map 264 on the display but is prevented from making any changes. A manager at a separate remote location may be able to see all elements on prediction map 264 and also be able to modify prediction map 264. In some cases, prediction map 264, accessible and modifiable by a manager at a remote location, can be used for machine control. This is an example of an achievable authorization hierarchy. Prediction map 264 or prediction control area map 265, or both, may also be configured in other ways, as indicated by box 297.

[0070] At box 298, the control system receives input from the geolocation sensor 204 and other field sensors 208. Specifically, at 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 additional information from various field sensors 208.

[0071] 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.

[0072] As an example, the generated prediction map 264, in the form of a predictive header setting map, can be used to control one or more subsystems 216. For example, the predictive header setting map may include predicted header setting values ​​georeferenced to locations within a harvesting field. For example, if the predictive header setting map includes header height setting values ​​as predicted values ​​of the header setting, indicating possible height settings for the header at a geographic location in the field, various other settings for the header (such as header roll setting, header tilt (pitch) setting, ground pressure setting, or header sensitivity setting) can be adjusted to correspond to the predicted header height setting provided by the predictive header setting map. Ground pressure refers to the force (e.g., pressure) exerted on the ground by the header due to its weight, which is controllably adjusted by a reaction buoyancy force applied to the header by one or more controllable actuators (e.g., hydraulic cylinders). The output force (e.g., buoyancy force) of the one or more actuators is controllably adjusted by the ground pressure setting. The foregoing example of using a predictive header setting map for header control is provided by way of example only. Therefore, values ​​obtained from the predictive cutter setup diagram or other types of predictive diagrams can be used to generate a variety of other control signals to control one or more controllable subsystems 216.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] In other examples, the learning trigger criterion may be based on how much field sensor data from field sensor 208 has changed over time or compared to previous values. For example, if the change in 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, 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 in the field sensor data is outside the selected range, greater than a defined amount, or above a threshold, 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.

[0077] 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.

[0078] 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, change the size, shape, position, or presence of control areas on prediction control area graph 265, or both. Box 321 shows that the edited information can be used as a learning trigger criterion.

[0079] 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 the 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.

[0080] 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 by box 326.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] Figure 4 yes Figure 1 A block diagram of a portion of an agricultural harvester 100. Specifically, among other things, Figure 4 Examples of the prediction model generator 210 and the prediction graph generator 212 are shown in more detail. Figure 4 The information flow between the various components is also shown. The prediction model generator 210 receives topographic map 332 as a priori information map. Topographic map 332 includes georegistered topographic feature values.

[0085] The predictive model generator 210 also receives a geographic location 334 or a geographic location indication from the geographic location sensor 204. The field sensor 208 exemplarily includes an operator input sensor (e.g., operator input sensor 336) and a processing system 338. The operator input sensor 336 senses a variety of different operator inputs, such as setting inputs for controlling one or more components of the agricultural harvester 100, such as controlling the header height, tumble or tilt header height setting, header tumble setting, or header tilt setting, respectively. In some cases, the operator input sensor 336 may be located on the agricultural harvester 100. The processing system 338 processes the sensor data generated from the operator input sensor 336 to generate processed sensor data 340, some examples of which are described below.

[0086] This discussion is conducted with reference to an example in which the operator input sensor 336 senses header setting inputs for controlling settings (such as header height setting, header tumble setting, or header tilt setting) on ​​the header 102 of an agricultural harvester 100. Figure 4 As shown, the exemplary prediction model generator 210 includes one or more of a terrain feature-based header height setting model generator 342, a terrain feature-based header tilt model generator 344, and a terrain feature-based header roll setting model generator 346. In other examples, the prediction model generator 210 may include more than Figure 4 The 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 power models. For example, the prediction model generator 210 may include specific terrain characteristic model generators, such as a slope-to-cutter height setting model generator, a slope-to-cutter roll setting model generator, or a slope-to-cutter tilt setting model generator.

[0087] The terrain feature-based header height setting model generator 342 identifies the relationship between the header height setting at a geographic location (corresponding to the geographic location where the operator input sensor 336 senses the header height setting input, which indicates the height of the header controlled by the operator's command) and terrain feature values ​​(e.g., one or more slope values) from the topographic map 332 corresponding to the same location in the field where the header height setting was sensed. Based on this relationship established by the terrain feature-based header height setting model generator 342, the terrain feature-based header height setting model generator 342 generates a predictive header setting model. The prediction map generator 212 uses the predicted header setting model to predict the header setting (e.g., header height setting) at different locations in the field based on the georeferenced terrain feature values ​​(e.g., slope values) included in the topographic map 332 at the same location in the field.

[0088] The terrain feature-based header tilt setting model generator 344 identifies the relationship between the header tilt setting at a geographic location (corresponding to the geographic location where the operator input sensor 336 senses the header tilt setting input (which indicates the operator-controlled tilt of the header)) and terrain feature values ​​(e.g., one or more slope values) from topographic map 332 corresponding to the same location in the field where the header tilt setting was sensed. Based on this relationship established by the terrain feature-based header tilt setting model generator 344, the terrain feature-based header tilt setting model generator 344 generates a predictive header setting model. The prediction map generator 212 uses the predicted header setting model to predict header settings (e.g., header tilt settings) at different locations in the field based on georeferenced terrain feature values ​​(e.g., slope values) included in topographic map 332 at the same location in the field.

[0089] The terrain feature-based header roll setting model generator 346 identifies a relationship between a geographic location (corresponding to the geographic location where the operator input sensor 336 senses the header roll setting (which indicates operator-controlled rollover of the header)) and terrain feature values ​​(e.g., one or more slope values) from topographic map 332 corresponding to the same location in the field where the header roll setting is sensed. Based on this relationship established by the terrain feature-based header roll setting model generator 346, the generator generates a predictive header setting model. The prediction map generator 212 uses the predicted header setting model to predict header settings (e.g., header roll settings) at different locations in the field based on georeferenced terrain feature values ​​(e.g., slope values) included in topographic map 332 at the same location in the field.

[0090] In view of the above, the prediction model generator 210 is operable to generate multiple prediction header setting models, such as one or more prediction header setting models generated by model generators 342, 344, 346, and 348. In another example, two or more of the above-mentioned prediction header setting models can be combined into a single prediction header setting model, which predicts two or more header characteristics, such as header height setting, header roll setting, and header tilt setting, based on different values ​​at different locations in the field. Any one or a combination of these header setting models is generated by... Figure 4 The cutter head setting model 350 is uniformly represented in the model.

