HEAT MAPPING SYSTEM
Systems and methods for providing visualization of health risks within a building. Health risk levels for building spaces are determined using occupancy data and health risk data relating to a risk of contracting or spreading an infectious disease. A visualization of the health risk levels is generated and presented on a user interface.
1 - 20 . (canceled)
21 . A building management system (BMS) comprising:
one or more processors; and
one or more computer-readable storage media having instructions stored thereon that, upon execution by the one or more processors, cause the one or more processors to implement operations comprising:
obtaining carbon dioxide (CO2) levels of a plurality of locations in a building;
determining, based on the CO2 levels of the plurality of locations in the building, one or more disinfection operations for the plurality of locations in the building; and
operating disinfectant mechanisms according to the one or more disinfection operations to reduce a risk of infection at one or more of the plurality of locations in the building.
22 . The BMS of claim 21 , wherein the disinfectant mechanisms comprises at least one of:
a lighting system configured to emit ultraviolet (UV) light to provide disinfection;
an access control system that limits access to a specific space;
an aerosol mechanism configured to apply a disinfectant aerosol to one or more spaces in the building; or
an economizer that draws fresh outdoor air and introduces the fresh outdoor air into one or more spaces in the building.
23 . The BMS of claim 21 , wherein the operations comprise:
obtaining healthcare data from the Centers for Disease Control (CDC) or the World Health Organization (WHO) regarding at least one of a spread of an infectious disease or one or more disinfection parameters for the disinfectant mechanism; and
wherein the one or more disinfection operations are determined based on both the CO2 levels of the plurality of locations in the building and the healthcare data.
24 . The BMS of claim 21 , wherein the disinfectant mechanisms comprise an ultraviolet (UV) light bulb configured to be operated to provide disinfection to reduce the risk of infection at one or more of the plurality of locations in the building, wherein the operations further comprise:
obtaining light intensity data from a photodetector at the UV light bulb; and
determining that the UV light bulb should be replaced based on the light intensity data.
25 . The BMS of claim 21 , wherein determining, based on the CO2 levels of the plurality of locations in the building, the one or more disinfection operations for the plurality of locations in the building, comprises:
determining, responsive to a comparison between the CO2 level of a specific space and a threshold value, a disinfection operation for the specific space, the CO2 level of the specific space indicated by the CO2 levels of the plurality of locations in the building.
26 . The BMS of claim 21 , wherein the operations further comprise:
obtaining the CO2 levels from a plurality of sensors and pathogen data from a health authority, the pathogen data comprising data regarding at least one of a spread of an infectious disease or one or more disinfection parameters for the disinfectant mechanism;
training a neural network to predict one or more disinfection parameters based on the CO2 levels and the pathogen data; and
determining the one or more disinfection operations by using the neural network to predict the one or more disinfection parameters using the CO2 levels of the plurality of locations in the building and the pathogen data as inputs to the neural network.
27 . The BMS of claim 21 , wherein the operations comprise:
determining, based on the CO2 levels of the plurality of locations, a schedule for the one or more disinfection operations; and
operating the disinfectant mechanisms over time according to the schedule to reduce the risk of infection at one or more of the plurality of locations in the building.
28 . A method in a building management system (BMS) performed by one or more processors, the method comprising:
obtaining carbon dioxide (CO2) levels of a plurality of locations in a building;
determining, based on the CO2 levels of the plurality of locations in the building, one or more disinfection operations for the plurality of locations in the building; and
operating disinfectant mechanisms according to the one or more disinfection operations to reduce a risk of infection at one or more of the plurality of locations in the building.
29 . The method of claim 28 , wherein the disinfectant mechanisms comprises at least one of:
a lighting system configured to emit ultraviolet (UV) light to provide disinfection;
an access control system that limits access to a specific space;
an aerosol mechanism configured to apply a disinfectant aerosol to one or more spaces in the building; or
an economizer that draws fresh outdoor air and introduces the fresh outdoor air into one or more spaces in the building.
