[Retracted] Safety Risk Assessment of Tourism Management System Based on PSO‐BP Neural Network

Abstract

With the development of science and technology, system management is gradually applied to tourism management. How to correctly assess the security risks of the tourism management system has become an important means to maintain passenger information. The security risk index of the travel management system is input into the PSO-BP network as a sample, and the corresponding risk value of the index is used as the network output. The results show that the error results, accuracy (96.53%), training time (216 s), number of iterations (275 times), and convergence speed are all better than traditional BP network. The relative error of PSO-BP network (0.32%) is better than that of BP network, with 300 iterations, and the error is close to 10–5. The average evaluation accuracy of S based on PSO-BP network is 99.72%, and the average time consumed is 2.512 s. It is superior to the evaluation model based on fuzzy set and entropy weight theory and the evaluation model based on gray correlation analysis and radial basis function neural network. In conclusion, the security risk assessment of the tourism management system based on PSO-BP network can effectively assess the security risk of the tourism management system.

Development of Tourism Management Imitation Real Training System Based on Virtual Reality Technology

“…The system uses professional 3 d modeling tools to build a variety of realistic virtual scenes to provide immersive support for the practical training of tourism management in different situations. Specifically, the system integrates Blender, an excellent 3 d modeling platform in the industry, and constructs more than 203 D virtual scenarios with different functions [8]. These scenes cover the hotel hall, tickets of various scenic spots, natural scenic spots and other key places of tourism management.…”

“…The scene details include rooms, furniture, natural vegetation, scenic spot landmarks and other elements, and the degree of refinement reaches more than 70% of the actual environment. These three-dimensional virtual scenes with comprehensive functions and excellent details provide highly realistic environment support for students' tourism management training, so that they can learn and use the knowledge learned in an immersive three-dimensional world, and obtain almost real learning experience [9]. The detailed construction of scenes is the basis of realizing virtual simulation training, and it is also a major feature and advantage of the system.…”

Research on Safety Risk Transfer in Subway Shield Construction Based on Text Mining and Complex Networks

“…In addition, Liu et al [3] combined qualitative analysis with quantitative analysis and used the set-pair analysis (SPA) method to evaluate the construction safety of subway tunnels. With the booming development of machine learning and artificial intelligence, some researchers have applied machine learning techniques to safety risk assessment, such as neural networks [29,30], random forests [31,32], Bayesian networks [33,34], support vector machines [35,36], etc. Zhang et al [37] proposed a method for assessing the safety of tunnels based on case-based reasoning, advanced geological prediction, and rough set theory.…”

A Projection Pursuit Dynamic Cluster Model for Tourism Safety Early Warning and Its Implications for Sustainable Tourism

“…(2) BPNN model. Artificial neural networks are the most popular machine learning algorithm chosen to perform a risk assessment and safety early warning[55][56][57][58][59][60], particularly the BPNN model. Establishing a BPNN model with generalization ability and practical value must follow the required principles and steps[28,51,53,54,61].First, the BPNN model is only suitable for modeling with large sample data, and it faces the defect of the "curse of dimensionality".…”