Power distribution network tower and equipment parameterized three-dimensional model construction method and system

Abstract

The invention discloses a power distribution network tower and equipment parameterized three-dimensional model construction method and system, and the method comprises the steps: S1, analyzing a tower and equipment, and recognizing and extracting basic geometric elements and key features of the tower and equipment; s2, quantifying shape parameters and position parameters of a tower and equipment based on the geometric elements and the key features extracted in S1, and constructing a standardized parameterized model; s3, adjusting and optimizing the standardized parameterized model constructed in S2, and reducing the complexity of the model and ensuring the accuracy of the structure through three-dimensional parameterized design; s4, verifying the parameterized model optimized in the step S3 by using actual inspection data, and correcting the deviation of geometric and physical characteristics; and S5, extracting texture and color features in combination with the visible light image, and optimizing the parameterized model verified in the step S4. The method has the advantages of high model construction precision and the like.

Description

Technical Field

[0001] This invention mainly relates to the inspection of overhead lines in power distribution networks, specifically to a method and system for constructing parametric three-dimensional models of power distribution network towers and equipment. Background Technology

[0002] Drones have shown great potential in the inspection of overhead power distribution lines. However, due to limitations imposed by unique terrain such as hills and mountains, numerous challenges arise during inspections, with high modeling costs being one of the biggest. Traditional laser scanning modeling methods require the purchase of expensive hardware such as large drones and LiDAR, placing extremely high demands on financial investment. LiDAR equipment is expensive, and the purchase and maintenance costs of large drones are also substantial, making the application of laser scanning technology in large-scale power distribution line inspections prohibitively expensive.

[0003] While oblique photogrammetry modeling is a relatively mature technology, its data acquisition efficiency is low, requiring significant manpower and time. Oblique photogrammetry technology necessitates capturing images of the inspection area from multiple angles and directions to generate a high-precision 3D model. Although this method has relatively low equipment costs, in practical applications, the sheer length of distribution network lines far exceeds that of transmission networks, resulting in a massive workload for data acquisition and processing, leading to overall low efficiency. Furthermore, distribution network lines are frequently relocated and altered, requiring much shorter update cycles for line models compared to transmission networks. Distribution networks often need to add, modify, or relocate lines based on actual needs, meaning frequent model updates are required, further increasing modeling costs and workload. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a method and system for constructing parametric three-dimensional models of power distribution network towers and equipment with high stability and accuracy.

[0005] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows: A method for constructing a parametric 3D model of power distribution network towers and equipment includes the following steps: S1. Analyze the towers and equipment, identify and extract their basic geometric elements and key features; S2. Based on the geometric elements and key features extracted in S1, quantify the shape and position parameters of the tower and equipment, and construct a standardized parametric model; S3. Adjust and optimize the standardized parametric model constructed in S2. Reduce model complexity and ensure structural accuracy through three-dimensional parametric design. Define global parameters to control overall structural features, local parameters to describe node and local geometric relationships, modularize high-frequency component parameters, establish dynamic association between global and local parameters, remove redundant connections and geometric details through topology optimization and geometric accuracy optimization, and retain key functional features. S4. Verify the parameterized model optimized in S3 using actual inspection data, and correct deviations in geometric and physical properties; S5. Combine visible light images to extract texture and color features, and optimize the parameterized model verified in S4.

[0006] Preferably, the specific process of step S1 is as follows: Based on existing engineering drawings and parameters of power distribution network poles and equipment, and using drones equipped with laser rangefinders and visible light cameras to collect images of the power distribution network poles and equipment to be modeled, a detailed analysis of the poles and their auxiliary equipment is conducted to identify and extract the basic geometric elements of the equipment, including points, lines, and surfaces; and to determine the spatial relationships between these elements, including parallelism, perpendicularity, and intersection.

[0007] Preferably, the specific process of step S2 is as follows: S201. Quantify the shape parameters of power distribution network towers and key equipment, including length L, width W, height H, radius R and angle θ; set the position parameters, including coordinates (X, Y, Z) and direction; S202. Create a virtual model of the equipment based on the rules of typical equipment, including geometry and size range.