30 . The method of claim 28 , wherein the method comprises:
obtaining healthcare data from the Centers for Disease Control (CDC) or the World Health Organization (WHO) regarding at least one of a spread of an infectious disease or one or more disinfection parameters for the disinfectant mechanism; and
wherein the one or more disinfection operations are determined based on both the CO2 levels of the plurality of locations in the building and the healthcare data.
31 . The method of claim 28 , wherein the disinfectant mechanisms comprise an ultraviolet (UV) light bulb configured to be operated to provide disinfection to reduce the risk of infection at one or more of the plurality of locations in the building, wherein the method further comprises:
obtaining light intensity data from a photodetector at the UV light bulb; and
determining that the UV light bulb should be replaced based on the light intensity data.
32 . The method of claim 28 , wherein determining, based on the CO2 levels of the plurality of locations in the building, the one or more disinfection operations for the plurality of locations in the building, comprises:
determining, responsive to a comparison between the CO2 level of a specific space and a threshold value, a disinfection operation for the specific space, the CO2 level of the specific space indicated by the CO2 levels of the plurality of locations in the building.
33 . The method of claim 28 , further comprising:
obtaining the CO2 levels from a plurality of sensors and pathogen data from a health authority, the pathogen data comprising data regarding at least one of a spread of an infectious disease or one or more disinfection parameters for the disinfectant mechanism;
training a neural network to predict one or more disinfection parameters based on the CO2 levels and the pathogen data; and
determining the one or more disinfection operations by using the neural network to predict the one or more disinfection parameters using the CO2 levels of the plurality of locations in the building and the pathogen data as inputs to the neural network.
34 . The method of claim 28 , wherein the method comprises:
determining, based on the CO2 levels of the plurality of locations, a schedule for the one or more disinfection operations; and
operating the disinfectant mechanisms over time according to the schedule to reduce the risk of infection at one or more of the plurality of locations in the building.
35 . A system for a building comprising:
a lighting system configured to emit ultraviolet (UV) light to provide disinfection for one or more spaces of a building;
a plurality of sensors configured to measure carbon dioxide (CO2) levels in a plurality of locations of the building;
one or more processors; and
one or more computer-readable storage media having instructions stored thereon that, upon execution by the one or more processors, cause the one or more processors to implement operations comprising:
obtaining the CO2 levels of the plurality of locations in the building from the plurality of sensors;
determining, based on the CO2 levels of the plurality of locations in the building, one or more disinfection operations for the plurality of locations in the building; and
operating the lighting system according to the one or more disinfection operations to reduce a risk of infection at one or more of the plurality of locations in the building.
36 . The system of claim 35 , wherein the operations comprise:
obtaining healthcare data from the Centers for Disease Control (CDC) or the World Health Organization (WHO) regarding at least one of a spread of an infectious disease or one or more disinfection parameters for the lighting system; and
wherein the one or more disinfection operations are determined based on both the CO2 levels of the plurality of locations in the building and the healthcare data.
37 . The system of claim 35 , wherein the lighting system comprises a UV bulb configured to emit the UV light, wherein the lighting system further comprises a photodetector at the UV bulb, and the operations further comprise:
obtaining light intensity data from the photodetector at the UV light bulb; and
determining that the UV light bulb should be replaced based on the light intensity data.
38 . The system of claim 35 , wherein the operations comprise:
determining, responsive to a comparison between the CO2 level of a specific space and a threshold value, a disinfection operation for the specific space, the CO2 level of the specific space indicated by the CO2 levels of the plurality of locations in the building.
39 . The system of claim 35 , wherein the operations comprise:
obtaining the CO2 levels and pathogen data from a health authority, the pathogen data comprising data regarding at least one of a spread of an infectious disease or one or more disinfection parameters for the disinfectant mechanism;
training a neural network to predict one or more disinfection parameters of the lighting system based on the CO2 levels and the pathogen data; and
determining the one or more disinfection operations by using the neural network to predict the one or more disinfection parameters using the CO2 levels of the plurality of locations in the building and the pathogen data as inputs to the neural network.
40 . The system of claim 35 , wherein the operations comprise:
determining, based on the CO2 levels of the plurality of locations, a schedule for the lighting system; and
operating the lighting system over time according to the schedule to reduce the risk of infection at one or more of the plurality of locations in the building.