Anticipatory modification of machine settings based on predicted operational state transition

A method for adjusting operating parameters of a machine in anticipation of a transition from a current operational state to a subsequent operational state, wherein the machine includes an excavator with a boom arm and a bucket coupled to a distal end of the boom arm, the method comprising:

• receiving, by an electronic controller, a data stream indicative of at least one selected from a group consisting of actuator settings, sensor outputs, and operator control settings;
• applying, by the electronic controller, a pattern detection AI, wherein the pattern detection AI is configured to
• receive the data stream as an input,
• determine a current operational state of the machine based on patterns detected in the data stream, and
• determine, based on the data stream, when the current operational state of the machine is a lifting of the bucket;
• applying, by the electronic controller, a reinforcement learning AI, wherein the reinforcement learning AI is configured to
• identify the predicted subsequent operational state by identifying a current operation of the machine based on the determined current operational state of the machine and a sequence of previously determined operational states of the machine, wherein a current operation of the machine is defined based on a sequence of operational states where the machine repeats the operational states of lifting the material at a first location, traveling to a second location, releasing the material at the second location, and returning to the first location, and wherein the current operation is a lift-and-carry operation, and
• produce as an output one or more target operating parameters based at least in part on the predicted subsequent operational state, wherein the output is an adjustment to a power distribution of the machine to provide more power to the drivetrain in anticipation of the machine transitioning from the lifting operational state to a driving operational state in the lift-and-carrying operation;
• applying, by the electronic controller, the one or more target operating parameters to the machine;
• monitoring at least one performance metric of the machine after application of the one or more target operating parameters;
• and
• retraining the reinforcement learning AI based on the at least one monitored performance metric.