Building health, safety, performance scoring

Abstract

Systems and methods for determining a comprehensive index, such as health, safety and performance indexes, of an enclosed area from weighted values of contribution indexes including sensor data and / or controlling one or more building systems based on the comprehensive index are disclosed.

Description

[0001] Cross - reference to related applications

[0002] This application claims the priority and benefit of U.S. Provisional Patent Application 63 / 376,863, filed on September 23, 2022, and titled "BUILDING HEALTH, SAFETY, PERFORMANCE SCORING"; this application is a partial continuation application of U.S. Patent Application No. 18 / 115,694, filed on February 28, 2023, and titled "SYSTEMS AND METHODS FOR MANAGING BUILDING WELLNESS", which is a continuation application of U.S. Patent Application No. 17 / 328,346, filed on May 24, 2021, and titled "SYSTEMS AND METHODS FOR MANAGING BUILDING WELLNESS", which claims the priority and benefit of U.S. Provisional Patent Application 63 / 030,507, filed on May 27, 2020; this application is also related to U.S. Patent Application No. 18 / 263,216, filed on July 27, 2023, and titled "MULTI-SENSOR SYNERGY", which is the national stage application of International PCT Application PCT / US2022 / 014135, filed on January 28, 2022, and titled "MULTI-SENSOR SYNERGY", which claims the benefit and priority of U.S. Provisional Patent Application 63 / 263,806, filed on November 9, 2021, and titled "MULTI-SENSOR SYNERGY"; International PCT Application PCT / US2022 / 014135 is also a partial continuation application of International PCT Application PCT / US2021 / 015378, filed on January 28, 2021, and titled "SENSOR CALIBRATION AND OPERATION";This application is also a continuation-in-part of U.S. Patent Application No. 18 / 007,047, filed on January 27, 2023, and titled "ATMOSPHERIC ADJUSTMENT IN AN ENCLOSURE". U.S. Patent Application No. 18 / 007,047 is the national stage application of International PCT Application PCT / US2021 / 043143, filed on July 26, 2021, and titled "ATMOSPHERIC ADJUSTMENT IN AN ENCLOSURE". The International PCT Application PCT / US2021 / 043143 claims the benefit and priority of U.S. Provisional Patent Application 63 / 080,899, filed on September 21, 2020, and titled "INTERACTION BETWEEN AND ENCLOSURE AND ONE OR MORE OCCUPANTS", U.S. Provisional Patent Application 63 / 057,120, filed on July 27, 2020, and titled "ATMOSPHERIC ADJUSTMENT IN AN ENCLOSURE", and U.S. Provisional Patent Application 63 / 078,805, filed on September 15, 2020, and titled "ATMOSPHERIC ADJUSTMENT IN AN ENCLOSURE". The International PCT Application PCT / US2021 / 043143 is a continuation-in-part of International PCT Application PCT / US2021 / 015378, filed on January 28, 2021, and titled "ATMOSPHERIC ADJUSTMENT IN AN ENCLOSURE". The International PCT Application PCT / US2021 / 015378 claims the priority of U.S. Provisional Patent Application 62 / 967,204, filed on January 29, 2020, and titled "SENSOR CALIBRATION AND OPERATION".International PCT application PCT / US2021 / 015378 is a partial continuation application of U.S. patent application 17 / 083,128, filed on October 28, 2020, and titled "BUILDING NETWORK", which is a continuation application of U.S. patent application 16 / 664,089, filed on October 25, 2019, and titled "BUILDING NETWORK", which is a partial continuation application of International PCT application PCT / US2019 / 030467, filed on May 2, 2019, and titled "EDGE NETWORK FOR BUILDING SERVICES", which claims priority to U.S. Provisional Patent Application 62 / 666,033, filed on May 2, 2018, and titled "EDGE NETWORK FOR BUILDING SERVICES"; International PCT application PCT / US2019 / 030467 is a partial continuation application of International PCT application PCT / US2018 / 029460, filed on April 25, 2018, and titled "TINTABLE WINDOW SYSTEMS FOR BUILDING SERVICES"; International PCT application PCT / US2018 / 029460 claims priority to U.S. Provisional Patent Application 62 / 489,703, filed on April 26, 2017, and titled "ELECTROCHROMIC WINDOWS WITH TRANSPARENT DISPLAY TECHNOLOGY";

[0003] It should be noted that there seems to be an incomplete sentence in the original text. I have translated it as accurately as possible based on the existing content. If you can provide the complete and correct text, the translation will be more accurate.the benefit and priority of U.S. Provisional Patent Application 62,490,457, filed on May 15, 2017, and titled "Electrochromic Windows with Transparent Display Technology"; U.S. Provisional Patent Application 62,506,514, filed on May 15, 2017, and titled "Electrochromic Windows with Transparent Display Technology"; U.S. Provisional Patent Application 62,507,704, filed on May 17, 2017, and titled "Electrochromic Windows with Transparent Display Technology"; U.S. Provisional Patent Application 62,523,606, filed on June 22, 2017, and titled "Electrochromic Windows with Transparent Display Technology"; and U.S. Provisional Patent Application 62,607,618, filed on December 19, 2017, and titled "Electrochromic Windows with Transparent Display Technology"; U.S. Patent Application 16 / 664,089 is also a continuation-in-part of International PCT Application PCT / US2018 / 029460; International PCT Application PCT / US2021 / 043143 is also a continuation-in-part of U.S. Patent Application 17 / 083,128, filed on October 28, 2021, and titled "Building Network"; International PCT Application PCT / US2021 / 043143 is also a continuation-in-part of International PCT Application PCT / US2021 / 027418, filed on April 15, 2021, and titled "Interaction Between an Enclosure and One or More Occupants"International PCT Application PCT / US2021 / 027418 claims the benefit and priority of U.S. Provisional Patent Application 63 / 080,899, filed on September 21, 2020; U.S. Provisional Patent Application 63 / 010,977, filed on April 16, 2020; U.S. Provisional Patent Application 63 / 052,639, filed on July 16, 2020; U.S. Provisional Patent Application 63 / 115 / 842, filed on November 19, 2020; U.S. Provisional Patent Application 63 / 085,254, filed on September 30, 2020; U.S. Provisional Patent Application 63 / 170,245, filed on February 4, 2021; and U.S. Provisional Patent Application 63 / 154,352, filed on February 26, 2021; International PCT Application PCT / US2021 / 027418 is a partial continuation application of U.S. Patent Application 17 / 249,148, filed on February 22, 2021, which is a continuation application of U.S. Patent Application 16 / 096,557, filed on October 25, 2018; U.S. Patent Application 16 / 096,557 is the national stage application of International PCT Application PCT / US2017 / 029476, filed on April 25, 2017, under 35 U.S.C. § 371, which claims the benefit and priority of U.S. Provisional Patent Application 62 / 327,880, filed on April 26, 2016; U.S. Patent Application 16 / 096,557 is also a partial continuation application of U.S. Patent Application 14 / 391,122, filed on October 7, 2014, which is the national stage application of International PCT Application PCT / US2013 / 036456, filed on April 12, 2013, under 35 U.S.C. § 371, which claims the benefit and priority of U.S. Provisional Patent Application 61 / 624,175, filed on April 13, 2012; International PCT Application PCT / US2021 / 027418 is also a partial continuation application of U.S. Patent Application 16 / 946,947, filed on July 13, 2020, which is a continuation application of U.S. Patent Application 16 / 462,916, filed on May 21, 2019;U.S. Patent Application 16 / 462,916 is a national stage application of International PCT Application PCT / US2017 / 062634, which was filed on November 20, 2017 under 35 U.S.C. § 371. International PCT Application PCT / US2017 / 062634 claims the benefit and priority of U.S. Provisional Patent Application 62 / 426,126 filed on November 23, 2016 and U.S. Provisional Patent Application 62 / 551,649 filed on August 29, 2017; U.S. Patent Application 16 / 462,916 is also a continuation application of U.S. Patent Application 16 / 082,793 filed on September 6, 2018. U.S. Patent Application 16 / 082,793 is a national stage application of International PCT Application PCT / US2017 / 020805, which was filed on March 3, 2017 under 35 U.S.C. § 371. International PCT Application PCT / US2017 / 020805 claims the benefit and priority of U.S. Provisional Patent Application 62 / 305,892 filed on March 9, 2016 and U.S. Provisional Patent Application 62 / 370,174 filed on August 2, 2016; U.S. Patent Application 16 / 462,916 is also a partial continuation application of U.S. Patent Application 14 / 951,410 filed on November 24, 2015. U.S. Patent Application 14 / 951,410 claims the benefit and priority of U.S. Provisional Patent Application 62 / 085,179 filed on November 26, 2014 and U.S. Provisional Patent Application 62 / 248,181 filed on October 29, 2015; U.S. Patent Application 14 / 951,410 is a partial continuation application of U.S. Patent Application 14 / 401,081 filed on November 13, 2014. U.S. Patent Application 14 / 401,081 is a national stage application of International PCT Application PCT / US2013 / 042765, which was filed on May 24, 2013 under 35 U.S.C. § 371. International PCT Application PCT / US2013 / 042765 claims the benefit of U.S. Provisional Patent Application BCPI-250156, page 5 / 120, filed on May 25, 2012;

[0004] Benefit and priority of 61 / 652,021; continuation-in-part of U.S. Patent Application 14 / 951,410 or U.S. Patent Application 14 / 468,778 filed on August 26, 2014, which is a continuation of U.S. Patent Application 13 / 479,137 filed on May 23, 2012, which is a continuation of U.S. Patent Application 13 / 049,750 filed on March 16, 2011; U.S. Patent Application 13 / 479,137 is also a continuation-in-part of U.S. Patent Application 12 / 971,576 filed on December 17, 2010, which claims the benefit and priority of U.S. Provisional Patent Application 61 / 289,319 filed on December 22, 2009; International PCT Application PCT / US2021 / 027418 is also a continuation-in-part of U.S. Patent Application 16 / 950 / 774 filed on November 17, 2020, which is a continuation of U.S. Patent Application 16 / 608,157 filed on October 25, 2019, which is filed under 35 U.S.C.National stage application of International PCT application PCT / US2018 / 02947 filed on April 25, 2018. International PCT application PCT / US2018 / 02947 claims the benefit and priority of U.S. Provisional Patent Application 62,490,457 filed on April 26, 2017 and titled "Electrochromic Windows with Transparent Display Technology", U.S. Provisional Patent Application 62,506,514 filed on May 15, 2017 and titled "Electrochromic Windows with Transparent Display Technology", U.S. Provisional Patent Application 62,507,704 filed on May 17, 2017 and titled "Electrochromic Windows with Transparent Display Technology", U.S. Provisional Patent Application 62,523,606 filed on June 22, 2017 and titled "Electrochromic Windows with Transparent Display Technology", and U.S. Provisional Patent Application 62,607,618 filed on December 19, 2017 and titled "Electrochromic Windows with Transparent Display Technology"; International PCT application PCT / US2021 / 027418 is also a partial continuation application of U.S. Patent Application 17 / 083,128 filed on October 28, 2021; International PCT application PCT / US2021 / 027418 is also a partial continuation application of U.S. Patent Application 17 / 081,809 filed on October 27, 2020. U.S. Patent Application 17 / 081,809 is a continuation application of U.S. Patent Application 16 / 608,159 filed on October 24, 2019. U.S. Patent Application 16 / 608,159 is under 35 U.S.C.National stage application of International PCT application PCT / US2018 / 029406 filed on April 25, 2018, and claiming the benefit and priority of U.S. Provisional Patent Application 62,490,457, U.S. Provisional Patent Application 62,506,514, U.S. Provisional Patent Application 62,507,704, U.S. Provisional Patent Application 62,523,606, and U.S. Provisional Patent Application 62,607,618; International PCT application PCT / US2021 / 027418 is also a partial continuation application of International PCT application PCT / US2020 / 053641 filed on September 30, 2020, and International PCT application PCT / US2020 / 053641 claims the benefit and priority of U.S. Provisional Patent Application 62 / 911,271 filed on October 5, 2019, U.S. Provisional Patent Application 62 / 952,207 filed on December 20, 2019, U.S. Provisional Patent Application 62 / 975,706 filed on February 12, 2020, and U.S. Provisional Patent Application 63 / 085,254 filed on September 30, 2020; International PCT application PCT / US2020 / 053641 is a partial continuation application of U.S. Patent Application 16 / 608,157 filed on October 24, 2019; all of these applications are incorporated herein by reference in their entirety for all purposes. Technical Field

[0005] Certain aspects generally relate to methods, devices, and systems for controlling one or more building systems. Background Art

[0006] In the context of the world's efforts to manage and recover from the global pandemic caused by COVID-19, information regarding building health may be crucial. For example, such information may be crucial for tenants to: (i) ensure that employees have a healthy environment so that they can be productive and feel secure in the office; (ii) quickly respond to the crisis through staffing processes and strategies to protect employee safety and ensure business continuity; and (iii) understand the performance of the buildings and spaces leased for real estate. Building health information may also be important for employees to: (i) manage personal safety when planning trips to and from the office, and (ii) feel comfortable and secure knowing that the environment and health conditions of the office are being strictly monitored. Summary of the Invention

[0007] Certain embodiments relate to systems having at least one controller for controlling one or more building systems based on a plurality of composite indices of one or more enclosed areas, the at least one controller being configured to determine the plurality of composite indices at least in part based on sensor data from a plurality of sensors.

[0008] Some embodiments relate to methods for controlling one or more building systems. The methods determine a plurality of composite indices for one or more enclosed areas based at least in part on sensor data from a plurality of sensors, and control the one or more building systems based on the determined plurality of composite indices.

[0009] Some embodiments relate to a non-transitory computer program product including a computer-readable memory storing computer-executable instructions for controlling one or more building systems including one or more tintable windows. The computer-executable instructions, when read by one or more processors operatively coupled to the one or more building systems, cause the one or more processors to perform operations of a method: determining a plurality of composite indices for one or more enclosed areas based at least in part on sensor data from a plurality of sensors, and controlling the one or more building systems based on the determined plurality of composite indices.

[0010] Some embodiments relate to a system having a plurality of sensors, at least one controller, and a user interface. The plurality of sensors are at a building and are configured to generate sensor data associated with an environment in the building. The at least one controller is configured to receive the sensor data and determine a first composite index and a second composite index for a space in the building based at least in part on the sensor data. The user interface is configured to present information associated with the first composite index and the second composite index.

[0011] Some embodiments relate to a system having at least one controller configured to receive sensor data acquired by a plurality of sensors and determine a plurality of composite indices for one or more enclosed areas based at least in part on the sensor data.

[0012] Some embodiments relate to a method of obtaining sensor data acquired by a plurality of sensors and determining a plurality of composite indices for one or more enclosed areas based at least in part on the sensor data from the plurality of sensors.

[0013] Some embodiments relate to a non-transitory computer program product including a computer-readable memory storing computer-executable instructions. The computer-executable instructions, when read by one or more processors, cause the one or more processors to perform operations of a method: obtaining sensor data acquired by a plurality of sensors and determining a plurality of composite indices for one or more enclosed areas based at least in part on the sensor data from the plurality of sensors.

[0014] These and other features are described in more detail below with reference to the associated drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram depicting a cross-section of an electrochromic device according to some embodiments.

[0016] Figure 2 Schematic diagram depicting a cross-section of an electrochromic device in a bleached state according to some embodiments.

[0017] Figure 3 Schematic diagram depicting a cross-section of an electrochromic device in a colored state according to some embodiments.

[0018] Figure 4 Drawing depicting a cross-section of an insulating glass unit (IGU) according to some embodiments.

[0019] Figure 5 Schematic diagram depicting an example of a sensor arrangement including one or more sensor sets in one or more enclosed areas according to some embodiments.

[0020] Figure 6 Schematic diagram depicting an example of the arrangement of a sensor set in an enclosed area according to some embodiments.

[0021] Figure 7A Schematic diagram depicting examples of various time windows including a time span for sensor data collection according to some embodiments.

[0022] Figure 7B Schematic diagram depicting examples of various time windows including a time span for sensor data collection according to some embodiments.

[0023] Figure 7C Schematic diagram depicting examples of various time windows including a time span for sensor data collection according to some embodiments.

[0024] Figure 7D Schematic diagram depicting examples of various time windows including a time span for sensor data collection according to some embodiments.

[0025] Figure 7E Schematic diagram depicting examples of various time windows including a time span for sensor data collection according to some embodiments.

[0026] Figure 8 Graph depicting the variation over time of sensor readings of carbon dioxide (CO2) concentration levels in an enclosed area according to some embodiments.

[0027] Figure 9 Contour map showing a top view of an example office environment of an enclosed area according to some embodiments, depicting various CO2 concentration levels.

[0028] Figure 10 Schematic diagram depicting an example of a BMS for managing one or more building systems and control systems in a building.

[0029] Figure 11 Depicting a ventilation system for ventilating an enclosed area within a building (e.g., room on page 9 / 120 of BCPI - 250156)

[0030] room).

[0031] Figure 12 Depicting a control system for controlling ventilation and other parameters in an enclosed area.

[0032] Figure 13 Schematic diagram depicting an example of a system for organizing sensors of a sensor set into sensor modules according to some embodiments.

[0033] Figure 14 Schematic diagram depicting an example of a control system architecture having a master controller that controls floor controllers, which in turn control local controllers, according to some embodiments.

[0034] Figure 15 Schematic diagram depicting an example of a computing system and network according to some embodiments.

[0035] Figure 16 Depicting a representation of an example of a digital twin according to some embodiments.

[0036] Figure 17 Schematic diagram depicting an example of a control system that can employ a digital twin when managing and controlling an interactive network device such as a building system, according to some embodiments.

[0037] Figure 18 Block diagram depicting an exemplary architecture for managing one or more building systems according to some embodiments.

[0038] Figure 19 Block diagram depicting an exemplary computer system for managing one or more building systems according to some embodiments.

[0039] Figure 20A Graph depicting an example of a scoring graph for determining a score of a first contribution metric according to some embodiments.

[0040] Figure 20B Depicting according to some embodiments the Figure 20A value of the first contribution metric with the metric score derived from the scoring graph in Figure 20A associated table.

[0041] Figure 21A A diagram depicting an example of a scoring graph for determining the score of a second contribution metric according to some embodiments.

[0042] Figure 21B Depicting according to some embodiments Figure 21A A table associating the values of the second contribution metric in with the metric scores derived from the scoring graph.

[0043] Figure 22 An example of a dashboard display with real-time data of a facility such as a building according to some embodiments.

[0044] Figure 23 An example of a dashboard display with real-time data of a certain space in a facility according to some embodiments.

[0045] Figure 24 An example of a dashboard display with real-time data of the carbon dioxide (CO2) level metric in a tenant space in a facility according to some embodiments.

[0046] Figure 25 A flowchart depicting an example of a method for determining multiple comprehensive indices of one or more enclosed areas and / or controlling one or more building systems based on the multiple comprehensive indices according to various embodiments.

[0047] Figure 26 A flowchart depicting the operation of a control system operatively coupled to one or more devices in an enclosed area according to various embodiments.

[0048] Figure 27A A graph depicting an example of how carbon dioxide sensor data values change over time according to various embodiments.

[0049] Figure 27B A graph depicting an example of how noise sensor data values change over time according to various embodiments.

[0050] The diagrams and components therein may not be drawn to scale. The various components in the diagrams described herein may not be drawn to scale. Detailed Description

[0051] Aspects are described below with reference to the accompanying drawings. Features shown in the drawings may not be drawn to scale. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the presented embodiments. The disclosed embodiments may be practiced without one or more of these specific details. In other instances, well-known operations have not been described in detail so as not to unnecessarily obscure the disclosed embodiments. Although the disclosed embodiments will be described in conjunction with specific embodiments, it should be understood that this is not intended to limit the disclosed embodiments.

[0052] Certain techniques disclosed herein generally relate to methods and systems for determining a comprehensive index for an enclosed area (e.g., a building or a space within a building) and / or controlling one or more building systems based on the comprehensive index. The comprehensive index can enable a building to perform a multi-modal assessment of the safety, health, and operational efficiency of its occupants and provide actionable insights into ways to improve the comprehensive index score. For example, these techniques can identify remedial measures to improve the score and notify (e.g., via a digital twin of the building) the occupants or others who can implement the remedial measures. For example, these techniques can calculate three comprehensive indexes for the enclosed area: a safety index, a health index, and a performance index. Each comprehensive index is calculated by combining weighted scores of contributing metrics from, for example, multiple sources such as sensors in a sensor set, including environmental sensors, and other data sources such as building access data, surveys, work orders, user input, and / or utility monitoring. In some cases, these techniques dynamically adjust the contributing metrics and their weighting factors based on data availability.

[0053] Numeric ranges include the numbers defining the range. Each maximum numeric limitation given in this specification will include every lower numeric limit, as if such lower numeric limits were expressly written herein. Each minimum numeric limitation given in this specification will include every upper numeric limit, as if such upper numeric limits were expressly written herein. Each numeric range given in this specification will include every narrower numeric range that falls within the broader numeric range, as if such narrower numeric ranges were expressly written herein.

[0054] The term "switchable window" refers to a window (e.g., an architectural window) that includes one or more light-switchable devices (e.g., electrochromic devices). An example of a switchable window is an electrochromic window having one or more electrochromic devices. In examples related to the commissioning of switchable windows, the switchable window is sometimes referred to as an "insulating glass unit" or "IGU".

[0055] The headings provided herein are not intended to limit the disclosure.

[0056] Unless otherwise defined herein, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. Various scientific dictionaries containing the terms herein are well known and available to those of skill in the art. Although only some methods and materials are described, any methods and materials similar or equivalent to those described herein are used in the practice or testing of the embodiments disclosed herein.

[0057] The terms defined below will be described more fully by reference to the entire specification. It should be understood that the present disclosure is not limited to the specific methods, protocols, and reagents described, as these may vary depending on the context used by those of skill in the art.

[0058] As used herein, unless the context clearly indicates otherwise, the singular terms "a", "an", and "the" include plural references.

[0059] I. Building Systems

[0060] To enable the reader to understand certain embodiments of the systems, devices, and methods disclosed herein, a discussion of building systems is provided, including, for example, one or more colorable windows (e.g., electrochromic windows). This initial discussion is provided only for background, and the embodiments described herein are not limited to the specific features or processes of this initial discussion. Additionally, it should be understood that in some aspects, a colorable window may include one or more electrochromic devices, and in other aspects, as a supplement or alternative, may also include one or more other light-switchable devices.

[0061] - Electrochromic Device

[0062] Figure 1 A cross-section of an electrochromic device 100 is schematically depicted. The electrochromic device 100 includes a substrate 102, a first conductive layer (CL) 104, an electrochromic layer (EC) 106, an ion-conductive layer (IC) 108, a counter electrode layer (CE) 110, and a second conductive layer (CL) 114. The layers 104, 106, 108, 110, and 114 are collectively referred to as the electrochromic stack 120. A voltage source 116 capable of applying an electric potential across the electrochromic stack 120 can cause the electrochromic device to transition from, for example, a bleached state to a colored state (depicted). The order of the layers may be reversed with respect to the substrate.

[0063] Electrochromic devices having the different layers can be fabricated as all-solid-state devices and / or all-inorganic devices. Such devices and methods of making them are described in more detail in the following applications: U.S. Patent Application No. 12 / 645,111, entitled "Fabrication of Low-Defectivity Electrochromic Devices," filed on December 22, 2009 and naming Mark Kozlowski et al. as inventors; and U.S. Patent Application No. 12 / 645,159, entitled "Electrochromic Devices," filed on December 22, 2009 and naming Zhongchun Wang et al. as inventors, both of which are incorporated herein by reference in their entirety. However, it should be understood that any one or more of the layers in the stack may contain a certain amount of organic material. The same is true for liquids, which may be present in small amounts in one or more layers. It should also be understood that solid materials can be deposited or otherwise formed by processes that employ liquid components, such as certain sol-gel or chemical vapor deposition processes.

[0064] Additionally, it should be understood that the reference to a transition between a bleached state and a colored state is non-limiting and is merely one example of the many electrochromic transitions that can be implemented. Unless otherwise specified herein (including the above discussion), when referring to a bleaching-coloring transition (or equivalently, a colorless-colored transition), the corresponding device or process encompasses other optical state transitions, such as non-reflective-reflective, transparent-opaque, and so on. Further, the terms "bleached" or "colorless" refer to an optically neutral state, such as uncolored, transparent, or translucent. Additionally, unless otherwise specified herein, the "color" or "hue" of an electrochromic transition is not limited to any particular wavelength or wavelength range. Those skilled in the art will understand that the selection of suitable electrochromic and counter electrode materials determines the associated optical transitions.

[0065] In the embodiments described herein, the electrochromic device cycles reversibly between a bleached / colorless state and a colored / colored state. In some cases, when the device is in the bleached state, a potential is applied to the electrochromic stack 120 such that the available ions in the stack are primarily present in the counter electrode 110. When the potential across the electrochromic stack is reversed, the ions are transported across the ion-conducting layer 108 to the electrochromic material 106 and cause the material to transition to the colored state. In a similar manner, the electrochromic devices of the embodiments described herein can be between different hue levels (e.g., the bleached state, the darkest state, and the intermediate between the bleached state and the darkest state) BCPI-250156 Page 13 / 120

[0066] cycles in a reversible manner between a first state and a second state.

[0067] Referring again to Figure 1 , the voltage source 116 can be configured to operate in conjunction with a radiation sensor and other environmental sensors. As described herein, the voltage source 116 interfaces with a device controller (not shown in this figure). Additionally, the voltage source 116 can interface with an energy management system that controls the electrochromic device according to various criteria, such as the time of year, the time of day, and the measured environmental conditions. Such an energy management system in conjunction with a large area electrochromic device (e.g., an electrochromic window) can significantly reduce the energy consumption of a building.

[0068] Any material having suitable optical, electrical, thermal, and mechanical properties can be used as the substrate 102. For example, such substrates include glass, plastic, and mirror materials. Suitable glasses include clear or tinted soda-lime glass, including soda-lime float glass. The glass can be tempered or non-tempered.

[0069] In many cases, the substrate is a pane of glass sized for residential window applications. The size of such a pane of glass can vary significantly depending on the specific needs of the residence. In other cases, the substrate is architectural glass. Architectural glass is typically used in commercial buildings but can also be used in residential buildings and generally, but not necessarily, separates the interior environment from the exterior environment. In certain embodiments, the architectural glass is at least 20 inches by 20 inches and can be much larger, e.g., up to about 80 inches by 120 inches. The thickness of architectural glass is typically at least about 2 mm and is generally between about 3 mm and about 6 mm. Of course, the electrochromic device can be extended to substrates smaller or larger than architectural glass. Additionally, the electrochromic device can be disposed on mirrors of any size and shape.

[0070] On top of the substrate 102 is a conductive layer 104. In certain embodiments, one or both of the conductive layers 104 and 114 are inorganic and / or solid-state. The conductive layers 104 and 114 can be made of several different materials, including conductive oxides, thin metal coatings, conductive metal nitrides, and composite conductors. Generally, the conductive layers 104 and 114 are transparent at least in the wavelength range in which the electrochromic layer exhibits electrochromism. Transparent conductive oxides include metal oxides and metal oxides doped with one or more metals. Examples of such metal oxides and doped metal oxides include indium oxide, indium tin oxide, doped indium oxide, tin oxide, doped tin oxide, zinc oxide, aluminum zinc oxide, doped zinc oxide, ruthenium oxide, doped ruthenium oxide, and the like. Since these layers typically use oxides, they are sometimes referred to as "transparent conductive oxide" (TCO) layers. Substantially transparent thin metal coatings can also be used, as well as combinations of TCO and metal coatings.

[0071] In some embodiments, commercially available substrates such as glass substrates contain a transparent conductive layer coating. Such products can be used for the substrate and the conductive layer. Examples of such glass include conductive layer coated glass sold by Pilkington of Toledo, Ohio under the trademark TEC Glass TM and conductive layer coated glass sold by PPG Industries of Pittsburgh, Pennsylvania under the trademark SUNGATE TM 300 and SUNGATE TM 500. TEC Glass TM is a glass coated with a tin fluoride oxide conductive layer.

[0072] In some embodiments of the present invention, two conductive layers use the same conductive layer (i.e., the conductive layer). In some embodiments, each conductive layer uses a different conductive material. For example, in some embodiments, the substrate uses TEC Glass TM (float glass), the conductive layer uses TEC Glass TM (tin fluoride oxide), and the conductive layer uses indium tin oxide (ITO). In some embodiments, with TEC Glass TM , there is a sodium diffusion barrier between the glass substrate and the TEC conductive layer. The function of the conductive layer is to disperse the electric potential provided by the voltage source 116 on the surface of the electrochromic stack 120 to the internal region of the stack, while the ohmic potential drop is relatively small. The electric potential is transferred to the conductive layer through an electrical connection with the conductive layer. In some embodiments, the busbars provide the electrical connection between the voltage source 116 and the conductive layers 104 and 114, where one busbar contacts the conductive layer 104 and one busbar contacts the conductive layer 114. The conductive layers 104 and 114 can also be connected to the voltage source 116 by other conventional means.

[0073] Overlying the conductive layer 104 is the electrochromic layer 106. In some embodiments, the electrochromic layer 106 is inorganic and / or solid state. The electrochromic layer can contain any one or more of several different electrochromic materials, including metal oxides. Such metal oxides include tungsten oxide (WO3), molybdenum oxide (MoO3), niobium oxide (Nb2O5), titanium oxide (TiO2), copper oxide (CuO), iridium oxide (Ir2O3), chromium oxide (Cr2O3), manganese oxide (Mn2O3), vanadium oxide (V2O5), nickel oxide (Ni2O3), cobalt oxide (Co2O3), and so on. During operation, the electrochromic layer 106 transfers ions to the counter electrode layer 110 and receives ions from the counter electrode layer 110 to cause an optical transition.

[0074] Typically, the coloring of electrochromic materials (or any change in optical properties - e.g., absorbance, reflectance, and transmittance -) is caused by reversible ion insertion (e.g., embedding) into the material and corresponding charge-balanced electron injection. Typically, some of the ions responsible for the optical transition irreversibly bind within the electrochromic material. Some or all of the irreversibly bound ions are used to compensate for "blind charges" within the material. In most electrochromic materials, suitable ions include lithium ions (Li+) and hydrogen ions (H+) (i.e., protons). However, in some cases, other ions will be suitable. In various embodiments, lithium ions are used to produce the electrochromic phenomenon. Lithium ion insertion into tungsten oxide (WO 3-y (0 < y ≤ ~0.3)) causes the tungsten oxide to change from transparent (bleached state) to blue (colored state).

[0075] Referring again to Figure 1 , in the electrochromic stack 120, the ion-conducting layer 108 is sandwiched between the electrochromic layer 106 and the counter electrode layer 110. In some embodiments, the counter electrode layer 110 is inorganic and / or solid-state. The counter electrode layer can comprise one or more of several different materials that act as ion reservoirs when the electrochromic device is in the bleached state. During an electrochromic transition initiated, for example, by applying an appropriate potential, the counter electrode layer transfers some or all of the ions it holds to the electrochromic layer, thereby changing the electrochromic layer to the colored state. At the same time, in the case of NiWO, the counter electrode layer changes color as it loses ions.

