Machine control using predictive velocity maps

Abstract

One or more infographics are acquired by agricultural machinery. These infographics map the values ​​of one or more agricultural characteristics at different geographic locations in the field. Field sensors on the agricultural machinery sense these agricultural characteristics as the machinery moves through the field. A predictive map generator generates predictive maps of the agricultural characteristics at different locations in the field, based on the relationships between the values ​​in the infographics and the agricultural characteristics sensed by the field sensors. These predictive maps 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 many detrimental effects on harvesting operations. Therefore, when encountering such conditions during harvesting, the operator may attempt to modify the harvester controls.

[0004] The above discussion is provided for general background information only 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 infographics are obtained from agricultural machinery. These infographics map the values ​​of one or more agricultural characteristics at different geographical locations in the field. Field sensors on the agricultural machinery sense these agricultural characteristics as the machinery moves through the field. A predictive map generator generates predictive maps of the agricultural characteristics at different locations in the field, based on the relationships between the values ​​in the infographics and the agricultural characteristics sensed by the field sensors. These predictive maps can be output and used for automated machine control.

[0006] This overview is provided to introduce, in a simplified form, some concepts that will be further described in the detailed description below. This overview 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 mentioned 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 The following are block diagrams illustrating some parts of an agricultural harvester in more detail, based on some examples of this disclosure.

[0009] Figures 3A to 3B (Referred to herein as Figure 3) shows a flowchart illustrating an example of the operation of an agricultural harvester when generating a diagram.

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

[0011] Figure 5 This is a flowchart illustrating an example of how an agricultural harvester receives a priori information map, detects speed characteristics, and generates a functional predictive speed map for controlling the agricultural harvester during harvesting operations.

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

[0013] Figure 7 The flowchart illustrates an example of how an agricultural harvester operates when receiving speed maps and detecting field sensor inputs while generating a functional predictive sensor data map.

[0014] Figure 8 This is a block diagram showing some examples of field sensors.

[0015] Figure 9 This is a block diagram illustrating an example of a control region generator.

[0016] Figure 10 It is a diagram. Figure 9 The flowchart shows an example of the operation of the control region generator.

[0017] Figure 11 The diagram illustrates an example of how a control system operates when selecting target settings to control an agricultural harvester.

[0018] Figure 12 This is a block diagram illustrating an example of an operator interface controller.

[0019] Figure 13 This is a flowchart illustrating an example of an operator interface controller.

[0020] Figure 14 This is a schematic diagram showing an example of an operator interface display.

[0021] Figure 15 This is a block diagram illustrating an example of an agricultural harvester communicating with a remote server environment.

[0022] Figures 16 to 18 An example of a mobile device that can be used in agricultural harvesters is shown.

[0023] Figure 19 This is a block diagram illustrating an example of a computing environment that can be used for agricultural harvesters and a previous diagram. Detailed Implementation

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

[0025] This specification relates to generating predictive maps by combining forecast data or prior data (previous data) with field data acquired simultaneously with agricultural operations, and more specifically, generating predictive speed maps. In some examples, predictive speed maps can be used to control agricultural machinery, such as combine harvesters. As discussed above, combine harvester performance can be improved by controlling the harvester's speed as it encounters different conditions in the field. For example, if the crop is mature, the weeds may still be green, thus increasing the moisture content of the biomass encountered by the harvester. This problem can be exacerbated when weed clumps are wet (such as shortly after rain or when they contain dew) and before they have a chance to dry. Therefore, when the harvester encounters an area with increased biomass, the operator may slow down the harvester to maintain a constant feed rate of material through it. Maintaining a constant feed rate helps maintain the harvester's performance. The performance of a combine harvester can be adversely affected based on many different criteria. These different criteria can include variations in biomass, crop condition, topography, soil properties, sowing characteristics, or other conditions. Therefore, it can also be useful to control the speed of agricultural harvesters based on other conditions that may exist in the field. For example, by controlling the speed of agricultural harvesters based on the biomass encountered by the harvester, the crop condition of the crop being harvested, the topography of the field being harvested, the soil properties of the soil in the field being harvested, the sowing characteristics of the field being harvested, the yield in the field being harvested, or other conditions present in the field, the performance of agricultural harvesters can be kept at an acceptable level.

[0026] Furthermore, given a specific speed of an agricultural harvester, it may be necessary to control the controllable subsystems on the harvester in a specific manner. For example, if the harvester is traveling at a first speed, it may be desirable to position the header at a first height, while if the harvester is traveling at a second speed, it may be desirable to position the header at a second height to maintain the material feed rate through the harvester at an ideal feed rate.

[0027] Some current systems provide vegetation index maps. Vegetation index maps schematically map vegetation index values ​​(which can indicate vegetation growth) at different geographic locations within a field of interest. An example of a vegetation index includes the normalized difference vegetation index (NDVI). Many other vegetation indices exist within the scope of this disclosure. In some examples, vegetation indices can be derived from sensor readings of one or more bands of electromagnetic radiation reflected by the vegetation. Without limitation, these bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

[0028] Vegetation index maps can be used to identify the presence and location of vegetation. In some examples, these maps enable the identification and georeferencing of vegetation in the presence of bare soil, crop residues, or other vegetation (including crops or other weeds).

[0029] In some examples, a biomass map is provided. A biomass map exemplarily maps measurements of biomass at different locations within a field being harvested. A biomass map can be generated from vegetation index values, historically measured or estimated biomass levels, images or other sensor readings acquired during a previous operation in the field, or otherwise. In some examples, biomass can be modulated by a factor representing a portion of the total biomass passing through an agricultural harvester. For maize, this factor is typically around 50%. For the moisture content of the harvested crop material, this factor is typically 10%–30%. In some examples, this factor may represent a portion of weed material or weed seeds.

[0030] In some examples, a crop status map is provided. Crop status can define whether the crop is drooping, upright, partially drooping, or the orientation of a drooping or partially drooping crop, among other things. The crop status map exemplarily maps the crop status at different locations within a field being harvested. The crop status map can be generated from aerial or other imagery of the field, from images or other sensor readings acquired during a previous operation in the field, or otherwise prior to harvesting.

[0031] In some examples, a seeding map is provided. A seeding map maps seeding characteristics (e.g., seed location, seed variety, or seed population) to different locations in the field. The seeding map can be generated during past seeding operations in the field. The seeding map can be derived from control signals used by the seeder when planting seeds or from sensors on the seeder confirming that seeds have been planted. The seeder may also include a geolocation sensor that geolocates seed characteristics in the field.

[0032] In some examples, a soil property map is provided. A soil property map, by way of example, maps measurements of one or more soil properties (e.g., soil type or soil moisture in a field being harvested) to different locations within the field. A soil property map may be generated from vegetation index values, historically measured or estimated soil properties, images or other sensor readings acquired during a previous operation in the field, or otherwise.

[0033] In some examples, additional prior information maps are provided. Such prior information maps may include topographic maps of fields being harvested, predictive yield maps of fields being harvested, or other prior information maps.

[0034] Therefore, this discussion focuses on systems that receive a priori information map of the field or a map generated during prior operations (previous operations) and also use field sensors to detect one or more variables indicating machine speed and outputs from a feed rate control system. The system generates a model that models the relationship between prior information values ​​on the priori information map and output values ​​from the field sensors. This model is used to generate a functional predictive speed map that predicts, for example, the target machine speed at different locations in the field. The functional predictive speed map generated during harvesting operations can be presented to the operator or other users, or used to automatically control the agricultural harvester during harvesting operations, or both.

[0035] This discussion also focuses on a system that receives speed maps that map predicted machine speed values ​​to different geographic locations in the field, and also uses field sensors to detect variables. The system generates a model that models the relationship between the values ​​on the speed map and the values ​​output by the field sensors. This model is used to generate a functional predictive data map that predicts values ​​at different locations in the field. The functional predictive data map generated during harvesting operations can be presented to the operator and used to automatically control the agricultural harvester during harvesting operations.

[0036] Figure 1This 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 should be understood that this description is also applicable 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. Moreover, this disclosure relates to other types of operating machines, such as agricultural seeders and sprayers in which the generation of predictive maps can be applied, construction equipment, forestry equipment, and lawn 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.

[0037] like Figure 1 As shown, the agricultural harvester 100 schematically 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 feed chamber 106, a feed accelerator 108, and a thresher generally indicated by 110. The feed chamber 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. Thus, the vertical position (header height) of the header 102 above the ground 111 over which the header 102 travels can be controlled by actuating the actuators 107. Figure 1 As not shown, the agricultural harvester 100 may also include one or more actuators that operate to apply a tilt angle, a lateral tilt angle, or both to the header 102 or multiple portions 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 more away from the ground. The lateral tilt angle refers to the orientation of the header 102 about the longitudinal axis of the agricultural harvester 100.

[0038] The threshing machine 110 schematically includes a threshing rotor 112 and a set of recesses 114. Additionally, the agricultural harvester 100 includes a separator 116. The agricultural harvester 100 also includes a cleaning subsystem or cleaning device (collectively referred to as cleaning subsystem 118), which includes a cleaning fan 120, a chaffer 122, and a sieve 124. The material handling subsystem 125 also includes a discharge beater 126, a tailings elevator 128, a clean grain elevator 130, and an unloading screw conveyor 134 and a spout 136. The clean grain elevator moves clean grain into a clean grain trough 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 comprising an engine that drives ground engagement components 144 (e.g., wheels or tracks). In some examples, combine harvesters within the scope of this disclosure may have more than one of any of the subsystems mentioned above. In some examples, the agricultural harvester 100 may have a left cleaning subsystem and a right cleaning subsystem, a separator, etc., which... Figure 1 Not shown in the image.

[0039] In operation, and as an overview, the combine harvester 100 schematically moves across the field in the direction indicated by arrow 147. As the combine harvester 100 moves, the header 102 (and associated reel 164) engages the crop to be harvested and collects the crop toward the cutter 104. The operator of the combine harvester 100 can be a local human operator, a remote human operator, or an automated system. The operator of the combine harvester 100 can determine one or more of the height setting, tilt angle setting, or side tilt angle setting of the header 102. For example, the operator inputs one or more settings (described in more detail below) to the control system of the control actuator 107. The control system can also receive settings from the operator for establishing the tilt angle and side tilt angle of the header 102 and implement the input settings by controlling associated actuators (not shown) that operate to change the tilt angle and side tilt angle of the header 102. Actuator 107 maintains the cutter head 102 at a height above ground 111 based on a height setting, and maintains it at desired tilt and yaw angles where applicable. Each of the height, tilt, and yaw settings can be implemented independently of the others. The control system responds to cutter head errors (e.g., the difference between the height setting and the measured height of the cutter head 104 above ground 111, and in some examples, tilt and yaw angle errors) with a responsiveness determined based on a selected sensitivity level. If the sensitivity level is set at a higher sensitivity level, the control system responds to smaller cutter head position errors and attempts to reduce the detected errors faster than if the sensitivity level were lower.

[0040] 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 moves toward the feed accelerator 108 via a conveyor in the feed chamber 106, which accelerates the crop material into the threshing machine 110. The crop material is threshed by the rotor 112, which rotates the crop against the recess 114. The threshed crop material is moved by the separator rotor in the separator 116, where a portion of the residue is moved toward the residue subsystem 138 by the discharge beater 126. This portion of residue conveyed to the residue subsystem 138 is shredded by the residue shredder 140 and spread on the field by the spreader 142. In other configurations, the residue is released from the agricultural harvester 100 into a stockpile. In other examples, the residue subsystem 138 may include a weed seed remover (not shown), such as a seed bagger or other seed collector, or a seed crusher or other seed destroyer.

[0041] The grain falls into the cleaning subsystem 118. A husk sieve 122 separates some of the larger pieces of material from the grain, and a screen 124 separates some of the finer pieces of material 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 moves the clean grain upwards, thereby storing it in a clean grain trough 132. Residue is removed from the cleaning subsystem 118 by an airflow generated by a cleaning fan 120. The cleaning fan 120 directs air upwards along an airflow path through the screen and the husk sieve. The airflow carries the residue in the agricultural harvester 100 backwards toward the residue treatment subsystem 138.

[0042] Tail feeder 128 returns the tail feed to thresher 110, where it is re-threshed. Alternatively, the tail feed may be conveyed by tail feeder or another conveyor to a separate re-threshing mechanism, where it is also re-threshed.

[0043] Figure 1 Also shown in one example, the agricultural harvester 100 includes a machine speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a front 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 cleaning subsystem 118.

[0044] Machine speed sensor 146 senses the travel speed of the agricultural harvester 100 on the ground. Machine speed sensor 146 can sense the travel speed of the agricultural harvester 100 by sensing the rotational speed of ground-engaging components (such as 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, long range navigation (LORAN) system, or various other systems or sensors that provide an indication of travel speed.

[0045] Loss sensor 152 schematically provides an output signal indicating the amount of grain loss occurring on the right and left sides of 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 cleaning subsystem 118. The impact sensors for the right and left sides of cleaning subsystem 118 can provide individual signals or combined or aggregated signals. In some examples, sensor 152 may include a single sensor, rather than providing a separate sensor for each cleaning subsystem 118.

[0046] Separator loss sensor 148 provides indication of the left and right separators (in 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 separate grain loss signals or combined or aggregated signals. In some cases, various different types of sensors can also be used to sense grain loss in the separator.

[0047] 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 vibration 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, create a pile, etc.; a cleaning device fan speed sensor for sensing the speed of the fan 120; a recess gap sensor that senses the gap between the rotor 112 and the recess 114; a threshing rotor speed sensor that senses the rotor speed of the rotor 112; a husk sieve gap sensor that senses the size of the opening in the husk sieve 122; a screen gap sensor that senses the size of the opening in the screen 124; and a material other than grain sensor. A grain (MOG) humidity sensor senses the humidity level of the MOG passing through the harvester 100; one or more machine setting sensors are configured to sense various configurable settings of the harvester 100; a machine orientation sensor senses the orientation of the harvester 100; and a crop property sensor senses various different types of crop properties (such as crop type, crop humidity, and other crop properties). The crop property sensor can also be configured to sense characteristics of the cut crop material as it is processed by the harvester 100. 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 feeding rate as the grain travels through the feed chamber 106, the clean grain elevator 130, or other locations within the harvester 100. The crop property sensor may also sense the feeding rate of biomass through the feed chamber 106, the separator 116, or other locations within the harvester 100. The crop property sensor can also sense the feeding rate via the elevator 130 or other parts of the agricultural harvester 100 as the grain mass flow rate, or provide other output signals indicating other sensed variables.

