A low-voltage electric control cabinet component assembly data checking method and system

Abstract

The application relates to the technical field of data consistency verification, and discloses a low-voltage electric control cabinet component assembly data verification method and system. The method comprises the following steps: acquiring assembled actual measurement three-dimensional surface data, performing adaptive voxel down-sampling on the actual measurement point cloud through statistical filtering and iterative closest point registration to obtain a down-sampled actual measurement point cloud; performing deep semantic segmentation and electrical topology potential label propagation on the down-sampled actual measurement point cloud; extracting a curvature adaptive grid and a directional protrusion index based on the labeled potential grouping actual measurement point cloud; calculating a verification insulation distance by using a directed distance field and a Dijkstra shortest path; and performing failure probability mapping and constraint optimization repair according to the verification result to generate risk point labeling and repair suggestions. Due to the layer-by-layer connection of point cloud preprocessing, potential grouping, insulation feature extraction, distance verification and risk repair, the application realizes assembly data consistency verification, and improves the assembly quality and safety of the low-voltage electric control cabinet.

Description

Technical Field

[0001] This invention relates to the field of data consistency verification technology, and in particular to a method and system for verifying assembly data of low-voltage electrical control cabinet components. Background Technology

[0002] Currently, after the installation and secondary wiring of components such as circuit breakers, contactors, knife switches, busbars, and insulating partitions are completed in low-voltage electrical control cabinets, the electrical clearances and creepage distances between phases, to ground, and between components at different potentials are usually checked based on the nominal spacing in the electrical schematic diagram, BOM (Bill of Materials), and cabinet design model. On-site quality inspectors typically use measuring tapes, feeler gauges, laser rangefinders, or special templates to conduct sampling measurements at key points such as busbar overlap surfaces, terminal outlets, and insulating partition edges, and use power frequency withstand voltage tests as the final verification method for the insulation performance of the entire cabinet. This processing path, which mainly relies on nominal geometric dimensions and manual point selection, makes the measurement results highly dependent on personnel experience. Moreover, the measured paths are mostly straight lines or regular curved surfaces, which cannot fully describe the complex surface morphology inside the assembled cabinet. Therefore, it can only reflect whether a few typical sections meet the design values ​​and cannot cover the most dangerous insulation paths under the combined effects of installation deviations and irregular structures.

[0003] For example, after the low-voltage electrical control cabinet is assembled, components such as circuit breakers, busbars, and insulating partitions may experience relative positional shifts due to installation errors, inconsistent tightening torques, or transportation vibrations. Irregularities such as injection molding flash on insulating partitions, rounded corners of busbar bends, edges of metal fastener holes, and inconsistent terminal heights of replacement components can create localized protrusions, grooves, or sharp points. When the phase-to-phase or phase-to-ground clearances fall within these areas, the nominal spacing in the design drawings often still appears to meet standards, but the actual minimum electrical clearance has been compressed. Creepage distances extend along the surface of insulating partitions or device housings, and are affected by protrusions, hole edges, roughness, and contamination, resulting in a significant difference between the actual effective creepage path and the nominal path. Manual sampling makes it difficult to accurately select the worst path. If only the nominal spacing and a few measured points are used to determine compliance, weak points in insulation caused by insufficient local creepage distances or concentrated electric fields are easily missed. This can lead to repeated failures in power frequency withstand voltage tests, or, in severe cases, phase-to-phase flashover, phase-to-ground breakdown, or surface discharge along the insulating partition during long-term operation, endangering equipment and personnel safety.

[0004] Therefore, there is an urgent need for a method to verify the assembly data of low-voltage electrical control cabinet components. After the low-voltage electrical control cabinet is assembled, and in the face of actual spacing changes caused by installation deviations and irregular shells, it should still be able to automatically identify different potential groups using the measured position and shape data after assembly, extract protrusions on the insulation surface and offsets of replacement components, calculate the minimum electrical clearance and creepage distance along the actual surface, issue warnings for parts that do not meet insulation standards and provide repair suggestions, so as to improve the coverage, consistency and reliability of on-site assembly verification, and reduce the risk of unqualified withstand voltage test and operational insulation failure. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, the purpose of this invention is to propose a method for verifying the assembly data of low-voltage electrical control cabinet components. This method aims to solve the problem that existing technologies, based on nominal spacing and manual sampling, are unable to detect insufficient local creepage distances. In particular, after assembly, the spacing between phases, to ground, and between components at different potentials is affected by installation deviations, irregular protrusions, or replacement components, making it impossible to automatically verify the minimum electrical clearance and creepage distance. This method aims to solve the technical problems of insulation failure or failure to pass the withstand voltage test.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a method for verifying the assembly data of low-voltage electrical control cabinet components.

[0007] The method for verifying the assembly data of low-voltage electrical control cabinet components includes: Step S10: Obtain the measured three-dimensional surface data of the assembled low-voltage electrical control cabinet. Based on the measured three-dimensional surface data of the assembled low-voltage electrical control cabinet, perform point cloud preprocessing tasks using an adaptive voxel downsampling mechanism of statistical filtering and iterative nearest point registration, and output the downsampled measured point cloud. Step S20: Based on the downsampled measured point cloud, the component potential grouping and replacement offset identification task is performed using deep semantic segmentation and electrical topology potential label propagation mechanism, and the measured point cloud with labeled potential grouping is output. Step S30: Based on the measured point cloud of the marked potential group, the insulation surface feature extraction task is performed by adopting the curvature adaptive grid reconstruction and directional convexity index extraction mechanism, and the insulation surface feature data is output. Step S40: Based on the insulation surface feature data, the insulation distance verification task is performed using the insulation distance verification mechanism calculated by the directed distance field and Dijkstra's shortest path, and the insulation distance verification result is output. Step S50: Based on the insulation distance verification results, the risk warning and repair suggestion generation task is executed using the failure probability mapping and constraint optimization repair mechanism, and the risk point marking and repair suggestions are output.

[0008] Preferably, in step S10, the steps of acquiring the measured three-dimensional surface data of the assembled low-voltage electrical control cabinet, and based on the measured three-dimensional surface data of the assembled low-voltage electrical control cabinet, performing point cloud preprocessing tasks using an adaptive voxel downsampling mechanism of statistical filtering and iterative nearest point registration, and outputting the downsampled measured point cloud, specifically include: Step S101: Perform statistical filtering on the measured three-dimensional surface data of the assembled low-voltage electrical control cabinet, calculate the neighborhood Euclidean distance statistics of each measured point, and remove outliers based on the preset outlier judgment rules to obtain a denoised point cloud. Step S102: Based on the denoised point cloud, read the CAD nominal model of the low-voltage electrical control cabinet, iteratively register the denoised point cloud with the CAD nominal model and unify it to the cabinet frame coordinate system to obtain the registered point cloud; Step S103: Based on the registered point cloud, the smaller of one-third of the local feature scale and one-fifth of the nominal gap value in the CAD design is used as the initial voxel size, wherein the local feature scale is the smaller of the local radius of curvature and twice the standard deviation of the scanner single-point measurement; using the initial voxel size as the sampling parameter, the registered point cloud is downsampled, and the root mean square of the difference between the measured unit normal vector and the local surface fitting unit normal vector is used as the feature preservation error; when the feature preservation error is greater than the preset feature preservation error threshold, the corresponding voxel is subdivided into octrees until the feature preservation error is not greater than the preset feature preservation error threshold or the voxel size reaches half of the standard deviation of the scanner single-point measurement, thus obtaining the downsampled measured point cloud.