[0076] In some embodiments, suitable materials for the counter electrode complementary to WO3 include nickel oxide (NiO), nickel tungsten oxide (NiWO), nickel vanadium oxide, nickel chromium oxide, nickel aluminum oxide, nickel manganese oxide, nickel magnesium oxide, chromium oxide (Cr2O3), manganese dioxide (MnO2), and Prussian blue. When charge is removed from the counter electrode 110 made of nickel tungsten oxide (i.e., ions are transferred from the counter electrode 110 to the electrochromic layer 106), the counter electrode layer will change from the transparent state to the colored state.

[0077] In the depicted electrochromic device, there is an ion-conducting layer 108 between the electrochromic layer 106 and the counter electrode layer 110. When the electrochromic device transitions between the bleached state and the colored state, the ion-conducting layer 108 acts as a medium through which ions pass (in the manner of an electrolyte). Preferably, the ion-conducting layer 108 has high conductivity for the relevant ions of the electrochromic layer and the counter electrode layer, but has a low enough electron conductivity such that electron transfer occurring during normal operation can be neglected. A thin ion-conducting layer with high ionic conductivity allows for rapid ion conduction, thus enabling fast switching of high-performance electrochromic devices. In certain embodiments, the ion-conducting layer 108 is inorganic and / or solid-state.

[0078] Examples of suitable ion-conductive layers (for electrochromic devices having different IC layers) include silicates, silicon oxides, tungsten oxides, tantalum oxides, niobium oxides, and borates. These materials can be doped with different dopants, including lithium. Lithium-doped silicon oxide includes lithium aluminosilicate. In some embodiments, the ion-conductive layer comprises a silicate-based structure. In some embodiments, the ion-conductive layer 108 uses silicon aluminum oxide (SiAlO).

[0079] The electrochromic device 100 can include one or more additional layers (not shown), such as one or more passive layers. Passive layers can be included in the electrochromic device 100 to improve certain optical properties. Passive layers can also be included in the electrochromic device 100 to provide moisture or scratch resistance. For example, the conductive layer can be treated with an antireflective or protective oxide or nitride layer. Other passive layers can be used to hermetically seal the electrochromic device 100.

[0080] Figure 2 is a cross-sectional schematic view of an electrochromic device in a bleached state (or transitioning to a bleached state). According to a particular embodiment, the electrochromic device 200 includes a tungsten oxide electrochromic layer (EC) 206 and a nickel tungsten oxide counter electrode layer (CE) 210. The electrochromic device 200 also includes a substrate 202, a conductive layer (CL) 204, an ion-conductive layer (IC) 208, and a conductive layer (CL) 214.

[0081] The power source 216 is configured to apply a potential and / or current to the electrochromic stack 220 through a suitable connection (e.g., a bus bar) to the conductive layers 204 and 214. In some embodiments, the voltage source is configured to apply a potential of several volts to drive the device from one optical state to another optical state. Figure 2 The polarity of the shown potential is such that ions (lithium ions in this example) are predominantly present (as indicated by the dashed arrows) in the nickel tungsten oxide counter electrode layer 210.

[0082] Figure 3 is Figure 2 a cross-sectional schematic view of the electrochromic device 200 shown but in a colored state (or transitioning to a colored state). In Figure 3In this case, the polarity of the voltage source 216 is reversed so that the electrochromic layer becomes more positive to accept additional lithium ions and thereby transitions to the colored state. As indicated by the dashed arrow, lithium ions are transported across the ion-conductive layer 208 to the tungsten oxide electrochromic layer 206. The tungsten oxide electrochromic layer 206 is shown in the colored state. The nickel tungsten oxide counter electrode 210 is also shown in the colored state. As explained, nickel tungsten oxide becomes increasingly opaque as it releases (intercalates) lithium ions. In this example, there is a synergistic effect where the transition of layers 206 and 210 to the colored state has an additive effect on reducing the amount of light transmitted through the stack and the substrate.

[0083] As described above, an electrochromic device can include an electrochromic (EC) layer and a counter electrode (CE) layer, separated by an ion-conductive (IC) layer that is highly conductive to counter ions and highly resistive to electrons. As is generally understood, the ion-conductive layer thus prevents a short circuit between the electrochromic layer and the counter electrode layer. The ion-conductive layer allows the electrochromic layer and the counter electrode layer to hold charge, thereby maintaining their bleached or colored states. In an electrochromic device having different layers, the components form a stack that includes an ion-conductive layer sandwiched between an electrochromic electrode layer and a counter electrode layer. The boundaries between these three stack components are defined by a sudden change in composition and / or microstructure. Thus, these devices have three distinct layers with two abrupt interfaces.

[0084] According to certain embodiments, the counter electrode layer and the electrochromic layer are formed adjacent to each other, sometimes in direct contact, without separately depositing an ion-conductive layer. In some embodiments, an electrochromic device is employed that has an interfacial region rather than distinct IC layers. Such devices and methods of making them are described in the following applications: U.S. Patent No. 8,300,298, filed April 30, 2010, and U.S. Patent Application Serial No. 12 / 772,075, and U.S. Patent Application Serial Nos. 12 / 814,277 and 12 / 814,279, filed June 11, 2010, the titles of all three patent applications and patents being "Electrochromic Devices", all naming Zhongchun Wang et al. as inventors, and all incorporated herein by reference in their entireties.

[0085] Figure 4Cross-sectional view depicting an example of an electrochromic window incorporating an insulating glass unit (“IGU”) 450, according to some embodiments. The IGU 450 includes a first pane 454 having a first surface S1 and a second surface S2. In this example, the first surface S1 of the first pane 454 faces an external environment, such as an outdoor or exterior environment. The IGU 450 also includes a second pane 456 having a third surface S3 and a fourth surface S4. In this example, the fourth surface S4 of the second pane 456 faces an internal environment, such as the interior of a home, building, vehicle, or a compartment thereof (e.g., an enclosed area therein, such as a room). In other examples, these surfaces may face other environments. For example, the first surface S1 and the fourth surface S4 may face the interior environment of a building, such as when a tintable window functions as a privacy window in a partition wall. The second pane 456 has an electrochromic device coating 455 disposed thereon. The electrochromic device coating 455 includes a first conductive layer, an electrochromic stack, and a second conductive layer. A first bus bar (BB1) is disposed on the first conductive layer, and a second bus bar (BB2) is disposed on the second conductive layer.

[0086] The IGU 500 also includes 460. The spacer 460 is used to separate the first electrochromic pane (lite) 454 from the second pane (lite) 456. The second pane 456 in the IGU 450 is a non-electrochromic lite, but the embodiments disclosed herein are not limited thereto. For example, in other embodiments, the second pane 456 may have an electrochromic device and / or one or more coatings thereon, such as a low-E coating, etc. As another example, the second pane 456 may be a laminated product of a glass pane laminated to a reinforcement pane using a laminate adhesive such as resin. Between the spacer 460 and the first pane 454 is a primary sealant 462. This primary sealant 462 is also between the spacer 460 and the second pane 456. Around the perimeter of the spacer 460 is a secondary seal 470. The secondary seal 470 may be much thicker than depicted. These seals help prevent moisture from entering the interior volume 458 of the IGU 450. They are also used to prevent argon or other gases in the interior volume 458 of the IGU 450 from escaping. The bus bar wiring / leads may pass through the seals and / or may pass through the spacer 460 to connect to a controller.

[0087] In some embodiments, the first and second panes of the IGU (e.g., panes 454 and 456) are transparent or translucent, such as at least for light in the visible spectrum. For example, each pane may be formed of a glass material. The glass material may include architectural glass and / or shatterproof glass. The glass may include silicon oxide (SO x)。The glass can include soda-lime glass or float glass. The glass can include at least about 75% silica (SiO2). The glass can include oxides such as Na2O or CaO. The glass can include alkali or alkaline earth oxides. The glass can include one or more additives. The first pane and / or the second pane can comprise any material having suitable optical, electrical, thermal, and / or mechanical properties. Other materials (e.g., substrates) that can be included in the first pane and / or the second pane are plastics, semi-plastics, and / or thermoplastic materials such as poly(methyl methacrylate), polystyrene, polycarbonate, allyl diglycol carbonate, styrene-acrylonitrile copolymer (SAN), poly(4-methyl-1-pentene), polyester, and / or polyamide. The first pane and / or the second pane can include a mirror material (e.g., silver). In some embodiments, the first pane and / or the second pane can be strengthened. Strengthening can include tempering, heating, and / or chemical strengthening.

[0088] The panes of the IGU can be a single substrate or a multi-substrate structure. The pane can include a laminate, such as a laminate of two substrates. The IGU (e.g., having a double-pane or triple-pane configuration) can offer several advantages compared to a single-pane configuration. For example, a multi-pane configuration can provide enhanced thermal insulation, sound insulation, environmental friendliness, and / or durability when compared to a single-pane configuration. The multi-pane configuration can provide enhanced protection for an electrochromic device (ECD). For example, an electrochromic film (e.g., along with associated layers and conductive interconnects) can be formed on the inner surface of the multi-pane IGU and be protected by an inert gas filling in the internal volume of the IGU. The inert gas filling can provide at least some isolation (thermal insulation) function for the IGU. The electrochromic IGU can have heat-blocking capabilities, e.g., through a colorable coating that absorbs (and / or reflects) heat and light.

[0089] In some embodiments, an insulating glass unit (IGU) includes two (or more) substantially transparent substrates. For example, the IGU can include two glass panes. At least one substrate of the IGU can have an electrochromic device disposed thereon. The one or more panes of the IGU can have a spacer disposed between them. The IGU can be a fully sealed structure, e.g., having an internal region isolated from the surrounding environment. The colorable window can include the IGU and / or a laminate. The colorable window can include one or more electrical leads for power supply and / or communication with one or more devices in the colorable window. For example, the electrical leads can operatively couple (e.g., connect) one or more electrochromic devices to a voltage source, a switch, etc., and can include a frame that supports the IGU or the laminate. The assembly of the colorable window (also referred to herein as a window assembly) can include a window controller and / or components of the window controller (e.g., a dock).

[0090] - Sensors and assemblies

[0091] In some embodiments, an enclosed area (e.g., a room) in a structure such as a building includes one or more sensors. The sensors can facilitate control of the environment of the enclosed area such that the inhabitants of the enclosed area can have a more comfortable, pleasant, beautiful, healthy, productive (e.g., in terms of inhabitant performance), easier to live (e.g., work) environment or any combination thereof. The sensors can be configured as low-resolution or high-resolution sensors. In some cases, the sensors can provide an on / off indication of the occurrence and / or presence of a particular environmental event (e.g., a pixel sensor).

[0092] In some embodiments, the accuracy and / or resolution of the sensors can be improved by performing artificial intelligence analysis on the measurement results of the sensors. Examples of available artificial intelligence techniques include: reactive, limited memory, theory of mind, and / or self-aware techniques known to those skilled in the art.

[0093] In various embodiments, multiple sensors can be configured to process, measure, analyze, detect, and / or respond to one or more of the following: data, temperature, humidity, sound, force, pressure, electromagnetic waves, position, distance, motion, flow, acceleration, velocity, vibration, dust, light, glare, color, gas, and / or other aspects (e.g., characteristics) of the environment (e.g., the enclosed area). For example, the gas can include volatile organic compounds (VOCs). Additionally or alternatively, the gas can include carbon monoxide, carbon dioxide, water vapor (e.g., moisture), oxygen, radon, and / or hydrogen sulfide.

[0094] In one embodiment, one or more sensors may be calibrated in a factory setting. For example, the sensors may be optimized to make accurate measurements of one or more environmental characteristics present in the factory setting. In some cases, such factory-calibrated sensors may be less optimized when operating in a target environment. For example, the factory setting may include an environment different from the target environment. The target environment may be the environment in which the sensor is deployed. The target environment may be the environment in which the sensor is expected and / or intended to operate. The target environment may be different from the factory environment. The factory environment corresponds to the location where the sensor is assembled and / or built. The target environment may include a location other than the factory where the sensor is not assembled and / or built. In some cases, the factory setting may be so different from the target environment that the sensor readings captured in the target environment are incorrect (e.g., to a measurable extent). In this context, "incorrect" may mean that the sensor readings deviate from a specified accuracy (e.g., specified by the sensor manufacturer). In some instances, when operating in the target environment, factory-calibrated sensors may provide readings that do not meet the accuracy specifications (e.g., specified by the manufacturer).

[0095] In one embodiment, one or more drawbacks in sensor operation may be at least partially corrected and / or mitigated by allowing the sensor to perform self-calibration in its target environment (e.g., where the sensor is installed). In some cases, the sensor may be calibrated and / or recalibrated after being installed in the target environment. In some cases, the sensor may be calibrated and / or recalibrated after operating in the target environment for a specific period of time. The target environment may be the location where the sensor is installed in an enclosed area. Calibrating and / or recalibrating the sensor after it is installed in the target environment may increase the measured (e.g., measurable) accuracy as compared to calibrating the sensor before installation. In certain embodiments, readings from one or more previously installed sensors in the enclosed area are provided for calibrating and / or recalibrating a newly installed sensor in the enclosed area. A calibrated and / or positioned component may be used as a standard for calibrating and / or positioning other components. Such a component may be referred to as a "gold component". The gold component may be used as a reference component. Such a component may be the most accurately calibrated and / or positioned component in the facility. Components (e.g., sensors, transmitters, or transceivers) may be calibrated and / or positioned by a traveler. The traveler may be human or non-human (e.g., a robot). The traveler may be a field service engineer. The traveler may include mobile robots such as drones, wheeled robots, or any other easily operable robot. Examples of components (e.g., devices), controls, calibrations, and travelers may be found in International Patent Application Serial Number PCT / US21 / 15378, which is incorporated herein by reference in its entirety.

[0096] In some embodiments, the target environment corresponding to the first enclosed area is different from the target environment corresponding to the second enclosed area. For example, the target environment corresponding to an enclosed area of a cafeteria or an auditorium may exhibit different sensor readings than the target enclosed area corresponding to a meeting room. The sensor may consider the target environment (e.g., one or more of its characteristics) when performing sensor readings and / or outputting sensor data. For example, during lunch, a carbon dioxide sensor installed in an occupied cafeteria may provide a higher reading than a sensor installed in an empty meeting room. In another example, an ambient noise sensor located in an occupied cafeteria during lunch may provide a higher reading than an ambient noise sensor located in a library.

[0097] In some embodiments, the sensor (e.g., occasionally) provides an output signal indicating an incorrect measurement result. The sensor may be operatively coupled to at least one controller. The controller may obtain the incorrect sensor reading from the sensor. The controller may obtain readings of the same type from one or more other (e.g., nearby) sensors at a similar time (e.g., or simultaneously). The one or more other sensors may be located in the same environment as the one sensor. The controller may evaluate the incorrect sensor reading in combination with one or more readings of the same type provided by one or more other sensors of the same type to identify the incorrect sensor reading as an outlier. For example, the controller may evaluate an incorrect temperature sensor reading and one or more temperature readings provided by one or more other temperature sensors. The controller may determine that the sensor reading is incorrect in response to considering (e.g., including evaluating and / or comparing) the sensor reading with one or more readings from other sensors in the same environment (e.g., the same enclosed area). The controller may instruct the one sensor providing the incorrect reading to be recalibrated (e.g., by undergoing a recalibration procedure). For example, the controller may transmit one or more values and / or parameters to the sensor providing the incorrect reading. The sensor providing the incorrect reading may use the transmitted values and / or parameters to adjust its subsequent sensor readings. For example, the sensor providing the incorrect reading may use the transmitted values and / or parameters to adjust its baseline for subsequent sensor readings. The baseline may be a value, a set of values, or a function.

[0098] In some embodiments, a sensor has a service life. The service life of the sensor may be related to one or more readings obtained by the sensor. Sensor readings from certain sensors may be more valuable and / or variable during certain time periods and less valuable and / or less variable during other time periods. For example, motion sensor readings may vary more during the day than at night. The service life of the sensor can be extended. By allowing the sensor to reduce sampling of environmental parameters during certain time periods (e.g., having a lower beneficial value), the extension of the service life can be achieved. Certain sensors can modify (e.g., increase or decrease) the frequency of sensor reading sampling. The timing and / or frequency of sensor operation may depend on the sensor type, location in the (e.g., target) environment, and / or day time. Sensor types may need to operate continuously and / or more frequently during the day (e.g., CO2 sensors, volatile organic compound (VOC) sensors, occupancy sensors, and / or lighting sensors). Volatile organic compounds can be emitted by animals and / or humans. VOCs can include compounds related to odors produced by humans. The sensor may need to operate infrequently during at least a portion of the night. Sensor types may need to operate infrequently during at least a portion of the day (e.g., temperature sensors and / or pressure sensors). Timing and / or frequency of operation can be assigned to the sensor. This assignment can be controlled (e.g., changed) manually and / or automatically (e.g., using at least one controller operatively coupled to the sensor). Operatively coupling can include communication coupling, electrical coupling, optical coupling, or any combination thereof. Modification of the timing and / or frequency of obtaining sensor readings can be in response to an event detected by the same type of sensor or a different type of sensor. Modification of the timing and / or frequency of sensor readings can utilize sensor data analysis. Sensor data analysis can utilize artificial intelligence (referred to herein simply as "AI"). The control can be fully automatic or partially automatic. Partially automatic control can allow the user to (i) veto an indication from the controller, and / or (ii) indicate any preferences (e.g., of the user).

[0099] In some embodiments, processing sensor data includes performing sensor data analysis. Sensor data analysis can include at least one rational decision-making process and / or learning. Sensor data analysis can be used to adjust the environment, for example, by adjusting one or more components that affect the environment of an enclosed area. The data analysis can be performed by a machine-based system (e.g., a circuit system). The circuit system can have a processor. Sensor data analysis can utilize artificial intelligence. Sensor data analysis can rely on one or more models (e.g., mathematical models). In some embodiments, sensor data analysis includes linear regression, least squares fitting, Gaussian process regression, kernel regression, nonparametric multiplicative regression (NPMR), regression trees, local regression, semiparametric regression, isotonic regression, multivariate adaptive regression splines (MARS), logistic regression, robust regression, polynomial regression, stepwise regression, ridge regression, lasso regression, elastic net regression, principal component analysis (PCA), singular value decomposition, fuzzy measure theory, Borel measure, Han measure, risk-neutral measure, Lebesgue measure, group method of data handling (GMDH), naive Bayes classifier, k-nearest neighbor algorithm (k-NN), support vector machine (SVM), neural network, support vector machine, classification and regression tree (CART), random forest, gradient boosting, or generalized linear model (GLM) techniques.

[0100] Figure 5 Schematic diagram 500 depicting an example of a sensor arrangement distributed between one or more enclosed areas. In Figure 5 In the example shown, the controller 505 is in communication link 508 with the following sensors: sensors 510(1), 510(2), 510(3), …… 510(n) located in enclosed area 1; sensors 515(1), 515(2), 515(3), ……, 515(n) located in enclosed area 2; sensors 520(1), 520(2), 520(3), ……, 520(n) located in enclosed area 3; and sensors 585(1), 585(2), 585(3), ……, 585(n) located in enclosed area m (where m = 1, 2, 3, 4, 5, 6, 7, 8, 9, etc., and n = 1, 2, 3, 4, 5, 6, 7, 8, 9, etc.). In other embodiments, there can be fewer or more sensors located in the enclosed areas, and / or there can be additional or fewer enclosed areas in communication link with the controller 505. The communication links include wired and / or wireless communication.

[0101] In some embodiments, a sensor set can refer to a collection of different sensors. In some cases, the sensor set includes at least two different types of sensors. In other embodiments, the sensor set can include at least two sensors of the same type.

[0102] Figure 5Depict a first set 511 including sensors 510(1), 510(2), 510(3), ……, 510(n), a second set 516 including sensors 515(1), 515(2), 515(3), ……, 515(n), a third set 521 including sensors 520(1), 520(2), 520(3), ……, 520(n) and an mth set 586 including sensors 585(1), 585(2), 585(3), ……, 584(n) (where m = 1, 2, 3, 4, 5, 6, 7, etc.). In other embodiments, fewer or more sets may be included. In the illustrated example, the first set 511 may represent a set of different sensors, where at least two of the sensors 510(1), 510(2), 510(3), ……, 510(n) have different types.

[0103] In some embodiments, at least two sensors in a set cooperate to determine an environmental parameter, such as an environmental parameter of an enclosed area in which they are disposed. For example, the set of sensors may include a carbon dioxide sensor, a carbon monoxide sensor, a volatile organic chemical sensor, an environmental noise sensor, a visible light sensor, a temperature sensor, and / or a humidity sensor. The set of sensors may include other types of sensors, and the claimed subject matter is not limited in this regard. The enclosed area may include one or more sensors that are not part of the set of sensors. The enclosed area may include multiple sets. At least two of the multiple sets may differ in at least one of their sensors. At least two of the multiple sets may have at least one similar (e.g., same type) sensor. For example, one set may have two motion sensors and one temperature sensor. For example, one set may have a carbon dioxide sensor and an IR sensor. The set may include one or more non-sensor devices. The one or more other non-sensor devices may include a sound emitter (e.g., buzzer), and / or an electromagnetic radiation emitter (e.g., light emitting diode). In some embodiments, a single sensor (e.g., not in a set) may be disposed adjacent to (e.g., immediately adjacent, such as in contact with) another non-sensor device.

[0104] In some embodiments, sensors in a sensor collection cooperate with each other (e.g., using a control system). The sensors can include a sensor array. The sensor arrays can cooperate in concert (e.g., using a network and / or a controller). The controller can be included in a control system (e.g., as disclosed herein). One type of sensor can be related to at least one other type of sensor. Conditions in an enclosed area can affect one or more different sensors. Sensor readings of the one or more different sensors can be related to and / or affected by the conditions. The correlation can be pre-determined. The correlation can be determined over a period of time (e.g., using a learning process). The period of time can be pre-determined. The period of time can have a cut-off value. The cut-off value can consider, for example, an error threshold (e.g., a percentage value) between predicted sensor data and measured sensor data in a similar situation. Time may still be continuing. The correlation can be derived from a learning set (also referred to herein as a "training set"). The learning set can include real-time observations in the enclosed area and / or can be derived therefrom. The observations can include data collection (e.g., from sensors). The learning set can include sensor data from similar enclosed areas. The learning set can include a third-party data set (e.g., of sensor data). The learning set can be derived from simulations of one or more environmental conditions affecting the enclosed area. The learning set can synthesize detected (e.g., historical) signal data with one or more types of noise added. The correlation can utilize historical data, third-party data, and / or real-time (e.g., sensor) data. A correlation between two sensor types can be assigned a value. The value can be a relative value (e.g., strong correlation, medium correlation, or weak correlation). The learning set that is not derived from real-time measurements can act as a benchmark (e.g., a baseline) to initiate the operation of sensors and / or various components affecting the environment (e.g., an HVAC system and / or a tinted window). Real-time sensor data can supplement the learning set, e.g., on an ongoing basis or over a defined period of time. During the deployment of sensors in the environment, the size of the (e.g., supplemented) learning set may increase. The size of the initial learning set may increase, e.g., by including additional (i) real-time measurements, (ii) sensor data from other (e.g., similar) enclosed areas, (iii) third-party data, (iv) other and / or updated simulations.

[0105] In some embodiments, data from sensors can be correlated. Once a correlation is established between two or more sensor types, a deviation from the correlation (e.g., a deviation from a correlation value) can indicate an irregularity and / or a failure of a sensor among the correlated sensors. A failure can include a drift in calibration. A failure can indicate a need to recalibrate the sensor. A failure can include a complete failure of the sensor. In an example, a motion sensor can cooperate with a carbon dioxide sensor. In an example, in response to the motion sensor detecting the movement of one or more individuals in an enclosed area, the carbon dioxide sensor can be activated to start carbon dioxide measurements. An increase in movement within the enclosed area can be correlated with an increase in carbon dioxide levels. In another example, the detection of an individual in an enclosed area by a motion sensor can be associated with an increase in noise detected by a noise sensor in the enclosed area. In some embodiments, the detection by a first type of sensor without the detection by a second type of sensor can cause the sensor to issue an error message. For example, if the motion sensor detects many individuals in an enclosed area but there is no increase in carbon dioxide and / or noise, then the carbon dioxide sensor and / or the noise sensor can be identified as having failed or having an incorrect output. An error message may be issued. The first plurality of different correlated sensors in a first set can include one sensor of a first type and a second plurality of sensors of different types. If the second plurality of sensors indicate a correlation and the one sensor indicates a reading different from the correlation, then the likelihood that the one sensor has failed increases. If the first plurality of sensors in a first set detect a first correlation and the third plurality of correlated sensors in a second set detect a second correlation different from the first correlation, then the likelihood that the situation experienced by the first set of sensors is different from the situation experienced by the third set of sensors increases.

[0106] Sensors in a sensor set can cooperate with each other. Cooperation can include considering the sensor data of another sensor in the set (e.g., having a different type). Cooperation can include trends predicted by another sensor in the set (e.g., of a type). Cooperation can include trends predicted by data related to another sensor in the set (e.g., of a type). Other sensor data can be derived from other sensors in the set, the same type of sensors in other sets, or data of the type collected by other sensors in the set that are not derived from other sensors. For example, a first set can include a pressure sensor and a temperature sensor. Cooperation between the pressure sensor and the temperature sensor can include considering the pressure sensor data when analyzing and / or predicting the temperature data of the temperature sensor in the first set. The pressure data can be: (i) the data of the pressure sensor in the first set, (ii) the data of the pressure sensors in one or more other sets, (iii) the pressure data of other sensors, and / or (iv) the pressure data of a third party.

[0107] In some embodiments, the sensor set is distributed throughout the enclosed area. Sensors of the same type can be dispersed in the enclosed area, for example, so as to be able to measure environmental parameters at various locations in the enclosed area. Sensors of the same type can measure gradients along one or more dimensions of the enclosed area. The gradient can include a temperature gradient, an environmental noise gradient, or any other variation (e.g., increase or decrease) in the measured parameter as the position varies from a point. The gradient can be used to determine if a sensor is providing an erroneous measurement result (e.g., the sensor has failed). BCPI-250156 page 26 / 120

[0108] Figure 6 Schematic diagram 600 depicting an example of the arrangement of sensor set 610 in enclosed area 620. In the example shown, the first set 612 is positioned at a distance D1 from vent 650, the second sensor set 614 is positioned at a distance D2 from vent 650, and the third sensor set 616 is positioned at a distance D3 from vent 650. Vent 650 can correspond to an air conditioning vent, which represents a relatively constant source of cooling gas and a relatively constant source of white noise. Thus, in this example, temperature and noise measurements can be made by the first sensor set 612. The temperature and noise measurements made by sensor 612 are shown by output reading profile 662. Output reading profile 612 indicates a relatively low temperature and a large amount of noise. The temperature and noise measurements made by the second sensor set 614 are shown by output reading profile 664. Output reading profile 664 indicates a slightly higher temperature and a slightly reduced noise level. The temperature and noise measurements made by the third sensor set 616 are shown by output reading profile 666. Output reading profile 666 indicates a temperature slightly higher than the temperatures measured by sensor sets 614 and 612. The noise measured by the third sensor set 616 indicates a level lower than the noise measured by sensor sets 612 and 614. In the example, if the temperature measured by the third sensor set 616 indicates a temperature lower than the temperature measured by the first sensor set 612, then one or more processors and / or controllers can identify the sensors of the third sensor set 616 as providing erroneous data.

[0109] In another example of a temperature gradient, a temperature sensor mounted near a window can measure an increased temperature fluctuation relative to the temperature fluctuation measured by a temperature sensor mounted at a position opposite the window. A sensor mounted near the midpoint between the window and the position opposite the window can measure a temperature fluctuation between the temperature fluctuation measured near the window and the temperature fluctuation measured at the position opposite the window. In the example, an environmental noise sensor mounted near an air conditioner (or near a heating vent) can measure a greater environmental noise compared to an environmental noise sensor mounted far from the air conditioner or heating vent.

[0110] In some embodiments, sensors of a first type cooperate with sensors of a second type. In an example, an infrared radiation sensor can cooperate with a temperature sensor. The cooperation between sensor types can include establishing a correlation (e.g., a negative or positive correlation) between readings from sensors of the same or different types. For example, an increase in the infrared energy measured by the infrared radiation sensor can be accompanied by an increase in the measured temperature (e.g., positively correlated therewith). A decrease in the measured infrared radiation can be accompanied by a decrease in the measured temperature. In an example, an increase in the infrared energy measured by the infrared radiation sensor, but not accompanied by an increase in the temperature that can be measured, can indicate a fault or degradation in the operation of the temperature sensor.

[0111] In some embodiments, an enclosed area contains one or more sensors. For example, the enclosed area can contain at least 1, 2, 4, 5, 8, 10, 20, 50, or 500 sensors. The enclosed area can contain a number of sensors within a range between any of the foregoing values (e.g., from about 1 to about 1000, from about 1 to about 500, or from about 500 to about 1000). The sensors can be of any type. For example, the sensors can be configured (e.g., and / or designed) to measure the concentration of a gas (e.g., carbon monoxide, carbon dioxide, hydrogen sulfide, volatile organic chemicals, or radon). For example, the sensors can be configured (e.g., and / or designed) to measure ambient noise. For example, the sensors can be configured (e.g., and / or designed) to measure electromagnetic radiation (e.g., RF, microwave, infrared, visible light, and / or ultraviolet radiation). For example, the sensors can be configured (e.g., and / or designed) to measure safety-related parameters, such as (e.g., glass) breakage and / or unauthorized entry into a restricted area. The sensors can cooperate with one or more (e.g., active) devices, such as radar or lidar. The devices can operate to detect the physical size of the enclosed area, the presence of people in the enclosed area, stationary objects in the enclosed area, and / or moving objects in the enclosed area.

[0112] In some embodiments, a sensor is operatively coupled to at least one controller. The coupling can include a communication link. The communication link (e.g., Figure 5 508 in) can include any suitable communication medium (e.g., wired and / or wireless). The communication link can include wires, such as one or more conductors arranged in twisted pairs, coaxial cables, and / or optical fibers. The communication link can include a wireless communication link, such as Wi-Fi, Bluetooth, ZigBee, cellular, or optical. One or more segments of the communication link can include a conductive (e.g., wired) medium, while one or more other segments of the communication link can include a wireless link.

