BUILDING AUTOMATION SYSTEMS WITH REGIONAL INTELLIGENCE

A method for troubleshooting a building automation system includes selecting an artificial intelligence tool from a set of available artificial intelligence tools based on the geographic region of the building automation system, ranking troubleshooting options for the building automation system by applying the artificial intelligence tool to data associated with the building automation system, and implementing at least a first troubleshooting option, the first troubleshooting option ranked higher than a remainder of the troubleshooting options by the artificial intelligence tool.

BACKGROUND

The present disclosure relates generally to building management systems (building automation systems). The present disclosure relates more particularly to challenges associated with location of building management systems across a wide variety of geographical regionals with different climates, architectures, infrastructure, utilities, equipment types, culture, terminology, languages, etc. For example, differences in technical terminology used across geographic regions, even across different regions having the same predominant language, can create confusion when distributing or supporting building management systems across multiple regions. As another example, differences in climate, architecture, types of equipment used, etc. can cause configuration settings, technician workflows (e.g., troubleshooting steps), etc. that are useful in one region to be unhelpful in other regions. As such, approaches for automatically handling regional differences would be beneficial.

A building management system (BMS) is, in general, a system of devices configured to control, monitor, and manage equipment in or around a building or building area. A BMS can include a heating, ventilation, or air conditioning (HVAC) system, a security system, a lighting system, a fire alerting system, another system that is capable of managing building functions or devices, or any combination thereof. BMS devices may be installed in any environment (e.g., an indoor area or an outdoor area) and the environment may include any number of buildings, spaces, zones, rooms, or areas. A BMS may include METASYS® building controllers or other devices sold by Johnson Controls, Inc., as well as building devices and components from other sources.

A BMS may include one or more computer systems (e.g., servers, BMS controllers, etc.) that serve as enterprise level controllers, application or data servers, head nodes, master controllers, or field controllers for the BMS. Such computer systems may communicate with multiple downstream building systems or subsystems (e.g., an HVAC system, a security system, etc.) according to like or disparate protocols (e.g., LON, BACnet, etc.). The computer systems may also provide one or more human-machine interfaces or client interfaces (e.g., graphical user interfaces, reporting interfaces, text-based computer interfaces, client-facing web services, web servers that provide pages to web clients, etc.) for controlling, viewing, or otherwise interacting with the BMS, its subsystems, and devices.

SUMMARY

One implementation of the present disclosure is a method for troubleshooting a building automation system. The method includes selecting an artificial intelligence tool from a set of available artificial intelligence tools based on the geographic region of the building automation system, ranking troubleshooting options for the building automation system by applying the artificial intelligence tool to data associated with the building automation system, and implementing at least a first troubleshooting option, the first troubleshooting option ranked higher than a remainder of the troubleshooting options by the artificial intelligence tool.

In some embodiments, the method also includes ignoring one or more of the troubleshooting options based on the geographic region of the building automation system. Implementing at the least the first troubleshooting option can include automatically adjusting operation of building equipment operated by the building automation system. Implementing at the least the first troubleshooting option can include adjusting an installation of one or more devices of the building automation system. Implementing at the least the first troubleshooting option can include changing configuration parameters of one or more devices of the building automation system.

In some embodiments, the method includes displaying the first troubleshooting options to a user via a mobile application. In some embodiments, selecting the artificial intelligence tool is further based on a domain of building equipment to be troubleshot.

Another implementation of the present disclosure is a method. The method includes communicating building automation information with a plurality of building automation systems located in a plurality of geographic regions, automatically adjusting the building automation information in accordance with different regional terminology used by the plurality of building automation systems, and controlling building equipment with at least a first building automation system of the plurality of building automation systems based on inputs provided by a first user in a first geographic region using a first regional terminology associated with the first geographic region and inputs provides by a second user in a second geographic region using a second regional terminology associated with the second geographic region.

In some embodiments, the first building automation system and the first user are located at the first geographic region and the second user is located at the second geographic region. The first user may be associated with the first building automation system and the second user may provide support for the plurality of building automation systems. In some embodiments, automatically adjusting the building automation information in accordance with different regional terminology comprises applying a machine learning model to the building automation information.

In some embodiments, the method also includes prompting, via the plurality of building automation systems, users at the plurality of geographic regions to answer questions relating to the different regional terminology used by the users and training the machine learning model based on answers to the questions.

In some embodiments, automatically adjusting the building automation information in accordance with different regional terminology used by the plurality of building automation systems comprises normalizing point labels for data provided by the plurality of building automation systems to facilitate analysis of aggregations of the data from the plurality of building automation systems.

DETAILED DESCRIPTION

Overview

Referring generally to the FIGURES, approaches relating to artificial intelligence in the context of building management systems (or other building equipment or collections thereof) distributed across geographic regions are shown, according to some embodiments. One aspect of the present disclosure is a determination that artificial intelligence for building systems will benefit from regional knowledge, that is, knowledge associated with or otherwise tuned for unique characteristics of different geographic regions.

For example, in the context of commissioning, configuring, or troubleshooting building equipment and building management systems, regional differences can have significant impacts on the relevant settings, workflows, installations, etc. suitable for providing a well-functioning system. As one example, buildings in the Northeastern region of the U.S. (e.g., Maine) often have hot water radiators to heat spaces, while buildings in the Midwest of the U.S. (e.g., Wisconsin) often uses natural gas furnaces and forced air heat and buildings in the southwest (e.g., Texas) often have electric heating systems. Because of such differences, technicians from different regions would have difficulty successful install, commission, troubleshooting, etc. building systems in other regions. The same challenge exists for artificial intelligence (AI) tools, which, if built and trained once by a manufacturer/designer in a geographically centralized or agnostic manner, inherently lack regional knowledge required for successful deployment across geographic regions. The approaches herein solve such challenges, including by providing sets of region-specific AI models selectable based on the region of interest, thereby improving the technical field of building management systems.

As another example, different terminology may be used in different regions to refer to the same physical parameters or equipment setpoints. Different terminology may result from different languages (e.g., English and French in different regions of Canada) or from variations within the industry in the same language (e.g., HVAC technicians in Iowa may use different terminology than technicians in New York or California). Such linguistic differences create confusion when distributing instructions, settings, dashboards, interfaces, recommendations, etc. from a centralized tool (e.g., manufacture support, software-as-a-service, etc.) to multiple geographic regions. The linguistic differences also create challenges for processing data received from multiple geographic regions, in which case one aspect of the present disclosure is to use AI trained on regional differences to normalize data from multiple regions to remove such linguistic differences and thereby enabling centralized learning and analysis from such aggregated dated and direct comparison between data from different regions even where originally created using labels of different terminology. The approaches herein solve such challenges, thereby improving the technical field of building management systems.