[0091] The predicted chute setting model 350 is provided to the predicted map generator 212. Figure 4 In one example, the prediction graph generator 212 includes a chute height setting graph generator 352, a chute tilt setting graph generator 354, and a chute roll setting graph generator 356. In other examples, the prediction graph generator 212 may include more, fewer, or different graph generators. Therefore, in some examples, the prediction graph generator 212 may include additional items 358, which may include other types of graph generators to generate chute setting graphs for other types of chute settings.

[0092] The header height map generator 352 receives the predicted header characteristic model 350, which predicts the header height setting based on the terrain characteristic values ​​in the topographic map 332 and the field sensor data indicating the header height setting. The header height map generator 352 generates a prediction map that maps the predicted header height setting at different locations of the header in the field.

[0093] The header tilt map generator 354 receives the predicted header characteristic model 350, which predicts the header tilt setting based on the topographic characteristic values ​​in the topographic map 332 and the field sensor data indicating the header tilt setting. The header tilt map generator 354 generates a prediction map that maps the predicted header tilt setting at different locations of the header in the field.

[0094] The cutter roll map generator 356 receives the predicted cutter characteristic model 350, which predicts the cutter roll setting based on the topographic characteristic values ​​in the topographic map 332 and the field sensor data indicating the cutter roll setting. The cutter roll map generator 356 generates a prediction map that maps the predicted cutter roll setting at different locations of the cutter in the field.

[0095] Prediction map generator 212 outputs one or more functional prediction header setting maps 360 that predict one or more header settings (such as header height setting, header roll setting, or header tilt setting). Each of the predicted header setting maps 360 predicts the header setting at different locations in the field. Each generated predicted header setting map 360 may be provided to control zone generator 213, control system 214, or both. Control zone generator 213 generates control zones and incorporates those control zones into the functional prediction maps (i.e., functional prediction maps 360) to provide a functional prediction map 360 with control zones. The functional prediction map 360 (with or without control zones) may be provided to control system 214, which generates control signals based on the functional prediction map 360 (with or without control zones) to control one or more controllable subsystems 216.

[0096] Figure 5 This is a flowchart illustrating an example of the operation of the prediction model generator 210 and the prediction map generator 212 in generating a prediction chute characteristic model 350 and a functional prediction chute characteristic map 360, respectively. At block 362, the prediction model generator 210 and the prediction map generator 212 receive a topographic map 332 or some other map 363. At block 364, the processing system 338 receives one or more sensor signals from field sensors 208 (e.g., operator input sensor 336). 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 indications of chute characteristics, such as chute height or chute tilt.

[0097] At box 372, processing system 338 processes the received one or more sensor signals to generate data indicating header settings. As shown in box 374, the header setting can be a header height setting commanded by the operator of the harvester 100. As shown in box 376, the header setting can be a header tilt setting commanded by the operator of the harvester 100. As shown in box 378, the header setting can be a header tumble setting commanded by the operator of the harvester 100. As shown in box 380, sensor data can indicate other header characteristics.

[0098] 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 of the captured or derived sensor data 340 based on machine latency, machine speed, sensor characteristics, etc.

[0099] At box 384, prediction model generator 210 generates one or more prediction models, such as cutter setting model 350, which model the relationship between terrain feature values ​​(e.g., slope values) obtained from a topographic map (e.g., topographic map 332) and cutter settings or related features sensed by field sensors 208 (e.g., operator input sensor 336). For example, prediction model generator 210 may generate a prediction cutter setting model that models the relationship between terrain feature values ​​(e.g., slope) and sensed cutter settings (such as cutter height setting, cutter roll setting, cutter tilt) indicated by sensor data obtained from field sensors 208 (e.g., operator input sensor 336).

[0100] At box 386, a prediction model, such as a predicted header setting model 350, is provided to a prediction map generator 212. This prediction map generator 212 generates a functional predicted header setting map 360 based on the topographic map 332 and the predicted header setting model 350. This functional predicted header setting map 360 maps the predicted header settings. For example, in some examples, the functional predicted header setting map 360 maps the predicted header height setting, the predicted header roll setting, or the predicted header tilt setting at multiple different locations across the field. Furthermore, the functional predicted header setting map 360 can be generated during agricultural operations. Therefore, the functional predicted header setting map 360 is generated while an agricultural harvester moves across the field to perform an agricultural operation.

[0101] At box 394, the prediction graph generator 212 outputs a functional prediction chute configuration diagram 360. At box 391, the prediction graph generator 212 outputs the functional prediction chute configuration diagram 360 to be presented to operator 260 for possible interaction. In some examples, as shown in box 393, the prediction graph generator 212 configures the functional prediction chute configuration diagram 360 for use by the control system 214. At box 395, the prediction graph generator 212 may also provide the functional prediction chute configuration diagram 360 to the control area generator 213 to generate and combine control areas. At box 397, the prediction graph generator 212 also configures the functional prediction chute configuration diagram 360 in other ways. The functional prediction chute configuration diagram 360 (with or without control areas) is provided to the control system 214. At box 396, control system 214 generates control signals based on functional predictive cutter setting diagram 360 (with or without a control area) to control controllable subsystem 216.

[0102] 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 a functional prediction map with added control areas, other controllers 246 control other controllable subsystems 256 on the agricultural harvester 100.

[0103] 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, tumbling, 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 the tilt of header 102 relative to the field surface from front to back. In yet another example, header / reel controller 238 may control header or other machine actuator 248 to adjust the tumbling of header 102, such as the tumbling of header 102 relative to the field surface across the width of header 102.

[0104] This demonstrates that the system receives a map mapping characteristics such as terrain features. The system also uses one or more field sensors to sense header settings (such as header height setting, header tumble setting, or header tilt setting) controlled by commands from the operator of the combine harvester 100, 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 prior information map. Therefore, the system uses the model, field data, and prior information map to generate a functional prediction map, and the generated functional prediction map can be configured for use by the control system, presented to a local operator or a remote operator or other user, or both. For example, the control system can use this map to control one or more systems of the combine harvester.

[0105] Figure 6A yes Figure 1 A block diagram of an example portion of an agricultural harvester 100 is shown. Specifically, among other things, Figure 6A Examples of the predictive model generator 210 and the predictive map generator 212 are shown. In the illustrated example, the prior information map 258 is the prior operation map 400. The prior operation map 400 may include header settings from previous or prior operations on the field or another field at multiple different locations within the field. For example, the prior operation map 400 may be a historical header setting map that includes historical header settings generated during harvesting operations in previous harvesting seasons at multiple different locations within the field. Figure 6AIt is also shown that the prediction model generator 210 and prediction map generator 212 can receive prediction ridge setting maps (e.g., functional prediction ridge setting map 360) in addition to prior information map 258. Functional prediction ridge setting map 360 can similarly serve as prior information map 258 in model generator 210, which models the relationship between information provided by functional prediction ridge setting map 360 and characteristics sensed by field sensors 208 to generate a prediction model. Therefore, map generator 212 can use the prediction model generated based on the relationship between information provided by functional prediction ridge setting map 360 and characteristics sensed by field sensors 208 to generate a functional prediction map that predicts characteristics sensed by field sensors 208 or characteristics indicating sensed characteristics at different locations in the field, based on one or more values ​​at different locations in the field in functional prediction ridge setting map 360 and based on the prediction model. Figure 6A As shown, the prediction model generator 210 and the prediction graph generator 212 can also receive other graphs 401, such as other prior information graphs or other prediction graphs, such as other prediction chute setting graphs generated in a different manner than those used to generate the functional prediction power graph 360.

