WiMi Hologram Cloud, the world’s leading hologram augmented reality (“AR”) technology provider, uses digital twin modeling based on multiple data sources to build more comprehensive, accurate and reliable digital twin models. announced that they had developed the technology.
This technology refers to integrating data from different sources into a unified model. Digital twin modeling uses multiple data source integration techniques to obtain more comprehensive and accurate data, increasing the accuracy and reliability of digital twin models.
Data gathering and preprocessing, data integration and integration, model building and training, model deployment and real-time updates, visualisation and analysis, and other key modules of an integrated digital twin modelling system based on many data sources are provided. They are interrelated, interact, and together form critical components of an integrated digital twin modelling system.
First, the system will collect data from numerous data sources and pre-process and clean it to assure the data’s quality and consistency, including data cleansing, conversion, merging, and other processes. The data from various data sources will then be combined into a uniform data model. Data mapping, data transformation, and data integration may be required to ensure that data from various data sources may be properly correlated and analysed. The model is then developed for digital twin modelling, and the integrated data is used for model training and optimisation by selecting appropriate modelling algorithms, defining the structure and parameters of the model, and training and validating the model using the training data.The trained model is then deployed to a real-time environment, where it receives and processes data from various data sources in real-time. This may include procedures such as model deployment, real-time data transfer, and model real-time updating to ensure that the digital twin model reflects real-world changes in real time. This module is in charge of visualising and analysing the results of the digital twin model so that users can understand and use the model’s output, as well as providing visualisation tools and analytical algorithms to aid users’ understanding and decision-making based on the model results.
As available data sources increase and data integration requirements become more sophisticated, future digital twin modeling systems will likely need to accommodate multimodal data such as images, audio, and video. To fully model and predict real-world behavior, many data source integrations must be able to process and analyze this multimodal data. Future digital twin modeling technologies are also likely to become more automated and intelligent, combining machine learning, artificial intelligence, and automation technologies to automate data integration and modeling processes and improve model accuracy and efficiency. It becomes possible.It also supports real-time data processing and model updates to better reflect real-world changes, as well as cross-domain applications and cross-domain integration for more comprehensive and holistic digital twin models. Emphasis is placed on This is the future trend of digital twin modeling technology based on multiple data sources.
The rapid development of big data, cloud computing, the Internet of Things, and other technologies has significantly improved data acquisition, storage, and processing capabilities, laying the technical groundwork and providing support for the implementation of digital twin modelling technology with multiple data sources. WiMi’s digital twin modelling technology with diverse data sources offers numerous application prospects in domains such as industrial Internet, smart city, virtual reality, and so on. This technology will be further improved and reinvented as data collecting and processing technology advances, as well as the growing desire for intelligent and sustainable development.