Embodiments of the present disclosure are described in detail below. In some embodiments, a set of available artificial intelligence models is provided. Each artificial intelligence model may be associated with a geographic region. A process can include determining the location of a unit of equipment, building management system, or other relevant device and automatically selecting the artificial intelligence model associated with the geographic region including the location of the equipment, BMS, or device. The selected artificial intelligence model can then be used online to control, commission, troubleshoot, monitor, detect or diagnose faults in, etc. the equipment, BMS, or device. Such an approach can be adapted for use with AI models in the context of fault detection and diagnosis (e.g., as in U.S. patent application Ser. Nos. 17/710,443, 17/710,597, 17/710,706, and 17/710,603, all filed Mar. 31, 2022, all of which are incorporated by reference herein), control of building equipment (e.g., as in U.S. Pat. Pub. No. 20200355391 or U.S. Pat. No. 10,901,373, incorporated by reference herein), and automated configuration and other applications (e.g., as in U.S. Provisional Patent App. Nos. 63/279,759, 63/315,442, 63/315,454, and 63/315,459, incorporated by reference herein), for example providing instances of such models for each of multiple geographic regions.

Building and Building Management System

Referring now toFIG.1, a perspective view of a building10is shown, according to an exemplary embodiment. A BMS serves building10. The BMS for building10may include any number or type of devices that serve building10. For example, each floor may include one or more security devices, video surveillance cameras, fire detectors, smoke detectors, lighting systems, HVAC systems, or other building systems or devices. In modern BMSs, BMS devices can exist on different networks within the building (e.g., one or more wireless networks, one or more wired networks, etc.) and yet serve the same building space or control loop. For example, BMS devices may be connected to different communications networks or field controllers even if the devices serve the same area (e.g., floor, conference room, building zone, tenant area, etc.) or purpose (e.g., security, ventilation, cooling, heating, etc.).

BMS devices may collectively or individually be referred to as building equipment. Building equipment may include any number or type of BMS devices within or around building10. For example, building equipment may include controllers, chillers, rooftop units, fire and security systems, elevator systems, thermostats, lighting, serviceable equipment (e.g., vending machines), and/or any other type of equipment that can be used to control, automate, or otherwise contribute to an environment, state, or condition of building10. The terms “BMS devices,” “BMS device” and “building equipment” are used interchangeably throughout this disclosure.

Referring now toFIG.2, a block diagram of a BMS11for building10is shown, according to an exemplary embodiment. BMS11is shown to include a plurality of BMS subsystems20-26. Each BMS subsystem20-26is connected to a plurality of BMS devices and makes data points for varying connected devices available to upstream BMS controller12. Additionally, BMS subsystems20-26may encompass other lower-level subsystems. For example, an HVAC system may be broken down further as “HVAC system A,” “HVAC system B,” etc. In some buildings, multiple HVAC systems or subsystems may exist in parallel and may not be a part of the same HVAC system20.

As shown inFIG.2, BMS11may include a HVAC system20. HVAC system20may control HVAC operations building10. HVAC system20is shown to include a lower-level HVAC system42(named “HVAC system A”). HVAC system42may control HVAC operations for a specific floor or zone of building10. HVAC system42may be connected to air handling units (AHUs)32,34(named “AHU A” and “AHU B,” respectively, in BMS11). AHU32may serve variable air volume (VAV) boxes38,40(named “VAV 3” and “VAV 4” in BMS11). Likewise, AHU34may serve VAV boxes36and110(named “VAV 2” and “VAV 1”). HVAC system42may also include chiller30(named “Chiller A” in BMS11). Chiller30may provide chilled fluid to AHU32and/or to AHU34. HVAC system42may receive data (i.e., BMS inputs such as temperature sensor readings, damper positions, temperature setpoints, etc.) from AHUs32,34. HVAC system42may provide such BMS inputs to HVAC system20and on to middleware14and BMS controller12. Similarly, other BMS subsystems may receive inputs from other building devices or objects and provide the received inputs to BMS controller12(e.g., via middleware14).

Middleware14may include services that allow interoperable communication to, from, or between disparate BMS subsystems20-26of BMS11(e.g., HVAC systems from different manufacturers, HVAC systems that communicate according to different protocols, security/fire systems, IT resources, door access systems, etc.). Middleware14may be, for example, an EnNet server sold by Johnson Controls, Inc. While middleware14is shown as separate from BMS controller12, middleware14and BMS controller12may integrated in some embodiments. For example, middleware14may be a part of BMS controller12.

Still referring toFIG.2, window control system22may receive shade control information from one or more shade controls, ambient light level information from one or more light sensors, and/or other BMS inputs (e.g., sensor information, setpoint information, current state information, etc.) from downstream devices. Window control system22may include window controllers107,108(e.g., named “local window controller A” and “local window controller B,” respectively, in BMS11). Window controllers107,108control the operation of subsets of window control system22. For example, window controller108may control window blind or shade operations for a given room, floor, or building in the BMS.

Lighting system24may receive lighting related information from a plurality of downstream light controls (e.g., from room lighting104). Door access system26may receive lock control, motion, state, or other door related information from a plurality of downstream door controls. Door access system26is shown to include door access pad106(named “Door Access Pad 3F”), which may grant or deny access to a building space (e.g., a floor, a conference room, an office, etc.) based on whether valid user credentials are scanned or entered (e.g., via a keypad, via a badge-scanning pad, etc.).

BMS subsystems20-26may be connected to BMS controller12via middleware14and may be configured to provide BMS controller12with BMS inputs from various BMS subsystems20-26and their varying downstream devices. BMS controller12may be configured to make differences in building subsystems transparent at the human-machine interface or client interface level (e.g., for connected or hosted user interface (UI) clients16, remote applications18, etc.). BMS controller12may be configured to describe or model different building devices and building subsystems using common or unified objects (e.g., software objects stored in memory) to help provide the transparency. Software equipment objects may allow developers to write applications capable of monitoring and/or controlling various types of building equipment regardless of equipment-specific variations (e.g., equipment model, equipment manufacturer, equipment version, etc.). Software building objects may allow developers to write applications capable of monitoring and/or controlling building zones on a zone-by-zone level regardless of the building subsystem makeup.

Referring now toFIG.3, a block diagram illustrating a portion of BMS11in greater detail is shown, according to an exemplary embodiment. Particularly,FIG.3illustrates a portion of BMS11that services a conference room102of building10(named “B1_F3_CR5”). Conference room102may be affected by many different building devices connected to many different BMS subsystems. For example, conference room102includes or is otherwise affected by VAV box110, window controller108(e.g., a blind controller), a system of lights104(named “Room Lighting17”), and a door access pad106.

Each of the building devices shown at the top ofFIG.3may include local control circuitry configured to provide signals to their supervisory controllers or more generally to the BMS subsystems20-26. The local control circuitry of the building devices shown at the top ofFIG.3may also be configured to receive and respond to control signals, commands, setpoints, or other data from their supervisory controllers. For example, the local control circuitry of VAV box110may include circuitry that affects an actuator in response to control signals received from a field controller that is a part of HVAC system20. Window controller108may include circuitry that affects windows or blinds in response to control signals received from a field controller that is part of window control system (WCS)22. Room lighting104may include circuitry that affects the lighting in response to control signals received from a field controller that is part of lighting system24. Access pad106may include circuitry that affects door access (e.g., locking or unlocking the door) in response to control signals received from a field controller that is part of door access system26.

