Fuzzy logic algorithm in the planning and design of tourist attraction facilities

Abstract

To address the uncertainties and complexities inherent in planning and designing tourist attraction facilities—challenges that traditional methods struggle to manage—this study utilizes fuzzy logic (FL) algorithms within artificial intelligence (AI) technology. Focusing on the planning of first aid facilities at tourist attractions, the research develops an FL reasoning system. The system’s performance is evaluated using mean squared error (MSE) and mean absolute error (MAE) as key indicators. Key data features can be extracted by preprocessing operations such as data cleaning and normalization on the collected historical data of tourist attractions, and the fuzzy sets and corresponding membership functions of input and output variables can be determined. A fuzzy rule library can be constructed using historical data and professional knowledge, and a fuzzy inference machine can be used to infer the fuzzy set of input variables based on fuzzy rules to obtain predicted results and perform deblurring transformation to output actual results. The research highlights the significant value of the FL system in optimizing the planning of emergency facilities in tourist attractions. The experiment demonstrated that the system achieved mean squared error (MSE) and mean absolute error (MAE) values of 1.90 and 1.10, respectively. These results underscore the system’s strong applicability and effectiveness, providing a reliable tool for enhancing emergency planning and ensuring safety in high-traffic areas.