Modular control system
Embodiments are directed towards automatically identifying, configuring, monitoring, controlling, managing, and maintaining a machine, via collection computers in communication with the machine components. The components include ID Tags that store identification data, such as a component type and a unique identifier. Interrogation of the ID Tags enables the automatic identification and configuration of the machine. Data provided by the sensors, during usage of the machine, enables the remote monitoring and managing of the usage, as well as maintaining of the machine. Machine maintenance includes automatically predicting and scheduling the replacement of various components. Embodiments provide suggestions for suppliers of replacement components, as well as suggestions for alternative components that may be better optimized for the configuration and usage of the machine. Heuristics and crowd-generated data, via machine user social networks, inform predictive analyses employed to automatically identify, configure, manage, operate, and maintain the machine.
1. A method for managing operation of a machine, comprising:
communicating, to a collection computer, component identification data separately stored in each of a plurality of component identification (ID) Tags that correspond to one or more components for the machine, wherein the component identification data includes a component type;
determining a combination of the component types based on at least the component identification data;
automatically determining a machine type for the machine based on a comparison of the combination of the component types and one or more previously provided combinations of component types that correspond to a previously determined machine type, wherein the likeliest comparison is employed to determine the machine type;
automatically determining a machine configuration for the machine based on one or more of the machine type and the combination of component types, wherein a statistical evaluation of a look up table that maps correlations between specific combinations of component types and machine types is employed to determine the machine type; and
controlling one or more characteristics of usage of the machine for one or more users permitted to operate the machine or maintenance of the machine based on at least the machine configuration.
2. The method of claim 1 , further comprising controlling the operation of the machine based on at least the machine configuration.
3. The method of claim 1 , further comprising:
determining an expected machine usage based on a previously determined machine usage; and
determining the machine configuration based on the expected machine usage.
4. The method of claim 1 , further comprising:
determining an actual machine usage based on data provided by one or more sensors that correspond to one or more characteristics of the one or more components for the machine; and
dynamically updating the machine configuration based on the actual machine usage.
5. The method of claim 1 , further comprising:
determining one or more of a geographic location or an environmental parameter of the machine, wherein the machine configuration is further determined based on the one or more of the global location or the environmental parameter.
6. The method of claim 1 , further comprising enabling a user to manually provide one or more additional component types for the machine, wherein the combination of component types is automatically updated.
7. The method of claim 1 , further comprising
uniquely identifying the one or more components based on the component identification data, wherein the machine is uniquely identified based on the one or more uniquely identified components; and
physically locating one or more of the plurality of component ID Tags on its corresponding component, wherein the one or more component ID tags is a radio frequency identification (RFID) Tag.
8. The method of claim 1 , further comprising:
determining a plurality of previously determined machine types having a machine type score, wherein each machine type score is based on a comparison between the combination of component types for the machine and other combinations of component types for the previously determined machine types; and
automatically further determining the machine type based on the previously determined machine type having the machine type score for a greatest likelihood of the other combination being equivalent to the combination.
9. The method of claim 1 , wherein the machine configuration is further determined based on one or more of a social network of user's of an equivalent machine type.
10. A processor readable non-transitory storage media that includes instructions for managing operation of a machine, wherein the execution of the instructions by a processor enables actions, comprising:
communicating, to a collection computer, component identification data separately stored in each of a plurality of component identification (ID) Tags that correspond to one or more components for the machine, wherein the component identification data includes a component type;
determining a combination of the component types based on at least the component identification data;
automatically determining a machine type for the machine based on a comparison of the combination of the component types and one or more previously provided combinations of component types that correspond to a previously determined machine type, wherein a statistical evaluation of a look up table that maps correlations between specific combinations of component types and machine types is employed to determine the machine type;
automatically determining a machine configuration for the machine based on one or more of the machine type and the combination of component types, wherein the machine configuration includes one or more component configurations for each component that corresponds to one or more of the plurality of component ID tags; and
controlling one or more characteristics of usage of the machine for one or more users permitted to operate the machine or maintenance of the machine based on at least the machine configuration.
11. The media of claim 10 , wherein the actions further comprise controlling the operation of the machine based on at least the machine configuration.