[0009] Preferably, in step S20, the step of performing component potential grouping and replacement offset identification tasks based on the downsampled measured point cloud, using deep semantic segmentation and electrical topology potential label propagation mechanism, and outputting the marked potential grouped measured point cloud, specifically includes: Step S201: Obtain a visible light image of the low-voltage electrical control cabinet, fuse the visible light image with the downsampled measured point cloud, and perform semantic segmentation on the fusion result to obtain a cluster of conductive point clouds with component type labels; Step S202: Based on the conductive point cloud cluster with component type tags, read the electrical schematic diagram and bill of materials of the low-voltage electrical control cabinet, and construct a potential node diagram according to the component terminals and wire or busbar connection relationships in the electrical schematic diagram and bill of materials; match the conductive point cloud cluster with the nodes of the potential node diagram, propagate the potential tags along the electrical connection edges, and determine the corresponding potential tags according to the adjacency relationship of the potential node diagram when the conductor is partially blocked by the insulating cover, so as to obtain the potential grouped point cloud; Step S203: Based on the potential grouping point cloud, robustly register the potential grouping point cloud with the CAD nominal model corresponding to the bill of materials to obtain the coordinate transformation relationship; calculate the local spatial block deviation and the global robust deviation using the coordinate transformation relationship, compare the local spatial block deviation and the global robust deviation with the corresponding preset deviation thresholds respectively, and mark the replacement device or offset device that exceeds the corresponding preset deviation threshold to obtain the marked potential grouping measured point cloud.

[0010] Preferably, in step S203, the local spatial block deviation is calculated based on the coordinate transformation relationship, and the formula for calculating the local spatial block deviation is:

[0011] in, Indicates the first Local spatial block deviation, in millimeters. Indicates the first A set of points in a local spatial block. This indicates the number of points in the point set. Represents a point index. The first point in the downsampled measured point cloud represents the... The coordinates of the measured points are in millimeters. Indicates the relationship between the nominal CAD model and the first The coordinates of the nearest point corresponding to each measured point, in millimeters. This represents the coordinate transformation operator obtained by robust registration; it compares the local spatial block deviation with a preset local spatial block deviation threshold, and identifies the corresponding components that exceed the preset local spatial block deviation threshold as replacement components or offset components.

[0012] Preferably, step S30, which involves performing the insulation surface feature extraction task based on the measured point cloud of the marked potential group and using a curvature adaptive mesh reconstruction and directional convexity index extraction mechanism to output insulation surface feature data, specifically includes: Step S301: Based on the measured point cloud of the marked potential group, the surface of the charged body and the surface of the insulator are reconstructed by curvature adaptive triangular mesh, and the regions marked as replacement devices or offset devices and the feature edges whose normal vector change angle exceeds the preset feature edge angle threshold are refined by anisotropic mesh to obtain the triangular mesh of the insulating surface. Step S302: Based on the triangular mesh of the insulating surface, calculate the average curvature and Gaussian curvature of each mesh vertex, extract the nearest point pairs between different potential groups, and determine the unit vector from the nearest point of the first potential group to the nearest point of the second potential group as the direction vector of the nearest point pair, thereby obtaining curvature and direction feature data; Step S303: Based on the curvature and orientation feature data, the orientation bulge index is determined using the projection height of the mesh vertex relative to the local reference plane and the average curvature as calculation parameters; the edge electric field concentration factor is determined using the edge curvature intensity and the preset electric field concentration amplification factor as calculation parameters, and the product of the orientation bulge index and the edge electric field concentration factor is used as the risk bulge index; the risk bulge index is compared with the preset risk bulge index threshold, and high-risk irregular bulges exceeding the preset risk bulge index threshold are marked; the insulating surface triangular mesh, the nearest point pair orientation vector, and the high-risk irregular bulge mark are combined to obtain the insulating surface feature data.

[0013] Preferably, step S40, which involves performing an insulation distance verification task based on the insulation surface feature data using an insulation distance verification mechanism calculated by a directed distance field and Dijkstra's shortest path, and outputting the insulation distance verification result, specifically includes: Step S401: Based on the insulation surface feature data, a directed distance field is established for the charged body surfaces of different potential groups. The minimum value of each point on the first charged body surface in the directed distance field of the second charged body surface is taken as the minimum electrical clearance. The standard deviation of single-point measurement of the scanner, the thermal expansion of the charged body, and the point cloud registration residual are input into the comprehensive uncertainty calculation process to obtain the comprehensive uncertainty. The high-risk irregular protrusion mark and the mesh reconstruction error are input into the shape confidence correction process to obtain the shape confidence correction term. The comprehensive uncertainty and the shape confidence correction term are subtracted from the minimum electrical clearance to obtain the effective electrical clearance. The reference electrical clearance value, the electric field non-uniformity correction coefficient, and the altitude correction coefficient are input into the electrical clearance requirement value calculation process to obtain the electrical clearance requirement value. Step S402: Based on the effective electrical clearance, construct a weighted graph on the triangular mesh of the insulating surface; input the length of each mesh edge, local average curvature, insulating surface roughness, electric field intensity components along the edge tangent direction, and metal fastener hole status into the mesh edge weight calculation process to obtain the mesh edge weight; use Dijkstra's shortest path algorithm to sum the mesh edge weights contained in the insulating surface path connecting different potential groups, and determine the minimum weight sum as the effective creepage distance to obtain the effective insulation distance data; Step S403: Based on the effective insulation distance data, input the reference creepage distance value, altitude correction factor, and material correction factor determined by the relative tracking index of the insulation material into the creepage distance requirement calculation process to obtain the creepage distance requirement value; compare the effective electrical clearance with the electrical clearance requirement value, and compare the effective creepage distance with the creepage distance requirement value; combine the effective electrical clearance, effective creepage distance, electrical clearance requirement value, creepage distance requirement value, and comparison results to obtain the insulation distance verification result.

[0014] Preferably, in step S50, based on the insulation distance verification result, the step of performing risk warning and repair suggestion generation task using a failure probability mapping and constraint optimization repair mechanism, and outputting risk point annotation and repair suggestions, specifically includes: Step S501: Obtain the effective distance and distance requirement value for each risk point from the insulation distance verification results. Divide the difference between the effective distance and the distance requirement value by the distance requirement value to obtain the local margin. Based on the local margin, perform failure probability mapping to obtain the failure probability. The formula for calculating the failure probability is:

[0015] in, Indicates the first The probability of failure at each risk point Indicates the risk point index. This represents the preset probability steepness coefficient. Indicates the first Local margin at each risk point This indicates the preset critical margin; the failure probability, probability steepness coefficient, local margin, and critical margin are all dimensionless quantities. Step S502: Based on the failure probability, the failure probability is weighted and summed using the importance weight of each risk point and the preset risk amplification power to obtain the overall cabinet risk index; the overall cabinet risk index is compared with the preset overall cabinet warning threshold, and a risk warning is generated when the overall cabinet risk index exceeds the preset overall cabinet warning threshold; the adjustable conductor displacement and insulation barrier thickness are used as repair variables, the minimum total repair cost is used as the optimization objective, and the effective distance after repair is not less than the corresponding distance requirement value is used as a constraint to obtain the repair optimization result; Step S503: Based on the repair optimization results, when using an insulation barrier for repair, the required insulation barrier thickness is determined by dividing the difference between the required distance value and the current effective distance by the effective electrical strength coefficient of the barrier material; the optimized values ​​of the risk warning and repair variables and the required insulation barrier thickness are mapped to the three-dimensional model of the low-voltage electrical control cabinet to obtain the risk point marking and repair suggestions.

[0016] This invention also provides a low-voltage electrical control cabinet component assembly data verification system, comprising: The point cloud preprocessing module is used to acquire the measured three-dimensional surface data of the assembled low-voltage electrical control cabinet. Based on the measured three-dimensional surface data of the assembled low-voltage electrical control cabinet, the point cloud preprocessing task is performed by an adaptive voxel downsampling mechanism of statistical filtering and iterative nearest point registration, and the downsampled measured point cloud is output. The potential grouping and replacement identification module is used to perform component potential grouping and replacement offset identification tasks based on the downsampled measured point cloud, using deep semantic segmentation and electrical topology potential label propagation mechanism, and outputting a marked potential grouped measured point cloud. The insulation surface feature extraction module is used to perform insulation surface feature extraction tasks based on the measured point cloud grouped by the marked potential, using curvature adaptive mesh reconstruction and directional convexity index extraction mechanism, and output insulation surface feature data. The insulation distance verification module is used to perform insulation distance verification tasks based on the insulation surface feature data, using an insulation distance verification mechanism calculated by a directed distance field and Dijkstra's shortest path, and output the insulation distance verification results. The risk warning and repair suggestion module is used to perform risk warning and repair suggestion generation tasks based on the insulation distance verification results, using a failure probability mapping and constraint optimization repair mechanism, and output risk point markings and repair suggestions.

