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Remote sensing imaging analysis and ubiquitous cloud-based mobile edge computing based intelligent forecast of forest tourism demand

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Abstract

With the development of society and the improvement of people's living standards, ecotourism based on forest ecological environment has become an urgent need of urban and rural people. Forest leisure tourism is different from traditional sightseeing tourism. The differences between them are not only reflected in consumption purpose, consumption behavior, consumption grade and consumption form, but also reflected in the differences between tourism products, service system and service quality demand. Therefore, in order to develop forest leisure tourism, we must first fully understand the objective status quo of the leisure tourism market and the real needs of the market, and correctly judge the basic characteristics of the current forest leisure tourism market. At present, the competition of tourism market is upgraded from the competition of tourism price and tourism product to the competition of tourism brand. However, the resource utilization efficiency of forest park tourism is insufficient, and the internal management and external marketing are difficult to adapt to the changes of the market, thus the image and attraction of forest park tourism need to be improved urgently. Therefore, based on remote sensing image analysis and neural computing model, this paper constructs a forest ecotourism evaluation scheme. We designed the novel cloud-based mobile edge computing model to construct the efficient scenario for the prediction. The experimental results show that the model proposed in this paper can effectively evaluate the development plan of forest eco-tourism.

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  1. School of Economics & Management, Beijing Forestry University, BeiJing, 100083, China

    Zhang Rui, Zhang Jingran & Wang Wukui

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  1. Zhang Rui
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  2. Zhang Jingran
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Correspondence to Zhang Rui.

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Rui, Z., Jingran, Z. & Wukui, W. Remote sensing imaging analysis and ubiquitous cloud-based mobile edge computing based intelligent forecast of forest tourism demand. Distrib Parallel Databases 41, 95–116 (2023). https://doi.org/10.1007/s10619-021-07343-0

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