On the afternoon of August 10, Tao Jianhui, founder and CEO of Beijing Taos Data Technology Co., Ltd., led a team to visit the Institute of Engineering Technology, USTB (hereinafter "IET, USTB") for technical exchange. Shao Jian, Deputy Dean of IET, USTB and Executive Deputy General Manager of Design and Research Institute Co., Ltd., USTB (hereinafter "DRI, USTB"), and Song Yong, Technical Director of DRI, USTB, attended the symposium, which was chaired by Shao Jian.

On behalf of IET, USTB and DRI, USTB, Shao Jian extended a warm welcome to the Taos Data team and reviewed the history of cooperation between the two sides: since 2022, the two sides have launched deep collaboration, initially under an OEM framework, jointly building metallurgical digital solutions based on the TDengine time-series engine, and subsequently continuously optimizing the cooperation model to build a solid foundation for technical coordination. He systematically introduced IET, USTB's technical accumulation and landmark industrial implementation achievements in many fields such as precision control, intelligent manufacturing, advanced materials, intelligent inspection, and process equipment. He stated that IET, USTB has been deeply rooted in steel and non-ferrous metal sites for decades, has accumulated a large number of industry scenario skills, and can provide personalized AI application scenarios that fit actual production, hoping to use this exchange to share technical ideas and explore new ideas for the intelligent transformation of the metallurgical industry.

Tao Jianhui introduced that Taos Data is a domestic time-series database enterprise with full independent intellectual property rights, bearing the mission of strengthening industrial basic software and promoting the autonomous controllability of industrial digitalization. It has independently developed the TDengine time-series database, focusing on building a localized industrial data foundation. He then detailed Taos Data's complete industrial digitalization support system. The platform uses a high-performance time-series database as the underlying foundation, builds an industrial ontology covering data catalog, data standardization, and data contextualization, and is equipped with atomic applications, industrial agents, flexible visualization interfaces, and a high-performance stream computing rule engine, providing support for lowering the threshold of industrial AI development, deployment, and operation in the metallurgical industry.

During the discussion phase, the two sides conducted full exploration of implementation pathways such as natural-language-driven metallurgical production-line data analysis, industrial ontology interfacing with unstructured data, industrial agents loading process skill libraries, and personalized process rules with lightweight visualization configuration.

Since 2022, the two sides have fully supported each other and established a stable and efficient long-term cooperative relationship. Going forward, the two sides will maintain normalized technical dialogue, continuously exchange innovative ideas on industry digitalization, integrate their respective technical advantages, and explore metallurgical industry AI solutions.