Power transmission line cableway stockyard point mode recognition method and system based on gradient compensation and road width dynamic correction, storage medium and computing device

Abstract

The present application belongs to the technical field of power transmission line engineering cableway design and construction, and particularly relates to a power transmission line cableway stockyard point mode recognition method and system based on slope compensation and road width dynamic correction, a storage medium and a computing device. The method comprises the following steps: S1, road feature extraction: including extracting road center line feature data, road final width feature data, and road unit direction feature data; S2, road feature enhancement processing; S3, calculating road curvature; S4, mode recognition classification: using a feature space dynamic weighting segmentation algorithm, through an iterative optimization process in a dynamic weighting road feature space, dynamically adjusting road feature weights, scaling the original road feature space, so that the left and right boundary points in the transformed space can be effectively separated by a hyperplane, realizing accurate two-classification of the stockyard points; S5, space topology consistency enhancement. The method can solve the problem of road width measurement distortion caused by steep slope terrain.

Description

Technical Field

[0001] This invention belongs to the field of cableway design and construction technology for power transmission line projects, specifically involving a method, system, storage medium, and computing device for identifying material yard patterns for power transmission line cableways based on slope compensation and dynamic road width correction. Background Technology

[0002] In the construction of power transmission lines in mountainous areas, cableways are the core channel for material transportation. Material yards (i.e., temporary material storage points) are distributed along both sides of the construction access road, and the left and right material yards must each correspond to an independent cableway system. Incorrect zoning will lead to secondary transfer of materials, extend the construction period by more than 30%, and severely reduce transportation efficiency.

[0003] Existing partitioning methods include the following:

[0004] The first method is manual surveying, which relies on experience and judgment. The error rate on curved road sections is >40%, and it is time-consuming (2 person-days for 1km section). In particular, for material yards on S-shaped curves, it is easy to cause confusion between the left and right side material yards.

[0005] The second method is the traditional K-means clustering method, which ignores the road direction and only uses Euclidean distance for clustering. It cannot perceive the orientation attribute and is prone to problems such as the material yard on the same side of the curved section being separated.

[0006] The third method is the road centerline constraint method, which requires a high-precision centerline model (UAV aerial survey + fitting), is costly, and is not suitable for temporary access roads. In particular, it addresses the issue of zone offset caused by centerline jitter on gravel access roads.

[0007] Chinese patent document CN120850439B discloses a method and system for spatial data clustering of engineering cableway material yards based on road feature enhancement. The method includes the following steps: S1, Road Feature Extraction: This involves extracting road centerline feature data, average road width feature data, and road unit direction feature data; S2, Road Feature Enhancement Processing: This includes calculating the minimum boundary distance feature data based on the road centerline feature data, determining the orientation of the material yard point relative to the road segment using the right-hand rule of the cross product, obtaining lateral offset feature data based on the cross product after signed distance normalization, and calculating longitudinal projection position feature data based on the road unit direction feature data; S3, Spatial Data Clustering: Based on the minimum boundary distance feature data, lateral offset feature data based on the cross product, and longitudinal projection position feature data, the K-means clustering algorithm is used to automatically divide the material yard points into a left-side material yard point set and a right-side material yard point set; S4, Spatial Consistency Optimization: Based on the principle of geographic spatial continuity, the actual material yard points are continuously distributed along the road extension direction, and outlier labeling is corrected through a neighbor point voting mechanism.

[0008] The above technical solution improves the accuracy and generalization of spatial data clustering for cableway material yards in power transmission line projects. For classifying curved road sections, the error rate using manual surveying methods is greater than 40%, while the error rate using this method is less than 5%, significantly improving accuracy. Processing time for 10,000 points is less than 10 minutes, meeting the 24-hour construction period requirement and improving efficiency. It supports roads with varying widths (3-15m) and sparse material yards (density <0.5 points / 10m), demonstrating strong engineering adaptability. It also avoids the risk of cableway collisions and reduces secondary transportation costs. However, in practical applications, road width measurement distortion can easily occur when encountering steep terrain. Summary of the Invention

[0009] The technical problem to be solved by the present invention is to provide a method, system, storage medium and computing device for pattern recognition of cableway material yards for power transmission lines based on slope compensation and dynamic correction of road width, which can solve the problem of road width measurement distortion caused by steep terrain.

[0010] The technical solution adopted by this invention to solve its technical problem is: a method for pattern recognition of cableway material yards for power transmission lines based on slope compensation and dynamic road width correction, including the following steps:

[0011] S1. Road Feature Extraction: This includes extracting feature data of the road centerline, feature data of the final width of the road, and feature data of the road in a unit direction.

[0012] Extracting the final width feature data of the road includes the following steps:

[0013] S101, through parameter verification, check whether the number of boundary points on both sides of the road is equal, and verify whether each point is represented by three-dimensional coordinates;

[0014] S102, let the boundary point sets on both sides of the road be the left boundary point set and the right boundary point set, respectively;

[0015] S103, calculate the distance between the left and right boundary point pairs of the road using the three-dimensional coordinates of the road boundary point pairs to obtain the road segment width;

[0016] S104, based on Box plot statistical method, robust processing to identify and remove road left and right boundary point pairs corresponding to abnormal road segment widths;

[0017] S105, calculate the original average width of the road by dividing the road into segments;

[0018] S106, the slope compensation algorithm is used to compensate the original average width of the road that is greater than the slope threshold, and then the final width feature data of the road is output.

[0019] S2. Road feature enhancement processing: This includes calculating the minimum distance feature data of the boundary based on the road centerline feature data, determining the orientation of the material yard point relative to the road segment using the right-hand rule of the cross product of vectors based on the final width feature data of the road, obtaining the lateral offset feature data based on the cross product after normalization of the signed distance, and calculating the longitudinal projection position feature data based on the road unit direction feature data.

[0020] S3. Calculate road curvature: The road curvature is calculated by analyzing the rate of change of the angle between adjacent line segments on the road centerline using the discrete point geometric differential method.

[0021] S4. Pattern Recognition and Classification: Based on the minimum boundary distance feature data, lateral offset feature data, longitudinal projection position feature data and road curvature, a dynamic weighted segmentation algorithm in the feature space is adopted. Through the iterative optimization process in the dynamic weighted road feature space, the road feature weights are dynamically adjusted to scale the original road feature space, so that the left and right boundary points of the road in the transformed space can be effectively separated by a hyperplane, thus achieving accurate binary classification of the material yard points.

[0022] S5, Spatial Topology Consistency Enhancement: Spatial topology consistency enhancement process is used to repair spatial isolated points and noise in accurate binary classification results.

[0023] Furthermore, step S3 includes the following steps:

[0024] S301, collect the three-dimensional point pairs of the left and right side boundaries of the road;

[0025] S302, calculate the road centerline point set by averaging the three-dimensional point pairs of the left and right side boundaries of the road;

[0026] S303, enter the loop of traversing the centerline point set of the road;

[0027] S304, in the S303 loop, calculate the adjacent vector based on the adjacent center point;

[0028] S305, calculate the angle between vectors;

[0029] S306, calculate the local curvature of the road;

[0030] S307, exit the loop of traversing the centerline point set of the road, obtain the average curvature value of the road by accumulating the local curvature of the road, and output the road curvature.

