Substation gis base intelligent drawing method, electronic equipment and storage medium

Abstract

The application provides a substation GIS foundation intelligent drawing method capable of realizing automatic conversion from a GIS foundation buried part drawing to a foundation construction drawing, intelligent drawing, an electronic device and a storage medium, and relates to the technical field of substation engineering design automation. The substation GIS foundation intelligent drawing method comprises the following steps: extracting geometric elements from a GIS foundation buried part drawing and performing redundant element filtering processing to obtain a GIS buried part geometric graph element data set; completing grouping of GIS buried parts and plane outer boundary construction; identifying spatial position attributes of the GIS buried parts and performing standardized data modeling; constructing a GIS foundation parameterized drawing data topology model, and generating a GIS foundation two-dimensional CAD construction drawing according to layer distribution rules. The application improves design efficiency and the accuracy and consistency of GIS foundation design.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of substation engineering design automation, and particularly relates to a substation GIS foundation intelligent drawing method, an electronic device and a storage medium. BACKGROUND

[0002] Substation GIS foundation design is an important link in substation construction, and its design quality and efficiency directly affect the progress and quality of the entire project. In the traditional GIS foundation design process, the designer needs to manually draw the foundation construction drawing according to the embedded part layout provided by the electrical professional. This method has many problems.

[0003] The traditional manual GIS foundation design method has the following problems: first, manually drawing the foundation construction drawing is time-consuming and laborious, especially for large substation projects, the number of foundations is large, and the embedded part layout is complex; second, manual drawing is prone to errors and inaccuracy, affecting construction quality and engineering safety; third, it is difficult to effectively handle multiple constraint conditions and optimization objectives, and it is difficult to achieve standardized design; finally, it lacks intelligent recognition and automated processing capabilities, and the design efficiency is low, which cannot meet the rapid development needs of modern engineering construction.

[0004] In the prior art, although there are some CAD auxiliary design tools, they cannot realize the automatic conversion from the GIS foundation embedded part drawing to the foundation construction drawing. Therefore, there is an urgent need for a method and system that can intelligently generate a substation GIS foundation construction drawing from a GIS foundation embedded part drawing. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a substation GIS foundation intelligent drawing method, an electronic device and a storage medium that can realize the automatic conversion from the GIS foundation embedded part drawing to the foundation construction drawing and intelligent drawing.

[0006] The technical solution adopted by the present application to solve the technical problem is: a substation GIS foundation intelligent drawing method, comprising the following steps:

[0007] S1, extracting geometric primitives from the GIS foundation embedded part drawing and performing redundancy filtering processing to obtain a GIS embedded part geometric primitive data set;

[0008] S2, based on the obtained GIS embedded part geometric primitive data set, completing grouping of the GIS embedded parts and constructing the plane outer expansion boundary through judging the irregularity of the GIS embedded part distribution law, a convex hull scanning algorithm, a spatial clustering algorithm and boundary point outer expansion;

[0009] S3, according to the completed grouping result of the GIS embedded parts, a spatial index is constructed by using a dual structure of R-tree and quad-tree, combined with grid row-column index and distance positioning, the spatial position attribute of the GIS embedded parts is identified and standardized data modeling is performed;

[0010] S4, based on the identified spatial position attribute of the GIS embedded parts, combined with the grouping result of the GIS embedded parts and the constructed plane outer boundary, a GIS basic parameterized drawing data topology model is constructed, and a GIS basic two-dimensional CAD construction drawing is generated according to the layer distribution rule;

[0011] S5, based on the generated GIS basic two-dimensional CAD construction drawing, a system overall engineering quantity list is generated.

[0012] Further, the S1 step comprises:

[0013] S11, extracting a geometric primitive data set G={gi} from the GIS basic embedded part drawing, wherein the primitive type of the primitive gi includes a straight line, a polyline and a block reference;

[0014] S12, based on a primitive type filtering function, filtering the primitives in the set G to obtain a set of quadrilaterals G1, then performing a vector perpendicular relationship judgment on the primitives in the G1, identifying the primitives that meet the rectangular condition, and screening them out to form a rectangular primitive set G2;

[0015] S13, primitive standardization and database construction:

[0016] S131, traversing the set G2, analyzing all block reference primitives therein by a recursive algorithm, decomposing them into the most basic straight line primitives, thereby generating a new set G3 containing only straight lines and polylines;

[0017] S132, based on the straight line primitives in the set G3, identifying the spatial relationships between the geometric entities including containing, adjacent and overlapping relationships by a computational geometry algorithm, and removing the redundant GIS embedded part primitives that overlap each other, to obtain a refined set G4;

[0018] S133, reconstructing the straight line and polyline primitives in the G4 that belong to the same GIS embedded part into a polyline, and adding a "GIS embedded part" layer attribute to all reconstructed primitives, outputting a standardized polyline set G5;

[0019] S134, identifying the planar size and planar area of each GIS embedded part in the G5 by geometric calculation;

[0020] S135, classifying the GIS embedded parts according to the calculated planar area, and assigning corresponding classification attributes to each GIS embedded part;

[0021] S136, a standardized GIS embedded component geometric primitive metadata set L(V, N) is established, where V represents a multi-segment line set G5, and N is a corresponding classification attribute set;

[0022] S14, a displacement vector Vmove=Ptarget-Pbase is calculated according to the user-specified reference point Pbase and the target point Ptarget, a bounding box Bi=[Pmin, Pmax] is calculated for each geometric primitive V in the set L, and a center point of the bounding box is determined as a translation anchor point, where Pmin is a two-dimensional point whose X and Y components are the minimum values of the X and Y coordinates of all vertices of the geometric primitive V, and Pmax is a two-dimensional point whose X and Y components are the maximum values of the X and Y coordinates of all vertices of the geometric primitive V;

[0023] S15, using the displacement vector Vmove as the translation amount, each geometric primitive V in the set L is translated and transformed to accurately place it at the target position in the global coordinate system, and finally a standard GIS embedded component geometric primitive metadata set is output.

