Given the importance of data safety for psychology, the present study investigated the influence of data leaking scandal on campus customers’ financial consumption behaviors at intelligent tourism platforms in China, and explored the roles that individual characteristics play in this process by focusing on a set of participants from colleges. Data were collected through sending out an online questionnaire, where respondents were asked to finish a series of questions about their background information, their trust, future consuming intention, and defensive behaviors toward intelligent platforms. After they finished these questions, a short description about an online tourism platform leaking customers’ personal information was presented to the respondents, following which they were asked to report about their future consuming intentions and defensive behaviors again. In total, 236 participants of college students and teachers were recruited. Paired samples mean comparison showed that after the stimulus was presented, the respondents had a significant decrease in future financial consumption intention, and a significant increase in defensive behaviors toward online tourism platforms due to risks perceived. Multiple regression analysis was conducted subsequently to investigate individual characteristics that may account for part of the decrease (increase) in consuming intention (defensive behaviors). Results showed that, customers with higher level of trust and monthly income, as well as older customers, tend to experience higher level of decrease in consuming intention, and increase in defensive behaviors. These findings highlighted the importance of online tourism platforms guaranteeing data security of their customers.

There are few studies on forest smart tourism currently, and most smart tourist service models are only studied and analyzed theoretically. Based on this, the Internet of Things (IoT), mobile communications technology, and smart tourism related ideas are discussed in depth in this study, followed by the construction of a forest smart tourist service model. The forest smart tourism evolutionary game model is then built based on the benefit relationships of various tourist sectors. Finally, the effect of the forest smart tourist service model and the forest smart tourism service evolutionary game model is evaluated using simulation experiments. The findings show that (a) daily passenger flow data from scenic spots in June was scattered, but the overall estimate gives that the daily passenger flow was more than 800; (b) daily passenger flow data from scenic spots in December was scattered, but overall estimate shows that the daily passenger flow was less than 300; and (c) the average profit value of the game model was 3.5, 3, and 6, respectively, under various circumstances. To summarize, the model provided in this paper is reliable and appropriate for optimizing forest smart tourist services. Through the constructed model, this work seeks to give theoretical reference for further boosting the development potential of forest recreation.

This paper aims to map the task of reliable transmission of wireless sensor networks. At the same time, this paper transforms the mapping problem of wireless sensor networks into a problem of reducing the energy consumption of mapping under many constraints such as reliability and scheduling length and uses discrete particle swarm optimization algorithm to map. For optimization, the algorithm performs iterative calculations to obtain the best mapping node for each operation so that the inertia coefficient of the existing particle swarm optimization algorithm is improved and linearly minimized with the number of iterations. When resource-demanding tasks need to allocate dynamic resources to multiple nodes to complete collaboratively, adding the mapping principle of the nearest node in the discrete particle swarm optimization mapping reduces the energy consumption of communication between tasks. An in-depth analysis of the influencing factors of the ice and snow tourism market shows that the per capita disposable income of urban residents and the number of urban residents have a significant impact on the ice and snow tourism market demand. In addition, regression analysis and demand-based forecasting are important methods to analyze the scale and development trend of tourism. At the same time, it shows an important position in the purpose of urban tourism and regional market share so as to provide a basis for decision-making in tourism destination marketing. This paper mainly studies and analyzes the wireless sensor network and further introduces it into dynamic resource allocation and ice and snow tourism, which can promote the continuous development of dynamic resource allocation and ice and snow tourism.