SYSTEM AND METHOD FOR OCCUPANCY ESTIMATION AND MONITORING

• US 20110213588A1
• Filed: 11/07/2008
• Published: 09/01/2011
• Est. Priority Date: 11/07/2008
• Status: Abandoned Application

• Current Assignee: UTC Fire & Security Corporation (Raytheon Technologies Corporation d/b/a RTX)
• Original Assignee: UTC Fire & Security (Raytheon Technologies Corporation d/b/a RTX)
• Inventors: Lin, Yiqing, Surana, Amit, Fedchenia, Igor I., Meyn, Sean, Oggianu, Stella M.
• Application Number:
  US13/126,519
• Publication Number:
  US 20110213588A1
• Time in Patent Office: Days
• US Class Current: 702/181
• CPC Class Codes: G05B 13/048 using a predictor

Abstract

A system computes occupancy estimates based on one or more inputs, including sensor data from one or more sensor devices, constraints on allowable occupancy levels, and one or more utility functions. An occupancy estimator organizes the sensor data, the utility functions and an occupancy estimate into an objective function and executes a constrained optimization algorithm that computes the occupancy estimate, subject to the constraints, such that the objective function is minimized. The computed occupancy estimate is provided as an output by the system to one or more control and/or monitoring systems.

Claims

1. A system for estimating occupancy in a region, the system comprising:

a first input operably connected to receive sensor data from one or more sensor devices;

a second input operably connected to receive constraints;

a third input operably connected to receive utility functions;

an occupancy estimator operably connected to the first input, the second input, and the third input, wherein the sensor data, the utility functions, and an occupancy estimate are organized into an objective function, wherein the occupancy estimator executes a constrained optimization algorithm that computes the occupancy estimate, subject to the constraints, such that the objective function is minimized; and

an output operably connected to the occupancy estimator to communicate the occupancy estimate generated by the occupancy estimator.

2. The system of claim 1, wherein the utility function describes a likely occupancy level associated with a zone within the region based on prior knowledge associated with how the zone will be utilized.

3. The system of claim 1, wherein the utility function describes a likely occupancy level associated with a zone within the region based on prior knowledge of scheduled events associated with the zone.

4. The system of claim 1, wherein the utility function describes a likely occupancy level for a zone within the region based on prior knowledge associated with a sensor model associated with the zone.

5. The system of claim 1, wherein the utility function describes a likely occupancy level for a zone within a region based on prior knowledge collected from previous occupancy estimates computed the occupancy estimator for the zone.

6. The system of claim 1, further including:

a fourth input operably connected to receive building information, wherein the objective function is organized based, in part, on the received building information.

7. The system of claim 1, wherein the objective function further includes a flow estimate that represents the number of occupants transitioning between adjacent zones, wherein the occupancy estimator executes a constrained optimization algorithm that computes the flow estimate, subject to the constraints, such that the objective function is minimized.

8. The system of claim 7, wherein the constraints define an occupancy minimum and maximum for each zone in the region and a flow minimum and maximum for the transition of occupants between adjacent zones within the region.

9. The system of claim 7, wherein the objective function includes a first penalty function that measures consistency between the sensor data and the flow estimate.

10. The system of claim 9, wherein the objective function includes a second penalty function that models a soft-constraint on changes in the occupancy estimate for adjacent time steps.

11. The system of claim 10, wherein the objective function includes a third penalty function that models a soft-constraint on changes in the flow estimate for adjacent time steps.

12. The system of claim 1, further including:

a parameter estimator operably connected to the output for receiving the occupancy estimates generated by the occupancy estimator, wherein the parameter estimator applies a statistical distribution to the occupancy estimate to generate parameter estimates; and

a statistical distribution generator operably connected to receive the parameter estimates generated by the parameter estimator and to generate in response to the parameter estimates a conditional probability distribution that represents a probability of a particular zone within the region having various levels of occupancy at a current time step, conditioned on occupancy estimate of the particular zone and zones neighboring the particular zone at a previous time step.

13. The system of claim 12, wherein the parameter estimator applies a one-sided truncated Poisson distribution to the occupancy estimates to generate a parameter estimate associated with arrivals of occupants to a zone within the region.

14. The system of claim 12, wherein the parameter estimator applies a two-sided truncated geometric distribution to the occupancy estimates to generate a parameter estimate associated with transitions of occupants between zones within the region.

15. The system of claim 12, wherein the statistical distribution generator employs a parameterized Markov model to generate the conditional probability distribution based on the parameters estimates provided by the parameter estimator.

16. The system of claim 1, wherein the system is a centralized system in which the occupancy estimator is operably connected to receive data from a plurality of heterogeneous sensors located throughout the region and in response generates occupancy estimates for each zone within the region.

17. The system of claim 1, wherein the system is a distributed system including a plurality of occupancy estimators, wherein each of the plurality of occupancy estimators receives sensor data associated with a proximate location of the region and executes a constrained optimization algorithm to generate an occupancy estimate for the proximate location based on the received sensor data, a utility function and constraints associated with the proximate location.