[0008] Preferably, in step S3, the global parameters The formula is:

[0009] Where H is the model height, W is the model width, and M is the model material property; Local parameters The formula is:

[0010] in, The angle between nodes. For node spatial coordinates, This refers to the node spacing; High-frequency component parameterization Modular processing:

[0011] Where R is the radius, For module height, This represents the module's rotation angle.

[0012] Preferably, in step S3, establishing the dynamic relationship between global and local parameters specifically involves:

[0013] in:

[0014]

[0015] Based on the constraint relationship, the synchronous changes of global and local parameters are constrained, and the stability of the overall structure is ensured.

[0016] Preferably, in step S3, during topology optimization, the node contribution rate is first calculated. For nodes Contribution rate is defined as:

[0017] in, Represents a node Weights between them; Use adjacency matrix Indicates the connection relationship between nodes, if:

[0018] Then trim the edges:

[0019] Remove low-contribution edges and redundant connections, retain the core topology structure, and ensure the rationality and consistency of topological relationships.

[0020] Preferably, in step S3, in the geometric accuracy optimization, a geometric error constraint simplification algorithm is applied, and an error threshold is set. Define the maximum allowable error:

[0021] in To determine the maximum permissible error for geometric accuracy, unimportant geometric details are removed through error constraints. The error distribution of the geometry is assessed, and redundant nodes or boundaries with errors below a threshold are removed. If the error is less than... ， This involves simplifying or removing geometry while maintaining the geometric accuracy of the model. Finally, redundant geometry is removed by combining bounding box constraints, and the parameter distribution in the boundary region is optimized.

[0022] Preferably, the verification in step S4 includes calculating the geometric error of each node, calculating the average error of all nodes, and if the average error exceeds a threshold, iteratively updating the global and local parameters through parameter correction until the model error meets the requirements.

[0023] Preferably, the extraction of texture and color features in step S5 includes applying Gaussian filtering to remove image noise, using the Canny algorithm for edge detection, statistically analyzing pixel distribution through color histograms, and extracting texture features using the gray-level co-occurrence matrix to support visual optimization of the 3D model.

[0024] The present invention also discloses a parametric three-dimensional model construction system for power distribution network towers and equipment, including a memory and a processor connected to each other. The memory stores a computer program, and the computer program executes the steps of the method described above when run by the processor.

[0025] Compared with the prior art, the advantages of the present invention are as follows: This invention, through parametric modeling, significantly reduces reliance on expensive equipment (such as large drones and LiDAR), simplifies the data acquisition process, and greatly improves data acquisition efficiency. Parametric modeling technology allows for flexible adjustment and optimization of parameter settings, resulting in a 3D parametric model that better adapts to complex terrain environments, ensuring the model's reliability and applicability. Furthermore, this method integrates image processing and computer vision technologies, improving the comprehensiveness and accuracy of the parametric model through the integrated use of multi-source data.

[0026] This invention ensures the stability and accuracy of parametric 3D model construction through a series of innovative steps. In addition, by combining visible light images with advanced image processing and computer vision technologies, the accuracy of modeling is greatly improved. This method has demonstrated strong stability, accuracy and wide applicability in the construction of parametric 3D models of power distribution network towers and equipment, providing strong support for the development and application of parametric 3D modeling related technologies. Attached Figure Description

[0027] Figure 1 The flowchart is a representation of the parametric three-dimensional model construction method for power distribution network towers and equipment of the present invention. Detailed Implementation

[0028] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0029] like Figure 1 As shown in the figure, the method for constructing a parametric three-dimensional model of power distribution network towers and equipment provided in this embodiment of the invention includes the following steps: S1. Analyze the towers and equipment, identify and extract their basic geometric elements and key features; S2. Based on the geometric elements and key features extracted in S1, quantify the shape and position parameters of the tower and equipment, and construct a standardized parametric model; S3. Adjust and optimize the standardized parametric model constructed in S2, reduce model complexity and ensure structural accuracy through three-dimensional parametric design; S4. Verify the parameterized model optimized in S3 using actual inspection data, and correct deviations in geometric and physical properties; S5. Combine visible light images to extract texture and color features, and optimize the parameterized model verified in S4.

