Patent Grant
11864483
The present description relates to agricultural machines, forestry machines, construction machines and turf management machines.
There are a wide variety of different types of agricultural machines. Some agricultural machines include harvesters, such as combine harvesters, sugar cane harvesters, cotton harvesters, self-propelled forage harvesters, and windrowers. Some harvester can also be fitted with different types of heads to harvest different types of crops.
Agricultural harvesters typically include an engine or other power source that produces a finite amount of power. The produced power is provided to the various subsystems of the agricultural harvester.
The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.
One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.
This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to examples that solve any or all disadvantages noted in the background.
For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to the examples illustrated in the drawings, and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is intended. Any alterations and further modifications to the described devices, systems, methods, and any further application of the principles of the present disclosure are fully contemplated as would normally occur to one skilled in the art to which the disclosure relates. In particular, it is fully contemplated that the features, components, steps, or a combination thereof described with respect to one example may be combined with the features, components, steps, or a combination thereof described with respect to other examples of the present disclosure.
The present description relates to using in-situ data taken concurrently with an agricultural operation, in combination with predictive or prior data, to generate a predictive map and, more particularly, a predictive power map. In some examples, the predictive power map can be used to control an agricultural work machine, such as an agricultural harvester. As discussed above, the power generation of a harvester has a finite limit and overall performance may be degraded when one or more subsystems have increased power demands.
Performance of a harvester may be deleteriously affected based on a number of different criteria. For example, areas of dense crop plants, weeds, or combinations thereof, may have deleterious effects on the operation of the harvester because subsystems require more power to process larger amounts of material, which includes crop plants and weeds. Vegetative index may signal where areas of dense crop plants, weeds, or combinations thereof may exist. Or for example, crop plants or weeds that have higher moisture content also take more power to process. Or for example, soil properties, such as type or moisture, can affect the power usage by the steering and propulsion systems. For instance, wet clay soils can cause additional slippage compared to dry soils which reduces the efficiency of the drive train. Or for example, topography of the field can change the power characteristics of an agricultural harvester. For instance, as the harvester ascends a hill some power needs to be diverted to the propulsion system to maintain a constant speed. Or for example, an area of the field having a higher grain yield may require that more power be diverted to the crop processing subsystems. Or for example, an area of the field containing a large biomass may require that more power be diverted to the crop processing subsystems.
A vegetative index map illustratively maps vegetative index values (which may be indicative of vegetative growth) across different geographic locations in a field of interest. One example of a vegetative index includes a normalized difference vegetation index (NDVI). There are many other vegetative indices that are within the scope of the present disclosure. In some examples, a vegetative index may be derived from sensor readings of one or more bands of electromagnetic radiation reflected by the plants. Without limitations, these bands may be in the microwave, infrared, visible or ultraviolet portions of the electromagnetic spectrum.
A vegetative index map can be used to identify the presence and location of vegetation. In some examples, these maps enable weeds to be identified and georeferenced in the presence of bare soil, crop residue, or other plants, including crop or other weeds. For instance, at the end of a growing season, when a crop is mature, the crop plants may show a relatively low level of live, growing vegetation. However, weeds often persist in a growing state after the maturity of the crop. Therefore, if a vegetative index map is generated relatively late in the growing season, the vegetative index map may be indicative of the location of weeds in the field.
A crop moisture map illustratively maps crop moisture across different geographic locations in a field of interest. In one example, crop moisture can be sensed prior to a harvesting operation by an unmanned aerial vehicle (UAV) equipped with a moisture sensor. As the UAV travels across the field, the crop moisture readings are geolocated to create a crop moisture map. This is an example only and the crop moisture map can be created in other ways as well, for example, the crop moisture across a field can be predicted based on precipitation, soil moisture or combinations thereof.
A topographic map illustratively maps elevations, or other topographical characteristics of the ground across different geographic locations in a field of interest. Since ground slope is indicative of a change in elevation, having two or more elevation values allows for calculation of slope across the areas having known elevation values. Greater granularity of slope can be accomplished by having more areas with known elevation values. As an agricultural harvester travels across the terrain in known directions, the pitch and roll of the agricultural harvester can be determined based on the slope of the ground (i.e., areas of changing elevation). Topographical characteristics, when referred to below, can include, but are not limited to, the elevation, slope (e.g., including the machine orientation relative to the slope), and ground profile (e.g., roughness).
A soil property map illustratively maps soil property values (which may be indicative of soil type, soil moisture, soil cover, soil structure, as well as various other soil properties) across different geographic locations in a field of interest. The soil property maps thus provide geo-referenced soil properties across a field of interest. Soil type can refer to taxonomic units in soil science, wherein each soil type includes defined sets of shared properties. Soil types can include, for example, sandy soil, clay soil, silt soil, peat soil, chalk soil, loam soil, and various other soil types. Soil moisture can refer to the amount of water that is held or otherwise contained in the soil. Soil moisture can also be referred to as soil wetness. Soil cover can refer to the amount of items or materials covering the soil, including, vegetation material, such as crop residue or cover crop, debris, as well as various other items or materials. Commonly, in agricultural terms, soil cover includes a measure of remaining crop residue, such as a remaining mass of plant stalks, as well as a measure of cover crop. Soil structure can refer to the arrangement of solid parts of the soil and the pore space located between the solid parts of the soil. Soil structure can include the way in which individual particles, such as individual particles of sand, silt, and clay, are assembled. Soil structure can be described in terms of grade (degree of aggregation), class (average size of aggregates), and form (types of aggregates), as well as a variety of other descriptions. These are merely examples. Various other characteristics and properties of the soil can be mapped as soil property values on a soil property map.
These soil property maps can be generated on the basis of data collected during another operation corresponding to the field of interest, for example, previous agricultural operations in the same season, such as planting operations or spraying operations, as well as previous agricultural operations performed in past seasons, such as a previous harvesting operation. The agricultural machines performing those agricultural operations can have on-board sensors that detect characteristics indicative of soil properties, for example, characteristics indicative of soil type, soil moisture, soil cover, soil structure, as well as various other characteristics indicative of various other soil properties. Additionally, operating characteristics or machine settings of the agricultural machines during previous operations along with other data can be used to generate a soil property map. For instance, header height data indicative of a height of an agricultural harvester's header across different geographic locations in the field of interest during a previous harvesting operation along with weather data that indicates weather conditions such as precipitation data or wind data during an interim period (such as the period since the time of the previous harvesting operation and the generation of the soil property map) can be used to generate a soil moisture map. For example, by knowing the height of the header, the amount of remaining plant residue, such as crop stalks, can be known or estimated and, along with precipitation data, a level of soil moisture can be predicted. This is merely an example.
The present discussion also includes predictive maps that predict a characteristic based on an information map and a relationship to an in-situ sensor. Two of these maps include a predictive yield map and a predictive biomass map. In one example, the predictive yield map is generated by receiving a prior vegetative index map and sensing a yield during a harvesting operation and determining a relationship between the prior vegetative index map and the yield sensor signal, and using the relationship to generate the predictive yield map based on the relationship and the prior vegetative index map. In one example, the predictive biomass map is generated by receiving a prior vegetative index map and sensing a biomass and determining a relationship between the prior vegetative index map and the biomass sensor signal, and using the relationship to generate the predictive biomass map based on the relationship and the prior vegetative index map. The predictive yield and biomass maps can be created based on other information maps or generated in other ways as well. For example, the predictive yield and biomass maps can be generated based on a satellite or growth model.
The present discussion thus proceeds with respect to examples in which a system receives one or more of a vegetative index, weed, crop moisture, soil property, topography, predictive yield or predictive biomass map, and also uses an in-situ sensor to detect a variable indicative of crop state, during a harvesting operation. The system generates a model that models a relationship between the vegetative index values, crop moisture values, soil property values, predictive yield values, or predictive biomass values from the maps and the in-situ data from the in-situ sensor. The model is used to generate a functional predictive power map that predicts an anticipated power characteristic of the agricultural harvester in the field. The functional predictive power characteristic map, generated during the harvesting operation, can be presented to an operator or other user or used in automatically controlling an agricultural harvester during the harvesting operation, or both.
As shown in
Thresher 110 illustratively includes a threshing rotor 112 and a set of concaves 114. Further, agricultural harvester 100 also includes a separator 116. Agricultural harvester 100 also includes a cleaning subsystem or cleaning shoe (collectively referred to as cleaning subsystem 118) that includes a cleaning fan 120, chaffer 122, and sieve 124. The material handling subsystem 125 also includes discharge beater 126, tailings elevator 128, clean grain elevator 130, as well as unloading auger 134 and spout 136. The clean grain elevator moves clean grain into clean grain tank 132. Agricultural harvester 100 also includes a residue subsystem 138 that can include chopper 140 and spreader 142. Agricultural harvester 100 also includes a propulsion subsystem that includes an engine that drives ground engaging components 144, such as wheels or tracks. In some examples, a combine harvester within the scope of the present disclosure may have more than one of any of the subsystems mentioned above. In some examples, agricultural harvester 100 may have left and right cleaning subsystems, separators, etc., which are not shown in
In operation, and by way of overview, agricultural harvester 100 illustratively moves through a field in the direction indicated by arrow 147. As agricultural harvester 100 moves, header 102 (and the associated reel 164) engages the crop to be harvested and gathers the crop toward cutter 104. An operator of agricultural harvester 100 can be a local human operator, a remote human operator, or an automated system. The operator of agricultural harvester 100 may determine one or more of a height setting, a tilt angle setting, or a roll angle setting for header 102. For example, the operator inputs a setting or settings to a control system, described in more detail below, that controls actuator 107. The control system may also receive a setting from the operator for establishing the tilt angle and roll angle of the header 102 and implement the inputted settings by controlling associated actuators, not shown, that operate to change the tilt angle and roll angle of the header 102. The actuator 107 maintains header 102 at a height above ground 111 based on a height setting and, where applicable, at desired tilt and roll angles. Each of the height, roll, and tilt settings may be implemented independently of the others. The control system responds to header error (e.g., the difference between the height setting and measured height of header 104 above ground 111 and, in some examples, tilt angle and roll angle errors) with a responsiveness that is determined based on a selected sensitivity level. If the sensitivity level is set at a greater level of sensitivity, the control system responds to smaller header position errors, and attempts to reduce the detected errors more quickly than when the sensitivity is at a lower level of sensitivity.
Returning to the description of the operation of agricultural harvester 100, after crops are cut by cutter 104, the severed crop material is moved through a conveyor in feeder house 106 toward feed accelerator 108, which accelerates the crop material into thresher 110. The crop material is threshed by rotor 112 rotating the crop against concaves 114. The threshed crop material is moved by a separator rotor in separator 116 where a portion of the residue is moved by discharge beater 126 toward the residue subsystem 138. The portion of residue transferred to the residue subsystem 138 is chopped by residue chopper 140 and spread on the field by spreader 142. In other configurations, the residue is released from the agricultural harvester 100 in a windrow. In other examples, the residue subsystem 138 can include weed seed eliminators (not shown) such as seed baggers or other seed collectors, or seed crushers or other seed destroyers.
Grain falls to cleaning subsystem 118. Chaffer 122 separates some larger pieces of material from the grain, and sieve 124 separates some of finer pieces of material from the clean grain. Clean grain falls to an auger that moves the grain to an inlet end of clean grain elevator 130, and the clean grain elevator 130 moves the clean grain upwards, depositing the clean grain in clean grain tank 132. Residue is removed from the cleaning subsystem 118 by airflow generated by cleaning fan 120. Cleaning fan 120 directs air along an airflow path upwardly through the sieves and chaffers. The airflow carries residue rearwardly in agricultural harvester 100 toward the residue handling subsystem 138.
Tailings elevator 128 returns tailings to thresher 110 where the tailings are re-threshed. Alternatively, the tailings also may be passed to a separate re-threshing mechanism by a tailings elevator or another transport device where the tailings are re-threshed as well.
Ground speed sensor 146 senses the travel speed of agricultural harvester 100 over the ground. Ground speed sensor 146 may sense the travel speed of the agricultural harvester 100 by sensing the speed of rotation of the ground engaging components (such as wheels or tracks), a drive shaft, an axel, or other components. In some instances, the travel speed may be sensed using a positioning system, such as a global positioning system (GPS), a dead reckoning system, a long range navigation (LORAN) system, or a wide variety of other systems or sensors that provide an indication of travel speed.
