The researchers analyzed the traditional method of reconstructing phase space and developed an improved method to identify chaos in time series data. By introducing the correlation integral and exploring different factors affecting it, they were able to effectively calculate the reconstruction parameters of chaotic signals. Using a time window, they described the relationship between embedding dimension and delay time, leading to the successful reconstruction of chaotic attractors. The results demonstrate that the improved method can accurately extract the characteristics of chaotic signals from the original data.