Scenic area tourist chain travel integration providing method
A technology for tourists and scenic spots, applied in neural learning methods, genetic rules, character and pattern recognition, etc., can solve problems such as interaction, lack of real-time data guidance, poor travel experience, etc., to save queuing time, improve service quality, and uniformity. The effect of passenger flow distribution
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[0117] 1. Prediction of queue time for scenic spots
[0118] (1) Use MATLAB to process the image into a grayscale image, convert the image into a matrix and classify it with a deep learning convolutional neural network;
[0119] (2) Calculate the pixel value of the picture to obtain the number of people in line;
[0120] (3) Recalculate the estimated queuing time according to the service rate.
[0121] 2. Optimizing the best play path
[0122] Assuming that there are 10 scenic spots in the surrounding area of tourists, the coordinates of tourists and each scenic spot and the queuing time of each scenic spot and other data are generated by random generation, and the distance between them is represented by the Euclidean distance between each point. The specific values are shown in Table 1. Show. The number of tourists is 0, the number of scenic spots is 1-10, and the per capita walking speed is set at about 1m / s.
[0123] Table 1 point information table
[0124]
[0...
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