Patent ID: 12248311

DETAILED DESCRIPTION

Overview

Referring generally to the FIGURES, the present disclosure is intended to provide a system and a method for predicting vulnerability of the BMS according to some embodiments.

In some embodiments a system and a method identifies risks and threats to which the devices of the BMS may be vulnerable to from the time the devices of the BMS are commissioned till end of life.

In some embodiments a system and method facilitates easy visualization and management of the devices associated with the BMS.

In some embodiments a system and a method facilitates proactive prediction of operational technology devices/IoT devices specific vulnerabilities.

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 Pad3F”), 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 Lighting 17”), 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 some embodiments, 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).

Still referring toFIG.4, memory138is shown to include an enterprise integration layer410, an automated measurement and validation (AM&V) layer412, a demand response (DR) layer414, a fault detection and diagnostics (FDD) layer416, an integrated control layer418, and a building subsystem integration later420. Layers410-420can be configured to receive inputs from building subsystems428and other data sources, determine optimal control actions for building subsystems428based on the inputs, generate control signals based on the optimal control actions, and provide the generated control signals to building subsystems428. The following paragraphs describe some of the general functions performed by each of layers410-420in BMS11.

Enterprise integration layer410can be configured to serve clients or local applications with information and services to support a variety of enterprise-level applications. For example, enterprise control applications426can 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 applications426may also or alternatively be configured to provide configuration GUIs for configuring BMS controller12. In yet other embodiments, enterprise control applications426can work with layers410-420to optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received at interface407and/or BMS interface132.

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.

Demand response layer414can 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 building10. The optimization can be based on time-of-use prices, curtailment signals, energy availability, or other data received from utility providers, distributed energy generation systems424, from energy storage427, or from other sources. Demand response layer414may receive inputs from other layers of BMS controller12(e.g., building subsystem integration layer420, integrated control layer418, 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 layer414includes control logic for responding to the data and signals it receives. These responses can include communicating with the control algorithms in integrated control layer418, changing control strategies, changing setpoints, or activating/deactivating building equipment or subsystems in a controlled manner. Demand response layer414may also include control logic configured to determine when to utilize stored energy. For example, demand response layer414may determine to begin using energy from energy storage427just prior to the beginning of a peak use hour.

In some embodiments, demand response layer414includes 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 layer414uses 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 layer414may 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 layer418can be configured to use the data input or output of building subsystem integration layer420and/or demand response later414to make control decisions. Due to the subsystem integration provided by building subsystem integration layer420, integrated control layer418can integrate control activities of the subsystems428such that the subsystems428behave as a single integrated supersystem. In some embodiments, integrated control layer418includes 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 layer418can 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 layer420.

Integrated control layer418is shown to be logically below demand response layer414. Integrated control layer418can be configured to enhance the effectiveness of demand response layer414by enabling building subsystems428and their respective control loops to be controlled in coordination with demand response layer414. This configuration may advantageously reduce disruptive demand response behavior relative to conventional systems. For example, integrated control layer418can 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 layer418can be configured to provide feedback to demand response layer414so that demand response layer414checks 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 layer418is also logically below fault detection and diagnostics layer416and automated measurement and validation layer412. Integrated control layer418can 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) layer412can be configured to verify that control strategies commanded by integrated control layer418or demand response layer414are working properly (e.g., using data aggregated by AM&V layer412, integrated control layer418, building subsystem integration layer420, FDD layer416, or otherwise). The calculations made by AM&V layer412can be based on building system energy models and/or equipment models for individual BMS devices or subsystems. For example, AM&V layer412may compare a model-predicted output with an actual output from building subsystems428to determine an accuracy of the model.

Fault detection and diagnostics (FDD) layer416can be configured to provide on-going fault detection for building subsystems428, building subsystem devices (i.e., building equipment), and control algorithms used by demand response layer414and integrated control layer418. FDD layer416may receive data inputs from integrated control layer418, directly from one or more building subsystems or devices, or from another data source. FDD layer416may 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 layer416can 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 layer420. In other exemplary embodiments, FDD layer416is configured to provide “fault” events to integrated control layer418which executes control strategies and policies in response to the received fault events. According to some embodiments, FDD layer416(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 layer416can be configured to store or access a variety of different system data stores (or data points for live data). FDD layer416may 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 subsystems428may generate temporal (i.e., time-series) data indicating the performance of BMS11and the various components thereof. The data generated by building subsystems428can 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 layer416to expose when the system begins to degrade in performance and alert a user to repair the fault before it becomes more severe.