[0031] Furthermore, step S4 specifically includes the following steps:

[0032] S401, input the road feature matrix and the average road curvature data, and dynamically adjust the importance weights of the lateral offset feature and the longitudinal projection feature according to the degree of road curvature;

[0033] S402, based on straight road and curved road scenarios, dynamically constructs a classification decision hyperplane based on boundary minimum distance feature data, lateral offset feature data, longitudinal projection position feature data and road curvature;

[0034] S403 employs a deterministic initialization method, assigning a signed lateral offset feature to each material yard point to be identified. Perform pre-classification, when , is a candidate point on the left; when , which are candidate points on the right; calculate the mean of the feature matrices of the candidate point set on the left and the candidate point set on the right respectively, and determine the left and right classification centers;

[0035] S404 iteratively optimizes the segmentation boundary, dynamically adjusts feature importance based on road curvature adaptive weights, and achieves feature space scaling transformation through weight vectors. Curves stretch the lateral offset axis, and straight roads stretch the longitudinal projection axis. In the dynamically weighted feature space, the distance from the point to the category center is calculated iteratively, and the classification label is dynamically updated according to the classification decision rule. After each iteration, the category feature center is recalculated, and the feature center moves with the classification result. The decision boundary gradually approaches the optimal segmentation hyperplane. The criterion for updating the cluster center is whether the classification label converges.

[0036] Furthermore, in step S404, the determination of whether the classification labels converge includes the following steps:

[0037] S4041, set the convergence threshold to 0.000001;

[0038] S4042, the maximum number of iterations is set to 100 to ensure that no infinite loop occurs;

[0039] S4043, calculate the distance to the classification center after weighting the feature matrix data of the material yard point;

[0040] S4044, dynamically assign new classification labels to material yard points based on the calculated distance;

[0041] S4045, Calculate the total number of different quantities of new and old labels and calculate the percentage change in category labels;

[0042] S4046, Determine whether the change ratio is less than the convergence threshold;

[0043] S4047, Update the classification center; when the change rate of the classification label of the material yard point is less than 0.000001 and the number of iterations is greater than 100, the classification is considered to have converged.

[0044] Furthermore, step S5 includes the following steps:

[0045] S501, through a topological potential energy model driven by road curvature, creates a functional mapping relationship between road curvature and potential energy bandwidth, realizes adaptive optimization of curves, and dynamically adjusts the spatial perception scale.

[0046] S502, Determine the spatial continuity of material yard points, and determine the correlation between the current material yard point and adjacent material yard points; adjacent or nearby objects in physical space have similar or related attributes.

[0047] S503, Local Topology Preservation Process, in spatial analysis, only considers spatial relationships on the horizontal plane and ignores the impact of elevation changes on spatial neighborhood relationships;

[0048] S504 creates a topology voting engine based on the principle that the spatial relationships of local neighborhoods remain unchanged, namely, the adjacency relationship remains unchanged, the connectivity remains unchanged, and the directional relationship remains unchanged. It corrects the topology classification labels of material yard points according to decision rules, including: correcting topology breakpoints, smoothing classification boundaries, and maintaining local topology structure.

[0049] S505 ensures the rationality of material yard classification boundaries by eliminating jagged boundaries, maintaining reasonable transitions, and protecting special areas through boundary regularization mechanisms.

[0050] Furthermore, step S501 includes the following steps:

[0051] S5011 establishes the dynamic relationship between road curvature and spatial scale through a potential energy bandwidth model;

[0052] S5012 determines the topological potential weights through the dynamic relationship between road curvature and spatial scale;

[0053] S5013 achieves adaptive neighborhood radius based on topological potential weights.

[0054] Furthermore, step S505 includes the following steps:

[0055] S5051 establishes a dual protection mechanism for boundary regularization processing;

[0056] S5052 applies the discrete Laplacian operator for boundary smoothing, including the following steps: inputting point cloud data, constructing KD-Tree graph data, calculating the weight matrix, constructing the graph Laplacian matrix, and solving for the linear system output smoothing label.

[0057] The transmission line cableway material yard pattern recognition system based on slope compensation and dynamic road width correction adopts a transmission line cableway material yard pattern recognition method based on slope compensation and dynamic road width correction, including a road feature extraction module, a road feature enhancement processing module, a road curvature calculation module, a pattern recognition classification module, and a spatial topology consistency enhancement module.

[0058] The road feature extraction module is configured to extract road centerline feature data, road final width feature data, and road unit direction feature data.

[0059] The road feature enhancement processing module is configured to include: calculating the minimum boundary distance feature data based on the road centerline feature data; determining the orientation of the material yard point relative to the road segment using the right-hand rule of the cross product of vectors based on the final width feature data of the road; obtaining the lateral offset feature data based on the cross product after normalization of the signed distance; and calculating the longitudinal projection position feature data based on the road unit direction feature data.

[0060] The road curvature calculation module is configured to use discrete point geometric differential method to analyze the rate of change of the included angle between adjacent line segments on the road centerline to calculate the road curvature;

[0061] The pattern recognition and classification module is configured to use a dynamic weighted segmentation algorithm based on boundary minimum distance feature data, lateral offset feature data, longitudinal projection position feature data, and road curvature. Through an iterative optimization process in the dynamic weighted road feature space, the weights of road features are dynamically adjusted to scale the original road feature space, so that the left and right boundary points of the road in the transformed space can be effectively separated by a hyperplane, thus achieving accurate binary classification of material yard points.

[0062] The spatial topology consistency enhancement module is configured to repair spatial isolated points and noise in accurate binary classification results through spatial topology consistency enhancement processing.

[0063] A storage medium storing a computer program, which, when executed by a processor, implements the steps of a method for pattern recognition of transmission line cableway material yards based on slope compensation and dynamic road width correction.

[0064] A computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a method for pattern recognition of transmission line cableway material yard points based on slope compensation and dynamic road width correction.

[0065] Compared with existing technologies, the beneficial effects of this invention are: This invention provides a method and system for pattern recognition of cableway material yard points for power transmission lines based on slope compensation and dynamic road width correction, which can solve the problem of road width measurement distortion caused by steep terrain. It also has the following advantages:

[0066] First, improved high-precision classification and robustness. By enhancing road geometry features and topological potential energy weighting, the accuracy of spatial location identification of material yard points was significantly improved. Experiments show that in complex mountainous terrain, the misclassification rate in curved areas was reduced by 86%, and the topological consistency was improved to 96.1%, effectively solving the classification inaccuracies caused by noise sensitivity, elevation interference, and topological breaks in traditional methods.

[0067] Second, it enhances dynamic adaptability. By adjusting dynamic feature weights driven by road curvature, the system can adaptively optimize the classification strategy based on the degree of road curvature. In straight road scenarios, longitudinal projection features are strengthened, while in curved road scenarios, lateral offset features are enhanced, ensuring classification accuracy under different road shapes. The misclassification rate is reduced by more than 35% compared to traditional fixed-weight methods.

[0068] Third, computational efficiency is optimized. By adopting an unsupervised dynamic weighted classifier and an iterative optimization strategy, the algorithm's time complexity is optimized to near linear (O(N)), and the processing time for tens of thousands of point clouds is less than 0.5 seconds, meeting the real-time requirements of construction. This represents an efficiency improvement of more than 10 times compared to the traditional O(N²) method.

[0069] Fourth, enhanced engineering applicability. Through slope compensation, differential treatment, and enhanced spatial topology consistency, the system can adapt to unstructured terrains such as steep slopes (slope > 25°) and sharp bends (curvature > 0.3), ensuring construction safety and rational material scheduling, with an accuracy rate of over 90% in steep slope areas.

[0070] Fifth, the level of automation and intelligence is improved. It eliminates the need to rely on millimeter-level road centerline models, achieving fully automated identification through feature fusion and intelligent classifiers, reducing manual intervention and providing reliable data support for cableway route planning, material scheduling, and safety early warning. Attached Figure Description

[0071] Figure 1 This is a flowchart of the present invention;

[0072] Figure 2 This is the overall flowchart of the present invention.

