This invention belongs to the field of
resource scheduling and management technology, and relates to a method and
system for intelligent scheduling and optimization of
soil acidification remediation resources based on
big data. The method includes: receiving remediation business requests to generate a demand matrix; constructing a resource topology map based on real-time supply data; extracting cancellation constraint data to generate a cancellation rate index reflecting feedback
delay; determining the
start time of the remediation task based on this index and applying
timestamp compensation to determine scheduling priority; generating a scheduling
instruction sequence, and transforming the demand matrix into a multi-band smooth logistics plan. This invention achieves precise matching between resource delivery frequency and environmental
absorption capacity by
coupling environmental absorption characteristics with logistics timing, effectively alleviating
trunk logistics pressure and improving the efficiency of
resource allocation for cross-regional ecological remediation tasks.