[0048] Before describing how the agricultural harvester 100 generates a functional predictive speed map and uses it for control, a brief description of some items on the agricultural harvester 100 and their operation will be provided first. Figure 2 The description in Figure 3 illustrates the following: A general type of prior information map is received, and information from the prior information map is combined with georeferenced sensor signals generated by field sensors. These sensor signals indicate characteristics, such as one or more characteristics of the field itself, one or more crop characteristics of the harvested material (e.g., crops or grains present in the field), one or more environmental characteristics of the harvester's environment, or one or more characteristics of the harvester itself. Field characteristics may include, but are not limited to, characteristics of the field such as slope, weed intensity, weed type, soil moisture, and surface quality. Crop characteristics may include one or more crop properties (such as crop height, crop moisture, crop density, and crop condition) and grain properties (such as grain moisture, grain size, and grain test weight). Environmental characteristics may include weather characteristics and the presence of standing water. Harvester characteristics may include characteristics indicating machine settings or operator inputs or machine operation (such as machine speed), outputs from the machine controller, and machine performance (such as loss level, job quality, fuel consumption, and power utilization). The relationship between characteristic values ​​obtained from or derived from field sensor signals and velocity map values ​​is identified, and this relationship is used to generate new functional predictive maps. The functional predictive maps predict values ​​at different geographic locations in the field, and one or more of these values ​​can be used to control one or more subsystems of a machine, such as an agricultural harvester. In some cases, the functional predictive maps can be presented to a user, such as an operator of an agricultural machine, which may be an agricultural harvester. The functional predictive maps can be presented to the user visually (e.g., via a display), tactilely, or audibly. Users can interact with the functional predictive maps to perform editing operations and other user interface operations. In some cases, the functional predictive maps can be used to control agricultural machines (such as agricultural harvesters), presented to operators or other users, and presented to operators or users for operator or user interaction, one or more of the following:

[0049] In reference Figure 2 After describing the overall method in Figure 3, refer to Figure 4 and Figure 5A more specific method for generating a predictive speed map is described, which can be presented to an operator or user, or used to control an agricultural harvester 100, or both. The use of the speed map to control the agricultural harvester 100 is then described. Again, although this discussion is directed to agricultural harvesters, and particularly combine harvesters, the scope of this disclosure extends to other types of agricultural harvesters or other agricultural machinery.

[0050] Figure 2 This is a block diagram showing some parts of an example agricultural harvester 100. Figure 2 The diagram illustrates an agricultural harvester 100, schematically including one or more processors or servers 201, a data storage device 202, a geolocation sensor 204, a communication system 206, and one or more field sensors 208 that sense one or more characteristics of the field during harvesting operations. The field sensors 208 generate values ​​corresponding to the sensed characteristics. The agricultural harvester 100 also includes a predictive model or relation generator (hereinafter collectively referred to as "predictive model generator 210"), a predictive graph 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 a variety of other agricultural harvester functions 220. The field sensors 208 include, for example, onboard sensors 222, remote sensors 224, and other sensors 226 that sense field characteristics during agricultural operations. Predictive model generator 210 schematically includes information variable to field variable model generator 228, and predictive model generator 210 may include other items 230. Control system 214 includes communication system controller 229, operator interface controller 231, setting controller 232, path planning controller 234, feed rate controller 236, header and reel controller 238, belt conveyor belt controller 240, table position controller 242, residue system controller 244, machine cleaning controller 245, area controller 247, and system 214 may include other items 246. Controllable subsystem 216 includes machine and header actuator 248, propulsion subsystem 250, steering subsystem 252, residue subsystem 138, machine cleaning subsystem 254, and subsystem 216 may include various other subsystems 256.

[0051] Figure 2 The agricultural harvester 100 is also shown to receive a priori information map 258. As described below, the priori information map 258 includes, for example, vegetation index maps, biomass maps, crop status maps, topographic maps, soil property maps, sowing maps, or maps from prior operations. However, the priori information map 258 may also encompass other types of data obtained prior to the harvesting operation or maps from prior operations. Figure 2 The diagram also illustrates that operator 260 can operate agricultural harvester 100. Operator 260 interacts with operator interface mechanism 218. In some examples, operator interface mechanism 218 may include joysticks, control levers, steering wheels, linkages, pedals, buttons, dials, keypads, user-actuable elements (such as icons, buttons, etc.) on a user interface display device, microphones and speakers (where speech recognition and speech synthesis are provided), and various other types of control devices. Where a touch-sensitive display system is provided, operator 260 may interact with operator interface mechanism 218 using touch gestures. The examples described above are provided as illustrative examples and are not intended to limit the scope of this disclosure. Therefore, other types of operator interface mechanisms 218 may be used, and other types of operator interface mechanisms are within the scope of this disclosure.

[0052] The prior information map 258 can be downloaded to the agricultural harvester 100 and stored in the data storage device 202 using the communication system 206 or other means. In some examples, the communication system 206 may be a cellular communication system, a system for communication over a wide area network or a local area network, a system for communication over a near-field communication network, or a communication system configured to communicate over any one or a combination of various other networks. The communication system 206 may also include systems that facilitate the downloading or transfer of information to or from a secure digital (SD) card or a universal serial bus (USB) card, or both.

[0053] The geolocation sensor 204 schematically senses or detects the geolocation or orientation of the agricultural harvester 100. The geolocation sensor 204 may include, but is not limited to, a GNSS receiver that receives signals from a Global Navigation Satellite System (GNSS) satellite transmitter. The geolocation sensor 204 may also include a real-time kinematic (RTK) component configured to improve the accuracy of the position data derived from the 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.

[0054] The field sensor 208 can be referenced above. Figure 1 Any of the sensors described. Field sensor 208 includes onboard sensor 222 mounted on the onboard agricultural harvester 100. Such sensors may include, for example, those described above relative to… Figure 1Any of the sensors discussed, sensing sensors (e.g., forward-looking monocular or stereo camera systems and image processing systems), and image sensors inside the agricultural harvester 100 (such as one or more grain cleaning cameras mounted to identify material leaving the agricultural harvester 100 through the residue subsystem or from the cleaning subsystem). Field sensor 208 also includes a remote field sensor 224 for capturing field information. Field data includes data acquired from sensors mounted on the harvester, or data acquired by any sensor in which data is detected during harvesting operations. Further examples of field sensor 208 are provided below relative to... Figure 8 Described.

[0055] Predictive model generator 210 generates a model indicating the relationship between values ​​sensed by field sensors 208 and metrics mapped to the field by prior information map 258. For example, if prior information map 258 maps vegetation index values ​​to different locations in the field, and field sensors 208 sense values ​​indicating machine speed, then prior information variable to field variable model generator 228 generates a predictive speed model that models the relationship between vegetation index values ​​and machine speed values. The predictive speed model can also be generated based on vegetation index values ​​from prior information map 258 and multiple field data values ​​generated by field sensors 208. Then, predictive map generator 212 uses the predictive speed model generated by predictive model generator 210 to generate a functional predictive speed map based on prior information map 258, which predicts the target machine speed sensed by field sensors 208 at different locations in the field.

[0056] In some examples, the type of values ​​in functional predictive graph 263 may be the same as the field data type sensed by field sensor 208. In some cases, the type of values ​​in functional predictive graph 263 may have a different unit than the data sensed by field sensor 208. In some examples, the type of values ​​in functional predictive graph 263 may be different from the data type sensed by field sensor 208, but related to the data type sensed by field sensor 208. For example, in some examples, the data type sensed by field sensor 208 may indicate the type of values ​​in functional predictive graph 263. In some examples, the type of data in functional predictive graph 263 may be different from the data type in prior information graph 258. In some cases, the type of data in functional predictive graph 263 may have a different unit than the data in prior information graph 258. In some examples, the type of data in functional predictive graph 263 may be different from the data type in prior information graph 258, but related to the data type in prior information graph 258. For example, in some examples, the data type in prior information graph 258 may indicate the type of data in functional predictive graph 263. In some examples, the type of data in the functional predictive graph 263 is different 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 predictive 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 predictive 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, but different from the other.

[0057] Continuing with the previous example, where prior information map 258 is a vegetation index map and field sensor 208 senses values ​​indicating machine speed, predictive map generator 212 can use the vegetation index values ​​from prior information map 258 and the model generated by predictive model generator 210 to generate a functional predictive map 263 predicting the target machine speed at different locations in the field. Predictive map generator 212 therefore outputs predictive map 264.

[0058] like Figure 2As shown, predictive map 264 uses information values ​​from prior information map 258 at various locations on the field and a predictive model to predict the values ​​of sensed characteristics (sensed by field sensors 208) or characteristics related to sensed characteristics at these locations. For example, if predictive model generator 210 has already generated a predictive model indicating the relationship between vegetation index values ​​and machine speed, then, given vegetation index values ​​at different locations on the field, predictive map generator 212 generates predictive map 264 predicting target machine speed values ​​at different locations on the field. The vegetation index values ​​at these locations obtained from the vegetation index map and the relationship between vegetation index values ​​and machine speed obtained from the predictive model are used to generate predictive map 264.

[0059] The following will describe some changes in the data types mapped in the prior information graph 258, the data types sensed by the field sensor 208, and the data types predicted on the predictive graph 264.

[0060] In some examples, the data type in the prior infographic 258 differs from the data type sensed by the field sensor 208, but the data type in the predictive infographic 264 is the same as that sensed by the field sensor 208. For example, the prior infographic 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be yield. The predictive infographic 264 could then be a predictive yield map that maps predicted yield values ​​to different geographic locations in the field. In another example, the prior infographic 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be crop height. The predictive infographic 264 could then be a predictive crop height map that maps predicted crop height values ​​to different geographic locations in the field.

[0061] 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 predictive map 264 differs from both the data type in the prior information map 258 and the data type sensed by the field sensor 208. For example, the prior information map 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be crop height. The predictive map 264 could then be a predictive biomass map that maps predicted biomass values ​​to different geographic locations in the field. In another example, the prior information map 258 could be a vegetation index map, and the variable sensed by the field sensor 208 could be yield. The predictive map 264 could then be a predictive velocity map that maps predicted harvester velocity values ​​to different geographic locations in the field.

[0062] In some examples, the prior information map 258 is derived from previous passage through the field during a priori operations, and its data type differs from the data type sensed by the field sensor 208, but the data type in the predictive map 264 is the same as that sensed by the field sensor 208. For example, the prior information map 258 could be a seed population map generated during planting, and the variable sensed by the field sensor 208 could be stem size. The predictive map 264 could then be a predictive stem size map that maps predicted stem size values ​​to different geographic locations in the field. In another example, the prior information map 258 could be a seed mix map, and the variable sensed by the field sensor 208 could be crop state, such as upright or lodged crop. The predictive map 264 could then be a predictive crop state map that maps predicted crop state values ​​to different geographic locations in the field.

[0063] In some examples, the prior information map 258 is derived from a field previously traversed during a prior operation, and its data type is the same as that sensed by the field sensor 208, and the data type in the predictive map 264 is also the same as that sensed by the field sensor 208. For example, the prior information map 258 could be a yield map generated during the previous year, and the variable sensed by the field sensor 208 could be yield. The predictive map 264 could then be a predictive yield map that maps predicted yield values ​​to different geographic locations within the field. In such an example, the predictive model generator 210 could use the relative yield differences from the geographic reference prior information map 258 from the previous year to generate a predictive model that models the relationship between the relative yield differences on the prior information map 258 and the yield values ​​sensed by the field sensor 208 during the current harvest operation. The predictive model is then used by the predictive map generator 210 to generate the predictive yield map.

[0064] In another example, the prior information map 258 could be a weed intensity map generated during a prior operation (such as from a spraying machine), and the variable sensed by the field sensor 208 could be weed intensity. The predictive map 264 could then be a predictive weed intensity map that maps predicted weed intensity values ​​to different geographic locations in the field. In such an example, a map of weed intensity at spraying time is georeferenced and provided to the agricultural harvester 100 as the weed intensity information map 258. The field sensor 208 can detect weed intensity at geographic locations in the field, and the predictive model generator 210 can then build a predictive model that models the relationship between weed intensity at harvest and weed intensity at spraying time. This is because the spraying machine affects weed intensity during spraying, but weeds may reappear in similar areas at harvest time. However, the weed area at harvest time may have different intensities based on harvest time, weather, weed type, etc.

[0065] In some examples, predictive graph 264 may be provided to control region generator 213. Control region generator 213 groups the multiple adjacent portions of a region into one or more control regions based on data values ​​associated with multiple adjacent portions of a region in predictive graph 264. A control region may include two or more consecutive portions of a region (such as a field), for which the control parameters corresponding to the control region used to control the controllable subsystem are constant. For example, the response time of changing the settings of controllable subsystem 216 may not be satisfactory in responding to changes in values ​​contained in a graph such as predictive graph 264. In this case, control region generator 213 analyzes the graph and identifies control regions with defined dimensions to accommodate the response time of controllable subsystem 216. In another example, the size of the control region may be determined to reduce wear caused by excessive actuator movement due to continuous adjustment. In some examples, different groups of control regions may be available for each controllable subsystem 216 or group of controllable subsystems 216. Control regions may be added to predictive graph 264 to obtain predictive control region graph 265. Predictive control area map 265 can therefore be similar to predictive map 264, except that predictive control area map 265 includes control area information that defines the control area. Therefore, as described herein, functional predictive map 263 may or may not include control areas. Both predictive map 264 and predictive control area map 265 are functional predictive maps 263. In one example, functional predictive map 263 does not include control areas, as in predictive map 264. In another example, functional predictive map 263 does include control areas, as in predictive control area map 265. In some examples, if an intercropping production system is implemented, multiple crops may coexist in the field. In this case, predictive map generator 212 and control area generator 213 are able to identify the location and characteristics of two or more crops and then generate predictive map 264 and predictive control area map 265 accordingly.

[0066] It should 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 predictive control region graph 265 or a separate graph to show only the generated control regions. In some examples, the control regions can be used to control or calibrate the agricultural harvester 100 or both. 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.

