Knowledge graph-based correlation analysis method for urban greening tasks

Abstract

The invention discloses a knowledge graph-based correlation analysis method for urban greening tasks, which includes the following steps: acquiring a standardized task data set, extracting entity nodes and relationship edges, and constructing a knowledge graph; processing the entity nodes and relationship edges in the knowledge graph by using a graph embedding algorithm, calculating the vector representation of each task to obtain a task vector set, and calculating an inter-task cosine similarity matrix through the task vector set to obtain a list of associated task pairs; applying a clustering algorithm to group similar tasks according to the list of associated task pairs to form an initial task cluster set, acquiring the resource demand characteristics of each cluster in the initial task cluster set, and calculating the cluster resource allocation weight through weighted summation to determine an optimized resource allocation scheme; adjusting the construction scheduling sequence according to the optimized resource allocation scheme to obtain a final task cluster scheduling plan. The invention significantly improves the resource utilization rate and scheduling efficiency of greening tasks.

Images