CN-122019826-A - Intersection entity extraction method and system based on large-scale vector road dataset

CN122019826ACN 122019826 ACN122019826 ACN 122019826ACN-122019826-A

Abstract

Traversing all road section data in the road data set, extracting a starting point and a finishing point corresponding to each road section data as an insertion point, and updating an ordered intersection point set according to a preset rule according to the position relation between the insertion point and coordinate points in the ordered intersection point set; and traversing all coordinate points in the ordered intersection point set, and outputting the coordinate points with the repetition times more than 2 as intersection entity points. The method reduces the algorithm time complexity and can meet the requirement of intersection entity extraction of a large-scale vector road dataset.

Inventors

YANG JIANYI
She yi
LI QIAN
DENG ZHIWEN
HUANG HOUDE
ZENG YILONG

Assignees

自然资源部四川测绘产品质量监督检验站(四川省测绘产品质量监督检验站)

Dates

Publication Date: 20260512
Application Date: 20260414

Claims (10)

1. The intersection entity extraction method based on the large-scale vector road data set is characterized by comprising the following steps of: s1, traversing all road section data in the road data set, extracting a starting point and an end point corresponding to each road section data as an insertion point, and updating the ordered intersection point set according to a preset rule according to the position relation between the insertion point and a coordinate point in the ordered intersection point set; s2, traversing all coordinate points in the ordered intersection point set, and outputting the coordinate points with the repetition times more than 2 as intersection entity points.
2. The intersection entity extraction method based on the large-scale vector road data set according to claim 1, wherein the preset rule comprises: s101, judging whether the positions of the insertion points are arranged behind the first point in the ordered intersection point set according to the position relation between the insertion points and the coordinate points in the ordered intersection point set, if so, executing the step S102; S102, judging whether the positions of the insertion points are arranged before the last point in the ordered intersection point set, if yes, adding the insertion points into the ordered intersection point set.
3. The method for extracting intersection entities based on the large-scale vector road data set according to claim 2, wherein the method for adding the insertion point to the ordered intersection point set comprises: Based on a binary search method, coordinate information of the insertion point and coordinate points in the ordered intersection point set is compared by halving, a search boundary is converged, and the position relation between the insertion point and the coordinate points in the ordered intersection point set is judged.
4. The intersection entity extraction method based on the large-scale vector road dataset according to claim 1, wherein the specific method for judging the position relationship between the insertion point P1 and the coordinate point P2 in the ordered intersection point set comprises the following steps: S111, comparing the difference value of the two points P1 and P2 on the abscissa as a first difference value, and if the first difference value is larger than a preset distance tolerance, judging that P1 is positioned behind P2; S112, comparing absolute values of differences of the two points P1 and P2 on the abscissa to serve as first absolute values, and executing a step S113 if the first absolute values are smaller than the preset distance tolerance, otherwise, judging that the P1 is located before the P2; s113, comparing the difference value of the two points P1 and P2 on the ordinate as a second difference value, and if the second difference value is larger than a preset distance tolerance, judging that P1 is positioned behind P2; S114, comparing the absolute value of the difference value of the two points P1 and P2 on the abscissa as a second absolute value, if the second absolute value is smaller than the preset distance tolerance, judging that the points P1 and P2 are repeated points, otherwise, judging that the point P1 is positioned before the point P2.
5. The method for extracting intersection entities based on the large-scale vector road data set according to claim 2, wherein in the step S101, if not, the insertion points are arranged before the first point in the ordered intersection point set; in the step S102, if not, the insertion points are arranged after the first point in the ordered intersection point set.
6. An intersection entity extraction system based on a large-scale vector road dataset, the system comprising: the intersection point inserting and updating module is used for traversing all road section data in the road data set, extracting a starting point and an end point corresponding to each road section data as an inserting point, and updating the ordered intersection point set according to a preset rule according to the position relation between the inserting point and coordinate points in the ordered intersection point set; and the intersection entity point acquisition module is connected with the intersection point insertion and updating module and is used for traversing all coordinate points in the ordered intersection point set and outputting the coordinate points with the repetition times more than 2 as intersection entity points.
7. The intersection entity extraction system based on the large-scale vector road data set according to claim 6, wherein the preset rule in the intersection point insertion and update module comprises: s101, judging whether the positions of the insertion points are arranged behind the first point in the ordered intersection point set according to the position relation between the insertion points and the coordinate points in the ordered intersection point set, if so, executing the step S102; S102, judging whether the positions of the insertion points are arranged before the last point in the ordered intersection point set, if yes, adding the insertion points into the ordered intersection point set.
8. The large-scale vector road dataset-based intersection entity extraction system of claim 7, wherein the method of adding the insertion point to the ordered set of intersection points in the intersection point insertion and update module comprises: Based on a binary search method, coordinate information of the insertion point and coordinate points in the ordered intersection point set is compared by halving, a search boundary is converged, and the position relation between the insertion point and the coordinate points in the ordered intersection point set is judged.
9. The intersection entity extraction system based on the large-scale vector road dataset according to claim 6, wherein the specific method for judging the position relationship between the insertion point P1 and the coordinate point P2 in the ordered intersection point set comprises the following steps: S111, comparing the difference value of the two points P1 and P2 on the abscissa as a first difference value, and if the first difference value is larger than a preset distance tolerance, judging that P1 is positioned behind P2; S112, comparing absolute values of differences of the two points P1 and P2 on the abscissa to serve as first absolute values, and executing a step S113 if the first absolute values are smaller than the preset distance tolerance, otherwise, judging that the P1 is located before the P2; s113, comparing the difference value of the two points P1 and P2 on the ordinate as a second difference value, and if the second difference value is larger than a preset distance tolerance, judging that P1 is positioned behind P2; S114, comparing the absolute value of the difference value of the two points P1 and P2 on the abscissa as a second absolute value, if the second absolute value is smaller than the preset distance tolerance, judging that the points P1 and P2 are repeated points, otherwise, judging that the point P1 is positioned before the point P2.
10. The intersection entity extraction system based on the large-scale vector road data set according to claim 7, wherein in the step S101, if not, the insertion points are arranged before the first point in the ordered intersection point set; in the step S102, if not, the insertion points are arranged after the first point in the ordered intersection point set.