Loss sensors 152 illustratively provide an output signal indicative of the quantity of grain loss occurring in both the right and left sides of the cleaning subsystem 118. In some examples, sensors 152 are strike sensors which count grain strikes per unit of time or per unit of distance traveled to provide an indication of the grain loss occurring at the cleaning subsystem 118. The strike sensors for the right and left sides of the cleaning subsystem 118 may provide individual signals or a combined or aggregated signal. In some examples, sensors 152 may include a single sensor as opposed to separate sensors provided for each cleaning subsystem 118. Separator loss sensor 148 provides a signal indicative of grain loss in the left and right separators, not separately shown in
Agricultural harvester 100 may also include other sensors and measurement mechanisms. For instance, agricultural harvester 100 may include one or more of the following sensors: a header height sensor that senses a height of header 102 above ground 111; stability sensors that sense oscillation or bouncing motion (and amplitude) of agricultural harvester 100; a residue setting sensor that is configured to sense whether agricultural harvester 100 is configured to chop the residue, produce a windrow, etc.; a cleaning shoe fan speed sensor to sense the speed of fan 120; a concave clearance sensor that senses clearance between the rotor 112 and concaves 114; a threshing rotor speed sensor that senses a rotor speed of rotor 112; a chaffer clearance sensor that senses the size of openings in chaffer 122; a sieve clearance sensor that senses the size of openings in sieve 124; a material other than grain (MOG) moisture sensor that senses a moisture level of the MOG passing through agricultural harvester 100; one or more machine setting sensors configured to sense various configurable settings of agricultural harvester 100; a machine orientation sensor that senses the orientation of agricultural harvester 100; and crop property sensors that sense a variety of different types of crop properties, such as crop type, crop moisture, and other crop properties. Crop property sensors may also be configured to sense characteristics of the severed crop material as the crop material is being processed by agricultural harvester 100. For example, in some instances, the crop property sensors may sense grain quality such as broken grain, MOG levels; grain constituents such as starches and protein; and grain feed rate as the grain travels through the feeder house 106, clean grain elevator 130, or elsewhere in the agricultural harvester 100. The crop property sensors may also sense the feed rate of biomass through feeder house 106, through the separator 116 or elsewhere in agricultural harvester 100. The crop property sensors may also sense the feed rate as a mass flow rate of grain through elevator 130 or through other portions of the agricultural harvester 100 or provide other output signals indicative of other sensed variables.
Examples of sensors used to detect or sense the power characteristics include, but are not limited to, a voltage sensor, a current sensor, a torque sensor, a fluid pressure sensor, a fluid flow sensor, a force sensor, a bearing load sensor and a rotational sensor. Power characteristics can be measured at varying levels of granularity. For instance, power usage can be sensed machine-wide, subsystem-wide or by individual components of the subsystems.
Prior to describing how agricultural harvester 100 generates a functional predictive power map, and uses the functional predictive power map for control, a brief description of some of the items on agricultural harvester 100, and their operation, will first be described. The description of
After the general approach is described with respect to
Information map 258 may be downloaded onto agricultural harvester 100 and stored in data store 202, using communication system 206 or in other ways. In some examples, communication system 206 may be a cellular communication system, a system for communicating over a wide area network or a local area network, a system for communicating over a near field communication network, or a communication system configured to communicate over any of a variety of other networks or combinations of networks. Communication system 206 may also include a system that facilitates downloads or transfers of information to and from a secure digital (SD) card or a universal serial bus (USB) card or both.
Geographic position sensor 204 illustratively senses or detects the geographic position or location of agricultural harvester 100. Geographic position sensor 204 can include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from a GNSS satellite transmitter. Geographic position sensor 204 can also include a real-time kinematic (RTK) component that is configured to enhance the precision of position data derived from the GNSS signal. Geographic position sensor 204 can include a dead reckoning system, a cellular triangulation system, or any of a variety of other geographic position sensors.
In-situ sensors 208 may be any of the sensors described above with respect to
After being retrieved by agricultural harvester 100, prior information map selector 209 can filter or select one or more specific information map(s) 258 that are prior information maps for usage by predictive model generator 210. In one example, prior information map selector 209 selects a map based on a comparison of the contextual information in the prior information map versus the present contextual information. For example, a historical yield map may be selected from one of the past years where weather conditions over the growing season were similar to the present year's weather conditions. Or, for example, a historical yield map may be selected from one of the past years when the context information is not similar. For example, a historical yield map may be selected for a prior year that was “dry” (i.e., had drought conditions or reduced precipitation), while the present year is “wet” (i.e., had increased precipitation or flood conditions). There still may be a useful historical relationship, but the relationship may be inverse. For instance, areas that are flooded in a wet year may be areas of higher yield in a dry year because these areas may retain more water in dry years. Present contextual information may include contextual information beyond immediate contextual information. For instance, present contextual information can include, but not by limitation, a set of information corresponding to the present growing season, a set of data corresponding to a winter before the current growing season, or a set of data corresponding to several past years, amongst others.
The contextual information can also be used for correlations between areas with similar contextual characteristics, regardless of whether the geographic position corresponds to the same position on information map 258. For instance, historical yield values from area with similar soil types in other fields can be used as information map 258 to create the predictive yield map. For example, the contextual characteristic information associated with a different location may be applied to the location on the information map 258 having similar characteristic information.
Predictive model generator 210 generates a model that is indicative of a relationship between the values sensed by the in-situ sensor 208 and a metric mapped to the field by the information map 258. For example, if the information map 258 maps a vegetative index value to different locations in the field, and the in-situ sensor 208 is sensing a value indicative of header power usage, then information variable-to-in-situ variable model generator 228 generates a predictive power model that models the relationship between the vegetative index value and the header power usage value. The predictive power model can also be generated based on vegetative index values from the information map 258 and multiple in-situ data values generated by in-situ sensors 208. Then, predictive map generator 212 uses the predictive power model generated by predictive model generator 210 to generate a functional predictive power map that predicts the value of a power characteristic, such as power usage by a subsystem, sensed by the in-situ sensors 208 at different locations in the field based upon the information map 258.
In some examples, the type of values in the functional predictive map 263 may be the same as the in-situ data type sensed by the in-situ sensors 208. In some instances, the type of values in the functional predictive map 263 may have different units from the data sensed by the in-situ sensors 208. In some examples, the type of values in the functional predictive map 263 may be different from the data type sensed by the in-situ sensors 208 but have a relationship to the type of data type sensed by the in-situ sensors 208. For example, in some examples, the data type sensed by the in-situ sensors 208 may be indicative of the type of values in the functional predictive map 263. In some examples, the type of data in the functional predictive map 263 may be different than the data type in the information map 258. In some instances, the type of data in the functional predictive map 263 may have different units from the data in the information map 258. In some examples, the type of data in the functional predictive map 263 may be different from the data type in the information map 258 but has a relationship to the data type in the information map 258. For example, in some examples, the data type in the information map 258 may be indicative of the type of data in the functional predictive map 263. In some examples, the type of data in the functional predictive map 263 is different than one of, or both of the in-situ data type sensed by the in-situ sensors 208 and the data type in the information map 258. In some examples, the type of data in the functional predictive map 263 is the same as one of, or both of, of the in-situ data type sensed by the in-situ sensors 208 and the data type in information map 258. In some examples, the type of data in the functional predictive map 263 is the same as one of the in-situ data type sensed by the in-situ sensors 208 or the data type in the information map 258, and different than the other.
Continuing with the preceding example, in which information map 258 is a vegetative index map and in-situ sensor 208 senses a value indicative of header power usage, predictive map generator 212 can use the vegetative index values in information map 258, and the model generated by predictive model generator 210, to generate a functional predictive map 263 that predicts the header power usage at different locations in the field. Predictive map generator 212 thus outputs predictive map 264.
As shown in
Some variations in the data types that are mapped in the information map 258, the data types sensed by in-situ sensors 208, and the data types predicted on the predictive map 264 will now be described.
In some examples, the data type in the information map 258 is different from the data type sensed by in-situ sensors 208, yet the data type in the predictive map 264 is the same as the data type sensed by the in-situ sensors 208. For instance, the information map 258 may be a vegetative index map, and the variable sensed by the in-situ sensors 208 may be yield. The predictive map 264 may then be a predictive yield map that maps predicted yield values to different geographic locations in the field. In another example, the information map 258 may be a vegetative index map, and the variable sensed by the in-situ sensors 208 may be crop height. The predictive map 264 may then be a predictive crop height map that maps predicted crop height values to different geographic locations in the field.
Also, in some examples, the data type in the information map 258 is different from the data type sensed by in-situ sensors 208, and the data type in the predictive map 264 is different from both the data type in the information map 258 and the data type sensed by the in-situ sensors 208. For instance, the information map 258 may be a vegetative index map, and the variable sensed by the in-situ sensors 208 may be crop height. The predictive map 264 may then be a predictive biomass map that maps predicted biomass values to different geographic locations in the field. In another example, the information map 258 may be a vegetative index map, and the variable sensed by the in-situ sensors 208 may be yield. The predictive map 264 may then be a predictive speed map that maps predicted harvester speed values to different geographic locations in the field.
In some examples, the information map 258 is from a prior pass through the field during a prior operation and the data type is different from the data type sensed by in-situ sensors 208, yet the data type in the predictive map 264 is the same as the data type sensed by the in-situ sensors 208. For instance, the information map 258 may be a seed population map generated during planting, and the variable sensed by the in-situ sensors 208 may be stalk size. The predictive map 264 may then be a predictive stalk size map that maps predicted stalk size values to different geographic locations in the field. In another example, the information map 258 may be a seeding hybrid map, and the variable sensed by the in-situ sensors 208 may be crop state such as standing crop or down crop. The predictive map 264 may then be a predictive crop state map that maps predicted crop state values to different geographic locations in the field.
In some examples, the information map 258 is from a prior pass through the field during a prior operation and the data type is the same as the data type sensed by in-situ sensors 208, and the data type in the predictive map 264 is also the same as the data type sensed by the in-situ sensors 208. For instance, the information map 258 may be a yield map generated during a previous year, and the variable sensed by the in-situ sensors 208 may be yield. The predictive map 264 may then be a predictive yield map that maps predicted yield values to different geographic locations in the field. In such an example, the relative yield differences in the georeferenced information map 258 from the prior year can be used by predictive model generator 210 to generate a predictive model that models a relationship between the relative yield differences on the information map 258 and the yield values sensed by in-situ sensors 208 during the current harvesting operation. The predictive model is then used by predictive map generator 210 to generate a predictive yield map.
In another example, the information map 258 may be a threshing/separating subsystem power usage map generated during a prior operation, and the variable sensed by the in-situ sensors 208 may be threshing/separating subsystems power usage. The predictive map 264 may then be a predictive threshing/separating subsystems power usage map that maps predicted threshing/separating subsystems power usage values to different geographic locations in the field.
In some examples, predictive map 264 can be provided to the control zone generator 213. Control zone generator 213 groups adjacent portions of an area into one or more control zones based on data values of predictive map 264 that are associated with those adjacent portions. A control zone may include two or more contiguous portions of an area, such as a field, for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant. For example, a response time to alter a setting of controllable subsystems 216 may be inadequate to satisfactorily respond to changes in values contained in a map, such as predictive map 264. In that case, control zone generator 213 parses the map and identifies control zones that are of a defined size to accommodate the response time of the controllable subsystems 216. In another example, control zones may be sized to reduce wear from excessive actuator movement resulting from continuous adjustment. In some examples, there may be a different set of control zones for each controllable subsystem 216 or for groups of controllable subsystems 216. The control zones may be added to the predictive map 264 to obtain predictive control zone map 265. Predictive control zone map 265 can thus be similar to predictive map 264 except that predictive control zone map 265 includes control zone information defining the control zones. Thus, a functional predictive map 263, as described herein, may or may not include control zones. Both predictive map 264 and predictive control zone map 265 are functional predictive maps 263. In one example, a functional predictive map 263 does not include control zones, such as predictive map 264. In another example, a functional predictive map 263 does include control zones, such as predictive control zone map 265. In some examples, multiple crops may be simultaneously present in a field if an intercrop production system is implemented. In that case, predictive map generator 212 and control zone generator 213 are able to identify the location and characteristics of the two or more crops and then generate predictive map 264 and predictive map with control zones 265 accordingly.
It will also be appreciated that control zone generator 213 can cluster values to generate control zones and the control zones can be added to predictive control zone map 265, or a separate map, showing only the control zones that are generated. In some examples, the control zones may be used for controlling or calibrating agricultural harvester 100 or both. In other examples, the control zones may be presented to the operator 260 and used to control or calibrate agricultural harvester 100, and, in other examples, the control zones may be presented to the operator 260 or another user or stored for later use.
Predictive map 264 or predictive control zone map 265 or both are provided to control system 214, which generates control signals based upon the predictive map 264 or predictive control zone map 265 or both. In some examples, communication system controller 229 controls communication system 206 to communicate the predictive map 264 or predictive control zone map 265 or control signals based on the predictive map 264 or predictive control zone map 265 to other agricultural harvesters that are harvesting in the same field. In some examples, communication system controller 229 controls the communication system 206 to send the predictive map 264, predictive control zone map 265, or both to other remote systems.
