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Driven by both industrial automation technologies and the technical demands of the metallurgy industry, metallurgical automation technology will exhibit three major trends. (1) Process control systems: Online continuous detection and monitoring systems for metallurgical processes. By utilizing advanced sensor technologies, optomechatronics, soft sensing techniques, data fusion and processing methods, as well as reliability techniques suitable for metallurgical environments, an online monitoring system for metallurgical processes is implemented. The objectives of this system are to achieve closed-loop control of key process parameters, tracking of material flow, control of energy balance, real-time monitoring of environmental emissions, and comprehensive control over product quality. It includes the detection and prediction of the composition and temperature of molten iron, steel, and slag; the detection and prediction of the purity of molten steel; the measurement and assessment of parameters such as temperature, size, microstructure, and defects in billets and steel products; as well as the monitoring of exhaust gases and dust throughout the entire process. High-performance closed-loop control of key variables in metallurgical processes. Based on techniques such as mechanism models, statistical analysis, predictive control, expert systems, fuzzy logic, neural networks, and support vector machines (SVM), and with the goals of ensuring process stability and improving technical and economic indicators, a comprehensive model is established upon the online continuous monitoring of the aforementioned key process parameters. An adaptive intelligent control mechanism is employed to achieve high-performance closed-loop control of the key variables in metallurgical processes, including an expert system for maintaining the proper operation of blast furnaces, closed-loop control of molten steel composition and temperature, and closed-loop control of slab and steel product dimensions as well as their microstructural properties. (II) Production management control system: Holistic integration of the metallurgical process. It achieves horizontal data integration and exchange among iron, steel, and rolling processes, as well as vertical information integration across management, planning, production, and control functions. Meanwhile, it integrates real-time production data with relational databases to form a data warehouse, and employs data mining techniques to provide decision support for production management and control. Full-process computer simulation to achieve science-based design and manufacturing. By utilizing computer simulation technology, multimedia technology, and computational mechanics, and based on various metallurgical models, offline process simulation and online integrated modeling are carried out to achieve optimization of production organization, production processes, design of new production processes, and development of new products. Enhance the intelligence of steel production and manufacturing. In terms of production organization and management, based on case-based reasoning, expert knowledge, as well as network rule techniques in production planning and operations research, it provides means and capabilities for rapid adjustment of work plans, thereby enhancing the flexibility and agility of production organizations ； Based on the parameters of each process, it automatically calculates the production sequence plan for each step as well as the production time and waiting time for each process, enabling full-track monitoring and control of the plan; it can also be adjusted flexibly according to on-site requirements and expert knowledge ； Recombination scheduling techniques in abnormal conditions, as well as human-machine collaborative dynamic production scheduling across various process routes. In terms of quality management, product quality is predicted, monitored, and analyzed using data mining, statistical calculation, and neural network analysis techniques ； Based on production process data and actual data, identify quality abnormalities that occur during production. In terms of equipment management, fault diagnosis and prediction techniques for production equipment are employed to establish models for predicting equipment failures and their lifespan, thereby enabling predictive maintenance. In terms of cost control, data mining and forecasting techniques are employed to develop dynamic cost models for predicting production costs ； By utilizing dynamic tracking control technology, the ratio of raw materials, the supply of energy sources, the maintenance schedule for production lines, and production scheduling management are optimized, while costs are calculated dynamically in order to reduce production costs. (III) Enterprise information systems Integration of enterprise information into industry information integration. One of the purposes of informatization is to achieve information sharing, to take advantages and avoid disadvantages in a context of fair competition. On the basis of standardizing the coding systems in corporate information systems and integrating heterogeneous data/information within enterprises, it further enables the integration of information across manufacturing enterprises, the development of industry-wide information networks, and macro-control information systems, ultimately leading to the creation of global industry information networks and macro-control information systems.