Agricultural Machine Driving Plan Management Across Manual Interventions
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Solution Overview
Problem
Existing technologies do not efficiently collect actual performance data during both self-driving and non-self-driving modes of agricultural machines, limiting the integration and management of automation in agricultural machinery.
Innovation Solution
A management apparatus and system that collects actual performance data from agricultural machines operating in self-driving and non-self-driving modes, including manual, remote, and self-driving, by storing data from interventions and changes in driving plans, and distinguishing between static and dynamic data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the agricultural machine operates in self-driving mode only, then automation level is improved, but data collection during manual intervention is lost
Solution Approach 1:
The system continuously collects performance data regardless of driving mode transitions. The data collection function operates continuously during both self-driving and manual driving periods, ensuring no useful information is lost when the operator intervenes to switch from automated to manual control.
Solution Approach 2:
The management apparatus receives and stores performance data transmitted from the agricultural machine during mode transitions. This feedback mechanism ensures that data from manual intervention periods is captured and integrated into the overall performance record, maintaining information continuity across different operating modes.
2Loss of information
If the system collects data from both self-driving and non-self-driving modes, then data completeness is improved, but system complexity increases
Solution Approach 1:
The management apparatus is designed with multi-functionality to handle diverse data types from different driving modes through a unified interface. The system can collect and process both self-driving performance data and manual driving intervention data using the same core infrastructure, avoiding the need for separate specialized systems.
Solution Approach 2:
The system segments data collection into distinct driving mode categories (self-driving, manual driving, remote driving) while maintaining a unified management framework. This segmentation allows organized handling of different data types without creating separate complex systems for each mode.
3Loss of information
If the management apparatus stores all actual performance data, then data availability is improved, but storage requirements increase
Solution Approach 1:
The system applies different data retention and processing strategies to different types of performance data based on their specific characteristics and importance. Critical data from manual intervention periods is prioritized for storage, while less critical self-driving data may be processed or archived differently, optimizing storage utilization.
Data Source
AI summary
A management apparatus for communicating with an agricultural machine to be operated in a driving mode including self-driving and non-self-driving, the management apparatus including a processor configured or programmed to create a self-driving plan for the agricultural machine, a communication module configured or programmed to transmit the self-driving plan to the agricultural machine and receive actual performance data of the agricultural machine collected by the agricultural machine, and a storage configured to store the received actual performance data. The actual performance data includes data collected by the agricultural machine during the non-self-driving performed by an intervention occurring in the self-driving performed in accordance with the self-driving plan.