Application of Intelligent Optimization Algorithms to the Design of Automatic Generation of Software Tests for Data Anomaly Identification

Abstract

This paper firstly constructs a system for monitoring precursor observation data and automatic identification of anomalies, designs the functions of downloading and format conversion of precursor data files and downloading and decompression of compressed files, and adopts the interpolation method to pre-process the precursor observation data. Secondly, a method based on the SURF intelligent optimization algorithm is adopted to automatically recognize and classify anomalous data from seismic big data. Finally, experiments were carried out to predict short-acuity earthquakes, and the experimental results were analyzed and contrasted. The results show that the accuracy of 2-day prediction and 4-day prediction gradually stabilizes at 0.728~0.785 when the training reaches 100 steps and gradually decreases and reaches about 0.65 with the increase of the prediction time period, which verifies the feasibility and practicality of the system. The research in this paper provides a new idea and method for earthquake precursor data anomaly identification, which has certain research and application value.