Some points in support of object-oriented narratives for embedded systems include requirements for objects to be efficient so Site Visit Executive can write about larger systems with fewer defects. Obtaining results in less time is realised using Digital Twin simulation techniques instead of structured methods, and advances can be implemented in assembly narratives, in addition to others.
An integrated engineering network, spanning across the entire value chain, is operated to intelligently connect various service divisions, and to generate a work space for products/services. The conditions for the Digital Twin are determined in which the digital space can be fed into the real, and the real world back into the digital to deal such intelligent products with rising variations.
Digital twins allow you to access large amounts of data in real time. But you don’t have to keep all that data to yourself. In fact, you’d be wise to share it. Creating a digital twin network makes it easy to share data with internal workforce, external supply chain partners, and even customers. With access to the same insight, you, your partners, and your customers can collaborate to improve products and processes.
Supply chain partners benefit from a network of Digital Twins with enhanced visibility. If an asset malfunctions, your maintenance provider knows it needs to mobilise a team to fix the equipment. If your company manufactures a product ahead of schedule, your logistics provider knows it can pick up the goods and deliver them early.
Digital Twin networks help you get invaluable insight from your customers. By monitoring how customers interact with your goods, you can remove underused features from future product iterations or develop new products that highlight popular features. Enabling an open, collaborative workspace through a network of digital twins offers you the chance to transform engineering, operations, and everything else in between.
With a Digital Twin network you share with your customer, you can monitor the condition of your asset around the clock and accurately track how much your customer consumes. This reliable and transparent method ensures you’re always standing by to repair the asset. Thinking outside the box and exploring innovative as-a-service business models is a surefire way to remain profitable in today’s ever-evolving digital world.
The term Digital Twin can be described as a digital copy of a real factory, machine, worker etc., that is created and can be independently expanded, automatically updated as well as being widely available in real time. Every real product and production site is permanently accompanied by a Digital Twin. First prototypes of Digital Twins already exist in Logistics Learning programmes built on a multidimensional data and information model.
A standardised language of the robot control systems via agents and positioning systems has to be integrated. The aspect of the continuity of the real workshop in the digital factory as an efficient means of ensuring continuous actuality of digital models can function as the basis for change
For localisation sensor combinations that in addition to the hardware already contain the application required for the sensor data fusion should be used. Processing systems, scenario-live-simulations and digital shop floor management results in a mandatory procedural combination. Essential to the Digital Twin is the ability to consistently provide all subsystems with the latest state of all required information/methods.
A Digital Twin is intended to be a digital replica of physical assets, processes, or systems, in other words, a model. It is most often referenced as an outcome of networks where the expanding world of devices with sensors provides an equally fast expanding body of data about those devices that can be broken down and assessed for efficiency,, design, maintenance, and many other factors.
Since the data continues to flow, the Digital Twin model can be continuously updated and ‘learn’ in near real time any change that may occur. Digital twins can produce value without machine learning and AI if the system is simple. If for example there are limited variables and linear relations are easily discovered between inputs and outputs then no data science may be required.
However, the vast majority of target systems have multiple variables and multiple streams of data and do require science discipline to make sense of what’s going on. Even while many experts tend to equate all this with AI, great majority of the benefit of modeling can be achieved with traditional machine learning tools to discover patterns in sensor readings.
For example, video feeds of components during manufacture can already be used to detect defective items and reject them. Similarly audio inputs of large generators can carry signals of impending malfunctions like vibration even before traditional sensors can detect the problem.
At first, an asset may be operating as expected. Inside the machine, however, it’s another story. A glitch in the system is causing your asset to gradually slow down. Later. it’ll fail completely. Without the right technology, you’d never know that. But Digital Twins help you anticipate issues and prevent problems before they even occur. They enable you to detect anomalies and automate repair processes at the first sign of weakness. And by coming to your asset’s rescue sooner rather than later, you can avoid serious service interruption or prolonged downtime.
If you already operate with advanced networks, especially those connected to industrial machines and processes you are probably in the clean up spot for Digital Twins. But any predictive model is potentially subject to drift over time and needs to be maintained. For example, some sensors are notoriously noisy and as you start to isolate signal from noise your sensors will undoubtedly need to be updated.
