Patent Application
20230417439
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.
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.
Various objects, aspects, features, and advantages of the disclosure will become more apparent and better understood by referring to the detailed description taken in conjunction with the accompanying drawings, in which like reference characters identify corresponding elements throughout. In the drawings, like reference numbers generally indicate identical, functionally similar, and/or structurally similar elements.
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.
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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 building 10. 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 building 10. The terms “BMS devices,” “BMS device” and “building equipment” are used interchangeably throughout this disclosure.
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Middleware 14 may include services that allow interoperable communication to, from, or between disparate BMS subsystems 20-26 of BMS 11 (e.g., HVAC systems from different manufacturers, HVAC systems that communicate according to different protocols, security/fire systems, IT resources, door access systems, etc.). Middleware 14 may be, for example, an EnNet server sold by Johnson Controls, Inc. While middleware 14 is shown as separate from BMS controller 12, middleware 14 and BMS controller 12 may integrated in some embodiments. For example, middleware 14 may be a part of BMS controller 12.
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Lighting system 24 may receive lighting related information from a plurality of downstream light controls (e.g., from room lighting 104). Door access system 26 may receive lock control, motion, state, or other door related information from a plurality of downstream door controls. Door access system 26 is shown to include door access pad 106 (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 subsystems 20-26 may be connected to BMS controller 12 via middleware 14 and may be configured to provide BMS controller 12 with BMS inputs from various BMS subsystems 20-26 and their varying downstream devices. BMS controller 12 may 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) clients 16, remote applications 18, etc.). BMS controller 12 may 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.
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Each of the building devices shown at the top of
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In some embodiments, BMS interface 132 and/or middleware 14 includes an application gateway configured to receive input from applications running on client devices. For example, BMS interface 132 and/or middleware 14 may 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 interface 132 may be configured to receive building management inputs from middleware 14 or directly from one or more BMS subsystems 20-26. BMS interface 132 and/or middleware 14 can include any number of software buffers, queues, listeners, filters, translators, or other communications-supporting services.
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Memory 138 may 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. Memory 138 may 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. Memory 138 may 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. Memory 138 may be communicably connected to processor 136 via processing circuit 134 and may include computer code for executing (e.g., by processor 136) one or more processes described herein. When processor 136 executes instructions stored in memory 138 for completing the various activities described herein, processor 136 generally configures BMS controller 12 (and more particularly processing circuit 134) to complete such activities.
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Equipment definitions 140 define 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 definitions 140 allow 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 definitions 140 may 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 definitions 140 define 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 definitions 140 allows each equipment definition to be referenced by or applied to multiple different variants of the same type of building equipment.
In some embodiments, equipment definitions 140 facilitate 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 definitions 140 specify 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 definitions 140 can 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 definitions 140 are created by equipment definition module 154, described in greater detail below.
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Equipment objects 144 can be created (e.g., by equipment object creation module 156) by referencing equipment definitions 140. 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 module 156.
Equipment objects 144 may be related with each other and/or with building objects 142. 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 objects 144 and building objects 142. For example, equipment objects 144 can be associated with building objects 142 representing 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 module 158.
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In some embodiments, application services 148 facilitate an applications gateway for conducting electronic data communications with UI clients 16 and/or remote applications 18. For example, application services 148 may be configured to receive communications from mobile devices and/or BMS devices. Client services 146 may provide client devices with a graphical user interface that consumes data points and/or display data defined by equipment definitions 140 and mapped by equipment objects 144.
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The building objects created by building object creation module 152 can be accessed by UI clients 16 and remote applications 18 to provide a comprehensive user interface for controlling and/or viewing information for a particular building zone. Building objects 142 can 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 module 152 uses 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 module 152 provides a user interface for guiding a user through a process of creating building objects. For example, building object creation module 152 may provide a user interface to client devices (e.g., via client services 146) that allows a new space to be defined. In some embodiments, building object creation module 152 defines 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 module 152 creates building objects automatically or semi-automatically. For example, building object creation module 152 may 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 module 152 references an existing hierarchy for BMS 11 to define the spaces within building 10. For example, BMS 11 may provide a listing of controllers for building 10 (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 module 152 may 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 objects 142.