[0067] Predictive map 264 or predictive control area map 265, or both, is 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 send predictive map 264, predictive control area map 265, or both, to other remote systems.

[0068] Operator interface controller 231 is operable to generate control signals to control operator interface mechanism 218. Operator interface controller 231 is also operable to present operator 260 with predictive graph 264 or predictive control area graph 265, or other information derived from or based on predictive graph 264, predictive control area graph 265, or both. Operator 260 can be a local operator or a remote operator. As an example, controller 231 generates control signals to control the display mechanism to display one or both of predictive graph 264 and predictive control area graph 265 to operator 260. Controller 231 can generate operator-actuable mechanisms that are shown and can be actuated by the operator to interact with the displayed graphs. The operator can edit the graph by, for example, correcting the type of weeds displayed on the graph based on the operator's observation. Setting controller 232 can generate control signals based on predictive graph 264, predictive control area graph 265, or both to control various settings on agricultural harvester 100. For example, the setting controller 232 can generate control signals to control the machine and header actuator 248. In one example, the setting controller 232 can control a sensitivity setting that, in response to a header position error, controls the responsiveness of the control system 214 when controlling said position (e.g., height, tilt, or lateral tilt) to conform to header position settings, such as header height setting, header tilt setting, or header position. In response to the generated control signals, the machine and header actuator 248 operate to control, for example, screen and chaff screen settings, recess clearance, rotor settings, cleaning fan speed settings, header height, header function, reel speed, reel position, belt conveyor function (where the agricultural harvester 100 is coupled to a belt conveyor header) (such as belt conveyor belt speed), corn header function, internal distribution control, and other actuators 248 that affect other functions of the agricultural harvester 100. In some examples, the machine and header actuator 248 can be controlled to adjust the rear axle speed (also referred to as header drive speed). For example, in the corn header example, the rear axle speed can be adjusted to control the speed of one or more of the straw rollers, collection chain, and auger conveyor on the corn header. In some examples, the machine and header actuator 248 may include a rotatable output mechanism such as a drive shaft, the output of which can be controlled to control the rear axle speed. Path planning controller 234 schematically generates control signals to control steering subsystem 252 to steer the harvester 100 according to a desired path. Path planning controller 234 can control the path planning system to generate a route for the harvester 100 and can control propulsion subsystem 250 and steering subsystem 252 to steer the harvester 100 along that route.The feed rate controller 236 can receive various inputs indicating the feed rate of material through the harvester 100 and can control various subsystems (such as the propulsion subsystem 250 and machine actuator 248) to control the feed rate based on predictive graph 264 or predictive control area graph 265, or both. For example, when the harvester 100 approaches a clump of weeds with a strength value higher than a selected threshold, the feed rate controller 236 can generate a control signal to control the propulsion subsystem 252 to reduce the speed of the harvester 100, thereby maintaining a constant feed rate of biomass through the harvester 100. The header and reel controller 238 can generate control signals to control the header or reel or other header functions. The belt conveyor controller 240 can generate control signals based on predictive graph 264, predictive control area graph 265, or both, to control the belt conveyor belt or other belt conveyor functions. The tabletop position controller 242 can generate control signals based on predictive map 264 or predictive control area map 265, or both, to control the position of the tabletop included on the harvester, and the residue system controller 244 can generate control signals based on predictive map 264 or predictive control area map 265, or both, to control the residue subsystem 138. The machine cleaning controller 245 can generate control signals to control the machine cleaning subsystem 254. For example, based on the different types of seeds or weeds passing through the harvester 100, a specific type of machine cleaning operation or the frequency of performing cleaning operations can be controlled. Other controllers included on the harvester 100 can also control other subsystems based on predictive map 264 or predictive control area map 265, or both.

[0069] Figure 3A and Figure 3B (Hereinafter referred to as Figure 3) shows a flowchart illustrating an example of the operation of an agricultural harvester 100 in generating a predictive map 264 and a predictive control region map 265 based on prior information map 258.

[0070] At 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 respect to boxes 281, 282, 284, and 286. As discussed above, as shown in box 282, priori information map 258 maps the values ​​of variables corresponding to a first characteristic to different locations in the field. As shown in box 281, receiving priori information map 258 may include selecting one or more of a plurality of possible priori information maps available. For example, one priori information map may be a vegetation index map generated from aerial imagery. Another priori information map may be a map generated during a previous pass through the field, which may be performed by a different machine (such as a spraying machine, a planting machine, a seeding machine, or other machine) performing the previous operation in the field. The process of selecting one or more priori information maps can 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, data can be collected based on aerial imagery acquired during the previous year, early in the current growing season, or at other times. Data can also be based on data detected in a manner different from that using aerial imagery. For example, a harvester 100 may be equipped with sensors (such as internal optical sensors) that identify weed seeds or other types of material leaving the harvester 100. Weed seed data or other data detected by sensors during the previous year's harvest can be used as data to generate a priori information map 258. The sensed weed data or other data can be combined with other data to generate the priori information map 258. For example, based on the quantity of weed seeds leaving the harvester 100 at different locations and based on other factors (such as whether the seeds were spread by a spreader or fell into a pile, weather conditions (such as wind) when the seeds fell or were spread, drainage conditions that might cause the seeds to move around in the field, or other information), the location of these weed seeds can be predicted, such that the priori information map 258 maps the predicted seed locations in the field. The data from the prior information diagram 258 can be transmitted to the agricultural harvester 100 using the communication system 206 and stored in the data storage device 202. The data from the prior information diagram 258 can also be provided to the agricultural harvester 100 in other ways using the communication system 206, as indicated by box 286 in the flowchart of Figure 3. In some examples, the prior information diagram 258 can be received by the communication system 206.

[0071] At the start of the harvesting operation, field sensor 208 generates sensor signals indicating one or more field data values, which indicate characteristics such as velocity characteristics, as shown in box 288. Examples of field sensor 288 are discussed with respect to boxes 222, 290, and 226. As explained above, field sensor 208 includes airborne sensor 222, remote field sensor 224 (such as a UAV-based sensor that collects field data on each flight (as shown in box 290)), or other types of field sensors specified by field sensor 226. In some examples, position, heading, or velocity data from geolocation sensor 204 is georeferenced against data from airborne sensors.

[0072] Predictive model generator 210 controls prior information variable to field variable model generator 228 to generate a model that models the relationship between the mapped values ​​contained in prior information graph 258 and the field values ​​sensed by field sensor 208, as shown in box 292. The characteristics or data types represented by the mapped values ​​in prior information graph 258 and the field values ​​sensed by field sensor 208 can be the same or different characteristics or data types.

[0073] The relation or model generated by the predictive model generator 210 is provided to the predictive graph generator 212. The predictive graph generator 212 uses the predictive model and prior information graph 258 to generate a predictive graph 264 that predicts the values ​​of characteristics at different geographical locations in the harvesting field sensed by the field sensor 208, or the values ​​of different characteristics related to the characteristics sensed by the field sensor 208, as shown in box 294.

[0074] 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. Each map in the two or more different maps, or each layer in the two or more different layers of a map, maps different types of variables to geographic locations in the field. In such an example, the predictive model generator 210 generates a predictive model that models the relationship between the field data and each of the different variables mapped by the two or more different maps or two or more different layers. Similarly, the field sensor 208 may include two or more sensors, each sensing a different type of variable. Therefore, the predictive model generator 210 generates a predictive model that models the relationship between each type of variable mapped by the prior information map 258 and each type of variable sensed by the field sensor 208. Predictive map generator 212 can use each of the graphs or layers in the predictive model and prior information graph 258 to generate a functional predictive map 263 that predicts the value of each sensed property (or property associated with the sensed property) at different locations in the field being harvested, as sensed by field sensor 208.

[0075] Predictive graph generator 212 configures predictive graph 264 such that predictive graph 264 can be operated (or consumed) by control system 214. Predictive graph generator 212 can provide predictive graph 264 to control system 214 or to control region generator 213 or both. Some examples of different ways in which predictive graph 264 can be configured or output are described with respect to boxes 296, 295, 299 and 297. For example, predictive graph generator 212 configures predictive graph 264 such that predictive graph 264 includes values ​​that can be read by control system 214 and used as the basis for generating control signals for one or more of the different controllable subsystems of agricultural harvester 100, as shown in box 296.

[0076] Control area generator 213 can divide predictive map 264 into control areas based on values ​​on predictive map 264. Values ​​that are consecutively geolocated within each other's thresholds can be grouped into control areas. The thresholds can be default thresholds, or they can be set based on operator input, input from the automation system, or other criteria. The size of the areas can be based on the responsiveness of control system 214, controllable subsystem 216, wear considerations, or other criteria, as shown in box 295. Predictive map generator 212 configures predictive map 264 for presentation to operators or other users. Control area generator 213 can configure predictive control area map 265 for presentation to operators or other users. This is indicated by box 299. When presented to an operator or other user, the presentation of predictive map 264 or predictive control area map 265, or both, may include one or more of the following: geographic location-related predictive values ​​on predictive map 264, geographic location-related control areas on predictive control area map 265, and setting values ​​or control parameters used based on the predictive values ​​on map 264 or the areas on predictive 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 that the predictive values ​​on predictive map 264 or the areas on predictive control area map 265 conform to the accuracy of measurements that can be measured by sensors on harvester 100 as harvester 100 moves through the field. Additionally, where information is presented to more than one location, an authentication and authorization system may be provided to implement the authentication and authorization process. For example, there may be a hierarchy of individuals authorized to view and modify maps and other presented information. As an example, an onboard display device may display the map locally on the machine in near 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 can be associated with a person or user permission level. User permission levels can be used to determine which display markers are visible on the physical display devices and which values ​​the corresponding person can change. For example, a local operator of machine 100 may not be able to see the information corresponding to predictive diagram 264 or make any changes to the machine's operation. However, a supervisor, such as a manager at a remote location, may be able to see predictive diagram 264 on the display but be prevented from making any changes. A manager at a separate remote location may be able to see all elements on predictive diagram 264 and also be able to change predictive diagram 264. In some cases, predictive diagram 264, accessible and modifiable by a remotely located manager, can be used for machine control. This is an example of an authorization hierarchy that can be implemented. Predictive diagram 264 or predictive control area diagram 265, or both, can also be configured in other ways, as shown in box 297.

[0077] 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 input from the geolocation sensor 204 identifying the geolocation of the harvester 100. Box 302 indicates that the control system 214 receives sensor input indicating the trajectory or heading of the harvester 100, and box 304 indicates that the control system 214 receives the speed of the harvester 100. Box 306 indicates that the control system 214 receives other information from various field sensors 208.

[0078] At block 308, control system 214 generates a control signal to control controllable subsystem 216 based on predictive map 264 or predictive 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 signal to the controllable subsystem. It should be understood that the specific control signal generated and the specific controllable subsystem 216 controlled can vary based on one or more different factors. For example, the generated control signal and the controllable subsystem 216 controlled can be based on the type of predictive map 264 or predictive control area map 265, or both, being used. Similarly, the generated control signal, the controllable subsystem 216 controlled, and the timing of the control signal can be based on various delays in the crop flow through agricultural harvester 100 and the responsiveness of controllable subsystem 216.

[0079] As an example, the generated predictive speed map 264, in the form of a predictive speed map, can be used to control one or more subsystems 216. For example, the predictive speed map may include georeferenced speed values ​​relative to a location within the field being harvested. Speed ​​values ​​from the predictive speed map can be extracted, and these speed values ​​are used to control the propulsion subsystem 250. By controlling the propulsion subsystem 250, the feeding rate of material moving through the agricultural harvester 100 can be controlled. Similarly, the header height can be controlled to harvest more or less material, and therefore, the header height can also be controlled to control the feeding rate of material through the agricultural harvester 100. In other examples, if the predictive map 264 maps weed height relative to a location in the field, header height control can be implemented. For example, if values ​​present in the predictive weed map indicate that one or more areas have weed heights at a first height amount, the header and reel controller 238 can control the header height such that, during the harvesting operation, the header is positioned above the first height amount of weeds in one or more areas having weeds at the first height amount. Therefore, the header and reel controller 238 can be controlled using geographic reference values ​​present in the predictive weed map to position the header above the predicted weed height value obtained from the predictive weed map. Additionally, the header height can be automatically changed by the header and reel controller 238 as the agricultural harvester 100 travels through the field using geographic reference values ​​obtained from the predictive weed map. The preceding examples involving weed height and intensity using the predictive weed map are provided by way of example only. Therefore, values ​​obtained from predictive speed maps or other types of predictive maps can be used to generate a variety of other control signals to control one or more of the controllable subsystems 216. In some examples, the rear axle speed, reel speed, conveyor belt speed, or header height sensitivity can be controlled based on the predictive speed map.

[0080] 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 read.

[0081] In some examples, at box 316, the agricultural harvester 100 can also detect learning trigger criteria to perform machine learning on one or more of the predictive graph 264, predictive control region graph 265, model generated by predictive model generator 210, region generated by control region generator 213, control algorithm implemented by controller in control system 214, and other learning trigger criteria.

[0082] The learning trigger criterion can include any of a variety of different criteria. Some examples of detecting the trigger criterion are discussed with regard to boxes 318, 320, 321, 322, and 324. For example, in some examples, triggering learning can include recreating the relationships used to generate a predictive model when a threshold amount of field sensor data is received from field sensor 208. In such an example, an amount of field sensor data received from field sensor 208 exceeding a threshold triggers or prompts predictive model generator 210 to generate a new predictive model used by predictive graph 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, the new predictive model can be used to regenerate a new predictive graph 264, a predictive control region graph 265, or both. Box 318 represents detecting a threshold amount of field sensor data used to trigger the creation of a new predictive model.

[0083] In other examples, the learning trigger criterion may be based on the degree of change in field sensor data from field sensor 208, such as the degree of change over time or compared to previous values. For example, if the change within the field sensor data (or the relationship between the field sensor data and the information in prior information graph 258) is within a selected range, less than a defined amount, or below a threshold, then a new predictive model is not generated by predictive model generator 210. As a result, predictive graph generator 212 does not generate a new predictive graph 264, predictive control region graph 265, or both. However, for example, if the change within the field sensor data is outside the selected range, greater than a defined amount, or above a threshold, then predictive model generator 210 uses all or part of the newly received field sensor data used by predictive graph generator 212 to generate a new predictive graph 264 to generate a new predictive model. At box 320, changes in field sensor data (such as the magnitude of data exceeding the selected range or the magnitude of changes in the relationship between field sensor data and information in prior information graph 258) can be used as triggers to generate new predictive models and predictive graphs. Continuing with the example described above, thresholds, ranges, and defined quantities can be set to default values, set by an operator or user through user interface interaction, set by the automation system, or otherwise.

