METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH A SELF-ORGANIZING ADAPTIVE SENSOR SWARM FOR INDUSTRIAL PROCESSES

Abstract

Methods and systems for detection in an industrial internet of things data collection environment with a self-organizing adaptive sensor swarm for industrial processes include a plurality of data collectors communicatively coupled to a plurality of input channels, wherein each of the plurality of data collectors is structured to collect detection values as collected data, an expert system circuit structured to self-organize one or more detection packages and an associated subset of the plurality of data collectors using a swarm optimization algorithm, a data acquisition circuit structured to interpret the collected data, a data analysis circuit structured to analyze the collected data, and a cognitive input selection facility for optimization of an input selection configuration for a collector route of the plurality of data collectors.

Claims

1. A system, comprising: a plurality of data collectors communicatively coupled to a plurality of input channels, wherein each of the plurality of data collectors is structured to collect detection values as collected data from sensors of an industrial system; an expert system circuit structured to self-organize one or more detection packages and an associated subset of the plurality of data collectors, each detection package comprising at least one of: collected data, data communication routing, data storage locations, or sensor data responsibility areas; a data acquisition circuit structured to interpret the collected data; a data analysis circuit structured to analyze the collected data; and wherein the expert system circuit includes a cognitive input selection facility structured to adaptively improve a data collection parameter.

2. The system of claim 1, wherein the data collection parameter includes at least one parameter selected from the parameters consisting of: data related resource utilization, data storage, data collection capability, data processing, data presentation availability, and data communication.

3. The system of claim 1, wherein cognitive input selection facility iteratively improves the data collection parameter based on feedback.

4. The system of claim 3, wherein the cognitive input selection facility further comprises a remote learning feedback facility associated with a data collection marketplace, and wherein the feedback is derived from user feedback metrics.

5. The system of claim 1, wherein the plurality of data collectors comprise a self-organized swarm of data collectors, wherein the self-organized swarm of data collectors organizes among themselves to optimize data collection based at least in part on a received data marketplace measure of success.

6. The system of claim 5, wherein the self-organized swarm of data collectors coordinate with one another to optimize data collection based at least in part on capabilities and conditions of members of the self-organized swarm.

7. The system of claim 5, wherein the self-organized swarm of data collectors coordinate with one another to optimize data collection based at least in part on optimizing sensed parameters from the collected data over time.

8. The system of claim 5, wherein the self-organized swarm of data collectors coordinate with one another to optimize data collection based at least in part on optimizing at least one of: resource utilization, data yield, or power utilization over time.

9. The system of claim 1, wherein the cognitive input selection facility adaptively improves the data collection parameter by modifying a hierarchical template for data collection.

10. The system of claim 1, wherein the cognitive input selection facility anticipates state information from machine learning and pattern recognition to adaptively improves the data collection parameter.

11. The system of claim 1, wherein the cognitive input selection facility adaptively improves the data collection parameter based on feedback to a machine learning facility regarding measures of success.

12. The system of claim 11, wherein the measures of success comprise at least one of: utilization measures, efficiency measures, measures of success in prediction or anticipation of states, productivity measures, yield measures, or profit measures.

13. A method, comprising: collecting data from a plurality of input channels by a plurality of data collectors communicatively coupled to the plurality of input channels, wherein each of the plurality of data collectors is structured to collect detection values as collected data from sensors of an industrial system; self-organizing, by an expert system circuit, one or more detection packages and an associated subset of the plurality of data collectors, each detection package comprising at least one of: data collected, data communication routing, data storage locations, or sensor data responsibility areas; interpreting and analyzing the collected data; and wherein the self-organizing further includes adaptively improving a data collection parameter.

14. The method of claim 13, wherein the data collection parameter includes at least one parameter selected from the parameters consisting of: data related resource utilization, data collection related resource utilization, data storage, data collection capability, data processing, data presentation availability, data communication, data collection throughput capacity, and data communication throughput capacity.

15. The method of claim 13, wherein the plurality of data collectors comprise a self-organized swarm of data collectors, wherein the self-organized swarm of data collectors organizes among themselves to optimize data collection based at least in part on a received data marketplace measure of success.

16. The method of claim 15, wherein the self-organized swarm of data collectors coordinate with one another to optimize data collection based at least in part on capabilities and conditions of members of the self-organized swarm.

17. An apparatus, comprising: a plurality of data collectors communicatively coupled to a plurality of input channels, wherein each of the plurality of data collectors is structured to collect detection values as collected data from sensors of an industrial system; an expert system circuit structured to self-organize one or more detection packages and an associated subset of the plurality of data collectors using a swarm optimization algorithm, each detection package comprising at least one of: data collected, data communication routing, data storage locations, or sensor responsibility areas; a data acquisition circuit structured to interpret the collected data; a data analysis circuit structured to analyze the collected data; and wherein the expert system circuit includes a cognitive input selection facility structured to adaptively improve a data collection parameter comprising at least one of a data collection throughput capacity or a data collection capability.

18. The apparatus of claim 17, wherein the plurality of data collectors is a self-organized swarm of data collectors, wherein the self-organized swarm of data collectors organizes among themselves to optimize data collection based at least in part on a received data marketplace measure of success.

19. The apparatus of claim 18, wherein the self-organized swarm of data collectors coordinate with one another to optimize data collection based at least in part on conditions of members of the self-organized swarm.

20. The apparatus of claim 19, wherein the self-organized swarm of data collectors coordinate with one another to optimize data collection based at least in part on optimizing sensed parameters from the collected data over time.