Still referring toFIG.3, BMS controller12is shown to include a BMS interface132in communication with middleware14. In some embodiments, BMS interface132is a communications interface. For example, BMS interface132may include wired or wireless interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wire terminals, etc.) for conducting data communications with various systems, devices, or networks. BMS interface132can include an Ethernet card and port for sending and receiving data via an Ethernet-based communications network. In another example, BMS interface132includes a Wi-Fi transceiver for communicating via a wireless communications network. BMS interface132may be configured to communicate via local area networks or wide area networks (e.g., the Internet, a building WAN, etc.).

In some embodiments, BMS interface132and/or middleware14includes an application gateway configured to receive input from applications running on client devices. For example, BMS interface132and/or middleware14may include one or more wireless transceivers (e.g., a Wi-Fi transceiver, a Bluetooth transceiver, a NFC transceiver, a cellular transceiver, etc.) for communicating with client devices. BMS interface132may be configured to receive building management inputs from middleware14or directly from one or more BMS subsystems20-26. BMS interface132and/or middleware14can include any number of software buffers, queues, listeners, filters, translators, or other communications-supporting services.

Still referring toFIG.3, BMS controller12is shown to include a processing circuit134including a processor136and memory138. Processor136may be a general purpose or specific purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable processing components. Processor136is configured to execute computer code or instructions stored in memory138or received from other computer readable media (e.g., CDROM, network storage, a remote server, etc.).

Memory138may include one or more devices (e.g., memory units, memory devices, storage devices, etc.) for storing data and/or computer code for completing and/or facilitating the various processes described in the present disclosure. Memory138may include random access memory (RAM), read-only memory (ROM), hard drive storage, temporary storage, non-volatile memory, flash memory, optical memory, or any other suitable memory for storing software objects and/or computer instructions. Memory138may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present disclosure. Memory138may be communicably connected to processor136via processing circuit134and may include computer code for executing (e.g., by processor136) one or more processes described herein. When processor136executes instructions stored in memory138for completing the various activities described herein, processor136generally configures BMS controller12(and more particularly processing circuit134) to complete such activities.

Still referring toFIG.3, memory138is shown to include building objects142. In some embodiments, BMS controller12uses building objects142to group otherwise ungrouped or unassociated devices so that the group may be addressed or handled by applications together and in a consistent manner (e.g., a single user interface for controlling all of the BMS devices that affect a particular building zone or room). Building objects can apply to spaces of any granularity. For example, a building object can represent an entire building, a floor of a building, or individual rooms on each floor. In some embodiments, BMS controller12creates and/or stores a building object in memory138for each zone or room of building10. Building objects142can be accessed by UI clients16and remote applications18to provide a comprehensive user interface for controlling and/or viewing information for a particular building zone. Building objects142may be created by building object creation module152and associated with equipment objects by object relationship module158, described in greater detail below.

Still referring toFIG.3, memory138is shown to include equipment definitions140. Equipment definitions140stores the equipment definitions for various types of building equipment. Each equipment definition may apply to building equipment of a different type. For example, equipment definitions140may include different equipment definitions for variable air volume modular assemblies (VMAs), fan coil units, air handling units (AHUs), lighting fixtures, water pumps, and/or other types of building equipment.

Equipment definitions140define the types of data points that are generally associated with various types of building equipment. For example, an equipment definition for VMA may specify data point types such as room temperature, damper position, supply air flow, and/or other types data measured or used by the VMA. Equipment definitions140allow for the abstraction (e.g., generalization, normalization, broadening, etc.) of equipment data from a specific BMS device so that the equipment data can be applied to a room or space.

Each of equipment definitions140may include one or more point definitions. Each point definition may define a data point of a particular type and may include search criteria for automatically discovering and/or identifying data points that satisfy the point definition. An equipment definition can be applied to multiple pieces of building equipment of the same general type (e.g., multiple different VMA controllers). When an equipment definition is applied to a BMS device, the search criteria specified by the point definitions can be used to automatically identify data points provided by the BMS device that satisfy each point definition.

In some embodiments, equipment definitions140define data point types as generalized types of data without regard to the model, manufacturer, vendor, or other differences between building equipment of the same general type. The generalized data points defined by equipment definitions140allows each equipment definition to be referenced by or applied to multiple different variants of the same type of building equipment.

In some embodiments, equipment definitions140facilitate the presentation of data points in a consistent and user-friendly manner. For example, each equipment definition may define one or more data points that are displayed via a user interface. The displayed data points may be a subset of the data points defined by the equipment definition.

In some embodiments, equipment definitions140specify a system type (e.g., HVAC, lighting, security, fire, etc.), a system sub-type (e.g., terminal units, air handlers, central plants), and/or data category (e.g., critical, diagnostic, operational) associated with the building equipment defined by each equipment definition. Specifying such attributes of building equipment at the equipment definition level allows the attributes to be applied to the building equipment along with the equipment definition when the building equipment is initially defined. Building equipment can be filtered by various attributes provided in the equipment definition to facilitate the reporting and management of equipment data from multiple building systems.

Equipment definitions140can be automatically created by abstracting the data points provided by archetypal controllers (e.g., typical or representative controllers) for various types of building equipment. In some embodiments, equipment definitions140are created by equipment definition module154, described in greater detail below.

Still referring toFIG.3, memory138is shown to include equipment objects144. Equipment objects144may be software objects that define a mapping between a data point type (e.g., supply air temperature, room temperature, damper position) and an actual data point (e.g., a measured or calculated value for the corresponding data point type) for various pieces of building equipment. Equipment objects144may facilitate the presentation of equipment-specific data points in an intuitive and user-friendly manner by associating each data point with an attribute identifying the corresponding data point type. The mapping provided by equipment objects144may be used to associate a particular data value measured or calculated by BMS11with an attribute that can be displayed via a user interface.

Equipment objects144can be created (e.g., by equipment object creation module156) by referencing equipment definitions140. For example, an equipment object can be created by applying an equipment definition to the data points provided by a BMS device. The search criteria included in an equipment definition can be used to identify data points of the building equipment that satisfy the point definitions. A data point that satisfies a point definition can be mapped to an attribute of the equipment object corresponding to the point definition.

Each equipment object may include one or more attributes defined by the point definitions of the equipment definition used to create the equipment object. For example, an equipment definition which defines the attributes “Occupied Command,” “Room Temperature,” and “Damper Position” may result in an equipment object being created with the same attributes. The search criteria provided by the equipment definition are used to identify and map data points associated with a particular BMS device to the attributes of the equipment object. The creation of equipment objects is described in greater detail below with reference to equipment object creation module156.

Equipment objects144may be related with each other and/or with building objects142. Causal relationships can be established between equipment objects to link equipment objects to each other. For example, a causal relationship can be established between a VMA and an AHU which provides airflow to the VMA. Causal relationships can also be established between equipment objects144and building objects142. For example, equipment objects144can be associated with building objects142representing particular rooms or zones to indicate that the equipment object serves that room or zone. Relationships between objects are described in greater detail below with reference to object relationship module158.

