Operator Performance Recommendation Generation for Mobile Machinery
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Solution Overview
Problem
Current methods for generating operator performance reports for mobile equipment, such as agricultural combines, lack comprehensive and real-time feedback on operator performance, leading to inefficiencies and suboptimal machine operation.
Innovation Solution
A system that evaluates sensed parameters from mobile machines to determine fulfillment of actionable conditions, generating recommendations for improving operator performance through a data evaluation layer, pillar score generation, and real-time reporting, utilizing a cloud computing architecture for data aggregation and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If comprehensive real-time data evaluation and recommendation systems are implemented, then operator performance feedback quality is improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex data evaluation process into distinct layers: a data collection layer that gathers sensor parameters from mobile equipment, a data evaluation layer that processes the collected data, and a recommendation generation layer that produces actionable insights. This segmentation allows each layer to be optimized independently, reducing overall system complexity while maintaining comprehensive feedback capabilities.
Solution Approach 2:
The patent introduces intermediary components including cloud-based processing servers that mediate between mobile equipment sensors and operator interfaces, and data evaluation algorithms that serve as intermediaries between raw sensor data and performance recommendations. These intermediaries handle the computational complexity remotely, allowing the mobile equipment itself to remain relatively simple.
2Productivity
If real-time data processing and recommendation generation are implemented, then operator performance improvement is enhanced, but computational energy consumption increases
Solution Approach 1:
The system transitions computational processing from the mobile equipment dimension to the cloud computing dimension. By moving data evaluation and recommendation generation to remote servers, the mobile equipment minimizes its computational energy consumption while still receiving real-time performance feedback and recommendations.
Solution Approach 2:
The system performs preliminary data processing and analysis in advance by continuously evaluating sensor data streams and pre-generating performance recommendations before operators need them. This allows the system to provide immediate feedback without requiring intensive real-time computation on the mobile equipment itself.
Data Source
AI summary
A set of data is received. The data is indicative of sensed parameters on a mobile machine. The data is evaluated against a set of actionable conditions to determine the degree of fulfillment of each condition. A recommendation for changing the operation of the mobile machine is identified based on the degree of fulfillment. An output is generated based on the recommendation.