[0084] 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), the switch to a different prior infographic can trigger relearning by the predictive model generator 210, the predictive map generator 212, the control region generator 213, the control system 214, or others. In another example, the transition of the agricultural harvester 100 to different terrain or to a different control region can also be used as a learning trigger criterion.

[0085] In some cases, operator 260 can also edit predictive graph 264 or predictive control area graph 265, or both. Editing can change the values ​​on predictive graph 264, change the size, shape, position, or presence of control areas on predictive control area graph 265, or both. Box 321 shows that the edited information can be used as a learning trigger criterion.

[0086] In some cases, operator 260 may also observe that the automatic control of the controllable subsystem is not as the operator expects. In this case, operator 260 may provide manual adjustments to the controllable subsystem, reflecting operator 260's expectation that the controllable subsystem will operate in a manner different from that commanded by control system 214. Therefore, manual changes to the settings by operator 260 may result in one or more of the following: based on operator 260's adjustments (as shown in box 322), predictive model generator 210 relearns the model, predictive graph generator 212 regenerates graph 264, control region generator 213 regenerates one or more control regions on predictive control region graph 265, and control system 214 relearns the control algorithm or performs machine learning on one or more of the controller components 232 to 246 in control system 214. Box 324 indicates the use of other triggering learning criteria.

[0087] In other examples, relearning can be performed periodically or intermittently, for example based on a chosen time interval, such as a discrete time interval or a variable time interval, as shown in box 326.

[0088] If relearning is triggered (whether based on a learning trigger criterion or on the elapsed time interval, as shown in box 326), one or more of the predictive model generator 210, predictive graph generator 212, control region generator 213, and control system 214 perform machine learning to generate new predictive models, new predictive graphs, new control regions, and new control algorithms, respectively, based on the learning trigger criterion. The new predictive models, new predictive graphs, and new control algorithms are generated using any additional data collected since the last learning operation. The execution of relearning is indicated by box 328.

[0089] If the harvesting operation is complete, the operation moves from box 312 to box 330, where one or more of the predictive map 264, predictive control region map 265, and predictive models generated by predictive model generator 210 are stored. The predictive map 264, predictive control region map 265, and predictive models can be stored locally on data storage device 202 or transmitted to a remote system using communication system 206 for later use.

[0090] It will be noted that while some examples in this paper describe the predictive model generator 210 and the predictive graph generator 212 receiving 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.

[0091] Figure 4 yes Figure 1 A block diagram of a portion of an agricultural harvester 100 is shown. Specifically, Figure 4 Examples of the predictive model generator 210 and the predictive graph generator 212 are shown in more detail. Figure 4 The information flow between the various components is also illustrated. The predictive model generator 210 receives a priori information map 258 as an information map, which may be a vegetation index map 332, a predictive yield map 333, a biomass map 335, a crop status map 337, a topographic map 339, a soil property map 341, or a sowing map 343. The predictive model generator 210 also receives a geographic location 334 or an indication of geographic location from the geographic location sensor 204. The field sensor 208 schematically includes a machine speed sensor 146, or a sensor sensing the output from the feed rate controller 236, and a processing system 338. The processing system 338 processes sensor data generated from the machine speed sensor 146, or from sensor 336, or from both the machine speed sensor 146 and sensor 336, to generate processed data, some examples of which are described below.

[0092] In some examples, sensor 336 may be a sensor that generates a signal indicating the control output from feed rate controller 236. The control signal may be a speed control signal or other control signal applied to controllable subsystem 216 to control the feeding rate of material through harvester 100. Processing system 338 processes the signal obtained via sensor 336 to generate processed data 340 identifying the speed of harvester 100. Processed data 340 may include the position of harvester 100 corresponding to its speed.

[0093] In some examples, raw or processed data from one or more field sensors 208 can be presented to operator 260 via operator interface mechanism 218. Operator 260 may be located on agricultural harvester 100 or at a remote location.

[0094] This discussion is based on the example where the field sensor 208 is the machine speed sensor 146. It should be understood that this is merely an example, and the aforementioned sensor is also considered herein as another example of a field sensor 208 from which machine speed can be derived. Figure 4 As shown, the exemplary predictive model generator 210 includes one or more of the following: vegetation index (VI) value to velocity model generator 342, biomass to velocity model generator 344, topography to velocity model generator 345, yield to velocity model generator 347, crop state to velocity model generator 349, soil properties to velocity model generator 351, and sowing characteristics to velocity model generator 346. In other examples, compared to Figure 4 The predictive model generator 210 may include additional components, fewer components, or different components, as shown in the examples. Therefore, in some examples, the predictive model generator 210 may also include other items 348, which may include other types of predictive model generators to generate other types of models.

[0095] Model generator 342 identifies the relationship between machine speeds detected in processed data 340 at geographic locations corresponding to where processed data 340 was obtained and vegetation index values ​​from vegetation index map 332 corresponding to the same locations in fields where weed characteristics were detected. Based on this relationship established by model generator 342, model generator 342 generates a predictive speed model. Speed ​​map generator 352 uses the predictive speed model to predict target machine speeds at different locations in the field based on georeferenced vegetation index values ​​in vegetation index map 332, which includes the same locations in the field.

[0096] Model generator 344 identifies the relationship between machine speed at a geographic location corresponding to the processed image data 340 and biomass values ​​at the same geographic location, as represented in the processed image data 340. Similarly, the biomass values ​​are geographic reference values ​​included in biomass map 335. Model generator 344 then generates a predictive speed model, which speed map generator 354 uses to predict the target machine speed at that location in the field based on the biomass values ​​at that location.

[0097] Model generator 346 identifies the relationship between machine speed at a specific location in the field, as identified by processed data 340, and sowing characteristics at the same location, as shown in sowing characteristic map 343. Model generator 346 generates a predictive speed model, which speed map generator 356 uses to predict the target machine speed at that specific location in the field based on the sowing characteristic values.

[0098] In light of the above, the predictive model generator 210 is operable to generate multiple predictive velocity models, such as one or more of the predictive velocity models generated by model generators 342, 344, 346, 347, 349, and 351. In another example, two or more of the predictive velocity models described above can be combined into a single predictive velocity model, which predicts the target machine velocity based on two or more of the following: vegetation index values, biomass values, topography, yield, sowing characteristics, crop status, or soil properties at different locations in the field. Any one of these velocity models or a combination thereof in Figure 4 The prediction model 350 is used to represent this.

[0099] The predictive model 350 is provided to the predictive graph generator 212. Figure 4 In one example, the predictive graph generator 212 includes a velocity graph generator 352. In other examples, the predictive graph generator 212 may include additional, fewer, or different graph generators. Therefore, in some examples, the predictive graph generator 212 may include additional items 358, which may include other types of graph generators used to generate velocity graphs. The velocity graph generator 352 receives a predictive model 350 based on values ​​from one or more prior information graphs 258 and generates predictive graphs that predict the velocity of the target machine at different locations in the field.

[0100] Predictive map generator 212 outputs one or more functional predictive speed maps 360 that predict the speed of one or more target machines. The functional predictive speed maps 360 predict the speed of the target machines at different locations in the field. The functional predictive speed maps 360 can be provided to control region generator 213, control system 214, or both. Control region generator 213 generates control regions and incorporates these control regions into the functional predictive maps (i.e., predictive maps) to produce a predictive control region map 265. One or both of predictive maps 264 and predictive control region maps 265 can be provided to control system 214, which generates control signals based on predictive maps 264, predictive control region maps 265, or both, to control one or more controllable subsystems 216 (e.g., propulsion subsystem 250).

[0101] Figure 5 This is a flowchart illustrating an example of the operation of the predictive model generator 210 and the predictive graph generator 212 in generating the predictive model 350 and the functional predictive velocity graph 360. At box 362, the predictive model generator 210 and the predictive graph generator 212 receive an information graph 258. The prior information graph 258 can be any of figures 332, 333, 335, 337, 339, 341, or 343. Furthermore, box 361 indicates that the received prior information graph can be a single graph. Box 363 indicates that the prior information graph can be multiple graphs or multiple layers. Box 365 indicates that the prior information graph 258 can also take other forms. At box 364, the processing system 338 receives one or more signals from the machine speed sensor 146 or sensor 336, or both.

[0102] At box 372, the processing system 338 processes one or more received signals to generate processed data 340 indicating the machine speed of the agricultural harvester 100.

[0103] At box 382, ​​the predictive model generator 210 also obtains the geographic location 344 corresponding to the processed data. For example, the predictive model generator 210 can obtain the geographic location from the geographic location sensor 204 and determine the precise geographic location of the processed data 340 based on machine latency, machine speed, etc.

[0104] At box 384, predictive model generator 210 generates one or more predictive models, such as predictive model 350, that model the relationship between values ​​in one or more prior information maps 258 and machine speeds sensed by field sensors 208. VI value to speed model generator 342 generates a predictive model that models the relationship between VI values ​​in VI map 332 and machine speeds sensed by field sensors 208. Biomass to speed model generator 344 generates a predictive model that models the relationship between biomass values ​​in biomass map 335 and machine speeds sensed by field sensors 208. Terrain to speed model generator 345 generates a predictive model that models the relationship between one or more terrain values ​​(e.g., pitch, roll, or slope in terrain map 339) and machine speeds sensed by field sensors 208. Seeding characteristics to speed model generator 346 generates a predictive model that models the relationship between seeding characteristics in seeding map 343 and machine speeds sensed by field sensors 208. Yield-to-velocity model generator 347 generates a predictive model that models the relationship between yield values ​​in yield map 333 and machine speeds sensed by field sensor 208. Crop state-to-velocity model generator 342 generates a predictive model that models the relationship between crop state values ​​in crop state map 337 and machine speeds sensed by field sensor 208. Soil property-to-velocity model generator 351 generates a predictive model that models the relationship between soil property values ​​in soil property map 341 and machine speeds sensed by field sensor 208. At box 386, predictive model 350 is provided to predictive map generator 212, which generates a functional predictive velocity map 360 based on prior information map 258 and predictive velocity model 350, which maps the predicted target machine speed. Velocity map generator 352 can use the predictive model 350 that models the relationship between VI values ​​in VI map 335 and machine speed, and VI map 332, to generate functional predictive velocity map 360. Speed ​​map generator 352 can use a predictive model 350 that models the relationship between yield values ​​and machine speed in yield map 333 and generate a functional predictive speed map 360 using yield map 333. Speed ​​map generator 352 can use a predictive model 350 that models the relationship between biomass values ​​and machine speed in biomass map 335 and generate a functional predictive speed map 360 using biomass map 335. Speed ​​map generator 352 can use a predictive model 350 that models the relationship between crop state values ​​and machine speed in crop state map 337 and generate a functional predictive speed map 360 using crop state map 337.Speed ​​map generator 352 can use a predictive model 350 that models the relationship between terrain values ​​and machine speed in terrain map 339 and terrain map 337 to generate a functional predictive speed map 360. Speed ​​map generator 352 can use a predictive model 350 that models the relationship between soil property values ​​and machine speed in soil property map 341 and soil property map 341 to generate a functional predictive speed map 360. Speed ​​map generator 352 can use a predictive model 350 that models the relationship between seeding characteristic values ​​and machine speed in seeding map 343 and seeding map 343 to generate a functional predictive speed map 360.

[0105] Therefore, as an agricultural harvester moves through a field where agricultural operations are being performed, one or more functional predictive speed maps 360 are generated during the agricultural operations.

[0106] At block 394, the predictive map generator 212 outputs a functional predictive speed map 360. At block 391, the functional predictive speed map generator 212 outputs the functional predictive speed map 360 to be presented to operator 260 for possible interaction. At block 393, the predictive map generator 212 can configure map 360 for use by control system 214. At block 395, the predictive map generator 212 can also provide map 360 to control region generator 213 to generate a control region. At block 397, the predictive map generator 212 also configures map 360 in other ways. The functional predictive speed map 360 (with or without a control region) is provided to control system 214. At block 396, control system 214 generates control signals based on the functional predictive speed map 360 to control controllable subsystem 216. The control system can control propulsion subsystem or other subsystems 399.

[0107] Figure 6 yes Figure 1 A block diagram of an example portion of an agricultural harvester 100 is shown. Specifically, Figure 6 Examples of predictive model generator 210 and predictive graph generator 212 are shown in particular. In the illustrated example, infographic 259 is one or more of a functional predictive speed graph 360, a speed graph 400 with a control region, or another speed graph 401. infographic 259 can be a priori infographic (e.g., infographic 258) or a predictive graph (e.g., a predictive graph generated during the operation of agricultural harvester 100).

[0108] In addition, Figure 6 In the example shown, the field sensor 208 may include one or more of a variety of different sensors 402 and processing systems 406. The following is about... Figure 8Examples of field sensors 208 and 402 are described below. Sensor 402 can sense any number of various agricultural characteristics or indicate values ​​for any number of various agricultural characteristics. Therefore, in some examples, sensor data generated by sensor 402 can indicate agricultural characteristics or can be used to deduce agricultural characteristics. Therefore, in some examples, the relationship between sensor data and other characteristics or characteristic values ​​can be identified and modeled. In other examples, sensor data can be used as an indicator of another characteristic or characteristic value, and therefore the relationship between a characteristic or characteristic value (indicated by the sensor data) and other characteristics or characteristics can be identified and modeled. Therefore, as used herein, sensor data can refer to the sensor data itself, or it can refer to the various characteristics or features that can be indicated by the sensor data.

[0109] In one example, sensor 402 may include an operator input sensor that senses various operator inputs. Inputs may be setting inputs or other control inputs used to control settings on the agricultural harvester 100, such as steering inputs and other inputs. Therefore, when the harvester operator changes settings or provides command inputs, for example through operator interface mechanism 218, such inputs are detected by the operator input sensor, which provides a sensor signal indicating the sensed operator input. This is merely one example of a variety of different sensors 402.