[0024] Further, the S2 step includes:

[0025] S21, initializing a GIS embedded component population;

[0026] S22, obtaining a GIS embedded component population data set after initialization, and inputting a preset GIS embedded component grouping spacing parameter and a GIS foundation outer expansion size parameter;

[0027] S23, according to the input GIS embedded component grouping spacing parameter, the GIS embedded component population is divided into multiple GIS embedded component groups by a spatial clustering algorithm, wherein a group of GIS embedded component groups corresponds to a GIS foundation to be constructed;

[0028] S24, traversing the multiple GIS embedded component groups, and performing the following processing on the current GIS embedded component group:

[0029] S241, obtaining the spatial position coordinates and size attributes of all GIS embedded components in the group;

[0030] S242, identifying GIS embedded components in the edge position based on the spatial position coordinates, and judging the distribution regularity or irregularity according to the proportional relationship of the size attributes and the size attributes of adjacent GIS embedded components; if it is judged that the distribution is irregular, a convex hull scanning algorithm is used to construct an irregular geometric enclosing contour of the group of GIS embedded components; if it is judged that the distribution is regular, a rectangular geometric enclosing contour is constructed based on the extreme points of the overall bounding box of all GIS embedded components in the group;

[0031] S243、According to the preset GIS base outward expansion size parameter, the constructed geometric enclosing contour is outwardly displaced and expanded to generate a GIS base plane outward expansion boundary;

[0032] S244、Adding layer attribute information to the GIS base plane outward expansion boundary;

[0033] S25、Outputting the GIS base plane outward expansion boundary of all GIS embedded parts groups to complete batch automatic processing.

[0034] Further, the condition for judging irregular distribution is that if the GIS embedded part at the edge position in the X-axis direction or the Y-axis direction has a size attribute value in the corresponding direction less than half of the size attribute value in the corresponding direction of the adjacent GIS embedded part, it is determined that the group of GIS embedded parts is irregularly distributed.

[0035] The condition for judging regular distribution is that if the GIS embedded part at the edge position in the X-axis direction or the Y-axis direction has a size attribute value in the corresponding direction greater than or equal to half of the size attribute value in the corresponding direction of the adjacent GIS embedded part, it is determined that the group of GIS embedded parts is regularly distributed.

[0036] Further, the S21 comprises:

[0037] S211、Randomly selecting a plurality of GIS embedded parts from the GIS embedded part geometric primitive data set to form a GIS base division scheme;

[0038] S212、Assigning an initial position and attribute information to each GIS embedded part, the attribute information at least including the GIS embedded part type and size;

[0039] S213、Optimizing the position and attribute of the GIS embedded part through an iterative update algorithm, the iterative update algorithm being represented as: ΔP(t+1)=ΔP(t)+β×(Pbest-P(t)), wherein ΔP(t) is the adjustment vector of the GIS embedded part in the current iteration step, Pbest is the optimal position scheme in the current iteration step, β is the learning rate, P(t) is the GIS embedded part position scheme in the current iteration step, and ΔP(t+1) is the adjustment vector of the GIS embedded part in the next iteration step.

[0040] Further, the convex hull scanning algorithm comprises the following steps:

[0041] a、Initial boundary identification:

[0042] a1、For the group of GIS embedded parts with irregular distribution, find the minimum point of the y coordinate, and take the point as the starting point;

[0043] a2、Sort the y coordinates of the group of GIS embedded parts with irregular distribution according to the polar angle of each point relative to the starting point;

[0044] a3, determining the convex hull boundary points of the irregularly distributed GIS buried component group through stack operation;

[0045] a4, creating a boundary point list and initializing it as a convex hull point set;

[0046] b, convex hull vertex geometry analysis:

[0047] b1, traversing each vertex on the convex hull to obtain its adjacent previous and next vertices;

[0048] b2, calculating the direction vectors of the current vertex to the previous and next vertices and performing unitization processing;

[0049] b3, calculating the included angle between adjacent edges through vector dot product;

[0050] c, vertex type judgment and screening:

[0051] c1, judging whether the included angle between adjacent edges is 0°, 180° or 90°;

[0052] c2, comparing the Y coordinate and X coordinate size relationship of the current vertex and adjacent vertex;

[0053] c3, determining whether a new boundary point needs to be inserted near the vertex based on the geometric condition, if the included angle between adjacent edges is neither 0° nor 180°, nor 90°, and the Y coordinate of the current vertex is less than the maximum Y coordinate of the adjacent vertex, then the insertion condition is met, and step d is executed to screen the convex hull vertex;

[0054] d, boundary point dynamic insertion:

[0055] d1, for the convex hull vertex meeting the insertion condition, finding the nearest vertex of the convex hull vertex in the irregularly distributed GIS buried component group geometric primitive;

[0056] d2, determining the insertion position according to the X coordinate comparison result of the convex hull vertex and the nearest vertex of the convex hull vertex: if the current point X coordinate is greater than the convex hull vertex X coordinate, then insert before the current position, otherwise insert at the next position;

[0057] d3, dynamically updating the boundary point list to form a more detailed boundary contour;

[0058] e, boundary contour generation:

[0059] e1, identifying internal boundary points through iterative processing;

[0060] e2, removing the convex hull internal points that have been processed and do not meet the boundary point insertion condition;

[0061] e3. The boundary insertion points obtained from the iterative processing and the convex hull point set are used together to construct a new set of irregular geometric enclosing contour boundary points for the GIS embedded parts group, thereby constructing the irregular geometric enclosing contour of the irregularly distributed GIS embedded parts group.

