LOGISTICS SAFETY OPERATIONS

Digital twin-based logistics operations are disclosed. A digital twin virtual environment models a physical environment and includes virtual nodes and virtual sensors that have corresponding physical nodes and physical sensors in the physical environment. Positions of the nodes are tracked in the digital twin. Using position data and other sensor data, the digital twin can be used to train machine learning models, label data for training, aggregate data, generate warnings, and cause a real or virtual display to be displayed when an event is predicted or determined.

FIELD OF THE INVENTION

Embodiments of the present invention generally relate to logistics, logistics operations, and digital twins. More particularly, at least some embodiments of the invention relate to systems, hardware, software, computer-readable media, and methods for performing digital twin based logistics operations.

BACKGROUND

Logistics operations are an important aspect of many environments. Many environments, such as warehouse environments, often have multiple devices operating therein, some or all of which may be automated. Consequently, there is a need to ensure that the devices operate in a safe manner. For example, collisions are a safety concern and attempts to avoid collisions should be performed. The likelihood of a collision may be based on the positions and/or trajectories of the devices operating in the warehouse environment.

DETAILED DESCRIPTION OF SOME EXAMPLE EMBODIMENTS

Embodiments of the present invention generally relate to logistics and logistics operations. More particularly, at least some embodiments of the invention relate to systems, hardware, software, computer-readable media, and methods for performing digital twin-based logistics operations, which include safety operations.

Embodiments of the invention thus relate to logistics operations that may be performed with respect to an environment such as a warehouse that is modeled using a digital twin. Multiple devices, such as forklifts, automated mobile robots (AMRs), and the like may operate in the environment. Embodiments of the invention relate to performing digital twin-based logistics operations for devices operating in these types of environments.

Logistics operations may benefit from machine learning models that can predict the trajectories of the devices and unsafe or potentially unsafe conditions using the captured data. More specifically, the data collected/received from the devices may be used in predicting and preventing collisions and other dangerous situations.

FIGS.1A and1Bdisclose aspects of an environment including a near edge environment and a far edge environment.FIG.1Amore specifically illustrates an environment100that includes a central node120. The central node120is an example of a near edge system, environment and may be referred to as a near edge node. The central node120is configured with computing resources such as processors, memory, and networking hardware. The central node120may be located in the environment100, cloud-based, or the like.

The environment100, which may be a warehouse, may house a number of mobile devices (forklifts, AMRs, etc.), which are represented by nodes110and114. The environment100also includes ultra-wideband (UWB) readers, Radio Frequency Identification (RFID) readers, or the like, which are represented by tag readers102,104, and106. The tag readers102,104, and106may be placed in various locations in the environment100.

The nodes110and114may include or be associated with tags, represented by tags112and116, such as UWB tags and/or RFID tags. The environment100may include other sensors128. Example sensors128include cameras, microphones, motion sensors, or the like or combination thereof. The tag readers102,104, and106may also be referred to as sensors.

In addition to the tags112and116, the nodes110and114may also include sensors130and132. The sensors130and132may include inertial sensors, position sensors, or the like. The tags114and116may also be examples of sensors.

The central node120is configured with services or applications that may be configured to extract/collect/receive and manage sensor data. For example, the central node120may receive data from the tag readers102,104and106and/or from the nodes110and114and/or data from the tags112and116and/or the sensors130and132placed on the nodes110and114. This data may be processed by services or applications such as, by way of example, a sensor reading application122, an event processing application124, and a digital twin application126.

For example, the tag reader102and the tag112, when within range, may coordinate to determine a position of the node110. This position can be transmitted to the central node120. This position data allows positions of the nodes110and114in the environment100to be determined and tracked over time. Generally, UWB has a range of 0-50 meters and a latency that is typically less than 1 millisecond. Consequently, the positions of the nodes110and114can be captured in substantially real time.

FIG.1Bdiscloses aspects of a far edge node or environment. More specifically,FIG.1Bdiscloses additional aspects of nodes operating in the environment100. In this example, the mobile devices or nodes are examples of a far edge environment andFIG.1Bdiscloses aspects of a node150. The node150is an example of a mobile device and/or computing resources of a mobile device operating in the environment100.FIG.1Billustrates a node150, which is similar to the nodes110and114. In this example, the node compute160may also include services or applications such as a driving assistance service162, a driver warning service164, and a virtual space service166. The node compute160may include processors, memory, and networking hardware.

