Predictive Power Map for Harvester Energy Allocation
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Solution Overview
Problem
Agricultural harvesters face performance degradation due to varying power demands caused by dense crops, weeds, soil conditions, and topography, which existing technologies fail to efficiently manage, leading to reduced efficiency and increased energy consumption.
Innovation Solution
The system generates a predictive power map using in-situ data and prior maps to control agricultural harvesters, accounting for vegetative index, crop moisture, soil properties, and topography, allowing for optimized power allocation across subsystems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the harvester operates at high power to maintain productivity in dense crops and difficult conditions, then processing capacity is maintained, but energy consumption increases and operational efficiency decreases
Solution Approach 1:
The system dynamically adjusts power distribution to subsystems based on real-time operating conditions detected by sensors (crop density, moisture, soil properties). The control system continuously modifies power allocation rather than maintaining fixed high power, enabling the harvester to adapt power consumption to actual processing needs while maintaining productivity.
Solution Approach 2:
The predictive power map provides location-specific power requirements for different areas of the field based on varying crop conditions, soil properties, and topography. The control system applies local quality by allocating power differently to subsystems depending on the specific geographic location and detected conditions, rather than using uniform power distribution throughout the field.
2Productivity
If the system uses multiple sensors and predictive maps to optimize power distribution, then operational efficiency improves, but device complexity increases
Solution Approach 1:
The control system serves multiple functions: it processes data from various sensors (moisture, crop density, soil properties), generates predictive power maps, allocates power to multiple subsystems, and monitors operational efficiency. This multi-functional approach consolidates what would otherwise require separate systems into a single integrated control platform, managing complexity through functional consolidation.
Solution Approach 2:
The predictive power map acts as an intermediary between sensor data and power allocation decisions. Rather than directly controlling each subsystem based on raw sensor inputs, the system first generates a predictive map that translates complex sensor data into actionable power distribution guidelines, simplifying the control process and reducing system complexity.
3Use of energy by moving object
If the harvester uses predictive power maps and real-time sensor data to adjust power allocation, then energy consumption decreases, but measurement and detection difficulty increases
Solution Approach 1:
The system performs preliminary actions by generating predictive power maps before harvesting operations begin and during the process. These maps pre-calculate power requirements based on sensor data collected during previous passes or from satellite imagery, allowing the control system to anticipate power needs rather than reactively adjusting to conditions, thereby simplifying real-time detection requirements.
Data Source
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Figure 3A
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.