OPERATOR PERFORMANCE RECOMMENDATION GENERATION

• US 20150199360A1
• Filed: 05/06/2014
• Published: 07/16/2015
• Est. Priority Date: 01/14/2014
• Status: Active Grant

• Current Assignee: Deere & Company
• Original Assignee: Deere & Company
• Inventors: Blank, Sebastian, Pfeiffer, Dohn W.
• Granted Patent: US 10,380,704 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 50/02 Agriculture; Fishing; Forestry; Mining

Abstract

A set of data is received. The data is indicative of sensed parameters on a mobile machine. The data is evaluated against a set of actionable conditions to determine the degree of fulfillment of each condition. A recommendation for changing the operation of the mobile machine is identified based on the degree of fulfillment. An output is generated based on the recommendation.

Claims

1. A computer-implemented method, comprising:

receiving a set of data based on sensor signals indicative of sensed parameters on a mobile machine;

evaluating the set of data to determine a degree of fulfillment of each of a plurality of actionable conditions;

identifying a recommendation for changing operation of the mobile machine based on the degree of fulfillment of each condition; and

generating an output based on the identified recommendation;

2. The computer-implemented method of claim 1 wherein generating an output comprises:

outputting the recommendation on a user interface.

3. The computer-implemented method of claim 2 wherein receiving a set of data comprises:

receiving operator performance data indicative of a performance of a given operator in operating the mobile machine.

4. The computer-implemented method of claim 2 wherein receiving a set of data comprises:

evaluating the set of data against reference data to obtain a performance metric indicative of how the set of data compares to the reference data.

5. The computer-implemented method of claim 2 wherein identifying a recommendation comprises:

determining which of the actionable conditions are triggered conditions based on the degree of fulfillment for each condition; and

identifying selected conditions from the triggered conditions, for which a recommendation is to be output.

6. The computer-implemented method of claim 5 wherein determining which of the actionable conditions are triggered conditions, comprises:

comparing predefined items in the set of data, corresponding to a selected condition, against reference data for the predefined items to obtain a degree of fulfillment for each predefined item; and

determining a degree of fulfillment of the selected condition based on the degree of fulfillment of each of the predefined items in the set of data.

7. The computer-implemented method of claim 6 wherein comparing predefined items comprises:

comparing items from different levels of aggregation of the set of data from the mobile machine.

8. The computer-implemented method of claim 5 wherein outputting the recommendation comprises:

outputting a recommendation, for a given triggered condition, that varies based on the degree of fulfillment of the given triggered condition.

9. The computer-implemented method of claim 5 wherein outputting the recommendation comprises:

outputting a recommendation, for a given triggered condition, that varies according to a predefined function associated with the given triggered condition.

10. The computer-implemented method of claim 5 wherein outputting the recommendation comprises:

outputting fixed recommendation, for a given triggered condition, that is a same recommendation, regardless of the degree of fulfillment of the given triggered condition.

11. The computer-implemented method of claim 5 wherein identifying a recommendation comprises:

identifying the selected conditions for which a recommendation is to be output based on a priority assigned to each condition.

12. The computer-implemented method of claim 5 wherein identifying a recommendation comprises:

identifying the selected conditions for which a recommendation is to be output based on an amount of time since a recommendation was last output.

13. The computer-implemented method of claim 5 wherein identifying a recommendation comprises:

identifying the selected conditions for which a recommendation is to be output based on a target number of recommendations to be output at any given time.

14. The computer-implemented method of claim 1 wherein evaluating the set of data to determine a degree of fulfillment of a plurality of actionable conditions comprises:

evaluating the set of data against a power utilization condition to determine whether changing operation of the mobile machine will improve power utilization.

15. The computer-implemented method of claim 1 wherein evaluating the set of data to determine a degree of fulfillment of a plurality of actionable conditions comprises:

evaluating the set data against a grain loss condition to determine whether changing operation of the mobile machine will improve grain loss.

16. The computer-implemented method of claim 1 wherein evaluating the set of data to determine a degree of fulfillment of a plurality of actionable conditions comprises:

evaluating the set of data against a grain quality condition to determine whether changing operation of the mobile machine will improve grain quality.

17. The computer-implemented method of claim 1 wherein evaluating the set of data to determine a degree of fulfillment of a plurality of actionable conditions comprises:

evaluating the set of data against a fuel economy condition to determine whether changing operation of the mobile machine will improve fuel economy.

18. The computer-implemented method of claim 1 wherein evaluating the set of data to determine a degree of fulfillment of a plurality of actionable conditions comprises:

evaluating the set of data against a productivity condition to determine whether changing operation of the mobile machine will improve productivity.

19. A computer system, comprising:

a data evaluation layer that receives a set of data based on sensor signals indicative of sensed parameters on a mobile machine and evaluates the set of data against a set of actionable conditions to identify a set of triggered conditions in the set of actionable conditions;

a recommendation engine that identifies a recommendation for changing operation of the mobile machine based on the set of triggered conditions; and

an output component that provides an output based on the recommendation.

20. The computer system of claim 19 wherein the output component comprises:

a user interface that indicates the recommendation.

21. The computer system of claim 20 and further comprising:

a plurality of different pillar score generators, each generating a different performance pillar score indicative of a relative performance of an operator in a given one of a plurality of different performance categories, relative to a performance level represented by a set of reference data in the given performance category, the data evaluation layer receiving the performance pillar scores as a portion of the set of data.

22. The computer system of claim 21 wherein the recommendation engine identifies the recommendation to indicate operational changes in operating the mobile machine to improve at least one of the performance pillar scores.

23. The computer system of claim 20 wherein the data evaluation layer evaluates a different subset of the received data against reference data, for each condition in the set of actionable conditions to identify the set of triggered conditions.

24. The computer system of claim 23 wherein the data evaluation layer determines a level of fulfillment of a set of symptoms corresponding to each condition in the set of actionable conditions by comparing each given item in the subset of received data against reference data for the given item.

25. The computer system of claim 24 wherein the data evaluation layer determines a level of fulfillment of a given condition based on the level of fulfillment determined for the set of symptoms corresponding to the given condition, and identifies the set of triggered conditions based on the level of fulfillment of each condition in the set of actionable conditions.

26. The computer system of claim 25 wherein the recommendation engine identifies the recommendation by identifying which of the triggered conditions a recommendation is to be output for.

27. A mobile machine, comprising:

a data sensing layer that senses a set of sensor data indicative of sensed parameters on the mobile machine;

a data evaluation layer that receives a set of data, based on the set of sensor data, and evaluates the set of data against a set of actionable conditions to identify a set of triggered conditions in the set of actionable conditions;

a recommendation engine that identifies a recommendation for changing operation of the mobile machine based on the set of triggered conditions; and

an output component that provides an output based on the recommendation.

28. The mobile device of claim 27 wherein the output component comprises:

a user interface that indicates the recommendation.

29. The mobile machine of claim 28 and further comprising:

a plurality of different levels of data aggregation, each level processing portions of the set of sensor data and other data to obtain processed data, the data evaluation layer including, in the set of data that is evaluated, processed data from the plurality of different data aggregation levels.

30. The mobile machine of claim 28 wherein the recommendation engine identifies the recommendation to be output based on a level of fulfillment and priority of each of the triggered conditions.

31. The mobile machine of claim 28 wherein the data sensing layer comprises:

a set of combine sensors that sense operating parameters on a combine.