An automatic reasoning method for the flow direction of waterway bridge tributaries based on conversion nodes

Abstract

The invention discloses an automatic reasoning method for the flow direction of waterway bridges and tributaries based on conversion nodes. Firstly, two estuary and mainstream statistical identification criteria are established; Finally, with the spatial proximity relationship as a constraint, the "global" and "local" flow direction reasoning consistency judgments are carried out, and the "segmented river section" is detected to divide the bridge tributary into the mainstream, and then complete the flow direction of the tributary according to the mainstream Automatic reasoning, thereby overcoming the shortcomings of traditional methods.

Description

technical field [0001] The invention relates to the technical field of map synthesis, and more specifically relates to an automatic reasoning method for the flow direction of waterway bridges and tributaries based on conversion nodes. Background technique [0002] Waterway transportation is one of the most important modes of transportation in the field of transportation. In a large-scale transportation area, the water network structure is complex and there are many conversion nodes. Correct river flow direction is a prerequisite for rationally carrying out route space analysis and waterway transportation mapping. However, due to the large scope of the watershed or the insufficient collection of attribute information, there are often problems such as non-existence or incomplete flow directions of various waterways in the database. For a complex water network with multiple estuaries, according to the relationship with the mainstream, tributaries can be divided into two types:...

AI Technical Summary

Problems solved by technology

However, the method of using digital elevation model (Digital Elevation Model, DEM) as auxiliary data to calculate the flow direction is only suitable in areas with severe terrain changes. In areas with flat terrain, the deficiency becomes very obvious. Due to the influence of DEM accuracy, it is difficult to accurately describe the flow direction of rivers in dense river areas

In addition, in the existing technology, the flow direction of most rivers depends on the mainstream flow direction for inference. However, in a complex river system with many mainstreams, the mainstream as the starting reference has randomness in selection

This is fine for rivers that are only associated with one mainstream, but for rivers that are locally associated with multiple mainstreams, it is difficult to ensure the accuracy of flow direction inference in this way

Images