[0106] 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 336, and the processing system 406. The field sensor 208 may also include other sensors 408.

[0107] Agricultural characteristic sensor 402 senses values ​​indicating agricultural characteristics. As discussed above, operator input sensor 336 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. Therefore, when the operator 260 changes settings or provides command inputs through operator interface mechanism 218, such inputs are detected by operator input sensor 336, which provides sensor signals indicating the sensed operator inputs.

[0108] The processing system 406 can receive sensor signals from one or more of the agricultural characteristic sensor 402 or the operator input sensor 336 and generate an output indicating the sensed variable. For example, the processing system 406 can receive sensor input from the agricultural characteristic sensor 402 and generate an output indicating the agricultural characteristic. The processing system 406 can also receive input from the operator input sensor 336 and generate an output indicating the sensed operator input.

[0109] Predictive model generator 210 may include a header setting to agricultural characteristic model generator 410 and a header setting 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 header setting model generators, such as a header height setting to agricultural characteristic model generator, a header tilt setting to agricultural characteristic model generator, a header tumble setting to agricultural characteristic model generator, a header height setting to command model generator, a header tumble setting to command model generator, or a header tilt setting to command model generator. The prediction model generator 210 can receive a geographic location 334 or a geographic location indication from the geographic location sensor 204 and generate a prediction model 426 that models the relationship between information in one or more of the aforementioned prior information diagram 258 or information in the functional prediction header setting diagram 360 or other diagram 401 and one or more of the agricultural characteristics sensed by the agricultural characteristic sensor 402 and the operator input commands sensed by the operator input sensor 336.

[0110] The header setting generates a relationship between the header setting value (which can be on the predictive header setting diagram 360, the prior operation diagram 400, or other diagram 401) and the agricultural characteristics sensed by the agricultural characteristic sensor 402 in the agricultural characteristic model generator 410. The header setting generates a predictive model 426 corresponding to this relationship in the agricultural characteristic model generator 410.

[0111] The cutter setting generates a model for the operator command model generator 414 that models the relationship between the cutter setting values ​​reflected in the predicted cutter characteristic diagram 360, the prior operation diagram 400, or other diagram 401, and the operator input commands sensed by the operator input sensor 336. The cutter setting generates a prediction model 426 corresponding to this relationship for the operator command model generator 414.

[0112] Other model generators 415 may include, for example, specific header setting model generators, such as header height setting to agricultural characteristics model generators, header tilt setting to agricultural characteristics model generators, header tumble setting to agricultural characteristics model generators, header height setting to command model generators, header tumble setting to command model generators, or header tilt to command model generators.

[0113] The prediction model 426 generated by the prediction model generator 210 may include one or more prediction models generated by the header setting to agricultural characteristics model generator 410, the header setting to operator command model generator 414, and other model generators that may be included as part of other items 415.

[0114] 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.

[0115] The predictive agricultural characteristic map generator 416 receives: a predictive model 426 (e.g., a predictive model generated by the agricultural characteristic model generator 410 based on the header setting) that models the relationship between header settings and agricultural characteristics sensed by the agricultural characteristic sensor 402, and one or more of the prior information map 258 or the functional predictive header characteristic map 360 or other maps 401. The predictive agricultural characteristic map generator 416 generates a functional predictive agricultural characteristic map 427 based on one or more header setting values ​​at different locations in the field in one or more of the prior information map 258, the functional predictive header characteristic map 360 or other maps 401, and based on the predictive model 426. This functional predictive agricultural characteristic map 427 predicts agricultural characteristic values ​​(or agricultural characteristics indicated by said values) at different locations in the field.

[0116] The predictive operator command generator 422 receives: a predictive model 426 (e.g., a predictive model generated by the header setting to command model generator 414) that models the relationship between header settings and operator command inputs detected by the operator input sensor 405, and one or more of the prior information map 258 or the functional predictive header characteristic map 360 or other maps 401. The predictive operator command generator 422 generates a functional predictive operator command map 440 based on header setting values ​​at different locations in the field from the prior information map 258 or the functional predictive header characteristic map 360 or other maps 401 and based on the predictive model 426. This functional predictive operator command map 440 predicts operator command inputs at different locations in the field.

[0117] 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 and combines control areas 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. Control system 214 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.

[0118] Figure 6B This is a block diagram showing some examples of the real-time (field) sensor 208. Figure 6B Some of the sensors shown, or different combinations thereof, may simultaneously have sensor 402 and processing system 406, while other sensors may be used as references. Figure 6A and Figure 7 The described sensor 402, in Figure 6A and Figure 7 The processing system 406 is either separate or independent. Figure 6B Some of the possible field sensors 208 shown are illustrated and described relative to the previous figures and are similarly numbered. Figure 6B The field sensors 208 shown may include operator input sensors 480, machine sensors 482, harvested material property sensors 484, field and soil property sensors 485, environmental property sensors 487, and may include a variety of other sensors 226. The operator input sensor 480 may be a sensor that senses operator input via operator interface mechanism 218. Therefore, the operator input sensor 480 can sense user movements of linkages, joysticks, steering wheels, buttons, dials, or pedals. The operator input sensor 480 can also sense user interactions with other operator input mechanisms, such as interactions with a touchscreen, a microphone utilizing voice recognition, or any of the various other operator input mechanisms.

