BIM model-based phase-split vertical layering arrangement GIS busbar cylinder maintenance method and system

Abstract

This invention relates to a method and system for the maintenance of phase-separated vertically stacked GIS busbars based on a BIM model. The method includes: constructing a three-dimensional model by acquiring point cloud data through laser scanning; extracting key feature points and determining displacement reference vectors; constructing a displacement constraint matrix and optimizing the path; generating an interactive three-dimensional spatial model; simulating disassembly and assembly sequences and assessing risks; generating emergency commands and tool trajectories; and optimizing the maintenance process by iteratively correcting the model through feedback loops. This invention achieves visualized simulation, precise control, and dynamic optimization of the entire maintenance process, significantly improving the accuracy, efficiency, and safety of maintenance operations.

Description

Technical Field

[0001] This invention relates to the field of GIS busbar maintenance technology, and in particular to a method and system for maintaining GIS busbars arranged in a phase-separated vertical stack based on a BIM model. Background Technology

[0002] As power systems evolve towards intelligence and high reliability, gas-insulated switchgear (GIS), as a core component of high-voltage transmission networks, plays a crucial role in ensuring grid security through its operational stability and maintenance efficiency. GIS busbars, due to their compact structure and small footprint, are widely used in urban substations and areas with complex terrain. However, their unique phase-by-phase, vertically stacked structure places extremely high demands on maintenance work. Traditional maintenance methods often require the removal of upper-layer components, resulting in extremely limited operating space, high difficulty, and long processing times. Furthermore, human error can easily lead to equipment collisions or insulation gas leaks, potentially causing more serious equipment accidents or even grid failures, severely impacting the reliability and security of the power grid.

[0003] Current GIS busbar maintenance methods primarily rely on manual experience and two-dimensional drawings, lacking intuitive representation and dynamic simulation of complex three-dimensional structures. This makes it difficult for maintenance personnel to accurately determine the spatial location and interrelationships of busbars arranged vertically and sequentially, especially in emergency situations, leading to low operational efficiency and a high risk of errors. Furthermore, existing methods often fail to predict the disassembly / reassembly sequence, spare parts requirements, and tool selection in advance when planning maintenance procedures, increasing the uncertainty and blindness of on-site operations. This static information-based maintenance approach is ill-suited to complex and ever-changing work scenarios.

[0004] In the maintenance of GIS busbars arranged in a vertically stacked phase configuration, the core technical challenge lies in achieving precise control over the spatial position of the busbars. The vertically stacked structure of the busbars requires high-precision displacement control in both the axial and radial directions during maintenance to avoid interference or damage to adjacent equipment. However, due to the narrow internal space and complex structure of the busbars, traditional measuring tools struggle to accurately capture their three-dimensional pose information. This lack of information acquisition directly leads to another critical problem: the absence of dynamic simulation and optimization of operational procedures during maintenance. Unsimulated operational processes may result in incorrect disassembly / reassembly sequences or improper tool use, thereby prolonging maintenance time or even causing secondary damage to equipment. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the defects of the existing technology, which relies on manual experience and lacks precise control and dynamic optimization capabilities for the maintenance of phase-separated vertically stacked GIS busbars, resulting in low work efficiency and high safety risks. The present invention provides a BIM model-based method and system for the maintenance of phase-separated vertically stacked GIS busbars, which can realize the visualization simulation, precise control and dynamic feedback optimization of the entire maintenance process, and significantly improve the maintenance accuracy, efficiency and safety.

[0006] To address the aforementioned technical problems, this invention provides a method for the maintenance of GIS busbars arranged in a phase-separated vertical stack based on a BIM model, comprising the following steps:

[0007] The phase-by-phase vertical stacking arrangement data of the GIS busbar is collected by a laser scanner, and the collected point cloud information is processed to construct an initial three-dimensional model.

[0008] Based on the initial 3D model, key feature points for axial displacement and radial control are extracted. A point cloud registration algorithm is used to fuse multi-angle scanning results to determine the reference vectors for axial displacement and radial control.