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Equipment definition module 154 may 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 BMS 11. 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 module 154 may 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 module 154 may 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 module 154 abstract 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 module 154 may 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 module 154 can 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 module 154 generates 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 module 154 may 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 module 154 may be displayed in conjunction with data values from BMS devices as part of a user-friendly interface.
In some embodiments, equipment definition module 154 generates 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 BMS 11 for 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 module 154 may 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 module 154 may store a name, label, and/or search criteria for each point definition in memory 138.
Equipment definition module 154 may 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 module 154 includes 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 module 154 may 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 module 154 provides 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 module 154 may generate an equipment definition for each different type of building equipment in BMS 11 (e.g., VMAs, chillers, AHUs, etc.). Equipment definition module 154 may store the equipment definitions in a data storage device (e.g., memory 138, equipment definitions 140, an external or remote data storage device, etc.).
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In some embodiments, equipment object creation module 156 determines 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 module 156 may retrieve the equipment definition for VMAs; whereas if the BMS device is a chiller, equipment object creation module 156 may 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 module 156 may 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 module 156 receives an equipment definition prior to selecting a BMS device. Equipment object creation module 156 may identify a BMS device of BMS 11 to which the equipment definition applies. For example, equipment object creation module 156 may 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 module 156), manually (e.g., by a user) or semi-automatically (e.g., by a user in response to an automated prompt from equipment object creation module 156).
In some embodiments, equipment object creation module 156 creates an equipment discovery table based on the equipment definition. For example, equipment object creation module 156 may 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 module 156 may 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 module 156 may 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 module 156 extracts a search criterion from each point definition of the equipment definition. Equipment object creation module 156 may 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 module 156 may 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 module 156 automatically 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 module 156 that 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 module 156 may determine that the search criterion is met. Accordingly, equipment object creation module 156 may map the data point of the selected BMS device to the corresponding column of the equipment discovery table.
Advantageously, equipment object creation module 156 may 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 module 156 may 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 module 156 can 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 module 156 may be stored in equipment objects 144.
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Object relationship module 158 may establish relationships between equipment objects 144 and building objects 142 (e.g., spaces). For example, object relationship module 158 may associate equipment objects 144 with building objects 142 representing particular rooms or zones to indicate that the equipment object serves that room or zone. In some embodiments, object relationship module 158 provides a user interface through which a user can define relationships between equipment objects 144 and building objects 142. 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.
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Building control services module 160 may 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 interface 132. Building control services module 160 may 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 building 10 (e.g., zone temperature, humidity, air flow rate, etc.).
In some embodiments, building control services module 160 is configured to control the environment of building 10 on a zone-individualized level. For example, building control services module 160 may 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 module 160 may operate BMS 11 to 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 module 160 uses 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 module 160 may 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 module 160 may 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 module 160 may determine which of a plurality of sensors to use in conjunction with a feedback control loop based on the locations of the sensors within building 10. For example, building control services module 160 may 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 module 160 automatically generates control algorithms for a controller or a building zone based on the location of the zone in the building 10. For example, building control services module 160 may 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 module 160 may 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 building 10. For control loops that are conducted at a larger scale (i.e., floor level) building control services module 160 may use the location of each building zone and/or BMS device to coordinate control functionality between building zones. For example, building control services module 160 may 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 module 160 is configured to optimize the energy efficiency of building 10 using the locations of various BMS devices and the control parameters associated therewith. Building control services module 160 may 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 module 160 may be configured to move warmer air from higher elevation zones to lower elevation zones by establishing pressure gradients between connected building zones.
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Each of building subsystems 428 can include any number of devices, controllers, and connections for completing its individual functions and control activities. HVAC subsystem 440 can include many of the same components as HVAC system 20, as described with reference to
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Interfaces 407, 132 can be or include wired or wireless communications interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wire terminals, etc.) for conducting data communications with building subsystems 428 or other external systems or devices. In various embodiments, communications via interfaces 407, 132 can be direct (e.g., local wired or wireless communications) or via a communications network 446 (e.g., a WAN, the Internet, a cellular network, etc.). For example, interfaces 407, 132 can include an Ethernet card and port for sending and receiving data via an Ethernet-based communications link or network. In another example, interfaces 407, 132 can include a Wi-Fi transceiver for communicating via a wireless communications network. In another example, one or both of interfaces 407, 132 can include cellular or mobile phone communications transceivers. In one embodiment, communications interface 407 is a power line communications interface and BMS interface 132 is an Ethernet interface. In other embodiments, both communications interface 407 and BMS interface 132 are Ethernet interfaces or are the same Ethernet interface.
