Method, system and equipment for vegetation restoration or rehabilitation based on machine learning

A system for a vegetation restoration or rehabilitation, the system being based on machine learning (ML) and comprising:

• a data acquisition module configured to acquire historical growth environment data of a vegetation community;
• a data extraction module configured to extract a site condition feature and a growth condition feature of each vegetation species from the historical growth environment data;
• a model constructing module configured to restore and rehabilitate a vegetation community structure selection model based on the site condition feature and the growth condition feature of the vegetation species, specifically to
• acquire growth condition features of a meteorological environment where the vegetation species is located and select a most correlated growth condition feature of the vegetation species;
• acquire site condition features of a geographical environment where the vegetation species is located and select a most correlated site condition feature of the vegetation species;
• establish, based on the selected most correlated growth condition feature and site condition feature of the vegetation species, a sample database of a one-to-one correspondence;
• and
• train, based on the sample database, the vegetation community structure selection model by an ML method, and determine a correspondence between the growth condition feature and the vegetation species and a correspondence between the site condition feature and the vegetation species;
• and
• a vegetation community constructing module configured to select, based on the vegetation community structure selection model, an optimal vegetation species according to current growth environment data of the vegetation community and restore and rehabilitate the vegetation community to simulate a natural ecosystem.