The invention relates to the technical field of
artificial intelligence, in particular to a personnel management and control method and
system based on
machine learning, and the method comprises the steps: obtaining productivity expected values of a
production line in different preset time periods, obtaining a productivity sequence, inputting the productivity sequence into a line speed prediction network, and predicting the real-
time line speed of the
production line in the next time period; the line speed of the
production line is adjusted to the real-
time line speed, the real-time personal
heart rate sequence and the historical
heart rate sequence of each operator on the production line are collected, and the historical
heart rate sequence comprises at least two
normal heart rate sequences and at least two
abnormal heart rate sequences; and calculating similar distances between the real-time personal heart rate sequence and the
normal heart rate sequences and between the real-time personal heart rate sequence and the
abnormal heart rate sequences, comparing and calculating the plurality of similar distances to judge whether the real-time personal heart rate sequence is in a normal operation state, and maintaining the real-time linear speed when the real-time personal heart rate sequence is in the normal operation state. According to the method, the
verification time waste is avoided by utilizing
machine learning, and the purpose of accurately predicting the line speed is effectively realized.