Abstract
Study on designing reasonable travel routes with the least time cost and the highest experience index was conducted. An artificial intelligence‐based wireless sensor travel route planning study is proposed. First, the improved TSP route planning model is built at the least time consumption and combines the normal distributed random number (ND) with the genetic algorithm (GA) and proposes the ND‐GA algorithm, analyzes the overall structure, node structure, communication mode, and network coverage of the wireless sensor network, and gives a mathematical model of wireless transmission energy consumption. Using the proposed algorithm to solve the travel route and detailed itinerary, with time, the 10‐year travel route design model based on multitarget dynamic optimization finally detailed analysis of the model results and sensitivity analysis results showing that the application of AI wireless sensor technology can also make the scenic work more efficient; for example, a face recognition system can improve the speed of ticket checking. Although the application of AI technology is widely used in tourism activities, there are some problems, which require the continuous optimization and innovation of AI wireless sensor technology by relevant practitioners, so that it can better serve tourists.
Highlights
With the rapid development of social economy, people’s living standard tourism has developed from a few luxury to popular consumption becoming an important content of people’s daily life with the deepening of the reform, and opening up tourism has become one of the important industries in China; in the future, tourism will become an important driving force to promote GDP growth, so scientifically planning an optimal tourism route makes tourist cost and tourism experience be of very important significance [1]
This paper mainly considers the following three problems
(3) Self-service is available for visitors. Artificial intelligence technologies such as face recognition, voice recognition, language translation, image consolidation, and tourist information sorting and transmission have been very widely used in the tourism industry, and the audience is growing wider and wider
Summary
With the rapid development of social economy, people’s living standard tourism has developed from a few luxury to popular consumption becoming an important content of people’s daily life with the deepening of the reform, and opening up tourism has become one of the important industries in China; in the future, tourism will become an important driving force to promote GDP growth, so scientifically planning an optimal tourism route makes tourist cost and tourism experience be of very important significance [1]. Tourism enterprises can analyze, calculate, and summarize according to big data, constantly tap the market demand of the tourism industry, and improve the satisfaction of tourists. Artificial intelligence has been integrated with the Internet of things, big data, mobile terminals, Internet+, and other concepts and constantly combined with the development of the times and national policies, so that it has entered a new stage of development and development field. At the same time, based on the different stages of travel experience index influence factors classification and constitute tourism experience index, using particle group algorithm based projection tracing method to index allocation weight, establishing the highest tourism experience index optimization model, based on the above two optimization model introduced tourist attitude parameter to establish 10 years travel route plan multi-target planning model, and by solving the model to get 10 years detailed travel plan. Considering the accommodation fee and catering fee in the 10 years, a combined prediction model is constructed to forecast the above cost and improve the multitarget planning model built above, obtaining a ten-year travel route design model based on multitarget dynamic optimization and a detailed travel plan by solving the model [5, 6]
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