System and method for monitoring a building

1. A method comprising:

receiving first sensor data from a plurality of sensors within a structure over a period of time, wherein the plurality of sensors are configured to sense one or more ambient conditions, wherein each of the plurality of sensors is associated with a baseline, and wherein a normal condition within the structure is determined based upon a combination of the baseline of at least a subset of the plurality of sensors;

comparing a sensed condition to the normal condition;

determining an abnormal state in response to the sensed condition being incompatible with the normal condition;

triggering an alert in response to determining the abnormal state; and dynamically updating the normal condition by adjusting at least one of an upper set-point or a lower set-point associated with the normal condition based upon an expected change in the one or more ambient conditions;

wherein determining the normal condition comprises determining a moving average based on the first sensor data, wherein the moving average comprises: a first moving average of the first sensor data over a first period of time; a second moving average of the first sensor data over a second period of time that is longer than the first period of time; and a third moving average of the first sensor data over a third period of time that is longer than the first period of time and the second period of time; and

computing a combined average based on the first moving average, the second moving average, and the third moving average; and

determining the upper set-point and the lower set-point based on the combined average, wherein the normal condition is based upon the upper set-point and the lower-set point.

2. The method of claim 1 , wherein the first sensor data comprises at least one of smoke data, temperature data, occupancy data, motion data, humidity data, infrared radiation data, power data, or carbon monoxide data.

3. The method of claim 1 , wherein comparing the sensed condition to the normal condition comprises:

sensing second sensor data; and

determining if the second sensor data falls within a data range associated with the normal condition.

4. The method of claim 1 , wherein determining the abnormal state comprises:

comparing second sensor data associated with the sensed condition with the upper set-point and the lower set-point; and

determining that the abnormal state exists upon the second sensor data falling outside of at least one of the upper set-point or the lower set-point.

5. The method of claim 1 , further comprising creating a distribution based on the combined average to determine the upper set-point and the lower set-point.

6. The method of claim 1 , wherein the sensor data is combined based on a Bayesian network.

7. A system comprising:

one or more sensors configured to monitor one or more ambient conditions within a structure; and

a server coupled to the one or more sensors, wherein the server:

receives first sensor data from the one or more sensors within the structure over a period of time;

determines a normal condition within the structure by computing at least one moving average based on the first sensor data;

compares a sensed condition to the normal condition; determines an abnormal state in response to the sensed condition being incompatible with the normal condition;

triggers an alert in response to determining the abnormal state;

computes a combined average based upon the at least one moving average;

computes an upper set-point and a lower set-point based on the combined average; and

dynamically updates the normal condition by adjusting at least one of the upper set-point or the lower set-point associated with the normal condition based upon an expected change in the one or more ambient conditions.

8. The system of claim 7 , wherein the server compares second sensor data associated with the sensed condition with the upper set-point and the lower set-point, and wherein the server determines that the abnormal state exists based upon the second sensor data falling outside of at least one of the upper set-point or the lower set-point.

9. The system of claim 7 , wherein the server creates a distribution based on the combined average to determine the upper set-point and the lower set-point.

10. The system of claim 7 , wherein the server dynamically updates the normal condition based upon the expected change in ambient temperature.

11. The system of claim 7 , wherein the server determines a probability the abnormal state exists based upon the comparison of the sensed condition to the normal condition.

12. The system of claim 7 , wherein the server predicts occurrence of a future abnormal state based on stored sensor data corresponding to at least one previous abnormal state.