Technological Innovation and Practical Progress in Mine Ecological Restoration from the Perspective of AI and Big Data Empowerment

Abstract

 With the continuous intensification of mineral resource development in China, ecological issues in mining areas have become increasingly prominent. Traditional ecological restoration technologies exhibit significant limitations in restoration efficiency, monitoring methods, and intelligent decision-making. In recent years, the rapid development of artificial intelligence (AI) and big data technologies has introduced new technical pathways and governance models for mine ecological restoration. From the perspective of AI and big data empowerment, this paper systematically reviews the current research background, mainstream methods, and typical challenges in mine ecological restoration. It focuses on summarizing the latest application advancements of AI and big data in ecological damage identification, dynamic monitoring, restoration effect evaluation, and intelligent decision support. The study reveals that deep learning algorithms based on remote sensing imagery, damage identification systems driven by knowledge graphs, and comprehensive restoration supervision systems built on big data platforms have been successfully implemented in multiple mine restoration cases. The paper also analyzes current technical challenges in data standardization, model interpretability, and cross-regional adaptability, proposing future efforts should focus on multi-source heterogeneous data integration, construction of industry-specific intelligent models, and coordinated policy-technology advancement to achieve digital, intelligent, and refined transformation in mine ecological restoration. This research provides technical support and theoretical references for green mining development and ecological civilization construction.