| 摘要 |
Systems and methods for real-time closed loop machine settings optimization are presented. The system can be configured to provide a real-time revenue/loss view of a field to identify underperforming machines whose revenue performance may be improved by adjusting settings for various machine parameters such as speed, engine revolutions, implement settings, and the like. Machine yield data can be provided to a remote fleet revenue assessment system (FRAS) that can be configured to determine a machine revenue metric, such as a revenue ratio, for a plurality of machines in a fleet. Machine revenue metrics can be aggregated to provide a fleet revenue metric to which machine revenue metrics can be compared to identify underperforming machines. By eliminating poor field artifacts, a determination can be made as to whether revenue may be increased by settings adjustments. An algorithm or look-up table can be used to optimize settings and determine the proper adjustments |
