Agricultural Harvester Prognosis Device for Harvest Target Accuracy
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Solution Overview
Problem
Operators of agricultural harvesting machines face challenges in accurately setting parameters to achieve desired harvest targets due to complex relationships with other vehicles in the harvesting process chain, leading to inefficiencies and a lack of reliable prognosis for crop compaction.
Innovation Solution
A storage and evaluation device provides a reliable forecast of achievable harvest targets by comparing current operating and crop parameters with expert-defined criteria, allowing for real-time adjustments to ensure desired compaction levels in the silo.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the operator relies on experience to set operating parameters, then the operator can make adjustments based on intuition, but the accuracy of achieving the desired harvest target is insufficient due to complex relationships with other vehicles in the harvesting process chain
Solution Approach 1:
A prognosis device is introduced as an intermediary between the operator and the complex harvesting process chain. This device calculates and displays prognoses about the achievability of harvest targets, mediating the complex relationships between multiple vehicles and parameters into comprehensible information for the operator, thereby improving measurement precision without requiring the operator to directly manage the complexity
Solution Approach 2:
The patent replaces the operator's intuitive experience-based decision-making with a computational prognosis system. The prognosis device uses algorithms to calculate harvest target achievability based on current operating parameters and crop conditions, substituting mechanical human judgment with an automated computational system that can process complex relationships more accurately
2Reliability
If the operator uses trial-and-error methods to optimize operating parameters, then the operator can eventually achieve desired results, but a very large amount of time is required due to time delays between harvesting starts and work result recordings
Solution Approach 1:
The prognosis device performs preliminary calculations about harvest target achievability before the operator commits to specific operating parameters. By providing advance prognoses based on current conditions and parameter settings, the system allows the operator to make informed decisions upfront, eliminating the need for time-consuming trial-and-error cycles and reducing the time loss associated with delayed feedback
Solution Approach 2:
The system implements a feedback mechanism where the prognosis device continuously monitors operating parameters and crop conditions, then provides real-time feedback about harvest target achievability. This immediate feedback loop allows the operator to adjust parameters based on prognostic information rather than waiting for delayed work result recordings, significantly reducing optimization time while maintaining reliability
3Measurement precision
If large capacity neural network models are used to determine harvester settings, then the model can provide comprehensive predictions, but prohibitive computational effort is required which limits practical use
Solution Approach 1:
The patent extracts only the essential calculations and prognostic functions needed for harvest target assessment, separating them from the complex full-scale neural network models. By implementing a dedicated prognosis device that performs only the necessary computations for harvest achievability based on specific operating parameters and crop conditions, the system achieves adequate prediction accuracy with significantly reduced computational effort and energy consumption
Data Source
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AI summary
The agricultural harvesting machine (2) has different working elements, an input and display unit (23), a memory and evaluation unit (28), and a control unit (24) for influencing adjustable operating parameters of the working elements. A reference criterion is stored in the memory and evaluation unit in a retrieval manner. The prognosis during the adjustment of the operating parameters is provided.