[0073] Figure 3 Here is a flowchart of the overall process for road feature extraction;

[0074] Figure 4 Flowchart for calculating road segment width;

[0075] Figure 5 Here is a flowchart of the robustness process;

[0076] Figure 6 Here is a flowchart of the slope compensation algorithm;

[0077] Figure 7 Flowchart for road feature enhancement;

[0078] Figure 8 A flowchart for calculating the minimum distance feature from a material yard point to its boundary;

[0079] Figure 9 The flowchart shows the calculation process for the lateral offset characteristics of the material yard points.

[0080] Figure 10 The flowchart shows the calculation process for the longitudinal projection characteristics of the material yard points.

[0081] Figure 11 This is a flowchart for calculating road curvature.

[0082] Figure 12 Flowchart of the multi-feature pattern classification algorithm for material yard points;

[0083] Figure 13 The flowchart shows the core objective of the dynamic weighting strategy;

[0084] Figure 14 Diagram of spatial topology consistency enhancement technology architecture;

[0085] Figure 15 Here is a flowchart of the spatial topology consistency enhancement algorithm;

[0086] Figure 16 Flowchart of the functional objectives of the topological potential energy model driven by road curvature;

[0087] Figure 17 This is a diagram illustrating the principle of adjacency invariance.

[0088] Figure 18 Diagram of the connectivity invariance principle;

[0089] Figure 19 This is a diagram illustrating the principle of invariance of directional relationships.

[0090] Figure 20 Schematic diagram of the boundary regularization dual protection mechanism;

[0091] Figure 21 For boundary smoothing flowchart;

[0092] Figure 22 Output a functional flowchart for classifying material yard locations. Detailed Implementation

[0093] The following is in conjunction with the appendix Figure 1 , 2 The invention is further illustrated by references 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, and 22, as well as by embodiments.

[0094] like Figure 1 ,2 As shown, the method for pattern recognition of cableway material yard points for power transmission lines based on slope compensation and dynamic road width correction includes the following steps:

[0095] like Figure 3 As shown, S1, Road Feature Extraction: This includes extracting feature data of the road centerline, feature data of the final width of the road, and feature data of the road in a unit direction;

[0096] Extracting the final width feature data of the road includes the following steps:

[0097] S101, through parameter verification, check whether the number of boundary points on both sides of the road is equal, and verify whether each point is represented by three-dimensional coordinates;

[0098] Get the number of rows of a point-to-vector pair: size ( ) size ( );

[0099] To obtain the number of columns of a point-to-vector pair: size ( ) size ( );

[0100] Suppose the road has N pairs of boundary points:

[0101] Left boundary point: ;

[0102] Right boundary point: ;

[0103] in,

[0104] : This represents the point with index 1 on the left boundary of the road. This represents the point with index 2 on the left boundary of the road. The index of the left boundary point of the road is point;

[0105] : This represents the point with index 1 on the right boundary of the road. This represents the point with index 2 on the right boundary of the road. The index of the right boundary point of the road is point;

[0106] Through points 、 The number of rows and columns of the vector is used to verify whether the number of two boundary points is equal and whether each point is represented by three-dimensional coordinates;

[0107] S102, let the boundary point sets on both sides of the road be the left boundary point set and the right boundary point set, respectively;

[0108] like Figure 4 As shown in S103, the distance between the points on the left and right sides of the road is calculated using the three-dimensional coordinates of the points on the left and right sides of the road to obtain the road segment width.

[0109] Suppose the road has N pairs of boundary points:

[0110] Left boundary point: ;

[0111] Right boundary point: ;

[0112] in,

[0113] : This represents the point with index 1 on the left boundary of the road. This represents the point with index 2, which is the left boundary point of the road. The index of the left boundary point of the road is point;

[0114] : This represents the point with index 1 on the right boundary of the road. This represents the point with index 2 on the right boundary of the road. The index of the right boundary point of the road is point;

[0115] Segment width calculation:

[0116] ;

[0117] in,

[0118] : The distance between the i-th pair of road boundary points;

[0119] Boundary 1 The coordinates of the point;

[0120] Boundary 2nd The coordinates of the point;

[0121] like Figure 5 As shown in S104, based on the IQR box plot statistical method, robust processing is used to identify and remove road left and right boundary point pairs corresponding to abnormal road segment widths;

[0122] (1) Quartile calculation:

[0123] First quartile ( ): The value located at the 25th percentile after sorting the dataset;

[0124] Third quartile ( ): The value at the 75th percentile after sorting the dataset;

[0125] ;

[0126] ;

[0127] in,

[0128] : The distance between the i-th pair of road boundary points;

[0129] (2) Interquartile Range (IQR):

[0130] and The difference reflects the dispersion of the data within the middle 50%.

[0131] ;

[0132] (3) Outlier boundaries:

[0133] Lower limit: ;

[0134] Maximum: ;

[0135] in,

[0136] ε: Empirical coefficient (usually taken as 1.5);

[0137] First quartile;

[0138] Third quartile;

[0139] (4) Valid data filtering:

[0140] ;

[0141] in,

[0142] x : Current data to be determined;

[0143] : Lower limit of normal value;

[0144] : Upper limit of normal value;

[0145] Any value below the lower limit or above the upper limit is considered an outlier and is filtered out.

[0146] S105, calculate the original average width of the road by dividing the road into segments;

[0147] ;

[0148] in,

[0149] : The number of road segment data;

[0150] : Effective data of segment width after robust processing;

[0151] : This represents the original average width of the road;

[0152] like Figure 6 As shown in S106, the original average width of the road that is greater than the slope threshold is compensated by the slope compensation algorithm, and then the final width feature data of the road is output.

[0153] (1) Traverse the original road average data matrix to calculate the local slope angle of the elevation difference of the current coordinate point;

[0154] ;

[0155] in,

[0156] The elevation difference between the starting and ending points of the segment;

[0157] The difference in planar coordinates between the starting and ending points of the segment;

[0158] (2) Determine if the slope angle is greater than the slope threshold (default 25°);

[0159] (3) When the current slope angle is greater than the slope threshold, road width slope compensation is performed;

[0160] ;

[0161] in,

[0162] The road width after slope compensation, which is the final road width characteristic data;

[0163] Original average width of the road;

[0164] : Slope compensation coefficient;

[0165] Local slope angle;

[0166] like Figure 7 As shown, S2, road feature enhancement processing: includes calculating the minimum distance feature data of the boundary based on the road centerline feature data, determining the orientation of the material yard point relative to the road segment based on the right-hand rule of the cross product of the vector based on the final width feature data of the road, obtaining the lateral offset feature data based on the cross product after normalization of the symbolic distance, and calculating the longitudinal projection position feature data based on the road unit direction feature data.

[0167] S3. Calculate road curvature: The road curvature is calculated by analyzing the rate of change of the angle between adjacent line segments on the road centerline using the discrete point geometric differential method.

[0168] S4. Pattern Recognition and Classification: Based on the minimum boundary distance feature data, lateral offset feature data, longitudinal projection position feature data and road curvature, a dynamic weighted segmentation algorithm in the feature space is adopted. Through the iterative optimization process in the dynamic weighted road feature space, the road feature weights are dynamically adjusted to scale the original road feature space, so that the left and right boundary points of the road in the transformed space can be effectively separated by a hyperplane, thus achieving accurate binary classification of the material yard points.

[0169] S5, Spatial Topology Consistency Enhancement: Spatial topology consistency enhancement process is used to repair spatial isolated points and noise in accurate binary classification results.

[0170] Specifically, the extraction of road centerline feature data in step S1 includes the following steps: based on the linear interpolation method of boundary point pairs on both sides of the road, a continuous centerline is constructed by calculating the center point coordinates of the corresponding boundary points.