Description

Intersection entity extraction method and system based on large-scale vector road dataset Technical Field The invention relates to the technical field of mapping geographic information, in particular to an intersection entity extraction method and system based on a large-scale vector road dataset. Background The intersection entity, also called road intersection entity, is an important basic geographic entity, forms a complete traffic entity set together with the road entity and the road accessory facility entity, is a basic data source for supporting GIS analysis algorithms such as road connectivity analysis, path analysis and the like, and is a main method for entity production by converting digital line Drawing (DLG) data, wherein the rapid extraction of the intersection entity from a large-scale DLG road data set is a basic mode for constructing the intersection entity. The method for extracting intersection entities based on the large-scale DLG road data set generally adopts a mode of firstly extracting intersection points and then calculating the repetition number of the intersection points, but has the defects that the algorithm complexity of the method is O (n 2), when the data volume is increased, the calculation time consumption is increased sharply, so that the efficiency is extremely low, the requirement on the system memory is extremely high under the scene of large data scale, the problem of insufficient memory is easy to occur, the task of extracting the intersection entities is difficult to finish in effective time, and the requirement of large-scale data processing cannot be met. In summary, the existing method for extracting the intersection entity has obvious defects, including low efficiency, high computational complexity and high memory pressure, and particularly is difficult to meet the requirement of large-scale data set extraction. Disclosure of Invention Aiming at the defects in the prior art, the invention provides a crossing entity extraction method and system based on a large-scale vector road dataset, which solve the problems of low efficiency, high calculation complexity and high memory pressure in the prior art of extracting crossing entities, and can meet the requirement of rapid extraction of the large-scale dataset, and the technical scheme provided by the invention comprises the following steps: the intersection entity extraction method based on the large-scale vector road data set comprises the following steps: s1, traversing all road section data in the road data set, extracting a starting point and an end point corresponding to each road section data as an insertion point, and updating the ordered intersection point set according to a preset rule according to the position relation between the insertion point and a coordinate point in the ordered intersection point set; s2, traversing all coordinate points in the ordered intersection point set, and outputting the coordinate points with the repetition times more than 2 as intersection entity points. Preferably, the preset rule includes: s101, judging whether the positions of the insertion points are arranged behind the first point in the ordered intersection point set according to the position relation between the insertion points and the coordinate points in the ordered intersection point set, if so, executing the step S102; S102, judging whether the positions of the insertion points are arranged before the last point in the ordered intersection point set, if yes, adding the insertion points into the ordered intersection point set. Preferably, the preset rule further includes: Based on a binary search method, coordinate information of the insertion point and coordinate points in the ordered intersection point set is compared by halving, a search boundary is converged, and the position relation between the insertion point and the coordinate points in the ordered intersection point set is judged. Preferably, the specific method for determining the positional relationship between the insertion point P1 and the coordinate point P2 in the ordered intersection point set includes: S111, comparing the difference value of the two points P1 and P2 on the abscissa as a first difference value, and if the first difference value is larger than a preset distance tolerance, judging that P1 is positioned behind P2; S112, comparing absolute values of differences of the two points P1 and P2 on the abscissa to serve as first absolute values, and executing a step S113 if the first absolute values are smaller than the preset distance tolerance, otherwise, judging that the P1 is located before the P2; s113, comparing the difference value of the two points P1 and P2 on the ordinate as a second difference value, and if the second difference value is larger than a preset distance tolerance, judging that P1 is positioned behind P2; S114, comparing the absolute value of the difference value of the two points P1 and P2 on the abscissa as a second ab