[0113] In some embodiments, the enclosed area is a facility (e.g., a building). The enclosed area can include walls, doors, or windows. In some embodiments, at least two of the plurality of enclosed areas are disposed within the facility. In some embodiments, at least two of the plurality of enclosed areas are disposed in different facilities. The different facilities can be a campus (e.g., and belong to the same entity). At least two of the plurality of enclosed areas can be on the same floor of the facility. At least two of the plurality of enclosed areas can be on different floors of the facility. Figure 5 The enclosed areas shown, such as enclosed areas 1, 2, 3, ……, m, can correspond to enclosed areas on the same floor of a building or to enclosed areas on different floors of a building. Figure 4 The enclosed areas can be in different buildings of a multi-building campus. Figure 4 The enclosed areas can be in different campuses of a multi-campus neighborhood.

[0115] In some embodiments, after the first sensor is installed, the sensor performs self-calibration to establish an operating baseline. The execution of the self-calibration operation can be initiated by a separate sensor, a nearby second sensor, or one or more controllers. For example, at the time of installation and / or afterwards, the sensors deployed in the enclosed area can perform a self-calibration procedure. The baseline can correspond to a lower threshold value, based on which it can be expected that the collected sensor readings include values higher than the lower threshold value. The baseline can correspond to an upper threshold value, based on which it can be expected that the collected sensor readings include values lower than the upper threshold value. The self-calibration procedure can start from a sensor search time window within which the fluctuations or perturbations of the relevant parameters are nominal. In some embodiments, the time window is sufficient to collect sensing data (e.g., sensor readings) such that signals and noise can be separated and / or identified from the sensing data. The time window can be pre-determined. The time window can be undefined. The time window can remain open (e.g., continuously) until a calibration value is obtained.

[0116] In some embodiments, the sensor may search (e.g., within a time window) for the optimal time of the measurement baseline. The optimal time (e.g., within a time window) may be a time span during which (i) the measured signal is most stable and / or (ii) the signal-to-noise ratio is highest. The measured signal may contain a certain level of noise. The complete absence of noise may indicate a sensor failure or insufficient environmental measurement. The sensed signal (e.g., sensor data) may include a timestamp of the data measurement. The sensor may be assigned a time window during which it can sense the environment. The time window may be pre-determined (e.g., using third-party information and / or historical data on the characteristics measured by the sensor). The signal may be analyzed within the time window, and an optimal time span may be found within the time window during which the measured signal is most stable and / or the signal-to-noise ratio is highest. The time span may be equal to or shorter than the time window. The time span may occur throughout the time window or within a portion of the time window.

[0117] Figure 7E An example of a time window 753 indicated as having a start time 751 and an end time 752 is shown. Within the time window 753, a time span 754 is indicated, which has a start time 755 and an end time 756. The sensor may sense the characteristic (e.g., VOC level) it is configured to sense within the time window 753, aiming to find the time span during which the best sensed data (e.g., best sensed data set) is collected, where the best data (e.g., data set) has the highest signal-to-noise ratio and / or indicates a stable signal is collected. The best sensed data may have a (e.g., low) noise level (e.g., indicating that the sensor is not malfunctioning). For example, the time window may be 12 hours between 5 pm and 5 am. Within the time span, the sensed VOC data is collected. The collected sensed data set may be analyzed (e.g., using a processor) to find the time span within the 12 hours during which the noise level is lowest (e.g., indicating that the sensor is operating) and (i) the signal-to-noise ratio is highest (e.g., the signal is distinguishable) and / or (ii) the signal is most stable (e.g., has low variability). This time may be the 1 hour between 4 am and 5 am. In this example, the time window is 12 hours between 5 pm and 5 am, and the time span is 1 hour between 4 am and 5 am.

[0118] In some embodiments, finding the best data (e.g., set) for calibration includes comparing sensor data collected over a time span (e.g., within a time window). Within the time window, the sensor may sense the environment over several time spans of (e.g., substantially) equal duration. Multiple time spans may fit within the time window. The time spans may or may not overlap. The time spans may contract with respect to each other. The data collected by the sensor over different time spans may be compared. The time span with the highest signal-to-noise ratio and / or with the most stable signal may be selected to determine the baseline signal. For example, the time window may include a first time span and a second time span. The first time span (e.g., having a first duration or first time length) may be shorter than the time window. The second time span (e.g., having a second duration) may be shorter than the time window. In some embodiments, evaluating the sensed data (e.g., to find the best sensed data for calibration) includes comparing a first sensed data set sensed (e.g., and collected) within the first time span with a second sensed data set sensed (e.g., and collected) within the second time span. The length of the first time span may be different from the length of the second time span. The length of the first time span may be equal to (or substantially equal to) the length of the second time span. The start time and / or end time of the first time span may be different from those of the second time span. The start time and / or end time of the first time span and the second time span may be within the time window. The start time of the first time span and / or the second time span may be equal to the start time of the time window. The end time of the first time span and / or the second time span may be equal to the end time of the time window.

[0119] Figure 7D An example is shown of a time window 743 having a start time 740 and an end time 749, a first time window 741 having a start time 745 and an end time 746, and a second time window 742 having a start time 747 and an end time 758. In Figure 7D the example shown, the start times 745 and 747 are within the time window 743, and the end times 746 and 748 are within the time window 743.

[0120] Figures 7A - 7D Examples of various time windows containing time spans are shown. Figure 7ADepict a time-shifted graph where the time window 710 is indicated to have a start time 711 and an end time 712. Within the time window 710, various time spans 701 - 707 are indicated, and the time spans overlap with each other. The sensor can sense a characteristic (e.g., humidity, temperature, or CO2 level) that it is configured to sense within at least two of the time spans (e.g., 701 - 707), for example, aiming to compare signals to find the time when the signal is most stable and / or has the highest signal-to-noise ratio. For example, the time window (e.g., 710) can be one day, and the time span (e.g., 701) can be 50 minutes. The sensor can measure the characteristic (e.g., CO2 level) within the 50-minute overlapping period (e.g., within the common time spans 701 - 707), and the data can later be divided into different (overlapping) 50-minute time spans, for example, by using timestamped measurements. The 50 minutes indicating a stable CO2 signal (e.g., at night) and / or having the highest signal-to-noise ratio can be designated as the best time span for measuring the baseline CO2 signal. In one case, the measured signal can be selected as the baseline for the sensor. Once the best time span is selected, other CO2 sensors (e.g., at other locations) can utilize this time span for baseline determination. Finding the best time for baseline determination can speed up the calibration process. Once the best time is found, other sensors can be programmed to measure the signal within the best time span to record their signals, which can be used for baseline calibration. Figure 7B Depict a time-shifted graph where the time window 723 is indicated, and within the time window 723, two time spans 721 and 722 are indicated, and the time spans overlap with each other. Figure 7C Depict a time-shifted graph where the time window 733 is indicated, and within the time window 733, two time spans 731 and 732 are indicated, and the time spans are in contact with each other, that is, the end of the first time span 731 is the start of the second time span 732. Figure 7D Depict a time-shifted graph where the time window 743 is indicated, and within the time window 743, two time spans 741 and 742 are indicated, and the time spans are separated by a time gap 744.

[0121] In an example, for a carbon dioxide sensor, the relevant parameter can correspond to the carbon dioxide concentration. In the example, the carbon dioxide sensor can determine that the time window with the least carbon dioxide concentration fluctuations during a period corresponds to a two-hour period, such as between 5:00 am and 7:00 am. Self-calibration may start at 5:00 am and continue while searching for the duration during which the measurement results are stable (e.g., with the least fluctuations) within these two hours. In some embodiments, the duration is long enough to allow separation between the signal and the noise. In the example, the data from the carbon dioxide sensor can facilitate determining that a 5-minute duration (e.g., between 5:25 am and 5:30 am) within the time window between 5:00 am and 7:00 am forms the best time period for collecting the lower limit baseline. This determination can be performed at least in part (e.g., completely) at the sensor level. This determination can be executed by one or more processors operatively coupled to the sensor. During the selected duration, the sensor can collect readings and establish a baseline that can correspond to the lower limit threshold.

[0122] In an example, for a gas sensor placed in a room (e.g., an office environment), the relevant parameter can correspond to the gas (e.g., CO2) level, where the required level is typically in the range of about 1000 ppm or lower. In the example, the CO2 sensor can determine that self-calibration should be performed within the time window when the CO2 level is the lowest, such as when there are no occupants near the sensor (e.g., see Figure 8 the CO2 level before 18000 seconds in

[0123] Figure 9 A contour map showing a top view of an example office environment of an enclosed area, depicting various CO2 concentration levels. The office environment includes a first occupant 901, a second occupant 902, a third occupant 903, a fourth occupant 904, a fifth occupant 905, a sixth occupant 906, a seventh occupant 907, an eighth occupant 908, and a ninth occupant 909. The gas (CO2) concentration can be measured by one or more sensors placed at various locations in the enclosed area (e.g., the office).

[0124] In some examples, multiple sensors in a room are used to locate the source chemical components (e.g., VOCs) of atmospheric materials. The spatial profile indicating the distribution of chemicals in an enclosed area can indicate various (e.g., relative or absolute) concentrations of chemicals that vary with space. Such a profile can be a two-dimensional or three-dimensional profile. The sensors can be placed at different locations in the room to allow sensing of chemicals at different room locations. Mapping (e.g., the entire) enclosed area (e.g., a room) may require (i) overlap of the sensor sensing areas, and / or (ii) inferring the distribution of chemicals in the enclosed area (e.g., in areas with low or no sensor coverage (e.g., sensing areas)). For example, Figure 9 An example is shown of a relatively steep and higher carbon dioxide concentration towards the location of the occupant 905, relative to the low concentration in the unoccupied area 910 of the enclosed area. This can indicate the presence of a carbon dioxide emission source at the location of the occupant 905. Similarly, by finding (e.g., relatively steep) low chemical concentrations in the environment, the location (e.g., source) of chemical elimination can be found. Relative is with respect to the general distribution of chemicals in the enclosed area.

[0125] - Network

[0126] Certain disclosed embodiments provide network infrastructure in an enclosed area (e.g., a building or other facility). The network infrastructure can be used for various purposes, such as for providing communication and / or power services. The communication services can include high-bandwidth (e.g., wireless and / or wired) communication services. The communication services can be for the occupants of the facility and / or users outside the facility (e.g., the building). The network infrastructure can work in cooperation with or as a partial replacement for the infrastructure of one or more cellular carriers. The network infrastructure can be provided in a facility that includes electro-switchable windows. Examples of components of the network infrastructure include high-speed backhaul. The network infrastructure can include at least one cable, switch, physical antenna, transceiver, sensor, transmitter, receiver, radio, processor, and / or controller (which can include a processor). The network infrastructure can be operatively coupled to and / or include a wireless network. The network infrastructure can include wiring. At least a portion of the wiring can be placed at the enclosure (e.g., the exterior wall of a building) of the enclosed area. As part of installing the network and / or after installing the network, one or more sensors can be deployed (e.g., installed) in the environment.

[0127] In various embodiments, a network infrastructure supports a control system. The control system can control one or more building systems, including, for example, windows, such as tintable (e.g., electrochromic) windows. The control system can include one or more controllers operatively (e.g., directly or indirectly) coupled to the one or more windows. The one or more windows can be light-switchable windows, tintable windows, and / or smart windows. The concepts disclosed herein for electrochromic windows can be applied to other types of smart and / or tintable windows (e.g., including switchable optical devices), including liquid crystal devices, electrochromic devices, suspended particle devices (SPD), nanochromic displays (NCD), organic electroluminescent displays (OELD), suspended particle devices (SPD), nanochromic displays (NCD), or organic electroluminescent displays (OELD). The display element can be attached to a portion of a transparent body (e.g., a window). Tintable windows can be disposed in a (non-transitory) facility, such as a building, and / or disposed in a transitory vehicle, such as a car, bus, train, airplane, helicopter, ship, recreational vehicle, or boat.

[0128] - Building management system

[0129] In some embodiments, a building management system (BMS) includes a control system installed in a building for controlling (e.g., monitoring) one or more building systems, such as mechanical and / or electrical equipment in an enclosed area. The control system can include a hierarchy of controllers (e.g., controllers configured to communicate in a hierarchical manner). The control system can include at least one controller for at least one tintable window. The at least one tintable window can change color, transparency, and / or hue in response to an electric current and / or a voltage difference. For example, the control system can control ventilation, lighting, power systems, elevators, fire protection systems, and / or security systems in an enclosed area of a building. The control systems described herein (e.g., including nodes and / or processors) can be adapted to integrate with a BMS.

[0130] A BMS can be composed of hardware, including interconnects to computers and / or associated software via communication channels for maintaining conditions in a building, e.g., in accordance with preferences set by at least one user. The user can be an occupant, owner, landlord, and / or building manager. For example, a BMS can be implemented using a local area network, such as Ethernet. The software can include open standards and / or be compliant with Internet protocol and cellular network protocols (e.g., at least third generation, fourth generation, or fifth generation cellular network protocols). An example is the software of Tridium, Inc. (Richmond, Virginia). A commonly used communication protocol for BMS is Building Automation and Control Network (BACnet).

[0131] In some embodiments, the BMS is placed in an enclosed area, such as a facility. The facility may include a building, such as a multi-story building. The BMS can be used at least to control the environment in the building. The control system and / or the BMS can control at least one environmental characteristic of the enclosed area. The at least one environmental characteristic may include temperature, humidity, fine mist (e.g., aerosol), sound, electromagnetic waves (e.g., glare and / or color), gas composition, gas concentration, gas velocity, vibration, volatile compounds (VOCs), debris (e.g., dust), and / or biological substances (e.g., airborne bacteria and / or viruses). The gas may include oxygen, nitrogen, carbon dioxide, carbon monoxide, hydrogen sulfide, nitrogen oxides (NO and NO2), inert gases, noble gases (e.g., radon), chloroform, ozone, formaldehyde, methane, and / or ethane. For example, the BMS can control the temperature, carbon dioxide level, and / or humidity in the enclosed area. The mechanical devices that can be controlled by the BMS and / or the control system may include lighting, heaters, air conditioners, fans, or vents. To control the environment of the enclosed area (e.g., a building), the BMS and / or the control system can turn on and off one or more devices it controls, for example, under defined conditions. The (e.g., core) function of a modern BMS and / or control system can be to maintain a comfortable, healthy, and / or productive environment for the occupants of the enclosed area, for example, while minimizing energy consumption (e.g., while minimizing heating and cooling costs / demand). A modern BMS and / or control system can be used to control (e.g., monitor) and / or optimize the coordination between various systems, such as saving energy and / or reducing the operating costs of the enclosed area (e.g., a facility).

[0132] In some embodiments, the control system controls at least one environmental characteristic of the enclosed area (e.g., the atmosphere of the enclosed area). The environmental characteristic can be any environmental characteristic disclosed herein. For example, the environmental characteristic can be the level of airborne and / or gaseous components in the atmosphere. For example, the environmental characteristic can be the level of an atmospheric accumulant. For example, the environmental characteristic can be the level of an atmospheric depletant.

[0133] In some embodiments, the control system is operatively coupled (e.g., communicatively coupled) to a set of devices (e.g., including one or more sensors and / or transmitters). The set facilitates controlling an environment and / or generating an alert. The control can utilize a control scheme such as feedback control, or any other control scheme described herein (e.g., feedforward, closed-loop, and / or open-loop). The set can include at least one sensor configured to sense electromagnetic radiation. The electromagnetic radiation can include (human) visible, infrared (IR), and / or ultraviolet (UV) radiation. The at least one sensor can include a sensor array. For example, the set can include an infrared (IR) sensor array. Additionally or alternatively, the set can include a sound detector and / or transmitter. Additionally or alternatively, the set can include a microphone. The set can include any sensor and / or transmitter disclosed herein.

[0134] In some embodiments, the set (or a set of sets) can be used to detect characteristics of an occupant in an enclosed area. The set can be used to detect abnormal physical characteristics of an occupant in an enclosed area. The abnormal physical characteristics can include body temperature, coughing, sneezing, sweating (e.g., humidity and / or VOC emissions), or CO2 levels. The set can be used for absolute and / or relative positioning of an occupant in an enclosed area. For example, the set can be used to measure the relative distance between occupants in an enclosed area and / or between an occupant and a hard and / or dense object (e.g., a fixture and / or a non-fixture) in the enclosed area. The hard and / or dense object can include a fixture (e.g., a wall, ceiling, floor, window, door, shelf, ceiling light, or wall light) or a moving piece of furniture (e.g., a chair, table, or lamp).

[0135] In some examples, one or more sensors in an enclosed area can be volatile organic compound (VOC) sensors. The VOC sensors can be specific to one VOC compound (e.g., as disclosed herein), or a class of compounds (e.g., having similar chemical characteristics). For example, the VOC sensors can be sensitive to aldehydes, esters, thiophenes, alcohols, aromatic hydrocarbons (e.g., benzene and / or toluene), or olefins. In some cases, one or more VOC sensors can output a total VOC output (also referred to herein as “TVOC”). The sensing can be performed over a period of time.

[0136] In one example, a set of chemical sensors (e.g., a sensor array) is sensitive to various compounds (e.g., VOCs) (e.g., having different chemical characteristics). The set of compounds can include identified or unidentified compounds. The chemical sensors can output sensed values for specific compounds, compound classes, or groups of compounds. The sensor output can be a total (e.g., cumulative) measurement of the sensed compound class or group of compounds. The sensor output can be a total (e.g., cumulative) measurement of multiple sensor outputs for (i) individual compounds, (ii) compound classes, or (iii) groups of compounds.

[0137] In some embodiments, a local (e.g., window) controller can be integrated with the BMS and / or control system. The local controller can be configured to control one or more devices, including a switchable window (e.g., including an electrochromic window), sensors, transmitters, antennas, or any other element communicatively coupled to a network (which can be controlled via communication). In one embodiment, the electrochromic window includes at least one all-solid-state and inorganic electrochromic device. The electrochromic window can include more than one electrochromic device, such as where at least two window panes (e.g., each window pane) are switchable. In one embodiment, the electrochromic window includes (e.g., consists of) all-solid-state and inorganic electrochromic devices. In one embodiment, the one or more electrochromic windows include organic electrochromic devices. In one embodiment, the electrochromic window is a multi-state electrochromic window. Examples of switchable windows and their control can be found in U.S. Patent Application Serial No. 12 / 851,514, filed on August 5, 2010, titled "Multi-pane Electrochromic Windows", which is incorporated herein by reference in its entirety.

[0138] In some embodiments, multiple devices may be operatively coupled (e.g., communicatively coupled) to a control system. The multiple devices may be disposed in a facility (e.g., including a building and / or room). The control system may include a controller hierarchy. The devices may include transmitters, sensors, or windows (e.g., IGU). The devices may be any of the devices disclosed herein. At least two of the multiple devices may be of the same type. For example, two or more IGUs may be coupled to the control system. At least two of the multiple devices may be of different types. For example, a sensor and a transmitter may be coupled to the control system. Sometimes, the multiple devices may include at least 20, 50, 100, 500, 1000, 2500, 5000, 7500, 10000, 50000, 100000, or 500000 devices. The multiple devices may have any number between the foregoing numbers (e.g., from 20 devices to 500000 devices, from 20 devices to 50 devices, from 50 devices to 500 devices, from 500 devices to 2500 devices, from 1000 devices to 5000 devices, from 5000 devices to 10000 devices, from 10000 devices to 100000 devices, or from 100000 devices to 500000 devices). For example, the number of windows on a floor may be at least 5, 10, 15, 20, 25, 30, 40, or 50. The number of windows on a floor may be any number between the foregoing numbers (e.g., from 5 to 50, from 5 to 25, or from 25 to 50). Sometimes, the devices may be in a multi-story building. At least a portion of the floors of the multi-story building may have devices controlled by the control system (e.g., at least a portion of the floors of the multi-story building may be controlled by the control system). For example, the multi-story building may have at least 2, 8, 10, 25, 50, 80, 100, 120, 140, or 160 floors controlled by the control system. The number of floors (e.g., the devices therein) controlled by the control system may be any number between the foregoing numbers (e.g., from 2 to 50, from 25 to 100, or from 80 to 160). The area of a floor may be at least about 150m 2 , 250m 2 , 500m 2 , 1000m 2 , 1500m 2 or 2000 square meters (m 2 ). The area of a floor may be between any of the foregoing floor area values (e.g., from about 150m 2 to about 2000m 2 , from about 150m 2 to about 500m 2 , from about 250m2 up to about 1000 m 2 or about 1000 m 2 up to about 2000 m 2 ). The area of the building can be at least about 1000 square feet (sqft), 2000 sqft, 5000 sqft, 10000 sqft, 100000 sqft, 150000 sqft, 200000 sqft or 500000 sqft. The area of the building can be between any of the above areas (e.g., about 1000 sqft to about 5000 sqft, about 5000 sqft to about 500000 sqft, or about 1000 sqft to about 500000 sqft). The area of the building can be at least about 100 m 2 200 m 2 500 m 2 1000 m 2 5000 m 2 10000 m 2 25000 m 2 or 50000 m 2 . The area of the building can be between any of the above areas (e.g., about 100 m 2 to about 1000 m 2 about 500 m 2 to about 25000 m 2 about 100 m 2 to about 50000 m 2)。The facilities can include commercial buildings or residential buildings. Commercial buildings can contain tenants and / or owners. Residential facilities can include multi-family or single-family buildings. Residential facilities can include apartment buildings. Residential facilities can include single-family homes. Residential facilities can include multi-family homes (e.g., apartments). Residential facilities can include townhouses. The facilities can include residential and commercial portions. The facilities can include at least about 1, 2, 5, 10, 50, 100, 150, 200, 250, 300, 350, 400, 420, 450, 500, or 550 windows (e.g., tintable windows). Components of the facilities (e.g., devices such as windows) can be assigned to different zones (e.g., at least in part based on their location, elevation, floor, ownership, use of the enclosed area (e.g., room), any other assignment metric, random assignment, or any combination thereof). The assignment of components (e.g., devices such as windows) to zones can be static or dynamic (e.g., based on heuristics). There can be at least about 2, 5, 10, 12, 15, 30, 40, or 46 components (e.g., devices such as sensors and / or windows) in each zone. These zones can be grouped (e.g., each with a distinguishable name and / or symbol). These zones can be clustered (e.g., each cluster with a distinguishable name and / or symbol). These zones, their groupings, and / or clusters can form a zone hierarchy.

[0139] In some embodiments, various components (e.g., IGU) are grouped into component (e.g., EC window) partitions. At least one partition (e.g., each partition) can contain a subset of components (e.g., devices). For example, at least one (e.g., each) component partition can be controlled by one or more corresponding floor controllers and one or more corresponding local controllers (e.g., window controllers) controlled by these floor controllers. In some examples, at least one (e.g., each) partition can be controlled by a single floor controller and two or more local controllers controlled by the single floor controller. For example, a partition can represent a logical grouping of components (e.g., devices). Each partition can correspond to a set of components (e.g., of the same type) in a particular location or area of a facility, which are driven together at least in part based on their location. For example, a facility (e.g., a building) can have four sides or faces (north, south, east, and west) and ten floors. In such a teaching example, each partition can correspond to a set of smart windows (e.g., switchable windows) on a particular floor and a particular one of the four faces. At least one (e.g., each) partition can correspond to a set of components (e.g., devices) that share one or more physical characteristics (e.g., device parameters such as size or lifespan). In some embodiments, the partitioning of components (e.g., devices) is grouped (e.g., defined) at least in part based on one or more non-physical characteristics (e.g., security designation or business hierarchy) (e.g., IGUs that define a manager's office can be grouped in one or more partitions, while IGUs that define a non-manager's office can be grouped into one or more different partitions).

[0140] In some embodiments, at least one (e.g., each) floor controller is capable of addressing all components (e.g., devices) in at least one (e.g., each) partition in one or more corresponding partitions. The components in a partition can be of the same type or of different types. For example, a master controller can issue a color command to a floor controller that controls a target partition.

[0141] In some embodiments, a facility may be divided into one or more zones. The zones may be defined at least in part by a customer or a facility manager. The zones may be defined at least in part automatically. For example, zones of devices (e.g., including colorable windows, sensors, or transmitters) may be associated with: (i) the facades of the buildings they face, (ii) the floors they are on, (iii) the buildings in the facility they are in, (iv) the functions of the enclosed areas they are in (e.g., conference rooms, gyms, offices, or cafeterias), (iv) the regulations and / or factual occupancy of the enclosed areas they are in (e.g., organizational functions), (v) the regulations and / or factual activities in the enclosed areas they are in, (vi) the tenants, owners, and / or managers of the enclosed areas in the facility (e.g., for a facility with multiple tenants, owners, and / or managers), and / or (vii) their geographical locations. The zones may be changeable (e.g., using a software application). The status of the zones (e.g., in conjunction with the status of components (e.g., devices) therein) may be displayed via an application (e.g., updated in real time or substantially in real time). One or more zones may be grouped. For example, all zones on a particular floor may be grouped. There may be a zone hierarchy using any of the zones associated with: (i) the facades of the buildings they face, (ii) the floors they are on, (iii) the buildings in the facility they are in, (iv) the functions of the enclosed areas they are in (e.g., conference rooms, gyms, offices, or cafeterias), (iv) the regulations and / or factual occupancy of the enclosed areas they are in (e.g., organizational functions), (v) the regulations and / or factual activities in the enclosed areas they are in, (vi) the tenants, owners, and / or managers of the enclosed areas in the facility (e.g., for a facility with multiple tenants, owners, and / or managers), and / or (vii) their geographical locations.

[0142] Figure 10Schematic diagram illustrating an example of a BMS 1010 for managing one or more building systems and a control system 1030 of a building 1002 according to an embodiment. In this example, the BMS 1010 manages several building systems of the building 1002, including a security system 1022, a heating, ventilation, and air conditioning (HVAC) system 1023, a lighting system 1024, an electrical system 1026, an elevator system 1027, a fire protection system 1028, and so on. The BMS 1010 can also manage the one or more switchable windows 1002 (e.g., electrochromic windows). The security system 1022 can include, for example, magnetic card access control, revolving doors, electromagnetic drive door locks, (e.g., surveillance) cameras, (e.g., burglar) alarms, and / or metal detectors. The BMS 1010 and / or the control system 1004 can control at least one fire protection system and / or a fire extinguishing system (e.g., the fire protection system 1028). The fire protection system can include one or more fire alarms. The fire extinguishing system can include water pipe control. The lighting system 1024 can include interior lighting, exterior lighting, emergency warning lights, emergency exit signs, and / or emergency floor (e.g., exit or entrance) lighting. The electrical system 1026 can include a main power supply for an enclosed area (e.g., a facility), a backup generator, and / or an uninterruptible power supply (UPS). According to certain aspects, the BMS 1010 can manage the control system 1030. In other aspects, the BMS 1010 can be managed by the control system. In one embodiment, the BMS 1010 can be included in the control system 1030. At Figure 10 the moment shown, the cloud 1090 passes over the building 1002, blocking sunlight from reaching at least one switchable window 1003.

[0143] At Figure 10 this time, the control system 1030 is depicted as a network of distributed controllers. The control system 1030 can have 1, 2, 3, or more hierarchical control levels. At Figure 10In this case, the control system 1030 includes a main controller 1032, intermediate controllers 1034a, 1034b, and 1034c (which can be floor controllers and / or network controllers), and local controllers (e.g., end or lobe controllers, such as window controllers) 1036. In other embodiments, other numbers of intermediate and local controllers can be used. The main controller 1032 may or may not be physically proximate to the BMS 1010. At least one floor (e.g., each floor) of the building 1002 may have one or more intermediate controllers 1034a, 1034b, and 1034c. At least one device (e.g., a window) may have its own local controller 1036. Each local controller 1036 can control any number of devices, such as at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 devices. In some embodiments, the control system 1030 may or may not have intermediate controllers.

[0144] In Figure 10 the example shown, at least one (e.g., each) local controller 1036 controls one or more devices. The one or more devices can include windows, sensors, transmitters, antennas, receivers, and / or transceivers. At least one (e.g., each) local controller 1036 can be located in a position separated from the device it controls or integrated into the device. In Figure 10 the example shown, ten (10) electrochromic windows of the building 1002 are depicted as being controlled by the main controller 1032. In alternative embodiments, there can be fewer or greater numbers of devices controlled by the main controller 1032.

[0145] In some embodiments, the control system can include the BMS or be operationally coupled (e.g., communicatively coupled) to the BMS. Through a feedforward (e.g., feedback) control scheme, the BMS and / or the control system can provide: (1) enhanced environmental control, (2) enhanced energy conservation, (3) enhanced security, (4) greater flexibility in control options, (5) increased reliability and service life of other systems (e.g., coordination of systems can reduce the total operating time of individual systems, thereby reducing system maintenance), (6) information availability and diagnostics, and / or (7) effective use of employees and higher productivity, and any combination thereof (e.g., because electrochromic windows can be automatically controlled). In some embodiments, (i) the BMS may not be present, (ii) the BMS may be present but may not communicate with the control system (e.g., the main controller), or (iii) the BMS may communicate with the control system (e.g., the main controller) at a high level. In certain embodiments, maintenance of the BMS does not interrupt the control of the one or more devices (e.g., electrochromic windows) coupled to the BMS and / or the control system.

[0146] In some embodiments, the BMS and / or control system controls ventilation within an enclosed area. A ventilation system (e.g., as part of an HVAC system) can provide a comfortable environment and good atmospheric (e.g., air) quality. The ventilation system may require a large amount of energy. Providing good atmospheric quality for the occupants of the enclosed area can improve well-being, comfort, and / or productivity. Such enclosed areas (e.g., facilities) may be occupied by a large number of individuals and / or may be occupied by individuals who are frequently replaced. Such enclosed areas can include large work environments, health and / or entertainment centers. For example, transportation hubs, sports centers, hospitals, exhibition centers, shopping malls, financial centers, movie theaters, museums, and / or cruise ships. Ventilation of the enclosed area can exchange the internal environment of the enclosed area with the external environment. For example, the ventilation system can introduce external atmosphere and discharge the internal atmosphere to the environment outside the enclosed area (e.g., outside the facility). The exchange of external and internal atmospheres can adjust one or more components of the internal atmosphere. For example, the exchange of external and internal atmospheres (e.g., via the ventilation system) can reduce any accumulated atmospheric components emitted within the enclosed area (e.g., CO2 from human respiration and VOCs from human respiration, saliva, and skin). For example, the exchange of external and internal atmospheres (e.g., via the ventilation system) can change the oxygen level and / or humidity level (when their internal and external levels are different). Industry standards can provide recommended ventilation flow rates based at least in part on full load (e.g., number of people), room size, and / or facility type (e.g., people in an office space produce less CO2 / VOC than people in a gym). Operating the ventilation rate according to industry standard recommendations may (e.g., significantly) over-ventilate the enclosed area (e.g., when the occupancy rate of the enclosed area (e.g., room) is below the maximum occupancy rate), which can lead to undesirable energy waste. Additionally, the ventilation system may ventilate using a mixture of external atmosphere and recirculated internal atmosphere (e.g., in an unknown ratio). Since the amount of external atmosphere (e.g., one or more of its components) may be unknown, the concentration of atmospheric components (e.g., pollutants) may vary (e.g., increase or decrease) to an undesirable level. Furthermore, the levels of atmospheric components may vary with occupancy (e.g., when they are emitted by occupants), so a constant ventilation rate may not adequately maintain the desired indoor environmental atmosphere. Therefore, it is desirable to optimize the ventilation rate in a manner that optimizes the concentration of one or more atmospheric components in the enclosed area and the energy consumption of the enclosed area (e.g., the energy consumption of the ventilation system serving the enclosed area).

