The invention provides an intelligent task optimization scheduling method for industrial data, and relates to the technical field of industrial scheduling. The intelligent task optimization scheduling method for industrial data comprises the steps of S1, starting a task, S2, running equipment, S3, inputting a new task, S4, calculating an emergency value, S5, collecting and preprocessing data, S6, storing old data, S7, predicting a fault, S8, replacing the equipment, and S9, ending the task. The
data acquisition in the step S5 comprises the equipment operation state, the production progress, the
raw material inventory, the
equipment temperature, the production speed, the yield and the
energy consumption. According to the invention, through analysis of historical data and real-
time data, potential risks of equipment faults can be effectively identified, possible risks are reduced through automatic adjustment, equipment abnormity is processed in time, production interruption caused by faults is avoided, and meanwhile,
downtime can be reduced through an
active fault management mode of the scheduling method, so that production efficiency is improved. The production loss is reduced.