Operator interface controller 231 is operable to generate control signals to control operator interface mechanisms 218. The operator interface controller 231 is also operable to present the predictive map 264 or predictive control zone map 265 or other information derived from or based on the predictive map 264, predictive control zone map 265, or both to operator 260. Operator 260 may be a local operator or a remote operator. As an example, controller 231 generates control signals to control a display mechanism to display one or both of predictive map 264 and predictive control zone map 265 for the operator 260. Controller 231 may generate operator actuatable mechanisms that are displayed and can be actuated by the operator to interact with the displayed map. The operator can edit the map by, for example, correcting a power characteristic displayed on the map, based on the operator's observation. Settings controller 232 can generate control signals to control various settings on the agricultural harvester 100 based upon predictive map 264, the predictive control zone map 265, or both. For instance, settings controller 232 can generate control signals to control machine and header actuators 248. In response to the generated control signals, the machine and header actuators 248 operate to control, for example, one or more of the sieve and chaffer settings, concave clearance, rotor settings, cleaning fan speed settings, header height, header functionality, reel speed, reel position, draper functionality (where agricultural harvester 100 is coupled to a draper header), corn header functionality, internal distribution control and other actuators 248 that affect the other functions of the agricultural harvester 100. Path planning controller 234 illustratively generates control signals to control steering subsystem 252 to steer agricultural harvester 100 according to a desired path. Path planning controller 234 can control a path planning system to generate a route for agricultural harvester 100 and can control propulsion subsystem 250 and steering subsystem 252 to steer agricultural harvester 100 along that route. Cooling controller 235 can control a cooling operation of cooling subsystem 255 of agricultural harvester 100. For instance, cooling controller 235 can adjust the fan speed or fan blade pitch of a fan of controlling subsystem 255. Or for instance, cooling controller 235 can increase fluid flow through a radiator or other heat dispersing device. Feed rate controller 236 can control various subsystems, such as propulsion subsystem 250 and machine actuators 248, to control a feed rate based upon the predictive map 264 or predictive control zone map 265 or both. For instance, as agricultural harvester 100 approaches an area having a predicted subsystem power usage value above a selected threshold, feed rate controller 236 may reduce the speed of agricultural harvester 100 to maintain power allocation to the predicted power usage requirements of the one or more subsystems. Header and reel controller 238 can generate control signals to control a header or a reel or other header functionality. Draper belt controller 240 can generate control signals to control a draper belt or other draper functionality based upon the predictive map 264, predictive control zone map 265, or both. Deck plate position controller 242 can generate control signals to control a position of a deck plate included on a header based on predictive map 264 or predictive control zone map 265 or both, and residue system controller 244 can generate control signals to control a residue subsystem 138 based upon predictive map 264 or predictive control zone map 265, or both. Machine cleaning controller 245 can generate control signals to control machine cleaning subsystem 254. Other controllers included on the agricultural harvester 100 can control other subsystems based on the predictive map 264 or predictive control zone map 265 or both as well.
At 280, agricultural harvester 100 receives information map 258. Examples of information map 258 or receiving information map 258 are discussed with respect to blocks 281, 282, 284 and 286. As discussed above, information map 258 maps values of a variable, corresponding to a first characteristic, to different locations in the field, as indicated at block 282. As indicated at block 281, receiving the information map 258 may involve selecting one or more of a plurality of possible information maps that are available. For instance, one information map may be a vegetative index map generated from aerial imagery. Another information map may be a map generated during a prior pass through the field which may have been performed by a different machine performing a previous operation in the field, such as a sprayer or other machine. The process by which one or more information maps are selected can be manual, semi-automated, or automated. The information map 258 is based on data collected prior to a current harvesting operation. This is indicated by block 284. For instance, the data may be collected based on aerial images taken during a previous year, or earlier in the current growing season, or at other times. As indicated by block 285, the information map can be a predictive map that predicts a characteristic based on an information map and a relationship to an in-situ sensor. A process of generating a predictive map is presented in
Upon commencement of a harvesting operation, in-situ sensors 208 generate sensor signals indicative of one or more in-situ data values indicative of a characteristic, for example, a power characteristic, such as a power usage by one or more subsystems, as indicated by block 288. Examples of in-situ sensors 288 are discussed with respect to blocks 222, 290, and 226. As explained above, the in-situ sensors 208 include on-board sensors 222; remote in-situ sensors 224, such as UAV-based sensors flown at a time to gather in-situ data, shown in block 290; or other types of in-situ sensors, designated by in-situ sensors 226. In some examples, data from on-board sensors is georeferenced using position, heading, or speed data from geographic position sensor 204.
Predictive model generator 210 controls the information variable-to-in-situ variable model generator 228 to generate a model that models a relationship between the mapped values contained in the information map 258 and the in-situ values sensed by the in-situ sensors 208 as indicated by block 292. The characteristics or data types represented by the mapped values in the information map 258 and the in-situ values sensed by the in-situ sensors 208 may be the same characteristics or data type or different characteristics or data types.
The relationship or model generated by predictive model generator 210 is provided to predictive map generator 212. Predictive map generator 212 generates a predictive map 264 that predicts a value of the characteristic sensed by the in-situ sensors 208 at different geographic locations in a field being harvested, or a different characteristic that is related to the characteristic sensed by the in-situ sensors 208, using the predictive model and the information map 258, as indicated by block 294.
It should be noted that, in some examples, the information map 258 may include two or more different maps or two or more different map layers of a single map. Each map layer may represent a different data type from the data type of another map layer or the map layers may have the same data type that were obtained at different times. Each map in the two or more different maps or each layer in the two or more different map layers of a map maps a different type of variable to the geographic locations in the field. In such an example, predictive model generator 210 generates a predictive model that models the relationship between the in-situ data and each of the different variables mapped by the two or more different maps or the two or more different map layers. Similarly, the in-situ sensors 208 can include two or more sensors each sensing a different type of variable. Thus, the predictive model generator 210 generates a predictive model that models the relationships between each type of variable mapped by the information map 258 and each type of variable sensed by the in-situ sensors 208. Predictive map generator 212 can generate a functional predictive map 263 that predicts a value for each sensed characteristic sensed by the in-situ sensors 208 (or a characteristic related to the sensed characteristic) at different locations in the field being harvested using the predictive model and each of the maps or map layers in the information map 258.
Predictive map generator 212 configures the predictive map 264 so that the predictive map 264 is actionable (or consumable) by control system 214. Predictive map generator 212 can provide the predictive map 264 to the control system 214 or to control zone generator 213 or both. Some examples of different ways in which the predictive map 264 can be configured or output are described with respect to blocks 296, 295, 299 and 297. For instance, predictive map generator 212 configures predictive map 264 so that predictive map 264 includes values that can be read by control system 214 and used as the basis for generating control signals for one or more of the different controllable subsystems of the agricultural harvester 100, as indicated by block 296.
Control zone generator 213 can divide the predictive map 264 into control zones based on the values on the predictive map 264. Contiguously-geolocated values that are within a threshold value of one another can be grouped into a control zone. The threshold value can be a default threshold value, or the threshold value can be set based on an operator input, based on an input from an automated system, or based on other criteria. A size of the zones may be based on a responsiveness of the control system 214, the controllable subsystems 216, based on wear considerations, or on other criteria as indicated by block 295. Predictive map generator 212 configures predictive map 264 for presentation to an operator or other user. Control zone generator 213 can configure predictive control zone map 265 for presentation to an operator or other user. This is indicated by block 299. When presented to an operator or other user, the presentation of the predictive map 264 or predictive control zone map 265 or both may contain one or more of the predictive values on the predictive map 264 correlated to geographic location, the control zones on predictive control zone map 265 correlated to geographic location, and settings values or control parameters that are used based on the predicted values on map 264 or zones on predictive control zone map 265. The presentation can, in another example, include more abstracted information or more detailed information. The presentation can also include a confidence level that indicates an accuracy with which the predictive values on predictive map 264 or the zones on predictive control zone map 265 conform to measured values that may be measured by sensors on agricultural harvester 100 as agricultural harvester 100 moves through the field. Further where information is presented to more than one location, an authentication and authorization system can be provided to implement authentication and authorization processes. For instance, there may be a hierarchy of individuals that are authorized to view and change maps and other presented information. By way of example, an on-board display device may show the maps in near real time locally on the machine, or the maps may also be generated at one or more remote locations, or both. In some examples, each physical display device at each location may be associated with a person or a user permission level. The user permission level may be used to determine which display markers are visible on the physical display device and which values the corresponding person may change. As an example, a local operator of agricultural harvester 100 may be unable to see the information corresponding to the predictive map 264 or make any changes to machine operation. A supervisor, such as a supervisor at a remote location, however, may be able to see the predictive map 264 on the display but be prevented from making any changes. A manager, who may be at a separate remote location, may be able to see all of the elements on predictive map 264 and also be able to change the predictive map 264. In some instances, the predictive map 264 accessible and changeable by a manager located remotely may be used in machine control. This is one example of an authorization hierarchy that may be implemented. The predictive map 264 or predictive control zone map 265 or both can be configured in other ways as well, as indicated by block 297.
At block 298, input from geographic position sensor 204 and other in-situ sensors 208 are received by the control system. Particularly, at block 300, control system 214 detects an input from the geographic position sensor 204 identifying a geographic location of agricultural harvester 100. Block 302 represents receipt by the control system 214 of sensor inputs indicative of trajectory or heading of agricultural harvester 100, and block 304 represents receipt by the control system 214 of a speed of agricultural harvester 100. Block 306 represents receipt by the control system 214 of other information from various in-situ sensors 208.
At block 308, control system 214 generates control signals to control the controllable subsystems 216 based on the predictive map 264 or predictive control zone map 265 or both and the input from the geographic position sensor 204 and any other in-situ sensors 208. At block 310, control system 214 applies the control signals to the controllable subsystems. It will be appreciated that the particular control signals that are generated, and the particular controllable subsystems 216 that are controlled, may vary based upon one or more different things. For example, the control signals that are generated and the controllable subsystems 216 that are controlled may be based on the type of predictive map 264 or predictive control zone map 265 or both that is being used. Similarly, the control signals that are generated and the controllable subsystems 216 that are controlled and the timing of the control signals can be based on various latencies of crop flow through the agricultural harvester 100 and the responsiveness of the controllable subsystems 216.
By way of example, a generated predictive map 264 in the form of a predictive power map can be used to control one or more subsystems 216. For instance, the predictive power map can include power usage requirement values georeferenced to locations within the field being harvested. The power usage requirement values from the predictive power map can be extracted and used to control the steering and propulsion subsystems 252 and 250. By controlling the steering and propulsion subsystems 252 and 250, a feed rate of material moving through the agricultural harvester 100 can be controlled. Similarly, the header height can be controlled to take in more or less material, and, thus, the header height can also be controlled to control feed rate of material through the agricultural harvester 100. In other examples, if the predictive map 264 maps predicted header power usage to positions in the field, power allocation to the header can be implemented. For example, if the values present in the predictive power map indicate one or more areas having higher power usage requirements for the header subsystems, then header and reel controller 238 can allocate more power from the engine to the header subsystems, this may require allocating less power to other subsystems, such as by reducing speed and reducing power to the propulsion subsystem. The preceding example involving header control using a predictive power map is provided merely as an example. Consequently, a wide variety of other control signals can be generated using values obtained from a predictive power map or other type of predictive map to control one or more of the controllable subsystems 216.
At block 312, a determination is made as to whether the harvesting operation has been completed. If harvesting is not completed, the processing advances to block 314 where in-situ sensor data from geographic position sensor 204 and in-situ sensors 208 (and perhaps other sensors) continue to be read.
In some examples, at block 316, agricultural harvester 100 can also detect learning trigger criteria to perform machine learning on one or more of the predictive map 264, predictive control zone map 265, the model generated by predictive model generator 210, the zones generated by control zone generator 213, one or more control algorithms implemented by the controllers in the control system 214, and other triggered learning.
The learning trigger criteria can include any of a wide variety of different criteria. Some examples of detecting trigger criteria are discussed with respect to blocks 318, 320, 321, 322 and 324. For instance, in some examples, triggered learning can involve recreation of a relationship used to generate a predictive model when a threshold amount of in-situ sensor data are obtained from in-situ sensors 208. In such examples, receipt of an amount of in-situ sensor data from the in-situ sensors 208 that exceeds a threshold triggers or causes the predictive model generator 210 to generate a new predictive model that is used by predictive map generator 212. Thus, as agricultural harvester 100 continues a harvesting operation, receipt of the threshold amount of in-situ sensor data from the in-situ sensors 208 triggers the creation of a new relationship represented by a predictive model generated by predictive model generator 210. Further, new predictive map 264, predictive control zone map 265, or both can be regenerated using the new predictive model. Block 318 represents detecting a threshold amount of in-situ sensor data used to trigger creation of a new predictive model.
In other examples, the learning trigger criteria may be based on how much the in-situ sensor data from the in-situ sensors 208 are changing, such as over time or compared to previous values. For example, if variations within the in-situ sensor data (or the relationship between the in-situ sensor data and the information in information map 258) are within a selected range or is less than a defined amount, or below a threshold value, then a new predictive model is not generated by the predictive model generator 210. As a result, the predictive map generator 212 does not generate a new predictive map 264, predictive control zone map 265, or both. However, if variations within the in-situ sensor data are outside of the selected range, are greater than the defined amount, or are above the threshold value, for example, then the predictive model generator 210 generates a new predictive model using all or a portion of the newly received in-situ sensor data that the predictive map generator 212 uses to generate a new predictive map 264. At block 320, variations in the in-situ sensor data, such as a magnitude of an amount by which the data exceeds the selected range or a magnitude of the variation of the relationship between the in-situ sensor data and the information in the information map 258, can be used as a trigger to cause generation of a new predictive model and predictive map. Keeping with the examples described above, the threshold, the range, and the defined amount can be set to default values; set by an operator or user interaction through a user interface; set by an automated system; or set in other ways.
