Research on deep learning based state detection and multi-channel warning model for nuclear security front-end equipment

To improve the accuracy and timeliness of nuclear security front-end equipment status detection, and reduce false alarms and omissions caused by environmental interference and equipment aging, this paper proposes a multi-channel comprehensive early warning model based on deep learning. Unlike existing single sensor analysis methods, the improved model not only integrates multi-source spatiotemporal features such as laser incidence and LiDAR, but also introduces a dynamic health assessment algorithm (DHEA) to quantify the impact of device performance degradation on diagnostic results, and designs an adaptive threshold adjustment mechanism to cope with meteorological changes. In the data processing stage, a three-level standardization process is adopted: data one is the raw signal of multiple sensors (laser point cloud, Doppler spectrum, etc.), and dynamic time warping (DTW) is used to achieve spatiotemporal alignment; Data 2: Construct a health assessment matrix based on historical parameters of device operation, such as fault frequency and response delay; Data three is the environmental monitoring indicators (temperature and humidity, electromagnetic noise), and an interference compensation model is established. Input three types of data into the Spatiotemporal Feature Fusion Network (STFN), jointly optimize feature weights, and generate a multi-channel warning index. The experimental results show that the model achieves 98.5% accuracy of fault identification on the domestic platform (Haiguang CPU/Kirin OS), the false alarm rate of rainstorm scene drops to 2.1%, the average response time of the system is shortened to 1.4 seconds, and the resource utilization rate of the traditional method is increased by 39%, effectively solving the evaluation deviation problem caused by environmental interference and hardware aging.