Agricultural Assistance System Optimizing Operating Costs
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Solution Overview
Problem
Existing driver assistance systems for agricultural working machines are inefficient in optimizing operating costs, requiring a long time to reach optimized parameter ranges and being heavily dependent on operator knowledge due to complex parameter interactions.
Innovation Solution
A driver assistance system with a control/regulating unit and mathematical models that derive efficiency parameters from working parameters, determining opportunity costs and visualizing them to optimize operating costs quickly, allowing for real-time or simulated cost optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of working parameters is performed for every parameter sequentially, then the operator can identify optimal values through trial and error, but the time required to reach optimized operation is excessively long
Solution Approach 1:
The control unit automatically adjusts working parameters based on sensor data and mathematical models without requiring manual operator intervention. The system performs self-optimization by continuously monitoring efficiency parameters and autonomously modifying working parameters to achieve optimal operation, thereby eliminating the time-consuming manual trial-and-error process while maintaining optimization accuracy.
Solution Approach 2:
The system implements continuous feedback loops where sensor data on efficiency parameters (grain loss, grain quality, tailings quantity) is fed back to the control unit, which then automatically adjusts working parameters. This closed-loop control enables rapid convergence to optimal settings by constantly monitoring results and making real-time adjustments, significantly reducing the time to reach optimized operation compared to manual methods.
2Ease of operation
If manual optimization procedures are used, then the operator can adjust working parameters, but the process is heavily dependent on the operator's level of knowledge about complex parameter interactions
Solution Approach 1:
The control unit autonomously performs the optimization function by processing sensor data and automatically adjusting working parameters based on mathematical models. This eliminates dependence on operator knowledge while ensuring consistent, reliable optimization results. The system serves itself by making all adjustment decisions based on objective data rather than subjective operator judgment.
Solution Approach 2:
The control unit acts as an intermediary between sensor data and working parameter adjustment. It processes the complex relationships between multiple parameters using stored mathematical models, translating raw sensor information into optimized control commands. This intermediary function removes the need for operator expertise in understanding complex parameter interactions while maintaining high optimization reliability through systematic, model-based decision-making.
3Loss of information
If traditional driver assistance systems are used, then working parameters can be monitored, but the systems cannot optimize operation based on actual operating costs in real-time
Solution Approach 1:
The system calculates operating costs in real-time based on current efficiency parameters and feeds this cost information back to the control unit, which then adjusts working parameters to optimize costs. This continuous cost-based feedback loop enables the system to respond dynamically to changing operating conditions and cost factors, achieving both real-time cost optimization and high productivity by automatically adapting to current operational context.
Solution Approach 2:
The system dynamically changes working parameters based on calculated operating costs and efficiency parameters. By continuously monitoring cost-relevant variables (grain loss, fuel consumption, production rate) and adjusting parameters such as ground speed, header height, and working part speeds, the system optimizes operation in real-time according to actual economic conditions rather than pre-set parameters, thereby improving both cost information availability and optimization speed.
Data Source
AI summary
An assistance system that optimizes the operation of a self-propelled agricultural working machine includes a device for determining working and efficiency parameters of the machine includes an arithmetic logic unit and a display unit. The arithmetic logic unit processes information generated by machine-internal sensor systems, external information and information stored in the arithmetic logic unit. One or more mathematical models describing the working process are stored in the arithmetic logic unit and derive efficiency parameters of the working machine from the available working parameters and, with consideration for monetary interrelationships, determine the opportunity costs of the working process and visualize the opportunity costs in the display unit.