[0017] The present invention also provides a computer program product, the computer program product including a low-voltage electrical control cabinet component assembly data verification program, which implements the above method when executed by a processor.

[0018] The beneficial effects of this invention are as follows: Outliers in the measured point cloud are removed by statistical filtering, and the denoised point cloud is unified to the cabinet frame coordinate system by iterative nearest point registration. Then, adaptive voxel downsampling constrained by local feature scale and design nominal clearance preserves insulation surface details. This ensures that the point cloud used for subsequent semantic segmentation and mesh reconstruction has both coordinate consistency and retains small features such as irregular protrusions, thereby reducing misjudgments of insulation distance caused by scanning noise and registration deviation, and improving the reliability of minimum electrical clearance and creepage distance calculation.

[0019] By using deep semantic segmentation and electrical topology potential tag propagation, the conductive point cloud clusters are matched with potential nodes in the electrical schematic and bill of materials. When the insulation shield is blocked, the potential tags are completed based on the adjacency relationship. At the same time, the coordinate transformation relationship obtained by robust registration is used to calculate the local spatial block deviation and the global robust deviation, so that the replacement device or the installed offset device is automatically marked. This ensures that the subsequent insulation distance verification is performed on the actual potential group and the actual assembly position, avoiding the underestimation or missed detection of the distance between different potentials due to device replacement or offset.

[0020] By reconstructing the curvature adaptive mesh and extracting the directional protrusion index, high-risk irregular protrusions are marked on the triangular mesh of the insulation surface. The effective electrical clearance is calculated using the directional distance field, and the effective creepage distance is calculated along the actual insulation surface using Dijkstra's shortest path. The distance values ​​are then conservatively corrected by combining comprehensive uncertainty and shape confidence correction, so that the verification results reflect the real most dangerous insulation path rather than the nominal geometric dimensions. This generates risk warnings and repair suggestions when the overall risk index of the cabinet exceeds the warning threshold, reducing the risk of failure in power frequency withstand voltage tests and long-term insulation failure. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the first embodiment of a method for verifying assembly data of low-voltage electrical control cabinet components according to the present invention. Detailed Implementation

[0022] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0023] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0024] Example 1: As Figure 1 The diagram shown is a flowchart of the first embodiment of a method for verifying assembly data of low-voltage electrical control cabinet components according to the present invention.

[0025] In the first embodiment, the method for verifying the assembly data of low-voltage electrical control cabinet components includes: Step S10: Obtain the measured three-dimensional surface data of the assembled low-voltage electrical control cabinet. Based on the measured three-dimensional surface data of the assembled low-voltage electrical control cabinet, perform point cloud preprocessing tasks using an adaptive voxel downsampling mechanism of statistical filtering and iterative nearest point registration, and output the downsampled measured point cloud. After the low-voltage electrical control cabinet is assembled, the three-dimensional surface data collected by the scanning equipment covers the surfaces of live parts, insulating supports, busbars, and mounting components inside the cabinet. This data contains scanning noise, outliers caused by obstructions, and actual position information after assembly. Step S10 first calculates the mean and standard deviation of the Euclidean distance within the neighborhood of each measured point. If the average neighborhood distance of a point is greater than the mean plus three times the standard deviation, that point is removed as an outlier, resulting in a denoised point cloud. Next, the nominal model of the low-voltage electrical control cabinet's computer-aided design is read, and the denoised point cloud is iteratively registered with the nominal model to transform the measured point cloud to the cabinet frame coordinate system, eliminating pose deviations caused by the scanner's mounting position and the cabinet's placement angle. After registration, the smaller of one-third of the local feature scale and one-fifth of the nominal gap value of the computer-aided design is used as the initial voxel size. The local feature scale is the smaller of the local radius of curvature and twice the standard deviation of the scanner's single-point measurement. The registered point cloud is downsampled using the initial voxel size, and the root mean square of the difference between the measured unit normal vector and the fitted unit normal vector of the local surface is calculated as the feature preservation error. When the feature preservation error is greater than a preset feature preservation error threshold, the corresponding voxels are subdivided into octrees until the feature preservation error is no greater than the preset threshold or the voxel size reaches half of the standard deviation of the scanner's single-point measurement. Finally, the downsampled measured point cloud is output.

[0026] After statistical filtering removes isolated outliers, the point cloud noise is constrained to near the standard deviation of the scanner's single-point measurement, making subsequent normal vector estimation and curvature calculation less susceptible to interference from isolated points. Iterative nearest-point registration places the measured point cloud and the nominal model of the computer-aided design in the same cabinet frame coordinate system, preventing coordinate deviations caused by scanning pose differences from being misjudged as assembly deviations in subsequent steps. Adaptive voxel downsampling refines the data based on the local surface feature preservation error, retaining higher point density in areas with significant surface changes such as busbar corners and insulation protrusions, while appropriately merging points in planar areas to reduce the computational load of subsequent triangular mesh reconstruction and protrusion extraction. Simultaneously, the feature preservation error is controlled below a preset threshold to avoid smoothing high-risk edges. The downsampled measured point cloud retains busbar edges and insulation surface features in a unified coordinate system, providing a stable geometric carrier for deep semantic segmentation and electrical topology potential tag propagation in step S20, thus controlling the discrete errors of subsequent minimum electrical clearance and creepage path search.

[0027] Traditional processing methods typically involve uniform downsampling of the original scanned point cloud or relying on manual point selection. Uniform downsampling easily loses local geometric variations at busbar edges and insulation protrusions, while manual point selection relies on the experience of the inspectors and is difficult to cover the entire interior area of ​​the cabinet. Low-voltage electrical control cabinets have densely packed components and irregular busbar routing, resulting in occlusion and uneven reflection in the scanned point cloud. Directly calculating the nearest point distance can easily misinterpret scanning noise as surface features. Step S10, through statistical filtering, iterative nearest point registration, and adaptive voxel refinement based on feature-preserving error, correlates the point cloud density distribution with surface curvature and standard gap values ​​without adding additional measurement equipment, reducing data redundancy while preserving high-risk geometric details required for subsequent insulation distance verification. Because registration residuals and scanning errors are explicitly incorporated into the processing, subsequent distance calculations have a consistent coordinate reference, reducing the risk of missed insulation detection due to inconsistent references.

[0028] After the assembly of a low-voltage electrical control cabinet, the nominal gap between the busbar and the frame, as designed by computer-aided design, was eight millimeters. However, after actual assembly, a local bend appeared at the corner of the busbar, and the scanned point cloud contained outliers reflecting off the cabinet edge. Step S10 performed statistical filtering on the scanned, measured 3D surface data of the assembled cabinet to remove outliers near the corner. Then, the denoised point cloud was iteratively registered with the nominal model of the computer-aided design to unify the busbar coordinates to the cabinet frame coordinate system. Adaptive voxel downsampling performed two octree subdivisions at the bend corner because the feature retention error exceeded a preset threshold, refining the voxel size from approximately 1.2 millimeters to 0.3 millimeters, thus preserving the bend radius at the corner. In the final output downsampled measured point cloud, the point density was higher in high-risk areas such as the busbar corner and insulation protrusions, and lower in planar areas, providing clear geometric boundaries for component potential grouping and replacement offset identification in step S20.