[0009] After obtaining the reference vector, a displacement constraint matrix is ​​constructed, and the radial control parameters are adjusted by applying the iterative nearest point algorithm to obtain the optimized displacement path.

[0010] A three-dimensional view is generated by optimizing the displacement path, and virtual reality rendering technology is used to overlay phase arrangement details to obtain an interactive three-dimensional spatial model for subsequent simulation.

[0011] Based on the input of the three-dimensional spatial model into the dynamic simulation engine, the disassembly and assembly sequence under axial displacement and radial control is simulated for the structure under maintenance to determine the process sequence with the highest operational efficiency.

[0012] After obtaining the process sequence, it is integrated into the damage risk assessment module. The sequence parameters are optimized through a genetic algorithm to obtain low-risk dynamic simulation results.

[0013] An emergency response instruction set is generated based on the results of low-risk dynamic simulations. A path planning algorithm is used to calculate the tool trajectory in a confined space environment to determine the execution steps for precise control.

[0014] The three-dimensional spatial model is updated by executing step feedback loops, and the model is corrected by applying point cloud registration algorithm to obtain the final optimized maintenance process.

[0015] In one embodiment of the present invention, phase-by-phase vertical stacking arrangement data of GIS busbars are acquired using a laser scanner, and the acquired point cloud information is processed to construct an initial three-dimensional model, including:

[0016] Data on the phase-separated vertical stacking arrangement of the busbar drum is acquired using a laser scanner to obtain point cloud information. The point cloud information is then preliminarily segmented using a stereomicroscope algorithm to obtain initial point cloud data.

[0017] The initial point cloud data is filtered, and noise points are removed using the mean filtering method. Feature points arranged in phases are extracted from the initial point cloud data to obtain a clean point cloud dataset.

[0018] By integrating multiple sets of point cloud data through data fusion, and processing the clean point cloud dataset using a surface fitting method, an initial 3D model is obtained.

[0019] In one embodiment of the present invention, key feature points for axial displacement and radial control are extracted based on an initial three-dimensional model. A point cloud registration algorithm is then used to fuse multi-angle scanning results to determine the reference vectors for axial displacement and radial control, including:

[0020] Three-dimensional point cloud data is obtained from the initial model, and a multi-angle point cloud set is generated by scanning with a stereo microscope. The multi-angle point cloud set is initially registered using the iterative nearest point algorithm to obtain the registered point cloud data.

[0021] For the registered point cloud data, principal component analysis is used to extract key feature points, which are then identified as candidate feature points for axial displacement and radial control.

[0022] Based on the candidate feature points, the spatial transformation matrix of the point cloud is calculated, and the initial parameters of the reference vector are obtained by fitting the axial displacement direction vector and the radial control constraint plane using the least squares method.

[0023] The point cloud data from the multi-angle scans are integrated using a data fusion algorithm, and the fused point cloud data is then denoised using voxel filtering to determine the final reference vector and control accuracy.

[0024] In one embodiment of the present invention, after obtaining the reference vector, a displacement constraint matrix is ​​constructed, and the radial control parameters are adjusted using an iterative nearest-point algorithm to obtain an optimized displacement path, including:

[0025] Obtain the reference vector from the input data, extract the key direction components using vector decomposition, and obtain the initial reference vector;

[0026] Using an initial reference vector and combining it with preset displacement constraints, a displacement constraint matrix is ​​constructed, and the structure of the constraint matrix is ​​determined.

[0027] By using the iterative nearest neighbor algorithm, the nearest neighbor distance of each point in the matrix is ​​calculated, and a set of distance distributions is obtained.

[0028] Based on the distance distribution set, adjust the radial control parameters, update the parameter values, and obtain the adjusted control parameter set.

[0029] By recalculating the trajectory points of the displacement path using the adjusted set of control parameters, a preliminary optimized path is obtained.

[0030] The least squares method is used to fit the path, resulting in a smooth optimized path;

[0031] Based on the smooth optimization path, the boundary conditions of the displacement constraint matrix are updated to obtain the final optimized displacement path.

