On March 23-24, the 2024 Global Developer Conference (GDC) was grandly held in Shanghai. With the theme of "Modular Speed Space - The Source of Developers' Modular Power," the conference gathered the most cutting-edge technologies, hottest topics, and leading talents worldwide, contributing wisdom to the national "AI+" initiative. Wang Jiangping, Vice Minister of the Ministry of Industry and Information Technology, Chen Jie, Vice Mayor of the Shanghai Municipal People's Government, and Ramesh Ramadoss, Global Chairman of IEEE Blockchain Technology Community attended the opening ceremony and delivered speeches.
On March 24, the sub-forum of the 2024 Global Developer Conference and the 8th Seminar for Knowledge that Yields, hosted by CASCO and Shanghai Engineering Research Center of Driverless Train Control of Urban Guided Transport, concluded successfully. With the theme of "Rail Transit Big Model and Reliability Engineering," this forum, as the only industry summit in rail transit at this conference, attracted numerous experts and technological leaders from the industry, academy, and research institutions to jointly discuss the development trends of digital rail transit in the era of large models. Wang Xiaoyong, General Manager of CASCO's Urban Rail Products Center and Director of Shanghai Engineering Research Center of Driverless Train Control of Urban Guided Transport, delivered the opening speech for this forum.
Through keynote speeches, participating experts discussed the limitations and innovative directions of traditional machine learning, the credibility of large models, and its potential in improving software reliability, focusing on the application scenarios of rail transit big models. Meanwhile, considering the characteristics of unmanned rail transit, they analyzed in detail the potential applications of vertical large models in rail transit, proposed new paths for innovation-empowered rail transit, and shared innovative applications of visual large models in rail transit pantograph video processing and self-optimization methods for electromagnetic space by wireless large models.
Addressing the promotional role of large models in reliability engineering, the forum delved into the connotation, system, and physical models of PHM (Prognostics and Health Management), sharing research and application achievements as well as practical challenges in PHM for rail transit signal equipment. It pointed out that the integration of mechanism analysis and AI analysis will become an important direction for the development of PHM, and the construction of a multi-subsystem PHM system will effectively enhance the reliability, utilization, and energy efficiency of rail transit equipment.
During the event, experts and scholars freely expressed their opinions on large models and reliability engineering, unanimously agreeing that leveraging AI to achieve ultra-large-scale digital upgrades and intelligent and credible operation and maintenance in rail transit is an inevitable trend in industry development. They also pointed out that there are still many challenges in accurately perceiving equipment status in complex environment, and technological innovation will bring new opportunities and breakthroughs.
As the enterprise with the most applications of fully automatic systems in China, CASCO is currently undergoing an iterative upgrade of its fully automatic system from automation to intelligence. Large models and reliability, as key technologies in digital rail transit, are the core of intelligent operation control and O&M, providing new momentum for sustainable rail transit development.