[0171] Specifically, calculating the center point coordinates of the corresponding boundary points in step S1 includes the following steps:

[0172] S111, let the boundary point sets on both sides of the road be the left boundary point set and the right boundary point set, respectively;

[0173] S112, Boundary sorting and point pair matching;

[0174] S113, use the center point calculation formula to find the coordinates of the center point.

[0175] Specifically, the extraction of the road unit direction feature data in step S1 includes the following steps:

[0176] S121, collect the coordinates of the first and last points of the road;

[0177] Road starting point coordinates: ;

[0178] Road end point coordinates: ;

[0179] The coordinates of the first point of the road are the first data point after the data matrix is ​​formed by scanning the road boundary cells, and the coordinates of the last point of the road are the last data point after the data matrix is ​​formed by scanning the road boundary cells.

[0180] S122, Calculate the vector difference between the first and last points of the road based on the coordinates of the first and last points of the road. ;

[0181] ;

[0182] S123, Calculate the road length L based on the coordinates of the first and last points of the road;

[0183] ;

[0184] in,

[0185] : Represents road vector Directional component, using the road endpoint Direction vector With the starting point of the road Direction vector The difference, i.e. ;

[0186] : Represents road vector Directional component, using the road endpoint Direction vector With the starting point of the road Direction vector The difference, i.e. ;

[0187] : Represents a road vector Directional component, using the road endpoint Direction vector With the starting point of the road Direction vector The difference, i.e. .

[0188] S124, Calculate the unit direction vector of the road based on the vector difference between the beginning and end points of the road and the road length. ;

[0189] ;

[0190] like Figure 8 As shown, the vector normalization result has a vector magnitude of 1, retaining only directional information. Specifically, step S2, which calculates the minimum boundary distance feature data based on the road centerline feature data, includes the following steps: based on the input material yard point coordinates and road boundary coordinates, the Euclidean distance between the current material yard point coordinates and the road boundary coordinates is taken, and then the minimum Euclidean distance from the material yard point to the left and right boundary points of the road is calculated using a KD-tree search. After normalization, the minimum boundary distance feature data is obtained.

[0191] Based on the nearest neighbor distance mapping of the road boundary on both sides, the three-dimensional spatial relationship between the material yard point and the boundary is quantified into three independent features, including: (1) boundary orientation, separating the calculation of the distance between the left and right boundaries of the road; (2) spatial symmetry, the distance features on both sides of the road are complementary; (3) scale invariance, the width is normalized to adapt to different road specifications.

[0192] set up:

[0193] Coordinates of the material yard point;

[0194] Road boundary coordinates;

[0195] Road boundary coordinates;

[0196] w Average road width;

[0197] Formula for calculating the minimum distance feature at the boundary:

[0198] ;

[0199] in,

[0200] For the Euclidean norm:

[0201] ;

[0202] The minimum distance from the material yard to the left boundary of the road;

[0203] The minimum distance from the material yard to the right boundary of the road;

[0204] The minimum distance from the material yard to the left boundary of the road is the normalized value of the road width to adapt to different road specifications.

[0205] The minimum distance from the material yard to the right boundary of the road is the normalized value of the road width to adapt to different road specifications.

[0206] like Figure 9 As shown, specifically, in step S2, based on the final road width feature data, the right-hand rule of the vector cross product is used to determine the orientation of the material yard point relative to the road segment. After normalization by signed distance, lateral offset feature data based on the cross product is obtained. This solves the problem of accurately determining the orientation (left / right) of the material yard point in curved road segments, overcomes the limitations of traditional Euclidean distance clustering in curved road segments, and realizes three-dimensional spatial orientation perception. The steps include:

[0207] Let the coordinates of the material yard be The calculation formula is as follows:

[0208] ;

[0209] in,

[0210] p : The three-dimensional coordinates of the material yard points to be classified;

[0211] : The three-dimensional coordinates of the lower right boundary of the road;

[0212] : The three-dimensional coordinates of the upper left boundary of the road;

[0213] : Road width direction vector (vector pointing from the bottom right to the top left);

[0214] : The vector pointing from the material yard point to the upper left boundary of the road;

[0215] : and The cross product vector;

[0216] : and The z-component of the cross product vector, the sign of which determines the orientation: negative value: the material yard is on the right side of the road, positive value: the material yard is on the left side of the road;

[0217] sgn : Symbolic piecewise function;

[0218] : and The magnitude of the cross product vector;

[0219] :vector The modulus length;

[0220] : and The perpendicular distance between the magnitude of the cross product vector and the magnitude of the road width direction vector;

[0221] Output the normalized horizontal offset of the feature value, with a value range of [-1, 1], where negative values ​​= left side and positive values ​​= right side;

[0222] Floating-point number calculation tolerance;

[0223] w Average road width;

[0224] like Figure 10 As shown, specifically, step S2, which involves calculating the longitudinal projection position feature data based on the road unit direction feature data, includes the following steps:

[0225] S201, Determine the road direction reference, with the road starting point as the origin and the road unit direction vector as the road direction reference axis;

[0226] S202, the projection length is obtained by projecting the dot product of the direction vector of any material yard point from the starting point of the road with the unit vector of the road direction.

[0227] The material yard point vector is the matrix of material yard point vectors to be input. It is the original data set to be processed and analyzed. The material yard point vector matrix is ​​formed by scanning the data matrix cell by cell through the road boundary in the previous process.

[0228] S203, calculate the road length based on the coordinates of the first and last points of the road;

[0229] S204, normalization process, the projected length is divided by the total road length to eliminate scale effects;

[0230] Let the coordinates of the material yard be The calculation formula is as follows:

[0231] ;

[0232] in,

[0233] : Coordinates of the starting point of the road;

[0234] : Unit vector of road direction;

[0235] Total road length;

[0236] : The direction vector of the coordinates from the starting point to any material storage area;

[0237] The projection length is obtained by projecting the dot product of the direction vector of any material site from the starting point and the unit vector of the road direction.

[0238] Output feature value: projected length divided by total road length to eliminate scale effect;

[0239] Formula explanation:

[0240] : 0 indicates the location is at the beginning of the road, and 1 indicates the location is at the end of the road;

[0241] when or When, it indicates that the material yard is outside the road extension line;

[0242] like Figure 11 As shown, specifically, step S3 includes the following steps:

[0243] S301, collect the three-dimensional point pairs of the left and right side boundaries of the road;

[0244] S302, calculate the road centerline point set by averaging the three-dimensional point pairs of the left and right side boundaries of the road;

[0245] ;

[0246] in,

[0247] : The three-dimensional coordinates of boundary point 1;

[0248] : The three-dimensional coordinates of boundary point 2;

[0249] i : Indicates the number of iterations;

[0250] S303, enter the loop of traversing the centerline point set of the road;

[0251] The sequence of center points generated through iteration forms a continuous centerline, maintaining the topological structure of the original point set in three-dimensional space;

[0252] S304, in the S303 loop, calculate the adjacent vector based on the adjacent center point;

[0253] ;

[0254] ;

[0255] in,

[0256] : Indicates the direction from the previous center point to the current point;

[0257] : Indicates the direction from the current point to the next center point;

[0258] S305, calculate the angle between vectors;

[0259] ;

[0260] ;

[0261] ;

[0262] ;

[0263] in,

[0264] : represents the area of ​​the parallelogram formed by the two vectors;

[0265] : Represents the scalar product of two vectors;

[0266] Use the arctangent function to avoid cases where the denominator is zero;

[0267] S306, calculate the local curvature of the road;

[0268] ;

[0269] ;

[0270] ;

[0271] in,

[0272] : Angle between adjacent vectors (in radians);

[0273] Take the angle between the vectors The absolute value of the curvature ensures that the curvature is always positive;

[0274] : Length of the forward vector;

[0275] : Length of the backward vector;

[0276] : The local curvature value at the current point;

[0277] S307, Exit the loop of traversing the road centerline point set, obtain the average curvature value of the road by accumulating the local curvature of the road and output the road curvature;

[0278] ;

[0279] ;

[0280] in,

[0281] : Valid number of points (total number of center points minus 2);

[0282] Total number of road center points;

[0283] The sum of all local curvatures;

[0284] : Average curvature value of the road;

[0285] : Number of valid calculation points (excluding the first and last points);

[0286] Avoid division by zero errors;

[0287] like Figure 13 , 16 As shown, specifically, step S4 includes the following steps:

[0288] S401, input the road feature matrix and the average road curvature data, and dynamically adjust the importance weights of the lateral offset feature and the longitudinal projection feature according to the degree of road curvature; so as to enhance the sensitivity of the intelligent classifier to lateral offset in the curve scene, while reducing the interference of road curvature on the longitudinal feature projection feature, and realize the nonlinear deformation of the feature space in the curve scene.