[0029] Step S20: Based on the downsampled measured point cloud, the component potential grouping and replacement offset identification task is performed using deep semantic segmentation and electrical topology potential label propagation mechanism, and the measured point cloud with labeled potential grouping is output. The downsampled measured point cloud output in step S10 only records the three-dimensional coordinates of the surfaces of each component inside the cabinet. Circuit breakers, contactors, busbars, and insulating supports are not yet distinguished, and there are no potential attributes. To provide the boundaries of charged bodies for subsequent insulation distance verification, a visible light image of the low-voltage electrical control cabinet is first acquired and fused with the downsampled measured point cloud. The device outlines, colors, and identification information in the image are used to assist semantic segmentation, so that the conductive areas in the point cloud are labeled with component types. Then, the electrical schematic diagram and bill of materials of the low-voltage electrical control cabinet are read, and a potential node diagram is constructed based on the component terminals and the connection relationships of wires or busbars. Nodes correspond to component terminals, and edges correspond to wire or busbar connections. The conductive point cloud clusters obtained by semantic segmentation are matched with the nodes in the potential node diagram, and the potential labels are propagated along the electrical connection edges. When a conductor is partially blocked by the insulating cover, the potential group to which the conductor belongs is inferred based on the adjacency relationship of the potential node diagram, resulting in a potential group point cloud. Based on this, robust registration is performed between the potential grouped point cloud and the nominal model of the computer-aided design corresponding to the bill of materials. The coordinate transformation relationship is obtained, and the local spatial block deviation and global robust deviation are calculated using this transformation relationship. The local spatial block deviation reflects the average deviation between the measured points in a local area of ​​a component and the corresponding points in the nominal model, while the global robust deviation reflects the residual positional difference after overall registration. The two types of deviations are compared with the corresponding preset deviation thresholds. Components exceeding the thresholds are marked as replacement components or offset components, and finally, the marked potential grouped measured point cloud is output.

[0030] After the above processing, each conductive point in the downsampled measured point cloud is labeled with a component type and a potential group label, and the replacement or offset regions are also clearly marked. This intermediate state allows step S30 to distinguish between charged and insulating surfaces during insulation surface feature extraction, and defines the closest point pairs only between different potential groups, preventing the distance between adjacent conductors at the same potential from being mistakenly used as electrical clearance. The replacement and offset markings also indicate which areas require finer mesh reconstruction, allowing curvature adaptive mesh reconstruction to invest more geometric expression accuracy in high-risk areas. In step S40, when calculating the directed distance field and shortest path, the marked potential grouped point cloud can be used directly to search for the minimum electrical clearance and creepage path for the insulating surfaces between different potential groups, avoiding including the spacing between conductors at the same potential in the verification scope. At the same time, the actual position of the replaced or offset device is retained in the point cloud, and subsequent insulation distance verification is based on the actual assembly state, rather than using nominal positions to replace actual positions, thereby reducing misjudgments of insulation risks caused by inconsistent device states.

[0031] Traditional verification typically assumes that the component installation positions match the nominal model in the computer-aided design based on the bill of materials, or only performs visual inspections on individual components. When similar-looking replacement components appear in the field, it is difficult to manually determine their wiring positions and changes in the insulation shell contour, easily leading to the nominal position being mistaken for the actual position when calculating insulation distance. Deep semantic segmentation associates the measured surface with the component type, reducing the problem of unclear correspondence between name and object; electrical topology potential tag propagation uses wiring relationships to fill in obscured areas, avoiding missed tagging of live parts due to scanning obstruction. Compared with methods that only compare global average deviations, local spatial block deviations can locate specific component areas. When the local block deviation exceeds a preset threshold or the global robust deviation exceeds a preset threshold, it is determined to be a replacement or offset component. This allows subsequent creepage distance and clearance calculations to be based on the actual assembly state, rather than using nominal positions to replace actual positions, reducing misjudgments of insulation risks caused by inconsistent component states.

[0032] Following the scenario in step S10, after the assembly of a low-voltage electrical control cabinet, the nominal gap between the feeder circuit busbar and the frame, designed by computer-aided design, is eight millimeters. However, after actual assembly, local bending occurs at the corner of the busbar. The downsampled measured point cloud output in step S10 shows a high point density in high-risk areas such as the busbar corner and insulation protrusions. First, a visible light image of the low-voltage electrical control cabinet is acquired. This image is then fused with the downsampled measured point cloud for semantic segmentation to identify component types such as circuit breakers, contactors, busbars, and insulation supports. Next, the electrical schematic and bill of materials are read to construct a potential node diagram. Conductor point cloud clusters are matched with nodes, and potential labels are propagated. Conductors obscured by the insulation shield are identified through adjacency relationships to determine potential groups. Finally, the potential group point cloud is robustly registered with the nominal computer-aided design model corresponding to the bill of materials, and local spatial block bias and global robust bias are calculated. In this cabinet, a molded case circuit breaker was replaced by a circuit breaker of the same series but with a different breaking capacity. The circuit breaker's external width was similar, but its terminal positions were offset. The local spatial block deviation exceeded a preset local spatial block deviation threshold, and the global robustness deviation also exceeded a preset global robustness deviation threshold. This device was marked as a replacement or offset device. In the final output measured point cloud of the marked potential group, the circuit breaker area showed a replacement offset mark. Subsequent step S30 performed anisotropic mesh refinement in this area, and step S40 used the measured offset position, rather than the nominal position, when calculating the actual electrical clearance between the circuit breaker terminals and the adjacent phase busbar.

[0033] Step S30: Based on the measured point cloud of the marked potential group, the insulation surface feature extraction task is performed by adopting the curvature adaptive grid reconstruction and directional convexity index extraction mechanism, and the insulation surface feature data is output. In the measured point cloud of the marked potential groups sent in step S20, each point, in addition to its three-dimensional coordinates, also carries the label of its corresponding potential group and the label of the replaced or offset device. For insulating surface feature extraction, a curvature-adaptive triangular mesh reconstruction is first performed on the charged body surface and the insulating surface. The mesh vertex density is automatically adjusted according to the surface curvature. The areas marked as replaced or offset devices in step S20, as well as feature edges whose normal vector change angle exceeds a preset feature edge angle threshold, are further refined. This ensures that sufficient number of vertices are retained at geometrically abrupt locations such as insulating shell edges, injection molding flash, and terminal corners. After reconstruction, the average curvature and Gaussian curvature are calculated for each mesh vertex. The average curvature reflects the local curvature of the surface, and the Gaussian curvature is used to distinguish between convex, concave, and saddle-shaped surfaces. Simultaneously, the nearest point pairs between different potential groups are extracted, and the unit vector pointing from the nearest point in the first potential group to the nearest point in the second potential group is determined as the direction vector of the nearest point pair. The directional bulge index is determined using the projected height and average curvature of the mesh vertices relative to the local reference plane as calculation parameters. The edge electric field concentration factor is determined using the edge curvature intensity and a preset electric field concentration factor as calculation parameters. The product of the directional bulge index and the edge electric field concentration factor is then used as the risk bulge index. The risk bulge index is compared with a preset risk bulge index threshold; vertices exceeding the threshold are marked as high-risk irregular bulges. Finally, the insulating surface triangular mesh, the nearest point pair direction vector, and the high-risk irregular bulge markers are combined to form the insulating surface feature data.

[0034] The insulation surface feature data output in step S30 includes, in addition to the vertex positions and connectivity of the triangular mesh on the insulation surface, the average curvature, Gaussian curvature, directional bulge index, edge electric field concentration factor, and high-risk irregular bulge markers for each vertex. These intermediate states provide region shape confidence information for the directed distance field construction and Dijkstra's shortest path calculation in step S40. In step S40, the directed distance field needs to calculate the shortest distance from spatial points to charged bodies and insulators based on the triangular mesh on the insulation surface. The curvature adaptive mesh reduces the discrete error of high-risk areas and avoids over-smoothing of irregular bulges. The nearest point pair direction vector provides a clear measurement direction for electrical clearance calculation, and the high-risk irregular bulge markers allow for stricter edge weight constraints on bulge areas in subsequent creepage distance calculations. Thus, when calculating the insulation distance between adjacent potential groups, step S40 can distinguish between smooth planes and high-risk bulge areas, avoiding treating irregular bulge areas as ordinary surfaces, thereby reducing the risk of missed detections due to insufficient creepage distance caused by local bulges.