[0062] Further, step S3 includes:

[0063] S31. Based on the grouping results of GIS embedded parts, construct a GIS embedded part spatial index matrix with dual structures, based on R-tree structure and quadtree structure;

[0064] S32. Based on the GIS embedded part spatial index matrix, the spatial relationship index of each GIS embedded part is calculated using the weighted distance method.

[0065] S33. Through spatial cluster analysis, spatial cluster evaluation is performed on the obtained spatial relationship indicators, and the spatial location attributes of GIS embedded parts are obtained based on the evaluation results.

[0066] S34. Based on the obtained spatial location attributes of the GIS embedded parts, perform standardized data modeling, construct structured data with GIS embedded parts and GIS foundation as objects, and transfer the data to the subsequent parametric drawing data topology model through object-oriented data modeling.

[0067] Further, S4 includes the step of:

[0068] S41. Generate GIS basic dimensions based on the constructed GIS embedded part boundary outline;

[0069] S42. Based on the preset GIS foundation reinforcement parameters, GIS foundation pad layer parameters, GIS foundation material parameters, GIS embedded part height parameters, and the spatial location attribute parameters of the identified GIS embedded parts, construct a GIS foundation parameterized drawing data topology model.

[0070] S43. Based on the GIS basic parametric drawing data topology model, establish a parametric CAD drawing model, and generate an initial reinforcement layout scheme through the intelligent analysis algorithm of reinforcement parameters.

[0071] S44. Based on the generated initial reinforcement layout scheme, a collision detection algorithm is used to perform collision detection. If a collision is found, an optimization algorithm is used to adjust the reinforcement layout until there is no collision.

[0072] S45. Generate a rebar layout scheme with no collisions in the detection results to obtain the final rebar layout scheme;

[0073] S46. Based on the final reinforcement layout scheme, perform parametric layer management and generate construction drawings.

[0074] An electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the above-described intelligent mapping method for substation GIS.

[0075] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described intelligent mapping method for substation GIS.

[0076] The beneficial effects of this invention are: this invention realizes the automated conversion and intelligent drawing generation from GIS foundation embedded part data extraction drawings to foundation construction drawings, reducing the workload of drawing, improving design efficiency, and enhancing the accuracy and consistency of foundation design. Attached Figure Description

[0077] Figure 1 This is a flowchart of the present invention;

[0078] Figure 2 This is a schematic diagram of the basic structure of GIS;

[0079] The figure shows: first-order rectangle 11, second-order rectangle 12. Detailed Implementation

[0080] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0081] like Figure 1 As shown, the intelligent mapping method for substation GIS infrastructure of the present invention includes the following steps:

[0082] S1. Extract geometric elements from the GIS basic embedded parts data map and perform redundancy filtering to obtain the GIS embedded parts geometric data set.

[0083] S2. Based on the obtained set of geometric data of GIS embedded parts, the basic grouping and planar expansion of GIS are constructed by judging the irregularity of the distribution pattern of GIS embedded parts, using convex hull scanning algorithm, spatial clustering algorithm and boundary point expansion.

[0084] S3. Based on the completed grouping results of the GIS embedded parts, a spatial index is constructed using a dual structure of R-tree and quadtree. Combined with grid row and column index and spacing positioning, the spatial location attributes of the GIS embedded parts are identified and standardized data modeling is performed.

[0085] S4. Based on the spatial location attributes of the identified GIS embedded parts, combined with the grouping results of the GIS embedded parts and the constructed planar expansion boundary, a GIS basic parametric drawing data topology model is constructed, and a GIS basic two-dimensional CAD construction drawing is generated according to the layer distribution rules and drawing evaluation methods.

[0086] S5, generating a system overall engineering quantity list based on the generated GIS foundation two-dimensional CAD construction drawing.

[0087] The method of the present application extracts geometric primitives from the GIS foundation buried member drawing and performs redundancy filtering processing to obtain a GIS buried member geometric primitive data set, then based on the obtained GIS buried member geometric primitive data set, through irregularity judgment of GIS buried member distribution, convex hull scanning algorithm (Graham Scan algorithm), spatial clustering algorithm and boundary point expansion, GIS foundation grouping and plane boundary expansion are completed, then according to the grouping result of GIS buried member, R-tree and quadtree dual structure are used to construct spatial index, combined with grid row-column index and distance positioning, the spatial position attribute of GIS buried member is identified, finally based on the spatial position attribute of GIS buried member identified, combined with the grouping result of GIS buried member and the plane boundary expansion constructed, a GIS foundation parameterized drawing data topology model is constructed, and according to the layer distribution rule and drawing evaluation method, a GIS foundation two-dimensional CAD construction drawing is generated, realizing automatic conversion and intelligent drawing from GIS foundation buried member drawing to foundation construction drawing, reducing the workload of drawing, improving the design efficiency and the accuracy and consistency of foundation design.

