Granted Patent B2

US 12,412,003 · App. 17/855,561 · Granted Sep 9, 2025

Inventors: Rajiv Ramanasankaran (San Jose, CA); Chenlu Zhang (Milwaukee, WI); Ambuj Shatdal (Madison, WI); Young Lee (Milwaukee, WI); Jonathan D. Douglas (Mequon, WI)

Assignee: TYCO FIRE & SECURITY GMBH

G06F30/13G05B15/02G06N5/04

Abstract

Claims (70)

1. A building system of a building comprising one or more non-transitory storage devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to:

store a digital twin of the building in the one or more non-transitory storage devices, the digital twin comprising a virtual representation of the building with an entity of the building, a data point, and an interrelationship between the entity of the building and the data point, wherein the digital twin further includes an artificial intelligence configured to generate a plurality of inference values of the data point for a plurality of future times;

generate a recommendation based on the plurality of inference values for the data point for the plurality of future times, the recommendation recommending making one or more updates to the building;

store data for the recommendation in the digital twin, wherein the digital twin includes a representation of a location within the building and associates the location with the recommendation; and

cause a graphic representation of the building to display an indication of the recommendation at the location within the graphic representation of the building associated with the entity based on the digital twin indicating the data point being related to the entity.

2. The building system of claim 1 , wherein the instructions cause the one or more processors to:

receive a plurality of data point values for the data point from building equipment, the building equipment generating the plurality of data point values by measuring an environmental condition of the building or recording operating decisions performed by the building equipment;

identify the data point based on the digital twin by identifying the interrelationship between the entity and the data point, the entity representing the building equipment; and

store the plurality of data point values, or a link to the plurality of data point values, in the data point of the digital twin.

3. The building of system of claim 2 , wherein the plurality of inference values include predicted future values of the data point;

wherein the plurality of inference values are stored in the digital twin; and

wherein the instructions cause the one or more processors to generate the plurality of inference values by:

identifying the data point of the digital twin and retrieving the plurality of data point values based on the data point of the digital twin; and

executing the artificial intelligence to generate the plurality of inference values based on the plurality of data point values.

4. The building system of claim 2 , wherein the plurality of inference values are predicted future values of a virtual indicator, wherein the virtual indicator is at least one of:

a clean air virtual indicator indicating a level of air quality of a building space;

an occupancy value indicating an amount of occupants in the building space; or

an infection risk value indicating a risk of spread of an infectious disease in a population.

5. The building system of claim 1 , wherein the digital twin includes a building graph data structure including a plurality of nodes representing a plurality of entities of the building and a plurality of edges between the plurality of nodes representing a plurality of relationships between the plurality of entities of the building.

6. The building system of claim 1 , wherein the instructions cause the one or more processors to:

model a predicted state of the building at the plurality of future times based on implementing the recommendation; and

cause the graphic representation of the building to display the modeled predicted state of the building at the plurality of future times.

7. The building system of claim 1 , wherein the recommendation includes a recommendation to update operating settings of building equipment of the building.

8. The building system of claim 7 , wherein the instructions cause the one or more processors to cause the graphic representation of the building to display updated operation of the building equipment through the plurality of future times.

9. The building system of claim 7 , wherein the recommendation to update the operating settings of the building equipment includes at least one of:

a recommendation to command an air handling unit to change an air flow through the building;

a recommendation to change air filtration of air within or being supplied to a space of the building;

a recommendation to command one or more disinfectant lights to activate; or

a recommendation to restrict access to one or more portions of the building.

10. The building system of claim 9 , wherein the recommendation to change air filtration of air within or being supplied to the space of the building includes a recommendation to perform at least one of:

changing operation of at least one of an air handing unit, a variable air volume device, or an in-zone air filtration device; or

modifying or replacing a filter in the at least one of the air handling unit, the variable air volume device, or the in-zone air filtration device.

11. The building system of claim 10 , wherein the recommendation to change the air filtration of air within or being supplied to the space of the building includes automatically implementing the recommendation within the building.

12. The building system of claim 9 , wherein the recommendation includes a status of the building equipment.

13. The building system of claim 1 , wherein the plurality of inference values include at least one of a predicted disease reproduction number indicating a predicted number of individuals that contract a disease from an infected individual, a predicted energy consumption value indicating a level of energy expected to be consumed by building equipment of the building, or a clean air score indicating a predicted measure of quality of air in the building.

