Intelligent optimization of mine environmental damage assessment and repair strategies based on deep learning

With the increase in mining activities, environmental damage problems have become increasingly prominent, and effective assessment and remediation strategies are urgently needed to mitigate their impacts. This study uses deep learning technology, combined with mine environmental monitoring data and remediation plans, to propose an intelligent optimization method aimed at improving the accuracy of environmental damage assessment and the efficiency of remediation strategies. First, a coal mine is used as a research case to comprehensively analyze the environmental damage assessment and restoration strategies of mining projects based on field investigation and geological survey results. It also combines mine environmental governance and restoration goals and mine environmental protection and governance tasks from ground subsidence restoration projects to aquifer damage. The content of mining environmental protection and governance and restoration work is introduced from three perspectives: governance and restoration projects, as well as topography, landscape and land resources governance and restoration projects. Secondly, deep learning models are used to predict and classify the degree of environmental damage, thereby achieving accurate assessment of environmental damage in mines. At the same time, based on deep learning optimization algorithms, the repair strategy is intelligently designed and adjusted to improve the repair effect and reduce costs. Experimental results show that the proposed method has made significant progress in assessing environmental damage and formulating remediation strategies, providing important support and guidance for mine environmental protection and sustainable development. In summary, this study provides an intelligent and efficient optimization solution to solve the problem of environmental damage in mines, and has broad application prospects and promotion value.