[0032] In one embodiment of the present invention, a three-dimensional rendering view is generated through an optimized displacement path, and virtual reality rendering technology is used to overlay phase arrangement details to obtain an interactive three-dimensional spatial model for subsequent simulation, including:

[0033] The input displacement path data is processed by Algorithm A to obtain the optimized displacement trajectory and determine the set of trajectory point coordinates.

[0034] A 3D view is generated using OpenGL rendering technology, and spatial position data is obtained from the set of trajectory point coordinates to obtain the 3D rendering view;

[0035] A layered rendering method is used to overlay phased arrangement details. Through the 3D presentation view and the preset material library, a view with enhanced details is obtained, resulting in the interactive 3D space model.

[0036] The user inputs interactive control commands, which are processed by the Unity3D engine to obtain the dynamically adjusted model state and simulate the application results.

[0037] In one embodiment of the present invention, a dynamic simulation engine is input based on a three-dimensional spatial model to simulate the disassembly and assembly sequence under axial displacement and radial control of the structure under maintenance, and to determine the process sequence with the highest operational efficiency, including:

[0038] Displacement data and control parameters are obtained from a three-dimensional spatial model. A preset dynamic simulation algorithm is used to generate an initial disassembly and assembly sequence for the axial displacement and radial control of the maintenance structure, thus obtaining a preliminary process sequence.

[0039] Based on the preliminary process sequence, and considering the constraints of axial displacement and radial control, a linear programming algorithm is used to optimize the assembly and disassembly sequence, and the optimization result is determined.

[0040] If the sequence optimization result meets the preset efficiency threshold, then key control parameters are extracted from the sequence optimization result to generate the final process sequence.

[0041] If the conditions are not met, adjust the control parameters and regenerate the sequence optimization results;

[0042] By combining the final process sequence with the dynamic simulation results, the disassembly and assembly sequence of the maintenance structure under axial displacement and radial control is verified, and the simulation results are obtained.

[0043] In one embodiment of the present invention, after obtaining the process sequence, it is integrated into a damage risk assessment module. The sequence parameters are optimized using a genetic algorithm to obtain low-risk dynamic simulation results, including:

[0044] Obtain process sequence data; extract process sequence data containing time series characteristics from the data source to obtain the original sequence dataset.

[0045] The original sequence dataset is cleaned and formatted through a data processing workflow to generate a standard sequence dataset.

[0046] The risk assessment module is used to calculate risk indicators on the standard sequence dataset to obtain a risk assessment indicator set.

[0047] The sequence parameters are optimized using a genetic algorithm to generate an optimized parameter set. Based on the optimized parameter set, the sequence parameters in the standard sequence dataset are adjusted to obtain the adjusted sequence dataset.

[0048] The dynamic simulation generation module performs simulation calculations on the adjusted sequence dataset to generate low-risk dynamic simulation results.

[0049] The evaluation result output module is used to format the low-risk dynamic simulation results and generate the final output results.

[0050] In one embodiment of the present invention, an emergency response instruction set is generated based on low-risk dynamic simulation results, a path planning algorithm is used to calculate the tool trajectory in a confined space environment, and the execution steps for precise control are determined, including:

[0051] Simulation data is obtained based on the dynamic simulation results. Environmental constraints and risk assessment parameters are extracted from the simulation data to generate an initial dataset for low-risk scenarios and determine the scenario risk level.

[0052] The initial dataset is processed using a path planning algorithm, and the trajectory is calculated using environmental constraints.

[0053] The tool trajectory is used to generate an instruction sequence. To meet the requirements of precise control, the tool trajectory is decomposed into a time series table to determine the execution steps of the instruction sequence.

[0054] In one embodiment of the present invention, the three-dimensional spatial model is updated by executing a step feedback loop, and the model is corrected by applying a point cloud registration algorithm to obtain the final optimized maintenance process, including:

[0055] During the axial and radial displacement operations performed on the disassembly and assembly platform, multiple sets of optical positioning sensors arranged in the work area collect the actual displacement data of the busbar drum in real time and record the spatial coordinate changes of the end effector of the tool.