Other learning trigger criteria can also be used. For instance, if predictive model generator 210 switches to a different information map (different from the originally selected information map 258), then switching to the different information map may trigger re-learning by predictive model generator 210, predictive map generator 212, control zone generator 213, control system 214, or other items. In another example, transitioning of agricultural harvester 100 to a different topography or to a different control zone may be used as learning trigger criteria as well.
In some instances, operator 260 can also edit the predictive map 264 or predictive control zone map 265 or both. The edits can change a value on the predictive map 264, change a size, shape, position, or existence of a control zone on predictive control zone map 265, or both. Block 321 shows that edited information can be used as learning trigger criteria.
In some instances, it may also be that operator 260 observes that automated control of a controllable subsystem, is not what the operator desires. In such instances, the operator 260 may provide a manual adjustment to the controllable subsystem reflecting that the operator 260 desires the controllable subsystem to operate in a different way than is being commanded by control system 214. Thus, manual alteration of a setting by the operator 260 can cause one or more of predictive model generator 210 to relearn a model, predictive map generator 212 to regenerate map 264, control zone generator 213 to regenerate one or more control zones on predictive control zone map 265, and control system 214 to relearn a control algorithm or to perform machine learning on one or more of the controller components 232 through 246 in control system 214 based upon the adjustment by the operator 260, as shown in block 322. Block 324 represents the use of other triggered learning criteria.
In other examples, relearning may be performed periodically or intermittently based, for example, upon a selected time interval such as a discrete time interval or a variable time interval, as indicated by block 326.
If relearning is triggered, whether based upon learning trigger criteria or based upon passage of a time interval, as indicated by block 326, then one or more of the predictive model generator 210, predictive map generator 212, control zone generator 213, and control system 214 performs machine learning to generate a new predictive model, a new predictive map, a new control zone, and a new control algorithm, respectively, based upon the learning trigger criteria. The new predictive model, the new predictive map, and the new control algorithm are generated using any additional data that has been collected since the last learning operation was performed. Performing relearning is indicated by block 328.
If the harvesting operation has been completed, operation moves from block 312 to block 330 where one or more of the predictive map 264, predictive control zone map 265, and predictive model generated by predictive model generator 210 are stored. The predictive map 264, predictive control zone map 265, and predictive model may be stored locally on data store 202 or sent to a remote system using communication system 206 for later use.
It will be noted that while some examples herein describe predictive model generator 210 and predictive map generator 212 receiving an information map in generating a predictive model and a functional predictive map, respectively, in other examples, the predictive model generator 210 and predictive map generator 212 can receive, in generating a predictive model and a functional predictive map, respectively other types of maps, including predictive maps, such as a functional predictive map generated during the harvesting operation.
Predictive yield map 341 includes georeferenced predictive yield values. Predictive yield map 341 can be generated using a process described in
Predictive biomass map 343 includes georeferenced predictive biomass values. Predictive biomass map 343 can be generated using a process described in
Predictive model generator 210 also receives a geographic location 334, or an indication of a geographic location, from geographic position sensor 204. In-situ sensors 208 illustratively include a power characteristic sensor, such as power sensor 336, as well as a processing system 338. Power sensor 336 senses power characteristics of one or more components of agricultural harvester 100. In some instances, power sensor 336 may be located on board the agricultural harvester 100. The processing system 338 processes sensor data generated from power sensor 336 to generate processed data, some examples of which are described below. Power sensor 336 can include, but is not limited to, one or more of a voltage sensor, a current sensor, a torque sensor, a fluid pressure sensor, a fluid flow sensor, a force sensor, a bearing load sensor and a rotational sensor. The outputs of one or more of these or other sensors can be combined to determine one or more power characteristic.
The present discussion proceeds with respect to an example in which power sensor 336 is one or more of the above listed. It will be appreciated that these are just examples, and other examples of power sensor 336, are contemplated herein as well. As shown in
Model generator 342 identifies a relationship between a power characteristic, at a geographic location corresponding to where power sensor 336 sensed the characteristic, and vegetative index values from the vegetative index map 332 corresponding to the same location in the field where the power characteristic was sensed. Based on this relationship established by model generator 342, model generator 342 generates a predictive power model 350. The predictive power model 350 is used by predictive map generator 212 to predict power characteristics at different locations in the field based upon the georeferenced vegetative index values contained in the vegetative index map 332 at the same locations in the field.
Model generator 343 identifies a relationship between a power characteristic, at a geographic location corresponding to where power sensor 336 sensed the characteristic, and crop moisture values from the crop moisture map 335 corresponding to the same location in the field where the power characteristic was sensed. Based on this relationship established by model generator 343, model generator 343 generates a predictive power model 350. The predictive power model 350 is used by predictive map generator 212 to predict power characteristics at different locations in the field based upon the georeferenced crop moisture values contained in the crop moisture map 335 at the same locations in the field.
Model generator 344 identifies a relationship between a power characteristic, at a geographic location corresponding to where power sensor 336 sensed the characteristic, and a topographic feature value from the topographic map 337 corresponding to the same location in the field where the power characteristic was sensed. Based on this relationship established by model generator 344, model generator 344 generates a predictive power model 350. The predictive power model 350 is used by predictive map generator 212 to predict power characteristics at different locations in the field based upon the georeferenced topographic feature values contained in the topographic map 337 at the same locations in the field.
Model generator 345 identifies a relationship between a power characteristic, at a geographic location corresponding to where power sensor 336 sensed the characteristic, and a soil property value from the soil property map 339 corresponding to the same location in the field where the power characteristic was sensed. Based on this relationship established by model generator 345, model generator 345 generates a predictive power model 350. The predictive power model 350 is used by predictive map generator 212 to predict power characteristics at different locations in the field based upon the soil property values contained in the soil property map 339 at the same locations in the field.
Model generator 346 identifies a relationship between a power characteristic, at a geographic location corresponding to where power sensor 336 sensed the characteristic, and a yield value from the yield map 341 corresponding to the same location in the field where the power characteristic was sensed. Based on this relationship established by model generator 346, model generator 346 generates a predictive power model 350. The predictive power model 350 is used by predictive map generator 212 to predict power characteristics at different locations in the field based upon the yield value contained in the predictive yield map 341 at the same locations in the field.
Model generator 347 identifies a relationship between a power characteristic, at a geographic location corresponding to where power sensor 336 sensed the characteristic, and a biomass value from the biomass map 343 corresponding to the same location in the field where the power characteristic was sensed. Based on this relationship established by model generator 347, model generator 347 generates a predictive power model 350. The predictive power model 350 is used by predictive map generator 212 to predict power characteristics at different locations in the field based upon the biomass value contained in the predictive biomass map 343 at the same locations in the field.
In light of the above, the predictive model generator 210 is operable to produce a plurality of predictive power models, such as one or more of the predictive power models generated by model generators 342, 343, 344, 345, 346, and 347. In another example, two or more of the predictive power models described above may be combined into a single predictive power model that predicts two or more power characteristics based upon the different values at different locations in the field. Any of these power models, or combinations thereof, are represented collectively by power model 350 in
The predictive power model 350 is provided to predictive map generator 212. In the example of
Crop engaging component map generator 351 receives the predictive power model 350, which predicts power characteristics based upon values in one or more of the vegetative index map 332, crop moisture map 335, topographic map 337, soil property map 339, predictive yield map 341, or predictive biomass map 343, and generates a predictive map that predicts the power characteristics of a crop engaging component at different locations in the field. For example, the crop engaging components could include a cutter and a reel and the crop engaging component map generator 351 generates a map of estimated power usage by the reel and cutter based on a predictive power model 350 that defines a relationship between crop moisture and power usage by the reel and cutter.
Header power map generator 352 receives the predictive power model 350, which predicts power characteristics based upon values in one or more of the vegetative index map 332, crop moisture map 335, topographic map 337, soil property map 339, predictive yield map 341, or predictive biomass map 343, and generates a predictive map that predicts the power characteristics of the header at different locations in the field. For example, the crop engaging components could include one or more of: a cutter, a reel, draper belts, augers, gathering components, stalk processing components, and header positioning actuators and the header power map generator 352 generates a map of estimated power usage by one or more of: the reel, the cutter, draper belts, augers, gathering components, stalk processing components, and header positioning actuators based on a predictive power model 350 that defines a relationship between crop moisture and topography, and power usage by one or more of the reel, the cutter, draper belts, auger, and header positioning actuators.
Feeder power map generator 353 receives the predictive power model 350, which predicts power characteristics based upon values in one or more of the vegetative index map 332, crop moisture map 335, topographic map 337, soil property map 339, predictive yield map 341, or predictive biomass map 343, and generates a predictive map that predicts the power characteristics of the feeder at different locations in the field.
Threshing power map generator 354 receives the predictive power model 350, which predicts power characteristics based upon values in one or more of the vegetative index map 332, crop moisture map 335, topographic map 337, soil property map 339, predictive yield map 341, or predictive biomass map 343, and generates a predictive map that predicts the power characteristics of the threshing subsystems at different locations in the field. For example, the threshing subsystems could include one or more threshing drums, concave adjustment actuators, and beaters, and the threshing power map generator 352 generates a map of estimated power usage by the one or more threshing drums, concave adjustment actuators, and beaters based on a predictive power model 350 that defines a relationship between vegetative index, and power usage by the one or more threshing drums, concave adjustment actuators, and beaters. Or for example, the threshing subsystems could include a threshing drum and a set of concaves at a given clearance, and the threshing power map generator 352 generates a map of estimated power usage by the threshing drum with the set of concaves at the given clearance based on a predictive power model 350 that defines a relationship between predictive biomass, and power usage by the threshing drum with the set of concaves at the given clearance. Or for example, the threshing subsystems could include one or more beaters at a given configuration, and the threshing power map generator 352 generates a map of estimated power usage by the one or more beaters at the given configuration based on a predictive power model 350 that defines a relationship between predictive biomass, and power usage by the one or more beaters at the given configuration.
Separator power map generator 355 receives the predictive power model 350, which predicts power characteristics based upon values in one or more of the vegetative index map 332, crop moisture map 335, topographic map 337, soil property map 339, predictive yield map 341, or predictive biomass map 343, and generates a predictive map that predicts the power characteristics of the separator subsystems at different locations in the field. For example, the separator subsystems could include one or more fans, sieves, chaffers, and straw walkers, and the separator power map generator 355 generates a map of estimated power usage by the one or more fans, sieves, chaffers, and straw walkers based on a predictive power model 350 that defines a relationship between predictive yield value, and power usage by the one or more fans, sieves, chaffers, and straw walkers. For example, the separator subsystems could include one or more fans running at a given speed and sieves, chaffers, and straw walkers in a given configuration, and the separator power map generator 355 generates a map of estimated power usage by the one or more fans running at the given speed and sieves, chaffers, and straw walkers in the given configuration based on a predictive power model 350 that defines a relationship between predictive yield value, and power usage by the one or more fans running at the given speed and sieves, chaffers, and straw walkers in the given configuration.
Residue handling power map generator 356 receives the predictive power model 350, which predicts power characteristics based upon values in one or more of the vegetative index map 332, crop moisture map 335, topographic map 337, soil property map 339, predictive yield map 341, or predictive biomass map 343, and generates a predictive map that predicts the power characteristics of the residue handling subsystems at different locations in the field. For example, the residue handling power map generator 356 generates a map of estimated power usage by a residue spreader based on a predictive power model 350 that defines a relationship between predictive biomass value, and power usage by the residue spreader. For example, the residue handling power map generator 356 generates a map of estimated power usage by a residue chopper based on a predictive power model 350 that defines a relationship between predictive yield value, and power usage by the residue chopper.
Propulsion power map generator 357 receives the predictive power model 350, which predicts power characteristics based upon values in one or more of the vegetative index map 332, crop moisture map 335, topographic map 337, soil property map 339, predictive yield map 341, or predictive biomass map 343, and generates a predictive map that predicts the power characteristics of the propulsion subsystems at different locations in the field. For example, the propulsion power map generator 357 generates a map of estimated power usage by the propulsion subsystem based on a predictive power model 350 that defines a relationship between topographic map value, and power usage by the propulsion system.
Predictive map generator 212 outputs one or more predictive power maps 360 that are predictive of one or more power characteristics. Each of the predictive power maps 360 predicts the respective power characteristic at different locations in a field. Each of the generated predictive power maps 360 may be provided to control zone generator 213, control system 214, or both. Control zone generator 213 generates control zones and incorporates those control zones into the functional predictive map 360. One or more functional predictive maps may be provided to control system 214, which generates control signals to control one or more of the controllable subsystems 216 based upon the one or more functional predictive maps (with or without control zones).
