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.

Abstract: A comprehensive evaluation method of military training level includes: selecting evaluation indicators of military training level, and establishing an evaluation indicator system of military training level by a multi-tree structure; constructing a leaf node evaluation indicator fusion model including a time indicator fusion model and a quality indicator fusion model, and performing multi-source data fusion processing on leaf node indicator data through the leaf node evaluation indicator fusion model; constructing a leaf node evaluation indicator model of military training level; constructing a parent node evaluation indicator model of military training level based on weight information of evaluation indicators of military training level of nodes in the multi-tree structure; and constructing a total indicator evaluation model by a method of breadth first search of multi-tree to complete the comprehensive evaluation of military training level.