Mapped Historical and Realtime Data for GPS-Loss Planting Continuity
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Solution Overview
Problem
Agricultural implements face operational challenges when one or more systems fail or become unavailable, leading to compromised performance and efficiency, particularly due to temporary loss of GPS or other critical sensor data, resulting in inaccurate actuator control and planting issues.
Innovation Solution
A computerized method and system that utilizes historical and real-time data to anticipate planting requirements, allowing for simultaneous display of both data sets on a shared map, with the ability to harmonize data post-availability, using a cloud-based storage system and high-speed data transmission protocols like Ethernet, ensuring continued precise operation even during system failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system relies on real-time GPS and sensor data for precise operation, then operational accuracy is improved, but system reliability deteriorates when GPS or sensors become unavailable
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical GPS and sensor data before actual operation. When real-time data becomes unavailable, the planter can continue operating using previously collected historical data, ensuring continuous operation without interruption.
Solution Approach 2:
The system introduces an intermediary mechanism (data buffering and prediction algorithms) that bridges the gap between real-time data requirements and actual system operation. This intermediary layer allows the system to function normally even when real-time GPS or sensor data is unavailable by using historical data as a substitute.
2Loss of information
If the system stores and processes large amounts of historical and real-time data, then data completeness is improved, but device complexity increases
Solution Approach 1:
The system extracts only the essential and most relevant features from the large volume of historical and real-time data, rather than processing all raw data. This extraction approach maintains data completeness for critical parameters while reducing the overall data management burden and system complexity.
Solution Approach 2:
The data management system is segmented into modular components that handle different types of data (GPS data, sensor data, operational data) separately. This segmentation allows for more manageable data processing and storage, reducing overall system complexity while maintaining complete data records.
3Reliability
If the system uses multiple data sources and prediction algorithms, then operational continuity is improved during system failures, but manufacturing complexity increases
Solution Approach 1:
The system employs multi-functional data processing algorithms that can handle multiple data sources (GPS, various sensors) and multiple operational modes (real-time operation, historical data mode, prediction mode) using a unified approach. This universality reduces manufacturing complexity compared to implementing separate specialized systems for each function.
Data Source
AI summary
Continued and precise operation of an agricultural implement exists even where a subsystem, such as a GPS receiver, wireless communicator, a sensor, or the like, fails, falters, or is otherwise unusable. Data is continually tracked to the extent possible during failure or faltering and is temporarily stored. The temporary data is later stitched or otherwise harmonized with historical data once the failing system is repaired or otherwise once again available. During failure or faltering, views, and even mapped views, of historical and real-time data are displayed. Predicted or anticipated data can be included within these views or can even be used when stitching.