[0030] In one specific embodiment, step S1 includes the following process: based on existing power distribution network pole and equipment engineering drawings and parameters, and using a drone equipped with a laser rangefinder and a visible light camera to collect relevant image data of the power distribution network pole and equipment to be modeled, a detailed analysis is performed on the pole and its auxiliary equipment (crossarms, pole-mounted switches, pole-mounted transformers), the basic geometric elements of the equipment are identified and extracted, including points, lines, and surfaces; and the spatial relationships between these elements (such as parallel, perpendicular, intersecting, etc.) are determined.

[0031] In one specific embodiment, the specific process of step S2 is as follows: S201. Quantify the shape parameters of power distribution network towers and key equipment, such as length L, width W, height H, radius R, and angle θ; set position parameters, including coordinates (X, Y, Z) and direction (rotation angle, etc.). S202. Based on the rules of typical equipment, such as geometry and size range, create a virtual model of the equipment. This virtual model is defined by a set of key parameters and their corresponding logic.

[0032] In one specific embodiment, the specific process of step S3 is as follows: First, define the parameter system, including global parameters. Controlling the overall structural features:

[0033] In the above formula, H is the model height, W is the model width, and M is the model material property.

[0034] Secondly, utilize local parameters. Describe the relationship between nodes and local geometry:

[0035] In the above formula, The angle between nodes. For node spatial coordinates, This represents the node spacing.

[0036] Finally, the parameterized representation of high-frequency components is defined. Modular processing:

[0037] In the above formula, R is the radius. For module height, This represents the module's rotation angle.

[0038] By precisely controlling the aforementioned parameters, a hierarchical parameter model is constructed, establishing a dynamic relationship between global and local parameters, thus ensuring the stability of global parameters. Control the overall structural shape, such as height (H) and width (W). Local parameters. Controlling the details of specific structures (such as the angles between nodes) through local geometric relationships. and spatial coordinates ).

[0039] Based on associative expressions, define the functional relationship between global and local parameters:

[0040] in:

[0041]

[0042] Based on the above constraints, the synchronous changes of global and local parameters are constrained, and the stability of the overall structure is ensured.

[0043] For high-frequency components (such as insulators), the geometric features of the high-frequency components are represented parametrically. Basic modules are repeated or spliced ​​together to form complex geometries, for example:

[0044] Modularization reduces redundant geometric details, thereby improving the model's storage and computation efficiency.

[0045] In topology optimization, the node contribution rate is calculated first. For nodes Contribution rate is defined as:

[0046] in, Represents a node Weights between edges (such as edge length, connection strength, etc.).

[0047] Use adjacency matrix Indicates the connection relationship between nodes, if:

[0048] Then trim the edges:

[0049] Based on the above formula, low-contribution edges and redundant connections are removed, while the core topology structure is preserved to ensure the rationality and consistency of the topology relationships.

[0050] In geometric accuracy optimization, a geometric error constraint simplification algorithm is applied by setting an error threshold. Define the maximum allowable error:

[0051] in To determine the maximum permissible error for geometric accuracy, unimportant geometric details are removed through error constraints. The error distribution of the geometry is assessed, and redundant nodes or boundaries with errors below a threshold are removed. If the error is less than... ， The geometry is simplified or removed while maintaining the model's geometric accuracy. Finally, redundant geometry is removed using boundary box constraints, and the parameter distribution in the boundary region is optimized. The optimized model preserves key functional features while effectively reducing computational overhead, thereby significantly improving the overall performance of the parametric power grid tower and equipment model and ensuring the model's efficiency and accuracy in practical applications.

