Abstract: Disclosed are a method and a system for modularized modeling of equipment entities in simulation field based on meta-model, where meta-modeling is combined with modularized modeling, and equipment simulation entities are divided into different model modules in terms of their physical attributes, behavior attributes, and task reliability attributes; a complete set of metadata is designed to characterize each dimensional attribute of the entity, and a meta-model representation protocol based on extensible markup language (XML) description is used to loosely couple the modeling of each module; after the simulation experiment is designed, the modules are bound for entity instantiation.

Abstract: Disclosed is an intelligent scheduling table generation method based on multi-source data analysis of buses, including following steps: integrating business data and cashier data of a bus scheduling system, analyzing the running situation of each line by using an intelligent scheduling algorithm, calculating passenger flow data of boarding and dropping off buses according to cashier data of card swiping for boarding buses, and scientifically and reasonably obtaining the data of each line in the intelligent scheduling table, such as of the number of upgoing and downgoing vehicles, departure time periods, planned shifts, planned circle time, so as to obtain the intelligent scheduling table which accords with the characteristics of morning, midday and evening peak of each line and adjusts the departure interval in real time according to the actual running situation of the lines.

Abstract: Disclosed is an intelligent scheduling table generation method based on multi-source data analysis of buses, including following steps: integrating business data and cashier data of a bus scheduling system, analyzing the running situation of each line by using an intelligent scheduling algorithm, calculating passenger flow data of boarding and dropping off buses according to cashier data of card swiping for boarding buses, and scientifically and reasonably obtaining the data of each line in the intelligent scheduling table, such as of the number of upgoing and downgoing vehicles, departure time periods, planned shifts, planned circle time, so as to obtain the intelligent scheduling table which accords with the characteristics of morning, midday and evening peak of each line and adjusts the departure interval in real time according to the actual running situation of the lines.