[0088] Since the GIS buried member geometric primitive is a rectangle, the present application extracts geometric primitives from the GIS foundation buried member drawing and performs redundancy filtering processing in the following way to obtain a GIS buried member geometric primitive data set:

[0089] S11, extracting geometric primitives from the GIS foundation buried member drawing to form a data set G={gi}, wherein the primitive gi includes a straight line (Line), a polyline (Polyline) and a block reference (BlockReference);

[0090] S12, filtering the primitives in the set G based on a primitive type filtering function to obtain a quadrilateral primitive set G1, then performing vector perpendicular relationship judgment on the primitives in the G1 to identify the primitives meeting the rectangle condition and screening them out to form a rectangle primitive set G2;

[0091] S13, primitive standardization and database construction:

[0092] S131, traversing the set G2, analyzing all block reference primitives (BlockReference) therein through a recursive algorithm to decompose them into the most basic straight line (Line) primitives, thereby generating a new set G3 containing only straight line (Line) and polyline (Polyline);

[0093] S132, based on the line (Line) primitives in the set G3, the spatial relationships between the geometric entities are identified by a geometric calculation algorithm, including inclusion, adjacency and overlapping relationships, and redundant GIS buried component primitives that overlap each other are removed to obtain a refined set G4;

[0094] S133, for the line (Line) and polyline (Polyline) primitives in G4 that belong to the same GIS buried component, reconstruct them into a polyline (PolyLine) and add a "GIS buried component" layer attribute to all reconstructed primitives, and output a standardized polyline set G5;

[0095] S134, by geometric calculation, identify the planar size (such as length, width) and planar area of each GIS buried component in G5;

[0096] S135, classify the GIS buried components according to the calculated planar area, and assign each GIS buried component a corresponding classification attribute;

[0097] S136, establish a standardized GIS buried component primitive data set L(V, N), where V represents the polyline set G5, and N represents the corresponding classification attribute set;

[0098] S14, according to the user-specified reference point Pbase and target point Ptarget, calculate the displacement vector Vmove=Ptarget-Pbase, calculate the boundary box Bi=[Pmin, Pmax] of each geometric primitive V in the set L, and determine the center point of the boundary box as the translation anchor point, where Pmin is a two-dimensional point, and the X and Y components are the minimum values of the X and Y coordinates of all vertices of the geometric primitive V, Pmax is a two-dimensional point, and the X and Y components are the maximum values of the X and Y coordinates of all vertices of the geometric primitive V;

[0099] S15, use the displacement vector Vmove as the translation amount to perform translation transformation on each geometric primitive V in the set L, and accurately place it to the target position in the global coordinate system, and finally output the standard GIS buried component geometric primitive data set.

[0100] Specifically, the ezdxf library using Python can be used to extract geometric primitives from GIS basic buried component data sets to generate data sets G={gi}.

[0101] The specific steps of determining whether the quadrilateral primitive is a rectangle are as follows:

[0102] The coordinates of the four vertices of the quadrilateral are obtained in order;

[0103] Four edge vectors v1, v2, v3, v4 are calculated, and dot products dot1, dot2, dot3, dot4 of adjacent edges are calculated:

[0104] dot1 = v1·v2, dot2 = v2·v3, dot3 = v3·v4, dot4 = v4·v1;

[0105] If abs(dot1) < tolerance and abs(dot2) < tolerance and abs(dot3) < tolerance and abs(dot4) < tolerance, then the quadrilateral is a rectangle. The tolerance is a tolerance value.

[0106] In the embodiment of the application, the specific S2 step comprises:

[0107] S21, initializing a GIS embedded part group;

[0108] S22, acquiring a GIS embedded part group data set after initialization, and inputting a preset GIS embedded part grouping spacing parameter and a GIS base outer expansion size parameter;

[0109] S23, dividing the GIS embedded part group into a plurality of GIS embedded part groups according to the input GIS embedded part grouping spacing parameter by a spatial clustering algorithm, wherein one group of GIS embedded part groups corresponds to one GIS base to be constructed;

[0110] S24, traversing the plurality of GIS embedded part groups, and performing the following processing on a current GIS embedded part group:

[0111] S241, acquiring spatial position coordinates and size attributes of all GIS embedded parts in the group;

[0112] S242, identifying GIS embedded parts at edge positions based on the spatial position coordinates, and judging distribution regularity or irregularity according to a proportional relationship between the size attributes and size attributes of adjacent GIS embedded parts; if it is judged that the distribution is irregular, a convex hull scanning algorithm is used to construct an irregular geometric enclosing contour of the group of GIS embedded parts; if it is judged that the distribution is regular, a rectangular geometric enclosing contour is constructed based on extreme points of an overall bounding box of all GIS embedded parts in the group;

[0113] S243, according to the preset GIS base outer expansion size parameter, performing displacement outer expansion on the constructed geometric enclosing contour to generate a GIS base plane outer expansion boundary;

[0114] S244, adding layer attribute information to the GIS base plane outer expansion boundary;

[0115] S25, output GIS base plane outer extension boundary of all GIS embedded part groups, complete batch automation processing.

[0116] In the present application, the following method is used to determine the distribution irregularity and regularity: if in the X-axis direction or the Y-axis direction, the GIS embedded part at the edge position has a size attribute value in the corresponding direction that is less than half of the size attribute value in the corresponding direction of the adjacent GIS embedded part, then it is determined that the group of GIS embedded parts is distributed irregularly. Specifically, in the Y-axis direction, if there exists or in the X-axis direction, if there exists then it is determined that the group of GIS embedded parts is distributed irregularly, wherein, is the length of the edge position embedded part in the Y-axis direction, is the length of the adjacent embedded part to the edge position embedded part in the Y-axis direction, is the length of the edge position embedded part in the X-axis direction, is the length of the adjacent embedded part to the edge position embedded part in the X-axis direction. If in the X-axis direction or the Y-axis direction, the GIS embedded part at the edge position has a size attribute value in the corresponding direction that is greater than or equal to half of the size attribute value in the corresponding direction of the adjacent GIS embedded part, then it is determined that the group of GIS embedded parts is distributed regularly. Specifically, in the Y-axis direction, if there exists or in the X-axis direction, if there exists then it is determined that the group of GIS embedded parts is distributed regularly. The above determination method is accurate, simple, and conforms to engineering practice.