[0035] Traditional mesh reconstruction often uses uniform meshes or reconstruction based solely on scan density, which can easily lead to over-smoothing at the edges and irregular protrusions of the insulating shell, weakening the information on directional protrusions and edge curvature. In electrical clearance standards, the electric field is more concentrated in areas with smaller radii of curvature, but uniform meshes cannot reflect this effect. This step uses curvature-adaptive mesh reconstruction to match the mesh vertex density with the surface curvature, anisotropically refining the replaced or offset device areas and feature edges, preserving the information on geometric abrupt changes in the insulating surface. The directional protrusion index combines the geometric protrusion height with the average curvature, highlighting irregularly shaped areas with large curvature and protruding along the shortest spacing direction; the edge electric field concentration factor further amplifies edges and narrow-edge regions. The extracted insulating surface feature data thus includes not only surface point locations but also the risk protrusion index and edge markers for each vertex, providing regional attributes for shape confidence correction and creepage distance edge weight calculation in electrical clearance calculations. This allows subsequent distance verification to distinguish smooth planes from high-risk protrusion areas, preventing irregularly shaped protrusion areas from being treated as ordinary surfaces. Compared to methods that rely solely on the straight-line distance between the nearest points to determine gaps, this method incorporates the local shape of the insulation surface into subsequent corrections, reducing the probability of missing irregular protrusions that could lead to insufficient creepage distances in certain areas.

[0036] Following the scenario in step S20, after the assembly of a low-voltage electrical control cabinet, the nominal gap between the feeder circuit busbar and the frame, designed by computer-aided design, is eight millimeters. However, after actual assembly, local bending occurs at the busbar corner. The measured point cloud of the marked potential group output in step S20 shows a high point density in high-risk areas such as the busbar corner and insulation protrusions. Furthermore, a molded case circuit breaker is replaced by a circuit breaker of the same series but with a different breaking capacity, and its terminal position is offset. This circuit breaker area has a replacement offset mark. Step S30 performs curvature adaptive triangular mesh reconstruction on the measured point cloud of the marked potential group. Anisotropic mesh refinement is performed in the circuit breaker replacement offset area and the characteristic edge of the busbar corner, making the mesh vertex density in these areas significantly higher than that on a flat surface. The average curvature and Gaussian curvature are calculated for each vertex. The average curvature is larger at the busbar corner, and the Gaussian curvature at the injection-molded flash on the insulation partition shows a local extremum. The nearest point pairs between different potential groups are extracted, such as the nearest point pair between the circuit breaker terminal and the adjacent phase busbar, and the direction vector of the nearest point pair is determined. The directional protrusion index is determined using the projected height and average curvature of the mesh vertices relative to the local reference plane as calculation parameters; the edge electric field concentration factor is determined using the edge curvature intensity and the preset electric field concentration amplification factor as calculation parameters. Protrusion indices at busbar corners and injection-molded flash that exceed the preset risk protrusion index threshold are marked as high-risk irregular protrusions. The final output insulation surface feature data includes the insulation surface triangular mesh, the nearest point pair direction vector, and high-risk irregular protrusion markings. Step S40 calculates the actual electrical clearance and creepage distance between the circuit breaker terminal and the adjacent phase busbar based on this data, and corrects for these distances using measured offset positions and protrusion markings.

[0037] Step S40: Based on the insulation surface feature data, the insulation distance verification task is performed using the insulation distance verification mechanism calculated by the directed distance field and Dijkstra's shortest path, and the insulation distance verification result is output. The insulation surface feature data obtained in step S40 includes surface point sets for different potential groups, triangular meshes of the insulation surface, direction vectors of nearest point pairs, and marked high-risk irregular protrusions. Insulation distance verification needs to handle two types of objects: the shortest spatial distance between charged bodies, i.e., the electrical clearance; and the shortest path length along the insulation surface connecting charged bodies at different potentials, i.e., the creepage distance. The electrical clearance calculation first establishes a directed distance field on the surface of the second charged body. The minimum value of each point on the surface of the first charged body in this distance field is the measured minimum electrical clearance. The standard deviation of single-point measurement by the scanner, the thermal expansion of the charged body, and the point cloud registration residuals can interfere with distance judgment; these factors are input into the comprehensive uncertainty calculation process to obtain the comprehensive uncertainty. The high-risk irregular protrusion markings and mesh reconstruction errors are input into the shape confidence correction process to obtain the shape confidence correction term. The comprehensive uncertainty and the shape confidence correction term are subtracted from the measured minimum electrical clearance to obtain the effective electrical clearance. The required electrical clearance is determined by a reference electrical clearance value, an electric field non-uniformity correction factor, and an altitude correction factor. The electric field non-uniformity correction factor is adjusted according to the trend of the radius of curvature of the charged body edge. Regarding creepage distance, a weighted graph is constructed on the triangular mesh of the insulation surface. The edge weights are determined by the edge length, local average curvature, insulation surface roughness, electric field strength components along the edge tangent, and the state of holes in the metal fasteners. The Dijkstra shortest path algorithm is used to sum the edge weights of the insulation surface paths connecting different potential groups, and the minimum weight sum is determined as the effective creepage distance. The required creepage distance is determined by a reference creepage distance value, an altitude correction factor, and a material correction factor determined by the relative tracking index of the insulation material. Finally, the effective electrical clearance is compared with the required electrical clearance value, and the effective creepage distance is compared with the required creepage distance value. The effective electrical clearance, effective creepage distance, required electrical clearance value, required creepage distance value, and comparison results are combined to form the insulation distance verification result.

[0038] The insulation distance verification result output in step S40 includes the effective electrical clearance, effective creepage distance, required electrical clearance value, required creepage distance value, and their respective comparison conclusions. Step S50 can directly locate risk points based on this. The effective electrical clearance has already deducted the effects of scanning error, temperature deformation, registration residual, and high-risk protrusions and mesh reconstruction errors, making it closer to the insulation withstand capability under actual operating conditions. The effective creepage distance is calculated using the shortest path on the weighted graph. The path automatically bypasses areas with large curvature, high roughness, strong tangential electric field strength, and holes in metal fasteners, avoiding misjudging the nominal smooth path as the actual surface discharge path. When the effective electrical clearance is less than the required electrical clearance value or the effective creepage distance is less than the required creepage distance value, step S50 can directly locate the corresponding charged body surface point pair or insulation surface path, and generate risk point markings and repair suggestions accordingly. Without these corrected effective distance data, subsequent risk classification can only rely on nominal clearance or manual sampling values, easily missing local insulation weaknesses caused by assembly deviations, irregular protrusions, or hole edges.

[0039] Traditional insulation distance verification typically relies on nominal spacing from computer-aided design or manual measurement with a measuring tape. The shortest straight line distance between different potentials is taken, and creepage distance is often assumed to follow a smooth path along the insulation surface. However, in actual cabinets, busbar corners, insulation partition protrusions, fastener holes, and surface roughness all affect the surface path. The edges of metal holes can further shorten the effective creepage path. This step establishes a directed distance field and weighted graph, incorporating measured geometric features into the distance calculation. It uses Dijkstra's algorithm to search for the shortest weighted path connecting two potential points, rather than simply taking the straight-line distance. This allows for the detection of insufficient creepage distances caused by irregular protrusions or holes. Traditional methods typically do not deduct scanning errors and temperature deformation, or only amplify the safety margin by a fixed ratio, failing to differentiate the shape confidence levels of different areas. This step sets up separate shape confidence correction terms for high-risk irregular protrusions and mesh reconstruction errors, making the insulation distance judgment for locally complex shapes more conservative and avoiding missed risks due to insufficient local shape confidence.