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Memory 138 (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. Memory 138 can be or include volatile memory or non-volatile memory. Memory 138 can 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, memory 138 is communicably connected to processor 136 via processing circuit 134 and includes computer code for executing (e.g., by processing circuit 134 and/or processor 136) one or more processes described herein.
In some embodiments, BMS controller 12 is implemented within a single computer (e.g., one server, one housing, etc.). In various other embodiments BMS controller 12 can be distributed across multiple servers or computers (e.g., that can exist in distributed locations). Further, while
Still referring to
Enterprise integration layer 410 can be configured to serve clients or local applications with information and services to support a variety of enterprise-level applications. For example, enterprise control applications 426 can be configured to provide subsystem-spanning control to a graphical user interface (GUI) or to any number of enterprise-level business applications (e.g., accounting systems, user identification systems, etc.). Enterprise control applications 426 may also or alternatively be configured to provide configuration GUIs for configuring BMS controller 12. In yet other embodiments, enterprise control applications 426 can work with layers 410-420 to optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received at interface 407 and/or BMS interface 132.
Building subsystem integration layer 420 can be configured to manage communications between BMS controller 12 and building subsystems 428. For example, building subsystem integration layer 420 may receive sensor data and input signals from building subsystems 428 and provide output data and control signals to building subsystems 428. Building subsystem integration layer 420 may also be configured to manage communications between building subsystems 428. Building subsystem integration layer 420 translate communications (e.g., sensor data, input signals, output signals, etc.) across a plurality of multi-vendor/multi-protocol systems.
Demand response layer 414 can be configured to optimize resource usage (e.g., electricity use, natural gas use, water use, etc.) and/or the monetary cost of such resource usage in response to satisfy the demand of building 10. The optimization can be based on time-of-use prices, curtailment signals, energy availability, or other data received from utility providers, distributed energy generation systems 424, from energy storage 427, or from other sources. Demand response layer 414 may receive inputs from other layers of BMS controller 12 (e.g., building subsystem integration layer 420, integrated control layer 418, etc.). The inputs received from other layers can include environmental or sensor inputs such as temperature, carbon dioxide levels, relative humidity levels, air quality sensor outputs, occupancy sensor outputs, room schedules, and the like. The inputs may also include inputs such as electrical use (e.g., expressed in kWh), thermal load measurements, pricing information, projected pricing, smoothed pricing, curtailment signals from utilities, and the like.
According to some embodiments, demand response layer 414 includes control logic for responding to the data and signals it receives. These responses can include communicating with the control algorithms in integrated control layer 418, changing control strategies, changing setpoints, or activating/deactivating building equipment or subsystems in a controlled manner. Demand response layer 414 may also include control logic configured to determine when to utilize stored energy. For example, demand response layer 414 may determine to begin using energy from energy storage 427 just prior to the beginning of a peak use hour.
In some embodiments, demand response layer 414 includes a control module configured to actively initiate control actions (e.g., automatically changing setpoints) which minimize energy costs based on one or more inputs representative of or based on demand (e.g., price, a curtailment signal, a demand level, etc.). In some embodiments, demand response layer 414 uses equipment models to determine an optimal set of control actions. The equipment models can include, for example, thermodynamic models describing the inputs, outputs, and/or functions performed by various sets of building equipment. Equipment models may represent collections of building equipment (e.g., subplants, chiller arrays, etc.) or individual devices (e.g., individual chillers, heaters, pumps, etc.).
Demand response layer 414 may further include or draw upon one or more demand response policy definitions (e.g., databases, XML, files, etc.). The policy definitions can be edited or adjusted by a user (e.g., via a graphical user interface) so that the control actions initiated in response to demand inputs can be tailored for the user's application, desired comfort level, particular building equipment, or based on other concerns. For example, the demand response policy definitions can specify which equipment can be turned on or off in response to particular demand inputs, how long a system or piece of equipment should be turned off, what setpoints can be changed, what the allowable set point adjustment range is, how long to hold a high demand setpoint before returning to a normally scheduled setpoint, how close to approach capacity limits, which equipment modes to utilize, the energy transfer rates (e.g., the maximum rate, an alarm rate, other rate boundary information, etc.) into and out of energy storage devices (e.g., thermal storage tanks, battery banks, etc.), and when to dispatch on-site generation of energy (e.g., via fuel cells, a motor generator set, etc.).