[0117] In order to more accurately construct the irregular geometric enclosing contour of the group of GIS embedded parts with irregular distribution, the present application uses a convex hull scanning algorithm to construct the irregular geometric enclosing contour, including the following steps:

[0118] a. Initial boundary identification:

[0119] a1. For a group of GIS embedded parts with irregular distribution, find the point with the minimum y-coordinate, and use this point as the starting point;

[0120] a2. Sort the y-coordinates of the group of GIS embedded parts with irregular distribution according to the polar angle of each point relative to the starting point;

[0121] a3. Determine the convex hull boundary points of the group of GIS embedded parts with irregular distribution through stack operation;

[0122] a4. Create a boundary point list and initialize it as a convex hull point set;

[0123] b. Convex hull vertex geometric analysis:

[0124] b1. Traverse each vertex on the convex hull to obtain its adjacent previous and next two vertices;

[0125] b2, calculate the direction vector of the current vertex to the previous and next vertices and perform unitization processing;

[0126] b3, calculate the included angle between adjacent edges by vector dot product;

[0127] c, vertex type judgment and screening:

[0128] c1, judge whether the included angle between adjacent edges is 0°, 180° or 90°;

[0129] c2, compare the Y coordinate and X coordinate size relationship of the current vertex and adjacent vertices;

[0130] c3, determine whether a new boundary point needs to be inserted near the vertex based on the geometric condition, if the included angle between adjacent edges is neither 0° nor 180°, nor 90°, and the Y coordinate of the current vertex is less than the maximum Y coordinate of the adjacent vertices, then the insertion condition is met, and then step d is executed to screen the convex hull vertex;

[0131] d, dynamic insertion of boundary points:

[0132] d1, for the convex hull vertex meeting the insertion condition, find the nearest vertex of the convex hull vertex in the irregularly distributed GIS buried component group geometric primitive;

[0133] d2, determine the insertion position according to the X coordinate comparison result of the convex hull vertex and the nearest vertex of the convex hull vertex: if the X coordinate of the current point is greater than the X coordinate of the convex hull vertex, then insert in front of the current position, otherwise insert in the next position;

[0134] d3, dynamically update the boundary point list to form a more detailed boundary contour;

[0135] e, boundary contour generation:

[0136] e1, identify the internal boundary points by iterative processing;

[0137] e2, remove the convex hull internal points that have been processed and do not meet the boundary point insertion condition;

[0138] e3, construct a new irregular geometric enclosing contour boundary point set of the GIS buried component group by combining the boundary insertion points obtained by iterative processing with the convex hull point set, and construct the irregular geometric enclosing contour of the irregularly distributed GIS buried component group.

[0139] The specific steps for initializing the buried component group in the application are:

[0140] S211, randomly select a plurality of GIS buried components from the GIS buried component geometric primitive data set to form a GIS basic division scheme;

[0141] S212, assign an initial position and attribute information to each GIS embedded part, the attribute information at least including GIS embedded part type and size;

[0142] S213, optimize the position and attribute of the GIS embedded part through an iterative updating algorithm, the iterative updating algorithm being represented as: ΔP(t+1) = ΔP(t) + β × (Pbest-P(t)), wherein ΔP(t) is an adjustment vector of the GIS embedded part in a current iteration step, Pbest is an optimal position scheme in the current iteration step, β is a learning rate, P(t) is a GIS embedded part position scheme in the current iteration step, and ΔP(t+1) is an adjustment vector of the GIS embedded part in a next iteration step.

[0143] The S3 step in the application comprises:

[0144] S31, according to the grouping result of the GIS embedded part, constructing a GIS embedded part space index matrix comprising a double structure based on an R-tree structure and a quadtree structure;

[0145] S32, according to the GIS embedded part space index matrix, respectively calculating a spatial relationship index of each GIS embedded part through a weighted distance method;

[0146] S33, through spatial clustering analysis, performing spatial clustering evaluation on the obtained spatial relationship index, and obtaining a spatial position attribute of the GIS embedded part according to the evaluation result.

[0147] S34, according to the obtained spatial position attribute of the GIS embedded part, performing standardized data modeling, constructing structured data of the GIS embedded part spatial position attribute taking the GIS embedded part and GIS foundation as objects, and delivering data to a subsequent parameterized drawing data topology model through an object-oriented data modeling manner.

[0148] The construction of the GIS embedded part space index matrix specifically comprises the following steps:

[0149] Extracting spatial coordinates P and corresponding attribute information Attr of all GIS embedded parts from the basic grouping division result;

[0150] Creating a space index matrix M, wherein each row represents a coordinate value of a GIS embedded part, and each column represents different spatial attributes (such as x coordinate, y coordinate, size, etc.);

[0151] Performing standardized processing on the space index matrix M to ensure that the range of each attribute value is convenient for spatial query and analysis.

[0152] Specifically, the calculation of the spatial relationship index of each GIS embedded part comprises the following steps:

[0153] According to the space index matrix M and a preset weight coefficient , , , ,calculate the spatial relationship index Spatial(i) of each GIS buried part;

[0154] The spatial relationship index is calculated using the weighted distance method , wherein, is the jth spatial attribute value of the ith GIS buried part after standardization.

[0155] Through spatial clustering analysis, the spatial relationship index obtained is evaluated by spatial clustering, and according to the evaluation result, the spatial position attribute of the GIS buried part is obtained, including the following steps:

[0156] A clustering evaluation set C={C1, C2, C3, C4, C5}={close, moderate, loose, separate, isolated} is established, and a quantitative value is assigned to each clustering level;

[0157] According to the spatial relationship index Spatial(i), a membership function μ i () is established, which maps Spatial(i) to each level C i of the clustering evaluation set C;

[0158] The clustering evaluation value E of each GIS buried part is calculated as C i ∑(μ i ()× V i ), wherein, V i is the quantitative value corresponding to the clustering level C i ;

[0159] The scheme with the optimal clustering evaluation value is selected as the accurate recognition result of the spatial position attribute of the GIS buried part.