The node150may also include a tag152that can be read/cooperate by/with a tag reader and sensors154, which may generate data. Example sensors154include inertial sensors, position sensors, proximity sensors, or the like. The sensors154may be dependent on the characteristics of the node150. If the node150corresponds to a forklift, for example, other sensors may include a load weight sensor, a mast height sensor, or the like.

The services162,164, and166may publish positioning data, subscribe to event topics to warn the driver or node regarding safety issues (e.g., collision scenarios), and the like.

Embodiments of the invention relate to modeling an environment, such as the environment100. The digital twin may be configured to reflect aspects of the environment such as doors, shelves, columns, or the like. The digital twin may also represent each node (each device) operating in the environment. As positions of the nodes are updated, the corresponding virtual node's position is updated in the digital twin.

More generally, a digital twin, generally, is a digital model of a physical system. In this case, the digital twin may include a virtual model of the warehouse environment, the nodes operating in the warehouse, and other aspects of the warehouse. The digital twin can model the environment in three dimensions and can model fixtures (e.g., shelves, columns, doors) of the environment. The digital twin may be able to actuate real sensors in the physical environment, virtual sensors in the virtual environment, perform tests using real and/or synthetic data, verify the accuracy of machine learning models, test machine learning models, or the like.

As discussed herein, the real environment may be referred to as the physical environment (or environment) and the digital twin environment may be referred to as a virtual environment or digital twin environment.

FIG.2Adiscloses aspects of a digital twin environment.FIG.2Aillustrates an environment200and a corresponding virtual environment202that is a model of the environment200. Devices in the environment200include a node202associated with sensors204, a node206associated with sensors208, and a tag reader210. These devices are modeled in the virtual environment202as a virtual node202vwith virtual sensors204v, a virtual node206vwith virtual sensors208v, and a virtual tag reader210v.

The environment200may include any number of devices, tags, and other structure and the digital twin virtual environment202can virtually represent the devices, tags, and other structure.

FIG.2Bdiscloses additional aspects of a virtual environment.FIG.2Bis similar toFIG.2A. However, an additional virtual node212vwith virtual sensors214vis represented in the virtual environment202. The node212vdoes not correspond to a physical device or node in the environment200. However, the node212vcan be modeled to include virtual sensors214vsuch as virtual inertial sensors, virtual RFID tags, virtual UWB tags (like other nodes). These virtual sensors214vand the virtual node212vcan be modelled and actuated as if present in the environment200. The virtual environment202can be used for testing purposes and warehouses. The environment202may also be used to test/verify new warehouse designs including logistic rules, sensor/reader placement, camera placement, or the like or combinations thereof. For example, the node212v, which does not have a physical counterpart, can test the efficiency of a machine learning model configured to detect a collision, a dangerous cornering, or the like. If the node212vis moved toward the node202(the node202vin the virtual environment), data generated by the virtual sensors214vand/or the sensors204can be used to determine whether the machine learning model will detect a potential collision and test the ability to generate alerts or perform other logistics operations.

FIGS.3A and3Bdisclose aspects of logistics operations using digital twins.FIG.3Aillustrates aspects of logistics operations performed at or from the perspective of a far edge node300(e.g., a forklift, AMR). The node300, by way of example, may be equipped with sensors302, which represents one or more sensors including an IMU (Inertial Measurement Unit) sensor. The node300may also be associated with other sensors such as a tag304(an RFID tag) and/or a tag306(UWB tag). The tags304and306may cooperate with corresponding readers to generate information including position information. In one example, the tag306may coordinate with a UWB reader to generate a position within an environment that is provided to the driving assistance service308operating on the node300. Similarly, inertial data and RFID data may be provided to the driving assistance service308.

The data collected by or received from the sensors302and the tags304and306may be transmitted to a message service322at a near edge node320. By transmitting position data, inertial data, or the like to the near edge node320, the data can be used in downstream capacities and applications including a digital twin application. The data from the sensors302may also be used locally at the node300. For example, the data may be input to machine learning models to generate inferences or predictions that may be related to logistics, such as collision avoidance.

Thus, the driving assistance service308may transmit data (e.g., events over a message bus or wireless connection) to the message service322. The driving assistance service308may also listen to or receive messages from the message service322. When the driving assistance service308receives events or messages from the message service, the driving assistance service308may respond to events received from the message service322. For example, the driving assistance service308may cause the warning service310to issue a warning to a driver. The driving assistant service308may use the virtual space service312to present data to a driver (e.g., visually, audibly, textually). More specifically, the virtual space service312may be configured to display to a driver or other user, on a display, aspects of the environment. The displayed data may be real (e.g., a frame from a physical camera) or virtual data (e.g., a rendering of the environment based on the digital twin). In one example, events received from the message service322may also be input to machine learning models at the node300.

