Combine Harvester Driver Assistance System Optimization
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Solution Overview
Problem
Current driver assistance systems for agricultural working machines, such as combine harvesters, require operators to manually adjust multiple weighting variables to optimize processing strategies, which is cumbersome and inefficient, especially when competing quality criteria need to be balanced.
Innovation Solution
The driver assistance system registers the operator's strategy selection behavior, predicts optimization targets, and automatically suggests changes to other weighting variables, allowing operators to simplify further optimizations by accepting or adjusting proposed changes through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the operator manually adjusts multiple weighting variables to optimize processing strategies, then the quality criteria can be balanced, but the operation becomes cumbersome and inefficient
Solution Approach 1:
The system automatically analyzes sensor data from the harvesting process and adjusts weighting variables itself without requiring manual operator intervention. The control system serves itself by autonomously optimizing the processing strategy based on real-time quality measurements, thereby resolving the contradiction between achieving precise quality balancing and maintaining ease of operation
Solution Approach 2:
The system implements a closed-loop feedback mechanism where sensor data from the harvesting process is continuously monitored, analyzed, and used to automatically adjust weighting variables. This feedback loop enables the system to self-correct and optimize quality criteria balancing dynamically, eliminating the need for cumbersome manual adjustments while maintaining high precision
2Adaptability or versatility
If the operator manually adjusts each weighting variable separately, then specific optimizations can be implemented, but the process requires multiple separate entries and is time-consuming
Solution Approach 1:
The system merges the adjustment of multiple weighting variables into a single automated process. Instead of requiring separate manual entries for each variable, the control system simultaneously analyzes all sensor data and adjusts all weighting variables together in one coordinated action, thereby maintaining full optimization flexibility while dramatically reducing the time required
Solution Approach 2:
The system performs preliminary analysis of the harvesting data and pre-calculates the optimal weighting variable adjustments before implementation. By preparing the optimization strategy in advance based on sensor data, the system can execute multiple variable adjustments simultaneously without requiring sequential manual input, thus preserving adaptability while minimizing time loss
Data Source
Figure 1
Figure 2a~2b
AI summary
The invention relates to a working machine, in particular a combine harvester, with several working elements (1-5) for carrying out or supporting work and with a driver assistance system (10) for controlling the working elements (1-5) according to at least one operator-definable processing strategy aimed at fulfilling at least one quality criterion (Q1, Q2, Q3, Q4, Q5, Q6, Q7, Q8), wherein the driver assistance system (10) has a memory (11) for storing data characterizing the at least one processing strategy, a computing device (12) for processing the data stored in the memory (11) and a graphical user interface (14), wherein competing quality criteria (Q1, Q2; Q3, Q4;Q5, Q6, Q7, Qs) are weighted relative to each other according to a weighting variable (G1, G2, G3, G4) and incorporated into the processing strategy. This weighting variable (G1, G2, G3, G4) is visualized via a virtual control element (16-19) of the graphical user interface (14) and can be specified by the operator. It is proposed that the driver assistance system (10) registers the operator's (13) strategy selection behavior, predicts an optimization target from this registered strategy selection behavior, and submits an optimization proposal (O) for this predicted optimization target.