[0056] The actual displacement data is compared with the predefined displacement path in the three-dimensional space model to calculate the position deviation and trigger the model correction mechanism.

[0057] The high-precision laser scanning unit is activated to perform a local rescan of the busbar structure in the current operation stage, acquire updated point cloud data, and perform feature matching with the point cloud of the corresponding area in the initial 3D model;

[0058] Based on the feature matching results, the pose of the busbar in the three-dimensional space model is dynamically adjusted to correct the model distortion caused by installation errors, structural deformation or sensor drift.

[0059] The corrected model data is fed back to the dynamic simulation engine to regenerate tool trajectories and execution steps that are adapted to the current actual environment, forming a closed-loop control process.

[0060] Repeat the above steps until the entire maintenance process is completed, and finally output a digital maintenance process library that is consistent with the actual operation process and has been optimized in multiple rounds.

[0061] To address the aforementioned technical problems, this invention also provides a phase-separated vertically stacked GIS busbar maintenance system based on a BIM model, used to implement the above method, comprising:

[0062] The data acquisition and processing module is used to acquire phase-by-phase vertical stacking arrangement data of GIS busbar cylinders through a laser scanner, and process the acquired point cloud information to construct an initial three-dimensional model.

[0063] The feature extraction and benchmark determination module is used to extract key feature points of axial displacement and radial control based on the initial three-dimensional model, and to determine the benchmark vectors of axial displacement and radial control by fusing multi-angle scanning results using a point cloud registration algorithm.

[0064] The path optimization module is used to construct a displacement constraint matrix after obtaining the reference vector, apply the iterative nearest point algorithm to adjust the radial control parameters, and obtain the optimized displacement path.

[0065] The 3D visualization module is used to generate a 3D presentation view through the optimized displacement path, and uses virtual reality rendering technology to overlay phase arrangement details to obtain an interactive 3D spatial model for subsequent simulation.

[0066] The simulation module is used to input the three-dimensional spatial model into the dynamic simulation engine, simulate the disassembly and assembly sequence under axial displacement and radial control for the structure under maintenance, and determine the process sequence with the highest operating efficiency.

[0067] The risk assessment and optimization module is used to integrate the damage risk assessment module after the process sequence is obtained, and to optimize the sequence parameters through a genetic algorithm to obtain low-risk dynamic simulation results.

[0068] The instruction generation and planning module is used to generate an emergency response instruction set based on the low-risk dynamic simulation results, and to use a path planning algorithm to calculate the tool trajectory in a confined space environment to determine the execution steps for precise control.

[0069] The feedback correction module is used to update the three-dimensional spatial model through feedback loops in the execution steps, apply point cloud registration algorithms to correct the model, and obtain the final optimized maintenance process.

[0070] The technical solution of the present invention has the following advantages compared with the prior art:

[0071] The present invention describes a BIM-based method for the maintenance of phase-separated vertically stacked GIS busbars. First, a laser scanner is used to collect actual point cloud data from the site, constructing a high-precision 3D model to ensure consistency between the model and the actual structure. Then, key feature points are extracted and multi-angle scanning results are fused to establish a displacement reference vector, providing accurate spatial reference for subsequent control. By constructing a displacement constraint matrix and optimizing parameters, a safe and reliable displacement path is generated. Furthermore, virtual reality technology is used to construct an interactive 3D scene, enabling a visual preview of the maintenance process. Based on this, a dynamic simulation engine is used to compare multiple disassembly and assembly sequences, optimizing the operation process and incorporating a risk assessment mechanism to further enhance the safety of the solution. Finally, path planning generates executable precision control commands to drive the PLC control system to complete automated disassembly and assembly operations.

[0072] The core of this solution lies in constructing a fully digital closed-loop system encompassing data acquisition, model building, simulation optimization, and control execution. Through real-time processing of point cloud data and dynamic model correction, continuous alignment between the virtual model and the physical entity is achieved, overcoming the problems of model distortion and control disconnect in traditional methods. Furthermore, BIM-based visualization simulation and risk assessment enable maintenance personnel to identify potential conflicts and risks in advance within the virtual environment, thereby preventing misoperation and equipment damage during actual operation. Simultaneously, by directly issuing optimized path commands to the control terminal, precise axial and radial displacement control is achieved, significantly improving the accuracy and reliability of operations.

