The invention discloses a working platform task
workload prediction method based on
deep learning. The method comprises the steps of obtaining historical
client publishing task data and employee completing task data of a working platform; carrying out missing value interpolation and normalization
processing on the
client publishing task data; training an LSTM
deep learning model as a single-factorprediction model; taking an LSTM
deep learning model based on a double-attention mechanism as a multi-factor prediction model; inputting the published task data into a single-factor prediction modelto obtain a prediction result of single-factor prediction; and fusing the prediction result into the lifting tree for regression calculation to obtain a prediction value of the
workload. According tothe invention, the single-factor prediction model and the multi-factor prediction model are constructed; a
work task failure reason is analyzed, a seq2seq model of the double-attention mechanism is selected, regression calculation is carried out in a lifting tree, a final
workload prediction value is obtained, prediction results of
multiple models are fused, and the optimal prediction value is solved through cooperation.