Mining ecological environment monitoring and management are paramount in constructing green mines, and their informatization and intelligent development have also become crucial directions for the sustainable development of the ecological environment. On the other hand, the rapid growth of technologies such as wireless communication, sensors, and cloud computing has enabled comprehensive three-dimensional monitoring of mining ecological environments. However, mining ecological environment monitoring involves numerous ecological elements,  covers vast geographical areas, and encompasses multiple scales. This results in massive environmental monitoring data with different structures and spatial scales, posing significant challenges to the fusion and analysis of environmental data in mining areas. In response to this challenge, this study proposes a data fusion method for digital mining ecological environments based on the rHEALPix global discrete grid model. Firstly, based on the principles of the rHEALPix model, the spatial region where the mine is located is divided and encoded at multiple levels, establishing a unified spatial reference system. Then, various types of ecological environment monitoring data collected by sensors are uniformly mapped to rHEALPix, using a globally unique spatial encoding to express spatial location information for various ecological elements. This enables the fusion of spatiotemporal data from different sources and different scales. Experimental results using real mining ecological environment remote sensing images, and other monitoring data demonstrate that this method can unify the representation and fusion management of monitoring data with traditional vector and raster structures, effectively improving the efficiency of spatiotemporal queries and multi-element joint queries of monitoring data. In summary, the method proposed in this study can provide fundamental data support for the informatization system of mining ecological environment monitoring and management. Its characteristics, such as discreteness and multi-scale capabilities, also make mining ecological environment data services more flexible and efficient. It is expected to establish a database for the future digital twinning of mining ecological environments.