System for Determining and Predicting Vulnerability of Building Management System

Referring now toFIG.5andFIG.6, the present disclosure shows a system500for determining and predicting vulnerability of a BMS (not specifically shown inFIGS.5and6). In some embodiments, the BMS referred to may be the BMS as disclosed inFIGS.2and3. In accordance with one embodiment of the present disclosure, the envisaged system500is enabled to determine and predict vulnerability of a plurality of BMSs.

In some embodiments, the system500may be enabled to communicate with a plurality of data sources, wherein the data sources may be selected from a group consisting of, but not limited to, a first data source506, a plurality of IoT-enabled devices508, one or more remote data sources510, and one or more remote controllers512. The system may be enabled to selectively extract data from each of the data sources (506-512) to determine and predict vulnerability of the BMS. In some embodiments, the BMS comprises a plurality of IoT-enabled devices508which may also be referred to as edge devices. In some embodiments, the IoT-enabled devices referred to may be and include the building subsystems428or building subsystem devices (e.g.) present in a BMS as disclosed inFIGS.1-4. In some embodiments, the plurality of IoT-enabled devices508are adapted to be connected with one or more peripheral devices such as the building subsystems428or building subsystem devices508. In some embodiments, the peripheral devices may be referred to as standard equipment capable of performing desired or routine tasks with none or limited communication capabilities.

In some embodiments, the system500of the present disclosure can be enabled to identify vulnerabilities of one or peripheral devices connected to the IoT-enabled device508based on the data received from the IoT-enabled device508.

In some embodiments, the system500of the present disclosure may be implemented using a cloud server.

In some embodiments of the present disclosure, the system500comprises a communication module612(also referred as “communication interface612”) and a processing circuit502. Although, the present disclosure describes the communication module612and the processing circuit502as a separate entity, it is to be understood that the communication module612can be integrated with the processing circuit502.

The processing circuit502may be enabled to establish a communication link with the plurality of data sources (506-512), wherein the data sources may be selected from the group consisting of, but is not limited to, a first data source506, a plurality of IoT-enabled devices508, one or more remote data sources510, and one or more remote controllers512.

In some embodiments, the first data source506may represent one or more compact disks, external storage devices, databases, floppy disks, diskettes, computers, servers, portable storage devices, virtual servers, and the like. In some embodiments, one or more remote data sources510may correspond to remote servers associated with open source networks. In some embodiments, the one or more remote controllers512may correspond to BMS controllers affiliated with one or more distant BMSs or automation systems.

In some embodiments, the processing circuit502is implemented using one or more processor(s). Referring specifically toFIG.6, processor606may 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. The processor606is configured to execute computer code or instructions stored in a memory or received from other computer readable media (e.g., CDROM, network storage, a remote server, etc.).

In some embodiments memory610may 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. Memory610may 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. Memory610may 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. In some embodiments memory610may be communicably connected to the processor606via processing circuit502and may include computer code for executing (e.g., by the processor) one or more processes described herein. When processor606executes instructions stored in the Memory610for completing the various activities described herein, processor606generally configures the processing circuit502and its modules/unit to complete such activities.

The processing circuit502may be communicatively coupled with the communication module612. In some embodiments, the processing circuit502may be configured to establish a communication link with the first data source506via the communication interface612to receive the first data. In some embodiments, the communication link established with the first data source506by the processing circuit502may be wired or wireless depending upon the capabilities of the communication module612and the first data source506. In some embodiments, the communication module612may be enabled to establish a plug and play type communication link of the processing circuit502with the first data source506. The processing circuit502may be configured to store the received first data in the memory610, wherein the first data may comprise a layout having a plurality of IoT-enabled devices508, and the location coordinate and the application software details pertaining to each of the IoT-enabled devices508.

In some embodiments, the first data may be a Building Information Modeling (BIM) file.

In some embodiments, the first data may be static and may be fetched by the processing circuit502only at the time of initializing the BMS. In some embodiments, the application software details contained within the first data corresponds to an initial version of the software installed within each of the IoT-enabled devices508.

