K-means clustering algorithm and discrete wavelet transform-based unmanned supermarket passenger flow forecasting method

The invention discloses a K-means clustering algorithm and discrete wavelet transform-based unmanned supermarket passenger flow forecasting method, including obtaining user flow data, wherein the flowdata includes passenger flow data; serializing the flow data into a plurality of time series according to the time characteristics and the store characteristics; clustering historical data by clustering algorithm; calculating a mean variance growth rate of the flow data and the like by a basic statistical algorithm and the plurality of time series data; using a wavelet transform algorithm for decomposing the time series data to obtain a time series wavelet, and inputting the time series wavelet, the mean value and the variance and the growth rate as feature values into a prediction model to obtain a flow data prediction value of the user in a predetermined period of time. The method of the invention can effectively predict the passenger flow of the unmanned supermarket in the future, andplays a guiding role in th