Still referring toFIG.3, memory138is shown to include client services146and application services148. Client services146may be configured to facilitate interaction and/or communication between BMS controller12and various internal or external clients or applications. For example, client services146may include web services or application programming interfaces available for communication by UI clients16and remote applications18(e.g., applications running on a mobile device, energy monitoring applications, applications allowing a user to monitor the performance of the BMS, automated fault detection and diagnostics systems, etc.). Application services148may facilitate direct or indirect communications between remote applications18, local applications150, and BMS controller12. For example, application services148may allow BMS controller12to communicate (e.g., over a communications network) with remote applications18running on mobile devices and/or with other BMS controllers.

In some embodiments, application services148facilitate an applications gateway for conducting electronic data communications with UI clients16and/or remote applications18. For example, application services148may be configured to receive communications from mobile devices and/or BMS devices. Client services146may provide client devices with a graphical user interface that consumes data points and/or display data defined by equipment definitions140and mapped by equipment objects144.

Still referring toFIG.3, memory138is shown to include a building object creation module152. Building object creation module152may be configured to create the building objects stored in building objects142. Building object creation module152may create a software building object for various spaces within building10. Building object creation module152can create a building object for a space of any size or granularity. For example, building object creation module152can create a building object representing an entire building, a floor of a building, or individual rooms on each floor. In some embodiments, building object creation module152creates and/or stores a building object in memory138for each zone or room of building10.

The building objects created by building object creation module152can be accessed by UI clients16and remote applications18to provide a comprehensive user interface for controlling and/or viewing information for a particular building zone. Building objects142can group otherwise ungrouped or unassociated devices so that the group may be addressed or handled by applications together and in a consistent manner (e.g., a single user interface for controlling all of the BMS devices that affect a particular building zone or room). In some embodiments, building object creation module152uses the systems and methods described in U.S. patent application Ser. No. 12/887,390, filed Sep. 21, 2010, for creating software defined building objects.

In some embodiments, building object creation module152provides a user interface for guiding a user through a process of creating building objects. For example, building object creation module152may provide a user interface to client devices (e.g., via client services146) that allows a new space to be defined. In some embodiments, building object creation module152defines spaces hierarchically. For example, the user interface for creating building objects may prompt a user to create a space for a building, for floors within the building, and/or for rooms or zones within each floor.

In some embodiments, building object creation module152creates building objects automatically or semi-automatically. For example, building object creation module152may automatically define and create building objects using data imported from another data source (e.g., user view folders, a table, a spreadsheet, etc.). In some embodiments, building object creation module152references an existing hierarchy for BMS11to define the spaces within building10. For example, BMS11may provide a listing of controllers for building10(e.g., as part of a network of data points) that have the physical location (e.g., room name) of the controller in the name of the controller itself. Building object creation module152may extract room names from the names of BMS controllers defined in the network of data points and create building objects for each extracted room. Building objects may be stored in building objects142.

Still referring toFIG.3, memory138is shown to include an equipment definition module154. Equipment definition module154may be configured to create equipment definitions for various types of building equipment and to store the equipment definitions in equipment definitions140. In some embodiments, equipment definition module154creates equipment definitions by abstracting the data points provided by archetypal controllers (e.g., typical or representative controllers) for various types of building equipment. For example, equipment definition module154may receive a user selection of an archetypal controller via a user interface. The archetypal controller may be specified as a user input or selected automatically by equipment definition module154. In some embodiments, equipment definition module154selects an archetypal controller for building equipment associated with a terminal unit such as a VMA.

Equipment definition module154may identify one or more data points associated with the archetypal controller. Identifying one or more data points associated with the archetypal controller may include accessing a network of data points provided by BMS11. The network of data points may be a hierarchical representation of data points that are measured, calculated, or otherwise obtained by various BMS devices. BMS devices may be represented in the network of data points as nodes of the hierarchical representation with associated data points depending from each BMS device. Equipment definition module154may find the node corresponding to the archetypal controller in the network of data points and identify one or more data points which depend from the archetypal controller node.

Equipment definition module154may generate a point definition for each identified data point of the archetypal controller. Each point definition may include an abstraction of the corresponding data point that is applicable to multiple different controllers for the same type of building equipment. For example, an archetypal controller for a particular VMA (i.e., “VMA-20”) may be associated an equipment-specific data point such as “VMA-20.DPR-POS” (i.e., the damper position of VMA-20) and/or “VMA-20.SUP-FLOW” (i.e., the supply air flow rate through VMA-20). Equipment definition module154abstract the equipment-specific data points to generate abstracted data point types that are generally applicable to other equipment of the same type. For example, equipment definition module154may abstract the equipment-specific data point “VMA-20.DPR-POS” to generate the abstracted data point type “DPR-POS” and may abstract the equipment-specific data point “VMA-20.SUP-FLOW” to generate the abstracted data point type “SUP-FLOW.” Advantageously, the abstracted data point types generated by equipment definition module154can be applied to multiple different variants of the same type of building equipment (e.g., VMAs from different manufacturers, VMAs having different models or output data formats, etc.).

In some embodiments, equipment definition module154generates a user-friendly label for each point definition. The user-friendly label may be a plain text description of the variable defined by the point definition. For example, equipment definition module154may generate the label “Supply Air Flow” for the point definition corresponding to the abstracted data point type “SUP-FLOW” to indicate that the data point represents a supply air flow rate through the VMA. The labels generated by equipment definition module154may be displayed in conjunction with data values from BMS devices as part of a user-friendly interface.

In some embodiments, equipment definition module154generates search criteria for each point definition. The search criteria may include one or more parameters for identifying another data point (e.g., a data point associated with another controller of BMS11for the same type of building equipment) that represents the same variable as the point definition. Search criteria may include, for example, an instance number of the data point, a network address of the data point, and/or a network point type of the data point.

In some embodiments, search criteria include a text string abstracted from a data point associated with the archetypal controller. For example, equipment definition module154may generate the abstracted text string “SUP-FLOW” from the equipment-specific data point “VMA-20.SUP-FLOW.” Advantageously, the abstracted text string matches other equipment-specific data points corresponding to the supply air flow rates of other BMS devices (e.g., “VMA-18.SUP-FLOW,” “SUP-FLOW. VMA-01,” etc.). Equipment definition module154may store a name, label, and/or search criteria for each point definition in memory138.

Equipment definition module154may use the generated point definitions to create an equipment definition for a particular type of building equipment (e.g., the same type of building equipment associated with the archetypal controller). The equipment definition may include one or more of the generated point definitions. Each point definition defines a potential attribute of BMS devices of the particular type and provides search criteria for identifying the attribute among other data points provided by such BMS devices.

In some embodiments, the equipment definition created by equipment definition module154includes an indication of display data for BMS devices that reference the equipment definition. Display data may define one or more data points of the BMS device that will be displayed via a user interface. In some embodiments, display data are user defined. For example, equipment definition module154may prompt a user to select one or more of the point definitions included in the equipment definition to be represented in the display data. Display data may include the user-friendly label (e.g., “Damper Position”) and/or short name (e.g., “DPR-POS”) associated with the selected point definitions.

