Intelligent Rural Tourism Environmental Suitability Evaluation System Based on a Wireless Sensor Network

Abstract

After years of vigorous development, the scale of rural tourism industry has gradually expanded throughout the country, and it has become an important means of coordinating urban and rural areas to drive farmers’ incomes and get rich. Nowadays, the level of informatization is constantly improving, the construction of smart cities is constantly advancing, the personalized demand of tourists is increasingly strong, especially the rural tourist spots scattered in the vast countryside which have a great spatial breadth, and the traditional management method can no longer meet the needs of the source objects whose main travel mode is self-driving. Therefore, only when rural tourism develops in the direction of intelligence can it successfully realize the transformation and upgrading of rural tourism industry. This paper takes a rural tourism resort in a certain area as the research object and builds an intelligent rural tourism environmental suitability evaluation system with three criterion levels: natural geographic conditions, traffic environmental conditions, and tourism spatial conditions. The weight of each indicator layer is obtained through the analytic hierarchy process, and the reclassified raster data of indicators is weighted and superimposed on the ArcGIS operating platform to obtain the evaluation result of the environmental suitability of smart rural tourism. According to the results of suitability evaluation, the most suitable area is selected as the potential area of the intelligent rural tourism environment in a certain area. According to the natural and cultural tourism resource conditions in the block, the potential block is divided into functions. A comprehensive comparison of the suitability spatial differentiation map of the smart rural tourism environment in a certain area with the suitability spatial differentiation map of each subsystem can more clearly find that the suitability score of the economic development subsystem and the basic support subsystem is a county-level village in a certain area. Among them, the suitability of the rural economic development subsystem is similar to the distribution of the suitability of the smart rural tourism environment in a certain area; that is, it is positively correlated with the suitability of the smart rural tourism environment. Therefore, improving the level of rural economic development and increasing investment in the construction of a smart rural tourism environment play an important role in improving the level of public services and infrastructure coverage.

Spatial distribution and suitability evaluation of nighttime tourism in Kunming utilizing multi-source data

“…The evaluation criteria of the indicators were determined according to the existing tourism suitability research [ [35] , [36] , [37] , [38] ], combined with the special geographical location and climate environment of Kunming City and considering the uniqueness of nighttime tourism ( Table 2 ). Among them, each factor is divided into corresponding grades by quantitative classification, and the single factor nighttime tourism suitability grade is classified.…”

A Personalized Recommendation Technique for Travel Route Based on Fuzzy Consistent Matrix

“…e authors conducted several experiments and the acquired outcomes indicate that the offered personalized recommendation system can effectively solve the problem of scarce tag data, and the recommendation performance of the trained recommendation system is significantly good. However, the accuracy of the personalized tourism recommendation algorithm is poor [5][6][7]. us, improving the efficiency, as well as, accuracy of such recommendation systems is the focus of this research.…”

“…e accuracy evaluation of the prediction score is the similarity between the score predicted by the algorithm and the user's actual score [6]. ere are many methods to evaluate the prediction accuracy, and the most commonly used are the mean square error (MSE) metric, mean absolute error (MAE) metric, root mean square error (RMSE), and standard mean absolute error (NMAE) metric.…”

“…Experiments of state-of-the-art literature demonstrate that this personalized recommendation algorithm and method can resolve the issue of scarce tag data, and the recommendation performance of the trained recommendation system is significantly good as compared to other techniques. However, unfortunately, the accuracy of the personalized tourism recommendation procedure is poor [5][6][7].…”

Construction of Rural Tourism Brand Value Management Model from the Perspective of Big Data

“…Since rural tourism has played an important role in promoting economic development and improving living standards in rural areas, vigorously developing the rural tourism industry has gradually become one of the industries promoted by the government [4][5][6]. However, the problems faced by rural tourism also follow [7][8][9][10][11][12]. For example, some rural tourism brands have a single supply structure, resulting in a mismatch between supply and demand.…”