This application provides a
machine learning-based intelligent cargo loading method and
system, applied in the fields of
artificial intelligence and air logistics technology. By collecting and
processing multi-dimensional
feature data of cargo, feature vectors and physical constraint parameters are constructed. With the help of a pre-trained
deep learning model group, the stackability,
fragility, and center of gravity stability of cargo after stacking are intelligently predicted. By combining the prediction results and constraint parameters, a multi-objective optimization model is established and the optimal stacking scheme that takes into account space, safety, and efficiency is solved. During execution, a real-time safety
verification and dynamic
backtracking mechanism are introduced to ensure the stability and reliability of the loading process. Through an
online learning mechanism, actual operation data is accumulated to continuously update
model parameters and optimization weights, realizing the
system's self-evolution and closed-
loop optimization. This achieves a fundamental transformation from unreplicable personal experience to sustainable evolution of
system intelligence, significantly improving the intelligence level, safety reliability, and long-term operational efficiency of
air cargo loading.