[0160] In the present application, specifically, the S4 comprises the steps of:

[0161] S41, generating GIS base dimensions according to the constructed GIS buried part boundary contour;

[0162] S42, constructing GIS base parameterized drawing data topology model according to the preset GIS base steel bar parameters, GIS base cushion layer parameters, GIS base material parameters and GIS buried part height parameters;

[0163] S43, establishing a parameterized CAD drawing model based on the GIS base parameterized drawing data topology model, and generating a steel bar arrangement initial scheme through a steel bar parameter intelligent analysis algorithm;

[0164] S44, according to the generated steel bar arrangement initial scheme, collision detection is carried out through a collision detection algorithm, if the result exists collision, an optimization algorithm is used to adjust the steel bar arrangement until the result has no collision;

[0165] S45, a steel bar arrangement scheme with no collision detection result is generated, and a final steel bar arrangement scheme is obtained;

[0166] S46, based on the final steel bar arrangement scheme, parameterized layer management is carried out, and a staged construction drawing is generated.

[0167] According to the constructed GIS embedded boundary contour, the GIS foundation size is generated, specifically: according to the constructed GIS embedded boundary contour, the width of the GIS foundation is obtained, and the width of the GIS foundation second-order rectangle 12 is taken as the width of the GIS foundation, and then according to the preset width-height ratio of the GIS foundation second-order rectangle, the width ratio of the GIS foundation first-order rectangle and the second-order rectangle, and the width-height ratio of the GIS foundation first-order rectangle 11, the GIS foundation size is generated (see Figure 2 ). In the embodiment of the application, the height-width ratio of the GIS foundation second-order rectangle is a fixed value 1.33, the width-height ratio of the GIS foundation first-order rectangle of the GIS foundation second-order rectangle is a fixed value 0.16, and the ratio of the width of the GIS foundation first-order rectangle to the width of the second-order rectangle is 1.66.

[0168] Specifically, the input steel bar parameters include: main reinforcement diameter, stirrup diameter, main reinforcement spacing, stirrup spacing, protective layer thickness, and steel bar grade.

[0169] According to the generated steel bar arrangement initial scheme, the specific steps of collision detection through the collision detection algorithm are as follows:

[0170] A three-dimensional steel bar space coordinate system is established, each steel bar is abstracted as a linear geometric entity, and a steel bar space position vector is defined ( , , , , , ) is the steel bar starting point coordinate, is the steel bar length, and are the steel bar direction angles;

[0171] The collision detection algorithm Collision_Detection(R x , R y ​​​​), calculate the minimum distance distmin(x, y) between the steel bars, and determine whether the steel bar spacing requirement distmin(x, y >= smin) is met, wherein smin is the minimum allowed steel bar spacing, and x and y represent the steel bars in the X direction and the Y direction, respectively.

[0172] An evaluation function of steel bar layout is established:

[0173] Evaluation(Layout) = W1 x Density + W2 x Uniformity + W3 x Accessibility, wherein Density is a steel bar density index, Uniformity is a layout uniformity index, Accessibility is a construction accessibility index, W1, W2, and W3 are weight coefficients, in the embodiment of the present application, W1 = 0.4, W2 = 0.3, and W3 = 0.3.

[0174] The steel bar layout adjustment is performed by using an optimization algorithm, and specifically includes:

[0175] An interference avoidance optimization model is established for the detected collision conflicts, and an optimization objective function Optimize_Target = minimize(∑Collision_Penalty + ∑Layout_Cost) is defined, wherein Collision_Penalty is a collision penalty term, which is used to quantify the collision interference degree between the steel bars; Layout_Cost is a layout cost term, which includes economic indexes such as steel bar adjustment amplitude and material consumption; minimize represents a minimum optimization process of the objective function, that is, the value of the objective function is continuously reduced through iterative search until a convergence condition is reached, and finally a global optimal solution or an approximate optimal solution with the least collision conflicts and the lowest layout cost is obtained;

[0176] The genetic algorithm is used to perform iterative optimization of the steel bar positions, and a mutation operator Mutation (Layout) is set to adjust the steel bar positions, and a crossover operator Crossover (Layout1, Layout2) is set to exchange the advantageous parts of different layout schemes. R i

[0177] The bounding box algorithm is applied to accurately adjust the positions of the steel bars in conflict, so as to ensure that the spacing constraints and avoidance requirements are met;

[0178] The constraint relationship matrix is updated in real time, and the geometric coordination relationship between the steel bars, the foundation, and the GIS embedded parts is maintained;

[0179] The optimized steel bar spatial layout scheme is output.

[0180] ​​​Based on the final steel bar arrangement scheme, parameterized layer management is carried out, and a generation step of construction drawings in stages is carried out, including:

[0181] A standardized layer system is established, including a basic contour layer, a GIS embedded part positioning layer, a main reinforcement arrangement layer, a stirrup arrangement layer, a dimension marking layer, a construction instruction layer and the like.

[0182] A parameterized drawing engine is applied to project three-dimensional steel bar arrangement information to generate two-dimensional construction drawings, including a basic plan, a steel bar reinforcement drawing, a detailed section drawing and the like, and a steel bar statistical table is automatically generated, including steel bar specifications, quantity, length, weight and the like.

[0183] The construction drawings are outputted.