[0073] The beneficial effects of this invention include: First, through high-precision 3D modeling and dynamic simulation, the entire maintenance process is made visible and controllable, significantly reducing operational risks caused by human error. Second, based on multi-source data fusion and path optimization, a safe and efficient disassembly and assembly sequence is generated, improving maintenance efficiency and shortening power outage time. Third, through closed-loop feedback and model correction mechanisms, the system's adaptability to complex on-site environments is enhanced, improving the robustness and practicality of the solution. Finally, this solution provides a complete, reliable, and scalable technical path for the intelligent maintenance of GIS equipment, possessing significant engineering application value and promising industry promotion prospects. Attached Figure Description

[0074] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein:

[0075] Figure 1 This is a flowchart of the steps of the maintenance method for phase-separated vertically stacked GIS busbar based on BIM model of the present invention.

[0076] Figure 2 This is a flowchart of the data acquisition and initial model construction steps of the present invention;

[0077] Figure 3 This is a flowchart of the steps for determining the reference vector in this invention;

[0078] Figure 4 This is a flowchart of the displacement path optimization steps of the present invention;

[0079] Figure 5 This is a flowchart of the three-dimensional visualization steps of the present invention;

[0080] Figure 6 This is a flowchart illustrating the steps involved in determining the process sequence of the present invention;

[0081] Figure 7 This is a flowchart of the risk assessment and optimization steps of the present invention;

[0082] Figure 8 This is a flowchart of the instruction generation and planning steps of the present invention;

[0083] Figure 9 This is a flowchart of the feedback correction steps of the present invention;

[0084] Figure 10 The structural framework diagram of the phase-separated vertically stacked GIS busbar maintenance system based on the BIM model of the present invention. Detailed Implementation

[0085] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0086] As mentioned earlier, the core technical challenge in the maintenance of GIS busbars arranged in a phase-separated, vertically stacked configuration lies in achieving precise control over the spatial position of the busbars. Taking a real emergency maintenance example, a substation's GIS busbar required rapid repair due to a sudden fault. Lacking three-dimensional spatial information, maintenance personnel could not accurately determine the relative position of the faulty busbar to other components, leading to accidental contact with adjacent equipment during disassembly and prolonging the power outage. Furthermore, the temporarily prepared spare parts did not match the actual needs, further exacerbating the maintenance difficulty. These problems reflect a significant deficiency in existing technologies when dealing with the dynamic operational requirements of complex structures.

[0087] How to achieve high-precision axial and radial displacement control within the narrow and complex space of a GIS busbar, and optimize the maintenance process through dynamic simulation, has become a key issue in improving the efficiency and safety of emergency maintenance.

[0088] To solve the above problems, refer to Figure 1 As shown, this invention proposes a maintenance method for phase-separated vertically stacked GIS busbars based on a BIM model. The method includes: acquiring phase-separated vertically stacked layout data of the GIS busbars using a laser scanner; processing the acquired point cloud information to construct an initial 3D model; extracting key feature points for axial displacement and radial control based on the initial 3D model; fusing multi-angle scanning results using a point cloud registration algorithm to determine the reference vectors for axial displacement and radial control; constructing a displacement constraint matrix after obtaining the reference vectors; applying an iterative nearest-point algorithm to adjust the radial control parameters to obtain an optimized displacement path; generating a 3D rendering view using the optimized displacement path; and overlaying the phase-separated vertically stacked layout data using virtual reality rendering technology. The layout details are analyzed to obtain an interactive 3D spatial model for subsequent simulations. Based on this model, a dynamic simulation engine is input to simulate the disassembly and assembly sequence under axial displacement and radial control of the structure under maintenance, determining the most efficient workflow sequence. This workflow sequence is then integrated into a damage risk assessment module, where genetic algorithms optimize sequence parameters to obtain low-risk dynamic simulation results. An emergency response instruction set is generated based on these low-risk results, and a path planning algorithm is used to calculate the tool trajectory in confined spaces, determining the precise control execution steps. The 3D spatial model is updated iteratively through execution step feedback, and a point cloud registration algorithm is applied to correct the model, resulting in the final optimized maintenance workflow.