In some embodiments the processing circuit502may be configured to establish a communication link with the plurality of IoT-enabled devices508, via the communication module612, and may be further configured to receive a second data from each of the IoT-enabled devices508. In some embodiments, the second data comprises one or more information pertaining to one or more parameters of the IoT-enabled device. Additionally, the processing circuit502may be configured to establish a communication link with at least one remote data source, via the communication module, to receive a third data from the remote data sources corresponding to one or more of the IoT-enabled devices508. In some embodiments, the third data comprises at least one of: information requested by the processing circuit502from the remote data source, wherein the information requested corresponds to the parameter(s) populated by the second data, and threat information, if any, pertaining to one or more of the IoT-enabled devices508.

In some embodiments, the processing circuit502may be configured to keep track of the second data received from each of the IoT-enabled devices508, wherein the processing circuit502may then establish the communication link with the remote data sources510to retrieve a third data such as intel feeds corresponding to the second data in order to perform one-to-one analysis and identify discrepancies between the third data and the second data. Alternatively, the processing circuit502may obtain threat information from the remote data source(s)510while the communication link is established. The threat information may be the intel feed provided by the remote data source510.

In some embodiments, the processing circuit502may be enabled to receive the third data from the remote data source(s)510pertaining to each of the IoT-enabled devices508, and may be further configured to filter the received third data, wherein the filtered third data may contain data corresponding to the parameters for which the second data is populated.

In some embodiments, the processing circuit502is configured to establish a communication link with one or more remote controllers512to receive the outlier data. Specifically, the outlier data is data collated by the remote controllers512which are beyond a desirable range, typically signifying the occurrence of a glitch or unexpected outcome which may be beyond the range of the IoT-enabled devices508interacting with the remote controller512.

In some embodiments, a thin agent may be installed in each of said IoT-enabled devices508, remote controllers512, and remote data sources510, wherein the thin agent may be configured to selectively transmit the data from the plurality of data sources508-512to the processing circuit. The second data, third data, and outlier data received from the IoT-enabled devices508, remote data sources510, and remote controllers512are respectively stored in the memory610by the processing circuit502as historical record, wherein each time the data is received the historical record is updated.

In some embodiments, the processing circuit502may be configured to analyze at least one of the first data from the first data source506and the received second data from the plurality of IoT-enabled devices508with at least one of the received third data, received outlier data, and stored historical record for each of the IoT-enabled devices508to determine the vulnerability of the IoT-enabled device(s)508by generating a vulnerability detection signal, wherein the vulnerability detection signal contains the location coordinates of the IoT-enabled device(s)508identified to be vulnerable. In some embodiments, based on the same data the processing circuit502may also be configured to predict the vulnerability of the IoT-enabled device(s)508and generate a prediction signal, wherein the vulnerability prediction signal contains the location coordinates of the IoT-enabled device508identified to be vulnerable.

In some embodiments, the processing circuit502is adapted to establish the communication link with each of the IoT-enabled device508, the remote data source510, and the remote controllers512at unanticipated time intervals. For example, the processing circuit502may be adapted to establish the communication link at unanticipated time intervals, the unanticipated time intervals being bounded by a predefined range, for example, at some time between 3 minutes and 10 minutes from the last the communication link was established. The unanticipated time intervals within the predefined range may account for attacks that try to anticipate the times when the communication link will be established and pause and/or hide during such times to avoid detection. If the communication links are established at constant, unchanging intervals, the attacks can continue to hide during each update in perpetuity. By establishing the communication link at unanticipated time intervals such attacks may not be able to avoid detection in such a manner. It should be understood that these time intervals are merely exemplary and may be different in various embodiments.

In some embodiments, the predefined range bounding the unanticipated time intervals may be a single range throughout the life of the system. In some embodiments, the range may periodically change. For example, in a first month the unanticipated time intervals may establish the communication link between 3 and 10 minutes from the last time the communication link was established. In a second month, the range may change to 10 to 20 minutes from the last time the communication link was established. It should be understood that these time intervals are merely exemplary and may be different in various embodiments. In some embodiments, the ranges may change weekly, monthly, or even annually. By changing the bounds within which the communication links are established the system may combat attacks that attempt to learn and adapt to the intervals. In some embodiments, the unanticipated time intervals might be limited by a user-defined range. Depending on the application of the BMS, the operator may choose to select a smaller range, for example 3 to 5 minutes for every unanticipated time interval in a high-security use case, or 10 to 15 minutes for every unanticipated time interval in a low-security use case. In some embodiments, the limits of the unanticipated time intervals may be bounded by a minimum and maximum time value to ensure that the detection and prediction systems function properly. When a user selects time intervals below the minimum or beyond the maximum, the system may notify the user that the selected time intervals is outside the allowed range, and require the user to select an intervals within the range. In some embodiments, the system may simply set the unanticipated time intervals to a default range whenever an improper range is selected by a user.

