Predictive Power Maps for Harvester Subsystem Power Control
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Solution Overview
Problem
Agricultural harvesters face challenges in maintaining efficient power distribution across changing field conditions due to finite power sources, with factors like dense crops, weeds, soil properties, and topography affecting subsystem performance.
Innovation Solution
The generation of predictive power maps using in-situ data and prior information maps to anticipate power requirements, allowing for optimized control of agricultural work machines by adjusting power allocation to subsystems based on real-time and geographically specific conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If power is allocated to subsystems based on finite power source capacity, then power availability is maintained, but power distribution efficiency deteriorates under changing field conditions
Solution Approach 1:
The system generates predictive power maps before harvesting operations to anticipate power requirements in different field zones. This preliminary action allows the control system to pre-plan power allocation strategies, adjusting subsystem power distribution in advance based on predicted conditions such as crop density, moisture content, and terrain variations, thereby improving power distribution efficiency while adapting to changing field conditions
Solution Approach 2:
The power management system dynamically adjusts power allocation to subsystems based on real-time sensor data and predictive maps. The system continuously monitors actual power consumption and field conditions, then modifies power distribution in real-time to optimize efficiency across varying operational conditions, resolving the contradiction between maintaining efficient power use and adapting to changing conditions
2Measurement precision
If in-situ sensors are deployed to sense agricultural characteristics, then measurement accuracy improves, but system complexity increases
Solution Approach 1:
The in-situ sensors are designed to perform multiple measurement functions simultaneously. For example, sensors can measure crop moisture content, biomass density, and other agricultural characteristics using the same hardware platform. This multi-functionality reduces the number of separate sensor systems needed, thereby improving measurement precision across multiple parameters while limiting the increase in overall system complexity
Solution Approach 2:
The system combines data from multiple sensor sources and integrates it with predictive map generation and control functions into a unified platform. By merging sensor data acquisition, predictive modeling, and control operations into an integrated system architecture, the patent achieves high measurement precision while managing system complexity through consolidation rather than separate independent systems
3Manufacturing precision
If predictive maps are generated using relationship modeling, then power allocation accuracy improves, but computational requirements increase
Solution Approach 1:
The system generates predictive power maps at appropriate levels of detail rather than exhaustive precision. The modeling focuses on the key relationships between agricultural characteristics and power consumption that are sufficient for effective power allocation decisions. This partial action approach achieves adequate power allocation accuracy while avoiding the excessive computational requirements that would result from attempting to model all possible variables and interactions with maximum precision
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.