Combine Harvester Fleet Metrics Aggregation for Remote Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for monitoring and controlling combine harvesters lack real-time performance metrics and remote management capabilities, making it difficult for fleet managers to assess operator and machine performance, leading to delayed adjustments and increased bandwidth consumption.
Innovation Solution
A logic system that aggregates metrics from multiple combine harvesters in near real-time, sending performance data and control inputs to both the machines and a remote user computing system, enabling real-time monitoring and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current monitoring systems are used for combine harvesters, then bandwidth consumption is reduced, but real-time performance metrics and remote management capabilities are lost
Solution Approach 1:
The system segments information transmission by sending different types of data at different frequencies. Critical performance metrics are transmitted in near real-time, while less critical data is aggregated and sent less frequently. This segmentation allows the system to maintain real-time monitoring capabilities while managing bandwidth consumption effectively.
Solution Approach 2:
The system implements periodic action by transmitting data at scheduled intervals rather than continuously. Performance metrics are collected and transmitted in periodic cycles, with the frequency adjusted based on the criticality of the data. This approach reduces overall bandwidth consumption while ensuring that important performance information is available in near real-time.
2Productivity
If manual adjustments are required for combine operations, then system complexity is reduced, but operational efficiency and response time deteriorate
Solution Approach 1:
The system implements feedback mechanisms where performance metrics from multiple combine harvesters are continuously monitored and transmitted to remote users. This feedback loop enables remote management and optimization of combine operations, improving productivity by allowing operators to make data-driven decisions without being physically present at each machine.
Solution Approach 2:
The system introduces an intermediary communication network that connects combine harvesters to remote users. This intermediary layer handles the complexity of data collection, aggregation, and transmission, allowing the combines to operate efficiently while the remote system manages the computational complexity of monitoring and control.
3Ease of operation
If data is transmitted from multiple combine harvesters, then remote management capability is improved, but bandwidth consumption increases
Solution Approach 1:
The system merges data from multiple combine harvesters into aggregated performance metrics. By combining individual machine data into fleet-wide statistics and trends, the system enables comprehensive remote management of multiple machines while reducing the total volume of data that needs to be transmitted over the network.
Solution Approach 2:
The communication system is designed with multi-functionality to handle different types of data transmission needs. It can simultaneously transmit individual machine performance metrics, aggregated fleet statistics, and remote control commands, making it a universal platform that supports various remote management functions while optimizing bandwidth usage through intelligent data prioritization.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A logic system aggregates metrics from a plurality of combine harvesters and controls a communication system to send aggregated metrics back to the combine harvesters and to send both machine performance metrics and aggregate metrics to a remote user computing system, for remote user control.