Safety Risk Assessment of Tourism Management System Based on PSO-BP Neural Network.

Wenru Guo,Syed Hassan Ahmed

doi:10.1155/2021/1980037

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.

Highlights

With economic and technological developments and the continuous improvement of people’s living standards, tourism has become a common leisure way for people to relieve pressure and enjoy their minds and bodies, and the tourism industry has brought about rapid development [1]
Traditional tourism management system security risk assessment schemes have the disadvantage of causing large assessment errors when performing system security risk assessments. erefore, this paper uses particle swarm optimization (PSO) to improve the slow convergence rate of traditional BP neural networks and is prone to local optimal solutions and the security risks of tourism management systems based on PSO-BP neural networks
We propose evaluation technology. e convergence time of the BP neural network algorithm, which can be shortened, improves the accuracy of risk value assessment. e results show that under the same number of iterations, the error results of the proposed PSO-BP neural network are always smaller than the error results of the traditional BP neural network

Summary

Introduction

With economic and technological developments and the continuous improvement of people’s living standards, tourism has become a common leisure way for people to relieve pressure and enjoy their minds and bodies, and the tourism industry has brought about rapid development [1]. In the process of research, the PSO algorithm is used to train the weights and thresholds of BP neural network and find out the optimal position of particle swarm optimization. Based on PSO-BP neural network, according to the basic content of the tourism security management system, the system security risk evaluation index is determined. The PSO-BP neural network is used to train the sample data of the security related risks of the tourism management system. In order to verify the practical operation effect of the PSO-BP neural network and the actual operational effect of the PSO-BP neural network model designed in this study, 10,000 data related to the safety risk evaluation index of the tourism management system were selected as samples and 3,000 were randomly tested.

86 PSO-BP

Types of security risk assessment models

Findings

Conclusion