| Abstract: |
| Multi-source information fusion is a multidisciplinary research field. Complex network-based information fusion explores the evolutionary relationships between units within the complex systems, providing a fresh perspective for real complex system analysis through the lens of topological dynamics. This report mainly introduces the development and application of this theory in two typical complex scenarios: the petroleum industry and human-machine hybrid intelligence. In the petroleum industry, by integrating multi-source sensor information through complex network, the challenges of high water cut, high gas void fraction, and non-steady-state flow in oil well output fluids are addressed. This technology enables effective monitoring of oil well output status, aiding in reservoir management and enhancing oil recovery rates. In the human-machine hybrid intelligence scenario, the focus is on tasks such as fatigue monitoring, emotional computing, and motor imagery rehabilitation. By constructing brain functional networks to integrate multi-source physiological data, the brain`s cognitive differences under various tasks are analyzed from a topological perspective, leading to more accurate and efficient brain state decoding. These advancements have been implemented in several tertiary hospitals. |
|