[0119] Machine sensor 482 can sense various characteristics of the agricultural harvester 100. For example, as discussed above, machine sensor 482 may include machine speed sensor 146, separator loss sensor 148, clean grain camera 150, forward-view image capture mechanism 151, loss sensor 152, or geolocation sensor 204, examples of which are described above. Machine sensor 482 may also include machine setting sensor 491 for sensing machine settings. (See above references) Figure 1Some examples of machine settings are described. A front-end device (e.g., header) position sensor 493 can sense the position of the header 102, reel 164, cutter 104, or other front-end devices relative to the frame of the harvester 100. For example, sensor 493 can sense the height of the header 102 above the ground. Machine sensor 482 may also include a front-end device (e.g., header) orientation sensor 495. Sensor 495 can sense the orientation of the header 102 relative to the harvester 100 or relative to the ground. Machine sensor 482 may include a stability sensor 497. Stability sensor 497 senses vibrational or bouncing movements (and amplitude) of the harvester 100. Machine sensor 482 may also include a residue setting sensor 499 configured to sense whether the harvester 100 is configured to shred residue, create a stockpile, or otherwise process residue. Machine sensor 482 may include a cleaning chamber fan speed sensor 551 that senses the speed of the cleaning fan 120. Machine sensor 482 may include a concave plate gap sensor 553 that senses the gap between the roller 112 and the concave plate 114 on the agricultural harvester 100. Machine sensor 482 may include a husk sieve gap sensor 555 that senses the size of the openings in the husk sieve 122. Machine sensor 482 may include a threshing drum speed sensor 557 that senses the drum speed of the roller 112. Machine sensor 482 may include a drum pressure sensor 559 that senses the pressure used to drive the roller 112. Machine sensor 482 may include a screen gap sensor 561 that senses the size of the openings in the screen 124. Machine sensor 482 may include a MOG humidity sensor 563 that senses the humidity level of the MOG passing through the harvester 100. Machine sensor 482 may include a machine orientation sensor 565 that senses the orientation of the harvester 100. Machine sensor 482 may include a material feed rate sensor 567 that senses the rate at which material is fed as it travels through the feeder housing 106, the clean grain elevator 130, or other locations within the harvester 100. Machine sensor 482 may include a biomass sensor 569 that senses the biomass traveling through the feeder housing 106, the separator 116, or other locations within the harvester 100. Machine sensor 482 may include a fuel consumption sensor 571 that senses the rate at which the harvester 100 consumes fuel over time.Machine sensor 482 may include a power utilization sensor 573 that senses power utilization in the harvester 100 (such as which subsystems are using power), or the rate at which subsystems are using power, or the power distribution among the subsystems in the harvester 100. Machine sensor 482 may include a tire pressure sensor 577 that senses the inflation pressure in the tires 144 of the harvester 100. Machine sensor 482 may include a variety of other machine performance sensors or machine characteristic sensors (as shown in box 475). The machine performance sensors and machine characteristic sensor 575 can sense the machine performance or characteristics of the harvester 100.

[0120] While crop material is being processed by the agricultural harvester 100, the harvest material property sensor 484 can sense the characteristics of the cut crop material. Crop properties may include things such as crop type, crop moisture content, grain quality (e.g., broken grain), MOG level, grain composition (e.g., starch and protein), MOG moisture content, and other crop material properties. Other sensors can sense straw “toughness,” corn and ear adhesion, and other characteristics that can be beneficially used to control processing to achieve better grain capture, reduced grain damage, lower power consumption, reduced grain loss, etc.

[0121] The field and soil property sensor 485 can sense the characteristics of fields and soils. Field and soil properties may include soil moisture, soil compaction, presence and location of waterlogging, soil type, and other soil and field characteristics.

[0122] The environmental characteristic sensor 487 can sense one or more environmental characteristics. Environmental characteristics may include things such as wind direction and speed, precipitation, fog, dust level, or other obstacles or other environmental features.

[0123] Figure 7A flowchart illustrating an example of the operation of the prediction model generator 210 and the prediction graph generator 212 in generating one or more prediction models 426 and one or more functional prediction graphs 427 and 440 is shown. At box 442, the prediction model generator 210 and the prediction graph generator 212 receive graphs. The graph received by the prediction model generator 210 or the prediction graph generator 212 in generating one or more prediction models 426 and one or more functional prediction graphs 427 and 440 may be a priori information graph 258, such as a priori operation graph 400 created using data obtained during previous or prior operations in the field. The graph received by the prediction model generator 210 or the prediction graph generator 212 in generating one or more prediction models 426 and one or more functional prediction graphs 427 and 440 may be a prediction graph, such as a functional prediction cutter setting graph 360. It can also receive other graphs, such as those indicated in box 401, such as other prior information graphs or other prediction graphs, such as other prediction cutter configuration graphs.

[0124] At box 444, the prediction model generator 210 receives sensor signals containing sensor data from field sensors 208. Field sensors 208 can be one or more of agricultural characteristic sensors 402 and operator input sensors 336. Agricultural sensor 402 senses agricultural characteristics. Operator input sensor 336 senses operator input commands. The prediction model generator 210 may also receive other field sensor inputs (as shown in box 408).

[0125] 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.

[0126] 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.

[0127] 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 chute settings, such as chute height settings, chute roll settings, or chute tilt settings; and prediction model generator 210 uses the mapping values ​​of the received graph and characteristics sensed by field sensors 208 (as represented in the processed data 409) or related characteristics (such as characteristics related to the characteristics sensed by field sensors 208) to generate prediction models.

[0128] 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 agricultural characteristic maps 427, functional prediction operator command maps 440, or any combination of these maps. The functional prediction agricultural characteristic map 427 predicts the values ​​of agricultural characteristics (or the agricultural characteristics indicated by those values) at different locations in the field. The functional prediction operator command map 440 predicts desired or possible operator command input values ​​at different locations in the field. Furthermore, one or more functional prediction maps 427 and 440 can be generated during agricultural operations. Therefore, when the agricultural harvester 100 moves across the field to perform agricultural operations, one or more prediction maps 427 and 440 are generated during the performance of those operations.

[0129] 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 and combining control areas. At box 476, the prediction graph generator 212 otherwise configures one or more prediction graphs 427 and 440. Functional prediction graphs 427 and 440 (with or without control areas) can be presented to operator 260 or another user, or also provided to control system 214.

[0130] 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 427 and 440 (or functional prediction maps 427 and 440 with control areas) and inputs from the geolocation sensor 204.

[0131] 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.

[0132] 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, tumbling, 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 102, such as the tilt of header 102 relative to the field surface from front to back. In yet another example, header / reel controller 238 may control header or other machine actuator 248 to adjust the tumbling of header 102 relative to the field surface across the width of header 102.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The target setting identifier component 498 sets the value of the target setting that will be used to control the WMA or WMA group in different control areas. For example, if the selected WMA is a cutting table or other machine actuator 248, and the functional prediction graph in the analysis is a functional prediction cutting table characteristic graph 360 (with control areas), then the target setting in each control area can be a target cutting table height setting, cutting table roll setting, or cutting table tilt setting based on the cutting table characteristic values ​​contained in the functional prediction cutting table setting graph 360.

