Network security situation prediction system based on improved grey wolf optimization algorithm in digital power enterprise architecture

Artificial intelligence enables the digital transformation of power enterprise architecture. This paper studies the problem t hat the difficulty of network security defense in the power enterprise architecture cannot meet the existing needs. Based on the gray wolf optimization algorithm, it optimizes from three perspectives: population initialization, convergence factor and nic he technology, and obtains an improved gray wolf optimization algorithm. In addition, a situation prediction model is constructe d by combining the long short-term memory network, and finally a network security situation prediction system is designed. The research results show that the mean absolute error, root mean square error, mean square error and mean absolute percentage error of the prediction model are 0.0237, 0.0012, 0.0366 and 0.071 respectively, and all functions of the system have passed the test. The above results show that the research method can still complete the tasks of network security defense and early warning in the complex digital power enterprise network environment, and improve the level of network security.