[0184] S51, based on the GIS basic parameterized drawing data topology model constructed, dimension parameter data related to system engineering quantity is screened out, including GIS embedded part dimensions (length, width and height), GIS embedded part quantity, GIS basic dimensions (length, width and height), GIS basic quantity, GIS basic material type, cushion dimensions (length, width and height), cushion quantity, cushion material type, the engineering quantity parameter data screened out is subjected to attribute assignment to each GIS basic geometric element in the two-dimensional construction drawing generated in the S5 step according to the GIS basic grouping division result in the S2 step, and is used for subsequent engineering quantity statistical calculation;

[0185] S52, the corresponding engineering quantity parameter data is obtained through the GIS basic geometric element whose attribute assignment of engineering quantity parameter data is completed by frame selection, the engineering quantity volume of the GIS embedded part, the GIS basic part and the cushion is respectively calculated according to the dimension parameters (length, width and height), and the classification and summary calculation is carried out according to the material type, and the system overall engineering quantity list is generated.

[0186] The application further provides an electronic device, including a processor, a memory and a computer program stored on the memory and executable on the processor, and the computer program is executed by the processor to realize the substation GIS basic intelligent drawing method.

[0187] The application further provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the substation GIS basic intelligent drawing method.

Claims

1. A basic intelligent mapping method for substation GIS, characterized in that, Includes the following steps: S1. Extract geometric elements from the GIS basic embedded parts data map and perform redundancy filtering to obtain the GIS embedded parts geometric data set. S2. Based on the obtained geometric data set of GIS embedded parts, the grouping and planar expansion boundary construction of GIS embedded parts are completed by judging the irregularity of the distribution pattern of GIS embedded parts, using convex hull scanning algorithm, spatial clustering algorithm, and boundary point expansion: S21. Initialize the GIS embedded component group; S22. Obtain the initialized GIS embedded parts group data set, and input the preset GIS embedded parts grouping spacing parameters and GIS foundation expansion dimension parameters; S23. Based on the input GIS embedded part group spacing parameters, the GIS embedded part group is divided into multiple GIS embedded part groups using a spatial clustering algorithm, where each GIS embedded part group corresponds to a GIS foundation to be constructed. S24. Traverse the multiple GIS embedded component groups and perform the following processing on the current GIS embedded component group: S241. Obtain the spatial location coordinates and size attributes of all GIS embedded parts in the group; S242. Identify GIS embedded parts located at the edge based on the spatial coordinates, and determine whether the distribution is regular or irregular based on the proportional relationship between their size attributes and the size attributes of adjacent GIS embedded parts; if the distribution is irregular, use the convex hull scanning algorithm to construct the irregular geometric enclosing contour of the group of GIS embedded parts; if the distribution is regular, construct a rectangular geometric enclosing contour based on the extreme points of the overall boundary box of all GIS embedded parts in the group. S243. According to the preset GIS basic expansion dimension parameters, the constructed geometric enclosing contour is displaced and expanded outward to generate the GIS basic plane expansion boundary. S244. Add layer attribute information to the outer boundary of the GIS basic plane; S25. Output the outer boundary of the GIS basic plane of all GIS embedded parts groups to complete batch automated processing; S3. Based on the completed grouping results of the GIS embedded parts, a spatial index is constructed using a dual structure of R-tree and quadtree. Combined with grid row and column index and spacing positioning, the spatial location attributes of the GIS embedded parts are identified and standardized data modeling is performed. S4. Based on the identified spatial location attributes of GIS embedded parts, combined with the grouping results of GIS embedded parts and the constructed planar expansion boundary, a GIS basic parametric drawing data topology model is constructed, and a GIS basic two-dimensional CAD construction drawing is generated according to the layer distribution rules. S5. Based on the generated GIS-based two-dimensional CAD construction drawings, generate the overall bill of quantities for the system.

2. The substation GIS basic intelligent mapping method as described in claim 1, characterized in that: Step S1 includes: S11. Extract geometric elements from the GIS basic embedded data map to form a data set G={gi}, where the element type of element gi includes line, polyline and block reference. S12. Based on the primitive type filtering function, the primitives in set G are filtered to obtain a set of quadrilateral primitives G1. Then, the vector perpendicularity relationship of the primitives in G1 is judged, the primitives that meet the rectangular condition are identified, and they are filtered out to form a set of rectangular primitives G2. S13. Graphical element standardization and database construction: S131. Traverse set G2 and use a recursive algorithm to parse all block reference primitives in it, decompose them into the most basic line primitives, and generate a new set G3 containing only lines and polylines. S132. Based on the linear primitives in set G3, spatial relationships between geometric entities are identified through computational geometry algorithms, including inclusion, adjacency and overlap relationships. Redundant GIS embedded primitives that overlap with each other are removed to obtain a refined set G4. S133. For straight lines and polyline elements belonging to the same GIS embedded element in G4, reconstruct them into polylines in a unified manner, add the "GIS embedded element" layer attribute to all reconstructed elements, and output the standardized polyline set G5. S134. Identify the planar dimensions and area of ​​each GIS embedded part in G5 through geometric calculations; S135. Classify the GIS embedded parts according to the calculated plane area, and assign corresponding classification attributes to each GIS embedded part; S136. Establish a standardized GIS embedded part map data set L(V, N), where V represents the polyline set G5 and N is the corresponding classification attribute set; S14. Calculate the displacement vector Vmove=Ptarget-Pbase based on the user-specified reference point Pbase and target point Ptarget. Calculate the bounding box Bi=[Pmin,Pmax] for each geometric primitive V in set L, and determine its bounding box center point as the translation anchor point. Here, Pmin is a two-dimensional point whose X and Y components are the minimum X and Y coordinates of all vertices of geometric primitive V, respectively. Pmax is a two-dimensional point whose X and Y components are the maximum X and Y coordinates of all vertices of geometric primitive V, respectively. S15. Using the displacement vector Vmove as the translation amount, perform translation transformation on each geometric primitive V in the set L, accurately place it at the target position in the global coordinate system, and finally output the GIS embedded part geometric primitive data set with accurate coordinates.