At block 372, processing system 338 processes the one or more received sensor signals to generate data indicative of a power characteristic. As indicated by block 374, the power characteristic may be identified at a machine wide level. For example, the entire power usage by the entire agricultural harvester. A power usage at this level can be used to calculate fuel consumption, efficiency, etc. As indicated by block 376, the power characteristic may be identified at a subsystem level. A characteristic at this level, for instance, can be used to allocate power across subsystems. As indicated by block 378, the power characteristic may be identified at a component level. The sensor data can include other data at other levels as well as indicated by block 380.
At block 382, predictive model generator 210 also obtains the geographic location corresponding to the sensor data. For instance, the predictive model generator 210 can obtain the geographic position from geographic position sensor 204 and determine, based upon machine delays, machine speed, etc., a precise geographic location where the sensor data 340 was captured or derived.
At block 384, predictive model generator 210 generates one or more predictive power models, such as power model 350, that model a relationship between a vegetative index value, a crop moisture value, a soil property value, a predictive yield value, or a predictive biomass value obtained from an information map, such as information map 258, and a power characteristic being sensed by the in-situ sensor 208 or a related characteristic. For instance, predictive model generator 210 may generate a predictive power model that models the relationship between a vegetative index value and a sensed characteristic including power usage indicated by the sensor data obtained from in-situ sensor 208.
At block 386, the predictive power model, such as predictive power model 350, is provided to predictive map generator 212 which generates a predictive power map 360 that maps a predicted power characteristic based on a vegetative index map, a crop moisture map, a soil property map, a predictive yield map, or a predictive biomass map, and the predictive power model 350. For instance, in some examples, the predictive power map 360 predicts power usage/requirements of various subsystems. Further, the predictive power map 360 can be generated during the course of an agricultural operation. Thus, as an agricultural harvester is moving through a field performing an agricultural operation, the predictive power map 360 is generated as the agricultural operation is being performed.
At block 394, predictive map generator 212 outputs the predictive power map 360. At block 391 predictive power map generator 212 outputs the predictive power map for presentation to and possible interaction by operator 260. At block 393, predictive map generator 212 may configure the map for consumption by control system 214. At block 395, predictive map generator 212 can also provide the map 360 to control zone generator 213 for generation of control zones. At block 397, predictive map generator 212 configures the map 360 in other ways as well. The predictive power map 360 (with or without the control zones) is provided to control system 214. At block 396, control system 214 generates control signals to control the controllable subsystems 216 based upon the predictive power map 360.
It can thus be seen that the present system takes an information map that maps a characteristic such as a vegetative index value, a crop moisture value, a soil property value, a predictive yield value, or a predictive biomass value or information from a prior operation pass to different locations in a field. The present system also uses one or more in-situ sensors that sense in-situ sensor data that is indicative of a power characteristic, such as power usage, power requirement, or power loss, and generates a model that models a relationship between the characteristic sensed using the in-situ sensor, or a related characteristic, and the characteristic mapped in the information map. Thus, the present system generates a functional predictive map using a model, in-situ data, and an information map and may configure the generated functional predictive map for consumption by a control system, for presentation to a local or remote operator or other user, or both. For example, the control system may use the map to control one or more systems of a combine harvester.
Also, in the example shown in
Temperature sensor 401 senses a temperature of a given subsystem. In some instances, temperature sensor 401 senses a coolant fluid temperature, a hydraulic fluid temperature, a lubricant, a surface of agricultural harvester 100 such as an inverter surface, a battery or an electronic power device; a moving mechanical component, such as a bearing or gear; an exhaust or other gas, an air temperature inside an enclosed portion of agricultural harvester 100, or some other temperature.
Operator input sensor 405 illustratively senses various operator inputs. The inputs can be setting inputs for controlling the settings on agricultural harvester 100 or other control inputs, such as steering inputs and other inputs. Thus, when operator 260 changes a setting or provides a commanded input through an operator interface mechanism 218, such an input is detected by operator input sensor 405, which provides a sensor signal indicative of that sensed operator input.
Processing system 406 may receive the sensor signals from one or more of temperature sensor 401, operator input sensor 405, and other sensor(s) 408 and generate an output indicative of the sensed variable. For instance, processing system 406 may receive a sensor input from temperature sensor 401 and generate an output indicative of temperature. Processing system 406 may also receive an input from operator input sensor 405 and generate an output indicative of the sensed operator input.
Predictive model generator 210 may include power-to-temperature model generator 410, power-to-operator command model generator 414, and power-to-sensor data model generator 441. In other examples, predictive model generator 210 can include additional, fewer, or other model generators 415. Predictive model generator 210 may receive a geographic location indicator 334 from geographic position sensor 204 and generate a predictive model 426 that models a relationship between the information in one or more of the information maps 258 and one or more of: the temperature sensed by temperature sensor 401; operator input commands sensed by operator input sensor 405; and another agricultural characteristic sensed by other sensor(s) 408.
Power-to-temperature model generator 410 generates a relationship between a power characteristic as reflected on historical power map 333, on predictive power map 360, or on prior operation map 400, or any combination thereof and the temperature sensed by temperature sensor 401. Power-to-temperature model generator 410 generates a predictive model 426 that corresponds to this relationship.
Power-to-operator command model generator 414 generates a model that models the relationship between a power characteristic as reflected on historical power map 333, on predictive power map 360, or on prior operation map 400, or any combination thereof and operator input commands that are sensed by operator input sensor 405. Power-to-operator command model generator 414 generates a predictive model 426 that corresponds to this relationship.
Power-to-sensor data model generator 441 generates a model that models the relationship between a power characteristic as reflected on historical power map 333, on predictive power map 360, or on prior operation map 400, or any combination thereof and sensor data that is sensed by one or more in-situ sensor(s) 208. Power-to-sensor data model generator 441 generates a predictive model 426 that corresponds to this relationship.
Predictive model 426 generated by the predictive model generator 210 can include one or more of the predictive models that may be generated by power-to-temperature model generator 410, power-to-operator command model generator 414, power-to-sensor data model generator 441 and other model generators that may be included as part of other items 415.
In the example of
Predictive temperature map generator 416 receives a predictive model 426 that models the relationship between a power characteristic and temperature (such as a predictive model generated by power-to-temperature model generator 410), and one or more of the information maps 258. Predictive temperature map generator 416 generates a functional predictive temperature map 425 that predicts a temperature of one or more components of agricultural harvester 100 at different locations in the field based upon one or more of the power characteristics in one or more of the information maps 258 at those locations in the field and based on predictive model 426.
Predictive operator command map generator 422 receives a predictive model 426 (such as a predictive model generated by power-to-command model generator 414), that models the relationship between the power characteristic and operator command inputs detected by operator input sensor 405 and generates a functional predictive operator command map 440 that predicts operator command inputs at different locations in the field based upon the power characteristic values from historical power map 333 or predictive power map 360 and the predictive model 426.
Predictive sensor data map generator 420 receives a predictive model 426 that models the relationship between a power characteristic and one or more characteristics sensed by an in situ sensor 408 (such as a predictive model generated by power-to-sensor data model generator 441) and one or more of the information maps 258. Predictive sensor data map generator 420 generates a functional predictive sensor data map 429 that predicts sensor data (or the characteristics the sensor data is indicative of) at different locations in the field based upon one or more of the power characteristics in one or more of the information maps 258 at those locations in the field and based on predictive model 426.
Predictive map generator 212 outputs one or more of the functional predictive maps 425, 429, and 440. Each of the functional predictive maps 425, 429, and 440 may be provided to control zone generator 213, control system 214, or both. Control zone generator 213 can generate and incorporate control zones into each map 425, 429, and 440. Any or all of functional predictive maps 425, 429, and 440 (with or without control zones) may be provided to control system 214, which generates control signals to control one or more of the controllable subsystems 216 based upon one or all of the functional predictive maps 425, 429, and 440. Any or all of the maps 425, 429, and 440 (with or without control zones) may be presented to operator 260 or another user.
Machine sensors 982 may sense different characteristics of agricultural harvester 100. For instance, as discussed above, machine sensors 982 may include machine speed sensors 146, separator loss sensor 148, clean grain camera 150, forward looking image capture mechanism 151, loss sensors 152 or geographic position sensor 204, examples of which are described above. Machine sensors 982 can also include machine setting sensors 991 that sense machine settings. Some examples of machine settings were described above with respect to
Harvested material property sensors 984 may sense characteristics of the severed crop material as the crop material is being processed by agricultural harvester 100. The crop properties may include such things as crop type, crop moisture, grain quality (such as broken grain), MOG levels, grain constituents such as starches and protein, MOG moisture, and other crop material properties. Other sensors could sense straw “toughness”, adhesion of corn to ears, and other characteristics that might be beneficially used to control processing for better grain capture, reduced grain damage, reduced power consumption, reduced grain loss, etc.
Field and soil property sensors 985 may sense characteristics of the field and soil. The field and soil properties may include soil moisture, soil compactness, the presence and location of standing water, soil type, and other soil and field characteristics.
Environmental characteristic sensors 987 may sense one or more environmental characteristics. The environmental characteristics may include such things as wind direction and wind speed, precipitation, fog, dust level or other obscurants, or other environmental characteristics.
At block 444, predictive model generator 210 receives a sensor signal containing sensor data from an in-situ sensor 208. The in-situ sensor can be one or more of a temperature sensor 401, or another sensor 408. Temperature sensor 401 senses a temperature. Predictive model generator 210 can receive other in-situ sensor inputs as well, as indicated by block 452.
At block 454, processing system 406 processes the data contained in the sensor signal or signals received from the in-situ sensor or sensors 208 to obtain processed data 409, shown in
Returning to
At block 458, predictive model generator 210 generates one or more predictive models 426 that model a relationship between a mapped value in an information map and a characteristic represented in the processed data 409. For example, in some instances, the mapped value in an information map may be a power characteristic and the predictive model generator 210 generates a predictive model using the mapped value of an information map and a characteristic sensed by in-situ sensors 208, as represented in the processed data 490, or a related characteristic, such as a characteristic that correlates to the characteristic sensed by in-situ sensors 208.
The one or more predictive models 426 are provided to predictive map generator 212. At block 466, predictive map generator 212 generates one or more functional predictive maps. The functional predictive maps may be functional predictive temperature map generator 425, functional predictive sensor data map 429, and a functional predictive operator command map 440, or any combination of these maps. Functional predictive temperature map generator 425 predicts desirable temperatures at different locations in the field. Functional predictive sensor data map 429 predicts sensor data values or characteristic values indicated by sensor data values at different locations in the field. Functional predictive operator command map 440 predicts likely operator command inputs at different locations in the field. Further, one or more of the functional predictive maps 425, 429, and 440 can be generated during the course of an agricultural operation. Thus, as agricultural harvester 100 is moving through a field performing an agricultural operation, the one or more predictive maps 425, 429, and 440 are generated as the agricultural operation is being performed.
At block 468, predictive map generator 212 outputs the one or more functional predictive maps 425, 429, and 440. At block 470, predictive map generator 212 may configure the map for presentation to and possible interaction by an operator 260 or another user. At block 472, predictive map generator 212 may configure the map for consumption by control system 214. At block 474, predictive map generator 212 can provide the one or more predictive maps 425, 429, and 440 to control zone generator 213 for generation of control zones. At block 476, predictive map generator 212 configures the one or predictive maps 425, 429, and 440 in other ways. In an example in which the one or more functional predictive maps 425, 429, and 440 are provided to control zone generator 213, the one or more functional predictive maps 425, 429, and 440, with the control zones included therewith, represented by corresponding maps 265, described above, may be presented to operator 260 or another user or provided to control system 214 as well.
At block 478, control system 214 then generates control signals to control the controllable subsystems based upon the one or more functional predictive maps 436, 437, 438, and 440 (or the functional predictive maps 425, 429, and 440 having control zones) as well as an input from the geographic position sensor 204.
In an example in which control system 214 receives the functional predictive map, the path planning controller 234 controls steering subsystem 252 to steer agricultural harvester 100. In another example in which control system 214 receives the functional predictive map, the residue system controller 244 controls residue subsystem 138. In another example in which control system 214 receives the functional predictive map, the settings controller 232 controls thresher settings of thresher 110. In another example in which control system 214 receives the functional predictive map, the settings controller 232 or another controller 246 controls material handling subsystem 125. In another example in which control system 214 receives the functional predictive map, the settings controller 232 controls crop cleaning subsystem. In another example in which control system 214 receives the functional predictive map, the machine cleaning controller 245 controls machine cleaning subsystem 254 on agricultural harvester 100. In another example in which control system 214 receives the functional predictive map, the communication system controller 229 controls communication system 206. In another example in which control system 214 receives the functional predictive map, the operator interface controller 231 controls operator interface mechanisms 218 on agricultural harvester 100. In another example in which control system 214 receives the functional predictive map, the deck plate position controller 242 controls machine/header actuators to control a deck plate on agricultural harvester 100. In another example in which control system 214 receives the functional predictive map, the draper belt controller 240 controls machine/header actuators to control a draper belt on agricultural harvester 100. In an example in which control system 214 receives the functional predictive map, cooling controller 235 controls the cooling subsystem 255 on agricultural harvester 100. For instance, cooling controller 235 can adjust a cooling fan speed. Or for instance, cooling controller 235 can adjust a cooling fan pitch. Or for instance, cooling controller 235 can adjust fluid flow through a radiator or other heat dispersing device. In another example in which control system 214 receives the functional predictive map, the other controllers 246 control other controllable subsystems 256 on agricultural harvester 100.
