Combine Harvester Control Interface for Real-Time Settings Adjustment
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Solution Overview
Problem
Current systems for managing combine harvesters lack real-time performance monitoring and control capabilities, making it difficult for operators and fleet managers to optimize operations, especially when dealing with multiple machines and varying conditions, leading to inefficiencies and delayed adjustments.
Innovation Solution
A control interface and system that detects operating conditions and performance metrics, allowing for real-time adjustments of settings through a user-friendly interface, enabling operators and managers to prioritize performance pillars and automatically implement optimal machine settings based on historical data and current conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustments and controls are used for combine harvester operations, then the operator can directly control machine settings, but it becomes difficult to monitor and optimize performance in real-time across multiple machines
Solution Approach 1:
The system implements automated feedback mechanisms that continuously monitor machine performance metrics (grain loss, productivity, fuel efficiency) and operator behaviors, then provide real-time feedback to operators through the interface and to fleet managers via remote monitoring, enabling performance optimization without manual intervention
Solution Approach 2:
The combine harvester system performs self-monitoring and self-optimization by automatically tracking its own performance parameters, comparing them against target values, and enabling automated control adjustments that allow the machine to maintain optimal performance without constant operator intervention
2Loss of information
If multiple performance metrics are monitored simultaneously, then comprehensive performance data is available, but it becomes difficult to prioritize which settings to adjust first
Solution Approach 1:
The interface applies local quality by providing different levels of information detail to different user roles (operators versus fleet managers) and by highlighting only the most critical performance parameters and recommended adjustments for each specific situation, rather than presenting all possible data equally
Solution Approach 2:
The system dynamically changes the prioritization and weighting of performance parameters based on current operating conditions, crop types, and machine state, automatically adjusting which metrics are most important to optimize at any given moment and presenting them in order of priority to the operator
3Productivity
If automated control adjustments are implemented, then operational efficiency is improved, but the system requires complex sensing and control mechanisms
Solution Approach 1:
The control system achieves multi-functionality by using a single integrated platform that combines performance monitoring, data analysis, recommendation generation, and control execution across multiple machines and user roles, reducing the need for separate specialized systems while maintaining comprehensive functionality
Solution Approach 2:
The system introduces an intermediary intelligence layer (the control interface and automated control logic) that sits between the sensors and the machine actuators, processing complex performance data and translating it into simplified control recommendations or automated adjustments, thereby managing system complexity while maintaining high productivity
Data Source
AI summary
Operating conditions corresponding to a harvesting operation being performed by a mobile harvesting machine are detected along with a priority of a first performance pillar metric relative to a second performance pillar metric. An operating characteristic of the mobile harvesting machine is detected and a performance pillar metric value is identified for the first performance pillar metric based on the detected operating characteristic. A performance limitation corresponding to the first performance pillar metric is identified based on the detected operating conditions and an aggressiveness setting is detected that is indicative of an operating settings change threshold. It is then determined whether a settings change is to be performed based on the first performance pillar metric value, the priority of the first performance pillar metric, the first performance limitation and the settings change threshold and if the settings change is to be performed, a settings change actuator is controlled to execute the settings change.