In some embodiments, equipment definition module154provides a visualization of the equipment definition via a graphical user interface. The visualization of the equipment definition may include a point definition portion which displays the generated point definitions, a user input portion configured to receive a user selection of one or more of the point definitions displayed in the point definition portion, and/or a display data portion which includes an indication of an abstracted data point corresponding to each of the point definitions selected via the user input portion. The visualization of the equipment definition can be used to add, remove, or change point definitions and/or display data associated with the equipment definitions.

Equipment definition module154may generate an equipment definition for each different type of building equipment in BMS11(e.g., VMAs, chillers, AHUs, etc.). Equipment definition module154may store the equipment definitions in a data storage device (e.g., memory138, equipment definitions140, an external or remote data storage device, etc.).

Still referring toFIG.3, memory138is shown to include an equipment object creation module156. Equipment object creation module156may be configured to create equipment objects for various BMS devices. In some embodiments, equipment object creation module156creates an equipment object by applying an equipment definition to the data points provided by a BMS device. For example, equipment object creation module156may receive an equipment definition created by equipment definition module154. Receiving an equipment definition may include loading or retrieving the equipment definition from a data storage device.

In some embodiments, equipment object creation module156determines which of a plurality of equipment definitions to retrieve based on the type of BMS device used to create the equipment object. For example, if the BMS device is a VMA, equipment object creation module156may retrieve the equipment definition for VMAs; whereas if the BMS device is a chiller, equipment object creation module156may retrieve the equipment definition for chillers. The type of BMS device to which an equipment definition applies may be stored as an attribute of the equipment definition. Equipment object creation module156may identify the type of BMS device being used to create the equipment object and retrieve the corresponding equipment definition from the data storage device.

In other embodiments, equipment object creation module156receives an equipment definition prior to selecting a BMS device. Equipment object creation module156may identify a BMS device of BMS11to which the equipment definition applies. For example, equipment object creation module156may identify a BMS device that is of the same type of building equipment as the archetypal BMS device used to generate the equipment definition. In various embodiments, the BMS device used to generate the equipment object may be selected automatically (e.g., by equipment object creation module156), manually (e.g., by a user) or semi-automatically (e.g., by a user in response to an automated prompt from equipment object creation module156).

In some embodiments, equipment object creation module156creates an equipment discovery table based on the equipment definition. For example, equipment object creation module156may create an equipment discovery table having attributes (e.g., columns) corresponding to the variables defined by the equipment definition (e.g., a damper position attribute, a supply air flow rate attribute, etc.). Each column of the equipment discovery table may correspond to a point definition of the equipment definition. The equipment discovery table may have columns that are categorically defined (e.g., representing defined variables) but not yet mapped to any particular data points.

Equipment object creation module156may use the equipment definition to automatically identify one or more data points of the selected BMS device to map to the columns of the equipment discovery table. Equipment object creation module156may search for data points of the BMS device that satisfy one or more of the point definitions included in the equipment definition. In some embodiments, equipment object creation module156extracts a search criterion from each point definition of the equipment definition. Equipment object creation module156may access a data point network of the building automation system to identify one or more data points associated with the selected BMS device. Equipment object creation module156may use the extracted search criterion to determine which of the identified data points satisfy one or more of the point definitions.

In some embodiments, equipment object creation module156automatically maps (e.g., links, associates, relates, etc.) the identified data points of selected BMS device to the equipment discovery table. A data point of the selected BMS device may be mapped to a column of the equipment discovery table in response to a determination by equipment object creation module156that the data point satisfies the point definition (e.g., the search criteria) used to generate the column. For example, if a data point of the selected BMS device has the name “VMA-18.SUP-FLOW” and a search criterion is the text string “SUP-FLOW,” equipment object creation module156may determine that the search criterion is met. Accordingly, equipment object creation module156may map the data point of the selected BMS device to the corresponding column of the equipment discovery table.

Advantageously, equipment object creation module156may create multiple equipment objects and map data points to attributes of the created equipment objects in an automated fashion (e.g., without human intervention, with minimal human intervention, etc.). The search criteria provided by the equipment definition facilitates the automatic discovery and identification of data points for a plurality of equipment object attributes. Equipment object creation module156may label each attribute of the created equipment objects with a device-independent label derived from the equipment definition used to create the equipment object. The equipment objects created by equipment object creation module156can be viewed (e.g., via a user interface) and/or interpreted by data consumers in a consistent and intuitive manner regardless of device-specific differences between BMS devices of the same general type. The equipment objects created by equipment object creation module156may be stored in equipment objects144.

Still referring toFIG.3, memory138is shown to include an object relationship module158. Object relationship module158may be configured to establish relationships between equipment objects144. In some embodiments, object relationship module158establishes causal relationships between equipment objects144based on the ability of one BMS device to affect another BMS device. For example, object relationship module158may establish a causal relationship between a terminal unit (e.g., a VMA) and an upstream unit (e.g., an AHU, a chiller, etc.) which affects an input provided to the terminal unit (e.g., air flow rate, air temperature, etc.).

Object relationship module158may establish relationships between equipment objects144and building objects142(e.g., spaces). For example, object relationship module158may associate equipment objects144with building objects142representing particular rooms or zones to indicate that the equipment object serves that room or zone. In some embodiments, object relationship module158provides a user interface through which a user can define relationships between equipment objects144and building objects142. For example, a user can assign relationships in a “drag and drop” fashion by dragging and dropping a building object and/or an equipment object into a “serving” cell of an equipment object provided via the user interface to indicate that the BMS device represented by the equipment object serves a particular space or BMS device.

Still referring toFIG.3, memory138is shown to include a building control services module160. Building control services module160may be configured to automatically control BMS11and the various subsystems thereof. Building control services module160may utilize closed loop control, feedback control, PI control, model predictive control, or any other type of automated building control methodology to control the environment (e.g., a variable state or condition) within building10.

Building control services module160may receive inputs from sensory devices (e.g., temperature sensors, pressure sensors, flow rate sensors, humidity sensors, electric current sensors, cameras, radio frequency sensors, microphones, etc.), user input devices (e.g., computer terminals, client devices, user devices, etc.) or other data input devices via BMS interface132. Building control services module160may apply the various inputs to a building energy use model and/or a control algorithm to determine an output for one or more building control devices (e.g., dampers, air handling units, chillers, boilers, fans, pumps, etc.) in order to affect a variable state or condition within building10(e.g., zone temperature, humidity, air flow rate, etc.).

In some embodiments, building control services module160is configured to control the environment of building10on a zone-individualized level. For example, building control services module160may control the environment of two or more different building zones using different setpoints, different constraints, different control methodology, and/or different control parameters. Building control services module160may operate BMS11to maintain building conditions (e.g., temperature, humidity, air quality, etc.) within a setpoint range, to optimize energy performance (e.g., to minimize energy consumption, to minimize energy cost, etc.), and/or to satisfy any constraint or combination of constraints as may be desirable for various implementations.

In some embodiments, building control services module160uses the location of various BMS devices to translate an input received from a building system into an output or control signal for the building system. Building control services module160may receive location information for BMS devices and automatically set or recommend control parameters for the BMS devices based on the locations of the BMS devices. For example, building control services module160may automatically set a flow rate setpoint for a VAV box based on the size of the building zone in which the VAV box is located.