Agricultural harvester 100, or other work machines, may have a wide variety of different types of controllable actuators that perform different functions. The controllable actuators on agricultural harvester 100 or other work machines are collectively referred to as work machine actuators (WMAs). Each WMA may be independently controllable based upon values on a functional predictive map, or the WMAs may be controlled as sets based upon one or more values on a functional predictive map. Therefore, control zone generator 213 may generate control zones corresponding to each individually controllable WMA or corresponding to the sets of WMAs that are controlled in coordination with one another.
WMA selector 486 selects a WMA or a set of WMAs for which corresponding control zones are to be generated. Control zone generation system 488 then generates the control zones for the selected WMA or set of WMAs. For each WMA or set of WMAs, different criteria may be used in identifying control zones. For example, for one WMA, the WMA response time may be used as the criteria for defining the boundaries of the control zones. In another example, wear characteristics (e.g., how much a particular actuator or mechanism wears as a result of movement thereof) may be used as the criteria for identifying the boundaries of control zones. Control zone criteria identifier component 494 identifies particular criteria that are to be used in defining control zones for the selected WMA or set of WMAs. Control zone boundary definition component 496 processes the values on a functional predictive map under analysis to define the boundaries of the control zones on that functional predictive map based upon the values in the functional predictive map under analysis and based upon the control zone criteria for the selected WMA or set of WMAs.
Target setting identifier component 498 sets a value of the target setting that will be used to control the WMA or set of WMAs in different control zones. For instance, if the selected WMA is propulsion system 250 and the functional predictive map under analysis is a functional predictive speed map 438, then the target setting in each control zone may be a target speed setting based on speed values contained in the functional predictive speed map 238 within the identified control zone.
In some examples, where agricultural harvester 100 is to be controlled based on a current or future location of the agricultural harvester 100, multiple target settings may be possible for a WMA at a given position. In that case, the target settings may have different values and may be competing. Thus, the target settings need to be resolved so that only a single target setting is used to control the WMA. For example, where the WMA is an actuator in propulsion system 250 that is being controlled in order to control the speed of agricultural harvester 100, multiple different competing sets of criteria may exist that are considered by control zone generation system 488 in identifying the control zones and the target settings for the selected WMA in the control zones. For instance, different target settings for controlling machine speed may be generated based upon, for example, a detected or predicted feed rate value, a detected or predictive fuel efficiency value, a detected or predicted grain loss value, or a combination of these. However, at any given time, the agricultural harvester 100 cannot travel over the ground at multiple speeds simultaneously. Rather, at any given time, the agricultural harvester 100 travels at a single speed. Thus, one of the competing target settings is selected to control the speed of agricultural harvester 100.
Therefore, in some examples, regime zone generation system 490 generates regime zones to resolve multiple different competing target settings. Regime zone criteria identification component 522 identifies the criteria that are used to establish regime zones for the selected WMA or set of WMAs on the functional predictive map under analysis. Some criteria that can be used to identify or define regime zones include, for example, crop type or crop variety based on an as-planted map or another source of the crop type or crop variety, weed type, weed intensity, soil type, or crop state, such as whether the crop is down, partially down or standing. Just as each WMA or set of WMAs may have a corresponding control zone, different WMAs or sets of WMAs may have a corresponding regime zone. Regime zone boundary definition component 524 identifies the boundaries of regime zones on the functional predictive map under analysis based on the regime zone criteria identified by regime zone criteria identification component 522.
In some examples, regime zones may overlap with one another. For instance, a crop variety regime zone may overlap with a portion of or an entirety of a crop state regime zone. In such an example, the different regime zones may be assigned to a precedence hierarchy so that, where two or more regime zones overlap, the regime zone assigned with a greater hierarchical position or importance in the precedence hierarchy has precedence over the regime zones that have lesser hierarchical positions or importance in the precedence hierarchy. The precedence hierarchy of the regime zones may be manually set or may be automatically set using a rules-based system, a model-based system, or another system. As one example, where a downed crop regime zone overlaps with a crop variety regime zone, the downed crop regime zone may be assigned a greater importance in the precedence hierarchy than the crop variety regime zone so that the downed crop regime zone takes precedence.
In addition, each regime zone may have a unique settings resolver for a given WMA or set of WMAs. Settings resolver identifier component 526 identifies a particular settings resolver for each regime zone identified on the functional predictive map under analysis and a particular settings resolver for the selected WMA or set of WMAs.
Once the settings resolver for a particular regime zone is identified, that settings resolver may be used to resolve competing target settings, where more than one target setting is identified based upon the control zones. The different types of settings resolvers can have different forms. For instance, the settings resolvers that are identified for each regime zone may include a human choice resolver in which the competing target settings are presented to an operator or other user for resolution. In another example, the settings resolver may include a neural network or other artificial intelligence or machine learning system. In such instances, the settings resolvers may resolve the competing target settings based upon a predicted or historic quality metric corresponding to each of the different target settings. As an example, an increased vehicle speed setting may reduce the time to harvest a field and reduce corresponding time-based labor and equipment costs but may increase grain losses. A reduced vehicle speed setting may increase the time to harvest a field and increase corresponding time-based labor and equipment costs but may decrease grain losses. When grain loss or time to harvest is selected as a quality metric, the predicted or historic value for the selected quality metric, given the two competing vehicle speed settings values, may be used to resolve the speed setting. In some instances, the settings resolvers may be a set of threshold rules that may be used instead of, or in addition to, the regime zones. An example of a threshold rule may be expressed as follows:
The settings resolvers may be logical components that execute logical rules in identifying a target setting. For instance, the settings resolver may resolve target settings while attempting to minimize harvest time or minimize the total harvest cost or maximize harvested grain or based on other variables that are computed as a function of the different candidate target settings. A harvest time may be minimized when an amount to complete a harvest is reduced to at or below a selected threshold. A total harvest cost may be minimized where the total harvest cost is reduced to at or below a selected threshold. Harvested grain may be maximized where the amount of harvested grain is increased to at or above a selected threshold.
At block 530, control zone generator 213 receives a map under analysis for processing. In one example, as shown at block 532, the map under analysis is a functional predictive map. For example, the map under analysis may be one of the functional predictive maps 436, 437, 438, or 440. Block 534 indicates that the map under analysis can be other maps as well.
At block 536, WMA selector 486 selects a WMA or a set of WMAs for which control zones are to be generated on the map under analysis. At block 538, control zone criteria identification component 494 obtains control zone definition criteria for the selected WMAs or set of WMAs. Block 540 indicates an example in which the control zone criteria are or include wear characteristics of the selected WMA or set of WMAs. Block 542 indicates an example in which the control zone definition criteria are or include a magnitude and variation of input source data, such as the magnitude and variation of the values on the map under analysis or the magnitude and variation of inputs from various in-situ sensors 208. Block 544 indicates an example in which the control zone definition criteria are or include physical machine characteristics, such as the physical dimensions of the machine, a speed at which different subsystems operate, or other physical machine characteristics. Block 546 indicates an example in which the control zone definition criteria are or include a responsiveness of the selected WMA or set of WMAs in reaching newly commanded setting values. Block 548 indicates an example in which the control zone definition criteria are or include machine performance metrics. Block 550 indicates an example in which the control zone definition criteria are or includes operator preferences. Block 552 indicates an example in which the control zone definition criteria are or include other items as well. Block 549 indicates an example in which the control zone definition criteria are time based, meaning that agricultural harvester 100 will not cross the boundary of a control zone until a selected amount of time has elapsed since agricultural harvester 100 entered a particular control zone. In some instances, the selected amount of time may be a minimum amount of time. Thus, in some instances, the control zone definition criteria may prevent the agricultural harvester 100 from crossing a boundary of a control zone until at least the selected amount of time has elapsed. Block 551 indicates an example in which the control zone definition criteria are based on a selected size value. For example, control zone definition criteria that are based on a selected size value may preclude definition of a control zone that is smaller than the selected size. In some instances, the selected size may be a minimum size.
At block 554, regime zone criteria identification component 522 obtains regime zone definition criteria for the selected WMA or set of WMAs. Block 556 indicates an example in which the regime zone definition criteria are based on a manual input from operator 260 or another user. Block 558 illustrates an example in which the regime zone definition criteria are based on crop type or crop variety. Block 560 illustrates an example in which the regime zone definition criteria are based on weed type or weed intensity or both. Block 562 illustrates an example in which the regime zone definition criteria are based on or include crop state. Block 564 indicates an example in which the regime zone definition criteria are or include other criteria as well. For example, the regime zone definition criteria can be based on topographic characteristics or soil characteristics.
At block 566, control zone boundary definition component 496 generates the boundaries of control zones on the map under analysis based upon the control zone criteria. Regime zone boundary definition component 524 generates the boundaries of regime zones on the map under analysis based upon the regime zone criteria. Block 568 indicates an example in which the zone boundaries are identified for the control zones and the regime zones. Block 570 shows that target setting identifier component 498 identifies the target settings for each of the control zones. The control zones and regime zones can be generated in other ways as well, and this is indicated by block 572.
At block 574, settings resolver identifier component 526 identifies the settings resolver for the selected WMAs in each regime zone defined by regimes zone boundary definition component 524. As discussed above, the regime zone resolver can be a human resolver 576, an artificial intelligence or machine learning system resolver 578, a resolver 580 based on predicted or historic quality for each competing target setting, a rules-based resolver 582, a performance criteria-based resolver 584, or other resolvers 586.
At block 588, WMA selector 486 determines whether there are more WMAs or sets of WMAs to process. If additional WMAs or sets of WMAs are remaining to be processed, processing reverts to block 436 where the next WMA or set of WMAs for which control zones and regime zones are to be defined is selected. When no additional WMAs or sets of WMAs for which control zones or regime zones are to be generated are remaining, processing moves to block 590 where control zone generator 213 outputs a map with control zones, target settings, regime zones, and settings resolvers for each of the WMAs or sets of WMAs. As discussed above, the outputted map can be presented to operator 260 or another user; the outputted map can be provided to control system 214; or the outputted map can be output in other ways.
At block 612, control system 214 receives a sensor signal from geographic position sensor 204. The sensor signal from geographic position sensor 204 can include data that indicates the geographic location 614 of agricultural harvester 100, the speed 616 of agricultural harvester 100, the heading 618 or agricultural harvester 100, or other information 620. At block 622, zone controller 247 selects a regime zone, and, at block 624, zone controller 247 selects a control zone on the map based on the geographic position sensor signal. At block 626, zone controller 247 selects a WMA or a set of WMAs to be controlled. At block 628, zone controller 247 obtains one or more target settings for the selected WMA or set of WMAs. The target settings that are obtained for the selected WMA or set of WMAs may come from a variety of different sources. For instance, block 630 shows an example in which one or more of the target settings for the selected WMA or set of WMAs is based on an input from the control zones on the map of the worksite. Block 632 shows an example in which one or more of the target settings is obtained from human inputs from operator 260 or another user. Block 634 shows an example in which the target settings are obtained from an in-situ sensor 208. Block 636 shows an example in which the one or more target settings is obtained from one or more sensors on other machines working in the same field either concurrently with agricultural harvester 100 or from one or more sensors on machines that worked in the same field in the past. Block 638 shows an example in which the target settings are obtained from other sources as well.
At block 640, zone controller 247 accesses the settings resolver for the selected regime zone and controls the settings resolver to resolve competing target settings into a resolved target setting. As discussed above, in some instances, the settings resolver may be a human resolver in which case zone controller 247 controls operator interface mechanisms 218 to present the competing target settings to operator 260 or another user for resolution. In some instances, the settings resolver may be a neural network or other artificial intelligence or machine learning system, and zone controller 247 submits the competing target settings to the neural network, artificial intelligence, or machine learning system for selection. In some instances, the settings resolver may be based on a predicted or historic quality metric, on threshold rules, or on logical components. In any of these latter examples, zone controller 247 executes the settings resolver to obtain a resolved target setting based on the predicted or historic quality metric, based on the threshold rules, or with the use of the logical components.
At block 642, with zone controller 247 having identified the resolved target setting, zone controller 247 provides the resolved target setting to other controllers in control system 214, which generate and apply control signals to the selected WMA or set of WMAs based upon the resolved target setting. For instance, where the selected WMA is a machine or header actuator 248, zone controller 247 provides the resolved target setting to settings controller 232 or header/real controller 238 or both to generate control signals based upon the resolved target setting, and those generated control signals are applied to the machine or header actuators 248. At block 644, if additional WMAs or additional sets of WMAs are to be controlled at the current geographic location of the agricultural harvester 100 (as detected at block 612), then processing reverts to block 626 where the next WMA or set of WMAs is selected. The processes represented by blocks 626 through 644 continue until all of the WMAs or sets of WMAs to be controlled at the current geographical location of the agricultural harvester 100 have been addressed. If no additional WMAs or sets of WMAs are to be controlled at the current geographic location of the agricultural harvester 100 remain, processing proceeds to block 646 where zone controller 247 determines whether additional control zones to be considered exist in the selected regime zone. If additional control zones to be considered exist, processing reverts to block 624 where a next control zone is selected. If no additional control zones are remaining to be considered, processing proceeds to block 648 where a determination as to whether additional regime zones are remaining to be consider. Zone controller 247 determines whether additional regime zones are remaining to be considered. If additional regimes zone are remaining to be considered, processing reverts to block 622 where a next regime zone is selected.