[0040] Following the scenario in step S30, after the low-voltage electrical control cabinet is assembled, the nominal clearance between the feeder circuit busbar and the frame, as designed by computer-aided design, is eight millimeters. However, after actual assembly, local bending occurs at the corner of the busbar. A molded case circuit breaker is replaced by a circuit breaker of the same series but with a different breaking capacity, and its terminal position is offset. This circuit breaker area has a replacement offset mark. In the insulation surface feature data output in step S30, the busbar corner and the injection molding flash are marked as high-risk irregular protrusions. The direction vector of the closest point between the circuit breaker terminal and the adjacent phase busbar has also been determined. Step S40 first establishes a directed distance field on the surface of the adjacent phase busbar, calculates the minimum value of each point on the surface of the circuit breaker terminal in this distance field, and obtains the measured minimum electrical clearance. This area has scanning noise, temperature deformation, and registration residuals, and the overall uncertainty is deducted from the measured minimum electrical clearance. The high-risk irregular protrusion marks at the edge of the circuit breaker terminal and the busbar corner trigger the shape confidence correction term, further deducting the margin caused by insufficient local shape confidence, to obtain the effective electrical clearance. The required electrical clearance value is calculated based on the baseline electrical clearance value, the electric field non-uniformity correction factor, and the altitude correction factor. The electric field non-uniformity correction factor increases due to the smaller radius of curvature at the busbar corner. A weighted graph is then constructed on the triangular mesh of the insulation surface. The path of the insulation partition between the circuit breaker terminal and the adjacent phase busbar passes through the edge of a metal fastener hole. The edge weight is multiplied by the hole penalty factor, and the minimum weight sum obtained by the Dijkstra algorithm is the effective creepage distance. The required creepage distance value is calculated based on the baseline creepage distance value, the altitude correction factor, and the material correction factor determined by the relative tracking index of the insulation material. The final comparison results show that the effective electrical clearance meets the electrical clearance requirement, but the effective creepage distance is less than the required creepage distance. This connection path is deemed unqualified. The insulation distance verification results record the specific location and comparison conclusion between the circuit breaker terminal and the adjacent phase busbar, providing a basis for generating risk point markings and repair suggestions in step S50.

[0041] Step S50: Based on the insulation distance verification results, the risk warning and repair suggestion generation task is executed using the failure probability mapping and constraint optimization repair mechanism, and the risk point marking and repair suggestions are output.

[0042] In the insulation distance verification results sent in step S40, each risk point includes an effective electrical clearance, an effective creepage distance, and its respective distance requirement value. For a single risk point, the effective distance is first subtracted from the distance requirement value, and then divided by the distance requirement value. The resulting local margin reflects the degree of excess insulation margin relative to the requirement value; a positive value indicates that the current distance meets the requirement, while a negative value indicates that the distance is insufficient. When the local margin is entered into the failure probability mapping, the mapping relationship is constructed using an exponential function with the natural constant as the base. A preset critical margin corresponds to half the failure probability, and the probability steepness coefficient controls the steepness of the curve near the critical margin. After the failure probability of each risk point is calculated, the failure probabilities are weighted by a preset risk amplification power according to their importance weights to form the overall cabinet comprehensive risk index. A risk warning is triggered when this index exceeds the preset overall cabinet warning threshold. The repair optimization uses the adjustable conductor displacement and insulation barrier thickness as repair variables, with the goal of minimizing the total repair cost. The total repair cost consists of the sum of the squares of each conductor displacement and the weighted cost of the barrier thickness. The constraint requires that the effective distance after repair is not less than the corresponding distance requirement value. The optimization process considers the sensitivity relationship between the effective distance of each risk point and conductor displacement, thereby coordinating the repair amount among multiple risk points. If an insulation barrier is used for repair, the required insulation barrier thickness is determined by dividing the difference between the required distance and the current effective distance by the effective electrical strength coefficient of the barrier material. Finally, the optimized values ​​of the risk warning and repair variables, along with the required insulation barrier thickness, are mapped onto the 3D model of the low-voltage electrical control cabinet to generate risk point location markings and repair suggestions.

[0043] The risk point markings and repair suggestions directly correspond to the specific coordinates and component locations in the 3D model of the low-voltage electrical control cabinet. Assembly personnel can use this information to locate the busbars, circuit breaker terminals, or insulation partitions that need adjustment. The conductor displacement and insulation barrier thickness given in the repair suggestions are the minimum cost solutions obtained through constraint optimization, under the premise of meeting the distance requirements of all non-compliant risk points. This avoids the problem of simply giving a qualified or unqualified conclusion without operability. The overall cabinet risk index summarizes the originally scattered insulation distance verification results into an overall risk level. When this index exceeds the preset warning threshold for the entire cabinet, even if individual risk points have not completely failed, it can indicate that there are systemic hidden dangers in the insulation status of the entire cabinet, prompting assembly personnel to intervene in advance. The repair optimization process also considers the mutual influence between multiple risk points. Moving a busbar may improve the gap of one risk point but worsen the creepage distance of another risk point. The constraints ensure that all risk points meet the distance requirements after repair, thus forming a closed-loop verification process from measurement, calculation, judgment to repair. The final output of risk point markings and repair suggestions can be directly used to guide on-site rework, reducing the number of repeated measurements and trial-and-error adjustments.

[0044] Traditional verification methods typically only provide conclusions for non-conformities or offer adjustment suggestions based on human experience, lacking a systematic calculation of repair quantities. Because the positions of conductors within the cabinet influence each other, moving a busbar might improve the clearance at one risk point but worsen the creepage distance at another, often requiring multiple attempts to find a feasible solution through manual adjustment. This step incorporates the sensitivity relationship between the displacement of multiple adjustable conductors and the effective distance of each risk point into the constraints through constraint optimization, aiming to obtain a set of coordinated repair quantities with the minimum cost, avoiding unintended consequences. The barrier thickness is determined based on the material's electrical strength utilization coefficient to avoid excessive space occupation due to excessive barrier additions. Traditional methods often rely on a binary conclusion of pass or fail, failing to distinguish between minor and severe deficiencies. This step quantifies the insulation distance verification results into risk weights through local margin and failure probability mapping, then summarizes them into a comprehensive risk index for the entire cabinet, reflecting the overall distribution and weak points of the cabinet's insulation level. The repair suggestions directly correspond to the coordinates of the 3D model, allowing assemblers to pinpoint the location of specific components or partitions and make adjustments based on the minimum displacement or barrier thickness, rather than providing general rework instructions.

[0045] Step S40 has determined that the effective creepage distance between the circuit breaker terminal and the adjacent phase busbar is less than the required creepage distance value. The specific location and comparison conclusion of this risk point are recorded in the insulation distance verification results. Step S50 extracts the effective creepage distance and the required creepage distance value of this risk point from this result, calculates the local margin. Since the effective creepage distance is less than the required value, the local margin is negative. Substituting this local margin into the failure probability mapping, a higher failure probability is obtained. The failure probabilities of other risk points in the cabinet are also calculated according to their respective local margins, and then weighted and summed according to their importance weights to obtain the overall cabinet risk index. If this index exceeds the preset overall cabinet warning threshold, a risk warning is triggered. The repair optimization uses the adjustable conductor displacement and insulation barrier thickness as repair variables, with the goal of minimizing the total repair cost. The constraint requires that the effective creepage distance after repair is not less than the required creepage distance value. During the optimization process, the influence of the circuit breaker terminal position and the displacement of the adjacent busbar on the creepage distance is considered. Since the space of this risk point is limited, it is not suitable to move the conductor, and the solution is to use insulation barrier repair. The required insulation barrier thickness is determined by dividing the difference between the required creepage distance and the current effective creepage distance by the effective electrical strength coefficient of the barrier material. Finally, the location of the risk point between the circuit breaker terminal and the adjacent phase busbar is marked on the 3D model of the low-voltage electrical control cabinet, and suggestions for increasing the insulation barrier thickness are given, completing the verification of the cabinet assembly data and generating repair suggestions.

