A method for simplifying vector data of linear elements of power infrastructure

Abstract

The present invention relates to a method for simplifying vector data of linear elements of power infrastructure. The method comprises the following steps: reading linear element information and substation element information for preprocessing; identifying the main line and branch line of the power line according to the site information of the nodes at both ends of the power infrastructure line; clustering the substation nodes on the branch line with appropriate granularity, omitting the branch line of the previous step, and simplifying the connecting line between the representative node in the cluster and the main line as the simplified branch line to generate arc segments and arc segment sets; traversing the arc segments, simplifying or splitting the arc segments according to the vertical distance from the node to the virtual straight line; checking whether the simplified arc segment set has self-intersection, and if so, identifying the target line segment and restoring the split, and repeating the steps until there is no self-intersection; recursively executing the arc segments obtained by the splitting until the splitting condition is no longer met, and then terminating the recursion; and outputting the simplified arc segments in geojson format.

Description

Technical Field

[0001] The invention belongs to the field of power informatization geographic information application, and specifically relates to a method for simplifying vector data of linear elements of power infrastructure. Background Art

[0002] Nowadays, with the growth of urban expansion, the acceleration of urbanization, and the development and comprehensive coverage of power systems, power grids have become increasingly large, complex, and diverse. Under the framework of urban power grids are countless large and small substations and intricate power grids, with thousands of main and branch lines crisscrossing. Today's power grid visualization technology is using computer graphics, digital image processing and other technical means to intuitively display non-intuitive and abstract data in the power grid system. When facing complex power infrastructure, if the visualization expression is only presented intact, various lines will overlap and interfere with each other in a large area and no meaningful information can be obtained. Therefore, it is of great significance to simplify the expression of power grid lines. How to effectively process power grid lines so that power grid analysts can obtain high-resolution and system-specific power grid contour maps through computer screens in a very short time, so as to quickly understand various geographical information of power grid lines to assist analysis and decision-making, is the main purpose of power grid line data simplification.

[0003] At present, the simplification methods of line elements are mainly based on local simplification elements: Douglas-Peucker algorithm uses nodes as simplification elements and thins out the nodes of linear elements through binary recursion; Li-Openshaw algorithm uses the bend of arc segments as the simplification object, connects the intersection of the minimum visible circle and the linear element, and takes the midpoint of the connection line as the simplified point to simplify the bend; the curve simplification method of cable-stayed / bidirectional cable-stayed bend division uses the bend as the simplification element, uses the cable-stayed line to identify the bend, and uses triples to classify the bends and simplify the bends using different strategies. Traditional methods mainly focus on simplifying the local area of ​​a single curve, and cannot grasp the degree and effect of simplification from the overall data. For a series of thematic maps with many fragmented line segments, such as fine-grained power grids, messy driving trajectory data, and water system maps of tributary rudders, the effect is very limited because the fragmented line segments cannot be simplified as a whole. Summary of the invention

[0004] The purpose of the present invention is to provide a method for simplifying vector data of linear elements of power infrastructure in order to address the deficiencies of the above-mentioned curve element simplification algorithm.

[0005] To achieve the above object, the technical solution of the present invention is: a method for simplifying vector data of linear elements of power infrastructure, comprising the following steps:

[0006] Step S1, reading linear element information and substation element information from the data and performing preprocessing;

[0007] Step S2: Identify the trunk line and branch line of the power infrastructure line according to the site information of the nodes at both ends of the power infrastructure line;

[0008] Step S3, clustering the substation nodes on the branch line in granularity, omitting the branch line obtained in step S2, simplifying the connection line between the representative node in the cluster and the trunk line, and generating arc segments and arc segment sets;

[0009] Step S4, draw an imaginary straight line between the first and last points of the arc segment, and calculate the vertical distance d from the remaining nodes on the arc segment to the straight line;

[0010] Step S5, determine whether to retain the node according to the maximum distance from the node to the straight line and the set threshold, if retained, divide the old arc segment into two parts, and execute step S7; otherwise, simplify the arc segment into a straight line, and execute step S6;

[0011] Step S6, for the simplified arc segment set, check whether there is self-intersection, if there is self-intersection, identify the target line segment and restore the split, repeat this step until there is no self-intersection, and execute step S7;

[0012] Step S7, recursively execute steps S4 to S6 for the two processed arc segments respectively, until the splitting condition is no longer met, and then the recursive execution of step S8 ends;

[0013] Step S8: Output the simplified arc segment in geojson format.