12. The media of claim 10 , wherein the actions further comprise:
determining an expected machine usage based on a previously determined machine usage; and
determining the machine configuration based on the expected machine usage.
13. The media of claim 10 , wherein the actions further comprise:
determining an actual machine usage based on data provided by one or more sensors that correspond to one or more characteristics of the one or more components for the machine; and
dynamically updating the machine configuration based on the actual machine usage.
14. The media of claim 10 , wherein the actions further comprise:
determining one or more of a geographic location or an environmental parameter of the machine, wherein the machine configuration is further determined based on the one or more of the global location or the environmental parameter.
15. The media of claim 10 , wherein the actions further comprise:
enabling a user to manually provide one or more additional component types for the machine, wherein the combination of component types is automatically updated.
16. The method of claim 10 , wherein the actions further comprise:
uniquely identifying the one or more components based on the component identification data, wherein the machine is uniquely identified based on the one or more uniquely identified components; and
physically locating one or more of the plurality of component ID Tags on its corresponding component, wherein the one or more component ID tags is a radio frequency identification (RFID) Tag.
17. The media of claim 10 , wherein the actions further comprising:
determining a plurality of previously determined machine types having a machine type score, wherein each machine type score is based on a comparison between the combination of component types for the machine and other combinations of component types for the previously determined machine types; and
automatically further determining the machine type based on the previously determined machine type having the machine type score for a greatest likelihood of the other combination being equivalent to the combination.
18. The media of claim 10 , wherein the machine configuration is further determined based on one or more of a social network of user's of an equivalent machine type.
19. A system for managing operation of a machine, comprising:
a plurality of component identification (ID) Tags that separately store component identification data, wherein each of the plurality of ID Tags corresponds to one or more components for the machine and the component identification data includes a component type; and
a network computer;
a memory for storing at least instructions; and
a processor device that executes the instructions to enable actions, including:
communicating component identification data;
determining a combination of the component types based on at least the component identification data;
automatically determining a machine type for the machine based on a comparison of the combination of the component types and one or more previously provided combinations of component types that correspond to a previously determined machine type, wherein a statistical evaluation of a look up table that maps correlations between specific combinations of component types and machine types is employed to determine the machine type;
automatically determining a machine configuration for the machine based on one or more of the machine type and the combination of component types, wherein the machine configuration includes one or more component configurations for each component that corresponds to one or more of the plurality of component ID tags; and
controlling one or more characteristics of usage of the machine for one or more users permitted to operate the machine or maintenance of the machine based on at least the machine configuration.
20. The system of claim 19 , wherein the actions further include controlling the operation of the machine based on at least the machine configuration.
21. The system of claim 19 , wherein the actions further include:
determining an expected machine usage based on a previously determined machine usage; and
determining the machine configuration based on the expected machine usage.
22. The system of claim 19 , wherein the actions further include:
determining an actual machine usage based on data provided by one or more sensors that correspond to one or more characteristics of the one or more components for the machine; and
dynamically updating the machine configuration based on the actual machine usage.
23. The media of claim 19 , wherein the actions further include:
determining one or more of a geographic location or an environmental parameter of the machine, wherein the machine configuration is further determined based on the one or more of the global location or the environmental parameter.
24. The system of claim 19 , wherein the actions further include:
enabling a user to manually provide one or more additional component types for the machine, wherein the combination of component types is automatically updated.
25. The system of claim 19 , wherein the actions further include:
uniquely identifying the one or more components based on the component identification data, wherein the machine is uniquely identified based on the one or more uniquely identified components; and
physically locating one or more of the plurality of component ID Tags on its corresponding component, wherein the one or more component ID tags is a radio frequency identification (RFID) Tag.
26. The system of claim 19 , wherein the actions further include:
determining a plurality of previously determined machine types having a machine type score, wherein each machine type score is based on a comparison between the combination of component types for the machine and other combinations of component types for the previously determined machine types; and
automatically further determining the machine type based on the previously determined machine type having the machine type score for a greatest likelihood of the other combination being equivalent to the combination.
27. The system of claim 19 , wherein the machine configuration is further determined based on one or more of a social network of user's of an equivalent machine type.