The invention belongs to the technical field of
intelligent control of industrial heating equipment, and particularly relates to a
big data self-learning evolution method for an intelligent burning furnace model of a
hot blast stove. According to the method, multi-
source data such as fuel components and temperature fields are collected in real time, and a standardized input
data set is constructed after
wavelet noise reduction and
principal component analysis processing. The model can dynamically optimize the air-fuel ratio, predict the hot
air temperature and correct the equipment parameter deviation, and stable
combustion is achieved. When effective samples are accumulated to a threshold value, the
system automatically triggers parameter iteration, key parameters are reserved and secondary parameters are updated by utilizing transfer learning,
encryption gradient aggregation and
knowledge sharing among multiple furnaces are realized by virtue of
federated learning, and distribution and deployment are performed after a
global optimization model is formed. And finally, according to the model output, a closed-loop regulation and control fuel valve, a fan and other execution mechanisms are realized, the temperature and
energy consumption are monitored in real time to evaluate the evolution effect, and a continuously optimized
intelligent control cycle is formed. According to the method, the control precision and the
combustion efficiency are remarkably improved, and
energy conservation and consumption reduction are effectively achieved.