[0147] In some embodiments, a ventilation system (e.g., as part of an HVAC system) supplies conditioned, fresh, outside, and / or recirculated atmosphere to an enclosed area. The ventilation system can include a heat pump and / or a gas (e.g., air) processor. The gas processor can include one or more blowers (e.g., single-speed or variable-speed), one or more mixing chambers, one or more filters, one or more dampers, and / or one or more ducts. The ventilation system can deliver the conditioned atmosphere to the enclosed area (e.g., rooms such as offices, meeting rooms, cafeterias, corridors, elevators, or lobbies) via supply and / or return ducts. In some embodiments, one or more sensors or sensor sets in the enclosed area are configured to (e.g., and do) measure the concentration of one or more atmospheric components, room occupancy, and / or ventilation flow rate. In some embodiments, the sensed (e.g., measured) quantities are used to estimate the concentration of atmospheric components, zone (e.g., room) occupancy, or ventilation rate. A control system can use the sensed and / or estimated (i) concentration of atmospheric components, (ii) occupancy, and / or (iii) ventilation rate, as well as knowledge of the outside (fresh air) concentration of atmospheric components, to issue commands to the ventilation system. The commands issued to the ventilation system can be to adjust the ventilation rate to optimize the atmospheric quality in the enclosed area and the energy consumption of the enclosed area (e.g., the energy consumption of the ventilation system serving the enclosed area). In some embodiments, by combining the detection of atmospheric components (e.g., VOCs, particulate matter, or CO2) and occupancy detection, the existing ventilation rate can be calculated and the ventilation rate required to clear stale atmosphere within a given time can be estimated. Particulate matter can include particles associated with smoke and / or soot (e.g., having a FLS of up to one micron). A particulate matter sensor can be used to detect smoke and / or a fire in the facility (or near the facility). Particulate matter can affect air quality (e.g., according to the air quality index). The rate of change of atmospheric components can be used to predict future levels and actively control ventilation (with or without considering occupancy). Additionally, by obtaining indoor and outdoor measurements of particulate matter, filter efficiency can be evaluated to detect whether a filter needs to be replaced and / or whether there is a pathogen accumulation.

[0148] In some embodiments, the particulate matter sensor may use the optical density of a sensed gas (e.g., air), such as the gas through which an energy beam passes. The particulate matter sensor may measure the dispersion of the energy beam as it passes through the gas (e.g., the dispersion pattern). The particulate matter sensor may measure the intensity of the energy beam after it passes through the gas (e.g., the optical density), such as compared to the intensity of the energy beam when it enters the gas (e.g., when emitted from an energy source such as a laser). The particulate matter sensor may utilize an energy beam that passes through the gas and is dispersed, for example, when encountering particulate matter in the gas (e.g., air). The energy beam may include a laser beam. The laser beam may be configured with an energy of at least 500 nanometers (nm), 525 nm, 550 nm, 600 nm, 650 nm, 660 nm, 700 nm, 750 nm, or 800 nm. The energy beam may include an infrared (IR) energy beam. The particulate matter may be sensed at a frequency of every 1 second (sec), 2.5 sec, 5 sec, 7.5 sec, 10 sec, 20 sec, 30 sec, or 60 sec. The particulate matter may be configured to sense particles at least nanometers or micrometers in size. The particulate matter sensed by the particulate matter sensor may include particles with a FLS (e.g., the diameter or the diameter of its bounding circle) of at least nanometers or micrometers. For example, the FLS of the particulate matter sensed by the particulate matter sensor may be at least 1 micrometer (μm), 2 μm, 2.5 μm, 5 μm, 7 μm, 10 μm, or 20 μm. The particulate matter sensed by the particulate matter sensor may have any value between the foregoing values. For example, from about 1 μm (PM1) to about 20 μm (PM 20 ), from about 1 μm (PM1) to about 5 μm (PM5), from about 2.5 μm (PM 2.5 ) to about 10 μm (PM 10 ), or from about 5 μm (PM5) to about 20 μm (PM 20)。The data of the particulate matter sensor, either alone or in combination with the data of other sensors (e.g., VOC sensors, light sensors, noise sensors, and / or personnel ID sensors), can be used to monitor, notify, and / or optimize the cleaning services in a facility. For example, the sensor can be used to alert that a part of the facility (e.g., an enclosed area) requires cleaning services, such as based on sensing an increase in malodor, an increase in particulate matter, and / or a high number of people (e.g., exceeding a threshold, and / or over time, e.g., within a specific time span). For example, the sensor can be used to alert that a cleaning service is in progress in a part of the facility (e.g., an enclosed area), such as based on sensing an elevated VOC level associated with cleaning supplies and / or particulate matter emitted during the cleaning process, the noise of the cleaning machine, sensing the ID of the cleaning personnel, and / or turning on and off the lights when the cleaning personnel pass by the facility. Such monitoring can allow the facility to be cleaned as needed, e.g., based on the sensor, rather than, for example, following a predetermined cleaning service that is insensitive to the required degree of cleaning. Such sensors can also allow monitoring of the cleaning rate, certain aspects of the cleaning method (e.g., the amount of cleaning supplies used, the time spent cleaning certain areas of the facility, the cleaning sequence, the cleaning path, or any combination thereof). The sensor (e.g., alone or in cooperation) can be used to detect odor in a facility (e.g., its enclosed areas, such as a bathroom or an office). The enclosed area can constitute a type of space, such as any type of space disclosed herein. The odor can include volatile organic compounds. Cooperation can have data from one type of sensor and data from other types of sensors. Cooperation can have data from one type of sensor and data from other sensors of the same type. At least two types of sensors can be placed (e.g., approximately) in the same location, such as as part of a set of devices. At least two types of sensors can be placed in different locations. The sensor can be placed inside the facility (e.g., in an enclosed area).

[0149] In some embodiments, occupants in a partition are discovered and / or located through a positioning technique (e.g., an automatic positioning technique). At least a portion of the positioning technique may be embedded in an identification tag of the occupant (e.g., as a microchip). In some embodiments, an identification (ID) tag of a user may include a microchip. The microchip may be a micro positioning chip. The microchip may incorporate an automatic positioning technique (also referred to herein as a "micro positioning chip"). The microchip may incorporate a technique for automatically reporting high-resolution and / or high-precision position information. The automatic positioning technique may include a Global Positioning System (GPS), Bluetooth, or radio wave technology. The automatic positioning technique may include electromagnetic wave (e.g., radio wave) emission and / or detection. The radio wave technology may be any RF technology disclosed herein (e.g., high frequency, ultra high frequency, super high frequency). The radio wave technology may include UWB technology. The microchip may facilitate determining its position within a precision of up to about 25 centimeters, 20 cm, 15 cm, 10 cm, or 5 cm. In various embodiments, a control system and / or an antenna (operatively coupled to a network) is configured to communicate with the micro positioning chip. In some embodiments, the ID tag may include the micro positioning chip. The micro positioning chip may be configured to broadcast one or more signals. The signals may be omnidirectional signals. One or more components operatively coupled to the network may (e.g., respectively) include the micro positioning chip. The micro positioning chip (e.g., placed at a stationary and / or known position) may act as an anchor point. The position of the ID tag may be determined by analyzing the time it takes for the broadcast signal to reach the anchor point within the transmission range of the ID tag. One or more processors (e.g., of the control system) may perform the analysis of the position-related signals. For example, the relative distance between the microchip and one or more anchor points and / or other microchips (e.g., within the transmission range limit) may be determined. The relative distance, position, and / or anchor point information may be aggregated. At least one anchor point may be placed in the floor, ceiling, wall, and / or mullion of a building. At least 1, 2, 3, 4, 5, 8, or 10 anchor points may be placed in an enclosed area (e.g., a room, a building, and / or a facility). At least two anchor points may have at least one of the same (Cartesian coordinate system) X coordinate, Y coordinate, and Z coordinate (e.g., substantially).

[0150] In some embodiments, the control system causes one or more devices to be located and / or tracked (e.g., including automatic positioning techniques such as micro-positioning chips) and / or at least one user carrying such a device. The relative position between two or more such devices can be determined based on information related to the received transmissions, e.g., at one or more antennas and / or sensors. Device positioning can include geolocation and / or geographical location. The position of a device can be determined by analyzing electromagnetic signals transmitted from the device and / or the micro-positioning chip. Information available for determining the position includes, for example, received signal strength, time of arrival, signal frequency, and angle of arrival. When determining the position of the one or more devices based on these metrics, a triangulation module can be implemented. The triangulation module can include calculations and / or algorithms. Triangulation can take into account and / or utilize the physical layout of the building. Automatic positioning can include geographical location and / or geolocation. An example of a positioning method can be found in International Patent Application Serial Number PCT / US17 / 31106, filed on May 4, 2017, titled "WINDOW ANTENNAS", which is incorporated herein by reference in its entirety.

[0151] In some embodiments, pulse-based Ultra-Wideband (UWB) technology (e.g., ECMA-368 or ECMA-369) is a wireless technology for transmitting large amounts of data over short distances (e.g., up to about 300 feet (')、250'、230'、200' or 150') at low power (e.g., less than about 1 milliwatt (mW), 0.75 mW, 0.5 mW or 0.25 mW). The short distance can be up to about 100 meters (m), 90 m, 80 m, 70 m, 60 m, 50 m, 40 m, 30 m, 20 m, 15 m, 10 m or 5 m. The UWB signal can occupy at least about 750 MHz, 500 MHz or 250 MHz of the bandwidth spectrum, and / or at least about 30%, 20% or 10% of its center frequency. The UWB signal can be transmitted by one or more pulses. The broadcast digital signal pulse components can be timed (e.g., precisely timed) on a carrier signal across multiple frequency channels simultaneously. For example, information can be transmitted by modulating the timing and / or positioning of the signal (e.g., pulse). The signal information can be transmitted by encoding the polarity, amplitude of the signal (e.g., pulse) and / or by using orthogonal signals (e.g., pulse). The UWB signal can be a low-power information transfer protocol. The UWB technology can be used for (e.g., indoor) positioning applications. The wide range of the UWB spectrum includes low frequencies with long wavelengths, which allows the UWB signal to pass through various materials, including various building fixtures (e.g., walls). The wide range of frequencies, including low penetration frequencies, can reduce the likelihood of multipath propagation errors (not wishing to be bound by theory, as some wavelengths may have line-of-sight trajectories). The UWB communication signal (e.g., pulse) can be very short (e.g., up to about 70 cm, 60 cm or 50 cm for pulses about 600 MHz, 500 MHz or 400 MHz wide; or up to about 20 cm, 23 cm, 25 cm or 30 cm for pulses with a bandwidth of about 1 GHz, 1.2 GHz, 1.3 GHz or 1.5 GHz). The short communication signal (e.g., pulse) can reduce the likelihood of the reflected signal (e.g., pulse) overlapping with the original signal (e.g., pulse).

[0152] Figure 11 Depicts a ventilation system 1100 for ventilating an enclosed area (e.g., a room) 1101 inside building 1120. A heat pump 1102 provides a heated or cooled heat exchange medium to a gas treatment system having a blower 1103, a filter 1104 and a mixing chamber 1105. After filtering, the conditioned atmosphere is delivered to the enclosed area 1101 and mixed with the atmosphere in the enclosed area 1101 to produce an internal atmospheric component concentration C in。The return atmosphere BCPI - 250156 from the enclosed area 1101, page 45 / 120, is conveyed to the mixing chamber 1105, where some or all of the return atmosphere can be directed to the exhaust port 1107 and replaced by fresh atmosphere (e.g., air) 1106 having an ambient external atmosphere component concentration C out The controller 1108 can be part of a controller network in the building 1120 for controlling, for example, one or more devices (e.g., tintable windows) and / or other aspects of the BMS. The controller 1108 is coupled to sensors 1109 and 1110 deployed in the enclosed area 1101 to monitor environmental characteristics such as the concentration of atmosphere components (e.g., CO2, VOC, and / or particulate matter concentration). The controller 1108 can be configured to perform operations to determine adjustments to the ventilation rate to optimize the atmosphere component concentration and atmosphere quality, and these adjustments are transmitted to the ventilation system 1100 (e.g., directly or through the BMS).

[0153] Industry standards (e.g., from the American Society of Heating, Refrigerating and Air - Conditioning Engineers under ANSI) recommend a minimum ventilation rate defined based on the size of the enclosed area (e.g., floor area or room volume), the occupancy of the enclosed area, and the use case (e.g., office). The occupancy and / or use case can indicate the required level of atmosphere components (e.g., pollutants) (e.g., CO2, hydrogen, methane, and / or VOC) generated within the room. The occupancy and / or use case can indicate the required level of any desired components (e.g., oxygen and / or moisture). A mass - balance equation can be used to calculate the necessary ventilation rate (e.g., including the entry of external atmosphere (e.g., fresh air)) to maintain the required concentration within the room. The concentration of atmosphere components (e.g., accumulants or consumables) in the external atmosphere may be lower than the concentration of said components in the enclosed - area atmosphere. The concentration of atmosphere components (e.g., moisture) in the external atmosphere may be higher than the concentration of said components in the enclosed - area atmosphere. The target (e.g., optimal, such as maximum or minimum) concentration of the atmosphere component to be maintained can be different from (e.g., higher or lower than) the outdoor concentration. For accumulants (e.g., VOC or CO2), the target can be a maximum optimum value. For consumables (e.g., O2), the target can be a minimum optimum value. At maximum room occupancy, the minimum ventilation rate can be determined, for example, by considering standard recommendations and / or a ventilation - rate lookup table such that the target concentration of the component is maintained at or below a threshold. The threshold can be a value or a function (e.g., a temperature - related function). The target concentration can be based on the internal concentration (C in ) of the component within the enclosed area and the external concentration (C out) The differential concentration (ΔPOL) between them is specified, and the external concentration of the component outside the enclosed area is, for example, the concentration in the ambient atmosphere. If the minimum ventilation rate for maximum occupancy is maintained when the occupancy rate in the room is low, over-ventilation may occur. (For example, health and / or regulatory) standards may recommend a lower minimum ventilation rate threshold, for example, for low occupancy levels in an enclosed area (such as a room). However, such a recommendation may over-ventilate the enclosed area (even at lower occupancy levels). Therefore, compared to following guidelines, an accurate ventilation rate relying on (for example, real-time and / or on-site) sensor measurements can provide more accurate guidance, can promote a reduction in energy (such as that of the ventilation system), and / or can promote a reduction in operating costs (such as ventilation costs). The lookup table can consider (and / or delineate) the partition type (such as building part types, like offices, meeting rooms, corridors, lobbies, etc.), the relative geographical location of the partition (such as relative to the sun and / or the building), weather conditions, the partition surface area, the partition volume, the partition temperature, and / or the expected activities in the partition (such as exercising in a gym, eating in a cafeteria, speaking in a meeting room, working quietly in an office). The data in the lookup table can be used to estimate the required ventilation rate. For example, compared to an office of the same (such as approximate) size, the occupants of a gym consume more oxygen. For example, compared to the occupants of an office of the same (such as approximate) size, the occupants of a gym emit more moisture, VOCs, and CO2. For example, occupants in a hot room (such as a south-facing room) emit more VOCs and / or other VOCs that become volatile than in a colder room (such as a north-facing room).

[0154] In some embodiments, at least one atmospheric component is a VOC. The atmospheric component (e.g., VOC) may include benzopyrrole volatiles (e.g., indole and skatole), ammonia, short-chain fatty acids (e.g., up to six carbons), and / or volatile sulfur compounds (e.g., hydrogen sulfide, methylmercaptan (also known as methanethiol), dimethyl sulfide, dimethyl disulfide, and dimethyl trisulfide). The atmospheric component (e.g., VOC) may include 2-propanone (acetone), 1-butanol, 4-ethylmorpholine, pyridine, 3-hexanol, 2-methyl-cyclopentanone, 2-hexanol, 3-methyl-cyclopentanone, 1-methyl-cyclopentanol, p-cymene, octanal, 2-methyl-cyclopentanol, lactic acid, methyl ester, 1,6-heptadien-4-ol, 3-methyl-cyclopentanol, 6-methyl-5-hepten-2-one, 1-methoxy-hexane, ethyl lactate, nonanal, 1-octen-3-ol, acetic acid, 2,6-dimethyl-7-octen-2-ol (dihydromyrcenol), 2-ethylhexanol, decanal, 2,5-hexanedione, 1-(2-methoxypropoxy)-2-propanol, 1,7,7-trimethylbicyclo[2·2·1]heptan-2-one (camphor), benzaldehyde, 3,7-dimethyl-1,6-octadien-3-ol (linalool), 1-methylhexyl acetate, propionic acid, 6-hydroxy-hexan-2-one, 4-cyanocyclohexene, 3,5,5-trimethylcyclohex-2-en-1-one (isophorone), butyric acid, 2-(2-propyl)-5-methyl-1-cyclohexanol (menthol), furfuryl alcohol, 1-phenyl-ethanone (acetophenone), isovaleric acid, ethyl carbamate (urethane), 4-tert-butylcyclohexyl acetate (vertenex), p-menth-1-en-8-ol (α-terpineol), dodecanal, 1-phenylethyl acetate, 2(5H)-furanone, 3-methyl, 2-ethylhexyl-2-ethylhexanoate, 3,7-dimethyl-6-octen-1-ol (citronellol), 1,1′-oxybis-2-propanol, 3-hexene-2,5-diol, 3,7-dimethyl-2,6-octadien-1-ol (geraniol), hexanoic acid, geranyl acetone 3, 2,4,6-tri-tert-butyl-phenol, unknown, 2,6-bis(1,1-dimethylethyl)-4-(1-oxopropyl)phenol, phenethyl alcohol, dimethyl sulfone, 2-ethylhexanoic acid, unknown, benzothiazole, phenol, Tetradecanoic acid, 1-methylethyl ester (isopropyl myristate), 2-(4-tert-butylphenyl)propanal (p-tert-butyldihydrocinnamaldehyde), octanoic acid, α-methyl-β-(p-tert-butylphenyl)propanal (lyral), 1,3-diacetyloxypropan-2-yl acetate (triacetin), p-cresol, cedrol, lactic acid, Hexadecanoic acid, 1-methylethyl ester (isopropyl palmitate), 2-hydroxy, hexyl ester benzoic acid (hexyl salicylate), palmitic acid, ethyl ester, Methyl 2-pentyl-3-oxo-1-cyclopentyl acetate (methyl dihydrojasmonate or hedione), 1,3,4,6,7,8-hexahydro-4,6,6,7,8,8-hexamethyl-cyclopenta-gamma-2-benzopyran (galaxolide), 2-ethylhexyl salicylate, propane-1,2,3-triol (glycerol), methoxyacetic acid, dodecyl ester, α-hexylcinnamaldehyde, benzoic acid, dodecanoic acid, 5-(hydroxymethyl)-2-furaldehyde, Homomethylsalicylate, 4-vinylimidazole, methoxyacetic acid, tetradecyl ester, tridecanoic acid, tetradecanoic acid, pentadecanoic acid, hexadecanoic acid, 9-hexadecenoic acid, heptadecanoic acid, 2,6,10,15,19,23-hexamethyl-2,6,10,14,18,22-tetracosahexaene (squalene), hexadecanoic acid and / or 2-hydroxyethyl ester.

[0155] In some embodiments, sensor data (e.g., indoor and outdoor) regarding atmospheric components of interest (e.g., consumables such as O2, and accumulants such as CO2) are used in combination with occupancy sensors to estimate the levels of atmospheric components in an enclosed area and / or the distribution of atmospheric components in the enclosed area. Sensors for measuring ventilation flow rates in ducts and / or into specific rooms (e.g., differential pressure sensors) are not used because they are costly and / or have low accuracy. Even if present, gas flow and / or pressure sensors cannot detect the composition of gases (e.g., gases arriving from the outdoor environment and / or recycled within the enclosed area). The lack of gas composition detection can impede and / or compromise the determination of: (i) the actual accurate flow rate of external atmosphere (e.g., fresh air) into the enclosed area, and / or (ii) the quality of the atmosphere within the enclosed area (e.g., at a given time). An enclosed area, a portion of an enclosed area, or a group of enclosed areas can define a zone. In some embodiments, the difference between the atmospheric component levels inside the zone and the atmospheric component levels outside the zone is calculated using the resident population of the zone, the area and / or volume of the zone, and the typical per capita production rate and / or consumption rate of the atmospheric components. A zone can be an enclosed area. Some of the atmospheric components of interest are accumulants because occupants exhale them. Some of the atmospheric components of interest are consumables because occupants consume and thus deplete them. It can be assumed that the rate of exhalation of atmospheric components of interest (e.g., VOCs and / or CO2) by each person is at an average level. It can also be assumed that the rate of consumption of atmospheric components of interest (e.g., O2) by each person is at an average level.

[0156] In some embodiments, the room occupancy rate, the ventilation rate, and the ΔPOL of one or more components are interrelated, such that any one of them can be derived (e.g., calculated) from the other two. In some embodiments, the occupancy rate (n) is calculated from the measured ΔPOL (e.g., ΔCO2 or ΔVOC) and a known gas flow rate. In some embodiments, the ΔPOL is determined (e.g., calculated) from a known gas flow rate and occupancy rate data. The occupancy rate data can be the detected occupancy rate (e.g., using an occupancy sensor). The occupancy rate data can take into account a schedule. The occupancy rate data can take into account historical occupancy rate data and / or predictive logic (e.g., using a learning algorithm). The learning algorithm can utilize historical data and / or a projected schedule as a learning set to predict the occupancy rate within an enclosed area. The predicted occupancy rate can be based on the schedule (e.g., calendar) of the enclosed area and / or the facility in which the enclosed area is located. The schedule can be an electronic schedule. The control system can take into account the schedule. In some embodiments, the gas flow rate is determined based on the occupancy rate data (e.g., detected and / or projected) and the measured ΔPOL. In some embodiments, once all three parameters are obtained, they can be used to adjust (e.g., with greater precision) the ventilation rate and / or the atmosphere (e.g., air) quality within a zone (e.g., an enclosed area). In some embodiments, depending on whether the actual ΔPOL is greater than or less than the target ΔPOL, the measured and / or determined (e.g., calculated) ΔPOL value is used (e.g., alone) to coarsely adjust the ventilation rate (e.g., increase or decrease the ventilation rate).

[0157] In some embodiments, a control system (e.g., including a processor) is adapted to control, determine, and / or implement a change in the ventilation rate. The control determination and / or implementation can be based on one or more relationships described herein, such as the relationship between zone occupancy, ventilation rate, and ΔPOL. The control system (e.g., its controller and / or processor) can store and / or retrieve one or more parameters and / or configuration data, e.g., depending on the control action to be performed and / or the available sensor data. In some embodiments, the ventilation rate is actively controlled before an expected change in the occupancy of a zone (e.g., an enclosed area such as a room) to better maintain air quality, e.g., when an occupant enters or leaves the room. For example, historical data recording regular fluctuations in the concentration of one or more air components (e.g., ΔPOL of CO2 and / or VOC) can be used to predict regular gatherings of people. The change in occupancy (e.g., expected) can be predicted at least in part based on other data sources, such as an online calendar for the room or specific individuals associated with the room. For example, electronic schedule information can provide the scheduled meetings and the list of attendees. Using the predicted change in occupancy, the generation amount of air components in the room can be predicted by multiplying the per capita air component generation rate or consumption rate according to the predicted occupancy. Before a significant change in the combined air component generation / consumption rate occurs, the ventilation rate bringing fresh air into the room can be changed to avoid peaks in the differential air component concentration.

[0158] Figure 12 Depicts a control system 1200 configured to at least control ventilation. The electronic memory 1201 stores parameters such as maximum occupancy, minimum ventilation rate, maximum ventilation rate, target ventilation rate, and / or target ΔPOL. In an analysis (e.g., including calculations), the parameters are used in the control system block 1202 to output a changed (e.g., altered) ventilation rate 1203. If the corresponding sensors are available, then the control system block 1202 obtains the measured actual indoor (e.g., on-site) air component concentration 1204 (e.g., in real time), the measured actual outdoor air component concentration 1205 (e.g., in real time), the measured actual ventilation rate 1206 (e.g., in real time), and / or the measured actual occupancy 1207 (e.g., in real time). When one or more sensors (e.g., sensor types) are not available to facilitate the measured data, the control system block 1202 can determine (e.g., estimate and / or predict) the corresponding actual values using available values (e.g., historically measured values and / or predicted values) as needed.

[0159] In some embodiments, a control system (e.g., a controller) can be configured to adjust a ventilation rate based on a zone (e.g., an enclosed area) occupancy rate and / or a target atmospheric component level of one or more atmospheric components to maintain a required air quality. The zone occupancy rate can be measured, estimated, and / or determined (e.g., calculated). For example, without knowing the absolute ventilation rate, the ventilation rate can be adjusted relative to the current ventilation rate, e.g., based on the difference between the measured atmospheric components and the target atmospheric components (e.g., pollutants, consumables, and / or accumulations). The measured zone (e.g., room) occupancy rate can be obtained using location technologies. Location technologies can include geographical location. Location technologies can utilize one or more sensors (e.g., an IR sensor array for detecting body heat signatures, a camera for identifying people using pattern recognition techniques on acquired images, or a UWB tracking receiver for detecting user security badges) and / or use schedule information (e.g., an online calendar for booking meeting rooms). In some embodiments, occupancy rate data can be used to determine a minimum ventilation rate based on industry standards and / or other empirical relationships. While maintaining the minimum ventilation rate, one or more sensors deployed in a zone (e.g., a room) can monitor the concentration of atmospheric components (e.g., O2, CO2, VOC, moisture, and PM) that affect air quality. The atmospheric components can be measured inside and outside the enclosed area to obtain a differential concentration ΔPOL. In some embodiments, when the ΔPOL of an atmospheric component exceeds a target value (e.g., an optimal value, such as a maximum or minimum value), the ventilation rate is increased to restore the required air quality. The increase in the ventilation rate can be proportional to the difference between the actual atmospheric component concentration and the target atmospheric component concentration. The increase in the ventilation rate can be made in pre-determined steps. In some embodiments, multiple (e.g., two or more) atmospheric components within a zone (e.g., an enclosed area) can be controlled. At least two of the multiple atmospheric components can be controlled simultaneously. At least two of the multiple atmospheric components can be controlled continuously. At least one of the multiple atmospheric components can be controlled persistently. At least one of the multiple atmospheric components can be controlled intermittently. One or more of the multiple atmospheric components can be incorporated into the recommended change in the ventilation rate of a zone (e.g., an enclosed area). When formulating the recommended change in the ventilation rate of a zone (e.g., an enclosed area), the standard ventilation rate associated with one or more of the multiple atmospheric components can be considered. When considering atmospheric components (e.g., or their standard ventilation rates) when formulating any change in the ventilation rate, at least two atmospheric components can have (e.g., substantially) the same weight, or at least two atmospheric components can be given different weights. For example, the main atmospheric component to be controlled (e.g., monitored and / or adjusted) can be CO2, while VOCs (from humans or other sources, such as aldehydes in sweat, carpets / furniture, etc.) and / or other substances are monitored and can be given a smaller weight when incorporated into the recommended change in the ventilation rate.CO2 levels can be continuously monitored and can be given the maximum weight. VOC levels can be intermittently monitored and can be given a weight lower than the weight given to CO2 levels.

[0160] The determination of occupancy can be performed by sensing the number of occupants in a zone using any suitable positioning technology (e.g., using occupancy sensors). The current occupancy or future occupancy can be determined and / or predicted (e.g., at least in part based on an electronic calendar, historical data, and / or a learning module). The minimum ventilation rate can be determined at least in part based on the obtained occupancy. The occupancy can be used to look up the corresponding ventilation rate according to a lookup table and / or industry standards applied to, for example, the size and / or type of use of an enclosed area. For example, for a room of 1000 ft 2 (approx. 92.9 m 2 ) with CO2 as the control variable and a maximum occupancy of 60 people, the total gas flow rate may be as follows: Total gas flow = 7.5 x 60 + 0.06 x 1000 = 510 cfm (approx. 896 m 3 / h). In this case of maximum occupancy, the maximum atmospheric component concentration is as follows: max(ΔCO2) = 60 x 10500 / 510 = 1235 ppm. Taking the outdoor ambient CO2 concentration of 400 ppm as an example, the indoor maximum absolute concentration (C design ) is as follows: C design = ΔCO2 + C out = 1235 + 400 = 1635 ppm. In the case of a lower room occupancy (e.g., 28 people), the industry standard minimum ventilation rate is: Total gas flow = 7.5 x 28 + 0.06 x 1000 = 270 cfm. Therefore, a ventilation rate command of 270 cfm (approx. 459 m 3 / h) can be sent to the ventilation system. At this occupancy and ventilation rate, the steady-state differential concentration of CO2 is: ΔCO2 = 28 x 10500 / 270 = 1089 ppm. In cases where higher air quality (lower atmospheric component concentration) is required, an increasing ventilation rate can be requested. For example, to limit the differential ΔCO2 to a value of 800 ppm, the ventilation rate determined above for 28 people will increase according to the differential concentration rate as follows: Required gas flow = 270 x (1089 / 800) = 368 cfm. Therefore, for higher air quality, the ventilation rate will increase to 368 cfm (approx. 625 m 3 / h).