[0137] 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 controlled in a header or other machine actuator 248 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, header tumble, or header tilt can be generated based on, for example, detected or predicted header setting values ​​(such as header height setting, header tumble setting, or header tilt setting), detected or predicted soil property values, detected or predicted agricultural characteristic values, detected or predicted terrain characteristic values ​​(e.g., slope value), 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 target settings for various different WMAs can be based on a variety of other values ​​or combinations of values. However, at any given time, the combine harvester 100 cannot simultaneously travel on the ground with multiple header heights, multiple header tumbles, or multiple header tilts. Instead, at any given time, the combine harvester 100 travels with a single header height, a single header tumble, and a single header tilt. Therefore, one of the competing objectives is selected to control the header height, header roll, or header tilt of the agricultural harvester 100.

[0138] Therefore, in some examples, the dynamic zone generation system 490 generates dynamic zones to resolve multiple different competing target settings. The dynamic zone criterion identification component 522 identifies the criteria used to establish dynamic zones for selected WMAs or WMA groups on the functional prediction map in the analysis. Some criteria that can be used to identify or define dynamic zones include, for example, header settings (such as header height setting, header roll setting, or header tilt setting), agricultural characteristics, topographic characteristics (e.g., slope), operator command inputs, soil properties (such as soil type, soil moisture, etc. provided by a soil property map), 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 region boundary definition component 524 identifies the boundaries of the dynamic regions on the functional prediction map in the analysis based on the dynamic region criteria identified by the dynamic region criterion identification component 522.

[0139] 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 overlapping crop state and crop type dynamic zones, the crop state dynamic zone can be assigned greater importance in the priority level than the crop type dynamic zone, thus giving priority to the crop state dynamic zone.

[0140] 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.

[0141] Once a setting resolver is identified for a specific dynamic zone, it can be used to resolve competing target settings, where more than one target setting is identified based on the control zone. Different types of setting resolvers can take different forms. For example, a setting resolver for each dynamic zone may include a manually selected resolver, in which the competing target settings are presented to the operator or other user for resolution. In another example, the setting resolver may include a neural network or other artificial intelligence or machine learning system. In this case, the setting resolver can resolve competing target settings based on predicted or historical quality metrics corresponding to each of the different target settings. As an example, an increased header height setting may reduce the likelihood of header scraping the soil (or otherwise deforming the soil), but may increase grain loss. A decreased header height setting may increase the likelihood of header scraping the ground, but may reduce grain loss. When grain loss or harvest time is selected as a quality metric, given two competing header height setting values, the predicted or historical value for the selected quality metric can be used to resolve the header height 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:

[0142] If the predicted slope value is greater than x (where x is the selected or predetermined value) within 20 feet of the header at 100 meters from the harvester, the target setting value selected based on header scraping rather than other competing targets is used; otherwise, the target setting value based on grain loss rather than other competing targets is used.

[0143] 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.

[0144] 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.

[0145] 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 360, 427, or 440. Box 534 indicates that the graph in the analysis could also be other graphs.

[0146] 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.

[0147] 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 560 shows an example where the dynamic zone defining criteria are based on cutter characteristics (such as cutter height, cutter inclination, or both). Box 564 indicates an example where the dynamic zone defining criteria are also or include other criteria, such as soil properties.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Figure 10An example is shown of a control system 214 controlling the operation of an agricultural harvester 100 based on a map output by a control zone generator 213. Thus, at box 592, the control system 214 receives a map of the work site. In some cases, this map may be a functional 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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 cutter configuration map (e.g., a functional prediction cutter configuration map 360), the displayed field may show cutter characteristics present in that field and georeferenced within the displayed field. 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 an example where the field shown also includes other items.

[0166] Figure 13 This illustration shows one example of a user interface display 720 that can be generated on a touch-sensitive display screen. In other embodiments, the user interface display 720 can be generated on other types of displays. The touch-sensitive display screen can be installed in the operator's compartment of the agricultural harvester 100 or on mobile equipment or elsewhere. (Continuing the description...) Figure 12 Before the flowchart shown, the user interface display unit 720 will be described.

[0167] 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.

[0168] 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.

[0169] 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 diagram being displayed is a predictive header setting diagram, such as a functional predictive header setting diagram 360. Therefore, multiple header setting markers are displayed on the field display section 728. A set of header setting display markers 732 is shown in the already visited area 714. A set of header setting display markers 732 is also shown in the upcoming area 712, and a set of cutting height characteristic display markers 732 is shown in the next work unit 730. Figure 13 The cutting characteristic display mark 732 is shown to consist of different symbols indicating areas with similar cutter settings. Figure 13 In the example shown, the "!" symbol represents a high-altitude area; the "*" symbol represents a low-altitude area; and the "#" symbol represents a low-altitude area.

[0170] 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 or derived by said values). As shown, the field display section 728 includes display markers at specific locations associated with specific locations on the field being displayed, in particular... Figure 13 The example shown includes a cutter setting display mark 732. In some cases, each location of the field may have a display mark associated with that location. Therefore, in some cases, display marks may be provided at each location of the field display portion 728 to identify attributes that map characteristics to each particular location of the field. Thus, this disclosure includes providing, for example, a cutter setting 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 background 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, the symbols can be any display feature, such as different colors, shapes, patterns, intensities, text, icons, or other display features.

[0171] 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.

[0172] exist Figure 13 In the example, the user interface display 720 also has a control display section 738. The control display section 738 allows the operator to view information and interact with the user interface display 720 in various ways.

[0173] 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 section 738 displays information for three different header height categories corresponding to the three symbols mentioned above. Display section 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 section 738 also includes multiple interactive tabs, such as header height tab 762, header tilt tab 764, header roll tab 766, and other tabs 770. Activating one of these tabs can modify the value displayed in display sections 728 and 738. For example, as shown, header height tab 762 is activated, and therefore, the value mapped on section 728 and displayed in section 738 corresponds to the header height setting value 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 header tilt setting of agricultural harvester 100. When operator 260 touches tab 766, touch gesture processing system 664 updates portions 728 and 738 to display characteristics related to the header tumble setting. When operator 260 touches tab 770, touch gesture management system 664 updates portions 728 and 738 to display other header setting characteristics related to the header of agricultural harvester 100, or a combination of one or more of the header height setting, header tilt setting, and header tumble setting.

[0174] 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 locations (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 (e.g., header setting characteristics). 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 header height. When operator 260 touches button 742, touch gesture management system 664 identifies the current location as a location where the harvester 100 has encountered an ideal header height. When operator 260 touches button 744, touch gesture management system 664 identifies the current location as a position where the harvester 100 has a low header height. 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 the field display portion 728 at the user-identified location. In this way, areas of the field where predicted values ​​cannot accurately identify actual values ​​can be marked for later analysis or for machine learning. In other examples, the operator can specify an area in front of or around the harvester 100 by actuating one of the marker actuators 740, 742, or 744, allowing control of the harvester 100 based on the values ​​specified by operator 260.