Integrated control layer 418 can be configured to use the data input or output of building subsystem integration layer 420 and/or demand response later 414 to make control decisions. Due to the subsystem integration provided by building subsystem integration layer 420, integrated control layer 418 can integrate control activities of the subsystems 428 such that the subsystems 428 behave as a single integrated supersystem. In some embodiments, integrated control layer 418 includes control logic that uses inputs and outputs from a plurality of building subsystems to provide greater comfort and energy savings relative to the comfort and energy savings that separate subsystems could provide alone. For example, integrated control layer 418 can be configured to use an input from a first subsystem to make an energy-saving control decision for a second subsystem. Results of these decisions can be communicated back to building subsystem integration layer 420.
Integrated control layer 418 is shown to be logically below demand response layer 414. Integrated control layer 418 can be configured to enhance the effectiveness of demand response layer 414 by enabling building subsystems 428 and their respective control loops to be controlled in coordination with demand response layer 414. This configuration may advantageously reduce disruptive demand response behavior relative to conventional systems. For example, integrated control layer 418 can be configured to assure that a demand response-driven upward adjustment to the setpoint for chilled water temperature (or another component that directly or indirectly affects temperature) does not result in an increase in fan energy (or other energy used to cool a space) that would result in greater total building energy use than was saved at the chiller.
Integrated control layer 418 can be configured to provide feedback to demand response layer 414 so that demand response layer 414 checks that constraints (e.g., temperature, lighting levels, etc.) are properly maintained even while demanded load shedding is in progress. The constraints may also include setpoint or sensed boundaries relating to safety, equipment operating limits and performance, comfort, fire codes, electrical codes, energy codes, and the like. Integrated control layer 418 is also logically below fault detection and diagnostics layer 416 and automated measurement and validation layer 412. Integrated control layer 418 can be configured to provide calculated inputs (e.g., aggregations) to these higher levels based on outputs from more than one building subsystem.
Automated measurement and validation (AM&V) layer 412 can be configured to verify that control strategies commanded by integrated control layer 418 or demand response layer 414 are working properly (e.g., using data aggregated by AM&V layer 412, integrated control layer 418, building subsystem integration layer 420, FDD layer 416, or otherwise). The calculations made by AM&V layer 412 can be based on building system energy models and/or equipment models for individual BMS devices or subsystems. For example, AM&V layer 412 may compare a model-predicted output with an actual output from building subsystems 428 to determine an accuracy of the model.
Fault detection and diagnostics (FDD) layer 416 can be configured to provide on-going fault detection for building subsystems 428, building subsystem devices (i.e., building equipment), and control algorithms used by demand response layer 414 and integrated control layer 418. FDD layer 416 may receive data inputs from integrated control layer 418, directly from one or more building subsystems or devices, or from another data source. FDD layer 416 may automatically diagnose and respond to detected faults. The responses to detected or diagnosed faults can include providing an alert message to a user, a maintenance scheduling system, or a control algorithm configured to attempt to repair the fault or to work-around the fault.
FDD layer 416 can be configured to output a specific identification of the faulty component or cause of the fault (e.g., loose damper linkage) using detailed subsystem inputs available at building subsystem integration layer 420. In other exemplary embodiments, FDD layer 416 is configured to provide “fault” events to integrated control layer 418 which executes control strategies and policies in response to the received fault events. According to some embodiments, FDD layer 416 (or a policy executed by an integrated control engine or business rules engine) may shut-down systems or direct control activities around faulty devices or systems to reduce energy waste, extend equipment life, or assure proper control response.
FDD layer 416 can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer 416 may use some content of the data stores to identify faults at the equipment level (e.g., specific chiller, specific AHU, specific terminal unit, etc.) and other content to identify faults at component or subsystem levels. For example, building subsystems 428 may generate temporal (i.e., time-series) data indicating the performance of BMS 11 and the various components thereof. The data generated by building subsystems 428 can include measured or calculated values that exhibit statistical characteristics and provide information about how the corresponding system or process (e.g., a temperature control process, a flow control process, etc.) is performing in terms of error from its setpoint. These processes can be examined by FDD layer 416 to expose when the system begins to degrade in performance and alert a user to repair the fault before it becomes more severe.