In some embodiments, the unanticipated time intervals within the range may be established based on a hardware or software ID associated with each IoT-enabled device. For example, using the MAC ID of the IoT-enabled devices the system may generate unique unanticipated time intervals for each device. In some embodiments, the time intervals may be the same across all devices. In some embodiments, the communication link is established with each of the data sources (the IoT-enabled device508, the remote data source510, and the remote controllers512) at same time interval. In some embodiments, the communication link is established with each of the IoT-enabled device508, the remote data source510, and the remote controllers512at different time intervals.

In some embodiments of the present disclosure, the system500includes a triggering unit616, implemented using one or more processor(s), that is configured to generate at least one trigger signal at unanticipated time intervals, wherein the processing circuit502is enabled to establish the communication link upon receiving the trigger signal. In some embodiments, the triggering unit616is configured to generate the trigger signal at unanticipated time intervals within a predefined range. For example, the system may be adapted to establish the communication links at unanticipated time intervals at some time between 3 and 10 minutes. The triggering unit616may generate at least one trigger signal within that range. In some embodiments, the triggering unit616may be integrated with the processing circuit502.

In some embodiments, the triggering unit616includes a timer618and a random pulse generator620. The timer618is configured to generate an output signal having a constant frequency and a constant duty cycle. The random pulse generator620is configured to cooperate with the timer618to receive the output signal having constant frequent and constant duty cycle. Further, the random pulse generator620may be configured to generate the triggering signal (s. In some embodiments, the random pulse generator620may be configured to generate the triggering signal(s) at a randomly opted duty cycle of the output signal received from the timer618but within a predefined range.

In some embodiments, the system500includes a user interface614to enable an operator to interact with the processing circuit502to provide first data by means of one or more external storage devices, wherein the external storage device(s) may act as first data source506. In another embodiment, the user interface614may facilitate the operator to mask and unmask the triggering signals generated by the triggering unit, and manually define time periods for establishing the communication link.

Referring again toFIG.6, the processing circuit502includes a data retrieving unit602, a vulnerability detection unit604, a vulnerability prediction unit608, the processor606, and the memory610. The data retrieving unit602, the vulnerability detection unit604, and the vulnerability prediction unit608may be implemented using one or more processor(s).

In some embodiments, the memory610may be configured to store a set of processing instructions, and the processor606may be enabled to cooperate with the memory610to receive the set of processing instructions to generate a set of processing commands. Additionally, the processor606may be configured to cooperate with the data retrieving unit602, the vulnerability detection unit604, and the vulnerability prediction unit608, wherein the data retrieving unit602, the vulnerability detection unit604, and the vulnerability prediction unit608may be enabled to perform desired operation(s) under the influence of the processing commands generated by the processor606.

In some embodiments the data retrieving unit602may be communicatively coupled with the communication module612. The data retrieving unit602may be enabled to receive the triggering signal generated by the triggering unit616, and may be further configured to generate a data logging signal. In some embodiments, since the triggering unit616is adapted to generate triggering signals at unanticipated time periods, the data logging signals generated by the data retrieving unit602are also generated at unanticipated time periods. In an alternative embodiment, the data retrieving unit602may be configured to of generate each data logging signal after a random delay time period subsequent to reception of the triggering signal which may enhance the security and add a factor of uncertainty to the system, thereby securing the communication link with the plurality of data feed sources, i.e., the IoT-enabled devices508, the remote data sources510, and the remote controllers512.

In some embodiments, the data logging signal generated by the data retrieving unit602is transmitted to the communication module612, wherein based on the data logging signal the communication module612is instructed to establish the communication link with one or more of the IoT-enabled devices508, the remote data sources510, and the remote controllers512.

