Predictive Power Mapping for Field-Adaptive Harvester Control
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Solution Overview
Problem
Agricultural harvesters face challenges in maintaining efficient power distribution across varying field conditions due to factors like dense crop plants, weeds, soil properties, topography, and crop yield, which increase power demands on subsystems.
Innovation Solution
Generate predictive power maps using in-situ sensors and prior data to anticipate power requirements, allowing for optimized control of agricultural machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If power is increased to meet varying field conditions, then operational performance is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts power distribution to subsystems based on real-time field conditions sensed by in-situ sensors. The predictive map generator creates spatially and temporally varying power allocation strategies, allowing the harvester to optimize performance in high-demand areas while reducing power consumption in low-demand areas, thereby resolving the contradiction between maintaining operational performance and reducing overall energy consumption.
Solution Approach 2:
The patent implements location-specific power management by generating predictive power maps that identify high-power and low-power zones within the field. The control system allocates power differently to subsystems based on the harvester's geographic location, providing enhanced power only when and where needed (e.g., during harvesting operations in dense crop areas) rather than maintaining high power levels throughout the entire field, thus improving productivity while reducing total energy consumption.
2Use of energy by moving object
If power distribution is optimized for specific conditions, then efficiency is improved, but system complexity increases
Solution Approach 1:
The system employs in-situ sensors that continuously monitor field conditions (crop density, moisture, yield) and feed this data back to the predictive map generator and control system. This feedback loop enables automatic adjustment of power distribution without requiring complex manual intervention, as the system self-regulates based on sensed conditions, thereby improving power distribution efficiency while keeping the control architecture manageable through automated decision-making algorithms.
Solution Approach 2:
The predictive map generator creates predictive power maps before and during harvesting operations, anticipating future power requirements based on current field conditions and historical data. By pre-calculating power allocation strategies and preparing predictive models in advance, the system reduces the need for complex real-time calculations during operation, simplifying the control system while maintaining optimized power distribution efficiency.
Data Source
AI summary
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.