CN-114303588-B - Machine control using predictive graphs

CN114303588BCN 114303588 BCN114303588 BCN 114303588BCN-114303588-B

Abstract

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 the field. In situ sensors on an agricultural work machine sense agricultural characteristics as the agricultural work machine moves through a field. The prediction graph generator generates a prediction graph that predicts predicted agricultural characteristics at different locations in the field based on a relationship between the values in the one or more information graphs and the agricultural characteristics sensed by the in-situ sensor. Predictive maps may be output and used for automatic machine control.

Inventors

Nathan R. Van Dick
Barnu Kiran Reddy Pala
Vandeven, Michael L.
Duane M. Bomleni

Assignees

迪尔公司

Dates

Publication Date: 20260508
Application Date: 20210908
Priority Date: 20201009

Claims (9)

1. An agricultural work machine (100), comprising: A communication system (206) that receives a graph (258) that includes values of an agricultural characteristic corresponding to different geographic locations in a field; a geographic position sensor (204) that detects a geographic position of the agricultural work machine (100); An in situ sensor (208) that detects a value of an ear of grain specification corresponding to the geographic location; A predictive map generator (212) that generates a functional predictive ear specification map of the field based on the values of the agricultural characteristics in the map and the values of the ear specifications detected by the field sensor, the functional predictive ear specification map mapping predicted values of ear specifications to the different geographic locations in the field; A controllable subsystem (216), and A control system (214) that generates control signals to control the controllable subsystem (216) based on the geographic location of the agricultural work machine (100) and based on a predicted value of a spike gauge in the functional predicted spike gauge map.
2. The agricultural work machine of claim 1, wherein the control system includes: A lid position controller that generates a lid position control signal based on the detected geographic position and the functional predicted ear specification map and controls the controllable subsystem to control spacing between at least one set of lids on the agricultural work machine based on the lid position control signal.
3. The agricultural work machine of claim 1, wherein the predictive map generator includes: A predictive operator command map generator that generates a functional predictive operator command map that maps predicted operator commands to the different geographic locations in the field.
4. The agricultural work machine of claim 3, wherein said 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 functionality predictive operator command map and controls the controllable subsystem to execute the operator command based on the operator command control signal.
5. The agricultural work machine of claim 1, further comprising: a predictive model generator that generates a predictive model that models a relationship between the agricultural property and the ear specification based on values of the agricultural property in the map at the geographic location and values of the ear specification detected by the field sensor corresponding to the geographic location, Wherein the prediction graph generator generates the functional prediction scion specification graph based on values of the agricultural characteristics in the graph and based on the prediction model, and Wherein the graph (258) is from a priori operation, or the type of data in the graph (258) is different from the type of data sensed by the in situ sensor.
6. The agricultural work machine of claim 1, wherein the graph is a vegetation index graph including values of vegetation index characteristics as values of the agricultural characteristics, and the agricultural work machine further comprises: a predictive model generator that generates a predictive model that models a relationship between the vegetation index characteristic and the ear specification based on values of the vegetation index characteristic in the vegetation index map at the geographic location and values of the ear specification detected by the in-situ sensor corresponding to the geographic location, Wherein the prediction graph generator generates the functional prediction scion specification graph based on values of the vegetation index characteristics in the vegetation index graph and based on the prediction model.
7. The agricultural work machine of claim 1, wherein the graph is a yield graph including a value of a yield characteristic as the value of the agricultural characteristic, and the agricultural work machine further comprises: A predictive model generator that generates a predictive model that models a relationship between the yield characteristic and the ear specification based on values of the yield characteristic in the yield map at the geographic location and values of the ear specification detected by the on-site sensor corresponding to the geographic location, Wherein the prediction graph generator generates the functional prediction scion specification graph based on values of the yield characteristics in the yield graph and based on the prediction model.
8. A computer-implemented method of controlling an agricultural work machine (100), comprising Obtaining a map (258) comprising values of an agricultural property corresponding to different geographic locations in the field; detecting a geographic location of the agricultural work machine (100); Detecting a value of an ear of grain specification corresponding to the geographic location with an in situ sensor (208); Generating a functional predicted ear specification map of a field based on the values of the agricultural characteristic in the map and the values of the ear specification detected by the in situ sensor, the functional predicted ear specification map mapping predicted values of ear specification to the different geographic locations in the field, and A controllable subsystem (216) is controlled based on the geographic location of the agricultural work machine (100) and based on a predicted value of an ear gauge in the functional predicted ear gauge map.
9. An agricultural work machine (100), comprising: A communication system (206) that receives a graph (258) that includes values of an agricultural characteristic corresponding to different geographic locations in a field; a geographic position sensor (204) that detects a geographic position of the agricultural work machine (100); An in situ sensor (208) that detects a value of an ear of grain specification corresponding to the geographic location; a predictive model generator (210) that generates a predictive model that models a relationship between the agricultural property and a spike gauge based on values of the agricultural property in the map (258) at the geographic location and values of the spike gauge detected by the site sensor that correspond to the geographic location; A prediction graph generator (212) that generates a functional prediction spike gauge graph of the field based on values of the agricultural characteristic in the graph (258) and based on the prediction model, the functional prediction spike gauge graph mapping predicted values of spike gauge to the different geographic locations in the field; A controllable subsystem (216), and A control system (214) that generates control signals to control the controllable subsystem (216) based on the geographic location of the agricultural work machine (100) and based on a predicted value of a spike gauge in the functional predicted spike gauge map.