[0289] ;

[0290] ;

[0291] in,

[0292] Average curvature of the road;

[0293] : Lateral offset feature weights;

[0294] δ : Vertical projection feature weights;

[0295] The meaning of the relevant fixed values,

[0296] 0: Straight-line curvature threshold, an empirical parameter;

[0297] 0.3: Curvature threshold for curves;

[0298] 0.4: Horizontal weight change coefficient, weight adjustment range;

[0299] 0.2: Vertical weight change coefficient, weight adjustment range;

[0300] Solve the following key problems:

[0301] (1) Lateral offset is dominant in curve scenarios;

[0302] In curve scenarios, the lateral offset of the material yard point (its vertical distance from the road centerline) is a key feature distinguishing the left and right sides. Traditional fixed weights cannot enhance the importance of this feature in curves; therefore, a dynamic weighting scheme is adopted.

[0303] ;

[0304] Lateral feature weights in curves Upgraded to version 1.0, enhancing the smart classifier's sensitivity to lateral offset.

[0305] (2) The dominance of longitudinal feature projection features in straight-line scenes;

[0306] In straight road scenarios, the longitudinal position of the material yard point (projection along the road direction) is the main distinguishing feature. In curved road scenarios, this feature is easily affected by curvature. Therefore, a dynamic weighting scheme is adopted.

[0307] ;

[0308] Vertical feature weights in straight sections Maintain 1.0, then reduce to 0.8 in curves to minimize curvature interference.

[0309] (3) Changes in road curvature cause distortion of the feature space;

[0310] In curves, the feature space undergoes nonlinear deformation, and fixed weights cannot adapt to the geometric changes in space. Therefore, adaptive reconstruction of the feature space is achieved by adjusting the weights. Increase → Stretch the lateral offset axis. Reduce → Compress the longitudinal projection axis.

[0311] S402, based on straight road and curved road scenarios, dynamically constructs a classification decision hyperplane based on boundary minimum distance feature data, lateral offset feature data, longitudinal projection position feature data and road curvature;

[0312] The decision plane equation is: ;

[0313] in,

[0314] Material yard Lateral offset characteristics;

[0315] Material yard The longitudinal projection characteristics;

[0316] Boundary slope, which is related to road curvature;

[0317] b : Boundary intercept, which is related to the location of the center point;

[0318] This equation indicates that in the weighted feature space, the decision boundary between the left and right categories is a hyperplane, and its projection in the horizontal offset-vertical projection subspace is a straight line with slope k and intercept b.

[0319] Region division: The left region (lower left of the decision boundary) has a relatively small lateral offset and a relatively small vertical projection. The right region (upper right of the decision boundary) has a relatively large lateral offset and a relatively large vertical projection.

[0320] Weighting effect: =1.0 (high), enhancing the discriminative power of lateral offset features; = 0.8 (low), reducing the influence of longitudinal projection features. The decision boundary is tilted, reflecting the geometric characteristics of the curve. Lateral offset dominates the classification decision, and the boundary slope is dynamically adjusted with the road curvature.

[0321] In a curve, the "left and right" attributes of the material yard point change with the direction of the road. At the starting point of the curve, the lateral offset determines the left and right sides; at the apex of the curve, the longitudinal position has a significant impact.

[0322] S403 employs a deterministic initialization method, assigning a signed lateral offset feature to each material yard point to be identified. Perform pre-classification, when <0, indicating a candidate point on the left; when >0 indicates a candidate point on the right; calculate the mean of the feature matrices of the left and right candidate point sets respectively to determine the left and right classification centers; including the following steps:

[0323] S4031, For each material yard point to be identified, its signed lateral offset feature Perform pre-classification;

[0324] ;

[0325] in,

[0326] Material yard Lateral offset characteristics;

[0327] Material yard Category tags;

[0328] 1 Category identifier on the left;

[0329] 2 Category identifier on the right;

[0330] S4032, Initialize the category feature centers by calculating the mean of the feature matrices of the left candidate point set and the right candidate point set respectively, and determining the left and right classification centers;

[0331] ;

[0332] in,

[0333] t Category identifier, 1 indicates the left side of the road, and 2 indicates the right side of the road;

[0334] :category t The feature center;

[0335] : Belongs to category t The set of points;

[0336] :category t The number of points in the middle;

[0337] : No. i The feature vectors of each point include boundary 1 distance feature, boundary 2 distance feature, lateral offset feature, and longitudinal projection feature.

[0338] like Figure 12 As shown in S404, the segmentation boundary is iteratively optimized. The road curvature adaptive weights dynamically adjust the feature importance. The feature space is scaled and transformed through the weight vector. Curves stretch the lateral offset axis, and straight roads stretch the longitudinal projection axis. The distance from the point to the category center is iteratively calculated in the dynamically weighted feature space. The classification label is dynamically updated according to the classification decision rule. The category feature center is recalculated after each iteration. The feature center moves with the classification result. The decision boundary gradually approaches the optimal segmentation hyperplane. The criterion for updating the cluster center is whether the classification label converges.

[0339] Category labels are the left boundary point label and the right boundary point label.

[0340] Specifically, in step S404, the determination of whether the classification labels have converged includes the following steps:

[0341] S4041, set the convergence threshold to 0.000001;

[0342] S4042, the maximum number of iterations is set to 100 to ensure that no infinite loop occurs;

[0343] S4043, calculate the distance to the classification center after weighting the feature matrix data of the material yard point;

[0344] ;

[0345] in,

[0346] t Category identifier;

[0347] :point To Category t The distance;

[0348] :point eigenvectors;

[0349] j Feature dimension index;

[0350] :feature j The weight, : Normalized distance to the left boundary : Normalized distance to the right boundary : Horizontal offset (with sign) : Vertical projection position;

[0351] :point i Features j The value;

[0352] :category t Central features j The value;

[0353] S4044, dynamically assign new classification labels to material yard points based on the calculated distance;

[0354] Decision-making rules: ;

[0355] in,

[0356] Category label for point i;

[0357] d 1 ( ) : The distance from point i to the center of the class on the left;

[0358] d 2 ( ) : The distance from point i to the center of the class on the right;

[0359] 1 Category identifier on the left;

[0360] 2 Category identifier on the right;

[0361] S4045, Calculate the total number of different quantities of new and old labels and calculate the percentage change in category labels;

[0362] S4046, Determine whether the change ratio is less than the convergence threshold;

[0363] ;

[0364] in,

[0365] : Indicator function, returns 1 if the condition is true;

[0366] new_labels : New label vector;

[0367] old_labels Old label vector

[0368] Total number of material yard locations;

[0369] S4047, Update the classification center; when the change rate of the classification label of the material yard point is less than 0.000001 and the number of iterations is greater than 100, the classification is considered to have converged.

