Safety Information Management in Mining Areas Based on Improved Convolutional Neural Networks and Automated Data Processing

Abstract

To improve the efficiency of safety information management in mining areas and better protect the life and property of workers, this study integrates intelligent automated data processing technology into the management framework. Addressing the challenge of inaccurate boundary value judgments during security risk level classification, an improved convolutional neural network is employed to enhance the visibility of numerical features. Furthermore, the research conducts a systematic analysis through experiments, gathering existing operational data via network platforms to quantify various risk factors within mining sites. Experimental results demonstrate that the proposed intelligent safety information management system plays a crucial role in identifying safety hazards. It exhibits high accuracy in recognizing risk factors and significantly enhances the overall data processing experience for users.