Description

Machine control using predictive graphs Technical Field The present description relates to agricultural, forestry, construction and lawn management machines. Background There are various different types of agricultural machines. Some agricultural machines include harvesters, such as combine harvesters, sugarcane harvesters, cotton harvesters, self-propelled forage harvesters, and cutter-windrowers. Some harvesters may also be equipped with different types of harvesting tables to harvest different types of crops. A wide variety of different conditions in the field can have several detrimental effects on the harvesting operation. Thus, upon encountering such conditions during a harvesting operation, an operator may attempt to modify the control of the harvester. The discussion above is provided merely as general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter. Disclosure of Invention 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 the field. In-situ sensors on the agricultural work machine sense agricultural characteristics as the agricultural work machine moves across the field. The prediction graph generator generates a prediction graph of predicted agricultural characteristics for different locations in the prediction field based on a relationship between values in the one or more information graphs and the agricultural characteristics sensed by the field sensor. Predictive maps may be output and used for automatic 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. Drawings FIG. 1 is a partially schematic, partially schematic illustration of one example of an agricultural harvester. Fig. 2 is a block diagram illustrating portions of an agricultural harvester in more detail, according to some examples of the present disclosure. Fig. 3A-3B show a flowchart illustrating an example of the operation of the agricultural harvester in generating a map. Fig. 4 is a block diagram showing one example of a prediction model generator and a prediction map generator. Fig. 5 is a flowchart illustrating an example of the operation of the agricultural harvester in receiving a map, detecting field characteristics, and generating a functional prediction map for presentation and/or use in controlling the agricultural harvester during a harvesting operation. Fig. 6 is a block diagram showing one example of a prediction model generator and a prediction map generator. FIG. 7 shows a flowchart illustrating one example of operations of an agricultural harvester to receive a prior information map and detect in-situ sensor inputs in generating a functional prediction map. FIG. 8 is a block diagram illustrating one example of an in-situ sensor(s). Fig. 9 is a block diagram showing one example of a control region generator. Fig. 10 is a flowchart illustrating one example of the operation of the control region generator shown in fig. 9. Fig. 11 is a flowchart showing an example of an operation of the control system in selecting a target setpoint to control the agricultural harvester. Fig. 12 is a block diagram showing one example of an operator interface controller. FIG. 13 is a flow chart illustrating one example of an operator interface controller. Fig. 14 is an illustrative diagram showing one example of the operator interface display section. Fig. 15 is a block diagram illustrating one example of an agricultural harvester in communication with a remote server environment. Fig. 16 to 18 show examples of mobile devices that may be used in an agricultural harvester. FIG. 19 is a block diagram illustrating one example of a computing environment that may be used with the agricultural harvester and the architecture shown in the previous figures. Detailed Description For the purposes of promoting an understanding of the principles of the disclosure, reference will now be made to the examples illustrated in the drawings and specific language will be used to describe the same. However, it will be understood that it is not intended to limit the scope of the present disclosure. Any alterations and further modifications in the described devices, systems, methods, and any further applications of the principles of the disclosure are contemplated as would normally occur to one skilled in the art to which the disclosure relates. In particular, it is fully contemplated that features, components, steps, or combinat