Study on the Characteristics of Special Cultural Tourism Securing and Enhancing Operations Based on Big Data

IF 0.5 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Advances in Data Science and Adaptive Analysis Pub Date : 2022-03-19 DOI:10.1142/s2424922x22500024
P. Peng
{"title":"Study on the Characteristics of Special Cultural Tourism Securing and Enhancing Operations Based on Big Data","authors":"P. Peng","doi":"10.1142/s2424922x22500024","DOIUrl":null,"url":null,"abstract":"After entering the new century, the country’s requirements for environmental protection are increasingly stringent. Many provinces with weak industrial bases regard tourism as an important industry for economic development and make great efforts to support and promote it. In order to adapt to the changes in the demand of the tourism industry under the internet, this paper is based on the research of the characteristics of cultural tourism planning strategies under the background of big data. After analyzing the advantages of the traditional ant colony algorithm in the design of tourism routes, in order to improve the rationality of the planning of travel routes, an optimized ant colony algorithm model is established to solve the characteristic tourism planning routes, making route planning more scientific and efficient. The final simulation experiment proves that the improvement of the ant colony algorithm in this study can effectively improve the effectiveness of the route planning and formulate special tourist routes that are more popular with tourists.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"24 1","pages":"2250002:1-2250002:17"},"PeriodicalIF":0.5000,"publicationDate":"2022-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Data Science and Adaptive Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/s2424922x22500024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

After entering the new century, the country’s requirements for environmental protection are increasingly stringent. Many provinces with weak industrial bases regard tourism as an important industry for economic development and make great efforts to support and promote it. In order to adapt to the changes in the demand of the tourism industry under the internet, this paper is based on the research of the characteristics of cultural tourism planning strategies under the background of big data. After analyzing the advantages of the traditional ant colony algorithm in the design of tourism routes, in order to improve the rationality of the planning of travel routes, an optimized ant colony algorithm model is established to solve the characteristic tourism planning routes, making route planning more scientific and efficient. The final simulation experiment proves that the improvement of the ant colony algorithm in this study can effectively improve the effectiveness of the route planning and formulate special tourist routes that are more popular with tourists.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于大数据的特色文化旅游保障与提升运营特点研究
进入新世纪后,国家对环境保护的要求越来越严格。许多工业基础薄弱的省份都把旅游业作为经济发展的重要产业,大力扶持和促进旅游业的发展。为了适应互联网下旅游行业需求的变化,本文基于对大数据背景下文化旅游规划策略特点的研究。在分析了传统蚁群算法在旅游路线设计中的优势后,为了提高旅游路线规划的合理性,建立了一种优化的蚁群算法模型来求解特色旅游规划路线,使路线规划更加科学高效。最后的仿真实验证明,本文对蚁群算法的改进可以有效地提高路线规划的有效性,制定出更受游客欢迎的旅游特色路线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Advances in Data Science and Adaptive Analysis
Advances in Data Science and Adaptive Analysis MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
自引率
0.00%
发文量
13
期刊最新文献
Assessment Of Mars Analog Habitation Plans Using Network Analysis Methodologies A Novel Genetic-Inspired Binary Firefly Algorithm for Feature Selection in the Prediction of Cervical Cancer Big Data Analytics for Predictive System Maintenance Using Machine Learning Models Data Mining for Estimating the Impact of Physical Activity Levels on the Health-Related Well-Being A Novel Autoencoder Deep Architecture for Detecting the Outlier in Heterogeneous Data Sets
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1