Real-Time Environment Digital Twin Simulation
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Solution Overview
Problem
Current environment simulation technologies fail to accurately and efficiently test alterations in real-world environments without incurring costs and risks, as they lack real-time data integration and visualization capabilities.
Innovation Solution
A computer-implemented method that receives real-time data from a real-world environment, generates an updated simulated environment, and initiates processes such as rendering user interfaces or applying trained models to simulate operational metrics, allowing for real-time visualization and testing of alterations before implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time data integration and visualization capabilities are added to environment simulation technologies, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The patent introduces a digital twin as an intermediary virtual model that bridges the real-world environment and the simulation system. This digital twin receives real-time data from sensors in the physical environment and translates it into a virtual representation, enabling accurate simulation without directly complicating the core simulation engine. The digital twin acts as a mediator that handles data integration and synchronization, thereby improving measurement precision while isolating the simulation system from direct complexity exposure.
Solution Approach 2:
The patent creates a virtual copy (digital twin) of the real-world environment that mirrors the physical system's behavior and characteristics. This copying approach allows the simulation to achieve high measurement precision by working with an exact virtual replica, while the complexity of maintaining real-time data synchronization is confined to the copying mechanism rather than propagating through the entire simulation system.
2Productivity
If real-time data collection and processing are implemented, then productivity improves, but use of energy increases
Solution Approach 1:
The patent implements selective data collection by focusing on only the critical parameters and metrics necessary for the specific simulation objectives. Rather than collecting and processing all possible data from the real-world environment, the system identifies and monitors only the essential elements required for accurate simulation, thereby reducing energy consumption while maintaining high productivity in environment testing.
Solution Approach 2:
The patent extracts and isolates only the essential data elements needed for simulation from the vast amount of available real-world data. By taking out only the critical information required for maintaining digital twin accuracy and simulation productivity, the system avoids the energy-intensive processing of redundant data while still achieving high testing efficiency.
3Reliability
If real-time simulation updating is performed continuously, then reliability improves, but loss of time in data processing increases
Solution Approach 1:
The patent implements periodic updating of the digital twin and simulation environment based on predetermined time intervals or trigger events rather than continuous real-time updates. This periodic action maintains reliability by ensuring that the simulation reflects the current state of the real-world environment at regular intervals, while avoiding the excessive processing time associated with continuous updating. The system updates the virtual model periodically to synchronize with new data from sensors or significant state changes.
Data Source
AI summary
Embodiments of the present disclosure provide for improved real-world environment simulation and processing thereof. Some embodiments enable real-time simulating of a real-world environment, and/or visualization of portion(s) of the corresponding simulated environment. Some embodiments enable accurate testing of alteration(s) to a real-world environment utilizing simulated environment(s) to determine the effects of such alteration(s), and apply alteration(s) that are determined from said simulations to improve operation of the real-world environment. Such alteration(s) may be applied automatically to enable a self-optimizing environment based on such simulation(s).