Agricultural Vehicle Settings Control via Aggregated Fleet Data
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Solution Overview
Problem
Agricultural equipment operators face challenges in determining optimal settings values, leading to suboptimal operation due to the complexity of the equipment and varying conditions, resulting in reduced yield and inefficient performance.
Innovation Solution
A system that aggregates settings data from multiple agricultural vehicles based on indexing criteria such as geographic location and operating conditions, providing aggregated setting values to assist in controlling controllable subsystems and improving operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If default values or operator-input settings are used for agricultural equipment subsystems, then the equipment can operate without complex optimization processes, but the operation becomes suboptimal resulting in reduced yield and inefficient performance
Solution Approach 1:
The system performs preliminary actions by collecting settings data from multiple agricultural vehicles and computing optimized setting values in advance. The server stores these pre-computed optimized settings indexed by job type and operating conditions, so when a vehicle needs settings, the optimal values are already prepared and available for immediate use, eliminating the need for complex real-time optimization by the operator.
Solution Approach 2:
A server acts as an intermediary between agricultural vehicles and the complex data processing required to determine optimal settings. The server collects settings data from multiple vehicles, processes this data to compute optimized values, and provides these settings to individual vehicles. This intermediary handles the complexity of settings determination, allowing operators to simply access pre-computed optimal settings without dealing with the underlying complexity.
2Productivity
If optimized settings values are determined through data aggregation from multiple vehicles, then operational efficiency and yield are improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The system merges settings data from multiple agricultural vehicles into a centralized database on the server. By combining data from multiple sources and computing aggregated optimized settings, the system leverages collective operational experience across the fleet. This merging approach improves the quality of settings recommendations while distributing the data collection burden across multiple vehicles contributing to the shared knowledge base.
Solution Approach 2:
The system implements feedback by collecting actual settings data and performance information from agricultural vehicles, processing this feedback to compute optimized settings, and then providing these settings back to the vehicles. This closed-loop feedback mechanism continuously improves operational efficiency by learning from real-world performance data and adjusting recommendations accordingly, while the server handles the complex processing of feedback information.
3Productivity
If settings values are manually determined by operators, then no additional data processing infrastructure is needed, but the complexity of agricultural equipment subsystems makes it difficult to determine optimal settings, leading to suboptimal operation
Solution Approach 1:
The server acts as an intermediary that handles the difficulty of identifying optimal settings by processing settings data from multiple vehicles and computing optimized values. Instead of requiring operators to manually determine complex optimal settings across multiple subsystems, the intermediary server performs this complex analysis and provides ready-to-use optimized settings to the vehicles.
Solution Approach 2:
The system enables self-service by allowing agricultural vehicles to automatically access and apply optimized settings from the server without requiring manual intervention from operators. The vehicles can autonomously retrieve pre-computed optimal settings based on their current job type and operating conditions, eliminating the need for operators to manually analyze complex equipment parameters and determine optimal configurations.
Data Source
AI summary
Settings data is collected from a plurality of different agricultural machines and is sorted based on indexing criteria. The sorted data is aggregated to obtain metric values and stored based on the indexing criteria. An agricultural vehicle accesses the indexed, aggregated data to obtain settings data. The agricultural vehicle is controlled based upon the settings data.