Geo-Spatial Harvester Control for Predicted Grain Tank Fill Points
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Solution Overview
Problem
Current combine harvesters face inefficiencies due to difficulty in predicting when the clean grain tank will be full and where, leading to idle time waiting for haulage units, especially when haulage units are not deployed optimally.
Innovation Solution
An agricultural harvesting machine system that uses georeferenced yield estimation with error mapping to generate a probability distribution of fill capacity along the harvester's path, allowing for controlled subsystems to optimize path and speed to rendezvous with haulage units efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the harvester operates without predictive capacity modeling, then the system is simpler to operate, but idle time increases due to unpredictable unloading needs
Solution Approach 1:
The system performs preliminary calculations of the georeferenced probability distribution to predict where the harvester will reach capacity before actually reaching that point. This allows proactive planning of unloading operations, reducing idle time by ensuring haulage units are positioned appropriately in advance.
Solution Approach 2:
The field is divided into multiple georeferenced segments along the harvester path, with capacity probability calculated for each segment. This segmentation allows the system to provide detailed, location-specific predictions rather than a single aggregate prediction, improving the precision of unloading planning.
2Productivity
If haulage units are deployed without predictive information, then deployment is simpler, but harvesting efficiency decreases due to mismatched arrivals
Solution Approach 1:
The system incorporates feedback from actual yield measurements and harvester operating data to continuously update and refine the probability distribution predictions. This feedback loop improves the accuracy of capacity predictions over time, enabling better coordination of haulage unit arrivals with actual unloading needs.
Solution Approach 2:
The system provides preliminary information about expected capacity points to haulage unit operators before the harvester reaches those points. This advance notice allows haulage units to position themselves optimally, reducing waiting time and improving harvesting efficiency through better-synchronized operations.
3Measurement precision
If the system uses detailed georeferenced probability distributions, then unloading timing is more accurate, but the system becomes more complex to operate
Solution Approach 1:
The system automatically calculates and updates the georeferenced probability distribution without requiring manual intervention from operators. The automated nature of the calculations and the intuitive presentation of results maintain operational simplicity while providing high-precision predictions.
Data Source
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AI summary
A georeferenced probability distribution is generated indicating a probability that a harvester will reach its full capacity at different locations in a field. A control signal is generated to control the harvester based upon the georeferenced probability distribution. The control signal is used to control one of a plurality of different controllable subsystems, such as the propulsion system (to control harvester speed), a steering subsystem (to control the harvester's path), or other controllable subsystems.