Digital Twin Database Architecture for Real-Time Parameter Updates

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Solution Overview

Problem

Legacy databases for managing data in factories and plants are limited in optimizing digital twins and smart factories, and existing digital twin technologies struggle to reflect real-time changes in equipment configurations.

Innovation Solution

A database architecture for real-time digital twin systems, comprising a first database for refined data processing, a second database for steady state determination, and a third database for model prediction, which updates parameters in real-time based on steady state plant data to synchronize digital twins with physical facilities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a legacy database is used to manage data from limited sensors, then data storage is simple, but the optimization for digital twin construction and smart factories is insufficient

Engineering Contradiction:

Improveadaptability for digital twin constructionVSAvoiddatabase architecture complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The database system is segmented into multiple specialized databases: a first database for refined data processing, a second database for steady state determination, and a third database for model prediction. Each database handles specific data types and processing tasks, enabling optimized digital twin construction while maintaining manageable complexity through functional separation.

Inventive Principle:

Principle #1Segmentation

2Speed

If digital twin technology updates data discontinuously at regular intervals, then data processing is simple, but real-time reflection of equipment changes is difficult

Engineering Contradiction:

Improvereal-time update speedVSAvoiddata processing complexity

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The system transitions from static, periodic updates to dynamic, event-driven updates. The steady state determination module continuously monitors data variation values and triggers model parameter updates only when steady state conditions are met, enabling real-time reflection of equipment changes while adapting processing complexity to actual system conditions.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements feedback mechanisms where the steady state determination module continuously compares current data with historical data, calculates variation values, and uses this feedback to determine when updates are necessary. This feedback loop enables real-time responsiveness while avoiding unnecessary processing during transient states.

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If a predetermined model is used to create a digital twin, then model creation is simple, but changes made to equipment are difficult to reflect in the model

Engineering Contradiction:

Improvemodel adaptability to equipment changesVSAvoidmodel accuracy

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The model parameters are transformed from static to dynamic through the parameter updater module. When the steady state determination module detects steady state conditions, it triggers parameter updates that reflect current equipment states. This dynamic parameter adjustment maintains model accuracy while adapting to equipment changes.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs preliminary data processing and steady state determination before updating model parameters. By preparing refined data in advance and validating steady state conditions, the system ensures that parameter updates are based on reliable, processed data, maintaining model accuracy while enabling adaptability.

Inventive Principle:

Principle #10Preliminary action

4Measurement precision

If all plant data is processed continuously, then data accuracy is improved, but processing time and computational resources increase

Engineering Contradiction:

Improvedata processing accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system applies partial processing by focusing computational resources only on data that meets steady state criteria. Rather than continuously processing all plant data, the system selectively processes data during steady state periods when parameter updates are meaningful, reducing processing time while maintaining accuracy for critical updates.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The steady state determination module extracts and isolates only the portions of plant data that are suitable for model parameter updates. By separating steady state data from transient data, the system processes only the relevant subset of data, improving efficiency without compromising the accuracy of parameter updates.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20260016798A1Database for implementing real-time digital twin system and system for creating digital twin

Publication Date: 2026.01.15 SIMACRO

AI summary

Provided are a database (DB) for implementing a real-time digital twin system, and a system for creating a digital twin. The DB includes a first DB configured to receive data from a legacy DB and store refined data that has undergone primary data processing, and a second DB configured to store model parameter update data that is derived by inputting the refined data stored in the first DB into a steady state determination module.