FIG.3Afurther illustrates a method350associated with operation of the node300. The node300(e.g., the driving assistance service308) may encapsulate data from the sensors302, the tag304and/or the tag306. The encapsulated data may include positioning data, inertial data, or the like. For example, UWB tag306may generate position data indicative of the position of the node300. The position data may have accuracy of 10-50 cm in one embodiment.

The encapsulated data (or simply data from the sensors302, the tag304, and/or the tag306) may be transmitted or sent354to the message service322as an event. The driving assistance service308may also listen356for events from the message service.

The driving assistance service308may include or have access to a machine learning model. Using events received from the message service322and/or data from the sensors302, the tag304, and/or the tag306, the driving assistance may generate predictions, such as a potential collision or dangerous cornering. The machine learning model may, in other embodiments, be located at the near edge node320such that events transmitted to the node300may constitute warnings or the like that can be conveyed to the user via a warning service310. In one example, the machine learning model may be incorporated into the digital twin application. This is possible, in part, due to the very low delay associated with positioning from the tag306. If there is not enough historical data to train the machine learning model, embodiments of the invention, including the digital twin, may model geographic zones within the warehouse environment. These zones can be marked as dangerous when a node is operating therein. Nodes entering a dangerous zone may receive a warning that another node is nearby (within the same geographic zone). Thus, whether using geo-zones or machine learning models, the warning service310may be invoked, which in turn generates an alert of some type.

In one example, the events communicated from the message service322to the driving assistance service308may be used to respond visually using the virtual space service312. The virtual space service312may show a view of a particular zone that could, for example, be related to a live camera display capturing the zone. The camera, via the digital twin, can be actuated based on events that relate back to sensors in the actual environment and/or geo-zones defined in the digital twin environment. This representation can be displayed360on a display device to a driver or other entity. Alternatively, the representation may be a virtual reconstruction of the zone to graphically illustrate relative positions of other nodes in the zone or other area.

FIG.3Bdiscloses aspects of a near edge or central node.FIG.3Billustrates aspects of digital-twin based logistics from the perspective of the near edge or central node320whileFIG.3Aillustrates aspects of digital-twin based logistics from the perspective of the far edge node300.

InFIG.3B, the central node320includes the message service322. The message service322may communicate events between the computing resources of the node320and the far edge node300. Events received at the message service322from the node300(or the driving assistance service308) may include position data such as UWB positioning data.

The digital twin application126, which may include the logistics service324, may listen for events via the message service322. These events are used by the logistics service324to keep a virtual environment340synchronized with a physical environment338. For example, the node300may move in the physical environment338. The driving assistance service308, may send UWB position data to the message service322as an event. The logistics service324may update the position of the virtual node300vin the virtual environment340. As previously stated, embodiments of the invention are not limited to UWB positioning data. Rather, the logistics service324may actuate or update virtual nodes/devices that exist in the virtual environment340as real-world events occur or are received at the digital twin. The logistics service324may be configured to illustrate events that occur and to illustrated the specific nodes (or devices) and/or sensors that are associated with the event and/or their associated readings (sensor/tag data). The logistics service324may present displays or user interfaces on various display, which may include displays on the nodes.

The digital twin application (or the logistics service324) is configured to form a virtual connection between real world devices (e.g., the far-edge nodes) and sensors that act independently of those nodes (e.g., cameras, UWB readers, RFID readers). The logistics service324, which may be part of the logistics service324, is configured to aggregate readings/data/events in the database326, which may be published via a visualization module328, which allows these data to be queried, visualized, or used for other downstream applications. Because the positions of these sensors that are independent of the nodes are known, their data can be used, for example, when a node is within a specified distance of those sensors. The visualization module328may allow sensors in the environment to be tied to specific nodes based on positional relationships.

FIG.3Balso illustrates an example of a physical environment338and a corresponding virtual environment340. In this example, the node300is in a zone334and the node332is operating in a different zone336. Thus, virtual counterpoints, node300vand node332v, are illustrated respectively in virtual zones334v, and336v.