18. A method for monitoring occupancy in a region, the method comprising:

acquiring sensor data from one or more sensor devices;

computing an occupancy estimate that minimizes a result of an objective function, wherein the objective function is organized to compare a penalty associated with differences in the sensor data and the occupancy estimate with a utility function that describes a likely occupancy level; and

generating an output that provides the occupancy estimate to one or more control and/or monitoring systems.

19. The method of claim 18, wherein the occupancy estimate includes a flow estimate that is computed as part of the objective function, wherein the flow estimate represents a number of occupants transitioning between adjacent zones within the region.

20. The method of claim 19, wherein computing the occupancy estimate includes computing the occupancy estimate and/or the flow estimate to satisfy one or more constraints that define occupancy minimums and maximums for each zone in the region and flow minimums and maximums defined for the transition of occupants between adjacent zones within the region.

21. The method of claim 19, wherein the objective function includes a first penalty function that measures consistency between the sensor data and the flow estimate, a second penalty function that models a soft-constraint on changes in the occupancy estimate between adjacent time periods, and third penalty function that models a soft-constraint on changes in the flow estimate between adjacent time periods.

22. The method of claim 19, further including:

calculating a parameter estimate associated with occupant movements within the region by applying a statistical distribution to the computed flow estimates and/or the computed occupancy estimates; and

generating a conditional probability distribution based on the calculated parameter estimate, the conditional probability distribution representing a probability of a particular zone within the region having various levels of occupancy at a current time step, conditioned on occupancy estimate of the particular zone and zones neighboring the particular zone at a previous time step; and

providing as an output the computed occupancy estimate, the computed flow estimate, and the conditional probability distribution.

23. The method of claim 19, wherein calculating a parameter estimate includes:

applying a first statistical distribution to the computed occupancy estimates and/or the computed flow estimates to calculate an arrival parameter that defines a probabilistic arrival law of occupants to the region; and

applying a second statistical distribution to the computed occupancy estimates and/or the computed flow estimates to calculate a transition parameter that defines a probabilistic transition law of occupants between regions.

24. The method of claim 23, wherein the first statistical distribution is a one-sided truncated Poisson distribution and the second statistical distribution is a two-sided geometric distribution.

25. The method of claim 22, wherein generating a conditional probability distribution based on the calculated parameter estimate includes applying a parameterized Markov model to the transition parameter estimate and the arrival parameter estimate.

26. A system for generating occupancy estimates for a region and conditional probability distributions defining occupant traffic in the region, the system comprising:

at least one sensor device for acquiring sensor data relevant to occupancy;

means for generating an occupancy estimate and/or flow estimate that executes a constrained optimization algorithm in conjunction with an objective function organized to compare the sensor data to the occupancy estimate and/or flow estimate, wherein the constrained optimization algorithm computes the occupancy estimate and/or flow estimate to minimize the result of the objective function, subject to a plurality of constraints on allowable levels of occupancy;

means for calculating an arrival parameter estimate associated with expected arrival of occupants to a zone within the region by applying a first statistical distribution to one or more calculated occupancy estimates and/or flow estimates and for calculating a transition parameter estimate associated with transition of occupants between zones in the region by applying a second statistical distribution to one or more calculated occupancy estimates and/or flow estimates;

means for generating a conditional probability distribution based on the calculated arrival parameter estimate and the calculated transition parameter estimate by applying a parameterized Markov model, wherein the conditional probability distribution represents a probability of a particular zone within the region having various levels of occupancy at a current time step, conditioned on occupancy estimate of the particular zone and zones neighboring the particular zone at a previous time step; and

means for providing as an output the occupancy estimate and/or flow estimate and the conditional probability distribution.

27. A computer readable storage medium encoded with a machine-readable computer program code for generating thereof occupancy estimates for a region and a conditional probability distribution describing normal occupant traffic for the region, the computer readable storage medium including instructions for causing a controller to implement a method comprising:

acquiring sensor data;

computing an occupancy estimate that minimizes a result of an objective function, wherein the objective function is organized to compare a penalty associated with differences in the sensor data and the occupancy estimate with a utility function that describes a likely occupancy level; and

generating an output that provides the occupancy estimate to selected systems within the region.

28. The computer readable storage medium of claim 27, the computer readable storage medium further including instructions for causing a controller to implement a method comprising:

calculating parameter estimates by applying a first statistical distribution to one or more computed occupancy estimates to generate a parameter estimate associated with occupant arrival to the region and a second statistical distribution to one or more computed occupancy estimates to generate a parameter estimate associated with occupant transitions between adjacent zones within the region; and

calculating a conditional probability distribution by applying a parameterized Markov model to the calculated parameter estimates, wherein the conditional probability distribution represents a probability of a particular zone within the region having various levels of occupancy at a current time step, conditioned on occupancy estimate of the particular zone and zones neighboring the particular zone at a previous time step; and

providing as an output the occupancy estimate and the conditional probability distribution.