Simulation application of virtual robots and artificial intelligence based on deep learning in enterprise financial systems

In the immersive virtual reality environment, the development of business intelligence models has ushered in new adjustments. Interactive entertainment robots and artificial intelligence can effectively improve the user experience. At the same time, with the advancement of digitalization and networking in enterprise financial systems, the threat of network intrusion poses a serious threat to enterprise financial security. This article analyzes the simulation application of virtual robots and artificial intelligence based on deep learning in enterprise financial systems, and learns how to identify and distinguish between normal network traffic and malicious intrusion behavior. After completing the training of the model, it is deployed to the enterprise financial system to monitor network traffic in real time and perform intrusion detection. The model can analyze and judge the network traffic entering the system in real time, and detect possible network intrusion behaviors based on the learned patterns and features. This paper adopts deep learning algorithm and virtual robot technology to develop a virtual robot system based on deep learning, which can automatically execute financial simulation tasks and generate accurate and reliable financial statements and data analysis results by learning and simulating real enterprise financial data and business processes. By automating financial simulation tasks, virtual robots greatly improve the simulation efficiency and accuracy, and provide more valuable data support for enterprise decisionmaking. Through experimental testing, it has been found that the network intrusion detection method based on deep neural networks has high accuracy and reliability in enterprise financial systems.