[0161] In some embodiments, the ventilation rate is controlled in response to occupancy, maximum or target atmospheric component concentration, and actual atmospheric component concentration. For example, the ventilation rate can be adjusted up or down to exchange fresh air into a zone (e.g., an enclosed area) to maintain the required atmospheric component concentration without knowing the ratio of fresh air to supplied recirculated air, without determining the value of the actual ventilation rate, and / or without measuring the actual ventilation rate. In some embodiments, occupancy is measured using at least one sensor operatively coupled (e.g., communicatively coupled) to a building network. The at least one sensor can be mounted in a sensor assembly (e.g., a networked module integrating sensors, transmitters, and / or actuators), which can include atmospheric component sensors (e.g., CO2 sensors, VOC sensors, humidity sensors, oxygen sensors, and / or PM sensors). In some embodiments, occupancy can be inferred from atmospheric component concentration measurements (e.g., using a mass balance equation), for example, if the actual fresh air ventilation rate is available. In some embodiments, the actual atmospheric component differential concentration can be estimated based on known occupancy and actual fresh air ventilation rate. The actual differential atmospheric component concentration ΔPOL can be compared to a target (e.g., maximum) ΔPOL to determine whether the current ventilation rate should be changed (e.g., increased or decreased). The change can be incremental, continuous, linear, or nonlinear (e.g., exponential). At least two increments of the incremental change can have the same duration. At least two increments of the incremental change can have different durations. At least two intervals of the incremental change can have different durations. At least two intervals of the incremental change can have the same duration. The duration and / or interval of the incremental change can follow a linear or nonlinear (e.g., exponential) function. If the measured atmospheric component ΔPOL is less than the maximum ΔPOL atmospheric component, then the ventilation gas flow rate can be decreased. The changed gas flow rate can be set to a threshold (e.g., value) expected to reach the target ΔPOL at time t (and subsequently maintain the threshold). The changed gas flow rate can deviate from the target threshold (e.g., briefly, at time <<t) to reach the target threshold faster. For example, the decreased gas flow rate can be set to a value expected to reach the target ΔPOL at t (and subsequently maintain this value). The decreased gas flow rate may decrease below the set threshold (e.g., briefly, at time <<t) to reach the target ΔPOL faster. The absolute value of the target ventilation rate required to reach and maintain the target concentration (e.g., maximum ΔCO2) can be determined at least in part based on actual occupancy and / or projected occupancy. For example, the gas flow per person can be calculated by dividing the atmospheric component production / consumption rate by the target differential concentration (e.g., 10500 / ΔCO2), and then multiplying it by the number of occupants in the zone (e.g., enclosed area) to calculate the required ventilation rate and set the demand accordingly.In some embodiments, the change in airflow demand is proportional to the difference between the current atmospheric component ΔPOL and the target ΔPOL. If the measured atmospheric component ΔPOL is greater than the optimal (e.g., maximum) atmospheric component ΔPOL, then the ventilation gas flow rate can be increased. By controlling the transitional ventilation rate, a selection time (t) required to reach a new steady state can be established, thereby generating an air (atmospheric) exchange rate (AER) for reducing ΔPOL. For example, AER can be calculated as follows: AER = [ln(C。 actual / C design )] / t. AER can be used to derive the transitional ventilation rate as follows: gas flow Vt = AER x room volume. Using a constant transitional ventilation rate can provide a linear slope for the changing differential concentration ΔPOL. In some embodiments, a variable ventilation rate is provided during a transition where the occupant is likely to be less discerning (e.g., the occupant is less distracted) to obtain an adaptive non-linear slope.

[0162] As an example of the transitional ventilation rate, assume the differential concentration ΔCO2 is 2000 ppm and the target max(ΔCO2) is 1235 ppm. The time to reach the target is 5 minutes. The required air exchange rate (using an external CO2 of 400 ppm) is as follows: AER = [ln(2400) - ln(1635)] / 5 = 0.077. The total gas flow converted to a 1000 square foot room with a 10 foot room height is: Vt = AER x room volume = 0.077 x 1000 x 10 = 770 cfm. Thus, using a ventilation rate of 770 cfm, the indoor CO2 concentration is reduced to 1635 ppm within 5 minutes. Instead of using a constant ventilation rate of 770 cfm (about 1308 m 3 / h), a variable rate can be used provided the average ventilation rate over the 5 minute period is 770 cfm.

[0163] In some embodiments, recommendations for ventilation rate changes are obtained by activating a ventilation mechanism (e.g., turning on and off an air handler). Sensors for measuring atmospheric component concentrations, room occupancy, ventilation pressure, and / or flow rate can be stand-alone. At least two sensors (e.g., at different times or of the same type) can be incorporated into a sensor set. One or more sensor sets can be placed in a room to be controlled (e.g., monitored). The sensor set can be operatively coupled (e.g., communicatively coupled and / or connection coupled) to a network. The network can operatively couple to a cooling control system and / or a BMS. The network can be operatively coupled to a ventilation system. At least a portion of the network can include wires placed within the enclosure (e.g., building) shell. The sensors can be configured for continuous or intermittent sensing. The continuous and / or intermittent sensing can be scheduled. For example, the schedule for sensing can take into account the past, present, and / or expected occupancy of the zone of interest. In some embodiments, the sensor set is installed in a window frame (e.g., in a mullion or transom). At least a portion of the devices in the set can be used to control a tintable window operatively coupled to the network (and thus to the control system). In some embodiments, the set and / or the window frame can incorporate an actuator (e.g., a fan or blower) configured to circulate the internal atmosphere and / or exchange atmosphere between the enclosed area and the external ambient atmosphere (as an exhaust and / or intake). Examples of ventilation systems, heat management system components (e.g., fans), smart windows, networks, sensors, and control systems can be found in International Patent Application Serial Number PCT / US15 / 14453, filed on February 4, 2015, titled "FORCED AIR SMART WINDOWS," which is incorporated herein by reference in its entirety.

[0164] In some embodiments, monitoring atmospheric components and ventilation rate aids in monitoring filter efficiency. Filter efficiency can deviate due to accumulated debris (e.g., a specific substance). The accumulation of debris on a filter can reduce its filtration efficiency and / or form a growth medium for pathogens. The efficiency of a filter can be determined using pressure sensors, airflow sensors, filter installation time, and / or particulate matter (PM) sensing. The air quality in an enclosed area can depend on the use of filters in an air handling system to remove various contaminants, such as particulate matter (e.g., dust, soot, viruses, bacteria, and / or fungi). Over time, the efficiency of a filter decreases as it accumulates more and more particulate matter. At least in part based on: (i) the external BCPI-250156 page 54 / 120 before filtration

[0165] Knowledge of the PM concentration and the internal PM concentration after filtration (e.g., differential concentration ΔPOL), (ii) the ventilation rate through the filter (e.g., the total volume of polluted air processed by the filter per unit time), (iii) the time elapsed since installation, (iv) the gas pressure before the filter, (v) the gas pressure after the filter, (vi) the filter morphology, (v) the optical density of the gas before the filter, (vi) the optical density of the gas after the filter, the actual filter efficiency can be determined and / or estimated. When the filtration efficiency is below a predetermined threshold of its nominal efficiency, the user (e.g., the building manager) can be notified to take corrective measures such as replacing the filter. For example, the notification can be generated as an immediately sent warning message or can be included in a regularly generated report.

[0166] The sensors in the sensor assembly can be organized into sensor modules. The sensor assembly can include a circuit board, such as a printed circuit board, to which several sensors are bonded or attached. The sensors can be removed from the sensor module. For example, the sensors can be inserted into and / or pulled out of the circuit board. The sensors can be individually activated and / or deactivated (e.g., using a switch). The circuit board can include a polymer. The circuit board can be transparent or opaque. The circuit board can include metals (e.g., elemental metals and / or metal alloys). The circuit board can include conductors. The circuit board can include insulators. The circuit board can have any geometry (e.g., rectangular or oval). The circuit board can be configured (e.g., shaped) to allow the assembly to be placed in a mullion (e.g., the mullion of a window). The circuit board can be configured (e.g., shaped) to allow the assembly to be placed in a frame (e.g., a door frame and / or a window frame). The mullion and / or the frame can include one or more holes to allow the sensors to obtain (e.g., accurate) readings. The circuit board can contain electrical connection ports (e.g., sockets). The circuit board can be connected to a power source (e.g., electricity). The power source can include a renewable power source or a non-renewable power source.

[0167] Figure 13FIG. is a schematic diagram illustrating an example of a system in which sensors of sensor assembly 1305 are organized into sensor modules. Sensors 1310A, 1310B, 1310C, and 1310D are shown as being included in sensor assembly 1305. A sensor assembly organized into sensor modules may include at least 1, 2, 4, 5, 8, 10, 20, 50, or 500 sensors. A sensor module may include a number of sensors within a range between any of the foregoing values (e.g., from about 1 to about 1000, from about 1 to about 500, from about 500 to about 1000). The sensors of a sensor module may include sensors configured or designed to sense parameters including, for example, temperature, humidity, carbon dioxide, particulate matter (e.g., from about 2.5 μm to about 10 μm), total volatile organic compounds (e.g., voltage potential changes caused by surface adsorption of volatile organic compounds), ambient light, audio noise level, pressure (e.g., of gases and / or liquids), acceleration, time, radar, lidar, radio signals (e.g., ultra-wideband radio signals), passive infrared, glass breakage, or motion. In some cases, a sensor assembly (e.g., sensor assembly 1305) may include one or more non-sensor devices such as buzzers and / or light emitting diodes. Examples of sensor assemblies and their use can be found in U.S. Patent Application Serial No. 16 / 447,169, filed June 20, 2019, titled “SENSING AND COMMUNICATIONS UNIT FOR OPTICALLY SWITCHABLE WINDOW SYSTEMS,” which is incorporated herein by reference in its entirety.

[0168] In some embodiments, increasing the number and / or type of sensors can increase the accuracy of one or more measured characteristics and / or the probability that a particular event measured by one or more sensors has occurred. In some embodiments, the sensors in a sensor assembly may cooperate with each other. In an example, a radar sensor in a sensor assembly may determine whether there are multiple individuals in an enclosed area. A processor (e.g., processor 1315) may determine that the detection of multiple individuals in the enclosed area is positively correlated with an increase in carbon dioxide concentration. In an example, a memory accessible by the processor may determine that an increase in detected infrared energy is positively correlated with an increase in temperature detected by a temperature sensor. In some embodiments, a network interface (e.g., 1350) may communicate with other sensor assemblies similar to the sensor assembly. The network interface may additionally communicate with a controller.

[0169] Each sensor in the sensor set (e.g., sensor 1310A, sensor 1310D, etc.) may include and / or utilize at least one dedicated processor. The sensor set may utilize a remote processor (e.g., 1354) using a wireless and / or wired communication link. The sensor set may utilize at least one processor (e.g., processor 1352), which may represent a cloud-based processor coupled to the sensor set via the cloud (e.g., 1350). The processors (e.g., 1352 and / or 1354) may be located in the same building, different buildings, buildings owned by the same or different entities, facilities owned by the manufacturer of the window / controller / sensor set, or any other location. In various embodiments, as indicated by the dashed line of Figure 13 , the sensor set 1305 does not need to include a separate processor and network interface. These entities may be separate entities and may be operatively coupled to the set 1305. Figure 13 The dashed line in indicates an optional feature. In some embodiments, the on-board processing and / or memory of one or more sensor sets may be used to support other functions (e.g., by allocating the memory and / or processing capabilities of the set to the network infrastructure of the building).

[0170] In some embodiments, multiple sensors of the same type may be distributed in an enclosed area. At least one of the multiple sensors of the same type may be part of a set. For example, at least two of the multiple sensors of the same type may be part of at least two sets. The sensor set may be distributed in an enclosed area. The enclosed area may include a meeting room. For example, multiple sensors of the same type may measure environmental parameters in the meeting room. In response to the measurement of the environmental parameters of the enclosed area, a parameter topology of the enclosed area may be generated. The parameter topology may be generated using the output signals from any type of sensor in the sensor set, e.g., the sensors disclosed herein. The parameter topology may be generated for any enclosed area of the facility, such as a meeting room, corridor, restroom, cafeteria, garage, auditorium, utility room, storage facility, equipment room, and / or elevator.

[0171] In certain embodiments, one or more sensors in a sensor collection provide readings. In some embodiments, the sensors are configured to sense parameters. The parameters can include temperature, particulate matter, volatile organic compounds, electromagnetic energy, pressure, acceleration, time, radar, lidar, glass breakage, motion, or gas. The gas can include noble gases. The gas can be a gas harmful to ordinary people. The gas can be a gas present in the surrounding atmosphere (e.g., oxygen, carbon dioxide, ozone, chlorinated carbon compounds, or nitrogen). The gas can include radon, carbon monoxide, hydrogen sulfide, hydrogen, oxygenated water (e.g., moisture). The electromagnetic sensors can include infrared, visible light, and ultraviolet sensors. The infrared radiation can be passive infrared radiation (e.g., blackbody radiation). The electromagnetic sensors can sense radio waves. The radio waves can include broadband or ultra-wideband radio signals. The radio waves can include pulsed radio waves. The radio waves can include radio waves for communication. The gas sensors can sense gas type, flow rate (e.g., rate and / or acceleration), pressure, and / or concentration. The readings can have an amplitude range. The readings can have a parameter range. For example, the parameter can be the electromagnetic wavelength, and the range can be the detected wavelength range.

[0172] In some embodiments, the sensor data responds to the environment in a closed area and / or any change-inducing factors in this environment (e.g., any environmental interference factors). The sensor data can respond to a transmitter operatively coupled to the closed area (e.g., being in the closed area) (e.g., an occupant, an appliance (e.g., a heater, a cooler, a ventilator, and / or a vacuum cleaner), an opening). For example, the sensor data can respond to an air-conditioning duct or an open window. The sensor data can respond to activities occurring within a room. The activities can include human activities and / or non-human activities. The activities can include electronic activities, gas activities, and / or chemical activities. The activities can include sensory activities (e.g., visual, tactile, olfactory, auditory, and / or gustatory). The activities can include electronic and / or magnetic activities. The activities can be sensed by a person. The activities can not be sensed by a person. The sensor data can respond to an occupant in the closed area, the flow rate of a substance (e.g., gas), the pressure of a substance (e.g., gas), and / or the temperature.

[0173] In some embodiments, sensor data from sensors within a closed area (e.g., and within a sensor collection) is collected and / or processed (e.g., analyzed). The data processing can be performed by a processor of a sensor, a processor of a sensor collection, another sensor, another collection, the cloud, a processor of a controller, a processor within the closed area, a processor external to the closed area, a remote processor (e.g., located in a different facility), a manufacturer (e.g., a sensor manufacturer, a window manufacturer, and / or a building network manufacturer). The sensor data can have a time indicator (e.g., can be timestamped). The sensor data can have a sensor location identifier (e.g., location-stamped). The sensors can be communicatively coupled to one or more controllers.

[0174] In some embodiments, the processed data derived from the sensors includes applying one or more models. The models can include mathematical models. The processing can include model fitting (e.g., curve fitting). The models can be multi-dimensional (e.g., two-dimensional or three-dimensional). The models can be represented as graphs (e.g., 2D graphs or 3D graphs). For example, the models can be represented as contour maps (e.g., as depicted in Figure 9 ). The models can include one or more matrices. The models can include topological models. The models can be topologically related to the parameters sensed in the closed area. The models can be topologically related to the temporal variations of the parameters sensed in the closed area. The models can be environment- and / or closed area-specific. The models can consider one or more characteristics of the closed area (e.g., dimensions, openness, and / or environmental interference factors (e.g., transmitters)). The processing of the sensor data can utilize historical sensor data and / or current (e.g., real-time) sensor data. The data processing (e.g., using the models) can be used to predict environmental changes in the closed area, and / or recommend measures to mitigate, adjust, or otherwise respond to the changes.

[0175] In some embodiments, the sensor is operatively coupled to at least one controller and / or processor. Sensor readings can be obtained by one or more processors and / or controllers. The controller can include a processing unit (e.g., CPU or GPU). The controller can receive inputs (e.g., from at least one sensor). The controller can include circuitry, wires, optical cabling, sockets, and / or outlets. The controller can deliver outputs. The controller can include multiple (e.g., sub-) controllers. The controller can be part of a control system. The control system can include a master controller, floor (e.g., including network controllers) controllers, local controllers. The local controller can be a window controller (e.g., controlling a light-switchable window), an enclosed area controller, or a component controller. For example, the controller can be part of a hierarchical control system (e.g., including a master controller indicating one or more controllers, such as floor controllers, local controllers (e.g., window controllers), enclosed area controllers, and / or component controllers). The physical location of the controller type in the hierarchical control system can be constantly changing. For example: At a first time: The first processor can act as the master controller, the second processor can act as the floor controller, and the third processor can act as the local controller. At a second time: The second processor can act as the master controller, the first processor can act as the floor controller, and the third processor can retain the local controller role. At a third time: The third processor can act as the master controller, the second processor can act as the floor controller, and the first processor can act as the local controller. The controller can control one or more devices (e.g., can be directly coupled to the device). The controller can be located near the one or more devices it controls. For example, the controller can control a light-switchable device (e.g., IGU), an antenna, a sensor, and / or an output device (e.g., a light source, a sound source, an odor source, a gas source, an HVAC outlet, or a heater). In one embodiment, the floor controller can indicate one or more window controllers, one or more enclosed area controllers, one or more component controllers, or any combination thereof. The floor controller can include one floor controller. For example, the floor (e.g., including network) controller can control multiple local (e.g., including window) controllers. The multiple local controllers can be located in a part of the facility (e.g., a part of a building). The part of the facility can be a floor of the facility. For example, the floor controller can be assigned to a floor. In some embodiments, a floor can include multiple floor controllers, e.g., depending on the floor size and / or the number of local controllers coupled to the floor controller. For example, a floor controller can be assigned to a part of a floor. For example, the floor controller can be assigned to a part of the local controllers located in the facility. For example, the floor controller can be assigned to a part of a facility floor. The master controller can be coupled to one or more floor controllers.A floor controller can be installed in a facility. A main controller can be installed in the facility or outside the facility. The main controller can be installed in the cloud. The controller can be part of a building management system or operationally coupled to a building management system. The controller can receive one or more inputs. The controller can generate one or more outputs. The controller can be a single-input single-output controller (SISO) or a multi-input multi-output controller (MIMO). The controller can interpret the received input signals. The controller can obtain data from one or more components (e.g., sensors). Obtaining can include receiving or extracting. The data can include measurement results, estimation results, determination results, generation results, or any combination thereof. The controller can include feedback control. The controller can include feedforward control. The control can include on / off control, proportional control, proportional-integral (PI) control, or proportional-integral-derivative (PID) control. The control can include open-loop control or closed-loop control. The controller can include closed-loop control. The controller can include open-loop control. The controller can include a user interface. The user interface can include (or be operationally coupled to) a keyboard, keypad, mouse, touch screen, microphone, speech recognition package, camera, imaging system, or any combination thereof. The output can include a display (e.g., a screen), a speaker, or a printer.

[0176] Figure 14 An example of a control system architecture 1400 is shown that includes a main controller 1408 that controls a floor controller 1406, which in turn controls local controllers 1404. In some embodiments, the local controllers control one or more IGUs, one or more sensors, one or more output devices (e.g., one or more transmitters), or any combination thereof. Figure 14 An example of a configuration is shown where the main controller 1408 is operationally coupled (e.g., wirelessly and / or wired) to a building management system (BMS) 1424 and a database 1420. Figure 14 The arrows in [figure] represent communication paths.

[0177] The controller can be operationally coupled (e.g., directly / indirectly and / or wired and wirelessly) to an external source (e.g., external source 1410). The external source can include a network. The external source can include one or more sensors and / or output devices. The external source can include cloud-based applications and / or databases. The communication can be wired and / or wireless. The external source can be installed outside the facility. For example, the external source can include one or more sensors and / or antennas installed on, for example, the walls or ceiling of the facility. The communication can be unidirectional or bidirectional. In Figure 14 the example shown, the communication intent of all communication arrows is bidirectional.

[0178] The controller can monitor and / or indicate (e.g., physical) changes in the operating conditions of the devices, software, and / or methods described herein. Control can include regulating, manipulating, restricting, indicating, monitoring, adjusting, modulating, varying, changing, constraining, checking, guiding, or managing. Control (e.g., by at least one controller) can include attenuating, modulating, varying, managing, suppressing, disciplining, regulating, constraining, supervising, manipulating, and / or guiding. Control can include controlling control variables (e.g., temperature, pressure, gas flow, occupancy, power, voltage, and / or current). Control can include real-time control or off-line control. Control can include on-site control. The calculations used by the controller can be performed in real-time and / or off-line. The controller can be a manual or non-manual controller. The controller can be an automatic controller. The controller can operate on request. The controller can be a programmable controller. The controller can be programmed. The controller can include a processing unit (e.g., CPU or GPU). The controller can receive inputs (e.g., from at least one sensor). The controller can deliver outputs. The controller can include multiple (e.g., sub-) controllers. The controller can be part of a control system. The control system can include a main controller, a floor controller, a local controller (e.g., an enclosed area controller or a window controller). The controller can receive one or more inputs. The controller can generate one or more outputs. The controller can be a single-input single-output controller (SISO) or a multi-input multi-output controller (MIMO). The controller can interpret the received input signals. The controller can obtain data from one or more sensors. Obtaining can include receiving or extracting. The data can include measurement results, estimation results, determination results, generation results, or any combination thereof. The controller can include feedback control. The controller can include feed-forward control. Control can include on / off control, proportional control, proportional-integral (PI) control, or proportional-integral-derivative (PID) control. Control can include open-loop control or closed-loop control. The controller can include closed-loop control. The controller can include open-loop control. The controller can include a user interface. The user interface can include (or be operatively coupled to) a keyboard, a keypad, a mouse, a touch screen, a microphone, a speech recognition package, a camera, an imaging system, or any combination thereof. The output can include a display (e.g., a screen), a speaker, or a printer. The methods, systems, and / or devices described herein can include a control system. The control system can communicate with any device (e.g., a sensor) described herein. The sensors can be of the same type or different types, as described herein. For example, the control system can communicate with a first sensor and / or a second sensor. The control system can control one or more sensors. The control system can control one or more components of a building management system (e.g., a lighting, security, and / or air conditioning system). The controller can regulate at least one (e.g., environmental) characteristic of an enclosed area. The control system can regulate the enclosed area environment using any component of the building management system.For example, the control system can adjust the energy supplied by the heating element and / or the cooling element. For example, the control system can adjust the rate of gas flowing through the vent to and / or from the enclosed area. The control system can include a processor. The processor can be a processing unit. The controller can include a processing unit. The processing unit can be a central processing unit. The processing unit can include a central processing unit (referred to herein as "CPU" for short). The processing unit can be a graphics processing unit (referred to herein as "GPU" for short). The controller or control mechanism (e.g., including a computer system) can be programmed to implement one or more methods of the present disclosure. The processor can be programmed to implement the methods of the present disclosure. The controller can control at least one component of the forming system and / or device disclosed herein.

[0179] Figure 15 Schematic example of a computing system 1500 and a network 1501 programmed or otherwise configured to perform one or more operations of any of the methods provided herein. The computing system 1500 can control (e.g., direct, monitor, and / or regulate) various features of the methods, devices, and systems described herein, such as controlling heating, cooling, lighting, and / or ventilation of an enclosed area or any combination thereof. The computing system 1500 can be part of or communicate with any of the sensors or sensor collections disclosed herein. The computing system 1500 can be coupled to one or more of the mechanisms and / or any part thereof disclosed herein. For example, the computing system 1500 can be coupled to one or more sensors, valves, switches, lights, windows (e.g., IGU), motors, pumps, optical components, or any combination thereof. The computing system 1500 can be an example of a controller (e.g., a main controller, a network controller, or a local controller).

[0180] The computing system 1500 can include one or more processing units 1506 (sometimes also referred to herein as one or more processors). The computing system 1500 can also include a memory or memory location 1502 (e.g., random access memory, read only memory, flash memory), an electronic storage unit 1504 (e.g., a hard disk), a communication interface for communicating with one or more other systems (e.g., a network adapter), and one or more peripheral devices 1505, such as a cache, other memory, data storage devices, and / or an electronic display adapter. Figure 15In the example shown, the memory 1502, storage unit 1504, interface 1503, and peripheral device 1505 communicate with the processing unit 1506 via a communication bus (represented by solid lines), such as a motherboard. The storage unit 1504 can be, for example, a data storage unit (or data repository) for storing data. The computing system 1500 can be operatively coupled to a computer network 1501 (network) via a communication interface. The network 1501 can be the Internet, an intranet, and / or an extranet, or an intranet and / or extranet that communicates with the Internet. In some cases, the network 1501 can be a telecommunications and / or data network. The network 1501 can include one or more computer servers, which can enable distributed computing, such as cloud computing. In some cases, via a computer system, the network 1501 can implement a peer-to-peer network, which can enable devices coupled to the computing system 1500 to act as clients or servers.

[0181] The processing unit 1506 can execute a sequence of machine-readable instructions, which can be embodied in a program or software. The instructions can be stored in a memory location, such as the memory 1502. The instructions can direct the processing unit 1506, which can then program or otherwise configure the processing unit to implement the methods of the present disclosure. Examples of operations performed by the processing unit 1506 can include fetching, decoding, executing, and writing back. The processing unit 1506 can interpret and / or execute instructions. The processing unit 1506 can include a microprocessor, data processor, central processing unit (CPU), graphics processing unit (GPU), system on a chip (SOC), coprocessor, network processor, application-specific integrated circuit (ASIC), application-specific instruction set processor (ASIP), controller, programmable logic device (PLD), chipset, field-programmable gate array (FPGA), or any combination thereof. The processing unit 1506 can be part of a circuit, such as an integrated circuit. One or more other components of the computing system 1500 can be included in the circuit.

[0182] The storage unit 1504 can store files, such as drivers, libraries, and saved programs. The storage unit 1504 can store user data (e.g., user preferences and user programs). In some cases, the computing system 1500 can include one or more additional data storage units external to the computing system 1500, such as data storage units located on remote servers that communicate with the computing system 1500 via an intranet or the Internet.

[0183] In some cases, the computing system 1500 can communicate with one or more remote computer systems via a network. For example, the computing system 1500 can communicate with a remote computer system of a user (e.g., an operator). Examples of remote computer systems include personal computers (e.g., portable PCs), tablets or tablet PCs (e.g., iPad, Galaxy Tab), phones, smartphones (e.g., iPhone, Android - enabled devices, ) or personal digital assistants. A user (e.g., a client) can access the computing system 1500 via the network 1501.

[0184] The methods described herein can be implemented by machine - executable code (e.g., computer processor - executable code) stored at an electronic storage location of the computing system 1500, such as code stored on the memory 1502 or the electronic storage unit 1504. The machine - executable or machine - readable code can be provided in software form. During use, the processing unit 1506 can execute the code. In some cases, the code can be retrieved from the storage unit and stored on the memory for access by the processor. In some scenarios, the electronic storage unit 1504 may not be included, and the machine - executable instructions are stored on the memory. The code can be pre - compiled and configured to be used with a machine having a processor for executing the code, or it can be compiled at runtime. The code can be provided in the form of a programming language, and this language can be chosen to enable the code to be executed in a pre - compiled or compiled manner.

[0185] In some embodiments, computing system 1500 includes at least one processor (e.g., processing unit 1506) that includes code. The code can be program instructions. The program instructions can cause the at least one processor (e.g., computer) to direct a feedforward and / or feedback control loop. In some embodiments, the program instructions cause the at least one processor to direct a closed-loop and / or open-loop control scheme. The control can be based at least in part on one or more sensor readings (e.g., sensor data). A controller can direct multiple operations. Different controllers can direct at least two operations. In some embodiments, a different controller can direct at least two of operations (a), (b), and (c). In some embodiments, multiple different controllers can direct at least two of operations (a), (b), and (c). In some embodiments, a non-transitory computer-readable medium causes a different computer to direct at least two of operations (a), (b), and (c). In some embodiments, multiple different non-transitory computer-readable media each cause a different computer to direct at least two of operations (a), (b), and (c). The controller and / or computer-readable medium can direct any device or its components disclosed herein. The controller and / or computer-readable medium can direct any operation of the methods disclosed herein.

[0186] In some embodiments, the at least one sensor is operatively coupled to a control system (e.g., a computing system). The sensor can include an optical sensor, an acoustic sensor, a vibration sensor, a chemical sensor, an electrical sensor, a magnetic sensor, a flow sensor, a motion sensor, a speed sensor, a position sensor, a pressure sensor, a force sensor, a density sensor, a distance sensor, or a proximity sensor. The sensor can include a temperature sensor, a weight sensor, a level (e.g., powder) sensor, a metering sensor, a gas sensor, or a humidity sensor. The metering sensor can include a measurement sensor (e.g., height, length, width, angle, and / or volume). The metering sensor can include a magnetic, acceleration, azimuth, or optical sensor. The sensor can transmit and / or receive sound (e.g., an echo), a magnetic signal, an electrical signal, or an electromagnetic signal. The electromagnetic signal can include a visible, infrared, ultraviolet, ultrasonic, radio wave, or microwave signal. The gas sensor can sense any gas described herein. The distance sensor can be a type of metering sensor. The distance sensor can include an optical sensor or a capacitive sensor. The temperature sensor can include a bolometer, a bimetal, a calorimeter, an exhaust gas thermometer, a flame detector, a Golay cell, a heat flux sensor, an infrared thermometer, a microbolometer, a microwave radiometer, a net radiometer, a quartz thermometer, a resistance temperature detector, a resistance thermometer, a silicon bandgap temperature sensor, a special sensor microwave / imager, a thermometer, a thermistor, a thermocouple, a thermometer (e.g., a resistance thermometer), or a pyrometer. The temperature sensor can include an optical sensor. The temperature sensor can include image processing. The temperature sensor can include a camera (e.g., an IR camera, a CCD camera). The pressure sensor can include a barometer, a pressure gauge, a pressure intensifier, a Bourdon gauge, a hot wire ionization gauge, an ionization gauge, a McLeod gauge, an oscillating U-tube, a permanent downhole pressure gauge, a pressure meter, a Pirani vacuum gauge, a pressure sensor, a pressure transducer, a tactile sensor, or a time pressure gauge. The position sensor can include an auxanometer, a capacitive displacement sensor, capacitive sensing, a free fall sensor, a gravimeter, a gyroscopic sensor, a shock sensor, an inclinometer, an integrated circuit piezoelectric sensor, a laser rangefinder, a laser surface velocimeter, a lidar, a linear encoder, a linear variable differential transformer (LVDT), a liquid capacitive inclinometer, an odometer, an optoelectronic sensor, a piezoelectric accelerometer, a rate sensor, a rotary encoder, a rotary variable differential transformer, a selsyn, a shock detector, a shock data recorder, a tilt sensor, a tachometer, an ultrasonic thickness gauge, a variable reluctance sensor, or a speed receiver.An optical sensor may include a charge-coupled device, a colorimeter, a contact image sensor, an electro-optical sensor, an infrared sensor, a kinetic inductance detector, a light-emitting diode (e.g., a light sensor), a light-addressable potentiometric sensor, a Nichols radiometer, an optical fiber sensor, an optical position sensor, a photodetector, a photodiode, a photomultiplier tube, a phototransistor, a photosensor, a photoionization detector, a photomultiplier, a photoresistor, an optical switch, a phototube, a scintillometer, a Shack-Hartmann, a single-photon avalanche diode, a superconducting nanowire single-photon detector, a transition-edge sensor, a visible-light photon counter, or a wavefront sensor. The one or more sensors may be connected to a control system (e.g., a processor, a computer).

