Life safety device with machine learning based analytics

1. A detector, comprising:

at least one sensor configured to detect airborne particulates at a premises;

processing circuitry in communication with the at least one sensor, the processing circuitry configured to:

determine at least one characteristic associated with the detected airborne particulates, the at least one characteristic associated with the detected airborne particulates comprises a composition of the detected airborne particulates;

compare the at least one characteristic associated with the detected airborne particulates to data associated with predefined characteristics of burned materials, the predefined characteristic of burned materials comprises at least one of a smoke composition or a soot composition;

detect, based at least on the comparison, a presence of a fire; and

determine, if the presence of the fire is detected, at least one characteristic of the fire based on the at least one characteristic associated with the detected airborne particulates and the comparison.

2. The detector of claim 1 , wherein the processing circuitry is further configured to determine, based on the at least one characteristic of the fire, at least one fire response characteristic.

3. The detector of claim 2 , wherein the fire response characteristic comprises at least one of an egress point or an ingress point.

4. The detector of claim 1 , wherein the processing circuitry is further configured to determine, based on the at least one characteristic associated with the detected airborne particulates, a visibility condition.

5. The detector of claim 1 , wherein the at least one sensor is configured to detect gas at the premises, and the processing circuitry is further configured to determine at least one characteristic associated with the detected gas.

6. The detector of claim 1 , wherein the at least one characteristic of the fire comprises at least one of burn rate, spread pattern, or an identity of a substance that is burning.

7. The detector of claim 1 , wherein the processing circuitry is further configured to transmit an alarm signal, the alarm signal comprising an indication of a fire response characteristic comprising one of an egress point or an ingress point.

8. The detector of claim 1 , wherein the predefined characteristics of burned materials further comprises at least one of smoke yield or soot yield.

9. The detector of claim 1 , wherein the processing circuitry is further configured to request data specific to the detected airborne particulates from a database to retrieve the predefined characteristics of burned materials.

10. The detector of claim 1 , wherein the processing circuitry is further configured to train a model to determine the at least one characteristic of the fire using machine learning.

11. A method implemented by a detector, the method comprising:

detecting airborne particulates at a premises;

determining at least one characteristic associated with the detected airborne particulates, the at least one characteristic associated with the detected airborne particulates comprises a composition of the detected airborne particulates;

comparing the at least one characteristic associated with the detected airborne particulates to data associated with predefined characteristics of burned materials, the predefined characteristic of burned materials comprises at least one of a smoke composition or a soot composition;

detecting, based at least on the comparison, a presence of a fire; and

determining, if the presence of the fire is detected, at least one characteristic of the fire based on the at least one characteristic associated with the detected airborne particulates and the comparison.

12. The method of claim 11 , further comprising determining, based on the at least one characteristic of the fire, at least one fire response characteristic.

13. The method of claim 12 , wherein the fire response characteristic comprises at least one of an egress point or an ingress point.

14. The method of claim 11 , further comprising determining, based on the at least one characteristic associated with the detected airborne particulates, a visibility condition.

15. The method of claim 11 , further comprising receiving, from at least one sensor, information pertaining to a detected gas at the premises, and determining at least one characteristic associated with the detected gas.

16. The method of claim 11 , wherein the at least one characteristic of the fire comprises at least one of burn rate, spread pattern, or an identity of a substance that is burning.

17. The method of claim 11 , further comprising transmitting an alarm signal, the alarm signal comprising an indication of a fire response characteristic comprising one of an egress point or an ingress point.

18. The method of claim 11 , wherein the predefined characteristics of burned materials further comprises at least one of smoke yield or soot yield.

19. The method of claim 11 , further comprising requesting data specific to the detected airborne particulates from a database to retrieve the predefined characteristics of burned materials.

20. The method of claim 11 , further comprising training a model to determine the at least one characteristic of the fire using machine learning.

21. A detector, comprising:

at least one sensor configured to detect airborne particulates at a premises;

processing circuitry configured to:

determine at least one characteristic associated with the detected airborne particulates, the at least one characteristic associated with the detected airborne particulates comprises a composition of the detected airborne particulates;

request data specific to the detected airborne particulates from a database to retrieve predefined characteristics of burned materials, the predefined characteristics of burned materials comprises at least one of a smoke composition or a soot composition;

compare the at least one characteristic associated with the detected airborne particulates to the predefined characteristics of burned materials;

detect, based at least on the comparison, a presence of a fire;

determine, if the presence of the fire is detected, at least one characteristic of the fire based on the at least one characteristic associated with the detected airborne particulates and the comparison; and

train a model to determine the at least one characteristic of the fire using machine learning.