This invention discloses an AI-based multi-objective optimization method for the automated cross-
scenario transfer of hazardous chemicals, comprising the following steps: S1: Constructing a data
support system for the cross-
scenario transfer of hazardous chemicals. This invention resolves the
coupling conflicts among the four objectives of safety, efficiency, cost, and
environmental protection by constructing an adaptive multi-objective optimization model and a dynamic weight allocation mechanism, achieving
dynamic balance among multiple objectives and improving optimization accuracy. Simultaneously, by leveraging an AI
algorithm with integrated dynamic interference prediction capabilities, a full-process dynamic
adaptation system is constructed to respond in real time to
traffic congestion, weather changes, and equipment failure interference factors, preventing scheduling scheme failures, reducing safety risks, shortening transfer cycles, and controlling transfer costs. Furthermore, a closed-loop iterative mechanism of "evaluation-optimization-
verification" continuously improves transfer performance. This method can adapt to various cross-
scenario transfer needs, balancing compliance and intelligence, and possesses high practicality and promotional value.