[0187] In some embodiments, measurements from one or more sensors (e.g., including a VOC sensor) can be used to adjust the odor (e.g., odor distribution), airborne compounds, and / or gaseous compounds of an environment. In some embodiments, airborne includes air-borne. The odor, airborne compounds, and / or gaseous compounds can be desired and / or preferred. The airborne compounds can be volatile compounds. The odor can have a distribution composed of one or more chemical substances (e.g., airborne chemical substances). The odor can be user-desired and / or preferred (e.g., as disclosed herein), and / or required and / or preferred by regulatory (e.g., hygiene) standards. The measurements from the one or more sensors can be used to form a sensed distribution (e.g., a sensed map). The distribution can be a function of space and / or time. The distribution can be two-dimensional, three-dimensional, or four-dimensional. At least one distribution data can be related to (i) space (e.g., compound concentration as a function of space) and / or (ii) time (e.g., compound concentration as a function of space). When there is a deviation between the sensed chemical substance distribution and the desired distribution, the distribution in the environment can be adjusted. The adjustment can be achieved at least in part by modifying the chemical composition of the ambient atmosphere, changing the air flow, and / or changing the atmospheric temperature. For example, the adjustment can be achieved by adding (e.g., injecting) and / or dispersing one or more chemical substances into the atmosphere. For example, the adjustment can be achieved by reducing (e.g., discharging, extracting, or ejecting) one or more chemical substances from the atmosphere. The reduction can be active (e.g., suction) or passive (e.g., absorption). At least one of the adjusted chemical substances can be the same as the sensed missing chemical substance. At least one of the adjusted chemical substances can be different from the sensed missing chemical substance. When there is a deviation between the desired chemical substance distribution and the desired chemical substance distribution, an adjustment of the entry / exit of chemical substances into / from the atmosphere can be made. The adjusted chemical substances can mask the sensed chemical distribution. This masking can be relative to an average user (e.g., the average user perceives the masked odor). The user can be an occupant of the environment. The adjustment can be made for individual compounds and / or compound mixtures. These chemical substances can be chemically identifiable or part of a mixture that cannot be (e.g., fully) identified.

[0188] In some embodiments, the control system adjusts the environment at least in part based on preferences. Preferences can include the user's (e.g., personal) preferences. Preferences can include governing (e.g., hygiene) preferences, standards, and / or recommendations. The user can input environmental preferences. Environmental preferences can include types of environmental characteristics, including temperature, chemical composition of the atmosphere, gas movement rate (e.g., ventilation speed), light intensity, or noise level. Environmental preferences can include suppression of one or more environmental conditions. For example, the user's input can include: (i) liking a certain environment, (ii) disliking a certain environment, and / or (iii) preferring another specified environment. Specific environments can be listed via a menu (e.g., a drop-down menu). The user can generate a specific environment by selecting one or more types of environmental characteristics from the menu. The types of environmental characteristics can have different levels. For example, the environmental characteristic of temperature can have different temperature levels, such as about 10, 15, 20, 25, or 30. The chemical composition of the atmosphere can include different levels (e.g., indicated as a percentage or ppm) of specific chemicals (e.g., CO2, O2, or a specific VOC). The user can indicate a preference for the chemical composition of the atmosphere in an enclosed area. The preference can be disliking the current odor, liking the current odor, or preferring another odor distribution. Preferences can be recorded as user input and combined with the time of the input entry and / or the space of the user entry. The learning system can use the various preferences of the user as a function of space and / or time. The learning system can use these preferences and predict future odor predictions, for example, optionally as a function of space. The learning system can use the preferences of multiple users (e.g., a group of users) and predict future odor predictions, for example, optionally as a function of space and / or space type. Users can occupy spaces adjacent to each other (e.g., within an open space area). Users can occupy similar space types. Space types can include similar room types, such as offices, meeting rooms, lounges, cafeterias, corridors, restrooms, or elevators. Space types can be defined and / or identified, for example, in a database. Space types can be identified by the functions performed by the occupants therein (e.g., learning, lecturing and / or listening to lectures, having meetings, eating, drinking, resting, secreting (e.g., urinating), excreting (e.g., defecating), washing, and / or waiting).

[0189] In some embodiments, the control system adjusts the environment at least in part based on a learning scheme. The control system can be communicatively coupled to a network (e.g., as disclosed herein). User input can be entered into a database operatively coupled to the network. The learning system can track the user input, e.g., as a function of space and / or time. The learning system can utilize the user input as a learning set. The learning system can make predictions about future times at least in part based on the user input. The learning system can include any learning scheme (e.g., algorithm) disclosed herein. For example, the learning system can utilize an artificial intelligence scheme. In some embodiments, the control system adjusts the chemical composition of the environment at least in part based on preferences. Preferences can include the (e.g., personal) preferences of the user (e.g., occupant). Preferences can include jurisdictional (e.g., hygiene) preferences, standards, and / or recommendations. The user can enter odor preferences. Odor preferences can include suppressing the current odor in the environment. Odor preferences can include liking the current odor in the environment. Odor preferences can include indicating a required odor in the environment (e.g., a citrus odor). The control system can utilize the input from at least one chemical sensor to form the current odor distribution in the environment. The control system can analyze (e.g., compare) the current odor distribution with the required odor distribution and generate a comparison result. The odor distribution can include indications of time, space, chemical species type, and / or levels of chemical species type. The control system can include one or more controllers and / or processors. The control system can analyze the comparison result relative to a threshold (e.g., a value and / or a function). The threshold function can have time, space, and / or chemical species type. When the comparison result is greater than the threshold, the control system can adjust the odor distribution in the environment by controlling the ventilation system and / or injecting odor components (e.g., citrus odor) into the environment. The control system can utilize the learning system to predict the requirements and / or preferences of the user. The control system can automatically (e.g., without explicit user requirements) adjust one or more environmental characteristics at least in part based on the learning system (e.g., a learning module). The user can (e.g., manually) override the environmental adjustments of the control system. The user input for environmental preferences can be done using an application. The application can be operatively coupled (e.g., communicatively coupled) to a mobile device. While examples of odor adjustment are provided, any other atmospheric component and / or characteristic can be adjusted similarly.

[0190] In some embodiments, the control system regulates various aspects of the enclosed area. For example, the control system can regulate the environment of the enclosed area. The control system can anticipate the user's future environmental preferences and regulate the environment in advance (e.g., at a future time) based on these preferences. Preferred environmental characteristics can be assigned based on (i) the user or user group, (ii) time, (iii) date, and / or (iv) space. Data preferences can include seasonal preferences. Environmental characteristics can include lighting, ventilation speed, atmospheric pressure, odor, temperature, humidity, carbon dioxide, oxygen, VOC, particulate matter (e.g., dust), or color. The environmental characteristic can be a preferred color scheme or theme for the enclosed area. For example, at least a portion of the enclosed area can project a preferred theme (e.g., projected colors, pictures, or videos). For example, the user is a heart patient and prefers (e.g., requires) an oxygen level higher than the ambient oxygen level (e.g., 20%) and / or a specific humidity level (e.g., 70%). When the heart patient is in a specific enclosed area, the control system can regulate the ambient atmosphere to reach the oxygen and humidity levels (e.g., by controlling the BMS). In some embodiments, the control system can operate components based on the preferences of the user or user group. In some embodiments, the control system can adjust the environment and / or components based on hierarchical preferences.

[0191] In some embodiments, the control system considers the results of environmental conditions (e.g., based on scientific and / or research results) that affect the health, safety, and / or performance of the occupants of the enclosed area. The control system can set thresholds and / or preferred window ranges for one or more environmental characteristics of the enclosed area (e.g., the atmosphere of the enclosed area). Thresholds can include levels of atmospheric components (e.g., VOC, particulate matter, and / or gases), temperature, and the time at a specific level. The specific level can be abnormally high, abnormally low, or at an average level. For example, the controller can allow abnormally high levels of VOC and / or particulate matter for a short period of time, but not for a long period of time. If the user's preferences conflict with the health and / or safety thresholds, then the control system may automatically override the user's preferences. The health and / or safety thresholds may be at a higher hierarchical level relative to the user's preferences. This hierarchy can prioritize the preferences of the majority. For example, if two occupants in a meeting room have one preference and the preference of a third occupant conflicts with it, then the preference of these two occupants will prevail (e.g., unless there is a conflict in their health and / or safety considerations).

[0192] Figure 26An example flowchart is shown depicting the operation of a control system operatively coupled to one or more devices in an enclosed area (e.g., a facility). In block 2600, the control system identifies the identity of a user. The identity can be identified by one or more sensors (e.g., a camera) and / or identification tags (e.g., scanned or otherwise sensed by one or more sensors). In block 2601, the location of the user can optionally be tracked while the user is within the enclosed area. The user can provide input regarding any preferences. The preferences can be related to components (e.g., target devices) and / or environmental characteristics. In block 2603, a learning module can optionally track such preferences and make predictions about the user's future preferences. The user's past selective preferences may be recorded (e.g., in a database) and used as a learning set for the learning module. As the learning process progresses and more input is provided by the user, the prediction accuracy of the learning module can increase. The learning module can include any learning scheme disclosed herein (e.g., including artificial intelligence and / or machine learning). The user can override the recommendations and / or predictions made by the learning module. The user can provide manual input to the control system. In block 2602, user input is provided to the control system (whether provided directly by the user or through the predictions of the learning module). The control system can use the input to change (or indicate a change to) one or more devices in the facility to achieve the user preferences (e.g., the input). The control system may or may not use the location of the user. The location can be a past location or a current location. For example, a user can enter a workplace by scanning a tag. Scanning an identification tag (ID tag) can inform the control system of the user's identity and the user's location at the time of scanning. The user can express a preference for a particular sound level that constitutes the input. The expression of the preference can be made through manual input (including tactile, voice, and / or gesture commands). Past expressions of preferences may be recorded in a database and associated with the user. The user can enter a meeting room at a predetermined time. The sound level in the meeting room can be adjusted according to the user's preferences at the following times: (i) when a scheduled meeting starts, and / or (ii) when one or more sensors sense that there is a user in the meeting room. The sound level in the meeting room may return to a default level and / or be adjusted according to the preferences of others at the following times: when a scheduled meeting is about to end, and / or (ii) when one or more sensors sense that there is no user in the meeting room.

[0193] In some embodiments, the detection correlation of human interaction with sensor data is excluded from the data. The sensor data may need to be analyzed. For example, the sensor data may need to find a baseline of a sensed characteristic (e.g., a sensed attribute). For example, the sensor data may need to be matched with a graph of manipulated data. Data manipulation may include filtering (e.g., high-pass or low-pass filtering); finding a mean, average, or median; discretizing the data (e.g., according to a threshold). The threshold may include a threshold value or a threshold function. Figure 27A An example of the change of carbon dioxide sensor data values over time is shown in graph 2700 showing sensor data 2701. The average baseline can be matched at 2702 and 2706. The carbon dioxide data can be discretized. For example, the discretized values 2703, 2704, and 2705 represent the discretization of sensor data 2701. The discretization can be matched with the number of people and / or their behavior. For example, a first person can enter a room equipped with carbon dioxide sensors. These sensors generate data 2701. When the first person enters the room, the sensor data may rise to level 2703. When a second person enters the room, the sensor data may rise to level 2704. When the second person leaves the room, the sensor data may decrease to level 2703; finally, when the first person leaves the room, the sensor data will return to the baseline level 2706. Other sensors can be used to confirm the entry of people into the room. For example, an ID sensor or a noise sensor. Such long-term data confirmation and / or accumulation can anticipate and / or characterize the behavior in the room (e.g., or facility). Figure 27B An example of the change of noise sensor data values over time is shown in graph 2750 showing first sensor data 2751, second sensor data 2752, and third sensor data 2753, which are placed at known and different locations in a facility. Sensor data 2751 reveals a lower noise level compared to sensor data 2752 depicting a higher noise environment. Sensor data 2752 depicts a regular noise oscillation that may match motor oscillation. The noise level can be monitored to avoid situations where the noise level exceeds a threshold. This provides an opportunity to mitigate such noise conditions when they occur (e.g., regardless of complaints and / or before a complaint is made). This level of understanding can provide an opportunity to monitor mobile devices, such as using machine learning or another control scheme. For example, when the sound oscillation becomes non-repetitive, and / or exhibits another change (e.g., a change in sound level, frequency change, full width at half maximum (FWHM) change, or any combination thereof), corresponding measures (e.g., issuing a notice) can be prescribed. Such information can monitor the facility or any component of the facility (e.g., service machinery and / or production machinery).

[0194] - Digital twin

[0195] To address the issue of users controlling other settings and states of devices such as sensors at conditions and / or facilities within a building, a digital model and associated files can be associated with the facility and one or more building systems. In some embodiments, the digital model and its associated files are referred to as the "digital twin" of the facility. An example of a digital twin is a Building Information Model or "BIM". Some examples of BIM files of a BIM model include Microdesk files such as Revit files, ModelStream files, IMAGINiT files, ATG USA files, or similar facility-related digital files. The digital twin can have an associated centralized file that integrates all assets in the facility, which can assist occupants and customer support personnel responsible for the facility and / or controlling one or more devices within the facility. For example, the digital twin can be stored in a cloud network that can be accessed and / or updated by occupants and customer support personnel.

[0196] In one aspect, the digital twin can contain the location and identifiers of devices in the building and the current settings and states of the devices in the building. The digital twin can also contain user preferences, such as preferred settings for one or more environmental conditions (e.g., the amount of natural light in a certain space) and preferred settings for one or more devices (e.g., the transmittance of a tintable window). In some embodiments, the digital twin of the facility can be updated to reflect in real time or substantially in real time the state and settings of the devices at the facility, which can assist in the deployment, maintenance, and control of the devices and environmental conditions at the facility. The digital twin can also act as an interactive tool for customers to control in real time or substantially in real time the environmental conditions (e.g., the amount of natural light or heat load) in their space and to see a visual display of how their changes to the environmental conditions will adjust the settings of the devices in the building on a three-dimensional model of the building, and / or to see a visual display of how their changes will adjust the settings of the devices in the building on the three-dimensional model of the building. For example, a customer can adjust the amount of light in their space and obtain a visual display of how the transmittance of the tintable windows on two adjacent facades of the space will be adjusted to accommodate their adjustment on the 3D model.

[0197] The digital twin can facilitate the control and management of devices and building systems at different levels. In some aspects, the digital twin can be a BIM that supplements device-related information, for example, through an application (software application). For example, a customer's input (e.g., through an application) can be fed into the control of the facility using a virtual building model. The digital twin can provide a visualization verification tool before commissioning or for the purpose of post-commissioning maintenance. The digital twin can also provide a virtual reality experience of the facility (including its assets, such as devices) to users of the software application.

[0198] In some cases, a three-dimensional (3D) building model can be used to initialize a digital twin file (e.g., a BIM file) to integrate the building elements of a facility. Ground truth verification (e.g., verification from a field service engineer) can be used to verify the device data in the digital twin file. The digital twin can be initialized before commissioning the devices at the facility. In some cases, the initialized BIM file (e.g., an Autodesk Revit file) will contain the building elements of the facility but not the devices installed within the facility. The BIM file can be updated during commissioning and / or by the occupants and / or customer support staff.

[0199] During commissioning of the devices at the facility, installers can install and distinguish individual devices, e.g., by consulting external tags with inscribed serial numbers, barcodes, quick response (QR) codes, radio frequency identification (RFID), and / or other printed information. In some cases, the process of locating and recording the devices during and / or after the commissioning process can be input into the digital twin through an automated and / or manual process. In some cases, commissioning can be performed to provide or correct the assignment of window controller addresses and / or other identification information, as well as the physical location of the windows and / or window controllers in the building, for specific windows and window controllers. For example, commissioning can be used to correct issues that occur when a switchable window is installed in the wrong location or when a cable is connected to the wrong window controller. The commissioning process for a specific switchable window involves associating the identification (ID) of the window or other window-related component with the network address of its corresponding window controller. This process can (e.g., additionally) assign a building location and / or an absolute location (e.g., latitude, longitude, and / or altitude) to the window or other component.

[0200] Colorable windows (e.g., including electrochromic devices), electronic assemblies (e.g., containing various sensors, actuators, and / or communication interfaces), and / or associated controllers (e.g., master controllers, network controllers, and / or other controllers, e.g., responsible for tint decisions) may be interconnected in a hierarchical network, e.g., for the purpose of coordinated control (e.g., monitoring). For example, one or more controllers may need to utilize the network address of a window controller connected to a specific window or array of windows. For this purpose, a commissioning function may be to provide the correct assignment of window controller addresses and / or other identification information for a specific window and window controller, as well as the physical location of windows and / or window controllers in a building. Another function of commissioning may be to correct the installation of a window in the wrong location or the connection of a cable to the wrong window controller. The commissioning process for a specific window (e.g., an insulating glass unit (IGU)) may involve associating the identification (ID) of the window or other window-related components with the network address of its corresponding window controller. This process may (e.g., additionally) assign a building location and / or an absolute location (e.g., latitude, longitude, and / or altitude) to the window or other components. Some examples of digital twins are described in PCT application PCT / US2021 / 057678, filed on November 2, 2021, titled "VIRTUALLY VIEWING DEVICES IN A FACILITY", and incorporated herein by reference in its entirety.

[0201] In some aspects, a control system and / or control interface includes, or communicates with, a "digital twin" of a facility of a building, for example. For example, the digital twin may include a representative model (e.g., a two-dimensional or three-dimensional virtual representation) containing structural elements (e.g., walls and doors), building fixtures / furniture, and one or more interactive target devices (e.g., color-tunable windows, sensors, transmitters, and / or media displays). The digital twin may exist on a server that can be accessed via a graphical user interface or using a virtual reality (VR) user interface. The VR interface may include augmented reality (AR) capabilities. The digital twin can be used to monitor and maintain the building infrastructure and / or control any interactive target devices, as well as to provide interactive input from a customer (e.g., preferences for one or more environmental settings) and provide feedback to the customer.

[0202] When a new device is installed in a facility (e.g., in its room or space) and operationally coupled to a network, the new device can be detected (e.g., and incorporated into a digital twin). The detection of the new device and / or the incorporation of the new device into the digital twin can be done automatically and / or manually. For example, the detection of the new device and / or the incorporation of the new device into the digital twin can be done without (e.g., any) manual intervention. Whether present in the original design plan of the enclosed area or added later, complete and detailed information about (e.g., each) device (including any unique identification code) can be stored in the digital twin, network profile, interconnection diagram, and / or building diagram (e.g., BIM file, such as a Revit file) to facilitate monitoring, maintenance, and / or control functions.

[0203] In some embodiments, the digital twin includes a virtual three-dimensional (3D) model of the facility. The facility can include static and / or dynamic elements. For example, the static elements can include representations of the facility's structural features (e.g., fixtures), and the dynamic elements can include representations of interactive devices with controllable features. The 3D model can include visual elements. The visual elements can represent the facility's fixtures. The fixtures can include walls, floors, walls, doors, shelves, structural (e.g., walk-in) closets, fixed lighting fixtures, electrical panels, elevator shafts, or windows. The fixtures can be attached to the structure. The visual elements can represent non-fixtures. The non-fixtures can include people, chairs, movable lighting fixtures, tables, sofas, movable closets, or media projections. The non-fixtures can include movable elements. The visual elements can represent the facility's features, including floors, walls, doors, windows, furniture, appliances, people, and / or interactive devices. The digital twin can be similar to the virtual worlds used in computer games and simulations, representing the environment of the real facility. The creation of the 3D model can include the analysis of a building information modeling (BIM) model (e.g., an Autodesk Revit file), e.g., exporting representations of (e.g., basic) fixed structures and movable objects (e.g., doors, windows, and elevators). In some embodiments, the digital twin is at least partially defined by using one or more sensors (e.g., optical, acoustic, pressure, gas velocity, and / or distance measurement sensors) to determine the layout of the real facility. The sensor data can be used (e.g., alone) to simulate the environment of the enclosed area. The sensor data can be used in combination with the 3D model of the facility (e.g., BIM model) to simulate and / or control the environment of the enclosed area. The BIM model of the facility can be obtained before, during (e.g., in real time), and / or after the construction of the facility. The BIM model of the facility can be updated (e.g., manually and / or using sensor data) during the operation and / or commissioning of the facility (e.g., in real time).

[0204] In some embodiments, the dynamic elements in the digital twin include device settings. The device settings can include (e.g., existing and / or predetermined): hue values, temperature settings, and / or light switch settings. The device settings can include available operations in a media display. The available operations can include menu items or hotspots in the displayed content. The digital twin can include virtual representations of devices and / or movable objects (e.g., chairs or doors) and / or occupants (actual images from cameras or stored avatars). In some embodiments, the dynamic elements can be devices newly connected to the network, and / or devices that disappear from the network (e.g., due to failure or migration). The digital twin can exist in any circuit system (e.g., a processor) operatively coupled to the network. The circuit system in which the digital circuit system resides can be located inside a facility, outside a facility, and / or in the cloud. In some embodiments, a two-way (e.g., bidirectional) link is maintained between the digital twin and the actual circuit system. The actual circuit system can be part of a control system. The actual circuit system can be included in any other node in a main controller, network controller, floor controller, local controller, or processing system (e.g., inside or outside a facility). For example, the actual circuit system can use the two-way link to notify the digital twin of changes in dynamic and / or static elements, so that the 3D representation of the enclosed area can be updated, for example, in real time or later (e.g., at a specified time). The digital twin can use the two-way link to notify the actual circuit system of manipulation (e.g., control) actions input by the user on a mobile circuit system. The mobile circuit system can be a remote control (e.g., including a handheld indicator, manual input buttons, or a touch screen).

[0205] Figure 16 Depicts a user interface 1600 with a visual representation of an example of a digital twin 1601 that can be at least partially based on one or more BIM (e.g., Revit) files 1620. In this implementation, the digital twin 1601 includes a 3D virtual construction of a facility that can be virtually navigated using an interface device to view and interact with target devices and / or different enclosed areas (e.g., spaces within a building). For example, the user can use the interface device to select and navigate to floor 1602 of the digital twin 1601, as Figure 16As depicted. The interface device can be a mobile device (e.g., a smartphone, a laptop computer, a tablet computer, a handheld controller, etc.) or another device (e.g., a desktop computer, a wall interface device, etc.). In some embodiments, the virtual representation of the enclosed area includes a virtual augmented reality representation of a digital twin displayed on a mobile device, where the virtual augmented reality representation includes a virtual representation of at least some of the actual target devices and / or spaces in the facility. Browsing the digital twin using the mobile device can be independent of the actual location of the mobile device or can be consistent with the movement of the mobile device within the actual enclosed area represented by the digital twin. The mobile device can be operatively coupled (e.g., communicatively coupled) to a network. The mobile device can record its current position in the actual facility and the corresponding position in the digital twin, e.g., using any geolocation technology. For example, a geolocation anchor coupled to the network.

[0206] In some embodiments, a mobile device (e.g., a smartphone, a tablet computer, or a handheld controller) is used to detect commissioning data of a corresponding target device and transmit the commissioning data to the digital twin and / or the BIM system. The mobile device can include geotracking capabilities (e.g., GPS, UWB, Bluetooth, and / or dead reckoning) such that the position coordinates of the mobile device can be transmitted to the digital twin using any suitable network connection established by the user between the mobile device and the digital twin. For example, the network connection can at least partially include the transmission link used by the hierarchical controller network within the facility. The network connection can be separate (e.g., completely) from the controller network of the facility (e.g., using a wireless network such as a cellular network). The target device can be equipped with an optically recognizable ID tag (e.g., a sticker with a barcode or a quick response (QR) code). The interaction between the mobile device and the target device can be used to populate the virtual representation of the target device in the digital twin with the unique identification code and / or other information associated with the target device, where the target device is associated with the ID code (e.g., included in the ID tag).

[0207] Figure 17 An example embodiment of a control system 1700 is shown that includes a controller network 1720 for managing and controlling interactive network devices, such as, for example, one or more building systems that include, for example, one or more colorable windows. The control system 1700 includes the controller network 1720, which has one or more controllers, such as one or more of a master controller, a network controller, and a local controller that includes a processor. The structure and content of an actual, physical building 1710 are represented in a 3D model digital twin 1730 as part of a modeling and / or simulation system that is executed to manage the computing assets of the building 1710 and / or control one or more environmental conditions and other conditions of the building 1710. The computing assets can be co-located with or remote from the building 1710 and / or the controller network 1720.

[0208] In the example shown, network link 1740 connects controller network 1720 to interactive target device 1712 (e.g., a colorable window, such as an electrochromic window). Interactive target device 1712 is represented as virtual object 1732 within digital twin 1730. Network link 1740 in building 1710 connects controller network 1720 to multiple network nodes that include one or more network interactive target computing devices. In the example shown, network link 1740 connects controller network 1720 to interactive target device 1712. Interactive target device 1712 is represented as virtual object 1732 within digital twin 1730. Network link 1750 connects controller network 1720 to digital twin 1730. In the example shown, customer 1714 is shown as being located within building 1700 and communicating with a mobile device (e.g., a handheld control unit) 1716. Building 1700 includes physical space 1718 associated with customer 1714. Physical space 1718 in building 1710 is represented by virtual space 1738 in digital twin 1730. In some embodiments, a physical colorable window in physical space 1718 is represented by a virtual colorable window in digital twin 1730. Mobile device 1716 may include integrated scanning capabilities (e.g., a camera for capturing bar code or QR code images), and / or may include an identification capture device (e.g., a handheld bar code scanner that is connected to mobile device 1716, for example, via a Bluetooth link) and may also communicate therewith. ID tags may be composed of, for example, RFID, UWB, emissive, reflective, or absorptive materials for use with various scanning tools (e.g., identification capture devices). The code or printed content on the ID tag may include device type, electronic and / or material characteristics of the target device, serial number, type, component identifier, manufacturer, manufacturing date, and / or any other relevant information.

[0209] In some embodiments, the digital twin of a facility stored on a server (e.g., a server on a cloud network and / or within a facility) may be updated in real-time or near real-time to include one or more customer preferences, such as the conditions of the interior space of the facility. In some cases, the digital twin model is updated to show, in real-time or near real-time, adjustments to one or more devices (e.g., interactive target devices) within the facility. For example, the digital twin may be updated in real-time or near real-time to simulate future characteristics of the facility. For example, a customer may interactively select an interior space in the digital twin to adjust the preferred amount of light in that space. The customer's preference for the preferred amount of light is transmitted to the server in real-time or near real-time and stored in the digital twin. A visual representation of the future adjusted tint level of a virtual window that meets the preferred conditions, and / or a visual representation of the future illuminance level of the interior space in the updated digital twin, may be provided to the customer in real-time or near real-time to show the future characteristics of the facility.

[0210] Digital twins, user interfaces, debugging, networks, 3D representations of intelligent objects, buildings, and spaces, colorable window partitions and other colorable window groups, and some examples of controlling colorable windows are described in U.S. Patent Application Serial No. 17,400,596, titled "AUTOMATED COMMISSIONING OF CONTROLLERS IN A WINDOW NETWORK," filed on August 12, 2021, and incorporated herein by reference in its entirety as pages 76 / 120 of BCPI-250156.

[0211] and is incorporated herein by reference.

[0212] II. Building Wellness Index and Composite Index

[0213] - Building Wellness Index

[0214] Certain embodiments relate to systems and methods that can be used to obtain and process building data to calculate a wellness index for a building (i.e., Building Wellness Index (BWI)). The data can be or include one or more parameters related to, for example, air and water quality, occupancy, body temperature, reported illnesses, and / or previous building maintenance or cleaning. Generally, the BWI can provide an overall measure or indication of the wellness status of a building. For example, the BWI can indicate how risky it is for people to enter or stay in a building from a personal health perspective. For example, the BWI can indicate the likelihood that a building may be contaminated by a virus (e.g., coronavirus) or other pathogen. Additionally or alternatively, the BWI can also indicate the likelihood that people entering or staying in the building may be exposed to a virus or other pathogen.

[0215] In some embodiments, the BWI and / or supporting data can be obtained by one or more occupants in a building, such as tenants or other people who access or enter the building (e.g., suppliers, maintenance personnel, etc.). Such information can be obtained through software applications (e.g., installed on a user's mobile phone, personal computer, etc.), digital signage, text messages or notifications, tenant interfaces (e.g., digital twins), and / or building management tools. The availability of the building wellness index and related data can enable occupants and building staff to make data-driven decisions regarding managing employees and resources in the context of any wellness risks associated with building conditions. For example, the BWI can be used to facilitate corrective actions to improve the current BWI. For example, such measures can be or include requiring people to evacuate the building or move to a specific part of the building, requiring people to reduce their occupancy of the building, requiring the use of personal protective equipment, and / or performing maintenance or cleaning on one or more contaminated or damaged building components or areas.

[0216] In various examples, the parameters used to calculate the BWI can include data related to building conditions and / or building occupants such as tenants and / or visitors. For example, the parameters can include building occupancy data (e.g., number of occupants and / or population density of the building), occupant health report data (e.g., data indicating that one or more occupants are currently sick or recovering from a recent illness), air quality data, water quality data (e.g., data describing the water quality of a cooling tower), building cleanliness data (e.g., the length of time since the last deep clean or recent exposure to pathogens), occupant body temperature data, historical building wellness index data (e.g., the rate of change or trend of the building wellness index), or any combination thereof. Such data can be collected or obtained from one or more building managers, occupants, medical professionals, cleaning professionals, other persons, or measuring devices.

[0217] In some embodiments, the building wellness index (BWI) can be calculated by combining the values of one or more parameters (also referred to herein as "metrics"). For example, a numerical value within a range (e.g., 0 to 1 or -1 to 1) can be assigned to each parameter, which can indicate the health risk associated with the parameter. For example, if a parameter is evaluated to have a relatively high risk of being harmful to health, then the score of the parameter can be at the high end of the range (e.g., set to 0 or -1). If a parameter is evaluated to have a relatively low risk of being harmful to health, then the score of the parameter can be set to the low end of the range (e.g., set to 1). In another example, a parameter evaluated to have a high risk to health can be assigned a value at the low end of the range, while a parameter evaluated to have a low risk to health can be assigned a value at the high end of the range.

[0218] A weight can be assigned to each parameter, and the BWI can be calculated by combining the weighted parameters as follows:

[0219]

[0220] Where: P i refers to one or more parameters, W i refers to one or more corresponding weighting factors, and N is the total number of parameters and corresponding weighting factors.

[0221] Other methods of calculating the BWI are envisioned. For example, one or more machine learning models or classifiers can be trained and used to calculate the BWI. For example, one or more parameters can be provided as input to the machine learning model, and then the machine learning model can provide the BWI as output. The machine learning model can be trained using training data that includes, for example, parameters for the BWI and their corresponding values to determine building wellness issues. Additionally or alternatively, one or more functional forms can be used to combine the parameters (in addition to the linear form in Equation 1) and calculate the BWI. Such functional forms can be or include, for example, non-linear functions, exponential functions, logarithmic functions, quadratic functions, etc.

[0222] Advantageously, the systems and methods described herein can improve data processing accuracy and / or automation. Data related to the BWI can be collected from various sources, including sensors in and around the building (e.g., temperature scanners, air quality sensors, security cameras, occupancy sensors, social distancing badges, etc.), buttons, medical testing laboratories, and / or information provided by the occupants (e.g., via surveys, work orders, or self-reporting). In some cases, the systems and methods can automatically aggregate such data to calculate the BWI and take corrective actions as needed. Compared to other methods that rely on manual data collection and analysis, some of the computer-implemented systems, connected sensors, algorithms, and machine learning techniques described herein can enable a more automated approach to processing data related to building wellness, which can help ensure that building wellness-related issues are identified and addressed in an efficient and accurate manner.