Although the definition of Digital Twins often includes specific reference to ‘processes’, examples of processes modeled with Digital Twins other than mechanical factory processes are difficult to find. Since many don’t have complex or capital intensive machinery and industrial processes, what is the role of Digital Twins in ordinary business processes.
There are applications available today that can automatically detect the beginning and end points of each step in the transaction from network logs thus providing the same sort of data stream for service origination as sensors might for aircraft.
The more that human activity is included in the data of what is being modeled, the less accurate the model will be. So for those of you who have modeled machine-based or factory-process based data where very little human intervention occurs can regularly achieve accuracy
But if we are modeling a business process such as customer-views-to-order in service centers then the complexity of operator action will mean the best models may be of limited complexity.
Digital Twins drive innovation and performance to give operators predictive tools and they give companies the ability to improve customer experience. Digital Twins allow for better understanding of customer needs, enhanced existing products, streamlined operations, and improved service-after-sales; all while creating headway for new products and services.
But even with industrial applications the error rate still exists. Models have error rates. So for example, when you use Digital Twin models to predict preventive maintenance or equipment failure, in some percentage of cases the maintenance is performed too early and in some there is the inability to forsee an unexpected failure. The model can continually be improved as new data and techniques are available but it will always be a model, not a one-to-one identity with reality.
Digital Twins can be used as a representation of current reality and new machines, processes, or components are designed and built up from scratch using those assumptions about operating reality. So you have to be sure to understand how the error rate in the underlying model might mislead designers into serious errors about how the newly designed machine or process might perform in the current reality.
The great majority of your interaction with digital systems is still request driven so once a condition is observed you instruct or request the system to take action. This is being rapidly supplanted by event driven processing. The modeling of machines, systems, and processes is a precondition for the optimisation work that determines when specifications and decisions are needed. As the Digital Twin movement expands, more streaming applications will be enabled with automated event driven decision making.
Object-Oriented Programming Simplifies Digital Twins
The digital twin model offers a breakthrough approach to structuring state tsream processing applications. This model organises key network information about each data source in application components that tracks data source changing state and to interpret the state and generating real-time feedback.
Using digital twins offers three key benefits over more traditional, pipelined stream-processing techniques: automatic event correlation by data source, deeper dives with enhanced state information, and parallel assessments to discover aggregate trends for all data sources in real time. It represents a big step forward for building stream-processing applications.
When using the digital twin model, each data source in a physical system has a corresponding object in the stream-processing platform that encapsulates both state information and code. State information includes a time-ordered list of the device’s incoming event messages along with key state information about the dynamic state of the data source. This information could include parameters, service history, known issues, and much more.
Application code handles the management of event list and the real-time analysis of incoming events for performing device commands. This code benefits from the rich context provided by dynamic state information, enabling deeper introspection than analyzing the event stream alone.
The secret to keeping event assessments low when handling events from many data sources is to host these Digital Twin objects in memory data grid with an integrated compute engine minimise network bottlenecks by assessing events within the grid.
Object-oriented storage precisely fits the requirements for Digital Twin objects, making it straightforward to deploy and host these objects with both scalable performance and high availability. The grid transparently distributes the Digital Twin objects across a cluster of networks for scalable processing.
Let’s take a look at how object-oriented techniques can simplify the design of digital twins. Because a digital twin encapsulates state information and associated code, it can be represented as a user defined type/class within an object-oriented language.
The use of an object class to represent the controller conveniently encapsulates the data and code as a single unit and allows for creation of many instances of this type to manage different devices. For example, consider the Digital Twin for a basic controller with class properties status /event collection describing the controller status and class methods for assessing events and performing device commands.
You can also can make use of the class definition to construct various special purpose digital twins as subclasses, taking advantage of the object-oriented technique called inheritance. For example, we can define the Digital Twin for a hot water valve as a subclass of a basic controller that adds new properties, such as temperature and flow rate, with associated methods for managing them.
This subclass inherits all of the properties of a basic controller while adding new capabilities to manage specialised controller types. Using this object-oriented approach maximises code reuse and saves development time.
