Harvester Capacity Prediction Control for Field Unloading Timing
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Solution Overview
Problem
Current combine harvesters face inefficiencies due to difficulty in predicting when their grain tanks will be full, leading to idle time and suboptimal deployment of harvesting machines and haulage units, as well as challenges in refilling material storage on machines that distribute materials.
Innovation Solution
A georeferenced probability distribution is generated to indicate the likelihood of a harvester reaching its capacity at different field locations, allowing for control signals to be used to manage the harvester's speed, path, and other subsystems to optimize operations and rendezvous with haulage units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the harvester operates without predictive capacity information, then the operation is simpler, but the harvester experiences increased idle time and reduced productivity
Solution Approach 1:
The system performs preliminary actions by generating a georeferenced probability distribution map before the harvester reaches capacity. This predictive mapping identifies likely fill points in advance, allowing the harvester to continue operating without idle time while maintaining manageable system complexity through automated calculations.
Solution Approach 2:
The system implements feedback by continuously monitoring the harvester's current fill level and comparing it against the predicted probability distribution. This feedback loop enables real-time adjustments to the harvesting path and speed, improving productivity while the automated nature of the feedback reduces the perceived complexity for the operator.
2Measurement precision
If the harvester waits for accurate capacity prediction, then unloading efficiency improves, but the harvester loses harvesting time
Solution Approach 1:
The system generates the georeferenced probability distribution map in advance, before the harvester actually reaches capacity. This preliminary prediction allows the system to identify optimal unloading points without requiring the harvester to pause or slow down, thereby maintaining both accuracy and time efficiency.
Solution Approach 2:
The system dynamically adjusts the harvesting operation based on the predicted probability distribution. As the harvester approaches predicted fill points, the system automatically modifies the operation, eliminating the need for static waiting periods while maintaining precise capacity management.
3Productivity
If multiple harvesters and haulage units operate without coordination, then deployment is simpler, but logistical efficiency decreases
Solution Approach 1:
The system merges the operational data of multiple harvesters and haulage units into a unified georeferenced probability distribution framework. This consolidation enables coordinated dispatch and routing decisions that improve logistical efficiency across the entire fleet while presenting a single integrated view to operators.
Solution Approach 2:
The georeferenced probability distribution system serves multiple functions simultaneously: it predicts fill points for individual harvesters, coordinates haulage unit dispatch, optimizes routing, and provides a common operational picture for all machines. This multi-functionality improves logistical efficiency without proportionally increasing complexity.
Data Source
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.