[0370] ;

[0371] in,

[0372] Number of iterations;

[0373] t Category identifier;

[0374] : New feature center;

[0375] : The set of points of the current iteration category t;

[0376] : The number of points in the current iteration category t;

[0377] : The eigenvector of point i;

[0378] like Figure 14 , 15 As shown in Figure 16, specifically, step S5 includes the following steps:

[0379] S501, through a topological potential energy model driven by road curvature, creates a functional mapping relationship between road curvature and potential energy bandwidth, realizes adaptive optimization of curves, and dynamically adjusts the spatial perception scale.

[0380] S501 includes the following steps:

[0381] S5011 establishes the dynamic relationship between road curvature and spatial scale through a potential energy bandwidth model;

[0382] ;

[0383] in,

[0384] R : Baseline neighborhood radius (defined by the user or calculated adaptively based on point density);

[0385] : A piecewise function for curvature adjustment, satisfying:

[0386] when (On a straight road) smaller smaller Weight decays rapidly (emphasizing proximity);

[0387] when When increasing (the curve), Increase Increase Slow weight decay (expands the scope of influence);

[0388] The detailed mathematical model is as follows:

[0389] ;

[0390] in,

[0391] Potential energy bandwidth function, which controls the rate of weight decay;

[0392] R: Neighborhood radius;

[0393] Average curvature of the road;

[0394] Basis for parameter selection:

[0395] (1) Determination of thresholds 0.1 and 0.3;

[0396] : Corresponding straight path (radius of curvature) ), with no obvious bending;

[0397] Gentle curve (radius of curvature) );

[0398] Sharp bend (radius of curvature) );

[0399] (2) Determination of coefficients 0.3 and 0.7;

[0400] Straight Road : Ensure that the neighborhood contains approximately 95% of the relevant points;

[0401] Straight Road Maximum neighborhood connectivity;

[0402] Linear interpolation in the transition region: smooth transition and avoid abrupt changes.

[0403] The physical significance of this is:

[0404] Consider two material yard points A and B at a bend in the road. Their distance along the road (arc length) is L, but their Euclidean distance (chord length) in three-dimensional space is: ;

[0405] when Larger ( When the curve is small and gentle, ;

[0406] when Smaller ( When the curve is large (or sharp), ;

[0407] Therefore, to capture adjacent points (with similar arc lengths) along the road in Euclidean space, the neighborhood radius R needs to be expanded at sharp bends so that the Euclidean neighborhood can cover these points. In spatial topology enhancement processing, it is desirable to maintain the topological structure of the material yard points distributed along the road (continuous on both sides, without intersections). At bends, without scale adjustment, the following may occur:

[0408] First, neighborhood breaks: points adjacent to each other along the road may not be included in the neighborhood due to their large Euclidean distance, resulting in topological breaks;

[0409] Second, incorrect connections: Points on the outer side of the road may be incorrectly connected to points on the inner side;

[0410] By increasing (That is, expanding the scope of influence of weights) allows more distant points (topologically adjacent but Euclidean distances) to participate in weighted voting, thereby maintaining topological continuity.

[0411] S5012 determines the topological potential weights through the dynamic relationship between road curvature and spatial scale;

[0412] ;

[0413] in,

[0414] Potential energy weight, the spatial influence of point j on point i;

[0415] : point xy Planar projection distance;

[0416] Average curvature of the road;

[0417] Potential energy bandwidth function, which controls the rate of weight decay;

[0418] use Replace fixed . On point Located on the curve ( When (increase), Increase Slow weight decay Further points It can have significant weight; when point Located on the straight road ( When (decreases), Decrease Rapid weight decay Only neighboring points have significant weights.

[0419] S5013, adaptive neighborhood radius is achieved based on topological potential energy weight;

[0420] Although neighborhood queries use a fixed radius R, the topological potential weight function varies. The change is equivalent to adjusting the relative importance of points within the neighborhood. In extreme cases:

[0421] On point At that time, all weights 1 (equal weight);

[0422] On point At that time, only the nearest point weight is used. 1, the rest 0.

[0423] Therefore, by adjusting This achieves dynamic neighborhood, expanding the neighborhood range at curves (because the weight of the far point is not 0) and effectively shrinking the neighborhood range at straight sections.

[0424] S502, Determine the spatial continuity of the material yard point, identifying the correlation between the current material yard point and adjacent material yard points. Objects that are adjacent or nearby in physical space share similar or correlated attributes.

[0425] In this algorithm step, it is specifically stated that adjacent material yard points should maintain consistency in classification attributes (left / right). Here, the Gaussian kernel is equivalent to the normal distribution probability density, and the weights are related to the points. Appear at point The probability is proportional to the domain.

[0426] This principle is quantitatively represented by the spatial autocorrelation function:

[0427] ;

[0428] in,

[0429] Local autocorrelation index, point The degree of spatial aggregation;

[0430] : Target point attribute value, point Category tags or attribute values;

[0431] : Neighboring point attribute value, neighboring point Category tags or attribute values;

[0432] Global attribute mean, the average attribute value across all points;

[0433] Global attribute variance, the degree of dispersion of attribute values;

[0434] Spatial weight, the spatial influence of point j on point i;

[0435] : Number of neighboring points, the number of neighboring points involved in the calculation;

[0436] (1) Local autocorrelation index , which represents the spatial correlation strength between the quantization point i and its neighborhood;

[0437] Explanation of the range:

[0438] Positive correlation (nearest points are of the same type);

[0439] : Negative correlation (neighboring outliers);

[0440] No correlation (random distribution);

[0441] Engineering significance:

[0442] High positive value: The classification results of the material yard points are spatially continuous;

[0443] High negative values: indicate the presence of classification errors (island phenomenon);

[0444] (2) Attribute deviation item and Its mathematical essence is the offset of the attribute value relative to the global mean;

[0445] Physical meaning:

[0446] :point i Attributes above average;

[0447] :point j Attributes are below average;

[0448] Engineering significance:

[0449] : Category label for point i (1=left, 2=right);

[0450] Global average label value (usually ≈1.5);

[0451] (3) Global attribute variance Its function is to act as a normalization factor, eliminating the influence of attribute dimensions;

[0452] Special cases for classification scenarios:

[0453] When all points belong to the same category: ,at this time Undefined;

[0454] In practical engineering, a small constant needs to be added to prevent division by zero;

[0455] (4) Summation term This represents the spatial weighted bias, which is the weighted average of the attribute biases of neighboring points.

[0456] Engineering significance: Characteristic point Spatial attribute trends of the neighborhood;

[0457] S503, Local Topology Preservation Processing, in spatial analysis, only considers spatial relationships on the horizontal plane and ignores the impact of elevation changes on spatial neighborhood relationships. This processing method is based on the following key assumption: In a road environment, the left and right classification of material yard points is mainly determined by horizontal positional relationships, and elevation changes do not affect basic topological relationships.

[0458] Projecting 3D points onto a horizontal plane (i.e., the xy plane):

[0459] ;

[0460] in,

[0461] : Three-dimensional coordinate data of the material yard point;

[0462] : Two-dimensional coordinate data of the material yard point.

[0463] Two-dimensional Euclidean distance as a substitute for three-dimensional distance:

[0464] ;

[0465] in, This represents the distance between two material yard points in a two-dimensional plane.

[0466] like Figure 17 , 18 As shown in Figure 19, S504, a topology voting engine is created based on the principle that the spatial relationship of the local neighborhood remains unchanged, namely, the adjacency relationship remains unchanged, the connectivity remains unchanged, and the directional relationship remains unchanged. The topology classification label of the material yard point is corrected according to the decision rules, including: correcting the topology breakpoint, smoothing the classification boundary, and maintaining the local topology structure.