As the nodes330and332move, their location may be collected, determined, or gathered using RFID and UWB sensor data (the tags). When the node300moves from the zone334to the zone336, the virtual space service344(of the node300) may notify the node300(or the driver) of the presence of the node332. Similarly, the node332may be advised of the presence of the node300in the zone336. This movement will be tracked and reflected in the virtual environment340.

In one example, the virtual space service344may present a visual representation of the zone336, either real or virtual. As indicated by the arrows in the environments338and340, the nodes330and332may be on a collision course, which is reflected in the virtual environment340. An image, whether acquired from a camera in the physical environment338or rendered using in the virtual environment240(e.g., using a virtual camera) may be presented on the nodes300and332.

More specifically, the physical environment338includes a camera346. The camera346may generate data independently of the nodes330and332. However, the data generated by the camera346may be added to the virtual environment340. Further, the data from the camera346can be related to the nodes300and332based on position or zone.

The visualization module328may be configured to display events that may be generated by nodes, including the virtual nodes330vand332v. More specifically, the data aggregated by the logistics service324may be subject to rules, input to machine learning models, or the like. When an event of interest is identified (e.g., a potential collision), the logistics service324may take action to notify the affected nodes/drivers. Further, information may be presented audibly, visually, or the like at the far edge nodes.

Thus, the logistics service324may aggregate information from multiple sensors and the nodes330and332. This information can be published over the message bus, such that downstream applications or environments, such as applications or services on the far edge node, can response to these events as previously described.

FIG.3Bfurther illustrates that a wall342(or other structure) is between the zones334and336. The wall is represented virtually as the wall342v. The position data allows the digital twin application to determine that the nodes300and332are on a collision course and take appropriate action, such as generating a warning, presenting a display (either real or virtual) to the nodes300and332. The logistics service324may cause a display of the zone336to be presented to the node300such that the node300is aware of the node332, which is behind the wall342.

FIGS.4A and4Billustrate examples of communications transmitted to a far edge node.FIG.4Aillustrates zones402and408of an environment. The display400may be presented in a display of a far edge node. The zone402is associated with a camera406and the zone408is associated with a camera412. The display400may be a real world display that includes a feed (or frames) from the cameras406and/or412. If the node404is entering the zone408, the camera412may be selected and a feed (or frames) from the camera412may be provided to the nodes404and410. The camera412, may be actuated based on events received and processed by the digital twin application. Thus, as the node404moves into the zone412, the camera412may be actuated.

FIG.4Billustrates a rendered display400. The horizontal and vertical lines may provide a perception of distance or the like. In this example, the rendered display410depicts the same nodes404and408in the same zones402and406. Thus, the rendered display410can apprise operators of other nearby nodes.

For example, if the digital twin detects a potential collision or detects that the node404is leaving the zone402and entering the zone408, the display400or the rendered display410may be provided. In one example, a virtual camera may be actuated to provide a view of the zone406. Additional warnings may be provided as well.

FIG.5discloses aspects of digital twin operations. The method500may include performing502digital twin or digital twin related operations. The services on the nodes and on the central node operate as discussed herein. Thus, a node may collect sensor data, send events to a central node, listen for events, and perform actions. The central node may similarly listen for events, update a digital twin virtual environment, send events to the nodes, and perform other actions.

In one example, the method500may include performing504digital twin positioning operations. The central node may aggregate IMU data, RFID positioning data, UWB positioning data. This information may be provided to prediction predicting models that can be replayed in a virtual environment and mitigate the impact of delayed positioning data. This may allow, for example, the predicted positions to be compared to actual positions. Alternatively, the path of a virtual only node can be predicted using the aggregated data.

In another example, data corresponding to potential collision events can be labeled506for training machine learning models. More specifically, the aggregated data corresponding to near collision events can be labeled as such. In another example, models can be tested508. A virtual environment allows virtual nodes or objects to be placed in the virtual environment. This allows the ability of a machine learning model to be tested. For example, a virtual only mode may move towards another virtual node (which may correspond to a real object). Based on position readings and other sensor data from the virtual only node from the real node (or another virtual only node), the ability of a machine learning model to predict a collision event can be tested. Thus, virtual nodes can be placed in specific settings and specific environments for testing purposes.

Similarly, the digital twin virtual environment also allows zones to be delineated. Thus, real world areas, using the virtual zones, can be tied to a danger ranking. Thus, real or virtual views of a zone being entered can be displayed.