You can build a group of Digital Twins that represent successively higher levels of control for complex systems to leverage object-oriented techniques. Consider the following set of interconnected Digital Twin instances used in managing a pump room:
In this example, the pump room has Digital Twin partners connected directly to devices, one for a hot water valve and another for a circuit breaker. These twins are both implemented as subclasses of a basic controller and add properties and methods specific to their devices. They feed telemetry to a higher-level Digital Twin instance which manages overall operations for the pump room.
This Digital Twin also can be implemented as a subclass of a basic controller even though it is not connected directly to a device. What’s important to observe about this example is how object inheritance and group rank play separate roles in defining the Digital Twin objects which work together to assess event streams. The behaviour of Digital Twin models to customise actions and build systems of interconnected Digital Twins customised to process events at successively higher levels of virtual expression.
Digital Twin models for state stream-processing have developed from concepts largely unrelated to object-oriented programming, in particular, product life cycle management and industrial networks device twins. Object-oriented techniques developers powerful tools for applying Digital Twins to break down state stream-processing and streaming processes.
Understand Digital Twins object models and spatial intelligence graph
Digital Twins services powers comprehensive virtual representations of physical work space and associated devices, sensors, and work force. It improves development by organising domain-specific concepts into useful models. The models are then situated within a spatial intelligence graph to model the relationships and interactions between workforce spaces and devices.
Digital Twins object models describe domain-specific concepts, categories, and properties. Models are predefined by users who want to match the solution to operational requirements. Together, these predefined Digital Twins object models describe/customise field-level regions/zones of interactions. With Digital Twins object models in place, you can populate a spatial graph.
Spatial graphs are virtual representations of the many relationships between spaces, devices relevant in network solutions, bringing together spaces, devices, sensors, and users. Each is linked together in a way that models the real world. For example, for workstations with many different areas users are associated with their workstations and given access to portions of the graph.
Spatial intelligence graph
Spatial graph is group graph of spaces, devices, and workforce defined in the Digital Twins object model. The spatial graph supports inheritance, filtering, traversing, scalability, and extensibility so you can manage and interact with your spatial graph.
If you deploy a Digital Twins service in your subscription, you become administrator of the root node. You're then automatically granted full access to the entire structure. You can provision spaces in the graph by using sensors. Open source tools also are available to provision the graph in bulk.
Graph inheritance applies to the permissions and properties that descend from a parent node to all nodes beneath it. For example, when a role is assigned to a user on a given node, the user has that role permissions to the given node and every node below it. Each property key and extended type defined for a given node is inherited by all the nodes beneath that node.
Graph filtering is used to narrow down request results. You can filter by identifiers, name, types, subtypes, parent space, and associated spaces. You also can filter by sensor data types, property keys and values
Graph traversing means you can move to new locations in the spatial graph through its depth and breadth. For depth, traverse the graph top-down or bottom-up by using the parameters You can traverse the graph to get sibling nodes directly attached to a parent space or one of its descendants for breadth. When you query an object, you can get all related objects that have relationships to that object.
Digital Twins guarantee Graph scalability so it can handle your real-world workloads. Digital Twins can be used to represent large portfolios of infrastructure, devices, sensors, telemetry, and more.
Finally, Digital Twins can be customised by utilising Graph extensibility to customise the underlying Digital Twins object models with new types/groups. Your Digital Twins data also can be enriched with extensible properties and values. The following Digital Twins Models Support Object Categories
1. Spaces are virtual or physical locations
2. Devices are virtual or physical pieces of equipment
3. Sensors are objects that detect events
4. Resources are attached to a space represent resources to be used by objects in the spatial graph.
5. Property keys/values are custom characteristics of spaces, devices, sensors, used along with built-in characteristics,
6. Roles are sets of permissions assigned to users and devices in the spatial graph
7. Role assignments are the association between a role and an object in the spatial graph. For example, a user or a service principal can be granted permission to manage a space in the spatial graph.
8. User-defined functions allow customised sensor processing within the spatial graph to: Set a sensor value, Perform custom logic based on sensor readings, Set the output to a space, Send notifications when predefined conditions are met.
9. Matchers are objects that determine which user-defined functions are executed for a given telemetry message.
10. Endpoints are the locations where Digital Twins events can be routed, for example, Event Hub, Service Bus, and Event Grid.