[0014] In one embodiment of the present invention, step S2, identifying the trunk line and branch line of the power infrastructure line specifically includes the following steps:

[0015] Step S21, traverse the substation site element information, identify the site type according to its attribute data, and divide it into primary site and secondary site;

[0016] Step S22, traverse the power infrastructure lines, classify the facility line segments whose tail nodes are secondary sites as branch lines, and the rest as trunk lines.

[0017] In one embodiment of the present invention, in step S3, granular clustering of substation nodes on the branch line and generating arc segments and arc segment sets are performed, which specifically includes the following steps:

[0018] Step S31: Divide each dimension into several grids, and calculate the Euclidean distance between each pair of samples only within its eight-neighborhood grid:

[0019]

[0020] Among them, d is the number of dimensions, ai is the value of node a in dimension i, b i is the value of node b in dimension i.

[0021] Step S32: Determine the size of the neighborhood RT according to the calculated distance;

[0022] Step S33, calculate the density and density vector: for each row of the rectangular matrix, count the number of adjacent regions whose radius RT is less than the number of adjacent regions, which is the distribution density of the corresponding data sample; obtain the density contour of the data sample from the density vector;

[0023] Step S34, determining a density threshold DT according to the density vector;

[0024] Step S35: for each pair of samples c and c', if their densities are both greater than DT and the distances between them are both less than RT, the two clusters of c and c' are merged into one cluster until all pairs of samples are merged;

[0025] Step S36: According to the calculation results, select the cluster center within the cluster as the representative point;

[0026] Step S37, cyclically compare whether the end points and starting points of the adjacent line segments coincide with each other. If the next line segment is not marked, it is recorded as the same arc segment and assigned an arc segment ID. It is added to the arc segment set until there is no unmarked arc segment whose starting point coincides with the previous end point. Then, the current arc segment is established and the next arc segment is entered. It is iterated until all arc segments are established.

[0027] In one embodiment of the present invention, in step S4, the distance d from the remaining points on the virtual straight line to the straight line is calculated as follows:

[0028] Assuming that the straight line exists, calculate the vertical distance d from each node to the straight line in turn according to the coordinates of the three points and the following formula;

[0029]

[0030] Among them, (x1, y1)(x2, y2), (x3, y3) are the coordinates of three points.

[0031] In one embodiment of the present invention, in step S6, checking whether there is a self-intersection, if there is a self-intersection, performing target line segment identification and restoration splitting specifically includes the following steps:

[0032] Step S61, traverse the line segments in all arc segment sets, and identify intersecting line segments using a fast repulsion test and a straddle test;

[0033] Step S62, identifying the corresponding convex hull according to the intersecting line segments;

[0034] Step S63, judging whether the endpoints of the intersecting line segments are inside the convex hull according to the convex hull characteristics, and if so, splitting the other line segment intersecting with it;

[0035] Step S64: Find the middle node of the line segment that needs to be restored and split into two line segments from this node according to the original arc segment nodes.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. The present invention fully considers the overall situation of the power facility data, solves the shortcomings of the traditional simplification method that only focuses on the local characteristics of a single line element and ignores the overall situation, grasps the simplification degree and simplification effect of the power line element from the overall data, and effectively avoids the overall simplification incoordination caused by too small a window.

[0038] 2. The present invention uses an iterative algorithm for self-intersection identification and splitting to avoid self-intersection caused by focusing on local optimization during the simplification process, which leads to changes in the topological relationship between line elements. Under the premise of retaining the topological relationship, simplification is achieved to the greatest extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of a flow chart of a method for simplifying vector data of linear elements of electric power infrastructure according to the present invention;

[0040] Figure 2 This is a schematic diagram for identifying primary and secondary sites (solid areas are primary sites; hollow areas are secondary sites);

[0041] Figure 3 It is a schematic diagram for identifying the main line and straight line;

[0042] Figure 4 It is a schematic diagram of the convex hull feature identification and restoration of split line segments;

[0043] Figure 5 This is a schematic diagram of the restored splitting result that avoids self-intersection. DETAILED DESCRIPTION

[0044] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings.

