HomeAI Core TechnologiesWiMi Announced Multi-scale and Multi-scenario Digital Twin Modeling

WiMi Announced Multi-scale and Multi-scenario Digital Twin Modeling

WiMi Hologram Cloud Inc., a leading global Hologram Augmented Reality (“AR”) Technology provider, today announced that the multi-scale and multi-scenario digital twin modeling it is working on is a technique that integrates the application of multi-scale and multi-scenario data, aiming to build highly realistic, reliable and comprehensive digital twin models. The multi-scale and multi-scenario digital twin modeling is dedicated to the integration of data from different sources and types, which includes sensor data, experimental data, simulation data, etc. By integrating the information from different data sources, the credibility and predictive performance of the model can be improved. Comprehensive modeling from macroscopic to microscopic, taking into account both large-scale system behavior as well as local details, can more accurately reflect the complexity of the real world in order to generate more comprehensive and accurate models. The generated digital twin models can be modeled based on data from different scenarios, including different environmental conditions, operating situations, or events as they occur, which allows the models to adapt to different application requirements and provide accurate prediction and simulation results.

WiMi’s multi-scale and multi-scenario digital twin modeling includes key technology modules such as data acquisition and pre-processing, multi-scale data integration, multi-scenario data integration, multi-scale modeling and simulation, and visualization and interaction, which work in tandem with each other to jointly build a comprehensive and accurate digital twin model.

Data acquisition and pre-processing: This involves the collection of a variety of data from different scales and scenarios, including sensor data, experimental data, simulation data, etc. Pre-processing of raw data, such as denoising, filtering, calibration, etc., is also required to ensure the quality and usability of the data.

Multiscale data integration: The integration of data from different scales to produce comprehensive models. This may involve operations such as alignment, collocation and scaling of multi-scale data, allowing the data to be compared and integrated into a unified coordinate system.

Multi-scenario data integration: Data from different scenarios are integrated to suit different applications. This may include considering data from different environmental conditions, operational situations, or events as they occur and incorporating them into models to provide more accurate predictions and simulation results.

Multi-scale modeling and simulation: Using multi-scale data to construct detailed and accurate digital twin models. This may include using physical models, statistical models, machine learning models, etc. to describe system behavior, and calibrate and validate them against actual data.

Visualization and Interaction: This module is responsible for presenting the digital twin model to the user in a visualized form and supporting the user to interact with the model. This allows the user to visualize the behavior, parameter changes, and event evolution of the system, as well as to modify and explore the model in real time.

Model Evaluation and Optimization: This involves evaluating and optimizing the digital twin model to improve the accuracy and reliability of the model. This may include such as model validation, sensitivity analysis, uncertainty quantification, and optimization of the model through parameter tuning and algorithm improvement.

The multi-scale and multi-scenario digital twin modeling technology is under-researched by WiMi enables more comprehensive and accurate system modeling and simulation by integrating data from different scales and scenarios, providing a comprehensive and reliable method to simulate and optimize real-world systems and facilitating the implementation of digital transformation. The rapid development of artificial intelligence and simulation technology also provides a technical foundation for multi-scale and multi-scenario digital twin modeling. In addition, technologies such as deep learning and machine learning have made it more feasible to process large-scale data and construct complex models, while driving innovation in multi-scale and multi-scenario modeling methods. Multi-scale and multi-scenario digital twin modeling technology has a wide range of application areas and plays an important role in many fields, and it can be applied to many fields such as smart cities, intelligent manufacturing, medical research, etc., to provide better decision support and problem solutions in these fields through accurate modeling and simulation.