[0175] 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 cutter setting characteristic, the symbol corresponds to that symbol. The display section 738 also includes an interactive specifier display section indicated approximately at 745. The interactive specifier display section 745 includes a specifier bar 748 that displays the symbol corresponding to the value or characteristic (in...). Figure 13 In the case of header setting characteristics, the designator is a category for identification (it can be a text designator or other designator). Without limitation, the symbols in the symbol bar 746 and the designators in the designator bar 748 can include any display features, such as different colors, shapes, patterns, intensities, text, icons or other display features, and can be customized through the interaction of the operator of the agricultural harvester 100.

[0176] 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 header heights of 18 inches or more are classified as "high header height," predicted or measured header heights of 12 inches or more are classified as "ideal header height," and predicted or measured header heights of 6 inches or less are classified as "low header height." In some examples, the selected value may include a range such that predicted or measured values ​​within the selected range are classified under the corresponding designator. For example, "ideal header height" may include a range of 11.5 inches to 12.5 inches, such that predicted or measured header heights falling within the 11.5-inch to 12.5-inch range are classified as "ideal header height." The value 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 for which the value will be displayed in bar 750. Therefore, the value in bar 750 may correspond to the value in display sections 712, 714, or 730.

[0177] 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 the value display bar 750, such that predicted or measured characteristic values ​​that reach or fall within that range satisfy the threshold. For example, in an example of header setting characteristics, a predicted header height value falling within 2 inches of a value deviating from 18 inches would satisfy the corresponding action threshold (within 2 inches of a value deviating from 18 inches), and the control system 214 would take actions such as adjusting header position settings (e.g., header height setting, header tumble setting, or header tilt setting), adjusting sensitivity settings, or adjusting the header ground pressure setting of the agricultural harvester. In other examples, the threshold in the threshold display bar 752 is separated from the selected value in the value display bar 750, such that the value in the value display bar 750 defines the classification and display of 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 header height of 6 inches might be designated as "low header height" for classification and display purposes, the action threshold could be 8 inches, such that an action will be taken when the predicted or measured header height is 2 inches higher than 6 inches. In other examples, the threshold in the threshold display bar 752 could include distance or time. For example, in the distance example, the threshold could be a threshold distance from the field to an area where the measured or predicted value is georeferenced, such that the harvester 100 must be in that area before taking action. For example, a threshold distance value of 5 feet means that an action will be taken when the harvester is located 5 feet or less from the field to the area where the measured or predicted value is georeferenced. In the example where the threshold is time, the threshold could be a threshold time for the harvester 100 to reach the field to the area where the measured or predicted value is georeferenced.For example, a 5-second threshold means that an agricultural harvester 100 will take action when it is 5 seconds away from the area of ​​the field where the measured or predicted value is georeferenced. In such an example, the harvester's current position and travel speed can be taken into account.

[0178] 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 setting, tilt setting, 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 increasing or decreasing), and the ground pressure setting can be adjusted (such as increasing or decreasing). These are just some examples.

[0179] The actions that can be set in column 754 can be any of a variety of different types of actions. For example, these actions can include a prohibition action that, when executed, prevents the combine harvester 100 from harvesting further in an area. These actions can include speed-changing actions that, when executed, change the speed at which the combine harvester 100 travels through the field. These actions can include setting-changing actions for changing the settings of an internal actuator or another WMA or WMA group, or for implementing changes to settings such as one or more header settings, such as header position settings, header sensitivity settings, and header ground pressure settings. These are merely examples, and a wide variety of other actions are considered herein.

[0180] 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 intensity, color, or pattern of the displayed item can be modified. Additionally, the item can be controlled to blink. As an example, a description of changes to the visual appearance of the item is provided. Therefore, other aspects of the visual appearance of the item can be changed. Thus, 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, or more or fewer specific items, can be included on the user interface display 720.

[0181] Now back Figure 12 The 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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".

[0186] Table 1

[0187] Operator: "Johnny, tell me about the current cutter settings."

[0188] Operator interface controller: "Cuttable height is currently high".

[0189] Operator: "Johnny, what should I do about this header height?"

[0190] Operator interface controller: "Adjust the position of the feeder housing".

[0191] 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).

[0192] Table 2

[0193] Operator interface controller: "The cutting table height has been lowered in the past 10 seconds."

[0194] Operator interface controller: "The predicted header height for the next 1 acre is 16 inches."

[0195] Operator interface controller: "Attention: Slope change is imminent, adjust header sensitivity setting."

[0196] 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 the header setting characteristic areas in a field being harvested.

[0197] Table 3

[0198] Human: "Johnny, mark the area of ​​high cutter height."

[0199] Operator interface controller: "High cutting table height area has been marked".

[0200] The example shown in Table 4 illustrates that the motion signal generator 660 can communicate with the operator 260 to start and stop marking the characteristic area of ​​the cutter.

[0201] Table 4

[0202] Human: "Johnny, begin marking the high cutter height area."

[0203] Operator interface controller: "Mark the high-level cutting table height area".

[0204] Human: "Johnny, stop marking the high cutter height area."

[0205] Operator interface controller: "Stop marking the high-cutting-table height area".

[0206] The example shown in Table 5 illustrates that the motion signal generator 160 can generate signals for marking the characteristic area of ​​the cutting platform in a manner different from that shown in Tables 3 and 4.

[0207] Table 5

[0208] Human: "Johnny, mark the last 100 feet as the low-cutting-edge height zone."

[0209] Operator interface controller: "The last 100 feet are marked as the low cutter height zone."

[0210] 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.

[0211] 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.

[0212] Therefore, one or more maps are obtained by an agricultural harvester, showing characteristic values ​​at different geographical locations in the field being harvested. Field sensors on the harvester sense these characteristics as the harvester moves through the field. A prediction map generator generates a prediction map based on the characteristic values ​​in the maps and the 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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).

[0224] 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 can 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] Figure 17 Similar to Figure 16 In addition to being a smartphone 71, the smartphone 71 has a touch-sensitive display 73 that shows icons, tiles, or other user input mechanisms 75. Users can use the mechanisms 75 to run applications, make calls, perform data transfer operations, etc. Generally, the smartphone 71 is built on a mobile operating system and offers more advanced computing power and connectivity than feature phones.

[0233] Note that other forms of device 16 are possible.

[0234] 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.

[0235] 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.

[0236] 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. 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.

[0237] 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).

[0238] 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), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), etc.

[0239] 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.

[0240] 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.

[0241] 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).

[0242] 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.

[0243] 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.

[0244] Example 1 is an example that includes any or all of the aforementioned examples, including:

[0245] A communication system that receives a map, the map including values ​​of an agricultural characteristic corresponding to different geographical locations in a field;

[0246] A geolocation sensor that detects the geolocation of the agricultural machinery;

[0247] A field sensor, wherein the field sensor detects a value set on the cutting table corresponding to the geographical location;

[0248] A predictive model generator generates a predictive agricultural model based on the values ​​of the agricultural characteristics in the graph at the geographic location and the values ​​of the header settings detected by the field sensors corresponding to the geographic location. The predictive agricultural model models the relationship between the agricultural characteristics and the header settings.