Regional Intelligence for Building Management Systems Referring now to
The BMSs 11a-11m can be various different building management systems similar to BMS 11 described above, adapted to serve buildings in different geographic regions as illustrated in
The computing system 502 is communicable with the BMSs 11a-m (e.g., via the Internet) and can be positioned in any geographic region or may be distributed across multiple geographic regions. The computing system 502 and provide various remote services, support, functions, etc. to the various BMSs 11a-m. The computing system 502 can also generate high level insights using data from the multiple BMSs 11a-m, for example facilitating enabling comparisons of performance across BMSs 11a-m. In some embodiments, the computing system 502 is configured to provide setpoints, control decisions, configuration parameters, control logic, automated troubleshooting routines, etc. to the BMSs 11a-m to affect operation of equipment and devices of such BMSs 11a-m. The computing system 502 may 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 BMSs 11a-m.
The system 500, building automation information can be communicated with the multiple BMSs 11a-m and the computing system 502, for example via one or more networks (e.g., via the Internet). Information can flow directly between the computing system 502, and the BMSs 11a-m, between different BMSs 11a-m, from a first BMS (e.g., BMS 11a) to a second BMS (e.g., BMS 11b) via the computing system 502, and/or otherwise communicated amongst the BMSs 11a-m and the computing system 502. 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 BMSs 11a-m and/or computing system 502 can be communicated in the system 500 in various embodiments.
In some embodiments, the system 500 is configured to automatically adjust the building automation information in accordance with different regional terminology used by the multiple BMSs 11a-m (and, in some embodiments, by the computing system 502). That is, building automation information can be automatically translated between different sets of local terminology when communicated within the system 500 which includes components distributed across multiple geographic regions as shown in
In some embodiments, the computing system 502 includes 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 BMSs 11a-m may 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 system 502) 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 BMSs 11a-m. For example, the BMSs 11a-m may 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 BMSs 11a-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 system 500 in terminology used in their respective regions. The system 500 seamlessly handles inputs in different regional terminologies and adjust operation of equipment accordingly.
In some embodiments, each user of the system 500 has 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 system 500 (e.g., the computing system 502, the BMSs 11a-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 BMSs 11a-m along with translation into regional terminologies suitable for the recipients of such information.
The system 500 thereby 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 to
At step 600, 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 in
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). Step 602 thereby 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 step 602 is further or alternatively based on building domain.
In the embodiments of
At step 606, 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 step 604. 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 process 600, troubleshooting options suitable for a particular location and prioritized using artificial intelligence can be implemented. Process 600 thereby 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 process 600, thereby efficiently solving technical issues with operation of building management systems and building equipment.
While the example of process 600 refers to troubleshooting options, process 600 can also be used to provide other workflow recommendations and execution. For example, process 600 can be adapted to automatically provide, with regional intelligence according to the teachings process 600, 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 process 600 so as to provide regionally-intelligent virtual assistance with a variety of installation, commissioning, and service tasks for building systems.
The construction and arrangement of the systems and methods as shown in the various exemplary embodiments are illustrative only. Although only a few embodiments have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.). For example, the position of elements can be reversed or otherwise varied and the nature or number of discrete elements or positions can be altered or varied. Accordingly, all such modifications are intended to be included within the scope of the present disclosure. The order or sequence of any process or method steps can be varied or re-sequenced according to alternative embodiments. Other substitutions, modifications, changes, and omissions can be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present disclosure.
The present disclosure contemplates methods, systems and program products on any machine-readable media for accomplishing various operations. The embodiments of the present disclosure can be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system. Embodiments within the scope of the present disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.
Although the figures show a specific order of method steps, the order of the steps may differ from what is depicted. Also two or more steps can be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations could be accomplished with standard programming techniques with rule based logic and other logic to accomplish the various connection steps, processing steps, comparison steps and decision steps.
This application claims priority to and the benefit of U.S. Provisional Application No. 63/354,269 filed Jun. 22, 2022, the entire disclosure of which is incorporated by reference herein.
| Number | Date | Country | |
|---|---|---|---|
| 63354269 | Jun 2022 | US |
