Self-learning control system for a mobile machine

1. A mobile machine computing system comprising:

machine interaction logic that receives machine data from a mobile machine, the machine data including control rule implementation data indicative of whether a control operation, corresponding to a control rule triggered on the mobile machine that maps performance to the control operation, was implemented by an operator of the mobile machine, and performance data indicative of an effect on machine performance of the mobile machine based on implementation of the control operation;

rule effectiveness analyzer logic that receives the machine data and generates a rule effectiveness output for the control rule based on the machine data;

learning logic that generates a rule rating output, corresponding to the control rule, indicative of a rule rating based on the effectiveness output;

an automated control rule modification engine that automatically modifies a set of control rules based on the rule rating output, the automated control rule modification engine comprising:

rule prioritization logic configured to modify control rules in the set of control rules, based on the rule rating output, to change a priority with which the control rules in the set of control rules are triggered by the performance data; and

a rule update engine that automatically updates the modified set of control rules to the mobile machine.

2. The mobile machine computing system of claim 1 wherein the rule effectiveness analyzer logic comprises:

implementation frequency logic configured to identify, from the control rule implementation data, a frequency with which the control operation corresponding to the control rule was implemented by the operator when the control rule was triggered on the mobile machine and generate a frequency of implementation output signal.

3. The mobile machine computing system of claim 2 wherein the rule effectiveness analyzer logic comprises:

rule performance evaluator logic configured to receive performance data indicative of machine performance before implementation of the control operation corresponding to the triggered rule on the mobile machine and performance data indicative of machine performance after implementation of the control operation corresponding to the triggered rule on the mobile machine and generate a performance comparison output signal.

4. The mobile machine computing system of claim 3 wherein the rule effectiveness analyzer logic is configured to generate the rule effectiveness output based on the performance comparison output signal and the frequency of implementation output signal.

5. The mobile machine computing system of claim 1 wherein the automated control rule modification engine comprises:

rule removal logic configured to automatically remove the control rule, from the set of control rules, based on the rule effectiveness output.

6. The mobile machine computing system of claim 1 wherein the learning logic comprises:

gap identifier logic configured to identify, as a control rule gap, an area of performance improvement for the mobile machine for which no effective control rule is present in the set of control rules.

7. The mobile machine computing system of claim 1 and further comprising:

analytics trigger logic configured to detect an analytics trigger and generate a trigger detected output, the rule effectiveness analyzer logic generating the rule effectiveness output based on the trigger detected output.

8. A method of controlling a mobile machine computing system comprising:

receiving machine data from a mobile machine, the machine data including control rule implementation data indicative of whether a control operation, corresponding to a control rule triggered on the mobile machine, was implemented by an operator of the mobile machine, and performance data indicative of an effect on machine performance of the mobile machine based on implementation of the control operation;

generating a rule effectiveness output for the control rule based on the machine data, wherein generating the rule effectiveness outputs comprises:

identifying, from the control rule implementation data, a frequency with which the control operation corresponding to the control rule was implemented by the operator when the control rule was triggered on the mobile machine; and

generating a frequency of implementation output signal; and

generating a rule rating output, corresponding to the control rule, indicative of a rule rating based on the effectiveness output;

automatically modifying a set of control rules based on the rule rating output; and

automatically synchronizing the modified set of control rules to the mobile machine.

9. The method of claim 8 wherein generating the rule effectiveness output further comprises:

receiving performance data indicative of machine performance before implementation of the control operation corresponding to the triggered rule on the mobile machine and performance data indicative of machine performance after implementation of the control operation corresponding to the triggered rule on the mobile machine: and

generating a performance comparison output signal.

10. The method of claim 9 wherein generating the rule effectiveness output further comprises generating the rule effectiveness output based on the performance comparison output signal and the frequency of implementation output signal.

11. The method of claim 8 wherein automatically modifying the set of control rules comprises:

modifying control rules in the set of control rules, based on the rule rating output, to change a priority with which the control rules in the set of control rules are triggered by the performance data and corresponding control operations are surfaced for the operator.

12. The method of claim 8 wherein automatically modifying the set of control rules comprises:

automatically removing the control rule, from the set of control rules, based on the rule effectiveness output.

13. The method of claim 8 and further comprising:

identifying, as a control rule gap, an area of performance improvement for the mobile machine for which no effective control rule is present in the set of control rules.

14. The method of claim 8 and further comprising:

detecting an analytics trigger; and

generating a trigger-detected output and generating the rule effectiveness output based on the trigger-detected output.

15. A mobile machine computing system comprising:

machine interaction logic that receives machine data from a mobile machine, the machine data including control rule implementation data indicative of whether a control operation, corresponding to a control rule triggered on the mobile machine that maps performance to the control operation, was implemented by an operator of the mobile machine, and performance data indicative of an effect on machine performance of the mobile machine based on implementation of the control operation;

rule effectiveness analyzer logic that receives the machine data and generates a rule effectiveness output for the control rule based on the machine data;

learning logic that generates a rule rating output, corresponding to the control rule, indicative of a rule rating based on the effectiveness output;

an automated control rule modification engine that automatically modifies a set of control rules based on the rule rating output, the automated control rule modification engine comprising:

rule removal logic configured to automatically remove the control rule, from the set of control rules, based on the rule effectiveness output; and

a rule update engine that automatically updates the modified set of control rules to the mobile machine.