Application of ipv6 technology based on improved ant colony algorithm in digital twin watershed

Abstract

With the Internet of Things, the rapid development of the Internet and the rising number of online users, IPv4 addresses used today have been allocated, and IP addresses have become scarce, unable to meet the growing demand for IP addresses. This paper provides a basic theory of big data analysis and research on social relationships and sense of location information based on the relevant information of a model and functions of the business processing layer, transmission layer, information processing and application layer extracted from the network and social material basis. The results show that this method has better fitting and prediction effect by using ipv6 technology of ant colony algorithm. It lays a foundation for further research in the future. It can be seen from the experimental results that the performance of the algorithm decreases obviously with the increase of feature proportion. This is because the increase of feature proportion makes the algorithm select redundant and irrelevant features, resulting in the decline of classification accuracy. The results show that the accuracy of eACO-GA algorithm is 0.98 to determine the optimal feature selection ratio of the current data set, and better classification results can be obtained.