This invention provides an adaptive energy-saving control method and
system for industrial environmental control systems based on large-
scale model-based operating condition prediction, applied in the field of
industrial data processing technology. It collects various types of data, including equipment, environmental, meteorological, and
energy consumption data, and performs unified benchmarking, cleaning, and
standardization preprocessing. Then, it constructs an operating condition identification model using time-series coding, sliding windows, and a large-
scale model, classifying four operating conditions. Combining equipment limits, energy efficiency curves, and
zoning requirements, it establishes a mapping relationship between
energy consumption and
control parameters, generating a multi-equipment collaborative control rule base. Relying on feedback closed-loop and adaptive PID algorithms, coupled with anti-
jitter and load balancing mechanisms, it ensures
stable system operation. Using
energy consumption, environmental indicators, and equipment stability as evaluation criteria, it constructs a dynamic weighted
loss function iterative optimization strategy to correct the model and
control parameters. Finally, it issues control commands, retains data, and outputs reports, achieving intelligent energy-saving management throughout the entire process.