Clone Digital Twin Predicts IoT Network States
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Solution Overview
Problem
Current smart traffic systems and digital twin technologies face challenges in processing real-time data from diverse sources to deliver predictive services efficiently, as they struggle to extrapolate future states of complex transport networks due to limitations in data stream processing and time dependency within Directed Acyclic Graph (DAG) structures.
Innovation Solution
The method involves creating a source digital twin and clone digital twins interconnected via a data stream synthesizer, which adds a time increment to the output of the source digital twin, allowing the clone to predict future states by emulating real-time data inputs and producing synthesized values, thereby maintaining a DAG structure while enabling predictive capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time data processing is implemented for smart traffic systems, then the system can deliver real-time services, but it becomes difficult to predict future states due to time dependency constraints in DAG structures
Solution Approach 1:
The patent creates a clone digital twin that replicates the source digital twin's structure and behavior. This clone operates with time-incremented synthesized data streams instead of real-time data, allowing predictive simulation of future states without modifying the original real-time processing system. The copying approach enables parallel evaluation of future scenarios while maintaining the integrity of current operational data processing.
Solution Approach 2:
The system performs preliminary actions by synthesizing future data streams in advance and feeding them to the clone digital twin before actual future events occur. This allows the system to pre-calculate and evaluate potential future states, enabling predictive services such as incident forecasting and trip time estimation before the actual events happen in the real world.
2Loss of time
If clone digital twins are created for predictive simulation, then future states can be forecasted, but data stream processing complexity increases
Solution Approach 1:
The data stream synthesizer acts as an intermediary component that bridges the source digital twin and the clone digital twin. It transforms real-time data streams into time-incremented synthesized data streams, allowing the clone to receive appropriately timed input data without requiring complex modifications to the underlying data processing infrastructure. This intermediary simplifies the overall system architecture by handling time transformation in a dedicated component.
3Measurement precision
If time-incremented synthesized data streams are used to drive clone digital twins, then predictive accuracy is improved, but the system requires additional processing nodes
Solution Approach 1:
The data stream synthesizer serves multiple functions: it generates time-incremented data, synthesizes future states based on current trends, and feeds processed data to the clone digital twin. This multi-functional component reduces the need for separate specialized processing nodes for each function, thereby improving prediction accuracy while minimizing the increase in system complexity.
Data Source
AI summary
A method of predicting the evolution of simulation results for an Internet of Things (IoT) network by creating a source digital twin for the IoT network, driven by real-time sensed data from objects fed to models of the objects interconnected as object nodes in a directed acyclic graph (DAG) with the interconnections representing flow of data, the source digital twin outputting a state of one or more of the objects in real time; creating a clone digital twin of the source digital twin; connecting input of the clone digital twin with output of the source digital twin via a data stream synthesizer node, the data stream synthesizer node adds a time increment to the output of the source digital twin to drive the clone digital twin at the incremented time. The source digital twin and the clone digital twin are executed to indicate an evolved state of one or more of the objects at the incremented time as the output of the clone digital twin.