In some embodiments, the vulnerability detection unit604is configured to cooperate with the data retrieving unit602, and may be further configured to, under the set of operating commands, receive the second data, the third data, and the outlier data associated with each of the IoT-enabled devices508from the data retrieving unit602. Additionally, the vulnerability detection unit604may also be configured to access the first data and the historical records data stored within the memory610. In some embodiments, the vulnerability detection unit604may receive the first data and the historical records pertaining to each of the IoT-enabled devices508via the processor606, wherein the processor606may be enabled to selectively extract data and records pertaining to each of the plurality of IoT-enabled devices508from the memory610.

Further, in some embodiments of the present disclosure, the vulnerability detection units604may be configured to analyze the outlier data with the second data, and generate the vulnerability detection signal. The vulnerability detection signal may be generated in the event information contained within the outlier data and the second data, pertaining to one or more IoT-enabled devices508, indicates the presence of vulnerability.

In another embodiment, the vulnerability detection unit604may be configured to analyze the second data with the third data received from the remote data source, and may be further configured to generate the vulnerability detection signal when the information contained within the third data and the second data, pertaining to one or more of the IoT-enabled devices508, indicates the presence of vulnerability. In some embodiments, the vulnerability detection unit604may be configured to analyze the first data and the third data, and in the event the information contained within the first data and the third data, pertaining to one or more IoT-enabled devices508, indicates the presence of vulnerability, the vulnerability detection unit604may be further configured to scrutinize the first data and the second data for the IoT-enabled device508identified to be vulnerable to confirm the vulnerability of said IoT-enabled device508.

In some embodiments, the vulnerability detection unit604may be configured to analyze at least one of or combination of the first data, the second data, the third data, and the outlier data, with the historical record to determine and predict vulnerability of a particular IoT-enabled device508.

In some embodiments, the second data may be selected from the group consisting of, but not limited to, current software version information, open port information, anomalous behavior information, health information, and information pertaining to one or more control signals being generated by the associated IoT-enabled device(s)508. The control signals may correspond to the signals being generated by the IoT-enabled device(s)508upon receiving sensed data generated by one or more peripheral devices connected with the IoT-enabled device. In another embodiment, each of the IoT-enabled device508may be provided with a thin agent configured to selectively transmit the second data subsequent to establishment of the communication link.

In some embodiments of the present disclosure, the second data may include information pertaining to one or more peripheral devices connected to the IoT-enabled device, wherein the associated IoT-enabled device508may be enabled to transmit the second data comprising data associated with said IoT-enabled device508and one or more peripheral devices connected to it. The peripheral devices may be sensors connected with the IoT-enabled device, and may have limited or no communication capabilities on their own.

In some embodiments the vulnerability prediction unit608of the processing circuit502is implemented using one or more processor(s). The vulnerability prediction unit608may be configured to, under the set of operating commands, receive the second data, the third data, and the outlier data associated with each of the IoT-enabled devices508from the data retrieving unit602. Additionally, the vulnerability prediction unit608may also be configured to access the first data and the historical records data stored within the memory610. In some embodiments, the vulnerability prediction unit608may be enabled to the access the historical records from the memory610via the processor606. The vulnerability prediction unit608may be enabled to generate a prediction signal for one or more IoT-enabled devices508, wherein the prediction signal generated may contain the location coordinates of the IoT-enabled device508predicted as vulnerable. The vulnerability prediction unit608may be configured to evaluate the information contained within the first data and the second data with one or more of the third data, outlier data, and historical records pertaining to each of the IoT-enabled devices508to generate the prediction signal.

In some embodiments, the prediction signal generated by the vulnerability prediction unit608comprises a value defining the probability of the IoT-enabled device508being vulnerable. The value defining the probability of being vulnerable may be based on at least source of data including the information contained within the second data, third data, and the outlier data. For example, the presence of outlier data and third data resulting in the prediction of an IoT-enabled device508being vulnerable may result in a high value of probability. On the contrary, if the prediction signal is generated based on only third data then the value of probability may be low. Still in other embodiments the value defining the probability of the IoT-enabled device508being vulnerable may be based on just the outlier data or the third data. The combinations of the various data sets that may be used to define the value of the probability that an IoT-enabled device508is vulnerable are merely exemplary, and it should be understood the combinations may vary in different embodiments.