[0110] Predictive model generator 210 may include a velocity characteristic to field sensor data model generator 416. In other examples, predictive model generator 210 may include additional, fewer, or other model generators 424. Predictive model generator 210 may receive a geographic location indicator 324 from geographic location sensor 204 and generate a predictive model 426 that models the relationship between information in one or more infographics and information in one or more field sensors 402. For example, the velocity to field sensor data model generator 416 generates a relationship between velocity characteristic values ​​(which may be located on Figure 360, Figure 400, or Figure 401) and values ​​sensed by sensor 402. The velocity to field data model generator 416 illustratively generates a model representing the relationship between the travel speed in the infographic 259 or a variable indicating travel speed and the characteristics sensed by field sensors 402. The predictive sensor data model 426 generated by the predictive model generator 210 may include a predictive model that can be generated by the velocity pair field sensor data model generator 416.

[0111] exist Figure 6In one example, the predictive map generator 212 includes a predictive sensor data map generator 428. In other examples, the predictive map generator 212 may include additional or other map generators 434. The predictive sensor data map generator 428 receives a predictive model 426 that models the relationship between machine speeds on Infographic 259 and characteristics detected by sensor 402. Based on machine speeds at different locations in the field in one or more of Infographic 259 and based on the predictive model 426, the predictive sensor data map generator 428 generates a functional predictive sensor data map 436 that predicts sensor data at said different locations in the field.

[0112] Predictive graph generator 212 outputs a functional predictive sensor data graph 436. The functional predictive sensor data graph 436 can be provided to a control region generator 213, a control system 214, or both. The control region generator 213 generates and merges control regions to provide a functional predictive sensor data graph 436 with control regions. The functional predictive sensor data graph 436 (with or without control regions) can be provided to the control system 214, which generates control signals to control one or more controllable subsystems 216 based on the functional predictive sensor data graph 436 (with or without control regions). The functional predictive sensor data graph 436 (with or without control regions) can be presented to an operator 260 or another user.

[0113] In one example, sensor 402, such as an operator input sensor, senses operator input instructing the harvester 100 to set a rear axle speed (or header drive speed) to control the speed of, for example, one or more straw rolls, one or more collection chains, or one or more screw conveyors operating on the corn header. Thus, in such an example, a speed characteristic-to-field sensor data model generator 416 models the relationship between speed values ​​from one or more graphs (functional predictive speed graph 360, speed graph with control area 400, or other speed graphs) and the rear axle speed setting indicated by the detected operator input, and a predictive sensor data graph generator 428 generates a functional predictive sensor data graph based on speed values ​​from one or more graphs at different locations in the field, which predicts the rear axle speed setting at said different locations in the field. This is merely one example, and various other machine settings indicated by operator input commands can be similarly modeled and mapped predictively, such as conveyor belt speed settings, reel speed settings, and header sensitivity settings.

[0114] Figure 7A flowchart illustrating an example of the operation of predictive model generator 210 and predictive graph generator 212 in generating predictive model 426 and functional predictive sensor data graph 436 is shown. At block 442, predictive model generator 210 and predictive graph generator 212 receive an information graph. The information graph may be a functional predictive velocity graph 360, a velocity graph 400 with a control region, or another velocity graph 406. At block 444, sensor 402 generates a sensor signal containing sensor data indicating characteristics sensed by field sensor 402. Field sensor 402 may be, for example, the following relative to… Figure 8 One or more of the aforementioned sensors.

[0115] At box 454, processing system 406 processes the data contained in the sensor signal received from field sensor 402 to obtain processed data 409, such as... Figure 6 As shown. The data contained in the sensor signal can be in its original format, processed to receive processed data 409. For example, a temperature sensor signal includes resistance data. This resistance data can be processed into temperature data. In other examples, processing may include digitizing, encoding, formatting, scaling, filtering, or classifying the data.

[0116] Return to Figure 7 At box 456, the predictive model generator 210 also receives geolocation 334 from geolocation sensor 204, such as Figure 6 As shown. Geographic location 334 can be associated with the geographic location of one or more sensed variables sensed by field sensor 402. For example, predictive model generator 210 can obtain geographic location 334 from geographic location sensor 204 and determine the precise geographic location of the processed data 409 derived from it based on machine latency, machine speed, etc.

[0117] At box 458, predictive model generator 210 generates predictive model 426, which models the relationship between the mapped velocity values ​​in the infographic and the characteristics or related characteristics represented in the processed data 409 (e.g., characteristics related to the characteristics sensed by the field sensor 402).

[0118] Predictive sensor data model 426 is provided to predictive map generator 212. At box 466, predictive map generator 212 generates a functional predictive sensor data map 436. The functional predictive sensor data map 436 predicts sensor data values ​​at different locations in the field, generated by field sensors 402 on the agricultural harvester 100. Therefore, the functional predictive sensor data map 436 is generated while the agricultural harvester 100 is moving through the field where agricultural operations are being performed.

[0119] At box 468, the predictive graph generator 212 outputs a functional predictive sensor data graph 436. At box 470, the predictive graph generator 212 can be configured to present the graph to operator 260 or another user, and for possible interaction by operator 260 or another user. At box 472, the predictive graph generator 212 can be configured to use the graph for control system 214. At box 474, the predictive graph generator 212 can provide the functional predictive sensor data graph 436 to control area generator 213 for the generation and merging of control areas. At box 476, the predictive graph generator 212 configures the functional predictive sensor data graph 436 in other ways. The functional predictive sensor data graph 436 (with or without control areas) can be presented to operator 260 or another user, or it can be provided to control system 214.

[0120] At block 478, control system 214 then generates control signals based on functional predictive sensor data map 436 (or functional predictive sensor data map 436 with a control area) and input from geographic location sensor 204 to control one or more controllable subsystems. For example, when functional predictive sensor data map 436 or functional predictive sensor data map 436 containing a control area is provided to control system 214. Header / reel controller 238 or other controller 246 generates control signals in response to control machine / header actuator 248, which may also include actuators for other front-end equipment.

[0121] In another example where the control system 214 receives functional predictive sensor data (Figure 436) or has a control area added to the functional predictive sensor data (Figure 436), the controller 232 is configured to control the propulsion subsystem 250 (shown as...). Figure 2 (One of the controllable subsystems 216 in the system).

[0122] In another example where the control system 214 receives predictive sensor data diagram 436 or adds a predictive sensor data diagram 436 with a control area, the path planning controller 234 controls the steering subsystem 252 to steer the agricultural harvester 100. In another example where the control system 214 receives predictive sensor data diagram 436 or adds a predictive sensor data diagram 436 with a control area, the residue system controller 244 controls the residue subsystem 138. In another example where the control system 214 receives predictive sensor data diagram 436 or adds a predictive sensor data diagram 436 with a control area, the setting controller 232 controls the threshing settings of the threshing machine 110. In another example where the control system 214 receives predictive sensor data diagram 436 or adds a predictive sensor data diagram 436 with a control area, the setting controller 232 or another controller 246 controls the material handling subsystem 125. In another example where the control system 214 receives predictive sensor data (Figure 436) or adds a control area to predictive sensor data (Figure 436), controller 232 controls the crop cleaning subsystem. In another example where the control system 214 receives predictive sensor data (Figure 436) or adds a control area to predictive sensor data (Figure 436), machine cleaning controller 245 controls the cleaning subsystem 254 on the agricultural harvester 100. In another example where the control system 214 receives predictive sensor data (Figure 436) or adds a control area to predictive sensor data (Figure 436), communication system controller 229 controls the communication system 206. In another example where the control system 214 receives predictive sensor data (Figure 436) or adds a control area to predictive sensor data (Figure 436), operator interface controller 231 controls the operator interface mechanism 218 on the agricultural harvester 100. In another example where the control system 214 receives functional predictive sensor data (Figure 436) or has added control areas, the platform position controller 242 controls the machine / header actuator to control the platform on the agricultural harvester 100. In another example where the control system 214 receives functional predictive sensor data (Figure 436) or has added control areas, the conveyor belt controller 240 controls the machine / header actuator to control the conveyor belt on the agricultural harvester 100. In another example where the control system 214 receives functional predictive sensor data (Figure 436) or has added control areas, other controllers 246 control other controllable subsystems 256 on the agricultural harvester 100.

[0123] Figure 8 This is a block diagram showing some examples of the real-time (field) sensor 208. Figure 8Some of the sensors shown, or different combinations thereof, may simultaneously have sensor 402 and processing system 406, while others may be used as the above relative to Figure 6 and Figure 7 The sensor 402, wherein Figure 6 and Figure 7 In this context, the processing system 406 is separate. Figure 8 Some of the possible field sensors 208 shown are relative to the previous ones. Figure 1-7 They are shown and described, and similarly numbered. Figure 8 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.

[0124] 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 that senses machine settings. (See above references) Figure 1Examples of machine setups are described. A front-end equipment (e.g., header) position sensor 493 can sense the position of the header 102, reel 164, cutter 104, or other front-end equipment 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 equipment (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 motions (and amplitude) of the harvester 100. Machine sensor 482 may also include a residue setup 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 device fan speed sensor 551 that senses the speed of the cleaning fan 120. Machine sensor 482 may include a recess gap sensor 553 for sensing the gap between the rotor 112 and the recess 114 on the harvester 100. Machine sensor 482 may include a husk sieve gap sensor 555 for sensing the size of the openings in the husk sieve 122. Machine sensor 482 may include a threshing rotor speed sensor 557 for sensing the rotor speed of the rotor 112. Machine sensor 482 may include a rotor pressure sensor 559 for sensing the pressure used to drive the rotor 112. Machine sensor 482 may include a sieve gap sensor 561 for sensing the size of the openings in the sieve 124. Machine sensor 482 may include a MOG humidity sensor 563 for sensing the humidity level of the MOG passing through the harvester 100. Machine sensor 482 may include a machine orientation sensor 565 for sensing the orientation of the harvester 100. Machine sensor 482 may include a material feed rate sensor 567 for sensing the feed rate of material as it travels through the feed chamber 106, the clean grain elevator 130, or other locations within the harvester 100. Machine sensor 482 may include a biomass sensor 569 that senses biomass traveling through feed chamber 106, separator 116, or other locations within the harvester 100. Machine sensor 482 may include a fuel consumption sensor 571 that senses the rate of fuel consumption of the harvester 100 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 utilizing power), or the rate at which subsystems are utilizing power, or the distribution of power among subsystems within 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 575).The machine performance sensor and machine characteristic sensor 575 can sense the machine performance or characteristics of the agricultural harvester 100.

[0125] While the crop material is being processed by the agricultural harvester 100, the post-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.

[0126] 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.

[0127] 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 obfuscation or other environmental features.

[0128] Figure 9 A block diagram illustrating an example of a control region generator 213 is shown. The control region generator 213 includes a work machine actuator (WMA) selector 486, a control region generation system 488, and a regime region generation system 490. The control region generator 213 may also include other items 492. The control region generation system 488 includes a control region standard identifier component 494, a control region boundary definition component 496, a target setting identifier component 498, and other items 520. The regime region generation system 490 includes a dynamic region standard identifier component 522, a dynamic region boundary definition component 524, a set resolver identifier component 526, and other items 528. Before describing the overall operation of the control region generator 213 in more detail, a brief description of some of the items in the control region generator 213 and their corresponding operations will first be provided.

[0129] 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 a function predictive map, or WMAs can be controlled in groups based on one or more values ​​on the function predictive map. Therefore, the control region generator 213 can generate control regions corresponding to each individual controllable WMA, or corresponding to groups of WMAs with coordinated control.

[0130] 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 boundary 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 identifying the boundary 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 function predictive graph in the analysis to define the boundary of the control region on the function predictive graph based on the values ​​on the function predictive graph in the analysis and based on the control region criteria of the selected WMA or WMA group.

[0131] The target setting identifier component 498 sets the value of the target setting, which will be used to control the WMA or WMA group in different control regions. For example, if the selected WMA is the propulsion system 250 and the functional predictive map in the analysis is the functional predictive speed map 438, then the target setting in each control region can be a target speed setting based on the speed values ​​contained in the functional predictive speed map 238 in the identified control region.

[0132] In some examples, when controlling the harvester 100 based on its current or future position, multiple target settings are possible for the WMA at a given position. In this case, the target settings may have different values ​​and may compete with each other. Therefore, it is necessary to resolve the target settings so that only a single target setting is used to control the WMA. For example, in the case where the WMA is an actuator controlled in the propulsion system 250 to control the speed of the harvester 100, there may be multiple different competing sets of criteria, which are considered by the control region generation system 488 when identifying the control region and the target settings of the WMA selected in the control region. For example, different target settings for controlling machine speed may be generated based on, for example, detected or predicted feed rate values, detected or predicted fuel efficiency values, detected or predicted grain loss values, or combinations of these values. However, at any given time, the harvester 100 cannot travel on the ground at multiple speeds simultaneously. Instead, at any given time, the harvester 100 travels at a single speed. Therefore, one of the competing target settings is selected to control the speed of the agricultural harvester 100.

[0133] Therefore, in some examples, the dynamic region generation system 490 generates dynamic regions to resolve multiple different competing target settings. The dynamic region criterion identification component 522 identifies the criteria used to establish dynamic regions on the selected WMA or WMA group on the functional predictive map in the analysis. Some criteria that can be used to identify or define dynamic regions include, for example, crop type or crop species based on a planting map or another source based on crop type or crop species, weed type, weed intensity, or crop state (such as whether the crop is lodged, partially lodged, or upright). Just as each WMA or WMA group may have a corresponding control region, different WMAs or WMA groups may have corresponding dynamic regions. The dynamic region boundary definition component 524 identifies the boundaries of the dynamic regions on the functional predictive map in the analysis based on the dynamic region criteria identified by the dynamic region criterion identification component 522.

[0134] In some examples, dynamic regions may overlap. For instance, a crop type dynamic region may partially or completely overlap with a crop state dynamic region. In such examples, different dynamic regions can be assigned priority levels such that, in the case of two or more overlapping dynamic regions, the dynamic region assigned a higher priority level or importance takes precedence over the dynamic region with a lower priority level or importance. The priority levels of dynamic regions can be set manually or automatically using rule-based systems, model-based systems, or other systems. As an example, in the case of an overlap between a lodged crop dynamic region and a crop type dynamic region, the lodged crop dynamic region can be assigned greater importance in the priority level than the crop type dynamic region, thus giving priority to the lodged crop dynamic region.