Building control services module160may determine which of a plurality of sensors to use in conjunction with a feedback control loop based on the locations of the sensors within building10. For example, building control services module160may use a signal from a temperature sensor located in a building zone as a feedback signal for controlling the temperature of the building zone in which the temperature sensor is located.

In some embodiments, building control services module160automatically generates control algorithms for a controller or a building zone based on the location of the zone in the building10. For example, building control services module160may be configured to predict a change in demand resulting from sunlight entering through windows based on the orientation of the building and the locations of the building zones (e.g., east-facing, west-facing, perimeter zones, interior zones, etc.).

Building control services module160may use zone location information and interactions between adjacent building zones (rather than considering each zone as an isolated system) to more efficiently control the temperature and/or airflow within building10. For control loops that are conducted at a larger scale (i.e., floor level) building control services module160may use the location of each building zone and/or BMS device to coordinate control functionality between building zones. For example, building control services module160may consider heat exchange and/or air exchange between adjacent building zones as a factor in determining an output control signal for the building zones.

In some embodiments, building control services module160is configured to optimize the energy efficiency of building10using the locations of various BMS devices and the control parameters associated therewith. Building control services module160may be configured to achieve control setpoints using building equipment with a relatively lower energy cost (e.g., by causing airflow between connected building zones) in order to reduce the loading on building equipment with a relatively higher energy cost (e.g., chillers and roof top units). For example, building control services module160may be configured to move warmer air from higher elevation zones to lower elevation zones by establishing pressure gradients between connected building zones.

Referring now toFIG.4, another block diagram illustrating a portion of BMS11in greater detail is shown, according to some embodiments. BMS11can be implemented in building10to automatically monitor and control various building functions. BMS11is shown to include BMS controller12and a plurality of building subsystems428. Building subsystems428are shown to include a building electrical subsystem434, an information communication technology (ICT) subsystem436, a security subsystem438, a HVAC subsystem440, a lighting subsystem442, a lift/escalators subsystem432, and a fire safety subsystem430. In various embodiments, building subsystems428can include fewer, additional, or alternative subsystems. For example, building subsystems428may also or alternatively include a refrigeration subsystem, an advertising or signage subsystem, a cooking subsystem, a vending subsystem, a printer or copy service subsystem, or any other type of building subsystem that uses controllable equipment and/or sensors to monitor or control building10.

Each of building subsystems428can include any number of devices, controllers, and connections for completing its individual functions and control activities. HVAC subsystem440can include many of the same components as HVAC system20, as described with reference toFIGS.2-3. For example, HVAC subsystem440can include a chiller, a boiler, any number of air handling units, economizers, field controllers, supervisory controllers, actuators, temperature sensors, and other devices for controlling the temperature, humidity, airflow, or other variable conditions within building10. Lighting subsystem442can include any number of light fixtures, ballasts, lighting sensors, dimmers, or other devices configured to controllably adjust the amount of light provided to a building space. Security subsystem438can include occupancy sensors, video surveillance cameras, digital video recorders, video processing servers, intrusion detection devices, access control devices and servers, or other security-related devices.

Still referring toFIG.4, BMS controller12is shown to include a communications interface407and a BMS interface132. Interface407may facilitate communications between BMS controller12and external applications (e.g., monitoring and reporting applications422, enterprise control applications426, remote systems and applications444, applications residing on client devices448, etc.) for allowing user control, monitoring, and adjustment to BMS controller12and/or subsystems428. Interface407may also facilitate communications between BMS controller12and client devices448. BMS interface132may facilitate communications between BMS controller12and building subsystems428(e.g., HVAC, lighting security, lifts, power distribution, business, etc.).

Interfaces407,132can be or include wired or wireless communications interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wire terminals, etc.) for conducting data communications with building subsystems428or other external systems or devices. In various embodiments, communications via interfaces407,132can be direct (e.g., local wired or wireless communications) or via a communications network446(e.g., a WAN, the Internet, a cellular network, etc.). For example, interfaces407,132can include an Ethernet card and port for sending and receiving data via an Ethernet-based communications link or network. In another example, interfaces407,132can include a Wi-Fi transceiver for communicating via a wireless communications network. In another example, one or both of interfaces407,132can include cellular or mobile phone communications transceivers. In one embodiment, communications interface407is a power line communications interface and BMS interface132is an Ethernet interface. In other embodiments, both communications interface407and BMS interface132are Ethernet interfaces or are the same Ethernet interface.

Still referring toFIG.4, BMS controller12is shown to include a processing circuit134including a processor136and memory138. Processing circuit134can be communicably connected to BMS interface132and/or communications interface407such that processing circuit134and the various components thereof can send and receive data via interfaces407,132. Processor136can be implemented as a general purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable electronic processing components.

Memory138(e.g., memory, memory unit, storage device, etc.) can include one or more devices (e.g., RAM, ROM, Flash memory, hard disk storage, etc.) for storing data and/or computer code for completing or facilitating the various processes, layers and modules described in the present application. Memory138can be or include volatile memory or non-volatile memory. Memory138can include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present application. According to some embodiments, memory138is communicably connected to processor136via processing circuit134and includes computer code for executing (e.g., by processing circuit134and/or processor136) one or more processes described herein.

In some embodiments, BMS controller12is implemented within a single computer (e.g., one server, one housing, etc.). In various other embodiments BMS controller12can be distributed across multiple servers or computers (e.g., that can exist in distributed locations). Further, whileFIG.4shows applications422and426as existing outside of BMS controller12, in some embodiments, applications422and426can be hosted within BMS controller12(e.g., within memory138).

Building subsystem integration layer420can be configured to manage communications between BMS controller12and building subsystems428. For example, building subsystem integration layer420may receive sensor data and input signals from building subsystems428and provide output data and control signals to building subsystems428. Building subsystem integration layer420may also be configured to manage communications between building subsystems428. Building subsystem integration layer420translate communications (e.g., sensor data, input signals, output signals, etc.) across a plurality of multi-vendor/multi-protocol systems.

Regional Intelligence for Building Management Systems Referring now toFIG.5, a diagram of a system500including a computing system502communicable with multiple building management systems (shown as BMSs11a-11m) is shown, according to some embodiments.FIG.5illustrates that the multiple BMSs11a-11mcan be located in a variety of geographic regions, for example various regions of a country (e.g., the United States as shown), various regions of a continent, various regions of the world, etc. Any number of BMSs can be included in any embodiment.

The BMSs11a-11mcan be various different building management systems similar to BMS11described above, adapted to serve buildings in different geographic regions as illustrated inFIG.5. For example, BMS11imay be located in a relatively warm climate as compared to BMS11d, and may therefore include more cooling-related components as compared to BMS11d(which may include more heating-related components). As another example, BMS11bmay be located in a region where the primary energy sources is electricity from an energy grid, while BMS11fmay be located in a region where natural gas is a widely-available source of energy for consumption by building equipment, with the result that suitable equipment used in the different BMSs is different in different regions. Other differences can be rooted in legacy equipment, building age, culture, etc. which drives differences common to different regions (e.g., radiant heat may be often used in one region whereas forced air heat may be used in another region). BMSs11a-mmay therefore be different from one another in ways driven by association of the BMSs11a-mwith different geographic regions. Different equipment, conditions, climate, architecture, etc. can result in a need for different configurations, parameters, settings, installations, devices, etc. for the different BMSs11a-min different regions.