In some embodiments, the operator may be enabled to weight one or more of said third data, outlier data, and historical record, wherein the value of prediction may be directly associated with the data used in the prediction of vulnerability. If, for an instance, each of the third data, outlier data, and the historical data points towards the IoT-enabled device508being vulnerable then the value of probability will be at its highest. In some embodiments, if one of the third data, the outlier data, and the historical data indicate that there is no vulnerability then, based on the weight assigned by the operator, the value of prediction for the IoT-enabled device508may be calculated considering the data indicating the possibility of vulnerability.

In some embodiments of the present disclosure, the vulnerability response unit504is communicatively coupled with the processing circuit502via the communication module612. The vulnerability response unit504is implemented using one or more processor(s). The vulnerability response unit504is configured to generate a first notification signal after reception of the vulnerability detection signal, wherein the first notification signal is enabled to provide at least one of audio, visual, and textual based alerts to an operator. In some embodiments, the alerts may also include location coordinates of the IoT-enabled device508being vulnerable. In some embodiments the vulnerability response unit504is configured to generate a second notification signal subsequent to reception of the prediction signal, wherein the second notification signal is enabled to provide at least one of audio, visual, and textual based alerts to the operator, and in some embodiments the alerts may also include location coordinates of the IoT-enabled device508being predicted as vulnerable. In some embodiments, the vulnerability response unit504is configured to quarantine or isolate the device(s) predicted or detected to be vulnerable after reception of the vulnerability detection signal.

In some embodiments of the present disclosure, the vulnerability response unit504may be integrated with the processing circuit502.

In some embodiments, the processing circuit502may be configured to compare the software version information contained within the third data with the software version information contained within the second data for each of the IoT-enabled devices508, and may be further configured to generate a comparison signal when the software version information contained within the second data is identical with the third data. Subsequently, the processing circuit502may be configured to generate a vigilant signal upon generation of the comparison signal, wherein the vigilant signal contains the location coordinates of the IoT-enabled device508identified by the vigilant signal. In some embodiments, the processing circuit502may be configured to quarantine or isolate the device(s) associated with the comparison signal. In some embodiments, the processing circuit502may be configured to perform a software patch on the identified vulnerable IoT-enabled device, wherein the IoT-enabled device508may be patched to the application software information of which are contained in the first data. Alternatively, the processing circuit502may be configured to notify the operator.

In some embodiments, the processing circuit502is enabled to employ artificial intelligence for analyzing at least one of first data and second data with at least one of second data, outlier data, and historical record for each of the IoT-enabled devices508.

In some embodiments of the present disclosure, the system for determining vulnerability of a BMS having a plurality of IoT-enabled devices508comprises a memory610and a processing circuit502. The memory610is configured to store a first data, wherein the first data may be a layout comprising the plurality of IoT-enabled devices508, and the location coordinates and application software details tagged to each of the IoT-enabled devices508. The processing circuit502is configured to cooperate with the memory610, and is communicatively coupled to a communication module612to establish a communication link for receiving a plurality of data feeds from a plurality of data sources at unanticipated time intervals, wherein the processing circuit502is configured to store the received data feeds in the memory610as historical feeds, and is further configured to analyze the one or more data feeds with the first data and the historical feed employing artificial intelligence for each of the IoT-enabled devices508to determine vulnerability of the business management system.

In some embodiments of the present disclosure, a system for predicting vulnerability of a BMS having a plurality of IoT-enabled devices508comprises a processing circuit502communicatively coupled to a communication module612to establish a communication link and receive a plurality of data feeds from a plurality of data sources at unanticipated time intervals, wherein the plurality of data feeds correspond to one or more of the IoT-enabled devices508, the processing circuit502configured to store the received data feeds in the memory610as historical feeds, and is further configured to analyze at least one of the data feeds and historical feeds for each of the IoT-enabled device508by employing artificial intelligence to predict vulnerability of the business management system.

Method for Determining and Predicting Vulnerability of Building Management System

Referring now toFIG.7aandFIG.7b, a method for determining and predicting vulnerability of a building management system (BMS) having a plurality of IoT-enabled devices508is disclosed.

The method performed by a processing circuit502in process700is shown to include the step of establishing a communication link (step702), with a first data source via a communication module612to receive a first data. In some embodiments, the first data comprises a layout having location coordinates and application software details pertaining to each of the IoT-enabled devices508. Further, the processing circuit502may be configured to store the received first data in a memory (step704).