At block 650, zone controller 247 determines whether the operation that agricultural harvester 100 is performing is complete. If not, the zone controller 247 determines whether a control zone criterion has been satisfied to continue processing, as indicated by block 652. For instance, as mentioned above, control zone definition criteria may include criteria defining when a control zone boundary may be crossed by the agricultural harvester 100. For example, whether a control zone boundary may be crossed by the agricultural harvester 100 may be defined by a selected time period, meaning that agricultural harvester 100 is prevented from crossing a zone boundary until a selected amount of time has transpired. In that case, at block 652, zone controller 247 determines whether the selected time period has elapsed. Additionally, zone controller 247 can perform processing continually. Thus, zone controller 247 does not wait for any particular time period before continuing to determine whether an operation of the agricultural harvester 100 is completed. At block 652, zone controller 247 determines that it is time to continue processing, then processing continues at block 612 where zone controller 247 again receives an input from geographic position sensor 204. It will also be appreciated that zone controller 247 can control the WMAs and sets of WMAs simultaneously using a multiple-input, multiple-output controller instead of controlling the WMAs and sets of WMAs sequentially.
Operator input command processing system 654 detects operator inputs on operator interface mechanisms 218 and processes those inputs for commands. Speech handling system 662 detects speech inputs and handles the interactions with speech processing system 658 to process the speech inputs for commands. Touch gesture handling system 664 detects touch gestures on touch sensitive elements in operator interface mechanisms 218 and processes those inputs for commands.
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 those outputs to other controllers in control system 214. Speech processing system 658 recognizes speech inputs, determines the meaning of those inputs, and provides an output indicative of the meaning of the spoken inputs. For instance, speech processing system 658 may recognize a speech input from operator 260 as a settings change command in which operator 260 is commanding control system 214 to change a setting for a controllable subsystem 216. In such an example, speech processing system 658 recognizes the content of the spoken command, identifies the meaning of that command as a settings change command, and provides the meaning of that input back to speech handling system 662. Speech handling system 662, in turn, interacts with controller output generator 670 to provide the commanded output to the appropriate controller in control system 214 to accomplish the spoken settings change command.
Speech processing system 658 may be invoked in a variety of different ways. For instance, in one example, speech handling system 662 continuously provides an input from a microphone (being one of the operator interface mechanisms 218) to speech processing system 658. The microphone detects speech from operator 260, and the speech handling system 662 provides the detected speech to speech processing system 658. Trigger detector 672 detects a trigger indicating that speech processing system 658 is invoked. In some instances, when speech processing system 658 is receiving continuous speech inputs from speech handling system 662, speech recognition component 674 performs continuous speech recognition on all speech spoken by operator 260. In some instances, speech processing system 658 is configured for invocation using a wakeup word. That is, in some instances, operation of speech processing system 658 may be initiated based on recognition of a selected spoken word, referred to as the wakeup word. In such an example, where recognition component 674 recognizes the wakeup word, the recognition component 674 provides an indication that the wakeup word has been recognized to trigger detector 672. Trigger detector 672 detects that speech processing system 658 has been invoked or triggered by the wakeup word. In another example, speech processing system 658 may be invoked by an operator 260 actuating an actuator on a user interface mechanism, such as by touching an actuator on a touch sensitive display screen, by pressing a button, or by providing another triggering input. In such an example, trigger detector 672 can detect that speech processing system 658 has been invoked when a triggering input via a user interface mechanism is detected. Trigger detector 672 can detect that speech processing system 658 has been invoked in other ways as well.
Once speech processing system 658 is invoked, the speech input from operator 260 is provided to speech recognition component 674. Speech recognition component 674 recognizes linguistic elements in the speech input, such as words, phrases, or other linguistic units. Natural language understanding system 678 identifies a meaning of the recognized speech. The meaning may be a natural language output, a command output identifying a command reflected in the recognized speech, a value output identifying a value in the recognized speech, or any of a wide variety of other outputs that reflect the understanding of the recognized speech. For example, the natural language understanding system 678 and speech processing system 568, more generally, may understand of the meaning of the recognized speech in the context of agricultural harvester 100.
In some examples, speech processing system 658 can also generate outputs that navigate operator 260 through a user experience based on the speech input. For instance, dialog management system 680 may generate and manage a dialog with the user in order to identify what the user wishes to do. The dialog may disambiguate a user's command; identify one or more specific values that are needed to carry out the user's command; or obtain other information from the user or provide other information to the user or both. Synthesis component 676 may generate speech synthesis which can be presented to the user through an audio operator interface mechanism, such as a speaker. Thus, the dialog managed by dialog management system 680 may be exclusively a spoken dialog or a combination of both a visual dialog and a spoken dialog.
Action signal generator 660 generates action signals to control operator interface mechanisms 218 based upon outputs from one or more of operator input command processing system 654, other controller interaction system 656, and speech processing system 658. Visual control signal generator 684 generates control signals to control visual items in operator interface mechanisms 218. The visual items may be lights, a display screen, warning indicators, or other visual items. Audio control signal generator 686 generates outputs that control audio elements of operator interface mechanisms 218. The audio elements include a speaker, audible alert mechanisms, horns, or other audible elements. Haptic control signal generator 688 generates control signals that are output to control haptic elements of operator interface mechanisms 218. The haptic elements include vibration elements that may be used to vibrate, for example, the operator's seat, the steering wheel, pedals, or joysticks used by the operator. The haptic elements may include tactile feedback or force feedback elements that provide tactile feedback or force feedback to the operator through operator interface mechanisms. The haptic elements may include a wide variety of other haptic elements as well.
At block 692, operator interface controller 231 receives a map. Block 694 indicates an example in which the map is a functional predictive map, and block 696 indicates an example in which the map is another type of map. At block 698, operator interface controller 231 receives an input from geographic position sensor 204 identifying the geographic location of the agricultural harvester 100. As indicated in block 700, the input from geographic position sensor 204 can include the heading, along with the location, of agricultural harvester 100. Block 702 indicates an example in which the input from geographic position sensor 204 includes the speed of agricultural harvester 100, and block 704 indicates an example in which the input from geographic position sensor 204 includes other items.
At block 706, visual control signal generator 684 in operator interface controller 231 controls the touch sensitive display screen in operator interface mechanisms 218 to generate a display showing all or a portion of a field represented by the received map. Block 708 indicates that the displayed field can include a current position marker showing a current position of the agricultural harvester 100 relative to the field. Block 710 indicates an example in which the displayed field includes a next work unit marker that identifies a next work unit (or area on the field) in which agricultural harvester 100 will be operating. Block 712 indicates an example in which the displayed field includes an upcoming area display portion that displays areas that are yet to be processed by agricultural harvester 100, and block 714 indicates an example in which the displayed field includes previously visited display portions that represent areas of the field that agricultural harvester 100 has already processed. Block 716 indicates an example in which the displayed field displays various characteristics of the field having georeferenced locations on the map. For instance, if the received map is a power map, the displayed field may show the different power characteristics existing in the field georeferenced within the displayed field. The mapped characteristics can be shown in the previously visited areas (as shown in block 714), in the upcoming areas (as shown in block 712), and in the next work unit (as shown in block 710). Block 718 indicates an example in which the displayed field includes other items as well.
In the example shown in
In the example shown in
In the example of
The actuators and display markers in portion 738 may be displayed as, for example, individual items, fixed lists, scrollable lists, drop down menus, or drop down lists. In the example shown in
Column 746 displays the symbols corresponding to each category of temperature that is being tracked on the field display portion 728. Designator column 748 shows the designator (which may be a textual designator or other designator) identifying the category of temperature. Without limitation, the temperature symbols in column 746 and the designators in column 748 can include any display feature such as different colors, shapes, patterns, intensities, text, icons, or other display features. The values displayed in column 750 can be predicted temperature values or temperature values measured by in-situ sensors 208. In one example, the operator 260 can select the particular part of field display portion 728 for which the values in column 750 are to be displayed. Thus, the values in column 750 can correspond to values in display portions 712, 714 or 730. Column 752 displays action threshold values. Action threshold values in column 752 may be threshold values corresponding to the measured values in column 750. If the measured values in column 750 satisfy the corresponding action threshold values in column 752, then control system 214 takes the action identified in column 754. In some instances, a measured value may satisfy a corresponding action threshold value by meeting or exceeding the corresponding action threshold value. In one example, operator 260 can select a threshold value, for example, in order to change the threshold value by touching the threshold value in column 752. Once selected, the operator 260 may change the threshold value. The threshold values in column 752 can be configured such that the designated action is performed when the measured value 750 exceeds the threshold value, equals the threshold value, or is less than the threshold value.
Similarly, operator 260 can touch the action identifiers in column 754 to change the action that is to be taken. When a threshold is met, multiple actions may be taken. For instance, at the bottom of column 754, a decrease cooling fan speed is identified as an action that will be taken if the measured value in column 750 meets the threshold value in column 752.
The actions that can be set in column 754 can be any of a wide variety of different types of actions. For example, the actions can include a keep out action which, when executed, inhibits agricultural harvester 100 from further harvesting in an area. The actions can include a speed change action which, when executed, changes the travel speed of agricultural harvester 100 through the field. The actions can include a setting change action for changing a setting of an internal actuator or another WMA or set of WMAs or for implementing a settings change action that changes a setting of a header. These are examples only, and a wide variety of other actions are contemplated herein.
The display markers shown on user interface display 720 can be visually controlled. Visually controlling the interface display 720 may be performed to capture the attention of operator 260. For instance, the display markers can be controlled to modify the intensity, color, or pattern with which the display markers are displayed. Additionally, the display markers may be controlled to flash. The described alterations to the visual appearance of the display markers are provided as examples. Consequently, other aspects of the visual appearance of the display markers may be altered. Therefore, the display markers can be modified under various circumstances in a desired manner in order, for example, to capture the attention of operator 260.
Returning now to the flow diagram of
At block 782, operator input command processing system 654 detects and processes operator inputs corresponding to interactions with the user interface display 720 performed by the operator 260. Where the user interface mechanism on which user interface display 720 is displayed is a touch sensitive display screen, interaction inputs with the touch sensitive display screen by the operator 260 can be touch gestures 784. In some instances, the operator interaction inputs can be inputs using a point and click device 786 or other operator interaction inputs 788.
At block 790, operator interface controller 231 receives signals indicative of an alert condition. For instance, block 792 indicates that signals may be received by controller input processing system 668 indicating that detected values in column 750 satisfy threshold conditions present in column 752. As explained earlier, the threshold conditions may include values being below a threshold, at a threshold, or above a threshold. Block 794 shows that action signal generator 660 can, in response to receiving an alert condition, alert the operator 260 by using visual control signal generator 684 to generate visual alerts, by using audio control signal generator 686 to generate audio alerts, by using haptic control signal generator 688 to generate haptic alerts, or by using any combination of these. Similarly, as indicated by block 796, controller output generator 670 can generate outputs to other controllers in control system 214 so that those controllers perform the corresponding action identified in column 754. Block 798 shows that operator interface controller 231 can detect and process alert conditions in other ways as well.
Block 900 shows that speech handling system 662 may detect and process inputs invoking speech processing system 658. Block 902 shows that performing speech processing may include the use of dialog management system 680 to conduct a dialog with the operator 260. Block 904 shows that the speech processing may include providing signals to controller output generator 670 so that control operations are automatically performed based upon the speech inputs.
Table 1, below, shows an example of a dialog between operator interface controller 231 and operator 260. In Table 1, operator 260 uses a trigger word or a wakeup word that is detected by trigger detector 672 to invoke speech processing system 658. In the example shown in Table 1, the wakeup word is “Johnny”.
Table 2 shows an example in which speech synthesis component 676 provides an output to audio control signal generator 686 to provide audible updates on an intermittent or periodic basis. The interval between updates may be time-based, such as every five minutes, or coverage or distance-based, such as every five acres, or exception-based, such as when a measured value is greater than a threshold value.
The example shown in Table 3 illustrates that some actuators or user input mechanisms on the touch sensitive display 720 can be supplemented with speech dialog. The example in Table 3 illustrates that action signal generator 660 can generate action signals to automatically mark a weed patch in the field being harvested.
The example shown in Table 4 illustrates that action signal generator 660 can conduct a dialog with operator 260 to begin and end marking of a weed patch.
The example shown in Table 5 illustrates that action signal generator 160 can generate signals to mark a weed patch in a different way than those shown in Tables 3 and 4.