[0052] In one specific embodiment, the specific process of step S4 is as follows: First, select a drone equipped with a high-precision laser rangefinder and a high-resolution camera, and use 3D modeling software to generate a high-precision model of the collected image data. and with parameterized models Perform geometric comparison and calculate geometric error. :

[0053] In the above formula, Represented as the first Geometric error of each node, For parameterized model node coordinates, These are the coordinates of the actual model nodes.

[0054] according to Calculate the average error of all nodes :

[0055] In the above formula, N is the total number of nodes; If the average error Then adjust the global parameters. and modular parameters :

[0056]

[0057] In the above formula, This is a correction factor. Through iterative verification, the geometric error of the model is ensured. To meet the high precision and high efficiency requirements of different application scenarios.

[0058] In one specific embodiment, step S5 specifically includes: First, visible light image data of the tower and its auxiliary equipment are acquired. Gaussian filtering is then applied to remove image noise and smooth the image. Then, the Canny algorithm in edge detection technology is used to accurately extract edge information from the image. Edge information is used to delineate the device outline, providing support for subsequent feature extraction. Finally, the color space distribution of the image is analyzed, and color features are extracted using methods such as color histograms. The gray-level co-occurrence matrix (GLCM) in texture analysis technology is applied to extract the device's texture features. The extracted features are converted into a format suitable for modeling, such as vectors, matrices, or specific data structures.

[0059] This method combines visible light images with image processing and computer vision techniques as an auxiliary step in modeling to optimize the constructed 3D parametric model.

[0060] This invention, through parametric modeling, significantly reduces reliance on expensive equipment (such as large drones and LiDAR), simplifies the data acquisition process, and greatly improves data acquisition efficiency. Parametric modeling technology allows for flexible adjustment and optimization of parameter settings, resulting in a 3D parametric model that better adapts to complex terrain environments, ensuring the model's reliability and applicability. Furthermore, this method integrates image processing and computer vision technologies, improving the comprehensiveness and accuracy of the parametric model through the integrated use of multi-source data.

[0061] This invention ensures the stability and accuracy of parametric 3D model construction through a series of innovative steps. In addition, by combining visible light images with advanced image processing and computer vision technologies, the accuracy of modeling is greatly improved. This method has demonstrated strong stability, accuracy and wide applicability in the construction of parametric 3D models of power distribution network towers and equipment, providing strong support for the development and application of parametric 3D modeling related technologies.

[0062] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0063] The method for constructing a parametric three-dimensional model of power distribution network towers and equipment provided by this invention includes the following steps: S1. Analyze the towers and equipment, identify and extract their basic geometric elements and key features; Specifically, based on existing model drawings and parameters of power distribution network poles and equipment, a detailed analysis of the poles and their auxiliary equipment (crossarms, pole-mounted switches, pole-mounted transformers) is conducted. According to the model drawings and known parameters, the basic geometric elements (points, lines, and surfaces) of the poles and their auxiliary equipment are extracted. Vector algebra and geometric methods are used to determine the spatial relationships between these geometric elements, such as parallelism, perpendicularity, and intersection. For the two crossarms of a tower to be considered parallel, the following condition must be met:

[0064] In the above formula, , This indicates the orientation of the tower crossarm, where Represented in vector coordinates as , Represented in vector coordinates as .

[0065] When the relationship between the poles or equipment is vertical, the following conditions must be met:

[0066] In the above formula, , Represented as pole or equipment elements, where Represented in vector coordinates as , Represented in vector coordinates as .

[0067] When the relationship between towers or equipment is intersecting, it can be represented by the intersection point of the lines they lie on. Let... ( These are two starting points, and their direction vectors are respectively... , The vector equation can be expressed as:

[0068]

[0069] In the above formula, , Let the two vectors be the parametric equations of the lines they lie on. Finding the equation of the intersection point of the two vectors is equivalent to solving the parametric equations of the two lines.