Process700is shown to include the processing circuit502establishing a communication link with the plurality of IoT-enabled devices508via the communication module612to receive the second data from each of the IoT-enabled devices508(step706). In some embodiments, the second data comprises one or more information pertaining to one or more parameters of the IoT-enabled devices508. In another embodiment, the second data may be selected from the group consisting of, but not limited to, current software version information, open port information, anomalous behavior information, health information, and information pertaining to one or more control signals being generated by associated IoT-enabled device508.

Process700is shown to include establishing a communication link with at least one remote data source510via the communication module612to receive a third data corresponding to one or more IoT-enabled devices508(step708).

Referring now specifically toFIG.7b, process700at step710includes establishing a communication link with one or more remote controllers512to receive outlier data. Process700is shown to include storing the received second data, the received third data, and the outlier data pertaining to each of the IoT-enabled devices508in the memory610as historical record (step712).

Process700is shown to include, at step714analyzing, with processing circuit502at least one of the first data and the second data with at least one of the third data, outlier data, and the historical record for each of the IoT-enabled devices508to determine vulnerability of one or more IoT-enabled devices508, or predict vulnerability of one or more IoT-enabled devices508, or both.

In some embodiments, the processing circuit502may be configured to generate a vulnerability detection signal upon determining the vulnerability of the IoT-enabled device, wherein the vulnerability detection signal comprises location coordinates of the IoT-enabled device. In another embodiment, the processing circuit502is configured to generate a prediction signal signifying the probability of the IoT-enabled device508being analyzed to be vulnerable, wherein the prediction signal comprises a value defining the probability of vulnerability and is determined based on the information contained within at least one of the second data, third data, historical record, and outlier data.

In some embodiments, the step of establishing the communication link, by the processing circuit, with each of the IoT-enabled devices508, remote data sources510, and the remote controllers512as shown in steps706,708and710may further comprise the steps of generating an output signal having a constant frequency and a constant duty cycle, randomly choosing a duty cycle of the generated output signal to generate a trigger signal, and establishing at least one communication link via said communication module612after generation of the trigger signal.

In some embodiments, the processing circuit502is configured to establish the communication link with at least one of the IoT-enabled devices508, remote data sources510, and remote controllers512at unanticipated time intervals. In some embodiments, the unanticipated time intervals are bounded by a range. In some embodiments, the range may change periodically. In some embodiments, the range may be user-selectable. In some embodiments, the unanticipated time intervals are selected from times within the range using a random number generator. In some embodiments, the unanticipated time intervals are selected from times within the range using hardware or software IDs associated with each IoT-enabled device. For example, the range of time intervals can be form 5 minutes to 30 minutes, the intervals can be random selections of 5 minutes, 12, minutes, 18, minutes, 14, minutes. 29 minutes, etc.

In some embodiments, the step of determining vulnerability of the IoT-enabled device508as shown in step714further comprises the step of analyzing the outlier data with the second data and generating an vulnerability detection signal in the event information contained within the outlier data and the second data does not match, wherein generation of the vulnerability detection signal signifies determining the vulnerability of the IoT-enabled device508. In some embodiments, determining the vulnerability of the IoT-enabled device may further include the step of analyzing the third data received from the remote data source510with the second data, and generating the vulnerability detection signal in the event information contained within the third data and the second data does not match, wherein generation of the vulnerability detection signal signifies determining the vulnerability of the IoT-enabled device. The step may further comprise analyzing the first data with the third data to generate a flag signal indicating detection of threat for the IoT-enabled device508, and scrutinizing the first data and the second data of the device having threat to generate vulnerability detection signal determining vulnerability of the IoT-enabled device508.

Referring back toFIG.6, in some embodiments of the present disclosure, the method comprises the step of providing notification to an operator by a vulnerability response unit504. The method comprises the steps of generating a first notification signal after reception of the vulnerability detection signal, wherein the first notification signal is enabled to provide at least one of audio, visual, and textual based alert to an operator indicating coordinates of the IoT-enabled device508being vulnerable, and generating a second notification signal after reception of the prediction signal, wherein the second notification signal is enabled to provide at least one of audio, visual, and textual based alerts to the operator indicating the coordinates of the IoT-enabled device508predicted to be vulnerable.

Configuration of Exemplary Embodiments

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.