[0249] A prediction map generator generates a functional predictive agriculture map of the field based on the values ​​of the agricultural characteristics in the map and based on the predictive agriculture model. The functional predictive agriculture map maps the predicted values ​​set by the harvester to the different geographical locations in the field.

[0250] Another example is one that includes any or all of the foregoing examples, wherein the predictive map generator configures the functional predictive agricultural map for use by a control system, which generates control signals based on the functional predictive agricultural map to control the controllable subsystems on the agricultural machinery.

[0251] Another example is one that includes any or all of the foregoing examples, wherein the field sensor detects operator input as a value for the header setting, the operator input indicating a header height setting value on the agricultural machine corresponding to the geographic location.

[0252] Another example is one that includes any or all of the foregoing examples, wherein the map includes a topographic map that includes a value of a topographic feature corresponding to the different geographic locations in the field as a value of the agricultural feature, and wherein the predictive model generator is configured to determine a relationship between the cutter height setting and the topographic feature based on a value of the cutter height setting corresponding to the geographic location detected by the field sensors and a value of the topographic feature in the topographic map at that geographic location, and the predictive agricultural model is configured to receive the value of the topographic feature as a model input and generate a predicted value of the cutter height setting as a model output based on the determined relationship.

[0253] Another example is one that includes any or all of the foregoing examples, wherein the field sensor detects operator input as a value for the header setting, the operator input indicating a header tilt setting value on the agricultural machine corresponding to the geographic location.

[0254] Another example is one that includes any or all of the foregoing examples, wherein the map includes a topographic map that includes a value of a topographic feature corresponding to the different geographic locations in the field as a value of the agricultural feature, and wherein the predictive model generator is configured to determine a relationship between the cutter tilt setting and the topographic feature based on a value of the cutter tilt setting corresponding to the geographic location detected by the field sensors and a value of the topographic feature in the topographic map at the geographic location, and the predictive agricultural model is configured to receive the value of the topographic feature as a model input and generate a predicted value of the cutter tilt setting as a model output based on the determined relationship.

[0255] Another example is one that includes any or all of the foregoing examples, wherein the field sensor detects operator input as a value for the header setting, the operator input indicating the header tumble setting on the agricultural machine.

[0256] Another example is one that includes any or all of the foregoing examples, wherein the map includes a topographic map that includes a value of a topographic feature corresponding to the different geographic locations in the field as a value of the agricultural feature, and wherein the predictive model generator is configured to determine a relationship between the cutter roll setting and the topographic feature based on a value of the cutter roll setting detected by the field sensors corresponding to the geographic location and a value of the topographic feature in the topographic map at the geographic location, and the predictive agricultural model is configured to receive the value of the topographic feature as a model input and generate a predicted value of the cutter roll setting as a model output based on the determined relationship.

[0257] Another example is one that includes any or all of the foregoing examples, and further includes:

[0258] A control system that generates control signals based on the functional predictive agriculture map to control the position of the cutting platform on the agricultural harvester relative to the field surface.

[0259] Another example is one that includes any or all of the foregoing examples, and further includes:

[0260] A control system that generates control signals based on the functional predictive agriculture map to control the ground pressure setting of the cutting platform on the agricultural machinery.

[0261] Another example is one that includes any or all of the foregoing examples, and further includes:

[0262] A control system that generates control signals based on the functional predictive agriculture map to control the sensitivity setting of the header on the agricultural machinery.

[0263] Another example is one that includes any or all of the aforementioned examples, including:

[0264] A map is received at an agricultural machinery, the map including values ​​of an agricultural characteristic corresponding to different geographical locations in the field;

[0265] Detect the geographical location of the agricultural machinery;

[0266] Use field sensors to detect the value set on the cutting table corresponding to the geographical location;

[0267] Generate a predictive agriculture model that models the relationship between the agricultural characteristics and the header settings; and

[0268] The control prediction map generator generates a functional predictive agriculture map of the field based on the values ​​of the agricultural characteristics in the map and the predictive agriculture model. The functional predictive agriculture map maps the predicted values ​​set by the harvester to the different geographical locations in the field.

[0269] Another example is one that includes any or all of the foregoing examples, and further includes:

[0270] The control system is configured with the functional predictive agriculture map, and the control system generates control signals based on the functional predictive agriculture map to control the controllable subsystems on the agricultural machinery.

[0271] Another example is one that includes any or all of the foregoing examples, wherein detecting the header setting value using field sensors includes: detecting operator input indicating the setting value for the header on the agricultural machine.

[0272] Another example is one that includes any or all of the foregoing examples, where obtaining the graph includes:

[0273] Obtain a topographic map, the topographic map including values ​​of a topographic feature corresponding to different geographical locations in the field as values ​​of the agricultural feature.

[0274] Another example is one that includes any or all of the foregoing examples, where generating predictive agricultural models includes:

[0275] The relationship between the terrain characteristics and the cutter settings is determined based on the values ​​of the cutter characteristics detected by the field sensors corresponding to the geographical location and the values ​​of the terrain characteristics at the geographical location on the topographic map; and

[0276] A predictive model generator is controlled to generate a predictive agricultural model, which receives values ​​of terrain characteristics as model inputs and generates predicted values ​​of the cutter settings as model outputs based on determined relationships.

[0277] Another example is one that includes any or all of the foregoing examples, and further includes:

[0278] Based on the functional predictive agricultural map, control signals are generated to control the setting of the cutting platform of the agricultural machinery.

[0279] Another example is one that includes any or all of the foregoing examples, further including:

[0280] The operator interface mechanism is controlled to present the predicted agricultural map.

[0281] Another example is one that includes any or all of the aforementioned examples, including:

[0282] A communication system that receives a topographic map, the topographic map including topographic characteristic values ​​corresponding to different geographical locations in the field;

[0283] A geolocation sensor that detects the geolocation of the agricultural machinery;

[0284] A field sensor detects a cutter setting value corresponding to the geographical location.

[0285] A prediction model generator generates a predictive cutter setting model based on terrain feature values ​​at the geographic location on the topographic map and cutter setting values ​​corresponding to the geographic location detected by the field sensors. The predictive cutter setting model models the relationship between the terrain feature values ​​and the cutter setting.

[0286] A prediction map generator generates a functional prediction cutter setting map of the field based on the terrain characteristic values ​​in the topographic map and based on the prediction cutter setting model. The functional prediction cutter setting map maps the predicted cutter setting values ​​to the different geographical locations in the field.