Sensor fusion can also be performed510. Data from multiple sensors can be aggregated to produce a collective output of relevant data for downstream applications. Sensor fusion512in a hybrid environment may also be enabled. This allows sensor data to include the generated from virtual and real-world sensors to produce a collective output of data in the form of events.

Embodiments of the invention relate to a virtual representation of real-world devices in a space. For example, in a space such as a logistics warehouse, a variety of objects can be modeled. A digital twin or virtual warehouse has multiple uses. The virtual objects in the virtual environment may be updated at a certain frequency. The digital twin can also be used to model virtual nodes that do not have a physical association.

More specifically, the physical environment may include mobile devices that have a variety of different sensors. The virtual representation of the real-world objects allows an event-based approach to their positions or to performing logistics operations, which may include collision avoidance operations, speed changing operations, or other actions. Further, the digital twin allows events to be replayed. If a collision occurs, the data can be collected (e.g., from the database326) and evaluated to determine whether a warning was generated, to determine why a collision was or was not predicted, or the like.

In one example, zones and rules are defined that can be applied to aggregated sensor readings and mobile device data. For example, nodes in adjacent zones may be made aware of each other. This may depend on the size of the zone. A node entering an occupied zone may be made aware of other nodes in the zone. When approaching a blind corner, a virtual or actual view of the area around the corner may be presented at a node. The rules can vary widely and may be constructed based on machine learning models.

If one goal is to prevent collisions or other issues, a digital twin allows scenarios to be tested. For example, trajectory prediction machine learning models can be tested. More generally, the digital twin allows real world devices to be mirrored and allows prediction models to be tested and/or executed to ensure that the models accurately represent real-world conditions. The digital twin can test scenarios that include different sets of rules for various devices (nodes), sensors, and their actuation. The digital twin may also be able to used trained machine learning models when generating events. Thus, warnings may be based on machine learning predictions and/or geo-zone based rules.

As previously stated in one example, it is assumed there is a zone containing 2 forklifts and a wall that obstructs the line-of-sight between the forklifts within the virtual environment (e.g.,FIG.3B). Using a digital twin, each of these forklifts can be placed in the virtual warehouse. Using readings from real world sensors, such as UWB and IMU sensors, the positioning trajectory can be tested using real and/or synthetic data. The virtual forklifts can be moved in an effort to determine if the prediction places the forklifts in a collision scenario.

By way of example only, embodiments of the invention aggregate IMU sensor data, RFID positioning data, UWB positioning data, and position predicting models which can be replayed in a virtual space, to mitigate the impact of delayed positioning data.

Further, the digital twin allows data to be captured, aggregated, and labelled for use in training machine learning models.

Embodiments of the invention also facilitate the actuation of nodes (e.g., devices or objects (real and/or virtual)). The ability to actuate devices nodes and other objects, whether real or virtual, enables a virtual replica modelling of a real-world scenario where virtual nodes or objects can be placed in specific settings within a specific environment. This allows prediction models, including collision detection models to be tested and/or validated.

Embodiments of the invention also facilitate the delineation of geo-zones, which highlight areas of increased or raised dangers. These zones serve as a virtual means of tying real-world areas to danger rankings. With sensor data from the real-world environment, the digital twin can show real or virtual zones being entered. Zones that include or are about to include more than one node may be deemed more dangerous. In some embodiments, data from multiple sensors can be aggregated to produce a collective output of data for downstream applications.

Generally, the digital twin may be configured to detect, manage, and process various scenarios including safety scenarios. Example safety scenarios include collision events, potential collision scenarios, unsafe operations, excessive speed, or the like. Scenarios may also detect safe scenarios (e.g., for employee reward/recognition or other purposes). More generally, safety scenarios are examples of logistics operations that are detected, managed, averted, controlled, tested, or the like or combination thereof.

In general, embodiments of the invention may be implemented in connection with systems, software, and components, that individually and/or collectively implement, and/or cause the implementation of, data protection operations which may include, but are not limited to, digital twin operations, data collection operations, position tracking operations, model testing operations, model verification operations, collision detection operations, or the like.

New and/or modified data (e.g., sensor data) collected and/or generated in connection with some embodiments, may be stored in a data protection environment that may take the form of a public or private cloud storage environment, an on-premises storage environment, and hybrid storage environments that include public and private elements. Any of these example storage environments, may be partly, or completely, virtualized. The storage environment may comprise, or consist of, a datacenter which is operable to perform or provide applications, services, or the like including digital twin related services and functionality.

