Digital Twins

a digital twin is a virtual representation of an industrial asset used to monitor, analyze, and optimize the performance of a device before investing in physical prototypes and assets, use digital twins throughout the product lifecycle to simulate, predict, and optimize the product and production system digital twins are also used to monitor the performance of physical assets in real time and optimize operations so that you can perform activities such as insights, alerts, and feedback loops note digital twins requires a scale or growth litmus license digital twins models models are data structures for digital twin instances you can configure the following parameters in a model attributes models store the static and dynamic attributes that instances use for identification and data collection static attributes model metadata, for example, device id , serial , and model dynamic attributes variable data for the model, for example, temperature , vibration , and downtime transformations rules that convert raw json data collected in instances to another format with a json based schema parameters key value pairs that you can insert into topic expressions for dynamic attributes parameters help you save time when creating and defining instances schema a logical representation of static and dynamic attributes you can drag and drop attributes into the hierarchy as needed and add folders to create a custom data structure digital twins instances instances represent the physical objects that contain static and dynamic data from different sources every instance is associated with a model and the attributes configured in the model determine the data that the instance collects every instance generates a new data topic and publishes it to the internal message broker data topics can be leveraged within litmus edge when you configure instances, you can configure static attributes in the instance map one to one relationships with data topics and dynamic attributes by configuring topic names for dynamic attributes in the instance view real time data in the data access tab set values for defined parameters publish attributes on individual topics publish flattened payloads send standardized and structured data to northbound integrations digital twins user journeys when setting up digital twins models, you follow the typical user journey configure static attributes that represent the metadata of the asset configure transformation rules for input data using jsonpath or jsonata to your desired json format define parameters to use as variables in your topic expressions for dynamic attributes configure dynamic attributes that represent the real time data of the asset define the schema of the resulting object optionally, import and export the model from a json file and clone the model as needed create models in litmus edge manager and deploy them to litmus edge instances when setting up digital twins instances you select the model to create the instance from specify the time interval to publish data to the message broker optionally, enable the flatten payload and publish attributes features update static attribute values as needed define dynamic attribute topics either by mapping input topics defining parameter values for each instance view the complete digital twins json payload in the data access page after setting up the instance, start and stop it as needed if you update the model associated with the instance, you must manually upgrade the instance with the updated model configuration digital twins how to guide see docid\ vdrp68hiwea7iylgo3ew7 to learn more about leveraging digital twins access digital twins ui to access digital twins log in to litmus edge from the navigation panel, select digital twins the models pane appears next steps docid\ zpadx1lmme0f44v7r6v6q docid\ hk0idxlg7zcoetajwcobo docid\ jzoth4t0hcrtrauusdryk docid\ req2rshywfuzlnezqls0t docid\ vdrp68hiwea7iylgo3ew7