Session: 08-06-01 non-presentations

Paper Number: 125757

125757 - Study on Intelligent Deep-Sea Management Platforms Based on Ai Multi-Modal Models 

Based on the holographic perception technology that integrates multi-modal AI models of “air-machine-sea”, our intelligent deep-sea management platform aims to achieve the purposes of spatio-temporal embedded meteorological and hydrological prediction, build models based on the fine-grained acquisition of medium and long-term weather and hydrological data of individual wind turbines, and provide high-precision wave prediction. In combination with the operation and maintenance requirements of the wind farm units, the platform can intelligently dispatch the operation and maintenance team and provide them with efficient and safe operation schemes. The platform can effectively solve the data island problem in the process of energy three-dimensional integration development, “smartly” enhance data governance, sharing, analysis and prediction, improve the management of the whole life cycle of marine integrated energy, and realize holographic perception, intelligent transmission and collaborative control. The platform relies on the integrated application of “sea-land-air” all-round satellite communication and submarine optical fiber to provide dual protection for energy equipment and provide “digital blood vessels” for the deep-sea energy group “smart brain”. Through AIS, satellites and other means, it builds a deep-sea safety monitoring network for ships in a wide area, sets up electronic safety fences for offshore wind farms, solves the problems of “no connection” and “invisibility” in deep-sea operations, and ensures information smoothness throughout the life cycle. The platform also coordinates comprehensive energy sources such as offshore wind power, seawater hydrogen production, marine ranching, offshore photovoltaic, energy storage, etc., identifies the reliability of each energy unit in real time, and ensures an ecological priority and diversified integration of blue energy new mode.

Presenting Author: Guangjun Li The University of Sydney

Presenting Author Biography: Professor Li Guangjun has rich research experience in artificial intelligence, energy and smart grid, especially in the field of demand-side energy systems, where he has a top international reputation. Professor Li Guangjun is a visiting professor at the University of Technology Sydney, a visiting scholar at the Kennedy School of Harvard University, a visiting scholar at Monash University, a visiting scholar at Victoria University, an expert at Taiji Co., Ltd., and a member of the academic committee of the Aerospace Remote Sensing Information Processing and Application Center of Hebei Province. He has been invited to give more than ten special lectures at Imperial College London, Tokyo University, Waseda University, Kyushu University, Cardiff University, Monash University and other places. He has served as a member of the organizing committee and sub-session chair of more than 10 international conferences/forums. He has published dozens of papers in international top journals (IEEE Transactions/Magazines series journals, Information Sciences, Applied Energy, IET series journals, etc.) and international conferences.

Authors:

Minxi Li The University of melbourne
Guangjun Li The University of Sydney