3. The substation GIS basic intelligent mapping method as described in claim 1, characterized in that, The condition for judging that the distribution is irregular is: if the size attribute value of the GIS embedded part at the edge in the X-axis or Y-axis direction is less than half of the size attribute value of its adjacent GIS embedded part in the corresponding direction, then the distribution of the group of GIS embedded parts is judged to be irregular. The condition for determining a distribution rule is: if the size attribute value of a GIS embedded part located at the edge in the X-axis or Y-axis direction is greater than or equal to half the size attribute value of its adjacent GIS embedded part in the corresponding direction, then the distribution rule of this group of GIS embedded parts is determined.

4. The substation GIS basic intelligent mapping method as described in claim 1, characterized in that, S21 includes: S211. Randomly select multiple GIS embedded parts from the GIS embedded part geometric data set to form a GIS basic partitioning scheme; S212. Assign initial location and attribute information to each GIS embedded part, wherein the attribute information includes at least the GIS embedded part type and size; S213. Optimize the location and attributes of GIS embedded parts through an iterative update algorithm, wherein the iterative update algorithm is expressed as: ΔP(t+1)=ΔP(t)+β×(Pbest-P(t)), where ΔP(t) is the adjustment vector of GIS embedded parts in the current iteration step, Pbest is the optimal location scheme in the current iteration step, β is the learning rate, P(t) is the location scheme of GIS embedded parts in the current iteration step, and ΔP(t+1) is the adjustment vector of GIS embedded parts in the next iteration step.

5. The substation GIS basic intelligent mapping method as described in claim 1, characterized in that, Constructing an irregular geometrically bounded contour using the convex hull scan algorithm includes the following steps: a. Initial boundary identification: a1. For irregularly distributed GIS embedded parts groups, find the point with the smallest y-coordinate and use that point as the starting point. a2. Sort the y-coordinates of the irregularly distributed GIS embedded parts group according to the polar angle of each point relative to the starting point; a3. Determine the convex hull boundary points of the irregularly distributed GIS embedded parts group through stack operations; a4. Create a list of boundary points and initialize it as a set of convex hull points; b. Geometric analysis of convex hull vertices: b1. Traverse each vertex on the convex hull and obtain its two adjacent vertices before and after it; b2. Calculate the direction vectors from the current vertex to the vertices before and after it and normalize them; b3. Calculate the angle between adjacent sides using the vector dot product; c. Vertex type determination and filtering: c1. Determine whether the included angle between adjacent sides is 0°, 180° or 90°; c2. Compare the Y and X coordinates of the current vertex with those of its neighboring vertices; c3. Based on geometric conditions, determine whether a new boundary point needs to be inserted near the vertex. If the included angle between adjacent sides is neither 0° nor 180° nor 90°, and the Y coordinate of the current vertex is less than the maximum Y coordinate of the adjacent vertex, then the insertion condition is met, and step d is executed to filter out the convex hull vertex. d. Dynamic insertion of boundary points: d1. For a convex hull vertex that satisfies the insertion condition, find the nearest vertex of the convex hull vertex in the irregularly distributed geometric primitives of the GIS embedded parts group; d2. Determine the insertion position based on the comparison of the X coordinates of the convex hull vertex and the nearest vertex of the convex hull: if the X coordinate of the current point is greater than the X coordinate of the convex hull vertex, insert it before the current position; otherwise, insert it at the next position. d3. Dynamically update the list of boundary points to form a more refined boundary profile; e. Boundary contour generation: e1. Identify internal boundary points through iterative processing; e2. Remove processed points inside the convex hull that do not meet the boundary point insertion conditions; e3. The boundary insertion points obtained from the iterative processing and the convex hull point set are used together to construct a new set of irregular geometric enclosing contour boundary points for the GIS embedded parts group, thereby constructing the irregular geometric enclosing contour of the irregularly distributed GIS embedded parts group.

6. The substation GIS basic intelligent mapping method as described in claim 1, characterized in that, Step S3 includes: S31. Based on the grouping results of GIS embedded parts, construct a GIS embedded part spatial index matrix with dual structures, based on R-tree structure and quadtree structure; S32. Based on the GIS embedded part spatial index matrix, the spatial relationship index of each GIS embedded part is calculated using the weighted distance method. S33. Through spatial cluster analysis, spatial cluster evaluation is performed on the obtained spatial relationship indicators, and the spatial location attributes of GIS embedded parts are obtained based on the evaluation results. S34. Based on the obtained spatial location attributes of the GIS embedded parts, perform standardized data modeling and construct structured data of the spatial location attributes of the GIS embedded parts using the GIS embedded parts and GIS foundation as objects.

7. The substation GIS basic intelligent mapping method as described in claim 3, characterized in that, The S4 step includes: S41. Generate GIS basic dimensions based on the constructed GIS embedded part boundary outline; S42. Based on the preset GIS foundation reinforcement parameters, GIS foundation pad layer parameters, GIS foundation material parameters, GIS embedded part height parameters, and the spatial location attribute parameters of the identified GIS embedded parts, construct a GIS foundation parameterized drawing data topology model. S43. Based on the GIS basic parametric drawing data topology model, establish a parametric CAD drawing model, and generate an initial reinforcement layout scheme through the intelligent analysis algorithm of reinforcement parameters. S44. Based on the generated initial reinforcement layout scheme, a collision detection algorithm is used to perform collision detection. If a collision is found, an optimization algorithm is used to adjust the reinforcement layout until there is no collision. S45. Generate a rebar layout scheme with no collisions in the detection results to obtain the final rebar layout scheme; S46. Based on the final reinforcement layout scheme, perform parametric layer management and generate construction drawings.

8. An electronic device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When executed by the processor, the computer program implements the substation GIS basic intelligent mapping method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements the substation GIS basic intelligent mapping method as described in any one of claims 1 to 7.

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