Seminar wsedfghjmkljuiytrtewsxdcfvg.pptx

ok

1. Content Introduction Approach of Disaster Management System Concept of Digital Twin Application of Digital Twin Disaster Management System (DT-DMS) Related Work in other countries Pros and Cons Future Scope Conclusion

2. Digital Twin Application for Disaster Management

3. A disaster is defined as a sudden, destructive occurrence that disrupts a community's or society's functioning and causes human, environmental damage , economic and material losses . It is greater than the community's or society's capacity to cope with using its resources. What is Disaster?

4. Current Approach of Disaster Management : “3S” Technology 1) REMOTE SENSING (RS) ( identification of hazard zones associated with flood plains, coastal inundation and erosion ) GEOGRAPHIC INFORMATION SYSTEM (GIS) ( computer-based tools used to store, visualize, analyze, and interpret geographic data ) 3) GLOBAL POSITIONING SYSTEM (GPS)

5. Rising Complexity and Frequency: Climate change contributes to an increase in the complexity and frequency of disasters, requiring ongoing research for effective mitigation. Technological Advancements: Rapid advances in AI, big data, and remote sensing demand further study to integrate these tools into disaster management strategies . Urbanization and Population Growth: Understanding urban resilience, evacuation strategies, and infrastructure planning is essential as more people inhabit disaster-prone areas. Need for further study?

6. What is Digital Twin Definition: Digital Twin is a virtual replica of a physical entity, system, or process, existing in the digital realm. Core Principles: Mirroring Reality: Exact representation of the physical object's characteristics and behavior. Real-time Synchronization: Changes in the physical entity reflect instantly in the digital twin. Data Integration: Utilizes data from sensors and IoT devices for comprehensive insights.

7. History of DT After the launch of Apollo 13 on April 1970 , no one could have predicted it would become a fight for survival as the oxygen tanks exploded early into the mission. It became a famous rescue mission as the world held its breath, with technical issues needing to be resolved from up to 200,000 miles away. A key to the rescue mission, however, was that NASA had a digital twin model of Apollo 13 on earth which allowed engineers to test possible solutions from ground level.

8. Digital twin can be defined as the bridge between the physical and virtual world.

9. Example of Digital Twin Framework Physical Layer It includes the study of physical geographical area in depth. It is necessary to install data acquisition sensors in physical space, including Internet of Things sensors and automatic monitors, to monitor the physical space in real time. MOUNTAIN GEOLOGICAL DISASTER

10. Information layer The information layer is a fully digital image of the physical layer. It is mainly responsible for the virtualization of the physical layer and real-time monitoring , prediction and early warning of the geological disaster process. In the DT monitoring module, the monitoring data, geological data and historical disaster data driven by physical layer data interact with each other to monitor and update the information of the mountain body, and provide data support for the DT prediction module. The data is analyzed and organized as a basis for decision making in the DT prediction module. The data in the DT monitoring module is analyzed and processed through the BP neural network algorithm to predict geological disasters. The DT early warning module and the DT prediction module interact in real time to warn the predicted results to the physical entities in the disaster area . DT monitoring module DT prediction module DT warning module

11. Visualization layer (GIS) It is a data-driven collection or general term. It is mainly responsible for displaying data and models of information layers through GIS and providing a display of geological environment. By analyzing actual needs, relying on information layer data, models, rules, the support of knowledge, the visualization layer to monitor the physical layer and guidance can be provided to people, vehicles, and related departments in geological disaster areas. .

12. Microsoft Corporation : [US$143 Billion] launched Azure Digital Twins it to support smart buildings, cities, and industrial applications 2. Bosch : [US$91.51 Billion] based on open source project Eclipse Ditto that helps devices to communicate directly and efficiently over an API. 3.General Electric Company : [US$75.619 Billion] M ost advanced Digital Twin. Their digital twin solutions focus on enhancing efficiency and predictive maintenance in industries such as aviation, healthcare, and energy. 4. IBM Corporation : [US$73.6 Billion] IBM Digital Twin Exchange for asset-intensive industries. Through Watson IoT platform. 5. Siemens : They offer a comprehensive suite of products and services for creating and utilizing digital twins in manufacturing and other industries. World’s Leading Digital Twin Companies: Top 5 by Revenue

13. Related work in other Countries Digital twins are used by many countries to model and monitor the infrastructure of tourist destinations, including transportation systems, hotels, and public spaces. This can facilitate better management, optimization of resources, and the ability to respond to the needs of visitors in real-time. NASA has used digital twin technology for space missions to simulate and analyze spacecraft performance. Companies in the automotive and manufacturing sectors have adopted digital twins for design optimization and predictive maintenance Singapore has been incorporating digital twin technology into its smart city initiatives, using it for urban planning, traffic management, and environmental monitoring.

14. Pros and Cons Digital twins enable predictive maintenance by analyzing real-time data from the physical counterpart Digital twins facilitate iterative testing and prototyping in a virtual environment, reducing the need for physical prototypes . However, there is a lack of academic studies that can be used for better solutions and standardization. The initial investment required for developing and maintaining digital twins can be substantial. The extensive data collection and sharing inherent in digital twins raise concerns about cybersecurity

15. Conclusion Thank You! In view of the current geological disaster monitoring and prediction, the environment encountered in the early warning process is complex, the amount of data is huge, the complexity of the geological disasters encountered during the monitoring process, randomness and regional differences, and poor forecasting. This prototype mentioned in the seminar uses a digital twin as the main driving method to establish digital twin physical space and information space . The two spaces integrate a large amount of data, establish a monitoring module, and combine GIS technology to visualize the information space. Through data analysis, the system quickly predicts and analyzes the monitoring module to achieve high_x0002_precision, automated and intelligent sensing of disaster monitoring information, and real-time query, rapid processing and analysis of geological disaster prediction and forecasting.