[0089] Among these, laser scanner data acquisition refers to obtaining the geometric information of the busbar cylinder surface through non-contact measurement, which can be achieved using a phase-type laser scanner. Its function is to establish high-precision 3D point cloud data. Point cloud registration algorithm refers to the technique of aligning multi-view scan data, which can be implemented using an iterative nearest-point algorithm to eliminate data deviations caused by differences in scanning perspectives. Displacement constraint matrix refers to the mathematical expression containing axial and radial motion constraints, which determines the feasible displacement region through matrix operations. Virtual reality rendering technology refers to the technique of converting 3D models into visualized scenes, which can be implemented using the OpenGL graphics interface to build an interactive maintenance simulation environment. Genetic algorithm optimization refers to a parameter optimization method that simulates the biological evolution process, using crossover and mutation operations to select the optimal solution set, thereby reducing operational risks.

[0090] Specifically, the initial 3D model of the busbar cylinder is first constructed using multi-angle laser scanning. A stereomicroscopy algorithm is then used to segment the point cloud data and remove noise. Key feature points are extracted based on principal component analysis, and a reference vector is determined using a spatial transformation matrix. After constructing a displacement constraint matrix, an iterative nearest-point algorithm is applied to optimize the motion trajectory, generating a smooth path. The path data is imported into a virtual reality system, and phase arrangement details are overlaid to form an interactive model. A dynamic simulation engine generates assembly / disassembly sequences based on model parameters, and a genetic algorithm is used to assess risks and optimize operating parameters. Finally, a path planning algorithm generates precise control commands, and displacement data is collected in real time during execution to provide feedback and correct the model, forming a closed-loop optimization mechanism.

[0091] Compared to existing technologies, traditional methods rely on two-dimensional drawings and human experience, making them unable to handle complex three-dimensional spatial relationships. This solution provides visualized operation guidance by constructing a dynamic 3D model and automatically generates low-risk disassembly and assembly sequences using optimization algorithms. Existing technologies lack real-time feedback mechanisms, while this solution uses optical positioning sensors to correct model deviations in real time, ensuring consistency between the virtual model and actual working conditions. Traditional risk assessment relies on manual judgment; this solution employs genetic algorithms to optimize operating parameters across multiple objectives, significantly improving the scientific rigor of decision-making.

[0092] Through the above technical solutions, this application achieves dynamic construction and real-time updating of three-dimensional spatial models, solving the problem of insufficient spatial perception in traditional maintenance. By combining virtual simulation and path planning, the tool movement trajectory in narrow environments is optimized, reducing the risk of equipment collisions. A closed-loop feedback mechanism is adopted to continuously correct the model accuracy, ensuring the matching degree between the maintenance process and actual working conditions. Ultimately, a dynamically adjustable optimized maintenance plan is formed, significantly improving operational safety and work efficiency.

[0093] Reference Figure 2As shown, this application further proposes to acquire point cloud information by using a laser scanner to perform phase-by-phase vertical stacking of busbar cylinders, and to use a stereomicroscope algorithm to perform preliminary segmentation of the point cloud information to obtain initial point cloud data; to perform filtering processing on the initial point cloud data, using the mean filtering method to remove noise points, and to extract the phase-by-phase arrangement feature points from the initial point cloud data to obtain a clean point cloud dataset; to integrate multiple sets of point cloud data through data fusion, and to process the clean point cloud dataset using a surface fitting method to obtain an initial three-dimensional model.