The computing system502is communicable with the BMSs11a-m(e.g., via the Internet) and can be positioned in any geographic region or may be distributed across multiple geographic regions. The computing system502and provide various remote services, support, functions, etc. to the various BMSs11a-m. The computing system502can also generate high level insights using data from the multiple BMSs11a-m, for example facilitating enabling comparisons of performance across BMSs11a-m. In some embodiments, the computing system502is configured to provide setpoints, control decisions, configuration parameters, control logic, automated troubleshooting routines, etc. to the BMSs11a-mto affect operation of equipment and devices of such BMSs11a-m. The computing system502may also be configured to provide (e.g., host) one or more graphical user interfaces accessible via a user device (e.g., smartphone via an Internet browser or mobile application) and displaying information to such users associated with configuring, troubleshooting, and/or otherwise monitoring or managing one or more of the BMSs11a-m.

The system500, building automation information can be communicated with the multiple BMSs11a-mand the computing system502, for example via one or more networks (e.g., via the Internet). Information can flow directly between the computing system502, and the BMSs11a-m, between different BMSs11a-m, from a first BMS (e.g., BMS11a) to a second BMS (e.g., BMS11b) via the computing system502, and/or otherwise communicated amongst the BMSs11a-mand the computing system502. Building automation can include sensor data (e.g., measurements of temperature, humidity, air quality, pressure, air flow, vibration, luminosity), meter data (e.g., power usage, energy consumption, gas usage, water usage, etc.), setpoints (e.g., temperature setpoints, airflow setpoints, etc.), pollution or emissions data (e.g., data relating to carbon emissions attributable to a building management system), configuration parameters and device capacities (e.g., network identities, CPU usage, memory usage, bandwidth requirements, etc.), equipment models, digital twin information, building utlilization (e.g., occupancy, events, productivity), operational changes, recommendations, troubleshooting instructions, insights, alerts, faults, alarms, etc. Various information relating to one or more BMSs11a-mand/or computing system502can be communicated in the system500in various embodiments.

In some embodiments, the system500is configured to automatically adjust the building automation information in accordance with different regional terminology used by the multiple BMSs11a-m(and, in some embodiments, by the computing system502). That is, building automation information can be automatically translated between different sets of local terminology when communicated within the system500which includes components distributed across multiple geographic regions as shown inFIG.5.

In some embodiments, the computing system502includes circuitry programmed (e.g., with instructions stored in non-transitory computer-readable media) configured provide such adjustments (translations, etc.) of regional terminology. In other embodiments, the BMSs11a-mmay include, locally at different geographic regions, circuitry programmed to translate inbound communications into a suitable regional terminology for that region and/or to translate outbound communications into suitable regional terminology (or genericized/normalized terminology) for an intended recipient (e.g., computing system502) of such outbound communications.

Automated translations may be performed using deep learning models, large language models, natural language processing, neural networks, machine learning models, artificial intelligence, etc. in various embodiments. In some embodiments, one or more artificial intelligence models (e.g., machine learning models) are used and are trained on data collected via the BMSs11a-m. For example, the BMSs11a-mmay prompt local users in the multiple geographic regions to answer questions relating to different regional terminology used by the users. For example, users in different regions may prompted input the local name for a particular type of equipment, equipment parameter, physical phenomenon, etc. and may input different terms due to regional differences in terminology. The prompt for information may include illustrations, images, graphics, etc., for example without words therein in order to avoid guiding the user to a different terminology than typically used by that user. Data can thereby be collected from users at multiple geographic regions that is indicative of terminology used in the multiple geographic regions. A training process (e.g., supervised learning, unsupervised learning) can then be applied using such data to train one or more artificial intelligence models to automatically translate between terminology used in such regions. Such a training and feedback process can be used to train (e.g., fine-tune) at least one generative AI model, for example according to the teachings of, the entire disclosure of which is incorporated by reference herein.

In some embodiments, automated translations are provided using tables or other maps storing relationships between terms as used in different regions. For example, for a given physical parameter, a generic identifier can be stored along with the different words used to describe such a parameter in the different geographic regions. Using such terminology dictionaries (e.g., populated using AI using a similar approach as above, populated by human experts, etc.) circuitry can be programmed to automatically uses such dictionaries to provide automated translations between geographic regions.

In some embodiments, these features enable building equipment of at least a first building automation system (e.g., any one of BMSs11a-m) to be controlled based on inputs provided by a first user in a first geographic region using a first regional terminology associated with the first geographic region and inputs provided by a second user in a second geographic region using a second regional terminology associated with the second geographic region. In some embodiments, the corresponding building equipment is at the first geographic region and a manufacturer support person or other service provider is at the second geographic region, enabling support across geographic regions while persons at both regions are able to work natively within the system500in terminology used in their respective regions. The system500seamlessly handles inputs in different regional terminologies and adjust operation of equipment accordingly.

In some embodiments, each user of the system500has a personalized terminology with individualized translations using models as described above. The terminology can be user-defined, trained via a set-up stage for a user account, etc. Such a feature enables user-level terminologies as an alternative or in addition to regional terminologies.

In some embodiments, the system500(e.g., the computing system502, the BMSs11a-m, and/or some combination thereof) is configured to normalize point labels for data provided by the plurality of building automation systems to facilitate analysis of aggregations of the data from the plurality of building automation systems. Data created in different terminologies can be automatically translated into a single terminology using the teachings herein, such that single terminology can then be used in analyses of aggregated data (e.g., for enterprise-wide analysis, for comparison of regions, for comparison of BMSs, etc.). Performance indicators, recommendations, control settings, and other insights driven by analysis of aggregated can be transmitted back to different BMSs11a-malong with translation into regional terminologies suitable for the recipients of such information.

The system500thereby enables seamless interoperability of building automation systems across multiple languages, dialects, regional terminologies, etc., thereby enabling broad geographic distribution (e.g., worldwide, nationwide, etc.) of equipment (e.g., building network devices, controllers, gateways, building equipment) and services (e.g., software-as-a-service, energy-management-as-a-service, carbon-neutral-as-a-service, net-zero-as-a-service) while eliminating confusion or barriers driven by terminological differences across geographic regions.

Referring now toFIG.6, a flowchart of a process600is shown, according to some embodiments. The process600can be executed by any of the various elements of the system500, in isolation and/or in combination, in various embodiments. In some embodiments, the process600is executed by or using a personal computing device, for example a personal computing device in communication with a remote computing system (e.g., cloud system). The personal computing device may be a smartphone, tablet, laptop computer, desktop computer, virtual or augmented reality headset, etc. in various embodiments. Some or all of process600can execute at the edge (e.g., on a unit of building equipment, on a gateway or controller of a building management system, etc.).