[0467] The core mathematical model of topological voting is the weighted voting model:

[0468] ;

[0469] ;

[0470] ;

[0471] in,

[0472] Topological votes, points neighborhood pairs of categories Support level;

[0473] : Neighborhood point set, point The neighborhood point index;

[0474] Topology labels, points Classification (left / right side of the road);

[0475] Potential energy weight, point right Its influence;

[0476] : point Planar projection distance;

[0477] Indicator function, when = The function returns 1 if the condition is met, otherwise 0.

[0478] Material yard classification (left / right side of the road);

[0479] :point Potential bandwidth at a given location, weight decay rate;

[0480] Decision threshold, correction sensitivity is typically 1.3-1.7;

[0481] Road curvature, point The curvature of the road at that location;

[0482] : Indicator function, returns 1 if the condition is true;

[0483] Material yard classification, classification on the left side of the road;

[0484] Topological labels, classification on the right side of the road.

[0485] The above methods achieve the following functional effects:

[0486] Repair isolated misclassified points (topological breaks) to ensure spatial continuity, eliminate jagged boundaries, achieve natural transitions, ensure consistent classification of adjacent material yard points, and prevent cross-classification on the left and right sides.

[0487] like Figure 20 As shown, S505 ensures the rationality of the material yard point classification boundary by eliminating jagged boundaries, maintaining a reasonable transition, and protecting special areas through the boundary regularization mechanism;

[0488] Step S505 includes the following steps:

[0489] S5051 establishes a dual protection mechanism for boundary regularization processing;

[0490] Boundary regularization is achieved through dual conditional constraints:

[0491] ;

[0492] Protection conditions:

[0493] ;

[0494] Voting Correction:

[0495] ;

[0496] in,

[0497] Center point Arrive at the material yard ;

[0498] : Road tangent direction;

[0499] Road normal vector (usually) axis);

[0500] :point Lateral offset from the center of the road;

[0501] :point Projection ratio along the road axis;

[0502] Center threshold, 0.2 for the road center protection zone;

[0503] The voting threshold is set to 1.5.

[0504] like Figure 21 As shown in Figure S5052, the boundary smoothing process using the discrete Laplacian operator includes the following steps: inputting point cloud data, constructing KD-Tree graph data, calculating the weight matrix, constructing the graph Laplacian matrix, and solving for the linear system output smoothing label.

[0505] The algorithm is based on the discrete Laplace operator, whose two-dimensional continuous space is defined as follows:

[0506] :

[0507] For discrete point cloud data, the graph Laplacian matrix approximation is used:

[0508] ;

[0509] in,

[0510] :point The neighborhood index set;

[0511] Gaussian weights control the influence of different neighboring points;

[0512] :point The tag value (1 or 2);

[0513] : Represents a neighboring point With the center point The difference in category labels;

[0514] when A time difference of 0 indicates a smooth and natural transition at the boundaries of points of the same type.

[0515] when When the non-zero value exhibits a sawtooth boundary, the boundary adjustment is driven.

[0516] The graph Laplacian matrix is ​​constructed as follows:

[0517] ;

[0518] in,

[0519] Weight matrix (sparse matrix) );

[0520] Degree matrix (diagonal matrix) );

[0521] The solution for the linear system is shown below.

[0522] Continuous Laplace equation:

[0523] ;

[0524] After discretization, we get:

[0525] ;

[0526] in,

[0527] Graph Laplace matrix (sparse matrix);

[0528] The label vector;

[0529] The solution to this equation corresponds to a smooth boundary, and the key procedure is as follows:

[0530] % Calculate the graph Laplacian matrix;

[0531] D = diag(sum(W,2)); % Degree matrix;

[0532] L = D - W; % Graph Laplacian matrix;

[0533] % Smoothing (explicit iteration);

[0534] for iter = 1:max_iter;

[0535] f_new = f_old - dt (L f_old); % dt is the step size;

[0536] f_old = f_new;

[0537] end

[0538] The matrix after iteration in the program f_new This is a smoothed label matrix.

[0539] like Figure 22 As shown, the left and right material yard point sets are obtained based on the optimized labels. Then, the left cableway is designed based on the left material yard point set, and the right cableway is designed based on the right material yard point set.

[0540] The transmission line cableway material yard pattern recognition system based on slope compensation and dynamic road width correction adopts a transmission line cableway material yard pattern recognition method based on slope compensation and dynamic road width correction, including a road feature extraction module, a road feature enhancement processing module, a road curvature calculation module, a pattern recognition classification module, and a spatial topology consistency enhancement module.

[0541] The road feature extraction module is configured to extract road centerline feature data, road final width feature data, and road unit direction feature data.

[0542] The road feature enhancement processing module is configured to include: calculating the minimum boundary distance feature data based on the road centerline feature data; determining the orientation of the material yard point relative to the road segment using the right-hand rule of the cross product of vectors based on the final width feature data of the road; obtaining the lateral offset feature data based on the cross product after normalization of the signed distance; and calculating the longitudinal projection position feature data based on the road unit direction feature data.

[0543] The road curvature calculation module is configured to use discrete point geometric differential method to analyze the rate of change of the included angle between adjacent line segments on the road centerline to calculate the road curvature;

[0544] The pattern recognition and classification module is configured to use a dynamic weighted segmentation algorithm based on boundary minimum distance feature data, lateral offset feature data, longitudinal projection position feature data, and road curvature. Through an iterative optimization process in the dynamic weighted road feature space, the weights of road features are dynamically adjusted to scale the original road feature space, so that the left and right boundary points of the road in the transformed space can be effectively separated by a hyperplane, thus achieving accurate binary classification of material yard points.

[0545] The spatial topology consistency enhancement module is configured to repair spatial isolated points and noise in accurate binary classification results through spatial topology consistency enhancement processing.

[0546] A storage medium storing a computer program, which, when executed by a processor, implements the steps of a method for pattern recognition of transmission line cableway material yards based on slope compensation and dynamic road width correction.

[0547] A computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a method for pattern recognition of transmission line cableway material yard points based on slope compensation and dynamic road width correction.