[0046] Example 2: Furthermore, the present invention provides a low-voltage electrical control cabinet component assembly data verification system, which employs a low-voltage electrical control cabinet component assembly data verification method described in the above embodiments, and can solve a technical problem related to low-voltage electrical control cabinet component assembly data verification. The beneficial effects of the low-voltage electrical control cabinet component assembly data verification system provided by the present invention are the same as those of the low-voltage electrical control cabinet component assembly data verification method described in the above embodiments, and other technical features of the low-voltage electrical control cabinet component assembly data verification system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0047] Example 3: This invention provides a low-voltage electrical control cabinet component assembly data verification device. The device includes at least one processor and a memory communicatively connected to the processor. The memory stores instructions executable by the processor, which are then executed to enable the processor to perform the low-voltage electrical control cabinet component assembly data verification method described in Example 1. This low-voltage electrical control cabinet component assembly data verification device can include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and vehicle terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. This low-voltage electrical control cabinet component assembly data verification device is merely an example and should not limit the functionality or scope of the invention. A low-voltage electrical control cabinet component assembly data verification device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in a read-only memory or a program loaded from a storage device into a random access memory. The random access memory also stores various programs and data required for the operation of the low-voltage electrical control cabinet component assembly data verification device. The processing unit, the read-only memory, and the random access memory are interconnected via a bus. An I / O interface is also connected to the bus. Typically, the following systems can be connected to the I / O interface: input devices including touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices including liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices including magnetic tapes, hard disks, etc.; and communication devices. The communication device allows the low-voltage electrical control cabinet component assembly data verification device to communicate wirelessly or wiredly with other devices to exchange data. Although a low-voltage electrical control cabinet component assembly data verification device with various systems has been described, it should be understood that it is not required to implement or possess all the systems described. It can be implemented alternatively or with more or fewer systems.

[0048] Example 4: This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the low-voltage electrical control cabinet component assembly data verification method described above. The computer program product provided by this invention can solve the technical problem of verifying low-voltage electrical control cabinet component assembly data. Compared with the prior art, the beneficial effects of the computer program product provided by this invention are the same as those of the low-voltage electrical control cabinet component assembly data verification method provided in the above embodiments, and will not be repeated here.

[0049] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a read-only memory. When the computer program is executed by a processing device, it performs the functions defined in the methods of the embodiments disclosed in this invention.

[0050] It should be understood that the various parts disclosed in this invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0051] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the present invention and its equivalents, the present invention also intends to include these modifications and variations.

Claims

1. A method for verifying assembly data of components in a low-voltage electrical control cabinet, characterized in that, The method includes: Step S10: Obtain the measured three-dimensional surface data of the assembled low-voltage electrical control cabinet. Based on the measured three-dimensional surface data of the assembled low-voltage electrical control cabinet, perform point cloud preprocessing tasks using an adaptive voxel downsampling mechanism of statistical filtering and iterative nearest point registration, and output the downsampled measured point cloud. Step S20: Based on the downsampled measured point cloud, the component potential grouping and replacement offset identification task is performed using deep semantic segmentation and electrical topology potential label propagation mechanism, and the measured point cloud with labeled potential grouping is output. Step S30: Based on the measured point cloud of the marked potential group, the insulation surface feature extraction task is performed by adopting the curvature adaptive grid reconstruction and directional convexity index extraction mechanism, and the insulation surface feature data is output. Step S40: Based on the insulation surface feature data, the insulation distance verification task is performed using the insulation distance verification mechanism calculated by the directed distance field and Dijkstra's shortest path, and the insulation distance verification result is output. Step S50: Based on the insulation distance verification results, the risk warning and repair suggestion generation task is executed using the failure probability mapping and constraint optimization repair mechanism, and the risk point marking and repair suggestions are output.

2. The method for verifying assembly data of low-voltage electrical control cabinet components as described in claim 1, characterized in that, In step S10, the measured three-dimensional surface data of the assembled low-voltage electrical control cabinet is acquired. Based on the measured three-dimensional surface data of the assembled low-voltage electrical control cabinet, an adaptive voxel downsampling mechanism of statistical filtering and iterative nearest point registration is used to perform point cloud preprocessing and output the downsampled measured point cloud. The steps specifically include: Step S101: Perform statistical filtering on the measured three-dimensional surface data of the assembled low-voltage electrical control cabinet, calculate the neighborhood Euclidean distance statistics of each measured point, and remove outliers based on the preset outlier judgment rules to obtain a denoised point cloud. Step S102: Based on the denoised point cloud, read the CAD nominal model of the low-voltage electrical control cabinet, iteratively register the denoised point cloud with the CAD nominal model and unify it to the cabinet frame coordinate system to obtain the registered point cloud; Step S103: Based on the registered point cloud, the smaller of one-third of the local feature scale and one-fifth of the nominal gap value in the CAD design is used as the initial voxel size, wherein the local feature scale is the smaller of the local radius of curvature and twice the standard deviation of the scanner single-point measurement; using the initial voxel size as the sampling parameter, the registered point cloud is downsampled, and the root mean square of the difference between the measured unit normal vector and the local surface fitting unit normal vector is used as the feature preservation error; when the feature preservation error is greater than the preset feature preservation error threshold, the corresponding voxel is subdivided into octrees until the feature preservation error is not greater than the preset feature preservation error threshold or the voxel size reaches half of the standard deviation of the scanner single-point measurement, thus obtaining the downsampled measured point cloud.

3. The method for verifying assembly data of low-voltage electrical control cabinet components as described in claim 1, characterized in that, In step S20, based on the downsampled measured point cloud, the step of performing component potential grouping and replacement offset identification tasks using deep semantic segmentation and electrical topology potential label propagation mechanism, and outputting the marked potential grouped measured point cloud, specifically includes: Step S201: Obtain a visible light image of the low-voltage electrical control cabinet, fuse the visible light image with the downsampled measured point cloud, and perform semantic segmentation on the fusion result to obtain a cluster of conductive point clouds with component type labels; Step S202: Based on the conductive point cloud cluster with component type tags, read the electrical schematic diagram and bill of materials of the low-voltage electrical control cabinet, and construct a potential node diagram according to the component terminals and wire or busbar connection relationships in the electrical schematic diagram and bill of materials; match the conductive point cloud cluster with the nodes of the potential node diagram, propagate the potential tags along the electrical connection edges, and determine the corresponding potential tags according to the adjacency relationship of the potential node diagram when the conductor is partially blocked by the insulating cover, so as to obtain the potential grouped point cloud; Step S203: Based on the potential grouping point cloud, robustly register the potential grouping point cloud with the CAD nominal model corresponding to the bill of materials to obtain the coordinate transformation relationship; calculate the local spatial block deviation and the global robust deviation using the coordinate transformation relationship, compare the local spatial block deviation and the global robust deviation with the corresponding preset deviation thresholds respectively, and mark the replacement device or offset device that exceeds the corresponding preset deviation threshold to obtain the marked potential grouping measured point cloud.

4. The method for verifying assembly data of low-voltage electrical control cabinet components as described in claim 3, characterized in that, In step S203, the local spatial block deviation is calculated based on the coordinate transformation relationship. The formula for calculating the local spatial block deviation is: ， in, Indicates the first Local spatial block deviation, in millimeters. Indicates the first A set of points in a local spatial block. This indicates the number of points in the point set. Represents a point index. The first point in the downsampled measured point cloud represents the... The coordinates of the measured points are in millimeters. Indicates the relationship between the nominal CAD model and the first The coordinates of the nearest point corresponding to each measured point, in millimeters. This represents the coordinate transformation operator obtained by robust registration; it compares the local spatial block deviation with a preset local spatial block deviation threshold, and identifies the corresponding components that exceed the preset local spatial block deviation threshold as replacement components or offset components.