[0045] The relevant parameters of the method for simplifying the vector data of linear elements of power infrastructure proposed by the present invention are shown in Table 1:

[0046] Table 1

[0047]

[0048] Please refer to Figure 1Algorithm flow, the present invention provides a method for simplifying vector data of linear elements of power infrastructure. Taking the power grid data of Greensboro, North Carolina, USA as an example, the specific implementation method is as follows:

[0049] Step S1: Read linear element information and substation element information from the data and perform preprocessing.

[0050] Step S2, reference Figure 2 , 3 As shown, the main power line and branch line are identified according to the site information of the nodes at both ends of the power infrastructure line. First, the point feature information of the substation point is traversed, and the site type is identified according to its Node attribute data, which is divided into main site and secondary site; then the power infrastructure line is traversed to identify the substation site and its type where the tail node is located. If it is a secondary site, the facility line segment is classified as a branch line, and the rest are main lines;

[0051] Step S3: cluster the substation nodes on the branch line with appropriate granularity according to the following steps:

[0052] 1. Divide each dimension into several grids, and calculate the similarity between each pair of samples only within its eight-neighborhood grid. Here it is the Euclidean distance.

[0053]

[0054] Among them, d is the number of dimensions, a i is the value of node a in dimension i, b i is the value of node b in dimension i.

[0055] 2. Determine the size of the neighborhood RT based on the calculated distance.

[0056] 3. Calculate the density and density vector. For each row of the rectangular matrix, count the number of adjacent regions with a radius less than RT, which is the distribution density of the corresponding data sample. The density contour of the data sample is obtained from the density vector;

[0057] 4. Determine DT (density threshold) based on the density vector;

[0058] 5. For each pair of samples c and c', if their density is greater than DT and the distance between them is less than RT, the two clusters of c and c' are merged into one cluster until all pairs of samples are merged;

[0059] 6. According to the calculation results, select the cluster center as the representative point;

[0060] Then, the loop compares whether the endpoints and starting points of the adjacent line segments coincide. If the next line segment is not marked, it is recorded as the same arc segment and assigned an arc segment ID. It is added to the arc segment set until there is no unmarked arc segment whose starting point coincides with the previous endpoint. The current arc segment is created and the next arc segment is created. It is iterated until all arc segments are created.

[0061] Step S4: Draw a straight line between the first and last points of the arc segment. Assuming that the straight line exists, calculate the vertical distance d from each node to the straight line in turn according to the coordinate values ​​of the three points and the following formula:

[0062]

[0063] Among them, (x1, y1)(x2, y2), (x3, y3) are the coordinates of three points.

[0064] Step S5, the maximum distance dmax is obtained and compared with the threshold value ρ. If it is greater than the threshold value ρ, the point is retained, and the old arc segment is divided into two arc segments from the retained point, and step S7 is executed; otherwise, the arc segment is simplified into a straight line, and step S6 is executed;

[0065] Step S6: for the simplified arc segment set, traverse all line segments in the arc segment set, and use the fast repulsion test and the straddle test to identify intersecting line segments; Figure 4 , it is recognized that the simplified AF and GH intersect, resulting in a change in the topological relationship. At this time, the ray method is used (a one-way ray from a point intersects the edge of the convex hull. The number of intersections is odd, it is inside the convex hull, and it is even, it is outside). It is identified that point G is located in the convex hull composed of AF and AB, BC...EF. At this time, it is determined whether the endpoints of the intersecting line segments AF and GH are inside the convex hull. It is concluded that the endpoint G is inside the convex hull. At this time, the other intersecting line segment AF is restored; the middle node D of the line segment AF to be split is found, and the split is restored into two line segments from this node. Refer to Figure 5 , and add the arc segment set. Repeat this step until there is no self-intersection, and then proceed to the next step;

[0066] Step S7, recursively execute steps S4 to S6 for each of the arc segments that have been processed and divided into two, until the splitting condition is no longer met, and then the recursion ends;

[0067] Step S8, traverse all simplified arcs, obtain the coordinates of all nodes of the arc through the arc set of the arc, and output the arc as a LineString type object in geojson format.