[0094] Among them, the stereomicroscope algorithm refers to a point cloud segmentation method based on the principle of binocular vision. Specifically, it can use a stereo matching algorithm to reconstruct three-dimensional spatial coordinates, and is used to separate independent point cloud sets of each phase of the busbar cylinder from multi-angle scanning data. The mean filtering method refers to eliminating random noise by calculating the average value of the point cloud coordinates in the neighborhood. Specifically, it can be implemented using a sliding window algorithm, with the window size set to, for example, a range of 5×5 pixels. Data fusion refers to the process of unifying point cloud data from different scanning angles into the same coordinate system. Specifically, it can use a rigid transformation matrix to achieve spatial alignment of multi-source data. The surface fitting method refers to approximating discrete point clouds to form a continuous surface using mathematical functions. Specifically, it can use the non-uniform rational B-spline (NURBS) algorithm to parametrically model the cylindrical structure of the busbar cylinder.

[0095] Specifically, during the busbar data acquisition phase, a laser scanner performs multi-angle scanning in a vertically stacked arrangement, generating an original point cloud containing phase distribution information. A stereomicroscope algorithm uses stereo matching technology to identify boundary features between phases, segmenting the mixed point cloud into independent phase datasets. Subsequently, mean filtering is used to denoise the segmented point cloud, replacing outlier points within the window with neighborhood means, effectively eliminating outliers caused by environmental interference. The filtered point cloud is then spatially registered using a rigid transformation matrix, integrating the multi-view scan data into a unified coordinate system. Finally, a NURBS surface fitting algorithm is used to generate a smooth and continuous initial 3D model based on the geometric characteristics of the busbar.

[0096] Compared to existing technologies, traditional methods rely on manual measurement of two-dimensional drawings, making it difficult to accurately obtain the three-dimensional spatial information of the busbar cylinder. This solution automatically separates each phase structure through a point cloud segmentation algorithm, combined with multi-angle data fusion technology, which can eliminate blind zone errors from a single scan. In contrast, existing technologies often suffer from misjudgment of phase boundaries due to insufficient experience when manually processing point cloud data. This solution employs a combination of mean filtering and surface fitting, which effectively preserves the integrity of geometric features.

[0097] Through the above technical solutions, this application achieves accurate 3D reconstruction of a phase-separated vertically stacked structure, solving the model distortion problem caused by point cloud mixing in traditional methods. Filtering effectively eliminates the interference of measurement noise on feature extraction, while the surface fitting algorithm ensures the geometric accuracy of the busbar cylindrical structure, providing a reliable model foundation for subsequent displacement control. Data fusion technology significantly improves the modeling completeness of complex structures in narrow spaces by integrating multi-angle scanning results.

[0098] Reference Figure 3 As shown, this application further proposes to obtain 3D point cloud data from an initial model, generate a multi-angle point cloud set by scanning with a stereomicroscope, and perform preliminary registration of the multi-angle point cloud set using an iterative nearest-point algorithm to obtain registered point cloud data. For the registered point cloud data, principal component analysis is used to extract key feature points, which are then identified as candidate feature points for axial displacement and radial control. Based on the candidate feature points, the spatial transformation matrix of the point cloud is calculated, and the direction vector of axial displacement and the constraint plane of radial control are fitted using the least squares method to obtain the initial parameters of the reference vector. The point cloud data from the multi-angle scans are integrated using a data fusion algorithm, and the fused point cloud data is denoised using voxel filtering to determine the final reference vector and control accuracy.

[0099] Among them, the iterative nearest neighbor algorithm refers to a method of registration by calculating the correspondence between two sets of point clouds and iteratively optimizing rigid body transformation parameters. Specifically, it can use KD-tree-accelerated nearest neighbor search combined with singular value decomposition to calculate the transformation matrix, which is used to eliminate pose differences between multi-angle scan data. Principal component analysis refers to a method of determining the principal direction of data distribution by calculating the eigenvectors of the point cloud covariance matrix. Specifically, it can use covariance matrix decomposition to extract the direction of maximum variance as the axial displacement reference, which is used to identify the spatial extension characteristics of the busbar structure. Voxel filtering refers to a method of dividing the three-dimensional space into a uniform grid and downsampling or statistically processing the point cloud in each grid. Specifically, it can use voxelization based on an octree structure to achieve point cloud noise reduction, which is used to improve the stability of the reference vector calculation.