At step600, an artificial intelligence tool is selected from a set of available artificial intelligence tools based on a geographic region, for example a geographic region of a building automation system or unit of building equipment. The geographic region can be determined automatically, for example using global positioning satellite (GPS) coordinates from a GPS chip included with the bundling automation system or unit of building equipment, using an IP address or other networking information relating to the building automation system or unit of building equipment, etc. In some embodiments, a graphical user interface is provided with allows a user to select a geographic region via the graphical user interface (e.g., from a list, from a map view, etc.).

The set of available artificial intelligence tools can include one or more artificial intelligence tool associated with each of the selectable geographic regions. The artificial intelligence tools can be configured to provide various building configuration, control, and management features, for example described in other sections of the present application. In the embodiments shown inFIG.6, at least a subset of the artificial intelligence tools relate to troubleshooting applications, for example to generating, ranking, filtering, etc. troubleshooting steps to facilitate system reconfiguration, testing, validation, etc. to improve system performance of a building automation system. The artificial intelligence tools can include models trained via supervised learning, decision tree learning, association rule learning, inductive logic programming, support vector machines, unsupervised learning, clustering, similarity and metric learning, sparse dictionary learning, genetic algorithms, reinforcement learning, Bayesian networks, neural networks, deep learning manifold learning, etc.

In some embodiments, each artificial intelligence tool is or includes a different generative artificial intelligence model, for example a generative artificial models fine-tuned based on different, regional training data to be adapted to regional details (e.g., regional service data, regional building operating data, regional warranty data, etc.). For example, model fine-tuning based on service and/or warranty data as in U.S. Provisional Patent Application No. 63/470,754 filed Jun. 2, 2023, incorporated by reference in its entirety herein, can be implemented to separately fine tune a generative AI model to have regional intelligence by separating the service and/or warranty data by geographical region, using the service and/or warranty data for a first region to fine-tune the generative AI model according to the teachings therein to get a first fine-tuned model, using the service and/or warranty data for a second region to fine-tune the generative AI model according to the teachings therein to get a second fine-tuned model for the second region, etc. for the number of desired geographic regions.

In some embodiments, each artificial intelligence tool includes as generative artificial intelligence model and a model augmentation functions (e.g., feature generation algorithm, translation tool, pre- and/or post-processing module, etc.). The different model augmentation functions augment the full generative artificial intelligence models, and may be substantially smaller than and/or more efficient (e.g., easier, faster, on less training data, less computationally expensive, etc.) to train, fit, adapted, etc. as compared to the generative artificial intelligence model, while operating to adjust operation of the generative artificial intelligence model to adapt to particular geographic/regional characteristics (e.g., by adjust vectors input to and/or output from the at least one generative AI model). Accordingly, in some embodiments, the same generative artificial intelligence model may be used with different model augmentation functions, where each model augmentation function is associated with a different geographic region such that multiple artificial intelligence tools can be provided for multiple geographic regions without requiring memory-intensive storage of multiple different generative AI models.

Selecting one or more artificial intelligence tools associated with a selected geographic region ensures that the artificial intelligence tools are suitable for the corresponding region. For example, the artificial intelligence tools for a region may be trained using training data specific to that region. As another example, the artificial intelligence tools may be designed or provided based on types of equipment typical in the corresponding region. As another example, the artificial intelligence tools may be adapted to use terminology or other context associated with a corresponding geographic region (e.g., by training, fine-tuning, or augmenting each artificial intelligence tool using training data from the corresponding geographic region). Step602thereby enables the geographic location of the building automation system, building equipment, or other relevant device to drive selection of an appropriate artificial intelligence tool (e.g., AI model, machine learning model, neural network, etc.) that may be better suited to that geographic region than an artificial intelligence tool intended to work across many geographic regions (e.g., thus providing more accurate classifications, more reliable predictions or troubleshooting recommendations, lower false-positive and false-negative rates, more optimal results in terms of energy, cost, and/or emissions savings, better occupant comfort, etc.). In some embodiments, the set of artificial intelligence tools is further narrowed based on a building domain to be trouble shot (e.g., HVAC, fire, security, access control, lighting, etc.), such that the selection in step602is further or alternatively based on building domain.

In the embodiments ofFIG.6, at step604troubleshooting options are ranked for the building automation system by applying an artificial intelligence tool selected in step602to data associated with the building automation system. The data can include sensor measurements, meter data, setpoints, settings, configuration parameters, digital twin information, equipment models, weather information, building utilization information, and/or other information relating to the building automation system in various embodiments. The artificial intelligence tool can be trained (e.g., using results of historical troubleshooting, using data representing successful configurations of other building management systems, in a supervised training approach, based on such information and/or supervised training particularly associated with the relevant geographic region) to rank troubleshooting options from a list of available troubleshooting options, for example using a neural network or other AI model that outputs a likelihood that each troubleshooting option will lead to resolution of an issue or otherwise provide a performance improvement. A ranked set of troubleshooting options can thus be output from step604.

At step606, at least a first troubleshooting option is implemented. The first troubleshooting option may be the highest-ranked by the artificial intelligence tool, e.g., ranked higher than other options in step604. Implementing the troubleshooting option may include providing instructions or recommendations to a technician, for example automatically generating a work order for a technician to carry out actions to implement the troubleshooting options. Implementing the troubleshooting option may include automatically changing control logic, settings, setpoints, configurations, parameters, etc. of building equipment or devices (e.g., in software), thereby automatically affecting operating of building equipment or devices to implementing a troubleshooting option (e.g., in a closed-loop manner). Implementing the troubleshooting option can include installing one or more devices or units or building equipment, for example to correct deficiencies in an existing building management system. Implementing the troubleshooting option can include adjusting (e.g., moving, reorienting, rearranging, reconnecting, rewiring, etc.) an installation of one or more devices or units of building equipment. All of these and other possibilities are within the scope of the present disclosure.

By following process600, troubleshooting options suitable for a particular location and prioritized using artificial intelligence can be implemented. Process600thereby solves challenges whereby typical training materials or troubleshooting suggestions are published from a central source (e.g., a manufacturer) in a single list which does not account for regional variation or have regional knowledge that can render troubleshooting options suitable for one region unsuitable for another region. Efficient troubleshooting and other services can thus be enabled by the features of process600, thereby efficiently solving technical issues with operation of building management systems and building equipment.

While the example of process600refers to troubleshooting options, process600can also be used to provide other workflow recommendations and execution. For example, process600can be adapted to automatically provide, with regional intelligence according to the teachings process600, building system design and engineering recommendations, installation workflow instructions, building system commissioning, testing, and validation instructions or control, maintenance recommendations, etc. to provide building engineers, managers, technicians, etc. with regionally-tuned information through the building life-cycle. For example, the various service recommendations, virtual assistant applications, etc. described in U.S. Provisional Application No. 63/466,603, filed May 15, 2023, incorporated by reference in its entirety herein, can be provided by selecting between different geographic-region-specific artificial intelligence tools as in process600so as to provide regionally-intelligent virtual assistance with a variety of installation, commissioning, and service tasks for building systems.

Configuration of Exemplary Embodiments