[0068] The above are preferred embodiments of the present invention. Any changes made according to the technical solution of the present invention, as long as the resulting functions do not exceed the scope of the technical solution of the present invention, belong to the protection scope of the present invention.

Claims

1. A method for simplifying vector data of linear elements of power infrastructure, characterized in that: The following steps are involved: Step S1, reading linear element information and substation element information from the data and performing preprocessing; Step S2: Identify the trunk line and branch line of the power infrastructure line according to the site information of the nodes at both ends of the power infrastructure line; Step S3, clustering the substation nodes on the branch line in granularity, omitting the branch line obtained in step S2, simplifying the connection line between the representative node in the cluster and the trunk line, and generating arc segments and arc segment sets; Step S4, draw an imaginary straight line between the first and last points of the arc segment, and calculate the vertical distance d from the remaining nodes on the arc segment to the straight line; Step S5, determine whether to retain the node according to the maximum distance from the node to the straight line and the set threshold, if retained, divide the old arc segment into two parts, and execute step S7; otherwise, simplify the arc segment into a straight line, and execute step S6; Step S6, for the simplified arc segment set, check whether there is self-intersection, if there is self-intersection, identify the target line segment and restore the split, repeat this step until there is no self-intersection, and execute step S7; Step S7, recursively execute steps S4 to S6 for the two processed arc segments respectively, until the splitting condition is no longer met, and then the recursive execution of step S8 ends; Step S8, outputting the simplified arc segment in geojson format; In step S3, the substation nodes on the branch line are clustered in granularity and arc segments and arc segment sets are generated, which specifically includes the following steps: Step S31: Divide each dimension into several grids, and calculate the Euclidean distance between each pair of samples only within its eight-neighborhood grid: Among them, d is the number of dimensions, a i is the value of node a in dimension i, b i is the value of node b in dimension i; Step S32: Determine the size of the neighborhood RT according to the calculated distance; Step S33, calculate the density and density vector: for each row of the rectangular matrix, count the number of adjacent regions whose radius RT is less than the number of adjacent regions, which is the distribution density of the corresponding data sample; obtain the density contour of the data sample from the density vector; Step S34, determining a density threshold DT according to the density vector; Step S35: for each pair of samples c and c', if their densities are both greater than DT and the distances between them are both less than RT, the two clusters of c and c' are merged into one cluster until all pairs of samples are merged; Step S36: According to the calculation results, select the cluster center within the cluster as the representative point; Step S37, cyclically compare whether the end points and starting points of the adjacent line segments coincide with each other. If the next line segment is not marked, it is recorded as the same arc segment and assigned an arc segment ID. It is added to the arc segment set until there is no unmarked arc segment whose starting point coincides with the previous end point. Then, the current arc segment is established and the next arc segment is entered. It is iterated until all arc segments are established.

2. A method for simplifying linear element vector data of electric power infrastructure according to claim 1, characterized in that: Step S2, identifying the trunk lines and branch lines of the power infrastructure line specifically includes the following steps: Step S21, traverse the substation site element information, identify the site type according to its attribute data, and divide it into primary site and secondary site; Step S22, traverse the power infrastructure lines, classify the facility line segments whose tail nodes are secondary sites as branch lines, and the rest as trunk lines.

3. The method for simplifying linear element vector data of electric power infrastructure according to claim 1, characterized in that: In step S4, the distance d from the remaining points on the arc segment to the imaginary straight line is calculated as follows: Assuming that the straight line exists, calculate the vertical distance d from each node to the straight line in turn according to the coordinates of the three points and the following formula; Among them, (x1, y1)(x2, y2), (x3, y3) are the coordinates of three points.

4. The method for simplifying linear element vector data of electric power infrastructure according to claim 1, characterized in that: In step S6, it is checked whether there is a self-intersection. If there is a self-intersection, the target line segment is identified and restored and split, which specifically includes the following steps: Step S61, traverse the line segments in all arc segment sets, and identify intersecting line segments using a fast repulsion test and a straddle test; Step S62, identifying the corresponding convex hull according to the intersecting line segments; Step S63, judging whether the endpoints of the intersecting line segments are inside the convex hull according to the convex hull characteristics, and if so, splitting the other line segment intersecting with it; Step S64: Find the middle node of the line segment that needs to be restored and split into two line segments from this node according to the original arc segment nodes.

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