[0070] In the above formula, after expanding the coordinate form:

[0071] The formula satisfies:

[0072]

[0073] In the above formula, when there exists , If both equations are true, then the vector... , intersect.

[0074] S2. Based on the geometric elements and key features extracted in S1, quantify the shape and position parameters of the tower and key equipment, and construct a standardized parametric 3D model; the specific steps are as follows: S201. Quantify the shape parameters of power distribution network towers and key equipment, such as length L, width W, height H, radius R, and angle θ; set position parameters, including coordinates (X, Y, Z) and direction (rotation angle, etc.). S202. Based on the rules of typical equipment, such as geometry and size range, create a virtual model of the equipment. Assume the length L of the tower or equipment, expressed by the following formula: ) In the above formula, , Maximum and minimum allowed lengths It is a length adjustment factor, with a value range of [0,1]. Assuming the width W of the tower or equipment, its expression formula is: ) In the above formula, , Maximum and minimum allowed width It is the width adjustment factor, with a value range of [0,1]. Assuming the height H of the tower or equipment, its formula is as follows: ) In the above formula, , Maximum and minimum allowed height, It is the height adjustment factor, with a value range of [0,1]. Assuming the equipment radius R of the tower or equipment, its expression formula is: ) In the above formula, , Maximum and minimum allowable radii It is the radius adjustment coefficient, with a value range of [0,1].

[0075] Based on the geometric parameters and boundary conditions defined above, a flexible and accurate virtual model of power distribution network towers and key equipment is created.

[0076] S3. Adjust and optimize the parameters of the 3D model constructed in S2. Reduce model complexity while ensuring structural accuracy through 3D parametric model design; Based on the constructed virtual model, a parameter system is defined, starting with defining global parameters. Controlling the overall structural features:

[0077] In the above formula, H is the model height, W is the model width, and M is the model material property.

[0078] Secondly, utilize local parameters. Describe the relationship between nodes and local geometry:

[0079] In the above formula, The angle between nodes. For node spatial coordinates, This represents the node spacing.

[0080] Finally, the parameterized representation of high-frequency components is defined. Modular processing:

[0081] In the above formula, R is the radius. For module height, This represents the module's rotation angle.

[0082] By precisely controlling the aforementioned parameters, a hierarchical parameter model is constructed, establishing a dynamic relationship between global and local parameters, thus ensuring the stability of global parameters. Control the overall structural form, such as height (H) and width (W); local parameters. Controlling the details of specific structures (such as the angles between nodes) through local geometric relationships. and spatial coordinates Based on associative expressions, the functional relationship between global and local parameters is defined:

[0083] in:

[0084]

[0085] Based on the above constraints, the synchronous changes of global and local parameters are constrained, and the stability of the overall structure is ensured.

[0086] For high-frequency components (such as insulators), the geometric features of the high-frequency components are represented parametrically. Basic modules are repeated or spliced ​​together to form complex geometries, for example:

[0087] Modularization reduces redundant geometric details, thereby improving the model's storage and computation efficiency.

[0088] In topology optimization, the node contribution rate is calculated first. For nodes Contribution rate is defined as:

[0089] in, Represents a node Weights between edges (such as edge length, connection strength, etc.).

[0090] Use adjacency matrix Representing the connection relationships between nodes, using node contribution rate and adjacency matrix Determine the validity of edges between nodes; if:

[0091] Then trim the edges:

[0092] Based on the above formula, low-contribution edges and redundant connections are removed, while the core topology structure is preserved to ensure the rationality and consistency of the topology relationships.

[0093] In geometric accuracy optimization, a geometric error constraint simplification algorithm is applied by setting an error threshold. Define the maximum allowable error:

[0094] in To determine the maximum permissible error for geometric accuracy, unimportant geometric details are removed through error constraints. The error distribution of the geometry is assessed, and redundant nodes or boundaries with errors below a threshold are removed. If the error is less than... ， The geometry is simplified or removed while maintaining the model's geometric accuracy. Finally, redundant geometry is removed using boundary box constraints, and the parameter distribution in the boundary region is optimized. The optimized model preserves key functional features while effectively reducing computational overhead, thereby significantly improving the overall performance of the parametric power grid tower and equipment model and ensuring the model's efficiency and accuracy in practical applications.

