Machine learning algorithm based on optical sensors for simulating sports tourism environment.

Aiming at sports tourism environment simulation, this paper aims to explore the application of machine learning algorithm based on light sensor in this field. We propose a new method for sports tourism simulation, which is based on optical sensor data acquisition and processing. The aim of this research is to develop an accurate and efficient algorithm to simulate the optical properties of sports event venues in order to provide a more realistic sports tourism experience. The research method consists of two main steps: optical sensor data collection and machine learning algorithm modeling and training. First, a network of optical sensors was set up to collect light intensity and color information at different locations in the stadium. This data is then used to train a machine learning algorithm to predict and simulate the sports tourism environment under different optical scenarios. Through experiments and evaluations, the proposed algorithm performs well in terms of accuracy and efficiency, and can simulate the lighting characteristics of sports venues with high accuracy. This provides a strong technical support for sports tourism simulation, which can provide users with a more realistic experience. [ABSTRACT FROM AUTHOR]