Returning again to
Once the operation is complete, then any desired values that are displayed, or have been displayed on user interface display 720, can be saved. Those values can also be used in machine learning to improve different portions of predictive model generator 210, predictive map generator 212, control zone generator 213, control algorithms, or other items. Saving the desired values is indicated by block 916. The values can be saved locally on agricultural harvester 100, or the values can be saved at a remote server location or sent to another remote system.
It can thus be seen that an information map is obtained by an agricultural harvester and shows power characteristic values at different geographic locations of a field being harvested. An in-situ sensor on the harvester senses a characteristic that has values indicative of an agricultural characteristic as the agricultural harvester moves through the field. A predictive map generator generates a predictive map that predicts control values for different locations in the field based on the values of the power characteristic in the information map and the agricultural characteristic sensed by the in-situ sensor. A control system controls controllable subsystem based on the control values in the predictive map.
A control value is a value upon which an action can be based. A control value, as described herein, can include any value (or characteristics indicated by or derived from the value) that may be used in the control of agricultural harvester 100. A control value can be any value indicative of an agricultural characteristic. A control value can be a predicted value, a measured value, or a detected value. A control value may include any of the values provided by a map, such as any of the maps described herein, for instance, a control value can be a value provided by an information map, a value provided by prior information map, or a value provided predictive map, such as a functional predictive map. A control value can also include any of the characteristics indicated by or derived from the values detected by any of the sensors described herein. In other examples, a control value can be provided by an operator of the agricultural machine, such as a command input by an operator of the agricultural machine.
The present discussion has mentioned processors and servers. In some examples, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. The processors and servers are functional parts of the systems or devices to which the processors and servers belong and are activated by and facilitate the functionality of the other components or items in those systems.
Also, a number of user interface displays have been discussed. The displays can take a wide variety of different forms and can have a wide variety of different user actuatable operator interface mechanisms disposed thereon. For instance, user actuatable operator interface mechanisms may include text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. The user actuatable operator interface mechanisms can also be actuated in a wide variety of different ways. For instance, the user actuatable operator interface mechanisms can be actuated using operator interface mechanisms such as a point and click device, such as a track ball or mouse, hardware buttons, switches, a joystick or keyboard, thumb switches or thumb pads, etc., a virtual keyboard or other virtual actuators. In addition, where the screen on which the user actuatable operator interface mechanisms are displayed is a touch sensitive screen, the user actuatable operator interface mechanisms can be actuated using touch gestures. Also, user actuatable operator interface mechanisms can be actuated using speech commands using speech recognition functionality. Speech recognition may be implemented using a speech detection device, such as a microphone, and software that functions to recognize detected speech and execute commands based on the received speech.
A number of data stores have also been discussed. It will be noted the data stores can each be broken into multiple data stores. In some examples, one or more of the data stores may be local to the systems accessing the data stores, one or more of the data stores may all be located remote form a system utilizing the data store, or one or more data stores may be local while others are remote. All of these configurations are contemplated by the present disclosure.
Also, the figures show a number of blocks with functionality ascribed to each block. It will be noted that fewer blocks can be used to illustrate that the functionality ascribed to multiple different blocks is performed by fewer components. Also, more blocks can be used illustrating that the functionality may be distributed among more components. In different examples, some functionality may be added, and some may be removed.
It will be noted that the above discussion has described a variety of different systems, components, logic, and interactions. It will be appreciated that any or all of such systems, components, logic and interactions may be implemented by hardware items, such as processors, memory, or other processing components, including but not limited to artificial intelligence components, such as neural networks, some of which are described below, that perform the functions associated with those systems, components, logic, or interactions. In addition, any or all of the systems, components, logic and interactions may be implemented by software that is loaded into a memory and is subsequently executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic and interactions may 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 may be used to implement any or all of the systems, components, logic and interactions described above. Other structures may be used as well.
In the example shown in
It will also be noted that the elements of
In some examples, remote server architecture 500 may include cybersecurity measures. Without limitation, these measures may include encryption of data on storage devices, encryption of data sent between network nodes, authentication of people or processes accessing data, as well as the use of ledgers for recording metadata, data, data transfers, data accesses, and data transformations. In some examples, the ledgers may be distributed and immutable (e.g., implemented as blockchain).
In other examples, applications can be received on a removable Secure Digital (SD) card that is connected to an interface 15. Interface 15 and communication links 13 communicate with a processor 17 (which can also embody processors or servers from other FIGS.) along a bus 19 that is also connected to memory 21 and input/output (I/O) components 23, as well as clock 25 and location system 27.
I/O components 23, in one example, are provided to facilitate input and output operations. I/O components 23 for various examples of the device 16 can include input components such as buttons, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors and output components such as a display device, a speaker, and or a printer port. Other I/O components 23 can be used as well.
Clock 25 illustratively comprises a real time clock component that outputs a time and date. It can also, illustratively, provide timing functions for processor 17.
Location system 27 illustratively includes a component that outputs a current geographical location of device 16. This can include, for instance, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. Location system 27 can also include, for example, mapping software or navigation software that generates desired maps, navigation routes and other geographic functions.
Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data store 37, communication drivers 39, and communication configuration settings 41. Memory 21 can include all types of tangible volatile and non-volatile computer-readable memory 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 according to the instructions. Processor 17 may be activated by other components to facilitate their functionality as well.
Note that other forms of the devices 16 are possible.
Computer 810 typically includes a variety of computer readable media. Computer readable media may be any available media that can be accessed by computer 810 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. Computer readable media includes hardware storage media including both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer 810. Communication media may embody computer readable instructions, data structures, program modules or other data in a transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.
The system memory 830 includes computer storage media in the form of volatile and/or nonvolatile memory or both such as read only memory (ROM) 831 and random access memory (RAM) 832. A basic input/output system 833 (BIOS), containing the basic routines that help to transfer information between elements within computer 810, such as during start-up, is typically stored in ROM 831. RAM 832 typically contains data or program modules or both that are immediately accessible to and/or presently being operated on by processing unit 820. By way of example, and not limitation,
The computer 810 may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,
Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (e.g., ASICs), Application-specific Standard Products (e.g., ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.
The drives and their associated computer storage media discussed above and illustrated in
A user may enter commands and information into the computer 810 through input devices such as a keyboard 862, a microphone 863, and a pointing device 861, such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 820 through a user input interface 860 that is coupled to the system bus, but may be connected by other interface and bus structures. A visual display 891 or other type of display device is also connected to the system bus 821 via an interface, such as a video interface 890. In addition to the monitor, computers may also include other peripheral output devices such as speakers 897 and printer 896, which may be connected through an output peripheral interface 895.
The computer 810 is operated in a networked environment using logical connections (such as a controller area network—CAN, local area network—LAN, or wide area network WAN) to one or more remote computers, such as a remote computer 880.
When used in a LAN networking environment, the computer 810 is connected to the LAN 871 through a network interface or adapter 870. When used in a WAN networking environment, the computer 810 typically includes a modem 872 or other means for establishing communications over the WAN 873, such as the Internet. In a networked environment, program modules may be stored in a remote memory storage device.
It should also be noted that the different examples described herein 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 contemplated herein.
Example 1 is an agricultural work machine comprising:
a communication system that receives an information map that includes values of a power characteristic corresponding to different geographic locations in a field;
a geographic position sensor that detects a geographic location of the agricultural work machine;
an in-situ sensor that detects a value of an agricultural characteristic corresponding to a geographic location;
a predictive map generator 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 the power characteristic in the information map and based on the value of the agricultural characteristic;
a controllable subsystem; and
a control system that generates a control signal to control the controllable subsystem based on the geographic position of the agricultural work machine and based on the control values in the functional predictive agricultural map.
Example 2 is the agricultural work machine of any or all previous examples, wherein the predictive map generator comprises:
a predictive temperature map generator that generates a functional predictive temperature map that maps predictive temperature values to the different geographic locations in the field.
Example 3 is the agricultural work machine of any or all previous examples, wherein the control system comprises:
a cooling controller that generates a cooling subsystem control signal based on the detected geographic location and the functional predictive temperature map and controls a cooling subsystem as the controllable subsystem based on the cooling subsystem control signal.
Example 4 is the agricultural work machine of any or all previous examples, wherein the control system controls the cooling subsystem to adjust a cooling fan speed.
Example 5 is the agricultural work machine of any or all previous examples, wherein the control system controls the cooling subsystem to adjust a cooling fan pitch.
Example 6 is the agricultural work machine of any or all previous examples, wherein the predictive map generator comprises:
a predictive operator command map generator that generates a functional predictive operator command map that maps predictive operator commands to the different geographic locations in the field.
Example 7 is the agricultural work machine of any or all previous examples, wherein the control system comprises:
a settings controller that generates an operator command control signal indicative of an operator command based on the detected geographic location and the functional predictive operator command map and controls the controllable subsystem based on the operator command control signal to execute the operator command.
Example 8 is the agricultural work machine of any or all previous examples, wherein the information map comprises a historical power map that maps historical power characteristic values to the different geographic locations in the field.
Example 9 is the agricultural work machine of any or all previous examples, wherein the control system further comprises:
an operator interface controller that generates a user interface map representation of the functional predictive agricultural map, the user interface map representation comprising a field portion with one or more markers indicating the predictive control values at one or more geographic locations on the field portion.
Example 10 is the agricultural work machine of any or all previous examples, wherein the operator interface controller generates the user interface map representation to include an interactive display portion that displays a value display portion indicative of a selected value, an interactive threshold display portion indicative of an action threshold, and an interactive action display portion indicative of a control action to be taken when one of the predictive control values satisfies the action threshold in relation to the selected value, the control system generating the control signal to control the controllable subsystem based on the control action.
Example 11 is a computer implemented method of controlling an agricultural work machine comprising:
obtaining an information map that includes values of a power characteristic corresponding to different geographic locations in a field;
detecting a geographic location of the agricultural work machine;
detecting, with an in-situ sensor, a value of an agricultural characteristic corresponding to a geographic location;
generating 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 the power characteristic in the information map and based on the value of the agricultural characteristic; and
controlling a controllable subsystem based on the geographic position of the agricultural work machine and based on the control values in the functional predictive agricultural map.
Example 12 is the computer implemented method of any or all previous examples, wherein generating a functional predictive map comprises:
generating a functional predictive temperature map that maps predictive temperature values to the different geographic locations in the field.
Example 13 is the computer implemented method of any or all previous examples, wherein controlling a controllable subsystem comprises:
generating a cooling subsystem control signal based on the detected geographic location and the functional predictive temperature map; and
controlling a cooling subsystem as the controllable subsystem based on the cooling subsystem control signal.
Example 14 is the computer implemented method any or all previous examples, controlling a cooling subsystem as the controllable subsystem based on the cooling subsystem control signal comprises:
controlling a fan speed of the cooling subsystem.
Example 15 is the computer implemented method of any or all previous examples, controlling a cooling subsystem as the controllable subsystem based on the cooling subsystem control signal comprises:
controlling a fan pitch of the cooling subsystem.
Example 16 is the computer implemented method of any or all previous examples, wherein generating a functional predictive map comprises:
generating a functional predictive operator command map that maps predictive operator commands to the different geographic locations in the field.
Example 17 is the computer implemented method of any or all previous examples, wherein controlling the controllable subsystem comprises:
generating an operator command control signal indicative of an operator command based on the detected geographic location and the functional predictive operator command map; and
controlling the controllable subsystem based on the operator command control signal to execute the operator command.
Example 18 is the computer implemented method of any or all previous examples, and further comprising:
generating a predictive agricultural model that models a relationship between the power characteristic and the agricultural characteristic based on a value of the power characteristic in the information map at the geographic location and a value of the agricultural characteristic sensed by the in-situ sensor at the geographic location, wherein generating the functional predictive agricultural map comprises generating the functional predictive agricultural map based on the values of the power characteristic in the information map and based on the predictive agricultural model.
Example 19 is an agricultural work machine comprising:
a communication system that receives an information map that includes values of a power characteristic corresponding to different geographic locations in a field;
a geographic position sensor that detects a geographic location of the agricultural work machine;
an in-situ sensor that detects a value of an agricultural characteristic corresponding to a geographic location;
a predictive model generator that generates a predictive agricultural model that models a relationship between the power characteristic and the agricultural characteristic based on a value of the power characteristic in the information map at the geographic location and a value of the agricultural characteristic sensed by the in-situ sensor at the geographic location;
a predictive map generator 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 the power characteristic in the information map and based on the predictive agricultural model;
a controllable subsystem; and
a control system that generates a control signal to control the controllable subsystem based on the geographic position of the agricultural work machine and based on the control values in the functional predictive agricultural map.
Example 20 is the agricultural work machine of any or all previous examples, wherein the control system comprises:
a cooling controller that generates a cooling control signal based on the detected geographic location and the functional predictive agricultural map and controls a cooling subsystem as the controllable subsystem based on the cooling control signal.
Although the subject matter has been described in language specific to structural features or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of the claims.
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| Number | Date | Country | |
|---|---|---|---|
| 20220110237 A1 | Apr 2022 | US |
