Emergency Response Management Apparatuses, Methods and Systems

• US 20120179421A1
• Filed: 12/02/2011
• Published: 07/12/2012
• Est. Priority Date: 12/07/2010
• Status: Active Application

• Current Assignee: Gautam Dasgupta
• Original Assignee: Gautam Dasgupta
• Inventors: Dasgupta, Gautam
• Application Number:
  US13/310,547
• Publication Number:
  US 20120179421A1
• Time in Patent Office: Days
• US Class Current: 702/181
• CPC Class Codes: G05B 23/0281 Quantitative, e.g. mathematical distance; Clustering; Neural networks; Statistical analysis

Abstract

The EMERGENCY RESPONSE MANAGEMENT APPARATUSES, METHODS AND SYSTEMS (“ERMS”) transform emergency related inputs and sensor information into a threat indication category, which is distributed to individuals and/or first responders for managing the threat. In one implementation, the method includes an emergency management processor-implemented method that receives sensor readings from one or more sensor devices and generates risk factors for the at least one sensor device. The generated risk factors are then curve fitted to a plurality of statistical distribution curves including both non-extreme and extreme statistical distributions, wherein each of the statistical distribution curves is indicative of a threat category. The threat category is then determined based on the generated risk factors that provide a best fit with one of the plurality of statistical distribution curves.

Claims

1. An emergency management processor-implemented method, comprising:

receiving sensor readings from at least one sensor device;

generating risk factors for the at least one sensor device using weighted sensor indications associated with the received sensor readings and a sensor statistical distribution associated with the at least one sensor device;

curve fitting the generated risk factors to a plurality of statistical distribution curves, wherein each of the statistical distribution curves is indicative of a threat category; and

determining the threat category based on the generated risk factors providing a best fit with one of the plurality of statistical distribution curves.

2. The method of claim 1, wherein the one of the plurality of statistical distribution curves comprises a Gumbel Distribution.

3. The method of claim 1, wherein the one of the plurality of statistical distribution curves comprises a Weibull Distribution.

4. The method of claim 1, wherein the one of the plurality of statistical distribution curves comprises a Fré

chet Distribution.

5. The method of claim 1, wherein providing the best fit with the one of the plurality of statistical distribution curves comprises a Gumbel Distribution best fitting the generated risk factors, the Gumbel Distribution representing a natural disaster threat category.

6. The method of claim 1, wherein providing the best fit with the one of the plurality of statistical distribution curves comprises a Weibull Distribution best fitting the generated risk factors, the Weibull Distribution representing an industrial disaster threat category.

7. The method of claim 1, wherein providing the best fit with the one of the plurality of statistical distribution curves comprises a Fré

chet Distribution best fitting the generated risk factors, the Fré

chet Distribution representing a terrorist threat category.

8. The method of claim 1, wherein providing the best fit with the one of the plurality of statistical distribution curves comprises a non-extreme distribution best fitting the generated risk factors, the non-extreme distribution representing a safety alert category.

9. The method of claim 1, wherein the non-extreme distribution representing the safety alert category comprises a Gaussian Distribution.

10. The method of claim 1, wherein the sensor statistical distribution associated with the at least one sensor device is a Beta distribution curve.

11. The method of claim 1, wherein the at least one sensor device comprises an intelligent sensor that is programmable to receive configuration information.

12. The method of claim 11, wherein the received configuration information comprise operation range information.

13. The method of claim 1, wherein the at least one sensor device comprises a plurality of sensor devices for detecting respective conditions associated with an environment.

14. The method of claim 1, further comprising:

retrieving, based on each of the sensor readings, tagged information corresponding to the at least one sensor device, wherein the tagged information include numerical components and textual components.

15. The method of claim 14, further comprising:

providing weighting factors from the textual components of the tagged information;

providing numerical values from the numerical components of the tagged information, the numerical values each being representative of at least one sensor reading from the received sensor readings; and

applying the weighted factors to the numerical components for generating the weighted sensor indications.

16. The method of claim 15, wherein the weighted sensor indications comprise a normalized fraction value ranging between 0-1.

17. The method of claim 14, wherein the textual components of the tagged information comprise at least one of an upper and lower cut-off range for the at least one sensor, a mean and a standard deviation for the at least one sensor over the upper and the lower cut-off range, a manufacturer reliability factor for the at least one sensor, and mathematical operations for utilizing the at least one sensor with at least one other sensor device.

18. The method of claim 17, wherein the sensor statistical distribution associated with the at least one sensor device is generated based on the mean and the standard deviation for the at least one sensor over the upper and the lower cut-off range.

19. The method of claim 1, further comprising:

receiving sensor readings from at least one other sensor device, the at least one other sensor device having an other statistical distribution that is combined with the statistical distribution of the at least one sensor device using interval mathematics.

20. The method of claim 1, wherein each of the risk factors are determined by calculating an area under the sensor statistical distribution, the area determined between each of the weighted sensor indications and a lower operating cut-off associated with the at least one sensor device.

21. The method of claim 1, wherein determining the threat category comprises providing each of the generated risk factors for generating the best fit with the one of the plurality of statistical distribution curves when each of the generated risk factors falls within a security risk region of the sensor statistical distribution.

22. The method of claim 1, further comprising:

adjusting, by a human operator, a current value corresponding to at least one of the weighted sensor indications to a new value corresponding to at least one new sensor indication;

determining, by the human operator, a new threat category based on the adjusted new value; and

replacing the current value with the new value when the new threat category is determined by the human operator to conform with a predetermined threat category expected by the human operator based on the new value.

23. An emergency management processor-implemented method, comprising:

receiving sensor readings from at least one sensor device;

generating risk factors for the at least one sensor device;

curve fitting the generated risk factors to a plurality of statistical distribution curves including both non-extreme and extreme statistical distributions, wherein each of the plurality of statistical distribution curves is indicative of a threat category; and

determining the threat category based on the generated risk factors providing a best fit with one of the plurality of statistical distribution curves.

24. The method of claim 23, wherein the non-extreme and extreme statistical distributions comprise a Gumbel Distribution, a Weibull Distribution, a Fré

chet Distribution, and a Gaussian Distribution.

25. The method of claim 23, further comprising:

generating other risk factors for an infrastructure that is associated with the at least one sensor.

26. The method of claim 23, wherein the generated risk factors and the generated other risk factors are selectively adjustable by a human expert via the at least one sensor device for use in future determinations of the threat category.

27. An emergency management system, comprising:

a memory; and

a processor disposed in communication with the memory and configured to issue processing instructions stored in the memory to;

receive sensor readings from at least one sensor device;

generate risk factors for the at least one sensor device using weighted sensor indications associated with the received sensor readings and a sensor statistical distribution associated with the at least one sensor device;

curve fit the generated risk factors to a plurality of statistical distribution curves, wherein each of the statistical distribution curves is indicative of a threat category; and

determine the threat category based on the generated risk factors providing a best fit with one of the plurality of statistical distribution curves.

28. A processor-readable tangible medium storing processor-issuable emergency management instructions to:

receive sensor readings from at least one sensor device;

generate risk factors for the at least one sensor device using weighted sensor indications associated with the received sensor readings and a sensor statistical distribution associated with the at least one sensor device;

curve fit the generated risk factors to a plurality of statistical distribution curves, wherein each of the statistical distribution curves is indicative of a threat category; and

determine the threat category based on the generated risk factors providing a best fit with one of the plurality of statistical distribution curves.