[0223] Certain aspects relate to computer-implemented methods of managing building wellness. In some embodiments, the method includes the steps of: obtaining parameters of a building (e.g., an office building) having occupants (e.g., employees); processing the parameters to determine the current BWI of the building; and based on the current BWI, sending a message about the current BWI to a recipient (e.g., a building occupant), (ii) displaying the current BWI for a user, and / or determining a remedial action to improve the current building wellness index. In some variations, the parameters can include building occupancy rate data, occupant health report data, air quality data, water quality data, building cleanliness data, occupant body temperature data, historical building wellness index data, and / or any combination thereof. In these cases, the current BWI can provide an indication of the risk of exposure to pathogens (e.g., viruses) within the building.

[0224] In some embodiments, the method may use data comprising one or more of the following: (i) building occupancy data may include the number of occupants in a building and / or the population density of the building, (ii) occupant health report data may include data indicating that an occupant is currently ill or recovering from a recent illness, (iii) water quality data may include data describing the water quality of a cooling tower, (iv) building cleanliness data may include the length of time since the last deep clean or since recent exposure to a pathogen, and / or (v) historical building wellness index data, which includes at least one of the rate of change or trend of the BWI.

[0225] In some applications, displaying the current BWI may include presenting the current BWI on a user's client device, and / or determining a remedial action may include instructing people to evacuate the building, move to a specific part of the building, use personal protective equipment inside the building, and / or clean areas of the building.

[0226] Certain aspects relate to a system for managing building wellness. In some embodiments, the system includes a computer processor for performing operations. In some embodiments, the instructions stored in the computer processor include: obtaining parameters of a building (e.g., an office building) having occupants (e.g., employees); processing the parameters to determine the current BWI of the building; and based on the current BWI, sending a message containing the current BWI to a recipient (e.g., an occupant), displaying the current BWI for a user, and / or determining a remedial action to improve the current BWI. In some variations, the parameters may include building occupancy data, occupant health report data, air quality data, water quality data, building cleanliness data, occupant body temperature data, historical building wellness index data, and / or any combination thereof. The current BWI provides an indication of the risk of exposure to a pathogen (e.g., a virus) within the building.

[0227] In some embodiments, building occupancy data may include the number of occupants in a building and / or the population density of the building; occupant health report data may include data indicating that an occupant is currently ill or recovering from a recent illness; water quality data may include data describing the water quality of a cooling tower; building cleanliness data may include the length of time since the last deep clean or since recent exposure to a pathogen; historical building wellness index data may include the rate of change and / or trend of the BWI.

[0228] In some applications, displaying the current BWI may include presenting the current BWI on a user's client device, and / or determining a remedial action may include instructing people to evacuate the building, move to a specific part of the building, use personal protective equipment inside the building, and / or clean areas of the building.

[0229] Some aspects relate to a non - transitory computer - readable medium having instructions stored thereon, which when executed by a computer processor cause the computer processor to perform operations. In some embodiments, the stored instructions include: obtaining parameters of a building (e.g., an office building) having an occupant (e.g., an employee); processing the parameters to determine the current Building Wellness Index (BWI) of the building; and based on the current BWI, sending a message containing the current BWI to a recipient (e.g., the occupant), displaying the current BWI for a user, or determining a remediation measure to improve the current BWI. In some variations, the parameters can include building occupancy data, occupant health report data, air quality data, water quality data, building cleanliness data, occupant body temperature data, historical building wellness index data, and / or any combination thereof. The current BWI provides an indication of the risk of exposure to pathogens (e.g., viruses) within the building.

[0230] Advantageously, the systems, methods, and devices of certain embodiments described herein can be constructed and arranged to calculate a Building Wellness Index (BWI) and classify the BWI score into one of multiple wellness levels. For example, the BWI score can be assigned one of four wellness levels: "Good", "Medium", "Use with Caution", and "Alert". One of ordinary skill in the art will understand that the number and names of the wellness levels may vary depending on the implementation, and the following description is intended to guide and illustrate an example of the BWI scoring technique. In calculating the BWI, assumptions and considerations can include a desire to avoid (i) recommendations that violate any lease and (ii) claims that may directly impact personal and / or individual health decisions. Additionally, the calculated BWI can: (i) be at least partially based on government guidelines when determining any occupancy thresholds; and (ii) be at least partially based on established (e.g., ASHRAE, CDC, EPA, etc.) baselines for any health and wellness - related thresholds. Further, while all data may be available for calculating the BWI, not all data will be incorporated into the risk - level calculation.

[0231] For example, when the data used to calculate the BWI reaches the "Good" level, it can be considered that there is no increased health risk for the occupants (e.g., tenants, building employees, building visitors, etc.); thus, the occupants can freely enter the building and work freely within the building without the need to wear additional personal protective equipment (PPE), maintain social distancing, or other restrictive practices. Alternatively, when the data used to calculate the BWI reaches the "Alert" level, the conditions within the building may be considered life-threatening, or in an alternative scenario, the building may need to comply with the government's "Shelter in Place" order. Under the "Alert" level conditions, potential occupants (e.g., tenants, building employees, building visitors, etc.) may avoid coming to the office building and work from home. The intermediate alert levels ("Moderate" and "Cautionary Use") are between the ideal "Good" conditions and the high-risk "Alert" conditions. Thus, the "Moderate" and "Cautionary Use" levels reflect a reduction in life-threatening conditions and / or government restrictions, resulting in a corresponding reduction in the usage restrictions and safety measures required by the occupants.

[0232] When calculating the BWI level, direct, indirect, and other parameters (also referred to herein as metrics) can be considered. As previously mentioned, although all data can be used to calculate the BWI, not all data will be incorporated into the BWI calculation and risk level assignment. Direct parameters can include direct indicators of potential risk, e.g., due to a pathogen (e.g., a virus such as COVID-19), and are the main factors for risk level escalation. Indirect parameters can indicate the overall risk trend or otherwise contribute to a possible risk, e.g., due to a pathogen (e.g., a virus such as COVID-19), and are secondary factors for risk level escalation. Other parameters that are neither direct nor indirect may be unrelated to the risks that a pathogen (e.g., a virus such as COVID-19) may pose; however, they are important for the overall health and well-being of the building occupants.

[0233] Table I provides exemplary risk rate criteria for each of the four well-being levels of "Good", "Moderate", "Cautionary Use", and "Alert" using direct parameters. In some embodiments, these direct parameters can include, for example, one or more of historical risk, building density, pathogen (e.g., COVID) spatial detection, reported pathogen (e.g., COVID) cases, or elevated employee body temperature.

[0234] Table I. Level Criteria (Direct Parameters)

[0235]

[0236]

[0237] Table II provides exemplary risk rate criteria for four BWI levels of "Good", "Medium", "Use with Caution", and "Alert" resulting from other parameters. In some embodiments, these parameters may include, for example, one or more of carbon monoxide (CO) level, particulate matter (PM10) level, ozone level, volatile organic compound (VOC) level, formaldehyde level, or Legionella level (for buildings that draw domestic drinking water from cooling towers). Unlike the parameters that directly affect the building itself, the other parameters in Table II are more related to the environment and how the environment affects humans.

[0238] Table II. Level Criteria (Other Parameters)

[0239]

[0240]

[0241] Table III provides exemplary risk rate criteria for each of the four levels of "Good", "Medium", "Use with Caution", and "Alert" resulting from indirect factors. These indirect factors may include, for example, one or more of carbon dioxide (CO2) level, humidity, particulate matter (PM2-5) level, or employee absenteeism. The indirect factors in Table III are related to the environment and how the environment affects humans.

[0242] Table III. Hierarchy Criteria (Indirect Parameters)

[0243]

[0244] B. Examples of System Architectures and Computing Systems

[0245] Figure 18 is a schematic diagram depicting an exemplary architecture of a system 1800 for managing building health and / or one or more building systems. The system 1800 includes a plurality of sensors 1820 (e.g., a set of sensors) and / or one or more data collection devices 1830, and a computing system 1890. The computing system 1890 is configured to calculate a building health index and / or one or more composite indices, in particular using sensor data from the plurality of sensors 1820 and / or other data from the data collection devices 1830. The data collection device can collect and process data, including, for example, sensor data from the plurality of sensors 1820, data from one or more surveys, questionnaires, or work orders, data from user input by an operator (e.g., an occupant), and so on.

[0246] System 1800 also includes a communication network 1840 that enables signals and data (e.g., communication and / or power) to be transferred between the computer-based system 1890 and the sensors 1820 and data collection devices 1830, so that the data collected by the multiple sensors 1820 and data collection devices 1830 can be used to evaluate what is happening, what might happen, and / or predict what is likely to happen within a building (e.g., an office building), in order to evaluate contribution metrics and calculate a business health index and / or one or more composite business health indices. Additionally, the evaluation of data and insights can be used to determine one or more remedial, preventive, and / or other actions that can be taken to improve the quality of life of one or more occupants within the building. In some embodiments, such actions can be communicated (e.g., via email, text message, phone call, digital twin, etc.) to the occupants and / or other individuals or building departments that may be responsible for implementing the one or more actions.

[0247] In some embodiments, the computing system 1890 includes stored instructions for performing one or more operations, which may include, for example, obtaining and processing data that can be used to determine the value of a contribution parameter for an enclosed area (e.g., a building, a room within a building such as an office, etc.) having one or more occupants (e.g., tenants, employees, and / or visitors).

[0248] For purposes of illustration and not limitation, some examples of data used to determine parameter values can include: building occupancy data, occupant health report data, air quality data, water quality data, building cleanliness data, occupant body temperature data, historical building health index data, and / or any combination thereof. Building occupancy data can include, for example, the current number of occupants in a building or an enclosed area of a space within the building. The current number of occupants can be an assessment, for example, on a floor-by-floor and room-by-room basis, and / or the population density of the building. Occupant health report data can include, for example, data indicating that one or more occupants may currently be ill or are recovering from a recent illness. Water quality data can include, for example, data describing the water quality of a cooling tower. Building cleanliness data can include, for example, the duration since the last cleaning or recent exposure to pathogens. Historical building health index data can include, for example, the rate of change of the building health index and / or the trend of the building health index.

[0249] The computing system 1890 may also include stored instructions for calculating parameter scores, determining their weighting factors, and calculating a current Building Wellness Index (BWI) for a building and / or one or more composite indices for a space within the building. In some cases, the calculated BWI or composite index may indicate a potential risk for an occupant, such as exposure to a pathogen (e.g., a virus, COVID-19, etc.). In one implementation, if the calculated BWI or composite index indicates a potential risk based on the calculated BWI or composite wellness index, a message or other notification may be sent to the occupant and / or an individual designated or capable of taking certain actions to address the potential risk and / or improve the current BWI or composite index. For example, a message or other notification with the current BWI or current composite index may be sent to the current occupant and / or one or more other individuals to display the current BWI or composite index to the user and / or to determine a remedial action to improve the current BWI or composite index. For example, the current BWI and / or current composite index may be displayed on a digital twin of the building or other user interface. The current BWI or composite index may provide an indication of potential or actual risk, such as the risk of exposure to a pathogen (e.g., a virus) within the building. In one example, displaying the current BWI or composite index may include presenting the current BWI or composite index on the user's client device, while determining the one or more remedial actions may include, for example, instructing the occupant to evacuate the building, move to a specific part of the building, use personal protective equipment within the building, and / or clean one or more areas of the building or otherwise perform one or more remedial actions.

[0250] In some cases, for the purpose of determining building occupancy (e.g., floor-by-floor and / or room-by-room occupancy), building density, building footfall, tenant usage, and tenant engagement, one or more sensors 1820 and / or data collection devices 1830 may include: one or more threshold counters (e.g., counters located at the entrances and exits of a building or other enclosed area) for counting and recording the number of building occupants (e.g., tenants and / or visitors) entering and leaving the enclosed area (e.g., a building, an office, or other space within the building); closed-circuit television (CCTV) for identifying discrete building occupants entering / leaving the enclosed area; and / or personal passes that may be automatically or manually scanned when an occupant (e.g., a tenant or visitor) enters / leaves the enclosed area. For example, in addition to predicting future occupancy, determining footfall trends, and managing elevator queues, this data may be particularly useful for ensuring that the number of people within the enclosed area does not exceed government (e.g., health and safety) guidelines and / or protocols.

[0251] In some cases, data collected by one or more sensors 1820 and / or data collection devices 1830 can be used to estimate optimal cleaning schedules and staffing in order to adjust the operations of cleaners and janitorial staff. For example, based on the "Caution", "Medium", and / or "Good" levels, the operations of cleaners and custodians can be adjusted to continuously clean all high-touch areas and high-touch points, such as building lobbies and common areas. For purposes of illustration and not limitation, high-touch points can include, for example, door handles, revolving doors, lobby service desks, elevator buttons, sneeze guards, revolving doors, etc. Additionally, when the calculated BWI or composite index indicates a high risk to public health and safety (e.g., the "Caution" level), cleaners and custodians can be instructed to perform additional cleaning in accordance with CDC guidelines, with a focus on the occupant traffic routes and common areas. The sensors 1820 and data collection devices 1830 installed in building restrooms can also include buttons to facilitate facility users indicating facility usage, so that restroom supplies replenishment and regular cleaning can be adjusted based on such usage. The sensors 1820 and data collection devices 1830 can also be installed in other building amenity centers (e.g., snack bars, cafeterias, etc.) to provide data on amenity usage, based on which cleaning schedules can be developed.

[0252] To determine the office space requirements and trends available for evaluating metrics, the sensors 1820 and data collection devices 1830 can include one or more occupancy sensors (e.g., floors and / or rooms). The occupancy sensors can be used to obtain sensor data that can be used to determine office assignments and meeting space requirements and utilization, employee space requirements, employee work habits, team collaboration, employee interactions, etc. Sensor data from amenity occupancy sensors can be used to evaluate amenity requirements and utilization.

[0253] To evaluate metrics associated with building and / or occupant health and / or occupant comfort, the sensors 1820 and data collection devices 1830 can include one or more air quality (AQ) sensors (e.g., indoor and / or outdoor), one or more temperature sensors (e.g., non-invasive body temperature elevation sensors), etc. The AQ sensors can include one or more humidity sensors, which can be particularly useful for maintaining humidity levels within enclosed areas (e.g., buildings), thereby, for example, inhibiting the spread of pathogens. For example, non-invasive high-density body temperature scanners can be installed in building lobbies and all occupants (e.g., tenants and / or visitors) can be required to pass through the scanners for measurement. In addition to being able to identify individual occupants who may pose a risk to the health of other building occupants, such data can also be used, for example, to determine when to replace and / or recalibrate the AQ sensors, when to mitigate AQ events, etc. As another example, social distancing badges and / or sensors can also be used to track contacts and / or distances between occupants.

[0254] In some embodiments, a building may be provided with medical screening or care facilities and / or medical testing laboratories. In addition to wearing masks in all public areas in accordance with CDC guidelines, occupants (tenants and / or visitors) may also be required to fill out a building access questionnaire (e.g., COVID-related) before entering the building (e.g., medical screening or care facilities). Public areas may include, for example, lobbies, elevators, stairwells, restrooms, convenience centers, etc. Medical screening or care facilities may also provide masks, protective gloves, hand sanitizer, etc. In some cases, such as in medical screening or care facilities and / or medical testing laboratories, pathogen (e.g., COVID) testing and / or vaccination may be performed.

[0255] In some embodiments, a tenant application may be provided for occupants. In some embodiments, the tenant application is a mobile application (“mobile app”) used by building occupants to perform daily activities, including completing health certificates. The tenant application also provides a mechanism for posting questionnaires to occupants to obtain their feedback on the conditions within the building (e.g., overall cleanliness). Data from the tenant application, as well as health certificates, surveys, or other employee activities, can be used as inputs to the methods described herein.

[0256] In some cases, publicly available data may also be used to determine the health status of a building. Some examples of publicly available data include: current data on the local hospitalization rate for influenza-like symptoms, COVID cases in a geographic area, and / or public transportation data that can be used to determine local levels of crowding and density.

[0257] Sensors 1820 and data collection devices 1830 may also include one or more sensors that are included in building systems, such as for monitoring aspects of a building or other enclosed area. These building system sensors 1820 and data collection devices 1830 include utility meters, water temperature sensors, air temperature sensors, furnace or boiler sensors, building work orders, etc. Data from these building system sensors can be used to evaluate energy usage and energy costs, and thus monitor the energy performance of the enclosed area. Such data can also be used to provide indications of the preventive maintenance needs of the general building and / or building plant, which can be addressed, for example, before an emergency repair. Additionally or alternatively, sensors 1820 and data collection devices 1830 may include one or more sensor assemblies, each having a different set of sensors or similar sensors.

[0258] Figure 19FIG. 1900 is a block diagram of an exemplary system 1900 showing a computing system 1990 that can be used to implement certain aspects of the techniques described herein. A general-purpose computer, a network device, a mobile device, or other electronic systems can also include at least portions of the computing system 1990. In some embodiments, the computing system 1990 can include a processor 1991, a memory 1992, a storage device 1993, and an input / output device 1994. Each of these components 1991, 1992, 1993, and 1994 can be interconnected, for example, using a system bus 1995.

[0259] Advantageously, the processor 1991 (e.g., single-threaded or multi-threaded) is capable of processing instructions for execution within the computing system 1990. In some variations, these instructions can be stored in the memory 1992 and / or on the storage device 1993.

[0260] The memory 1992 stores information within the computing system 1990. In some embodiments, the memory 1992 can be a non-transitory computer-readable medium. In some embodiments, the memory 1992 can be a volatile memory unit. In some embodiments, BCPI-250156 page 87 / 120

[0261] the memory 1992 can be a non-volatile memory unit.

[0262] The storage device 1993 is capable of providing large-capacity (e.g., data) storage for the computing system 1990. In some embodiments, the storage device 1993 can be a non-transitory computer-readable medium. In various different embodiments, the storage device 1993 can include, for example, a hard disk device, an optical disk device, a solid-state drive, a flash drive, and / or some other large-capacity storage device. For example, the storage device 1993 can store long-term data (e.g., database data, file system data, etc.).

[0263] In some embodiments, the input / output device 1994 performs input / output operations of the computing system 1990. For example, in some embodiments, the input / output device 1994 can include one or more of the following: a network interface device (e.g., an Ethernet card), a serial communication device (e.g., an RS-232 port), a wireless interface device (e.g., an 802.11 card), a 3G wireless modem, and / or a 4G wireless modem. In some embodiments, the input / output device 1994 can include a drive device that is configured to receive input data and send output data to other input / output devices 1996, such as a keyboard, a printer, and a display device. In some examples, mobile computing devices, mobile communication devices, and other devices can be used.

[0264] In some embodiments, at least a portion of the methods described above can be implemented by instructions that, when executed, cause one or more processing devices to perform the processes and functions described above. Such instructions can include, for example, interpreted instructions (such as script instructions) or executable code, or other instructions stored on a non-transitory computer-readable medium. The storage device 1993 can be implemented distributively over a network, such as as a server cluster or a set of widely distributed servers, or can be implemented in a single computing device.

[0265] Although the exemplary processing system 1990 has been described above, embodiments of the subject matter, functional operations, and processes described in this specification can be implemented in other types of digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware (including the structures disclosed in this specification and their structural equivalents), or combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non-transitory program carrier for execution by, or to control the operation of, a data processing apparatus. Alternatively or additionally, the program instructions can be encoded on an artificially generated propagated signal, such as a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to a suitable receiving device for execution by the data processing apparatus. A computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of the foregoing. Figure 19

[0266] C. Composite Index

[0267] Certain embodiments relate to methods and systems that can dynamically evaluate the safety, health, and / or operational efficiency of an enclosed area (e.g., a building or a space within a building) based on, for example, data from environmental sensors, occupancy counters, building access data, surveys, work orders, and / or utility monitoring. In certain embodiments, one or more composite indices of the enclosed area can be calculated. For example, three composite indices can be calculated: a health index, a safety index, and a performance index. The safety index can measure whether the enclosed area is safe for its current occupants, avoiding hazards such as physical hazards, security risks, and / or the spread of infectious diseases. The health index can measure whether the enclosed area space is generally healthy, comfortable, and efficient for its current occupants. The performance index can measure whether the enclosed area is operating sustainably from the perspective of, for example, energy, water, and waste, and / or whether one or more building systems at the location of the enclosed area are operating properly.

[0268] ​Each composite index can be associated with one or more subgroups of contributing metrics. The scoring of each subgroup of contributing metrics can be referred to as a subgroup index or subgroup score. The composite index can be calculated by combining the weighted scores of the subgroups as follows:

[0269]

[0270] where m (e.g., 1, 2, 3, etc.) refers to the number of subgroups associated with the composite index, GI j refers to the subgroup index, and GW j refers to the subgroup weighting factor corresponding to the subgroup index GI j .

[0271] In some embodiments, multiple composite indices can be calculated according to the following formula:

[0272]

[0273] where i = 1 to p, p (e.g., 2, 3, etc.) refers to the number of composite indices calculated, m (e.g., 1, 2, 3, etc.) refers to the number of subgroups of contributing metrics associated with the composite index, GI i,j refers to the jth subgroup index associated with the ith composite index, and GW i,j refers to the subgroup weighting factor.

[0274] Each subgroup index can be calculated by combining the weighted scores of the contributing metrics associated with the subgroup as follows:

[0275]

[0276] where n (e.g., 1, 2, 3, etc.) refers to the number of contributing metrics in the subgroup, j refers to the subgroup, k refers to the composite index, and P i,j,k refers to the kth contributing metric associated with the jth subgroup and the ith composite index, and W i,j,k refers to the corresponding weighting factor of the contributing metric (sometimes referred to as "sub-weight" herein).

[0277] In one embodiment, three composite indices for a closed area can be calculated according to the following equations: a safety index, a health index, and a performance index:

[0278]

[0279] where: j = 1 to q, and q (e.g., 1, 2, 3, etc.) refers to the number of subgroups of contributing metrics associated with the composite safety index.

[0280]

[0281] where: j = 1 to r, and r (e.g., 1, 2, 3, etc.) refers to the number of subgroups of contribution indicators associated with the comprehensive health index.

[0282]

[0283] where: j = 1 to s, and s (e.g., 1, 2, 3, etc.) refers to the number of subgroups of contribution indicators associated with the comprehensive performance index.

[0284] Each subgroup index (GW 1,j ) of the safety index in Equation 5a can be calculated as follows:

[0285]

[0286] where k = 1 to n, n (e.g., 1, 2, 3, etc.) refers to the number of contribution indicators in the jth subgroup, P 1,j,k refers to the kth contribution indicator associated with the jth subgroup of the safety index, and W 1,j,k refers to the corresponding weighting factor of the contribution indicator.

[0287] Each subgroup index (GW 2,j ) of the health index in Equation 5b can be calculated as follows:

[0288]

[0289] where k = 1 to n, n (e.g., 1, 2, 3, etc.) refers to the number of contribution indicators in the jth subgroup, P 2,j,k refers to the kth contribution indicator associated with the jth subgroup of the health index, and W 2,j,k refers to the corresponding weighting factor of the contribution indicator.

[0290] Each subgroup index (GW 3,j ) of the performance index in Equation 5b can be calculated as follows:

[0291]

[0292] where k = 1 to n, n (e.g., 1, 2, 3, etc.) refers to the number of contribution indicators in the jth subgroup, P 3,j,k refers to the kth contribution indicator associated with the jth subgroup of the performance index, and W 3,j,k refers to the corresponding weighting factor of the contribution indicator.

[0293] The composite index can be based on data of contribution metrics from various data sources, including, for example, sensor data from one or more sensors (e.g., a set of sensors), data from one or more surveys, questionnaires, and / or work orders, utility data, public data, and / or user input. In some cases, data from different sources can be used to determine values for scoring the contribution metrics. For example, the filtration efficiency metric can be scored based on pressure sensor data, airflow sensor data, filter installation time in building data, and / or particulate matter (PM) sensor data. Generally, if scores of multiple parameters are used to calculate a subgroup index or a composite index, the weighting factors applied to these scores total 100%. For example, the weighting factors (also referred to herein as sub-weights) applied to the contribution metric scores of a subgroup total 100%, and / or the weighting factors applied to the subgroup scores of the composite index total 100%. For example, a safety index can be associated with a virus risk subgroup, a safety subgroup, and a safety hazard subgroup, and the subgroup weighting factor applied to the virus risk subgroup can be 35%, the weighting factor applied to the safety subgroup can be 35%, and the weighting factor applied to the safety hazard subgroup can be 30%, such that the subgroup weighting factors total 100%.

[0294] Table IV is an example of criteria that can be used to calculate one or more composite indexes, such as a health index, a safety index, or a performance index for an enclosed area. Equations 5a - 5c and 6a - 6c or equations 3 and 4 can be used to calculate a first composite index CI1, a second composite index CI2, and a third composite index CI3; in some embodiments, these indexes can be a health index, a safety index, or a performance index, respectively. In Table IV, the subgroups associated with the first composite index CI1 include the GI 1,1 subgroup, the GI 1,2 subgroup, and the GI 1,3 subgroup. The subgroups associated with the second composite index CI2 include the GI 2,1 subgroup, the GI 2,2 subgroup, the GI 2,3 subgroup, the GI 2,4 subgroup, and the GI 2,5 subgroup. The subgroups associated with the third composite index CI3 include the GI 3,1 subgroup, the GI 3,2 subgroup, and the GI 3,3 subgroup. Some examples of subgroups used to calculate the composite index include a virus risk subgroup, a safety subgroup, a safety hazard subgroup, a ventilation subgroup, a filtration subgroup, a thermal comfort subgroup, an environmental satisfaction subgroup, a complaint subgroup, a utility subgroup, a system operation subgroup, and an occupancy subgroup.

[0295] Some examples of contribution metrics in the virus risk subgroup can include air exchange rate (AER), current carbon dioxide (CO2) level, filtration efficiency, relative humidity (RH) level, and / or occupancy data (e.g., occupancy capacity limit). Examples of contribution metrics in the safety subgroup can include building access card usage / issuance ratio. Some examples of contribution metrics in the safety hazard subgroup can include carbon monoxide (CO) level and / or volatile organic compound (VOC) level. Some examples of contribution metrics in the ventilation subgroup can include air exchange rate (AER), current carbon dioxide (CO2) level, ozone level, formaldehyde level, and / or total volatile organic compound (TVOC) level. Some examples of contribution metrics in the filtration subgroup can include filtration efficiency, the perceivedness of particulate matter of about 2.5 μm (PM 2.5 ), and / or the perceivedness of particulate matter of about 10 μm (PM 10 ). Some examples of metrics in the thermal comfort subgroup can include ASHRAE thermal comfort, temperature, and / or relative humidity level. Some examples of contribution metrics in the environmental satisfaction subgroup can include the occupant survey response rate (%) and / or the occupant survey response rate (%). Some examples of contribution metrics in the complaints subgroup can include the number of environmental complaints from occupants. Some examples of contribution metrics in the utilities subgroup can include energy consumption intensity, water, steam, and / or waste. Some examples of contribution metrics in the system operation subgroup can include window (e.g., tintable window) operation and / or the on-time operation of the occupancy sensor. Some examples of contribution metrics in the occupancy subgroup can include dwell time, percentage of room / floor capacity, and / or percentage of building capacity. In other embodiments, different criteria can be used to calculate one or more composite indices. For example, in other embodiments, fewer, more, and / or different subgroups can be incorporated into the calculation of the composite index. Additionally or alternatively, in other embodiments, the subgroups can include fewer, more, or different contribution metrics.

[0296] Table IV. Example Criteria for Calculating Health, Safety, and Performance Indices

[0297]

[0298] In some embodiments, a safety index can be calculated by combining weighted scores of subgroup indices associated with the safety index. Each weighted score of a subgroup index is a combination of weighted scores of contributing metrics. Some examples of contributing metrics that can be used to determine the safety index include environmental sensor data, air exchange rate, current carbon dioxide (CO2) level, filtration efficiency, relative humidity (RH) level, occupancy data such as occupancy capacity limits, and building access card usage to access control issuance ratio. Some examples of subgroups that can affect the safety index include an infectious disease transmissibility (sometimes also referred to herein as "virus risk") subgroup, a safety subgroup, a ventilation subgroup, and / or a safety hazard subgroup. Building managers, facility managers, and / or tenants can use these safety index scores to take corrective actions to improve the safety of their buildings or to compare their buildings to each other. Some examples of contributing metrics in the infectious disease transmissibility subgroup include air exchange (AER) rate, current carbon dioxide (CO2) level, filtration efficiency, relative humidity (RH) level, and / or occupancy data (e.g., occupancy capacity limits). An example of a contributing metric that can be included in the safety subgroup is the ratio of the number of building access credentials used during a certain period (e.g., last month) to the total number of building access credentials issued. Some examples of contributing metrics that can be included in the safety hazard subgroup include carbon monoxide (CO) level and volatile organic compound (VOC) level.

[0299] One or more safety-related metrics can affect the safety subgroup. An example of a safety-related contributing metric is the ratio of the number of building access credentials used during a certain period (e.g., a week, a month, etc.) to the total number of building access credentials issued. A security system (e.g., Figure 10 security system 1022 in) can include, for example, magnetic card access control, turnstiles, electromagnetic drive door locks, cameras (e.g., surveillance cameras), alarms (e.g., burglar alarms), and / or metal detectors, which can be used to determine the number of building access credentials used during a certain period. In some cases, one or more sensors placed in an enclosed area can be configured to measure safety-related parameters such as breakage (e.g., glass breakage) and / or the presence of unauthorized personnel. The sensors can cooperate with one or more devices (e.g., active devices), such as radar or lidar. The devices can be used to detect the physical dimensions of the enclosed area, the presence of people in the enclosed area, stationary objects in the enclosed area, and / or moving objects in the enclosed area.

[0300] The air exchange rate (AER) (sometimes also referred to herein as the atmospheric exchange rate) can be an indicator of an increase in virus risk, ventilation risk, etc. For example, AER can be calculated as follows: AER = [ln(C actual / C design / t, where C actual is the measured indoor carbon dioxide (CO2) concentration, Cdesign refers to the maximum absolute indoor carbon dioxide (CO2) concentration. C design can be calculated as follows: C design = ΔCO2 + C out , where C out refers to the ambient external atmospheric component concentration, and ΔCO2 refers to the CO2 steady-state differential concentration. In one embodiment, the AER can be determined by isolating a time period during which the carbon dioxide (CO2) concentration decay differential is linear. The AER can be calculated as the slope of the linear best-fit line of the change in the carbon dioxide (CO2) concentration level differential over time. The carbon dioxide (CO2) level decay can be linear, for example, during lunchtime or after 5 pm.

