In order to meet the needs of efficient management and application of massive, multi-sourc and heterogeneous data in the process of intelligent development of coal mines, this paper proposes an intelligent lakehouse system for coal mine big earth data based on multiple big data technologies. In response to the needs in efficient indexing of multi-source heterogeneous coal mine geological data, this paper proposes a spatiotemporal classification method based on Hilbert curves and GeoHash coding, which uniformly encodes multidimensional attributes such as time, space and classification, reduces the data index dimension, and improves retrieval efficiency. The construction of the intelligent lake house system not only optimizes the integration and utilization efficiency of coal mine geological data, but also provides important practical experience for the digital transformation and transparent development of coal mining enterprises, and promotes the coal mining industry to move towards intelligence, transparency, and high efficiency.

To practice the development concept of "lucid waters and lush mountains are invaluable assets", and balance mineral development and ecological environment protection, it is urgent to carry out dynamic monitoring and scientific evaluation of the ecological environment in mining areas.Based on the characteristics of the ecological environment in mining areas, this paper analyzes the temporal and spatial change characteristics and differences of the ecological environment elements in the mining area, the impact mechanism of the mining and restoration activities on the ecological environment elements, and the cooperative evolution law of each element.Guided by the requirements on ecological environment monitoring and evaluation in mining areas in the new era, a technology framework for quantitative remote sensing-based monitoring and evaluation of the ecological environment in mining areas is proposed from the perspective of 'data-monitoring-evaluation-application'.The framework makes full use of multi-source big data in mining areas with remote sensing images as the main body and takes advantage of the emerging technologies such as artificial intelligence and quantitative remote sensing.This research archives quantitative monitoring and evaluation of the ecological environment elements in the mining areas with the features of high frequency, large-extent, long-term, continuous, all-factor observation and quantitative inversion, which can support the operational applications including mining activity monitoring, ecological environment diagnosis and early warning and restoration effect evaluation in mining areas.Finally, two real-world cases are introduced to illustrate the effectiveness and application flow of the proposed framework.