[0287] Another example is one that includes any or all of the foregoing examples, and further includes:

[0288] A control system that generates control signals based on the functional predictive header setting diagram to control the setting of the header on the agricultural machine.

[0289] 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: Controllable subsystem; The operator inputs a sensor, which is configured to sense the input value set by the header control that corresponds to a first geographical location in the field; The control system includes a header controller, the header controller being configured to: The input value set by the cutting table control is received as the control input; as well as Based on the control input, the cutting platform of the agricultural machine is controlled in the area of ​​the field corresponding to the first geographical location; A communication system (206) is configured to receive an agricultural characteristic map (258) comprising values ​​of agricultural characteristics mapped to different geographical locations in the field; A predictive model generator (210) is configured to obtain a first value of the agricultural characteristic mapped to the first geographic location in the agricultural characteristic map and generate a predictive agricultural model based on the first value of the agricultural characteristic mapped to the first geographic location and the input value of the header control setting corresponding to the first geographic location, the predictive agricultural model modeling the relationship between the agricultural characteristic and the header control setting. and A prediction map generator (212) is configured to generate a functional predictive agricultural map of the field using the predictive agricultural model. The functional predictive agricultural map maps multiple predicted values ​​set by the harvester control to multiple locations in the field, wherein each corresponding predicted value of the multiple predicted values ​​corresponds to a corresponding location among the multiple locations, and is based on the corresponding value of the agricultural characteristic in the agricultural characteristic map corresponding to the corresponding geographical location. The control system is configured to control the controllable subsystem based on the plurality of predicted values ​​in the functional predictive agriculture map.

2. The agricultural work machine of claim 1, wherein, The prediction map generator configures the functional predictive agricultural map for use by the control system, wherein the control system controls at least one of the position and orientation of the header based on the control input.

3. The agricultural work machine of claim 1, wherein, The control system is configured to receive operator input that limits the input value of the header control setting, which includes a header height setting on the agricultural machine corresponding to the first geographical location.

4. The agricultural work machine of claim 3, wherein, The agricultural characteristic map includes a topographic map, which includes values ​​representing the agricultural characteristic corresponding to different geographical locations within the field. The predictive model generator is configured to determine the relationship between the cutter height setting and the terrain feature based on the input value of the cutter height setting corresponding to the first geographical location detected by the operator input sensor and the value of the terrain feature corresponding to the first geographical location on the topographic map. The predictive agricultural model is configured to receive the value of the terrain feature as model input and generate a predicted value of the cutter height setting as model output based on the determined relationship.

5. The agricultural work machine of claim 1, wherein, The control system is configured to receive operator input that limits the input value of the header control setting, which includes a header tilt setting on the agricultural machine corresponding to the first geographical location.

6. The agricultural machinery according to claim 5, wherein, The agricultural characteristic map includes a topographic map, which includes values ​​representing the agricultural characteristic corresponding to different geographical locations within the field. The predictive model generator is configured to determine the relationship between the cutter tilt setting and the terrain feature based on the input value of the cutter tilt setting corresponding to the first geographical location detected by the operator input sensor and the value of the terrain feature corresponding to the first geographical location on the topographic map. The predictive agricultural model is configured to receive the value of the terrain feature as model input and generate a predicted value of the cutter tilt setting as model output based on the determined relationship.

7. The agricultural work machine of claim 1, wherein, The control system is configured to receive operator input that limits the input value of the header control setting, which includes a header tumbling setting on the agricultural machine corresponding to the first geographical location.

8. The agricultural work machine of claim 7, wherein, The agricultural characteristic map includes a topographic map, which includes values ​​representing the agricultural characteristic corresponding to different geographical locations within the field. The predictive model generator is configured to determine the relationship between the cutter roll setting and the terrain feature based on the input value of the cutter roll setting corresponding to the first geographical location detected by the operator input sensor and the value of the terrain feature corresponding to the first geographical location on the topographic map. The predictive agriculture model is configured to receive the value of the terrain feature as model input and generate a predicted value of the cutter roll setting as model output based on the determined relationship.

9. A computer-implemented method for generating functional predictive agricultural maps, comprising: Agricultural characteristic map (258) is received at the agricultural operation machine (100), which includes agricultural characteristics mapped to different geographical locations in the field. The operator input sensor detects the input value set by the cutting table control that corresponds to the first geographical location; The cutting table of the agricultural machine is controlled based on the input value set by the cutting table control. Obtain a first value that maps the agricultural characteristic to the first geographical location in the agricultural characteristic map; A predictive agricultural model is generated based on the first value of the agricultural characteristic mapped to the first geographical location and the input value of the header control setting corresponding to the first geographical location. The predictive agricultural model models the relationship between the agricultural characteristic and the header control setting. and The control prediction map generator (212) uses the prediction agriculture model to generate a functional prediction agriculture map of the field, which maps multiple prediction values ​​set by the harvester control to multiple locations in the field, wherein each of the multiple prediction values ​​corresponds to a corresponding location in the multiple locations, and is based on the corresponding value of the agricultural characteristic in the agricultural characteristic map corresponding to the corresponding geographical location. as well as A controllable subsystem for controlling agricultural machinery based on the multiple predicted values ​​in the functional predictive agriculture map.

10. An agricultural operation system (100), comprising: A communication system (206) receives an agricultural characteristic map (258) which includes agricultural characteristic values ​​mapped to different geographical locations in the field; An operator inputs a sensor that detects an input value set by a header control corresponding to a first geographical location in the field. The input value set by the header control is limited to a control input of a header controller, which is configured to control at least one of the position and orientation of the header of an agricultural machine in the area of ​​the field corresponding to the first geographical location based on the input value set by the header control. A prediction model generator (210) obtains a first value of the agricultural characteristic mapped to the first geographical location in the agricultural characteristic map and generates a prediction header setting model based on the first value of the agricultural characteristic mapped to the first geographical location and the input value of the header control setting corresponding to the first geographical location detected by the operator input sensor (208). The prediction header setting model models the relationship between the agricultural characteristic and the header control setting. and A prediction map generator (212) generates a functional prediction header setting map of the field using the prediction header setting model. This functional prediction header setting map maps multiple predicted values ​​of the header control settings to multiple locations within the field. Each corresponding predicted value corresponds to a specific location among the multiple predicted values, and is based on the corresponding values ​​of the agricultural characteristics in the agricultural characteristic map corresponding to the corresponding geographical locations. A control system that controls a controllable subsystem of the agricultural operation system based on a plurality of predicted values ​​in the functional predictive header setting diagram. The control system is configured to receive operator input, which limits the input value of the header control setting, which includes one of the header height setting, header tilt setting, and header tumble setting on the agricultural machine corresponding to the first geographical location.

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