[0301] In some cases, one or more gas sensors can be placed within or at a closed area to provide sensor data (readings / measurements) of the concentration levels of one or more gases in the atmosphere around the closed area. Some examples of the concentration level sensor data that can be obtained by one or more gas sensors within the closed area include carbon dioxide (CO2) levels, carbon monoxide (CO) levels, ozone levels, hydrogen sulfide levels, hydrogen levels, oxygen levels, formaldehyde levels, and / or relative humidity (RH) levels.

[0302] In some embodiments, one or more sensors placed within or at a closed area are VOC sensors. The VOC sensors can be specific to a VOC compound or a class of compounds (e.g., having similar chemical characteristics). For example, the VOC sensors can be sensitive to aldehydes, esters, thiophenes, alcohols, aromatic hydrocarbons (e.g., benzene and / or toluene), or olefins. In some examples, a set of sensors (e.g., a sensor array) can sense a set of VOCs having different chemical characteristics. This set of VOCs can include identified or unidentified compounds. The VOC sensor or the set of sensors can output sensed values of specific compounds, compound classes, or compound groups. The sensor output can be the total measured value (e.g., the cumulative measured value) of the sensed compound class or compound group. The sensor output can be (i) a single compound, (ii) a certain class of compounds, or (iii) the total measured value (e.g., the cumulative measured value) of multiple sensor outputs of a certain group of compounds. The one or more VOC sensors can output VOC levels or total VOC (also referred to herein as TVOC) levels.

[0303] The filtration efficiency can be determined using data from pressure sensors, airflow sensors, filter installation time data, and / or particulate matter (PM) sensing data. The air quality within an enclosed area can depend on the use of filters in an air treatment system for removing various pollutants, such as particulate matter (e.g., dust, soot, viruses, bacteria, and / or fungi). In some cases, a particulate sensor can sense particulate matter of a specific size, e.g., particulate matter of about 1 μm (PM1), about 2.5 μm (PM 2.5 ), particulate matter of about 5 μm (PM5), particulate matter of about 10 μm (PM 10 ), or particulate matter of about 20 μm (PM 20 ). In some cases, a particulate sensor can sense particulate matter within a size range, e.g., between about 1 μm (PM1) and about 20 μm (PM 20 ), between about 1 μm (PM1) and about 5 μm (PM5), between about 2.5 μm (PM 2.5 ) and about 10 μm (PM 10 ), or between about 5 μm (PM5) and about 20 μm (PM 20 ). In one embodiment, the filtration efficiency is determined at least in part based on the ratio of the indoor PM concentration to the outdoor PM concentration. In one embodiment, the filtration efficiency is determined or estimated at least in part based on the external sensing of particulate matter before filtration (e.g., particulate matter of about 2.5 μm (PM 2.5 )) and the internal sensing of particulate matter after filtration (e.g., PM 2.5 ). In other embodiments, the following can be used to determine the filter efficiency: (i) the ventilation rate through the filter (e.g., the total volume of polluted air processed by the filter per unit time), (ii) the time elapsed since installation, (iii) the gas pressure before filtration, (iv) the gas pressure after filtration, (v) the filter morphology, (iv) the optical density of the gas before filtration, and / or (v) the optical density of the gas after filtration. In one example, the filtration efficiency can be determined based on particulate matter sensed by one or more Wellstat sensors, where the air type is "outside air" or "fresh air". In another example, the filtration efficiency can be determined based on a weather API such as Breezometer or a similar API that provides values for particulate matter of about 2.5 μm (PM 2.5 ).

[0304] Occupancy data can include, for example, data regarding social distancing and / or occupancy capacity limits in an enclosed area. Some examples of occupancy data can include the number of occupants in an enclosed area, the occupancy capacity limit, and / or the ratio of the number of occupants in an enclosed area to the occupancy capacity limit. In one embodiment, the occupancy capacity limit (sometimes referred to herein as the "occupancy limit") can be based on building code requirements. In some cases, the number of occupants in an enclosed area can be determined based on one or more sensors in or around the enclosed area, such as a temperature scanner, an air quality sensor, a security camera, an occupancy sensor, a social distancing badge, etc., a button, a medical testing laboratory, and / or information provided by the occupants (e.g., provided via a survey or self-report). The occupancy capacity limit can be determined, for example, by a building manager or building data. In one example, the occupancy capacity limit is stored in the data of a digital twin, for example, as part of a REVIT model.

[0305] In a particular embodiment, for example, one or more sensors in a set of sensors (e.g., Figure 13 the set of sensors 1305 shown) can provide sensor data for determining values of contribution metrics to determine one or more composite indices. Some examples of sensor data that can be used include temperature readings, particulate matter perception, volatile organic compound (VOC) perception or readings, electromagnetic energy measurements, pressure readings, acceleration readings, time, radar readings, lidar readings, glass breakage sensing, motion sensing or measurements, and / or gas levels. In some cases, different sensors can be used to determine the value of a single contribution metric. For example, pressure sensor data, airflow sensor data, and particulate matter (PM) sensor data can be used to determine the value of a filtration efficiency metric. The values of the contribution metrics can be scored and weighted, and the weighted scores are used to calculate the scores of one or more subgroup indices. Then, the weighted scores of the subgroup indices of the composite index are used to calculate the composite index score.

[0306] In certain embodiments, a health index can be calculated by combining the weighted scores of subgroup indices associated with the health index. Each weighted score of a subgroup index is a combination of the weighted scores of contribution metrics. Some examples of contribution metrics that can affect the health index include environmental sensor data, air exchange rate, filtration efficiency, ASHRAE temperature / relative humidity (Temp / RH), current carbon dioxide (CO2) level, current carbon monoxide (CO) level, total volatile organic compound (TVOC) level, formaldehyde level, ozone level, perception of particulate matter of approximately 2.5 μm (PM 2.5 ) and perception of particulate matter of approximately 10 μm (PM 10) Perceptibility, temperature readings, relative humidity levels, and the health index includes the percentage (%) of the occupant survey response rate, the percentage (%) of the occupant survey response rate, the number of environmental complaints from occupants, acoustic data, and lighting data. Data for the contributing metrics of the health index can be provided by, for example, one or more sensors (e.g., a sensor set), one or more surveys or questionnaires, and / or one or more work orders.

[0307] Some examples of subgroups or health indices include a ventilation subgroup, a filtration subgroup, a thermal comfort subgroup, an environmental satisfaction subgroup, and / or a complaint subgroup. Building managers, facility managers, and / or tenants can use these health index scores to take corrective actions to improve the health performance of their buildings or to compare their buildings to each other. The ventilation subgroup score is a composite score of the contributing metrics associated with airborne compounds generated inside an enclosed area. Some examples of contributing metrics that can be included in the ventilation subgroup of the health index include the air exchange (AER) rate, the current carbon dioxide (CO2) level, the ozone level, the formaldehyde level, and / or the total volatile organic compound (TVOC) level. Some examples of contributing metrics that can be included in the filtration subgroup of the health index include filtration efficiency, the perceptibility of particulate matter such as particulate matter of about 2.5 μm (PM 2.5 ) and / or particulate matter of about 10 μm (PM 10 ). The perceptibility of particulate matter can be based on one or more sensors used outside the enclosed area to sense particulate matter before filtration and / or inside the enclosed area to sense particulate matter after filtration. Some examples of contributing metrics that can be included in the thermal comfort subgroup of the health index include ASHRAE thermal comfort, temperature, radiant temperature, occupant survey responses, the airspeed of HVAC equipment, the clothing value (CLO) of the occupants, and the measurement results of the relative humidity level. Some examples of contributing metrics that can be included in the environmental satisfaction subgroup of the health index include the percentage (%) of the occupant survey response rate and the percentage (%) of the occupant survey response rate. Examples of contributing metrics that can be included in the complaint subgroup of the health index include the number of environmental complaints from occupants and / or the number of maintenance or repair tickets submitted. In other embodiments, other soundness factor subgroups and / or other contributing metrics can be used to calculate the health index.

[0308] The ASHRAE thermal comfort metric can be associated with the ASHRAE standard for the thermal environmental conditions of human occupancy, such as ANSI / ASHRAE Standard 55 - 2010. The % occupant survey response rate metric can refer to the % of the number of occupants who completed the survey out of the total number of occupants in the building. The Net Promoter Score (NPS) can refer to the percentage (%) of respondents who give a score of 9 or 10 (out of 10) in terms of the likelihood of recommending their workplace to a colleague. The number of environmental complaints metric can refer to the daily complaint count received through the facility management reporting system.

[0309] In some embodiments, a performance index can be calculated by combining weighted scores of subgroup indices associated with the performance index. Each weighted score of a subgroup index is a combination of weighted scores of contributing metrics. Some examples of contributing metrics that can affect the performance index include one or more of the following: energy consumption intensity, water level, steam level, waste level, window operation data, run sensor uptime data, dwell time data, percentage room / floor capacity data, and percentage building capacity data. Some examples of subgroups of the performance index include a utilities subgroup, a system operation subgroup, and / or an occupancy subgroup. Building managers, facility managers, and / or tenants can use these performance index scores to take corrective actions to improve the environmental performance of their buildings or to compare their buildings to each other. Some examples of contributing metrics of the utilities subgroup of the performance index include energy consumption intensity, water, steam, and / or waste. Some examples of contributing metrics of the system operation subgroup of the performance index include window (e.g., tintable window) operation and / or run sensor uptime. Some examples of contributing metrics of the occupancy subgroup of the performance index include dwell time, percentage room / floor capacity, and / or percentage building capacity.

[0310] BCPI - 250156 Page 97 / 120

[0311] - Contributing Metric Scores and Score Charts

[0312] In some embodiments, a value of a contributing metric (e.g., sensor data) can be assigned a numerical score within a range (e.g., 0 to 100 or 0 to 1). In one embodiment, the high end of the range (e.g., 100) can indicate the lowest risk and the low end of the range (e.g., 0) can indicate the highest risk. In another embodiment, the high end of the range (e.g., 100) can indicate the highest risk and the low end of the range (e.g., 0) can indicate the lowest risk. In some cases, the score of a contributing metric can be determined by employing a score chart (curve) of the metric score range relative to different metric values.

[0313] In some embodiments, a score (e.g., a metric score, a subgroup index score, and / or an overall index score) can be assigned a grade. For example, if the score range is between 0 (high risk) - 100 (low risk), then > a score of 75 can be assigned an "excellent" grade, < 75 and > a score of 50 can be assigned a "good" grade, < 50 and > a score of 25 can be assigned a "medium" grade, and a score < 25 can be assigned a "poor" grade. In another example, if the score range is between 0 (low risk) - 100 (high risk), then > a score of 75 can be assigned a "poor" grade, < 75 and> A score of 50 can be assigned a "medium" rating, <50 and > A score of 25 can be assigned a "good" rating, and a score <25 can be assigned an "excellent" rating. One or more ratings (e.g., ratings of contribution metrics, ratings of subgroups, and / or ratings of composite indices) can be provided to the current occupant of the enclosed area, e.g., via a digital twin (e.g., digital twin 1730). For example, an occupant can select a space in a building, e.g., a floor or an office of the digital twin, and an indicator (e.g., color) indicating the rating can be displayed on the digital twin.

[0314] Figure 20A A diagram depicting an example of a scoring graph 2001 that can be used to determine the score of a first contribution metric. Figure 21A A diagram depicting an example of a scoring graph 2101 that can be used to determine the score of a second contribution metric. In these illustrated examples, the scores assigned to the contribution metrics are in the range of 0 (high risk) to 100 (low risk). In other embodiments, other ranges can be used. In Figure 20A it, when the value of contribution metric 1 is its lowest value of 0, it has the highest score of 100 (lowest risk). In Figure 21A it, when the value of contribution metric 2 is its highest value of 60, it has the highest score of 100 (lowest risk).

[0315] Figure 20B Depicting related to Figure 20A A table of examples of the values 500, 140, 55, 40, and 0 of the first contribution metric associated with the metric scores 0, 25, 50, 75, and 100 derived from the scoring graph 2001 in Figure 21B Depicting related to Figure 21B The metric scores BCPI - 250156 page 98 / 120 taken from the scoring graph 2101 in

[0316] A table of examples of the values 0, 4.7, 10, 20, and 60 of the second contribution metric associated with the metric scores 0, 25, 50, 75, and 100. In Figure 20A and 21B it, the graph is segmented in 25 - point increments, where > A metric score of 75 is assigned an "excellent" rating, <75 and > A metric score of 50 is assigned a "good" rating, <50 and >In some cases, the scores of the contribution indicators in the subgroups can be determined using different scoring graphs. In other cases, the same scoring graph can be implemented to determine the scores of multiple contribution indicators.

[0317] In some embodiments, the contribution indicators of the composite index are grouped into contribution indicator subgroups. In these cases, the composite index score can be calculated using the weighting factors of the subgroups (sometimes referred to herein as "weights") and the weighting factors of the contribution indicators (sometimes referred to herein as "sub-weights"). Referring to the criteria provided in Table IV, for example, if a first composite index CI1 is to be calculated, which in some embodiments can be a safety index, the subgroup GW can be calculated. 1,1 GW 1,2 and GW 1,3 The score of each subgroup is determined by combining the weighted scores of the contribution indicators in the corresponding subgroup. In one embodiment, each score of the contribution indicator can be obtained from a score map (e.g., Figure 20A The score charts in 2001 and Figure 21A For example, referring to the criteria in Table IV, the contribution index P 1,1,1 , P 1,1,2 , P 1,1,3 , P 1,1,4 , P 1,1,5 , P 1,2,1 , P 1,3,1 and P 1,3,2 The score of can be determined using the values ​​of the indicators according to eight different score plots. The scores plotted can be P 1,1,1 50.P 1,1,2 50.P 1,1,3 45. P 1,1,4 70. and P 1,1,5 80. In one example, GI 1,1 The weighting factors for the subgroups have an equal weight of 20%. The calculated GI 1,1 The subgroup index can be calculated based on Equation 4 as 50x20% (P 1,1,1 )+50x20%(P 1,1,2 )+45x20%(P 1,1,3 )+70x20%(P 1,1,4 )+80x20%(P 1,1,5 )=59. In one case, > A subgroup index score of 75 would represent an “excellent” rating, and a subgroup index score of <75 and> A score of 50 would represent a “good” rating, and <50 and > A score of 25 may represent a "moderate" grade, and a score < 25 may represent a "poor" grade. In this case, the calculated subgroup index score of 59 is a "good" grade.

[0318] - Overlapping contribution indicators

[0319] In some cases, the same indicator (e.g., sensor data) may affect more than one subgroup index and / or more than one composite index. For example, the air exchange rate affects ventilation in a building and also affects the virus risk level in the building. In certain embodiments, the composite index can be calculated by combining the weighted scores of the jointly contributing indicators (sometimes referred to as overlapping indicators in this article). For example, one or more of the same contributing indicators can be used to calculate different composite indices. The weighting factors used to calculate the subgroup indices of the jointly contributing indicators can be different. For example, the carbon dioxide indicator can be part of a subgroup of the health index and can also be part of a subgroup of the safety index. The weighting factor W of the carbon dioxide indicator applied to the subgroup of the health index is 2,1,2 The weighting factor W of the CO2 indicator applied to the subgroups of the safety index may be different 2,1,1 In some cases, each composite index is calculated using at least one common contribution indicator and another composite index. In one embodiment, a first composite index may be based on sensor data from one or more sensors of a first group, and a second composite index may be based on sensor data from one or more sensors of a second group, wherein the first group of sensors has at least one common sensor with the second group of sensors.

[0320] - Dynamically available data

[0321] Different indicator data (e.g., sensor data) may be available at different times. For example, certain sensors may not be operational at certain times of the day or on certain days, for example, occupancy sensors may not be operational on federal holidays. As another example, a sensor may be unavailable due to a sensor failure or power outage. In certain embodiments, the techniques described herein may dynamically adjust the contribution indicators, subgroups, and / or weighting factors used to calculate the composite index based on data availability. In some cases, the calculated composite index may be considered independent of any technical data stack or individual sensor and may be adjusted based on the available data.

[0322] In certain embodiments, a system comprising one or more controllers and / or processors thereof (e.g., Figure 12 The control system 1200 in Figure 11 Ventilation system 1100, Figure 14 The control system 1400 inFigure 15 The computing system 1500 in Figure 17 The control system 1700 in Figure 18 System 1800 or Figure 19 The computing system 1990 in the example may store, retrieve, or utilize certain contribution indicators based on data availability. The memory (e.g., memory 1502, Figure 15 The electronic storage unit 1504, Figure 15 Memory 1502 in Figure 19 The memory 1992 in the memory 1992 can store one or more indicators of an enclosed area (e.g., a building or a space in a building). At least one stored value can be an estimate of a measured value derived from one or more (e.g., two or more) other indicators. These indicators can be used to calculate a composite index for the enclosed area, and / or determine remedial measures to improve the scores of one or more composite indices and / or one or more subgroup indices. If the corresponding sensor or other data source is available to provide data, then the one or more controllers and / or processors can obtain the data. According to certain embodiments, when one or more sensors (e.g., sensor types) or other data sources are not available to provide data, the one or more controllers and / or processors can be dynamically adjusted to use only available data.

[0323] For example, in one embodiment, when calculating the composite index, the one or more controllers and / or processors may omit the contribution indicators for which no data is available and adjust the weighting factors of the remaining contribution indicators in the subgroup. For example, referring to the criteria in Table IV, if P 1,1,5 If the indicator data is not available, then GI 1,1 The subgroup index can be calculated using the remaining index P in the subgroup. 1,1,1 , P 1,1,2 , P 1,1,3 and P...

Claims

1. A system, characterized in that: Comprising: At least one controller configured to control one or more building systems based on a plurality of comprehensive indices of one or more enclosed areas, the at least one controller being configured to determine the plurality of comprehensive indices at least in part based on sensor data from a plurality of sensors.

2. The system according to claim 1, wherein: The plurality of comprehensive indices include: A safety index, A health index, and A performance index.

3. The system according to claim 2, characterized in that: The one or more enclosed areas include a building in or at which the plurality of sensors are installed, and / or a space in the building.

4. The system according to claim 1, characterized in that: The plurality of sensors include a set of sensors including two or more environmental sensors.

5. The system according to claim 1, wherein: The plurality of comprehensive indices are also determined at least in part based on data from one or more of surveys, questionnaires, work orders, or user inputs.

6. The system according to claim 1, wherein: At least one of the comprehensive indices is determined based on a weighted score of a value of a contribution metric including the sensor data from the plurality of sensors.

7. The system according to claim 1, wherein: The plurality of comprehensive indices are determined based on weighted scores of a plurality of subgroup indices, and The weighted score of each subgroup index is determined based on a weighted score of a value of a contribution metric including the sensor data from the plurality of sensors.

8. The system according to claim 1, wherein: The at least one controller is further configured to: Determine a first comprehensive index among the plurality of comprehensive indices based on a weighted score of a value of a first set of contribution metrics, and Determine a second comprehensive index among the plurality of comprehensive indices based on a weighted score of a value of a second set of contribution metrics, and The first set of contribution metrics and the second set of contribution metrics include the sensor data from the plurality of sensors.

9. The system according to claim 8, characterized in that: The first set of contribution metrics overlaps with the second set of contribution metrics.

10. The system according to claim 8, wherein: The first set of contribution metrics and the second set of contribution metrics only partially overlap.

11. The system according to claim 8, wherein: Dynamically adjust one or both of the first set of contribution metrics and the second set of contribution metrics based on the availability of sensor data from the plurality of sensors.

12. The system according to claim 8, wherein: The at least one controller is further configured to determine a third comprehensive index, and The third comprehensive index is based on a weighted score of a value of a third set of contribution metrics.

13. The system according to claim 12, wherein: The third set of contribution metrics does not overlap with the first set of contribution metrics or the second set of contribution metrics.

14. The system according to claim 1, characterized in that: The at least one controller is configured to dynamically calculate the plurality of comprehensive indices based on currently available data.

15. The system according to claim 1, wherein: The at least one controller is further configured to: Determine whether data of a set of contribution metrics is available; If data of a contribution metric in the set of contribution metrics is not available, then remove the contribution metric from the set of contribution metrics and adjust each weighting factor of the remaining contribution metrics in the set of contribution metrics; and Determine at least one of the comprehensive indices based on the remaining contribution metrics and the corresponding adjusted weighting factors.

16. The system according to claim 1, wherein: The at least one controller is further configured to determine the data availability of a minimum set of contribution metrics.

17. The system according to claim 16, wherein: The minimum set of contribution metrics includes: (A) Air exchange rate, carbon dioxide (CO2) level, filtration efficiency, and relative humidity level; or (B) Air exchange rate, carbon dioxide (CO2) level, total volatile organic compound level, filtration efficiency, perceptibility of particulate matter of about 2.5 μm, perceptibility of particulate matter of about 10 μm, thermal comfort level, temperature reading, and relative humidity level; or (C) Energy use intensity level, operating sensor uptime level, and percentage building capacity level.

18. The system according to claim 16, characterized in that: The at least one controller is further configured to send a notification to at least one occupant of the one or more enclosed areas in or at which the plurality of sensors are disposed as to whether data is unavailable for the minimum set of contributing metrics.

19. The system according to claim 1, characterized in that: The plurality of composite indices are determined based on a weighted score of the values of contributing metrics including air exchange rate, carbon dioxide (CO2) level, filtration efficiency, relative humidity level, occupancy capacity limit, and building access card usage / issuance ratio, carbon monoxide level, and volatile organic compound level.

20. The system according to claim 1, characterized in that: The plurality of composite indices are determined based on a weighted score of the values of contributing metrics including air exchange rate, carbon dioxide (CO2) level, ozone level, formaldehyde level, total volatile organic compound level, filtration efficiency, perceptibility of particulate matter of about 2.5 μm, perceptibility of particulate matter of about 10 μm, thermal comfort level, temperature reading, relative humidity level, percentage occupant survey response rate, net promoter score, and number of environmental complaints.

21. The system according to claim 1, wherein: The plurality of composite indices are determined based on a weighted score of the values of contributing metrics including energy consumption intensity, water usage rate, steam usage rate, waste disposal rate, window operation level, operating sensor uptime rate, dwell time, percentage room / floor capacity, and building capacity percentage.

22. The system according to claim 1, wherein: The at least one controller is further configured to determine a first composite index, a second composite index, and a third composite index among the plurality of composite indices, The first composite index is determined based on a weighted score of the values of contributing metrics including air exchange rate, carbon dioxide (CO2) level, filtration efficiency, relative humidity level, occupancy capacity limit, building access card usage / issuance ratio, carbon monoxide level, and volatile organic compound level. The second composite index is determined based on a weighted score of the values of contribution metrics including air exchange rate, carbon dioxide (CO2) level, ozone level, formaldehyde level, total volatile organic compound level, filtration efficiency, perceptibility of particulate matter of about 2.5 μm, perceptibility of particulate matter of about 10 μm, thermal comfort level, temperature reading, relative humidity level, percentage of occupant survey response rate, net promoter score, and number of environmental complaints, and The third composite index is determined based on a weighted score of the values of contribution metrics including energy consumption intensity, water usage rate, steam usage rate, waste treatment rate, window operation level, operation sensor on-time level, dwell time, percentage of room / floor capacity, and building capacity percentage.

23. The system according to claim 1, wherein: The at least one controller is further configured to send control instructions to adjust one or more building systems to improve the plurality of composite indices.

24. The system according to claim 1, wherein: The at least one controller is further configured to change the tint of one or more tintable windows to improve the plurality of composite indices.

25. The system according to claim 1, wherein: The at least one controller is configured to determine one or more measures configured to improve the plurality of composite indices.

26. The system according to claim 25, wherein: The at least one controller is configured to send a notification of the one or more measures to one or more occupants of one or more enclosed areas in or at which the plurality of sensors are disposed.

27. The system according to claim 26, wherein: The at least one controller is configured to send the notification to a digital twin of the one or more enclosed areas.

28. A method for controlling one or more building systems, characterized in that: The method includes: determining a plurality of composite indices of one or more enclosed areas based at least in part on sensor data from a plurality of sensors; and controlling the one or more building systems based on the plurality of composite indices.

29. The method according to claim 28, wherein: The plurality of composite indices include those of the one or more enclosed areas: a safety index, a health index, and a performance index.

30. The method according to claim 29, wherein: The one or more enclosed areas include a building in or at which the plurality of sensors are disposed, and / or a certain space in the building.

31. The method according to claim 28, wherein: The plurality of sensors include a set of sensors including two or more environmental sensors.

32. The method according to claim 28, wherein: The determination of the plurality of composite indices is also based at least in part on data from one or more of surveys, questionnaires, work orders, or user inputs.

33. The method according to claim 28, wherein: Further includes determining the plurality of composite indices based on a weighted score of the values of contribution metrics including the sensor data from the plurality of sensors.

34. The method according to claim 28, characterized in that: Further includes: determining a weighted score for each contribution metric associated with the plurality of composite indices; determining a weighted score for the subgroup index by combining the weighted scores of a set of contribution metrics associated with each subgroup index among the plurality of subgroup indices; and determining the composite index by combining the weighted scores of the subgroup indices associated with each composite index among the plurality of composite indices.

35. The method according to claim 28, wherein: Further includes: determining a first composite index among the plurality of composite indices based on a weighted score of the values of a first set of contribution metrics; and Determine a second composite index among the plurality of composite indices based on a weighted score of values of a second set of contribution metrics; wherein the first set of contribution metrics and the second set of contribution metrics include sensor data from the plurality of sensors.

36. The method according to claim 35, wherein: The first set of contribution metrics overlaps with the second set of contribution metrics.

37. The method according to claim 35, wherein: The first set of contribution metrics and the second set of contribution metrics only partially overlap.

38. The method according to claim 35, characterized in that: Further include dynamically adjusting one or both of the first set of contribution metrics and the second set of contribution metrics based on the availability of sensor data from the plurality of sensors.

39. The method according to claim 35, characterized in that: Further include determining a third composite index based on a weighted score of values of a third set of contribution metrics.

40. The method according to claim 39, wherein: The third set of contribution metrics does not overlap with the first set of contribution metrics or the second set of contribution metrics.

41. The method according to claim 28, wherein: Further include dynamically calculating the plurality of composite indices based on currently available data.

42. The method according to claim 28, wherein: Further include: Determine whether data of a set of contribution metrics is available; and If data of a contribution metric in the set of contribution metrics is not available, then remove the contribution metric from the set of contribution metrics and adjust each weighting factor of each of the remaining contribution metrics in the set of contribution metrics; and Determine the plurality of composite indices based on the remaining contribution metrics and the corresponding adjusted weighting factors.

43. The method according to claim 28, wherein: Further include determining whether data of a minimum set of contribution metrics is available.

44. The method according to claim 43, characterized in that: The minimum set of contribution metrics includes: (A) Air exchange rate, carbon dioxide (CO2) level, filtration efficiency, and relative humidity level; or (B) Air exchange rate, carbon dioxide (CO2) level, total volatile organic compound level, filtration efficiency, perception of particulate matter of about 2.5 μm, perception of particulate matter of about 10 μm, thermal comfort level, temperature reading, and relative humidity level; or (C) Energy consumption intensity level, operating sensor uptime level, and percentage building capacity level.

45. The method according to claim 43, characterized in that: Further include sending a notification to at least one occupant of a closed area in the one or more closed areas where the plurality of sensors are placed indicating whether data of the minimum set of contribution metrics is unavailable.

46. The method according to claim 28, characterized in that: At least one of the composite indices is determined based on a weighted score of values of contribution metrics including air exchange rate, carbon dioxide (CO2) level, filtration efficiency, relative humidity level, occupancy capacity limit, and building access card usage / issuance ratio, carbon monoxide level, and volatile organic compound level.

47. The method according to claim 28, wherein: At least one of the composite indices is determined based on a weighted score of values of contribution metrics including air exchange rate, carbon dioxide (CO2) level, ozone level, formaldehyde level, total volatile organic compound level, filtration efficiency, perception of particulate matter of about 2.5 μm, perception of particulate matter of about 10 μm, thermal comfort level, temperature reading, relative humidity level, percentage occupant survey response rate, net promoter score, and number of environmental complaints.

48. The method according to claim 28, wherein: At least one of the composite indices is determined based on a weighted score of values of contribution metrics including energy consumption intensity, water usage rate, steam usage rate, waste treatment rate, window operation level, operating sensor on-time rate, dwell time, percentage room / floor capacity, and building capacity percentage.

49. The method according to claim 28, characterized in that: Further comprising: Determining a first composite index of the plurality of composite indices based on a weighted score of values of contribution metrics including air exchange rate, carbon dioxide (CO2) level, filtration efficiency, relative humidity level, occupancy capacity limit, and building access card usage / issuance ratio, carbon monoxide level, and volatile organic compound level; Determining a second composite index of the plurality of composite indices based on a weighted score of values of contribution metrics including air exchange rate, carbon dioxide (CO2) level, ozone level, formaldehyde level, total volatile organic compound level, filtration efficiency, perceived level of particulate matter of about 2.5 μm, perceived level of particulate matter of about 10 μm, thermal comfort level, temperature reading, relative humidity level, percentage occupant survey response rate, net promoter score, and number of environmental complaints; And Determining a third composite index of the plurality of composite indices based on a weighted score of values of contribution metrics including energy consumption intensity, water usage rate, steam usage rate, waste treatment rate, window operation level, operating sensor on-time rate, dwell time, percentage room / floor capacity, and building capacity percentage.

50. The method according to claim 28, wherein: Further comprising sending control instructions to adjust the one or more building systems to improve the plurality of composite indices.

51. The method according to claim 28, wherein: Further comprising sending control instructions to change the tint of one or more tintable windows to improve the plurality of composite indices.

52. The method according to claim 28, wherein: Further comprising determining one or more measures configured to improve the plurality of composite indices.

53. The method according to claim 52, characterized in that: Further comprising sending a notification of the one or more measures to one or more occupants of the one or more enclosed areas where the plurality of sensors are located therein or thereon.

54. The method according to claim 53, wherein: Further comprising sending the notification to a digital twin of the one or more enclosed areas.

55. A non - transitory computer program product, characterized in that: A computer-readable memory storing computer-executable instructions for controlling one or more building systems, the one or more building systems including one or more tintable windows, the computer-executable instructions, when read by one or more processors operatively coupled to the one or more building systems, cause the one or more processors to perform the operations of the method according to any one of claims 28 to 54.

56. A system, characterized in that: Comprising: A plurality of sensors located at a building and configured to generate sensor data associated with the environment in the building; At least one controller configured to: Receive the sensor data, and Determine a first composite index and a second composite index of a space in the building based at least in part on the sensor data; and A user interface configured to present information associated with the first composite index and the second composite index.

57. A system, characterized in that: Comprising: At least one controller configured to: Receive sensor data acquired by a plurality of sensors; And Determine a plurality of composite indices of one or more enclosed areas based at least in part on the sensor data.

58. A method, characterized in that: Comprising: Obtaining sensor data acquired by a plurality of sensors; And Determining a plurality of comprehensive indices of one or more enclosed areas at least partially based on the sensor data from the plurality of sensors.

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