Abstract: The present disclosure provides an inversion method for determining a pollution source list based on artificial intelligence and big data, an inversion system for determining the pollution source list based on artificial intelligence and big data, and applications thereof, which provides basic data support for government sectors to formulate relevant environmental protection measures. The specific technical solution employed in the present disclosure is as follows: finding out an emission source that makes the highest contribution to the pollutant concentration of any cell with an advanced 3D CNN artificial intelligence algorithm based on artificial intelligence and big data, establishing a model of the relationship between pollutant concentration and emission, and finding out the relationship between pollutant concentration and emission with machine learning technology, i.e., estimating an emission from a given pollutant concentration, and estimating a pollutant concentration from a given emission.

Abstract: The invention provides a nano-silicon agglomerate composite negative electrode material of pine needle and branch-shaped three-dimensional network structure and a method for preparing the same. The nano-silicon agglomerate composite negative electrode material comprises nano-sized core particles, a nano-silicon agglomerate of pine needle and branch-shaped three-dimensional network structure growing around the nano-sized core particles, and a composite coating layer over the nano-silicon agglomerate of needles and branch-shaped three-dimensional network structure. With measurements, it is shown that the nano-silicon agglomerate composite negative electrode material, when being applied in lithium ion battery, has excellent battery charge-discharge cycle performances and rate capability, and it has an initial discharge capacity per gram of more than 2600 mAh/g, and an initial coulombic efficiency of no less than 85%.