5. The method for verifying assembly data of low-voltage electrical control cabinet components as described in claim 3, characterized in that, Step S30, based on the measured point cloud of the marked potential group, employs a curvature adaptive mesh reconstruction and directional convexity index extraction mechanism to perform the insulation surface feature extraction task and output insulation surface feature data. Specifically, this includes: Step S301: Based on the measured point cloud of the marked potential group, the surface of the charged body and the surface of the insulator are reconstructed by curvature adaptive triangular mesh, and the regions marked as replacement devices or offset devices and the feature edges whose normal vector change angle exceeds the preset feature edge angle threshold are refined by anisotropic mesh to obtain the triangular mesh of the insulating surface. Step S302: Based on the triangular mesh of the insulating surface, calculate the average curvature and Gaussian curvature of each mesh vertex, extract the nearest point pairs between different potential groups, and determine the unit vector from the nearest point of the first potential group to the nearest point of the second potential group as the direction vector of the nearest point pair, thereby obtaining curvature and direction feature data; Step S303: Based on the curvature and orientation feature data, the orientation bulge index is determined using the projection height of the mesh vertex relative to the local reference plane and the average curvature as calculation parameters; the edge electric field concentration factor is determined using the edge curvature intensity and the preset electric field concentration amplification factor as calculation parameters, and the product of the orientation bulge index and the edge electric field concentration factor is used as the risk bulge index; the risk bulge index is compared with the preset risk bulge index threshold, and high-risk irregular bulges exceeding the preset risk bulge index threshold are marked; the insulating surface triangular mesh, the nearest point pair orientation vector, and the high-risk irregular bulge mark are combined to obtain the insulating surface feature data.

6. The method for verifying assembly data of low-voltage electrical control cabinet components as described in claim 5, characterized in that, Step S40, based on the insulation surface feature data, involves performing an insulation distance verification task using an insulation distance verification mechanism calculated by a directed distance field and Dijkstra's shortest path, and outputting the insulation distance verification result. This step specifically includes: Step S401: Based on the insulation surface feature data, a directed distance field is established for the charged body surfaces of different potential groups. The minimum value of each point on the first charged body surface in the directed distance field of the second charged body surface is taken as the minimum electrical clearance. The standard deviation of single-point measurement of the scanner, the thermal expansion of the charged body, and the point cloud registration residual are input into the comprehensive uncertainty calculation process to obtain the comprehensive uncertainty. The high-risk irregular protrusion mark and the mesh reconstruction error are input into the shape confidence correction process to obtain the shape confidence correction term. The comprehensive uncertainty and the shape confidence correction term are subtracted from the minimum electrical clearance to obtain the effective electrical clearance. The reference electrical clearance value, the electric field non-uniformity correction coefficient, and the altitude correction coefficient are input into the electrical clearance requirement value calculation process to obtain the electrical clearance requirement value. Step S402: Based on the effective electrical clearance, construct a weighted graph on the triangular mesh of the insulating surface; input the length of each mesh edge, local average curvature, insulating surface roughness, electric field intensity components along the edge tangent direction, and metal fastener hole status into the mesh edge weight calculation process to obtain the mesh edge weight; use Dijkstra's shortest path algorithm to sum the mesh edge weights contained in the insulating surface path connecting different potential groups, and determine the minimum weight sum as the effective creepage distance to obtain the effective insulation distance data; Step S403: Based on the effective insulation distance data, input the reference creepage distance value, altitude correction factor, and material correction factor determined by the relative tracking index of the insulation material into the creepage distance requirement calculation process to obtain the creepage distance requirement value; compare the effective electrical clearance with the electrical clearance requirement value, and compare the effective creepage distance with the creepage distance requirement value; combine the effective electrical clearance, effective creepage distance, electrical clearance requirement value, creepage distance requirement value, and comparison results to obtain the insulation distance verification result.

7. The method for verifying assembly data of low-voltage electrical control cabinet components as described in claim 6, characterized in that, In step S50, based on the insulation distance verification result, a risk warning and repair suggestion generation task is performed using a failure probability mapping and constraint optimization repair mechanism, outputting risk point annotations and repair suggestions. This specifically includes: Step S501: Obtain the effective distance and distance requirement value for each risk point from the insulation distance verification results. Divide the difference between the effective distance and the distance requirement value by the distance requirement value to obtain the local margin. Based on the local margin, perform failure probability mapping to obtain the failure probability. The formula for calculating the failure probability is: ， in, Indicates the first The probability of failure at each risk point Indicates the risk point index. This represents the preset probability steepness coefficient. Indicates the first Local margin at each risk point This indicates the preset critical margin; the failure probability, probability steepness coefficient, local margin, and critical margin are all dimensionless quantities. Step S502: Based on the failure probability, the failure probability is weighted and summed using the importance weight of each risk point and the preset risk amplification power to obtain the overall cabinet risk index; the overall cabinet risk index is compared with the preset overall cabinet warning threshold, and a risk warning is generated when the overall cabinet risk index exceeds the preset overall cabinet warning threshold; the adjustable conductor displacement and insulation barrier thickness are used as repair variables, the minimum total repair cost is used as the optimization objective, and the effective distance after repair is not less than the corresponding distance requirement value is used as a constraint to obtain the repair optimization result; Step S503: Based on the repair optimization results, when using an insulation barrier for repair, the required insulation barrier thickness is determined by dividing the difference between the required distance value and the current effective distance by the effective electrical strength coefficient of the barrier material; the optimized values ​​of the risk warning and repair variables and the required insulation barrier thickness are mapped to the three-dimensional model of the low-voltage electrical control cabinet to obtain the risk point marking and repair suggestions.

8. A low-voltage electrical control cabinet component assembly data verification system, applied to the low-voltage electrical control cabinet component assembly data verification method according to any one of claims 1 to 7, characterized in that, The system includes: The point cloud preprocessing module is used to acquire the measured three-dimensional surface data of the assembled low-voltage electrical control cabinet. Based on the measured three-dimensional surface data of the assembled low-voltage electrical control cabinet, the point cloud preprocessing task is performed by an adaptive voxel downsampling mechanism of statistical filtering and iterative nearest point registration, and the downsampled measured point cloud is output. The potential grouping and replacement identification module is used to perform component potential grouping and replacement offset identification tasks based on the downsampled measured point cloud, using deep semantic segmentation and electrical topology potential label propagation mechanism, and outputting a marked potential grouped measured point cloud. The insulation surface feature extraction module is used to perform insulation surface feature extraction tasks based on the measured point cloud grouped by the marked potential, using curvature adaptive mesh reconstruction and directional convexity index extraction mechanism, and output insulation surface feature data. The insulation distance verification module is used to perform insulation distance verification tasks based on the insulation surface feature data, using an insulation distance verification mechanism calculated by a directed distance field and Dijkstra's shortest path, and output the insulation distance verification results. The risk warning and repair suggestion module is used to perform risk warning and repair suggestion generation tasks based on the insulation distance verification results, using a failure probability mapping and constraint optimization repair mechanism, and output risk point markings and repair suggestions.

9. A device for verifying assembly data of low-voltage electrical control cabinet components, characterized in that, The low-voltage electrical control cabinet component assembly data verification device includes: a memory, a processor, and a low-voltage electrical control cabinet component assembly data verification program stored in the memory and executable on the processor. When the low-voltage electrical control cabinet component assembly data verification program is executed by the processor, it implements a low-voltage electrical control cabinet component assembly data verification method according to any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a low-voltage electrical control cabinet component assembly data verification program, which, when executed by a processor, implements a low-voltage electrical control cabinet component assembly data verification method according to any one of claims 1 to 7.

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