[0135] 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 predictive plot in the analysis, and for a specific setting resolver for the selected WMA or WMA group.

[0136] Once a setting resolver is identified for a specific dynamic region, it can be used to resolve competing target settings, in which more than one target setting is identified based on the control region. Different types of setting resolvers can take different forms. For example, a setting resolver for each dynamic region may include a human-selected resolver, in which the competing target setting is presented to an operator or other user for resolution. In another example, the setting resolver may include a neural network or other artificial intelligence or machine learning system. In this case, the setting resolver can resolve competing target settings based on a predicted or historical quality metric corresponding to each of the different target settings. As an example, an increased vehicle speed setting may reduce harvesting time and corresponding time-based labor and equipment costs, but may increase grain loss. A decreased vehicle speed setting may increase harvesting time and corresponding time-based labor and equipment costs, but may reduce grain loss. When grain loss or harvesting time is selected as a quality metric, given two competing vehicle speed setting values, the predicted or historical value of the selected quality metric can be used to resolve the speed setting. In some cases, setting the parser can be a set of threshold rules that can be used to replace or supplement dynamic regions. Examples of threshold rules can be expressed as follows:

[0137] If the predicted biomass value within 20 feet of the header of the agricultural harvester 100 is greater than x (where x is the selected or predetermined value), the target setting value selected based on the feed rate rather than other competing target settings is used; otherwise, the target setting value based on grain loss rather than other competing target settings is used.

[0138] A setting parser can be a logical component that executes logical rules when identifying a target setting. For example, a setting parser can parse a target setting 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 settings. Harvesting time can be minimized when the amount harvested is reduced to or below a selected threshold. Total harvest cost can be minimized when it is reduced to or below a selected threshold. Harvested grain can be maximized when the amount harvested is increased to or above a selected threshold.

[0139] Figure 10 This is a flowchart illustrating an example of the operation of the control region generator 213 when it receives a graph for region processing (e.g., a graph in analysis) and generates control regions and dynamic regions.

[0140] At box 530, control region 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 predictive graph. For example, the graph in the analysis could be one of functional predictive graphs 436, 437, 438, or 440. Box 534 indicates that the graph in the analysis could also be other graphs.

[0141] At box 536, WMA selector 486 selects the WMA or WMA group for which a control region will be generated on the graph in the analysis. At box 538, control region criterion identification component 494 obtains the control region definition criteria for the selected WMA or WMA group. Box 540 indicates an example where the control region criteria are or include the wear characteristics of the selected WMA or WMA group. Box 542 indicates an example where the control region definition criteria are or include the amplitude and variation of input source data, such as the amplitude and variation of values ​​on the graph in the analysis or the amplitude and variation of inputs from various field sensors 208. Box 544 indicates an example where the control region definition 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 region definition 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 region definition criteria are or include machine performance metrics. Box 550 indicates an example where the control area definition criterion is or includes operator preferences. Box 552 indicates an example where the control area definition criterion is also or includes other items. Box 549 indicates an example where the control area definition criterion is time-based, meaning that the harvester 100 will not cross the boundary of the control area until a selected amount of time has elapsed since the harvester 100 entered the specific control area. In some cases, the selected amount of time may be a minimum amount of time. Therefore, in some cases, the control area definition criterion can prevent the harvester 100 from crossing the boundary of the control area until at least the selected amount of time has elapsed. Box 551 indicates an example where the control area definition criterion is based on a selected size value. For example, a control area definition criterion based on a selected size value can exclude the definition of control areas smaller than the selected size. In some cases, the selected size may be a minimum size.

[0142] At box 554, the dynamic zone standard identification component 522 obtains the dynamic zone definition standard of the selected WMA or WMA group. Box 556 indicates an example where the dynamic zone definition standard is based on manual input from operator 260 or another user. Box 558 shows an example where the dynamic zone definition standard is based on crop type or crop variety. Box 560 shows an example where the dynamic zone definition standard is based on weed type or weed intensity or both. Box 562 shows an example where the dynamic zone definition standard is based on or includes crop status. Box 564 indicates an example where the dynamic zone definition standard is also or includes other standards.

[0143] At box 566, the control region boundary definition component 496 generates the boundary of the control region on the graph in the analysis based on control region criteria. The dynamic region boundary definition component 524 generates the boundary of the dynamic region on the graph in the analysis based on dynamic region criteria. Box 568 indicates an example in which the boundaries of the control region and the dynamic region are identified. Box 570 shows that the target setting identifier component 498 identifies the target settings for each in the control region. The control region and the dynamic region can also be generated in other ways, and this is indicated by box 572.

[0144] 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 definition 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.

[0145] 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 for which a control region and dynamic region are defined is selected. When no additional WMAs or WMA groups remain for which a control region or dynamic region is to be generated, processing moves to box 590, where control region generator 213 outputs a graph for each WMA or WMA group, containing a control region, target settings, dynamic region, and settings resolver. 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.

[0146] Figure 11An example is shown of a control system 214 controlling the operation of an agricultural harvester 100 based on a map output by a control area generator 213. Thus, at box 592, the control system 214 receives a map of the work site. In some cases, this map may be a functional predictive map that can include control areas and dynamic areas (as shown in box 594). In some cases, the received map may be a functional predictive map that excludes control areas and dynamic areas. Box 596 indicates an example where the received work site map may be an information map with control areas and dynamic areas identified thereon. 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.

[0147] At box 612, control system 214 receives sensor signals from geolocation sensor 204. Sensor signals from geolocation sensor 204 may include data indicating the geolocation 614 of harvester 100, the speed 616 of harvester 100, the heading 618 of harvester 100, or other information 620. At box 622, area controller 247 selects a dynamic area, and at box 624, area controller 247 selects a control area on a map based on the geolocation sensor signals. At box 626, area controller 247 selects a WMA or WMA group to be controlled. At box 628, area controller 247 obtains one or more target settings for the selected WMA or WMA group. The target settings obtained for the selected WMA or WMA group can come from 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 sensor 208. Box 636 illustrates an example where one or more target settings are obtained from one or more sensors on other machines operating simultaneously with agricultural harvester 100 in the same field, or from one or more 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.

[0148] At box 640, the zone controller 247 accesses the settings resolver for the selected dynamic zone and controls the settings resolver to parse the competing target settings into a resolved target setting. As discussed above, in some cases, the settings resolver may be a human resolver, in which case the zone controller 247 controls the operator interface mechanism 218 to present the competing target settings to the operator 260 or another user for resolution. In some cases, the settings resolver may be a neural network or other artificial intelligence or machine learning system, and the zone controller 247 submits the competing target settings to the neural network, artificial intelligence, or machine learning system for selection. In some cases, the settings resolver may be based on predicted or historical quality metrics, threshold rules, or logic components. In any of these later examples, the zone controller 247 executes the settings resolver to obtain the resolved target setting based on predicted or historical quality metrics, threshold rules, or, when using logic components.

[0149] At box 642, if the area 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 the control signals to the selected WMA or WMA group. For example, if the selected WMA is a machine or header actuator 248, the area 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 box 644, if an additional WMA or additional WMA group is to be controlled at the current geographic location of the agricultural harvester 100 (as detected at box 612), the process returns to box 626, where the next WMA or WMA group is selected. The process represented by boxes 626 to 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 WMAs or WMA groups remain 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 there is an additional dynamic area to be considered. The area controller 247 determines whether there is an additional dynamic area to be considered. If there is an additional dynamic area to be considered, the process returns to box 622, where the next dynamic area is selected.

[0150] At box 650, the area controller 247 determines whether the operation being performed by the harvester 100 has been completed. If not, the area controller 247 determines whether control area criteria have been met to continue processing, as shown in box 652. For example, as mentioned above, control area definition criteria may include criteria defining when the harvester 100 can cross the control area boundary. For example, whether the harvester 100 can cross the control area boundary may be defined by a selected time period, meaning that the harvester 100 is prevented from crossing the boundary until the selected amount of time has elapsed. In this case, at box 652, the area controller 247 determines whether the selected time period has elapsed. Additionally, the area controller 247 can perform processing continuously. Therefore, the area 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, the area controller 247 determines that it is time to continue processing, and then processing continues at box 612, where the area controller 247 again receives input from the geolocation sensor 204. It should also be understood that the area 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.

[0151] Figure 12 This is a block diagram illustrating an example of an operator interface controller 231. In the illustrated example, 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 processing system 662, a touch gesture processing 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 unit 674, a synthesis unit 676, a natural language understanding system 678, a dialogue management system 680, and other items 682. The motion signal generator 660 includes a visual control signal generator 684, an audio control signal generator 686, a tactile control signal generator 688, and other items 690. Figure 12 Before processing various operator interface actions, the example operator interface controller 231 shown in the figure first provides a brief description of some of the items in the operator interface controller 231 and their associated operations.

[0152] The operator input command processing system 654 detects operator input on the operator interface mechanism 218 and processes these command inputs. The voice processing system 662 detects voice input and processes interaction with the voice processing system 658 to process voice command inputs. The touch gesture processing system 664 detects touch gestures on touch-sensitive elements in the operator interface mechanism 218 and processes these command inputs.

[0153] Other controller interaction system 656 handles interactions 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 input, determines the meaning of these inputs, and provides outputs indicating the meaning of the spoken input. For example, voice processing system 658 can recognize voice input from operator 260 as a setting change command, where operator 260 is instructing control system 214 to change the settings of controllable subsystem 216. In such an example, voice processing system 658 recognizes the content of the spoken command, identifies the meaning of the command as a setting change command, and provides the meaning of the input back to voice processing system 662. Voice processing system 662 then interacts with controller output generator 670 to provide command outputs to the appropriate controllers in control system 214 to complete the spoken setting change command.

[0154] The voice processing system 658 can be invoked in various ways. For example, in one example, the voice processing 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 processing 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 processing system 662, the voice recognition unit 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 spoken word (referred to as a wake-up word). In such an example, when the recognition unit 674 recognizes the wake-up word, the recognition unit 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 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.

[0155] Once the speech processing system 658 is invoked, speech input from operator 260 is provided to the speech recognition unit 674. The speech recognition unit 674 recognizes linguistic elements in the speech input, such as words, phrases, or other linguistic units. The 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, the natural language understanding system 678 and the speech processing system 568 can understand the meaning of speech recognized in the environment of the agricultural harvester 100.

[0156] In some examples, the voice processing system 658 can also generate output for navigating operator 260 through user experience based on voice input. For example, the dialogue management system 680 can generate and manage dialogues with users to identify what the user wants to do. The dialog box can disambiguate user commands, identify one or more specific values ​​required to execute user commands, or obtain other information from or provide other information to the user, or both. The synthesis component 676 can generate speech synthesis, which can be presented to the user through an audio operator interface mechanism such as a speaker. Therefore, dialogues managed by the dialogue management system 680 can be exclusively verbal dialogues or a combination of visual and verbal dialogues.

[0157] 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 or force feedback to the operator through the operator interface mechanism. Tactile elements may also include a wide variety of other tactile elements.

[0158] Figure 13 This is a flowchart illustrating an example of the operation of the operator interface controller 231 when generating an operator interface display on an operator interface mechanism 218 that may include a touch-sensitive display screen. Figure 13 An example of how the operator interface controller 231 can detect and process operator interactions with the touch-sensitive display is also shown.

[0159] At box 692, operator interface controller 231 receives a graph. Box 694 indicates that the graph is an example of a functional predictive 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.

[0160] At box 706, the vision control signal generator 684 in the operator interface controller 231 controls the touch-sensitive display screen 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 showing 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 identifying 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, while box 714 indicates an example where the displayed field includes a previously visited display showing areas of the field after the harvester 100 has processed. Box 716 indicates an example where the displayed field shows various characteristics of the field having a geographic reference location on the map. For example, if the received map is a weed map, the displayed fields can show the different types of weeds present in the georeferenced fields within the displayed fields. Mapping characteristics can be shown in previously visited areas (as shown in box 714), upcoming areas (as shown in box 712), and the next work unit (as shown in box 710). Box 718 indicates that the fields displayed therein also include examples of other items.

[0161] Figure 14 This illustration shows an example of a user interface display 720 that can be generated on a touch-sensitive display screen. In other embodiments, the user interface display 720 can be generated on other types of displays. The touch-sensitive display screen can be installed in the operator's cab of an agricultural harvester 100, on a mobile device, or elsewhere. (Continuing the description...) Figure 13 Before the flowchart shown, the user interface display 720 will be described.

[0162] exist Figure 14In 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 coupled 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, soft keypads, links, icons, switches, etc. Operator 260 can actuate the user interface control actuators to perform various functions.

[0163] exist Figure 14 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 the harvester 100 is operating. The field display portion 728 is shown having a current position marker 708 corresponding to the current position of the harvester 100 in the portion of the field shown in the field display portion 728. In one example, the operator can control the touch-sensitive display to zoom in on multiple portions 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 field area 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.

[0164] 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, the area of ​​the next work unit 730 may be larger when the harvester 100 travels faster compared to when the harvester 100 travels slower. The field display section 728 is also shown to display previously visited areas 714A, 714B, 714C, and 714D and upcoming areas 712A, 712B, 712C, and 712D. The previously visited areas 714A, 714B, 714C, and 714D represent areas that have already been harvested, while the upcoming areas 712A, 712B, 712C, 712D, and 712E represent areas that still need to be harvested. The field display section 728 is also shown to display different characteristics of the field. Figure 14In the example shown, the graph being displayed is a speed graph. Therefore, multiple different speed markers are displayed on the field display section 728. A set of speed display markers 732 is shown in the already visited areas 714A, 714B, 714C, and 714D. A set of speed display elements 734 is also shown in the upcoming areas 712A, 712B, 712C, 712D, and 712E, and a set of speed display elements 736 is shown in the next work unit 730. Figure 14 The speed display elements 732, 734, and 736 are shown to be composed of different symbols. Each symbol represents a speed range. Figure 14 In the example shown, the @ symbol represents a low-speed range of less than 3.5 mph; the * symbol represents a medium-speed range of 3.5 mph to 5.5 mph; and the # symbol represents a high-speed range of greater than 5.5 mph. Therefore, the field display section 728 shows different speed ranges located in different areas within the field. As previously described, display elements 732 can be composed of different symbols, and as described below, symbols can be any display feature, such as different colors, shapes, patterns, intensities, text, icons, or other display features. In some cases, each location in the field can have a display marker associated with that location. Therefore, in some cases, display markers can be provided at each location of the field display section 728 to identify the nature of the characteristics being mapped for each specific location in the field. Therefore, this disclosure includes providing display markers (e.g., loss level display markers 732, as shown in...) at one or more locations on the field display section 728. Figure 11 In the context of this example, the nature, degree, etc. of the characteristic being displayed are identified, thereby identifying the characteristic at the corresponding location in the field being displayed.

