Abnormal Recognition Technology of 3D Virtual Scene Image Based on Wireless Network Sensor

The performance and reliability requirements of wireless network sensors are getting higher and higher. In the complex 3D virtualization scene, abnormal recognition of sensor data becomes a challenging task. Therefore, this study aims to explore an effective technique for identifying anomalies in wireless network sensor images in 3D virtualized scenarios. In this study, deep learning techniques were used, combined with image processing and pattern recognition methods, to achieve accurate identification of anomalies in wireless network sensor images. Through training and testing on a large number of 3D virtualized scene images, the experimental results show that the proposed method has high accuracy in identifying anomalies in wireless network sensor images. The recall rate of the algorithm in this paper is 77%-85%, compared with the traditional method, the method in this paper can more accurately detect and classify various types of abnormal conditions, including signal interference, equipment failure and abnormal data. In addition, the method studied in this paper also shows good real-time performance, which can detect and identify anomalies in real time when processing large-scale sensor data.