[0095] S4. Validate the optimized parametric model from S3 based on actual inspection results to ensure it conforms to the geometric and physical characteristics of the actual equipment. Make necessary corrections and adjustments to the model to meet the actual inspection scenarios and requirements.

[0096] First, select a drone equipped with a high-precision laser rangefinder and a high-resolution camera, and use 3D modeling software to generate a high-precision model of the collected image data. and with parameterized models Perform geometric comparison and calculate geometric error. :

[0097] In the above formula, Represented as the first Geometric error of each node, For parameterized model node coordinates, These are the coordinates of the actual model nodes.

[0098] according to Calculate the average error of all nodes :

[0099] In the above formula, N is the total number of nodes. If the average error Then update the parameters:

[0100]

[0101] In the above formula, This is a correction amount.

[0102] Through iterative verification, the geometric error of the model is ensured. To meet the high precision and high efficiency requirements of different application scenarios.

[0103] S5. Combine visible light images to extract texture and color features, and optimize the parameterized model verified in S4. Specifically, a drone equipped with a high-precision laser rangefinder and a high-resolution camera is selected to collect visible light image data of the tower and its auxiliary equipment; the collected images are then smoothed using a Gaussian filter, which smooths the images through a convolution operation.

[0104] In the above formula, It is the original image. The standard deviation is Two-dimensional Gaussian function; Based on the above method, high-frequency noise is removed while retaining the main structural information, completing the image preprocessing. The preprocessed image data is then processed using the Canny algorithm in edge detection techniques to extract precise edge information. This edge information is used to delineate the device outline and will serve as the basis for subsequent feature extraction and model construction. Furthermore, the color space distribution of the image is analyzed, and color features are extracted using methods such as color histograms.

[0105] In the above formula, It is a color value The number of pixels, It's a Dirac function. These are pixel color values, and the color features extracted using the above method are used to distinguish different types of poles or equipment.

[0106] Finally, the Gray Co-occurrence Matrix (GLCM) technique in texture analysis is applied. GLCM is the joint probability distribution matrix of pixel gray values ​​in an image. By statistically analyzing the spatial relationships between gray values, texture features are extracted, and then the extracted features are converted into a format suitable for modeling, such as vectors, matrices, or specific data structures.

[0107] This method combines visible light images with image processing and computer vision techniques as an auxiliary step in modeling to optimize the constructed 3D parametric model.

[0108] The present invention also discloses a parametric three-dimensional model construction system for power distribution network towers and equipment, including a memory and a processor connected to each other. The memory stores a computer program, and the computer program executes the steps of the method described above when run by the processor.

[0109] The system of the present invention corresponds to the method described above and has the same advantages as the method described above.

[0110] The present invention can implement all or part of the processes in the methods of the above embodiments, or it can be implemented by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium includes: any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. The memory is used to store computer programs and / or modules. The processor implements various functions by running or executing the computer programs and / or modules stored in the memory, and by calling data stored in the memory. The memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0111] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for constructing a parametric three-dimensional model of power distribution network towers and equipment, characterized in that, Including the following steps: S1. Analyze the towers and equipment, identify and extract their basic geometric elements and key features; S2. Based on the geometric elements and key features extracted in S1, quantify the shape and position parameters of the tower and equipment, and construct a standardized parametric model; S3. Adjust and optimize the standardized parametric model constructed in S2. Reduce model complexity and ensure structural accuracy through three-dimensional parametric design. Define global parameters to control overall structural features, local parameters to describe node and local geometric relationships, modularize high-frequency component parameters, establish dynamic association between global and local parameters, remove redundant connections and geometric details through topology optimization and geometric accuracy optimization, and retain key functional features. S4. Verify the parameterized model optimized in S3 using actual inspection data, and correct deviations in geometric and physical properties; S5. Combine visible light images to extract texture and color features, and optimize the parameterized model verified in S4.