[0165] exist Figure 14 In the example, the user interface display 720 also includes a current speed indicator 721 that displays the current speed of the agricultural harvester 100 and 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.

[0166] The actuators and display elements in part 738 can be displayed as, for example, individual items, fixed lists, scrollable lists, drop-down menus, or drop-down lists. Figure 14 In the example shown, display portion 738 displays information corresponding to three different speed ranges for the three symbols mentioned above. Display portion 738 also includes a set of touch-sensitive actuators that operator 260 can interact with by touch. For example, operator 260 can touch a touch-sensitive actuator with a finger to activate the corresponding actuator.

[0167] The marker column 739 displays markers that have been set automatically or manually. The marker actuator 740 allows operator 260 to mark the current position and then add information indicating the speed at which the harvester 100 is traveling at that current position. For example, when operator 260 actuates the marker actuator 740 by touching it, the touch gesture processing system 664 in the operator interface controller 231 identifies the current position as a position with a low speed range. When operator 260 touches button 742, the touch gesture processing system 664 identifies the current position as a position with a medium speed range. When operator 260 touches button 744, the touch gesture processing system 664 identifies the current position as a position with a high speed range. The touch gesture processing system 664 also controls the visual control signal generator 684 to add a symbol corresponding to the identified speed on the field display section 728 at the user-identified position before, after, or during the actuation of buttons 740, 742, or 744.

[0168] Column 746 displays a symbol corresponding to each speed range tracked on the field display section 728. Column 748 shows a designator (which can be a text designator or other designator) identifying the speed range. Without limitation, the speed range symbols in column 746 and the designators in column 748 can include any display features, such as different colors, shapes, patterns, intensities, text, icons, or other display features. Column 750 displays the speed range values. Figure 14 In the example shown, the speed range values ​​are speed values ​​in miles per hour corresponding to each speed range. Column 752 displays the action threshold. The action threshold in column 752 can be a threshold distance indicating where an action should be taken (in column 754 described below) as the harvester 100 moves through the field. In one example, the action identified in column 754 is taken when the harvester 100 is within 10 feet of the low-speed range area entering the field. In some examples, operator 260 can select the threshold, for example, to change the threshold by touching it in column 752. Once selected, operator 260 can change the threshold. The threshold in column 752 can be configured such that a specified action is performed when the measured distance value exceeds, is equal to, or is less than the threshold.

[0169] Similarly, operator 260 can touch the action identifier in column 754 to change the action to be taken. Multiple actions can be taken when a threshold is met. For example, at the bottom of column 754, increasing fan speed and raising the cutting table are identified as actions to be taken if the measured distance value meets the threshold in column 752.

[0170] 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 the area. These actions can include a mitigation activation that, when executed, performs a mitigation action. These actions can include setting change actions for altering the settings of an internal actuator or another WMA or WMA group, or setting change actions for implementing changes to the header settings. These are merely examples, and a wide variety of other actions are considered herein.

[0171] The display elements shown on the user interface display 720 can be visually controlled. Visually controlling the interface display 720 can be performed to capture the attention of the operator 260. For example, the display elements can be controlled to modify the intensity, color, or pattern of the displayed elements. Additionally, the display elements can be controlled to blink. As an example, a description of changes to the visual appearance of the display elements is provided. Therefore, other aspects of the visual appearance of the display elements can be changed. Thus, the display elements can be modified in a desired manner in various situations to, for example, capture the attention of the operator 260.

[0172] Various functions that can be performed by operator 260 using user interface display 720 can also be performed automatically, such as through other controllers in control system 214. For example, it is possible that the agricultural harvester 100 always travels at a speed of 5.0 mph to 6.0 mph. In this case, operator interface controller 231 can add a new speed range, such as a "high-speed range" of 5.0 mph to 6.0 mph, and can automatically add a flag at the current position of the agricultural harvester 100 (corresponding to the position of the new speed range), generating a display in the flag column, generating a corresponding symbol in the symbol column, and generating a specifier in specifier column 748. When identifying different speed ranges, operator interface controller 231 can also generate speed range values ​​in column 750 and thresholds in column 752. Operator interface controller 231 or another controller can also automatically identify actions added to column 754.

[0173] Now back Figure 13The flowchart continues to describe the operation of the operator interface controller 231. At block 760, the operator interface controller 231 detects input for setting a flag and controls the touch-sensitive user interface display 720 to display the flag on the field display section 728. The detected input can be operator input (as shown in 762) or input from another controller (as shown in 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 control signals 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 flags and modify the characteristics of these flags. For example, the user can modify the speed range corresponding to a flag. Block 772 indicates that the action thresholds in column 752 are displayed. Box 776 indicates that the action in column 754 is displayed, and box 778 indicates that the field data of the measurement in column 750 is displayed. Box 780 indicates that various other information and actuators can also be displayed on the user interface display 720.

[0174] At box 782, the operator input command processing system 654 detects and processes operator input corresponding to the interaction performed by operator 260 with user interface display 720. If the user interface mechanism displayed on user interface display 720 is a touch-sensitive display, the interaction input performed by operator 260 with the touch-sensitive display can be a touch gesture 784. In some cases, operator interaction input can be input using a clicking device 786 or other operator interaction input 788.

[0175] At box 790, operator interface controller 231 receives a signal indicating an alarm condition. For example, box 792 indicates that the signal may be received by controller input processing system 668, indicating that the detected value meets a threshold condition present in column 752. As previously explained, threshold conditions may include values ​​below, at, or above a threshold. Box 794 shows that action signal generator 660 may alert operator 260 in response to receiving an alarm condition by generating 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. Similarly, as shown in box 796, controller output generator 670 may 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 may also detect and process alarm conditions in other ways.

[0176] Box 900 illustrates that the voice processing 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 a signal to the controller output generator 670 to automatically perform control operations based on the voice input.

[0177] 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 voice 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".

[0178] Table 1

[0179] Operator: "Johnny, tell me about the current speed."

[0180] Operator interface controller: "Current speed is 3.7 mph in the medium range and the threshold is 20 feet. The high-speed range is approaching and is 120 feet away."

[0181] Operator: "Johnny, what should I do because a speed change is approaching?"

[0182] Operator interface controller: "Increase fan speed by 10% and raise the cutting table by 10%".

[0183] Table 2 illustrates an example in which the speech synthesis unit 676 provides output to the audio control signal generator 686 to provide auditory 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 is greater than a threshold).

[0184] Table 2

[0185] Operator interface controller: "The driving speed has reached 5.0 mph in the past 10 minutes."

[0186] Operator interface controller: "The next 1 acre of land includes the highway range."

[0187] Operator interface controller: "Warning: Current speed is higher than the expected speed range. Reduce machine speed to 5.0 mph."

[0188] Operator interface controller: "Attention: Biomass too high. Machine speed reduced to 4.0 mph."

[0189] 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 high-speed ranges in a field being harvested.

[0190] Table 3

[0191] Human: "Johnny, mark the high-speed range."

[0192] Operator interface controller: "High-speed range marked".

[0193] The example shown in Table 4 illustrates that the motion signal generator 660 can communicate with the operator 260 to start and end high-speed range marking.

[0194] Table 4

[0195] Human: "Johnny, start marking the high-speed range."

[0196] Operator interface controller: "Marking high-speed range".

[0197] Human: "Johnny, stop marking high-speed range."

[0198] Operator interface controller: "High-speed range marker stop".

[0199] The examples shown in Table 5 illustrate that the motion signal generator 160 can generate signals for marking high-speed ranges in a manner different from that shown in Tables 3 and 4.

[0200] Table 5

[0201] Human: "Johnny, mark the next 100 feet as the high-speed range."

[0202] Operator interface controller: "The next 100 feet is marked as high-speed range".

[0203] Return again Figure 13 Box 906 illustrates that the operator interface controller 231 can also detect and process situations for outputting messages or other information in other ways. For example, the other controller interaction system 656 can detect input from other controllers indicating alarms or output messages that should be presented to the operator 260. Box 908 illustrates that the output can be an audio message. Box 910 illustrates that the output can be a visual message, and Box 912 illustrates that the output can be a tactile message. Until the operator interface controller 231 determines that the current harvesting operation is complete (as shown in Box 914), processing returns to Box 698, where the geographical location of the harvester 100 is updated, and processing continues as described above to update the user interface display 720.

[0204] Once the operation is complete, any desired values ​​displayed or already displayed on the user interface display 720 can be saved. These values ​​can also be used in machine learning to improve different parts of the predictive model generator 210, predictive graph generator 212, control region generator 213, control algorithm, or other components. The desired values ​​are saved as 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.

[0205] As can be seen, the infographic is obtained by an agricultural harvester and shows the machine speed values ​​at different geographical locations in the field being harvested. Field sensors on the harvester sense characteristics as the harvester moves through the field. A predictive map generator produces a predictive map based on the machine speed values ​​in the infographic and the characteristics sensed by the field sensors. This predictive map predicts control values ​​for different locations in the field. The control system controls the controllable subsystems based on the control values ​​in the predictive map.

[0206] 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, such as a functional predictive graph. A control value can also include any characteristic indicated by or derived from a value detected by any of the sensors described herein. 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.

[0207] The current discussion has already mentioned processors and servers. 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 and facilitated by other components or items in these systems.

[0208] Furthermore, numerous user interface displays have been discussed. Displays can take various forms and can have various user-actuable operator interface structures set upon 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, user-actuable operator interface mechanisms 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 user-actuable operator interface mechanism. Moreover, user-actuable operator interface mechanisms can be actuated using voice commands utilizing speech recognition capabilities. Speech recognition can be implemented using speech detection devices (such as microphones) and software for recognizing the detected speech and executing commands based on the received speech.

[0209] Many data storage devices are also discussed. It should be noted that data storage 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 devices; one or more data storage devices may all be located remotely from the system utilizing the data storage devices; or one or more data storage devices may be local while others are remote. This disclosure considers all of these configurations.

[0210] Furthermore, the accompanying diagram shows multiple boxes, with each function belonging to a specific box. It should be noted that fewer boxes can be used to illustrate that functions attributed to multiple different boxes are performed by fewer components. Moreover, more boxes can be used to show that the function can be distributed across more components. In different examples, some functions can be added, and some functions can be removed.

[0211] It should be noted that the foregoing discussion has described various different systems, components, logics, and interactions. It should be understood that any or all of such systems, components, logics, and interactions can be implemented by hardware items such as processors, memory, or other processing units, including but not limited to artificial intelligence units (such as neural networks, some of which are described below) that perform functions associated with those systems, components, logics, or interactions. Furthermore, any or all of the systems, components, logics, and interactions can be implemented by software loaded into memory and subsequently executed by a processor or server or other computing unit, as described below. Any or all of the systems, components, logics, 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, logics, and interactions described above. Other structures may also be used. Figure 15 This is a block diagram of the 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 without requiring end users to know the physical location or configuration of the system delivering the services. In various examples, the remote server can deliver 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 via a web browser or any other computing component. Figure 2 The software or components shown herein, along with their associated data, can be stored on servers at remote locations. Computing resources in a remote server environment can be consolidated at a remote data center location, or they can be distributed across multiple remote data centers. Remote server infrastructure can deliver services through a shared data center, even if the service appears as a single access point to the user. Therefore, the components and functions described herein can be provided from remote servers at remote locations using a remote server architecture. Alternatively, components and functions can be provided from servers, or they can be installed directly or otherwise on client devices.

[0212] exist Figure 15 In the examples shown, some items are similar to Figure 2The items shown are numbered similarly. Figure 15 Specifically, a predictive model generator 210 or a predictive graph generator 212, or both, can be located at a server location 502, away from the agricultural harvester 600. Therefore, in Figure 15 In the example shown, the agricultural harvester 600 accesses the system via a remote server location 502.

[0213] Figure 15 Another example of a remote server architecture is also described. Figure 15 It shows Figure 2 Some components can be located at remote server location 502, while others can be located elsewhere. As an example, data storage device 202 can be placed at a location separate from location 502 and accessed via a remote server at location 502. Regardless of their location, these components can be directly accessed by the combine harvester 600 via a network (such as a wide area network or local area network), hosted by a service at a remote site, 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 or by an operator, user, or system. For example, a physical carrier can be used in addition to a physical carrier. In some examples, where wireless telecommunications service coverage is poor or absent, 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 the 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. Then, when the machine containing the received information reaches a location where wireless telecommunications service coverage or other wireless coverage is available, the collected information can be forwarded to another network. For example, when a 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 agricultural harvester 600 until the agricultural harvester 600 enters an area with wireless communication coverage. The agricultural harvester 600 itself can transmit the information to another network.

[0214] It will also be noted that Figure 2 The components or parts thereof can be located 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.

[0215] In some examples, the remote server architecture 500 may include network security measures. Without limitation, these measures include encryption of data on storage devices, encryption of data sent between network nodes, authentication of personnel or processes accessing data, and the use of a ledger for recording 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).

[0216] Figure 16 This is a simplified block diagram illustrating a schematic example of a handheld or mobile computing device 16 that can be used as a user's or customer's handheld device in which this system (or part thereof) may be deployed. For example, a mobile device could be deployed in the operator's cab of an agricultural harvester 100 for use in generating, processing, or displaying the diagrams discussed above. Figures 17 to 18 Examples are handheld or mobile devices.

[0217] Figure 16 A general block diagram of the components of client device 16, which can operate... Figure 2 The device 16 includes some of the components shown, interacts with them, 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, the communication link 13 provides a channel for automatically receiving information (e.g., by scanning). Examples of the communication link 13 include those that allow 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.

[0218] In other examples, the application can receive data 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 implemented from a processor or server in other figures), which is also connected to memory 21 and input / output (I / O) components 23, as well as clock 25 and position system 27.

