This invention provides a dynamic
route scheduling optimization method for open-pit mine trucks based on deep
reinforcement learning, comprising the following steps: S1. Construction of static basic dataset and dynamic real-time dataset; S2. Construction of preprocessed data
feature set and scheduling environment model; S3. Construction of a deep
reinforcement learning model integrating improved Dijkstra's
algorithm, improved deep Q-network, and deep deterministic policy gradient; S4. Offline training and iterative optimization of the deep
reinforcement learning model; S5. Real-
time data acquisition and preprocessing; S6. Real-time
route decision and continuous action generation. This invention overcomes the limitations of single algorithms, achieving dynamic
road condition adaptation, discrete-continuous decision
collaboration, and intelligent response to abnormal scenarios. It effectively improves the transportation efficiency of open-pit mine trucks, reduces unit
transportation fuel consumption and
waiting time, adapts to the dynamic changes in mining area operation needs and equipment types, and provides integrated
technical support for intelligent, low-cost, and highly reliable
truck scheduling in open-pit mines.