Early Warning Monitoring System for Fresh Food Safety Based on K-means Clustering Algorithm

In recent years, with the increase of people's income and living standard, people's demand for quality of life has become higher and higher. With the continuous development of social economy and network technology, etc., people's demand for food has increased dramatically, which has led to the increase of per capita consumption level of Chinese residents. Therefore, this has to a certain extent driven the rapid growth of the domestic catering industry and related industries, but due to factors such as consumer eating habits, food safety awareness is not strong, making some fresh products have serious hidden problems. Some of the food products contain harmful substances, which are harmful to human health and even endanger life and property, causing great economic loss and social panic to people and affecting normal life order. The K-means algorithm is of great significance for early warning and monitoring of food safety. Based on the K-means algorithm and combined with the safety warning and monitoring system of fresh products, this paper analyzes it and establishes the corresponding index system.