[0219] In one example, I / O components 23 are provided to facilitate input and output operations. Various examples of I / O components 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.

[0220] Clock 25 schematically includes a real-time clock component that outputs the time and date. Schematically, it may also provide timing functions for processor 17.

[0221] Location system 27 schematically includes components that output the current geographic location of device 16. This 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. Location system 27 may also include, for example, mapping or navigation software that generates desired maps, navigation routes, and other geographic functions.

[0222] 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 their functionality.

[0223] Figure 17 The illustration shows an example where device 16 is a tablet computer 600. Figure 16 In the diagram, computer 601 is shown as having a user interface display screen 602. Screen 602 can be a touchscreen or a pen-enabled interface that receives input from a pen or stylus. Tablet 600 can also utilize an on-screen virtual keyboard. Of course, computer 601 can also be attached to a keyboard or other user input device, for example, via a suitable attachment structure (such as a wireless link or USB port). Computer 601 can also schematically receive voice input.

[0224] Figure 18 Similar to Figure 17 In addition to being a smartphone 71, the smartphone 71 has a touch-sensitive display 73 that shows icons, blocks, or other user input mechanisms 75. Users can use these mechanisms 75 to run applications, make calls, perform data transfer operations, etc. Generally, smartphones 71 are built on a mobile operating system and offer more advanced computing power and connectivity than feature phones.

[0225] Note that other forms of device 16 are possible.

[0226] Figure 19 It is one of the deployable ones Figure 2 An example of a computing environment for the components. Reference Figure 19An 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 the 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 couples various system components, including the system memory, to the processing unit 820. The 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 19 In the corresponding part.

[0227] 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 of its characteristics set or changed in a manner that encodes information in the signal.

[0228] System memory 830 includes computer storage media in the form of volatile and / or non-volatile memory, or both, such as read-only memory (ROM) 831 and random access memory (RAM) 832. The basic input / output system 833 (BIOS) (which contains basic routines such as those that help transfer information between components within computer 810 during startup) is typically stored in ROM 831. RAM 832 typically contains data and / or program modules, or both, that are readily accessible to and / or currently being operated by processing unit 820. This is by way of example and not limitation. Figure 18 The operating system 834, application program 835, other program modules 836, and program data 837 are shown.

[0229] Computer 810 may also include other removable / non-removable volatile / non-volatile computer storage media. This is just one example. Figure 19 A hard disk drive 841 is shown that reads from or writes to a non-removable, non-volatile magnetic medium, an optical disk drive 855, and a non-volatile optical disk 856. The hard disk drive 841 is typically connected to the system bus 821 via a non-removable memory interface (such as interface 840), and the optical disk drive 855 is typically connected to the system bus 821 via a removable memory interface (such as interface 850).

[0230] 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 Systems (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.

[0231] The above discussion and Figure 19 The driver and its associated computer storage medium shown provide the computer 810 with storage for computer-readable instructions, data structures, program modules, and other data. For example, in Figure 19In 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.

[0232] 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 keyboards, satellite dishes, scanners, etc. These and other input devices are typically connected to processing unit 820 via user input interface 860 coupled to the system bus, but may also be connected via other interfaces and bus structures. Visual display 891 or other types of display devices are also connected to system bus 821 via an interface such as video interface 890. In addition to the monitor, the computer may also include other peripheral output devices, such as speakers 897 and printers 896, which can be connected via peripheral output interface 895.

[0233] 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)) between one or more remote computers (such as remote computer 880).

[0234] 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 19 This demonstrates that remote application 885 can reside on remote computer 880.

[0235] It should also be noted that the different examples described in this article can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of this is considered in this article.

[0236] 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.

[0237] Example 1 is an agricultural operating machine, comprising:

[0238] A communication system that receives an information map, the information map including machine speed values ​​corresponding to different geographical locations in the field;

[0239] A geolocation sensor that detects the geolocation of agricultural machinery;

[0240] A field sensor that detects agricultural characteristics corresponding to geographical location;

[0241] A predictive map generator generates a functional predictive agricultural map of a field based on machine speed values ​​in an information map and on agricultural characteristic values. The functional predictive agricultural map maps predictive control values ​​to different geographic locations in the field.

[0242] Controllable subsystem; and

[0243] A control system that generates control signals to control a controllable subsystem based on the geographical location of the agricultural machinery and control values ​​in a functional predictive agriculture map.

[0244] Example 2 is any or all of the agricultural machinery described in the preceding examples, wherein the infographic includes:

[0245] A predictive machine speed map, which maps machine speed values ​​as control values ​​to indicate the predictive speed of an agricultural harvester at different locations in the field.

[0246] Example 3 is any or all of the agricultural machinery described in the foregoing examples, and also includes:

[0247] A speed-to-field sensor data model generator generates a predictive sensor data model based on the predictive machine speed value at a geographical location in a predictive machine speed map and the value of the agricultural characteristic corresponding to the geographical location. The predictive sensor data model models the relationship between the predictive machine speed value and the agricultural characteristic.

[0248] Example 4 is any or all of the agricultural operating machines of the foregoing examples, wherein the predictive map generator includes:

[0249] A predictive sensor data map generator generates functional predictive sensor data maps that map predictive values ​​of agricultural characteristics to different geographic locations in the field.

[0250] Example 5 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system includes:

[0251] The controller generates control signals based on geographic location and functional predictive sensor data maps, and controls the controllable subsystem based on the control signals.

[0252] Example 6 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system further includes:

[0253] An operator interface controller generates a user interface representation of a functional predictive agriculture map, the user interface representation including a field portion.

[0254] Example 7 is any or all of the agricultural operating machines of the foregoing examples, wherein the user interface diagram further includes:

[0255] Machine speed symbols indicate the value of machine speed at one or more geographical locations on a field portion.

[0256] Example 8 is any or all of the agricultural operating machines of the foregoing examples, wherein the operator interface controller generates a user interface diagram representation to include an interactive display portion showing the detected agricultural characteristics, an interactive threshold display portion showing the action threshold, and an interactive action indicator showing the control action to be taken when the detected agricultural characteristics meet the action threshold, and the control system generates control signals based on the control actions to control the controllable subsystem.

[0257] Example 9 is a computer-implemented method for controlling agricultural machinery, comprising:

[0258] Obtain an information map containing machine speed values ​​corresponding to different geographical locations in the field;

[0259] Detecting the geographical location of agricultural machinery;

[0260] The values ​​of agricultural characteristics corresponding to the geographical location are detected using on-site sensors;

[0261] Based on machine speed values ​​from the infographic and agricultural characteristic values, a functional predictive agriculture map of the field is generated, which maps predictive control values ​​to different geographical locations within the field; and

[0262] Controllable subsystems are controlled based on the geographical location of agricultural machinery and control values ​​in a functional predictive agriculture map.

[0263] Example 10 is a computer-implemented method of any or all of the foregoing examples, wherein obtaining the infographic includes:

[0264] Obtain a predictive machine speed map that maps machine speed values ​​indicating the predicted speed of an agricultural harvester at different locations in the field.

[0265] Example 11 is a computer-implemented method of any or all of the foregoing examples, and also includes:

[0266] Using a speed-to-field model generator, a predictive sensor data model is generated based on the predictive machine speed values ​​at geographical locations in the predictive machine speed map and the values ​​of agricultural characteristics corresponding to those geographical locations. The predictive sensor data model models the relationship between the predictive machine speed values ​​and the agricultural characteristics.

[0267] Example 12 is a computer-implemented method of any or all of the foregoing examples, and also includes:

[0268] A predictive sensor data map is generated using a predictive sensor data map generator, which serves as a predictive agricultural map. This predictive sensor data map maps predictive values ​​of agricultural characteristics as predictive control values ​​to different geographical locations in the field.

[0269] Example 13 is a computer-implemented method of any or all of the foregoing examples, and also includes:

[0270] Control signals are generated based on geographic location and functional predictive sensor data maps, and controllable subsystems are controlled based on these control signals.

[0271] Example 14 is a computer-implemented method of any or all of the foregoing examples, and also includes:

[0272] A user interface diagram representation for generating functional predictive agriculture maps, the user interface diagram representation including field portions.

[0273] Example 15 is a computer-implemented method of any or all of the foregoing examples, wherein the user interface diagram representation for generating a functional predictive agriculture map further includes:

[0274] Generate machine speed symbols as part of the user interface diagram representation, the machine speed symbols indicating the value of the machine speed at one or more geographic locations on a field portion.

[0275] Example 16 is a computer-implemented method of any or all of the foregoing examples, which further includes a user interface graphical representation for generating functional predictive agriculture maps:

[0276] An interactive display portion is generated as part of the user interface diagram representation. The interactive display portion displays a detected characteristic display indicating the detected agricultural characteristics, an interactive threshold display portion indicating the action threshold, and an interactive action specifyer indicating the control action to be taken when the detected agricultural characteristics meet the action threshold. The control system generates control signals based on the control actions to control the controllable subsystem.

[0277] Example 18 is an agricultural operating machine, comprising:

[0278] A communication system that receives an information map containing machine speed values ​​corresponding to different geographical locations in the field;

[0279] A geolocation sensor that detects the geolocation of agricultural machinery;

[0280] A field sensor that detects agricultural characteristics corresponding to geographical location;

[0281] A predictive model generator generates a predictive agricultural model based on the machine speed value at a geographic location in an information map and the agricultural characteristics corresponding to the geographic location sensed by field sensors. The predictive agricultural model models the relationship between machine speed and agricultural characteristics.

[0282] A predictive map generator generates a functional predictive agriculture map of a field, which is based on machine speed values ​​in an infographic and maps predictive control values ​​to different geographic locations in the field based on a predictive agriculture model.

[0283] Controllable subsystem; and

[0284] A control system that generates control signals to control a controllable subsystem based on the geographical location of the agricultural machinery and control values ​​in a functional predictive agriculture map.

[0285] Example 18 is an agricultural operating machine of any or all of the foregoing examples, wherein field sensors generate sensor signals indicative of agricultural characteristics, and further includes:

[0286] A processing system that receives sensor signals and is configured to identify agricultural characteristics corresponding to geographical locations based on the sensor signals.

[0287] Example 19 is any or all of the agricultural operating machines of the foregoing examples, wherein the control system further includes:

[0288] An operator interface controller generates a user interface representation of a functional predictive agriculture map, the user interface representation including field portions and machine speed symbols, the machine speed symbols indicating the value of machine speed at one or more geographic locations on the field portion.

[0289] Example 20 is any or all of the agricultural operating machines of the foregoing examples, wherein the predictive map generator includes:

[0290] A predictive sensor data map generator generates a functional predictive sensor data map as a functional predictive agricultural map, which maps predictive values ​​of agricultural characteristics as predictive control values ​​to different geographical locations in the field.

[0291] 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 system comprising: a communication system (206) that receives an information map (258) that includes values of machine speed corresponding to different geographic locations in a field; a geographic location sensor (204) that detects a geographic location of an agricultural work machine; a field sensor (208) that detects values of an agricultural property corresponding to the geographic location; a predictive map generator (212) that generates a functional predictive agricultural map of the field that maps predictive control values to the different geographic locations in the field based on the values of machine speed in the information map (258) and based on the values of the agricultural property; and a control system (214) that generates control signals to control a controllable subsystem (216) of the agricultural work machine based on the geographic location of the agricultural work machine (100) and based on the control values in the functional predictive agricultural map.

2. The agricultural system of claim 1, wherein, The information map includes: a predictive machine speed map that maps machine speed values as the control values that indicate a predicted speed of the agricultural work machine at the different geographic locations in the field.

3. The agricultural system of claim 2, further comprising: a speed versus field sensor data model generator that generates a predictive sensor data model based on a predictive machine speed value at the geographic location in the predictive machine speed map and the values of the agricultural property corresponding to the geographic location, the predictive sensor data model modeling a relationship between the predictive machine speed value and the agricultural property.

4. The agricultural system of claim 3, wherein, The predictive map generator includes: a predictive sensor data map generator that generates a functional predictive sensor data map that maps predictive values of the agricultural property to the different geographic locations in the field.

5. The agricultural system of claim 4, wherein, The control system includes: a controller that generates control signals based on the geographic location and the functional predictive sensor data map and controls the controllable subsystem based on the control signals.

6. The agricultural system of claim 1, wherein, The control system further includes: an operator interface controller that generates a user interface graphical representation of the functional predictive agricultural map, the user interface graphical representation including a field portion.

7. The agricultural system of claim 6, wherein, The user interface graphical representation further includes: a machine speed symbol that indicates values of the machine speed at one or more geographic locations on the field portion.

8. A computer-implemented method of controlling an agricultural work machine, comprising: obtaining an information map (258) that includes values of machine speed corresponding to different geographic locations in a field; detecting a geographic location of an agricultural work machine (100); detecting values of an agricultural property corresponding to the geographic location with a field sensor (208); generating a functional predictive agricultural map of the field based on values of machine speed in the information map (258) and based on values of agricultural properties, the functional predictive agricultural map mapping predictive control values to the different geographical locations in the field; and controlling a controllable subsystem (216) based on a geographical location of an agricultural work machine (100) and based on the control values in the functional predictive agricultural map.

9. The computer-implemented method of claim 8, wherein, obtaining an information map comprises: obtaining a predictive machine speed map, the predictive machine speed map mapping machine speed values indicative of a predicted speed of the agricultural work machine at the different geographical locations in the field.

10. An agricultural system comprising: a communication system (206) receiving an information map (258), the information map (258) comprising values of machine speed corresponding to different geographical locations in a field; a geographical location sensor (204) detecting a geographical location of an agricultural work machine (100); a field sensor (208) detecting values of an agricultural property corresponding to the geographical locations; a predictive model generator (210) generating a predictive agricultural model based on values of the machine speed at the geographical locations in the information map (258) and values of the agricultural property detected by the field sensor (208) corresponding to the geographical locations, the predictive agricultural model modeling a relationship between the machine speed and the agricultural property; a predictive map generator (212) generating a functional predictive agricultural map of the field based on values of machine speed in the information map (258) and based on the predictive agricultural model, the functional predictive agricultural map mapping predictive control values to the different geographical locations in the field; and a control system (214) generating control signals to control a controllable subsystem (216) of the agricultural work machine based on a geographical location of the agricultural work machine and based on the control values in the functional predictive agricultural map.

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