Some example cloud computing environments in connection with which embodiments of the invention may be employed include, but are not limited to, Microsoft Azure, Amazon AWS, Dell EMC Cloud Storage Services, and Google Cloud. More generally however, the scope of the invention is not limited to employment of any particular type or implementation of cloud computing environment.

Particularly, devices in the operating environment may take the form of software, physical machines, containers, or VMs, or any combination of these, though no particular device implementation or configuration is required for any embodiment. Similarly, data system components such as databases, storage servers, storage volumes (LUNs), storage disks, services, backup servers, servers, for example, may likewise take the form of software, physical machines, containers, or virtual machines (VMs), though no particular component implementation is required for any embodiment.

As used herein, the term ‘data’ is intended to be broad in scope. Thus, that term embraces, by way of example and not limitation, sensor data, position data, events, display data, rendered data, or the like.

Example embodiments of the invention are applicable to any system capable of storing and handling various types of objects or other data, in analog, digital, or other form.

Embodiment 1. A method comprising: preparing sensor data at a node in a physical environment for transmission to a central node, wherein the sensor data includes position data, sending the sensor data to a message service at the central node, listening for events from the message service, and predicting a presence of a safety scenario.

Embodiment 2. The method of embodiment 1, wherein the safety scenario comprises a potential collision between the node and a second node.

Embodiment 3. The method of embodiment 1 and/or 2, wherein the event indicates that the node is entering a zone occupied by the second node.

Embodiment 4. The method of embodiment 1, 2, and/or 3, further comprising generating a display at the node associated with the event.

Embodiment 5. The method of embodiment 1, 2, 3, and/or 4, further comprising displaying a feed from a camera in a physical environment.

Embodiment 6. The method of embodiment 1, 2, 3, and/or 5, further comprising displaying a rendered environment based on a virtual environment.

Embodiment 7. A method comprising: receiving events from nodes operating in a physical environment, publishing the events to a digital twin comprising a virtual environment, aggregating the events from the nodes, determining that a probability of a safety scenario is above a threshold and constitutes a safety event, and publishing the safety event to nodes impacted by the safety event.

Embodiment 8. The method of embodiment 7, wherein the virtual environment includes a virtual node for each of the nodes.

Embodiment 9. The method of embodiment 7 and/or 8, wherein the virtual environment further includes virtual only nodes.

Embodiment 10. The method of embodiment 7, 8, and/or 9, further comprising replaying positions of nodes in the virtual environment using the virtual nodes and/or the virtual only nodes.

Embodiment 11. The method of embodiment 7, 8, 9, and/or 10, further comprising testing collision models in the digital twin and testing collision prediction models in the digital twin.

Embodiment 12. The method of embodiment 7, 8, 9, 10, and/or 11, further comprising actuating virtual sensors in the digital twin.

Embodiment 13. The method of embodiment 7, 8, 9, 10, 11, and/or 12, generating an output from sensors in the physical environment and virtual only sensors in the digital twin.

Embodiment 15. The method of embodiment 7, 8, 9, 10, 11, 12, 13, and/or 14, further comprising applying rules based on nodes entering the zones to determine that the safety scenario has occurred.

Embodiment 16. The method of embodiment 7, 8, 9, 10, 11, 12, 13, 14, and/or 15, further comprising causing a node to display an interface that includes real data from a sensor in the environment or rendered data that includes data from a virtual only sensor.

Embodiment 19. A method comprising any one or more of embodiments 1-17 or any portions or combinations thereof.

As used herein, the term module, component, engine, agent, client, or the like may refer to software objects or routines that execute on the computing system. The different components, modules, engines, and services described herein may be implemented as objects or processes that execute on the computing system, for example, as separate threads. While the system and methods described herein may be implemented in software, implementations in hardware or a combination of software and hardware are also possible and contemplated. In the present disclosure, a ‘computing entity’ may be any computing system as previously defined herein, or any module or combination of modules running on a computing system.

In the example ofFIG.6, the physical computing device600includes a memory602which may include one, some, or all, of random access memory (RAM), non-volatile memory (NVM)604such as NVRAM for example, read-only memory (ROM), and persistent memory, one or more hardware processors606, non-transitory storage media608, UI device610, and data storage612. One or more of the memory components602of the physical computing device600may take the form of solid state device (SSD) storage. As well, one or more applications614may be provided that comprise instructions executable by one or more hardware processors606to perform any of the operations, or portions thereof, disclosed herein.