14. The building system of claim 13 , wherein the instructions further cause the one or more processors to generate diagnostics for at least one of the predicted disease reproduction number, the predicted energy consumption value, or the clean air score, wherein the diagnostics include text indicating a cause for the predicted disease reproduction number, the predicted energy consumption value, or the clean air score.

15. The building system of claim 1 , wherein the instructions cause the one or more processors to:

receive outdoor air quality forecast data from one or more outdoor air quality sensors;

model a predicted state of the building at the plurality of future times based on the outdoor air quality forecast data; and

cause the graphic representation of the building to display the modeled predicted state of the building at the plurality of future times.

16. The building system of claim 15 , wherein the recommendation includes a recommendation to update operating settings of building equipment of the building in response to the predicted state of the building based on the one or more outdoor air quality sensors.

17. A method comprising:

storing a digital twin of a building in one or more storage devices, the digital twin comprising a virtual representation of the building with an entity of the building, a data point, and an interrelationship between the entity of the building and the data point, wherein the digital twin further includes an artificial intelligence configured to generate a plurality of inference values of the data point for a plurality of future times;

generating a recommendation based on the plurality of inference values for the data point for the plurality of future times, the recommendation recommending making one or more updates to the building;

storing data for the recommendation in the digital twin, wherein the digital twin includes a representation of a location within the building and associated the location with the recommendation; and

causing a graphic representation of the building to display an indication of the recommendation at the location within the graphic representation of the building associated with the entity based on the digital twin indicating the data point being related to the entity.

18. The method of claim 17 , further comprising:

receiving a plurality of data point values for the data point from building equipment, the building equipment generates the plurality of data point values based on operation to control an environmental condition of the building;

identifying the data point based on the digital twin by identifying the interrelationship between the entity and the data point, the entity representing the building equipment; and

storing the plurality of data point values, or a link to the plurality of data point values, in the data point of the digital twin.

19. The method of claim 18 , wherein the plurality of inference values are stored in the digital twin; and

wherein the plurality of inference values include predicted future values of the data point, wherein the method generates the plurality of inference values by:

identifying the data point of the digital twin and retrieving the plurality of data point values based on the data point of the digital twin; and

executing the artificial intelligence to generate the plurality of inference values based on the plurality of data point values.

20. The method of claim 18 , wherein the plurality of inference values are predicted future values of a virtual indicator, wherein the virtual indicator is at least one of:

a clean air virtual indicator indicating a level of air quality of a building space;

an occupancy number indicating a number of occupants in the building space; or

an infection risk value indicating a risk of spread of an infectious disease in a population.

21. The method of claim 17 , further comprising generating diagnostics for at least one of a predicted disease reproduction number, a predicted energy consumption value, or a clean air score, wherein the diagnostics include text indicating a cause for the predicted disease reproduction number, the predicted energy consumption value, or the clean air score.

22. The method of claim 17 , wherein the recommendation includes a recommendation to update operating settings of building equipment of the building, the operating settings updating operation of the building equipment.

23. The method of claim 22 , wherein the recommendation to update the operating settings of the building equipment includes at least one of:

a recommendation to make a command for an air handling unit to change an air flow through the building;

a recommendation to command the air handling unit to change an air filtration setting for the building;

a recommendation to command one or more disinfectant lights to activate; or

a recommendation to command building security mechanisms to restrict access to one or more portions of the building.

24. One or more non-transitory computer readable media containing program instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

storing a digital twin of a building in one or more storage devices, the digital twin comprising a virtual representation of the building with an entity of the building, a data point, and an interrelationship between the entity of the building and the data point, wherein the digital twin further includes an artificial intelligence configured to generate a plurality of inference values of the data point for a plurality of future times;

generating a recommendation based on the plurality of inference values for the data point for the plurality of future times, the recommendation recommending making one or more updates to the building;

storing data for the recommendation in the digital twin, wherein the digital twin includes a representation of a location within the building and associates the location with the recommendation; and

causing a graphic representation of the building to display an indication of the recommendation at the location within the graphic representation of the building associated with the entity based on the digital twin indicating the data point being related to the entity.

Assignments (2)

Continuity (2)

References Cited (400)