[0548] The specific embodiments described are preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. All equivalent changes made to the structure, shape, and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for pattern recognition of cableway material yards for power transmission lines based on slope compensation and dynamic road width correction, characterized in that, Including the following steps: S1. Road Feature Extraction: This includes extracting feature data of the road centerline, feature data of the final road width, and feature data of the road in a single direction. The extraction of the final road width feature data includes the following steps: S101, through parameter verification, check whether the number of boundary points on both sides of the road is equal, and verify whether each point is represented by three-dimensional coordinates; S102, let the boundary point sets on both sides of the road be the left boundary point set and the right boundary point set, respectively; S103, calculate the distance between the left and right boundary point pairs of the road using the three-dimensional coordinates of the road boundary point pairs to obtain the road segment width; S104, based on Box plot statistical method, robust processing to identify and remove road left and right boundary point pairs corresponding to abnormal road segment widths; S105, calculate the original average width of the road by dividing the road into segments; S106, the slope compensation algorithm is used to compensate the original average width of the road that is greater than the slope threshold, and then the final road width feature data is output. S2. Road feature enhancement processing: This includes calculating the minimum distance feature data of the boundary based on the road centerline feature data, determining the orientation of the material yard point relative to the road segment using the right-hand rule of the cross product of vectors based on the final width feature data of the road, obtaining the lateral offset feature data based on the cross product after normalization of the signed distance, and calculating the longitudinal projection position feature data based on the road unit direction feature data. S3. Calculate road curvature: The road curvature is calculated by analyzing the rate of change of the angle between adjacent line segments on the road centerline using the discrete point geometric differential method. S4. Pattern Recognition and Classification: Based on the minimum boundary distance feature data, lateral offset feature data, longitudinal projection position feature data and road curvature, a dynamic weighted segmentation algorithm in the feature space is adopted. Through the iterative optimization process in the dynamic weighted road feature space, the road feature weights are dynamically adjusted to scale the original road feature space, so that the left and right boundary points of the road in the transformed space can be effectively separated by a hyperplane, thus achieving accurate binary classification of the material yard points. Step S4 specifically includes the following steps: S401, input the road feature matrix and the average road curvature data, and dynamically adjust the importance weights of the lateral offset feature and the longitudinal projection feature according to the degree of road curvature; S402, based on straight road and curved road scenarios, dynamically constructs a classification decision hyperplane based on boundary minimum distance feature data, lateral offset feature data, longitudinal projection position feature data and road curvature; S403 employs a deterministic initialization method, assigning a signed lateral offset feature to each material yard point to be identified. Perform pre-classification, when , is a candidate point on the left; when , which are candidate points on the right; calculate the mean of the feature matrices of the candidate point set on the left and the candidate point set on the right respectively, and determine the left and right classification centers; S404 iteratively optimizes the segmentation boundary, dynamically adjusts feature importance based on road curvature adaptive weights, and achieves feature space scaling transformation through weight vectors. Curves stretch the lateral offset axis, and straight roads stretch the longitudinal projection axis. In the dynamically weighted feature space, the distance from the point to the category center is calculated iteratively, and the classification label is dynamically updated according to the classification decision rule. After each iteration, the category feature center is recalculated, and the feature center moves with the classification result. The decision boundary gradually approaches the optimal segmentation hyperplane. The criterion for updating the cluster center is whether the classification label converges. S5, Spatial Topology Consistency Enhancement: Spatial topology consistency enhancement process is used to repair spatial isolated points and noise in accurate binary classification results.

2. The method for pattern recognition of transmission line cableway material yards based on slope compensation and dynamic road width correction as described in claim 1, characterized in that, Step S3 includes the following steps: S301, collect the three-dimensional point pairs of the left and right side boundaries of the road; S302, calculate the road centerline point set by averaging the three-dimensional point pairs of the left and right side boundaries of the road; S303, enter the loop of traversing the centerline point set of the road; S304, in the S303 loop, calculate the adjacent vector based on the adjacent center point; S305, calculate the angle between vectors; S306, calculate the local curvature of the road; S307, exit the loop of traversing the centerline point set of the road, obtain the average curvature value of the road by accumulating the local curvature of the road, and output the road curvature.

3. The method for pattern recognition of transmission line cableway material yards based on slope compensation and dynamic road width correction as described in claim 1, characterized in that, In step S404, the determination of whether the classification labels have converged includes the following steps: S4041, set the convergence threshold to 0.000001; S4042, the maximum number of iterations is set to 100 to ensure that no infinite loop occurs; S4043, calculate the distance to the classification center after weighting the feature matrix data of the material yard point; S4044, dynamically assign new classification labels to material yard points based on the calculated distance; S4045, Calculate the total number of different quantities of new and old labels and calculate the percentage change in category labels; S4046, Determine whether the change ratio is less than the convergence threshold; S4047, Update the classification center; when the change rate of the classification label of the material yard point is less than 0.000001 and the number of iterations is greater than 100, the classification is considered to have converged.

4. The method for pattern recognition of transmission line cableway material yards based on slope compensation and dynamic road width correction as described in claim 1, characterized in that, Step S5 includes the following steps: S501, through a topological potential energy model driven by road curvature, creates a functional mapping relationship between road curvature and potential energy bandwidth, realizes adaptive optimization of curves, and dynamically adjusts the spatial perception scale. S502, Determine the spatial continuity of material yard points, and determine the correlation between the current material yard point and adjacent material yard points; adjacent or nearby objects in physical space have similar or related attributes. S503, Local Topology Preservation Process, in spatial analysis, only considers spatial relationships on the horizontal plane and ignores the impact of elevation changes on spatial neighborhood relationships; S504 creates a topology voting engine based on the principle that the spatial relationships of local neighborhoods remain unchanged, namely, the adjacency relationship remains unchanged, the connectivity remains unchanged, and the directional relationship remains unchanged. It corrects the topology classification labels of material yard points according to decision rules, including: correcting topology breakpoints, smoothing classification boundaries, and maintaining local topology structure. S505 ensures the rationality of material yard classification boundaries by eliminating jagged boundaries, maintaining reasonable transitions, and protecting special areas through boundary regularization mechanisms.

5. The method for pattern recognition of transmission line cableway material yards based on slope compensation and dynamic road width correction as described in claim 4, characterized in that, Step S501 includes the following steps: S5011 establishes the dynamic relationship between road curvature and spatial scale through a potential energy bandwidth model; S5012 determines the topological potential weights through the dynamic relationship between road curvature and spatial scale; S5013 achieves adaptive neighborhood radius based on topological potential weights.

6. The method for pattern recognition of transmission line cableway material yards based on slope compensation and dynamic road width correction as described in claim 4, characterized in that, Step S505 includes the following steps: S5051 establishes a dual protection mechanism for boundary regularization processing; Boundary regularization is achieved through dual conditional constraints: ； Protection conditions: ； Voting Correction: ； in, Center point Arrival at the material yard ; : Road tangent direction; : Road normal vector; :point Lateral offset from the center of the road; :point Projection ratio along the road axial position; Center threshold, 0.2 for the road center protection zone; The voting threshold is set to 1.

5. S5052 applies the discrete Laplacian operator for boundary smoothing, including the following steps: inputting point cloud data, constructing KD-Tree graph data, calculating the weight matrix, constructing the graph Laplacian matrix, and solving for the linear system output smoothing label.

7. A pattern recognition system for cableway material yards along power transmission lines based on slope compensation and dynamic road width correction, characterized in that, The transmission line cableway material yard pattern recognition method based on slope compensation and dynamic road width correction as described in any one of claims 1-6 includes a road feature extraction module, a road feature enhancement processing module, a road curvature calculation module, a pattern recognition classification module, and a spatial topology consistency enhancement module. The road feature extraction module is configured to extract road centerline feature data, final road width feature data, and road unit direction feature data. The road feature enhancement processing module is configured to include: calculating the minimum distance feature data of the boundary based on the road centerline feature data; determining the orientation of the material yard point relative to the road segment using the right-hand rule of the cross product of vectors; obtaining the lateral offset feature data based on the cross product after normalization of the signed distance; and calculating the longitudinal projection position feature data based on the road unit direction feature data. The road curvature calculation module is configured to use discrete point geometric differential method to analyze the rate of change of the included angle between adjacent line segments on the road centerline to calculate the road curvature; The pattern recognition and classification module is configured to use a dynamic weighted segmentation algorithm based on boundary minimum distance feature data, lateral offset feature data, longitudinal projection position feature data, and road curvature. Through an iterative optimization process in the dynamic weighted road feature space, the weights of road features are dynamically adjusted to scale the original road feature space, so that the left and right boundary points of the road in the transformed space can be effectively separated by a hyperplane, thus achieving accurate binary classification of material yard points. The spatial topology consistency enhancement module is configured to repair spatial isolated points and noise in accurate binary classification results through spatial topology consistency enhancement processing.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the transmission line cableway material yard pattern recognition method based on slope compensation and dynamic road width correction as described in any one of claims 1-6.

9. A computing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the transmission line cableway material yard pattern recognition method based on slope compensation and dynamic road width correction as described in any one of claims 1-6.

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