This book integrates innovative theoretical research with practical application, providing a comprehensive and in-depth guide for practitioners, researchers in the field of intelligent manufacturing, and readers from all walks of life who are curious about industrial artificial intelligence.It delves into the challenges of various typical application scenarios of industrial artificial intelligence, systematically expounds on various new data-driven modeling methods, and provides a wealth of rich industrial practice cases. The book introduces data-driven industrial intelligence from two main threads: the practical application problems faced by industrial big data analysis and the construction methods of industrial AI models, achieving a deep integration of theory and practical application. It combines the modeling ideas of industrial AI models with the introduction of theoretical methods, enabling readers to grasp the methods and processes of thinking about problems, achieving "teaching people to fish." In the introduction to the development of data-driven industrial AI models, it not only focuses on the introduction of theoretical knowledge but also connects the knowledge points of each chapter to form a three-dimensional and complete industrial AI data analysis system, enhancing readers' macro thinking on industrial intelligence and industrial big data analysis.This book can serve as a reference for technical experts in the industry, IT system developers, and researchers in the academic fields of intelligent manufacturing, artificial intelligence, industrial internet, and data science. It can also be used as a textbook for industrial artificial intelligence courses in computer science, automation, mechanical engineering, and other related majors in colleges and universities. Additionally, it can serve as a self-study material or reference book for enthusiasts and developers in the industrial field of new generation artificial intelligence, deep learning, and blockchain.The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.

This book integrates innovative theoretical research with practical application, providing a comprehensive and in-depth guide for practitioners, researchers in the field of intelligent manufacturing, and readers from all walks of life who are curious about industrial artificial intelligence.It delves into the challenges of various typical application scenarios of industrial artificial intelligence, systematically expounds on various new data-driven modeling methods, and provides a wealth of rich industrial practice cases. The book introduces data-driven industrial intelligence from two main threads: the practical application problems faced by industrial big data analysis and the construction methods of industrial AI models, achieving a deep integration of theory and practical application. It combines the modeling ideas of industrial AI models with the introduction of theoretical methods, enabling readers to grasp the methods and processes of thinking about problems, achieving "teaching people to fish." In the introduction to the development of data-driven industrial AI models, it not only focuses on the introduction of theoretical knowledge but also connects the knowledge points of each chapter to form a three-dimensional and complete industrial AI data analysis system, enhancing readers' macro thinking on industrial intelligence and industrial big data analysis.This book can serve as a reference for technical experts in the industry, IT system developers, and researchers in the academic fields of intelligent manufacturing, artificial intelligence, industrial internet, and data science. It can also be used as a textbook for industrial artificial intelligence courses in computer science, automation, mechanical engineering, and other related majors in colleges and universities. Additionally, it can serve as a self-study material or reference book for enthusiasts and developers in the industrial field of new generation artificial intelligence, deep learning, and blockchain.The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.