2. The method for constructing a parametric three-dimensional model of power distribution network towers and equipment according to claim 1, characterized in that, The specific process of step S1 is as follows: Based on existing engineering drawings and parameters of power distribution network poles and equipment, and using drones equipped with laser rangefinders and visible light cameras to collect images of the power distribution network poles and equipment to be modeled, a detailed analysis of the poles and their auxiliary equipment is conducted to identify and extract the basic geometric elements of the equipment, including points, lines, and surfaces; and to determine the spatial relationships between these elements, including parallelism, perpendicularity, and intersection.

3. The method for constructing a parametric three-dimensional model of power distribution network towers and equipment according to claim 1, characterized in that, The specific process of step S2 is as follows: S201. Quantify the shape parameters of power distribution network towers and key equipment, including length L, width W, height H, radius R and angle θ; set the position parameters, including coordinates (X, Y, Z) and direction; S202. Create a virtual model of the equipment based on the rules of typical equipment, including geometry and size range.

4. The method for constructing a parametric three-dimensional model of power distribution network towers and equipment according to claim 1, 2, or 3, characterized in that, In step S3, global parameters The formula is: Where H is the model height, W is the model width, and M is the model material property; Local parameters The formula is: in, The angle between nodes. For node spatial coordinates, This refers to the node spacing; High-frequency component parameterization Modular processing: Where R is the radius, For module height, This represents the module's rotation angle.

5. The method for constructing a parametric three-dimensional model of power distribution network towers and equipment according to claim 4, characterized in that, In step S3, establishing the dynamic relationship between global and local parameters specifically involves: in: Based on the constraint relationship, the synchronous changes of global and local parameters are constrained, and the stability of the overall structure is ensured.

6. The method for constructing a parametric three-dimensional model of power distribution network towers and equipment according to claim 5, characterized in that, In step S3, during topology optimization, the node contribution rate is first calculated. For nodes Contribution rate is defined as: in, Represents a node Weights between them; Use adjacency matrix Indicates the connection relationship between nodes, if: ;in The contribution rate threshold; Then trim the edges: Remove low-contribution edges and redundant connections, retain the core topology structure, and ensure the rationality and consistency of topological relationships.

7. The method for constructing a parametric three-dimensional model of power distribution network towers and equipment according to claim 6, characterized in that, In step S3, during geometric accuracy optimization, a geometric error constraint simplification algorithm is applied by setting an error threshold. Define the maximum allowable error: in This is the error threshold; in To determine the maximum permissible error for geometric accuracy, unimportant geometric details are removed through error constraints. The error distribution of the geometry is assessed, and redundant nodes or boundaries with errors below a threshold are removed. If the error is less than... ， This involves simplifying or removing geometry while maintaining the geometric accuracy of the model. Finally, redundant geometry is removed by combining bounding box constraints, and the parameter distribution in the boundary region is optimized.

8. The method for constructing a parametric three-dimensional model of power distribution network towers and equipment according to claim 1, 2, or 3, characterized in that, The verification in step S4 includes calculating the geometric error of each node, calculating the average error of all nodes, and if the average error exceeds the threshold, iteratively updating the global and local parameters through parameter correction until the model error meets the requirements.

9. The method for constructing a parametric three-dimensional model of power distribution network towers and equipment according to claim 1, 2, or 3, characterized in that, The extraction of texture and color features in step S5 includes applying Gaussian filtering to remove image noise, using the Canny algorithm for edge detection, statistically analyzing pixel distribution through color histograms, and extracting texture features using the gray-level co-occurrence matrix to support visual optimization of the 3D model.

10. A parametric three-dimensional model construction system for power distribution network towers and equipment, comprising an interconnected memory and a processor, wherein the memory stores a computer program, characterized in that, The computer program, when run by a processor, performs the steps of the method as described in any one of claims 1-9.

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