Method for realizing shopping mall and supermarket large screen point location people flow prediction based on deep learning
A deep learning and traffic prediction technology, applied in the field of human traffic prediction, to achieve the effect of simple structure, reliable design principle and significant progress
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Embodiment 1
[0046] like figure 1 As shown in the figure, the present invention provides a method for predicting the flow of people on a super-large screen based on deep learning, comprising the following steps:
[0047] S1. Obtain the data collected from each large screen point of the supermarket, process the collected data and extract the eigenvalues for the prediction of people flow, and construct the people flow prediction based on the LSTM long short-term memory neural network according to the extracted eigenvalue data. Model;
[0048] S2. According to the processed data and the extracted feature values, generate a training data set and a test data set, train the human flow prediction model according to the set step size through the training data set, and then verify the trained human flow through the test data set prediction model;
[0049] S3. Input real-time feature value data to the people flow prediction model, and predict the flow of people at each large screen point within ...
Embodiment 2
[0052] like figure 2 As shown in the figure, the present invention provides a method for predicting the flow of people on a super-large screen based on deep learning, comprising the following steps:
[0053] S1. Obtain the data collected from each large screen point of the supermarket, process the collected data and extract the eigenvalues for the prediction of people flow, and construct the people flow prediction based on the LSTM long short-term memory neural network according to the extracted eigenvalue data. model; the specific steps are as follows:
[0054] S11. Obtain the data collected by the major screen points in the supermarket, and generate a data set;
[0055] S12. Sort the data in the data set according to the time series, clean the abnormal data in the time series; delete the data with abnormal values in the time series, and update the vacant data values in the time series that exceed the set time period. smooth insertion complement;
[0056] S13. Select ...
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