Harvester Process Model Adaptation via Cloud Feedback
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Solution Overview
Problem
Existing methods for operating agricultural working machines, such as harvesting machines, often result in suboptimal performance due to user inputs, especially under unknown conditions, requiring continuous adaptation and failing to achieve consistent agricultural work results across different locations.
Innovation Solution
A method involving a process model running on a working machine that generates control commands based on working data sets, with machine data sets transmitted to an EDP device for adaptation, allowing for remote optimization and adaptation of the process model considering feedback and sensor information from multiple machines, enabling better adaptation to varying conditions without local optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the process model is adapted locally on the working machine based on user inputs, then the working machine can be optimized at the usage location, but the working machine experiences worse agricultural working performance with unpracticed users and requires continuous adaptation
Solution Approach 1:
A cloud-based server acts as an intermediary between multiple working machines, collecting data from various locations and users. The server processes this data to generate optimized process models that are then distributed back to the machines, eliminating the need for unreliable local adaptations by unpracticed users
Solution Approach 2:
The system implements a feedback loop where working data from multiple machines is continuously transmitted to the server, which processes this feedback information to automatically adapt and improve the process model. This ensures that adaptations are based on actual performance data rather than user inputs
2Adaptability or versatility
If the process model is continuously adapted at each usage location, then the working machine can be optimized locally, but the machine requires repeated adaptation at further locations and cannot function optimally under unknown conditions
Solution Approach 1:
The server pre-processes working data from multiple sources and prepares optimized process models in advance. When a working machine needs adaptation, it receives pre-computed optimized parameters rather than requiring time-consuming on-site adaptation, enabling immediate optimal operation under unknown conditions
3Ease of manufacture
If only location-related information is processed and transmitted, then data transmission is simple, but the agricultural work result cannot be consistently improved across different locations and machines
Solution Approach 1:
The system merges working data from multiple different working machines and locations into a comprehensive data set on the server. This combined data is then processed to generate process models that achieve consistent agricultural work results across all machines, going beyond simple location-based adaptations
Data Source
AI summary
A method is provided in which a particularly good agricultural work result is achieved upon use both at the same location and also at other locations for one or multiple agricultural working machines, which may be one or multiple harvesting machines.