With the integration of informatization and industrialization, the Internet of Things and industrial Internet have flourished, resulting in a large amount of industrial big data represented by time series. There are many valuable patterns in time series, among which symmetric patterns are widespread in various time series. Mining symmetric patterns has important research value in the fields of behavior analysis, trajectory tracking, anomaly detection, etc. However, the data volume of time series is often as high as tens or even hundreds of gigabytes. It can take months or even years to mine symmetric patterns using a direct nested query algorithm, and typical acceleration techniques such as indexing, lower bounds, and triangular inequalities can only produce speedup of one or two orders of magnitude at most. Therefore, based on the inspiration of the dynamic time warping algorithm, this study proposes a method that can mine all the symmetric patterns of the time series within the time complexity of O(w×|T|). Specifically, given the symmetric pattern length constraint, the symmetric subsequences can be calculated based on the interval dynamic programming. Then the largest number of non-overlapping symmetric patterns can be selected according to the greedy strategy. In addition, we also study the algorithm for mining symmetric patterns in the time series data stream, and dynamically adjusts the window size according to the characteristics of the data in the window to ensure the integrity of the symmetric pattern data. Using one artificial data set and three real data sets to experiment with the above method under different data volumes, it can be seen from the experimental results that compared with other symmetric pattern mining methods, this method has better performance in terms of pattern mining results and time overhead.