2results about How to "Reduce schedule pressure" patented technology

The application provides a warehouse virtual node generation method and device for emergency events, which comprises the following steps: taking the position of a warehouse center point corresponding to an emergency event instruction as a first warehouse node, and taking the position of an emergency distribution point as a second warehouse node; constructing a material complementary relationship network based on the material surplus and deficiency states among the second warehouse nodes; constructing an emergency event relationship tree based on the first warehouse node, the second warehouse nodes, and the material complementary relationship network corresponding to the second warehouse nodes; constructing a material allocation relationship network based on the material sources pointed to by the material allocation instructions issued by each second warehouse node, and constructing a material allocation graph based on the material allocation relationship network and the hierarchical connection relationship of the nodes in the emergency event relationship tree; determining a first target warehouse node and a second target warehouse node based on the material allocation graph, and performing a material entity allocation operation based on the first target warehouse node and the second target warehouse node. The application improves the emergency response efficiency.

The present application relates to the technical field of intelligent warehousing, automatic navigation and multi-robot collaborative scheduling, in particular to an AGV intelligent path planning method and system based on multi-layer modeling and adaptive scheduling; the method comprises the following steps: step S1: integrating all nodes for AGV operation in the warehouse environment to build a warehouse graph model; step S2: collecting the position, speed and state data of each AGV through the warehouse graph model, performing risk prediction and evaluation, and generating a real-time risk map; step S3: combining the warehouse graph model and the real-time risk map, introducing a weight adjustment mechanism adaptive to power, and outputting the comprehensive optimal path of each AGV; step S4: analyzing the virtual information field based on the warehouse graph model, defining the field value update rule, and outputting the current field value; step S5: analyzing the warehouse graph model, evaluating the comprehensive load rate of the corresponding warehouse environment, and optimizing the comprehensive optimal path combined with the current field value. The method can actively respond to unexpected situations, significantly reduce path conflicts, and improve operation safety.