Mapped Data Continuity for Agricultural Implement Operation
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Solution Overview
Problem
Agricultural implements face challenges in maintaining precise operation when one or more systems fail, falter, or become temporarily unavailable, leading to compromised planting efficiency.
Innovation Solution
The system uses historical and sensed information to anticipate planting requirements and expected productivity, allowing for continued operation by integrating real-time and historical data on a shared map, and storing and accessing data remotely in a cloud-based system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the agricultural implement uses fully automated systems with multiple sensors and GPS, then productivity and precision are improved, but reliability deteriorates when systems fail or become unavailable
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing historical operational data, sensor readings, and GPS information before system failures occur. This historical data serves as a backup that can be retrieved and used when real-time systems fail, ensuring continued operation without compromising planting efficiency or reliability.
Solution Approach 2:
The system introduces an intermediary layer that harmonizes and integrates data from multiple sources including GPS, sensors, and historical records. This intermediary data harmonization layer allows the system to switch between real-time data and historical data seamlessly, maintaining reliability even when primary systems become unavailable.
2Measurement precision
If the system integrates multiple data sources and systems for comprehensive monitoring, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system merges multiple data sources including GPS location data, sensor readings, and historical operational data into a unified harmonized view. By combining these diverse data streams into a single integrated system, the patent achieves comprehensive measurement precision while managing complexity through unified data handling rather than separate processing systems.
Solution Approach 2:
The system creates a universal data harmonization framework that can handle multiple types of data (geospatial, sensor, historical) through a single platform. This multi-functional approach allows the same system to process various data types uniformly, improving measurement precision across all parameters without proportionally increasing device complexity.
3Loss of information
If the system stores and processes large amounts of historical and real-time data, then loss of information is reduced, but loss of time increases due to data processing
Solution Approach 1:
The system extracts only the essential and relevant features from large volumes of historical and real-time data for immediate processing and display. By extracting key information rather than processing complete raw datasets, the system maintains data completeness while significantly reducing processing time and computational overhead.
Solution Approach 2:
The system performs preliminary data processing and organization in advance, structuring historical data and real-time data streams before they need to be analyzed. This pre-processing ensures that when data needs to be accessed or compared, the information is already prepared and organized, minimizing processing delays while maintaining complete information availability.
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.