[0100] Specifically, after the initial 3D model's 3D point cloud data is scanned using a stereomicroscope to generate a multi-angle point cloud set, the iterative nearest-point algorithm is first used to perform preliminary registration of the multi-angle data to eliminate pose deviations caused by different scanning angles. The registered point cloud data is then subjected to principal component analysis to extract the principal eigenvectors reflecting the axial extension direction of the busbar cylinder, which are used as candidate reference directions for axial displacement. Simultaneously, the radial constraint plane is determined by calculating the secondary eigenvectors of the point cloud covariance matrix, and the plane equation is fitted using the least squares method as the radial control reference. After the multi-angle scan data is integrated using a data fusion algorithm, voxel filtering is applied to the fused point cloud for noise reduction, eliminating the influence of isolated noise points on the reference vector calculation. Finally, a high-precision axial displacement direction vector and radial control plane parameters are output.

[0101] Compared to existing technologies, traditional methods rely on manual measurement to determine the axial reference of the busbar drum, which is susceptible to operator experience and error accumulation. This solution, however, automatically extracts the principal direction of the point cloud through principal component analysis, avoiding subjective judgment bias. Existing technologies only perform simple stitching of multi-angle scan data, failing to consider the impact of pose differences on reference vector calculation. This solution employs an iterative nearest-point algorithm for accurate registration, ensuring spatial consistency of multi-source data. Furthermore, traditional filtering methods struggle to effectively handle complex noise after fusion; this solution introduces voxel filtering for noise reduction, improving the robustness of reference vector calculation.

[0102] Through the above technical solution, this application achieves automated and high-precision determination of the axial displacement and radial control reference vectors of the busbar cylinder, solving the problems of low efficiency and error susceptibility of traditional manual measurement. Precise registration and fusion of multi-angle scanning data effectively eliminates blind zone errors from single-view scanning, ensuring the comprehensiveness of the reference vector calculation. Voxel filtering reduces the impact of noise interference on the reference vector, resulting in higher reliability for subsequent displacement path planning.

[0103] Reference Figure 4 As shown, this application further proposes to obtain the reference vector in the input data, extract the key direction components through vector decomposition to obtain the initial reference vector; use the initial reference vector, combined with preset displacement constraints, to construct a displacement constraint matrix and determine the structure of the constraint matrix; use an iterative nearest-neighbor algorithm to calculate the nearest neighbor distance of each point in the matrix to obtain a distance distribution set; adjust the radial control parameters according to the distance distribution set, update the parameter values, and obtain the adjusted control parameter set; recalculate the trajectory points of the displacement path using the adjusted control parameter set to obtain the preliminary optimized path; fit the path using the least squares method to obtain the smoothed optimized path; update the boundary conditions of the displacement constraint matrix according to the smoothed optimized path to obtain the final optimized displacement path.

[0104] The reference vector refers to the directional reference parameters for the axial displacement and radial control of the busbar cylinder. Specifically, principal component analysis can be used to extract the spatial principal direction components from the point cloud data, thereby establishing a reference coordinate system for displacement control. The displacement constraint matrix is ​​a set of mathematical constraints used to limit the displacement range of the busbar cylinder. Specifically, a multi-dimensional matrix structure can be constructed using preset parameters such as the maximum axial offset and the allowable radial deformation threshold, thus providing boundary constraints for path optimization. The iterative nearest point algorithm is a method of registration optimization by calculating the spatial distance between corresponding points in the point cloud data. Specifically, it can use Euclidean distance matching and iterative updates of the transformation matrix to dynamically adjust control parameters and reduce path deviation. Least squares fitting is a method of solving for the optimal curve by minimizing the sum of squared errors. Specifically, a polynomial function can be used to make the discrete trajectory points continuous, thereby generating a smooth displacement path.

[0105] Specifically, the reference vector is decomposed into initial direction components, which are then combined with preset axial displacement thresholds and radial deformation constraints to construct a multi-dimensional constraint matrix. The iterative nearest-neighbor algorithm calculates the nearest-neighbor distance distribution between each trajectory point in the current displacement path and the target position, generates parameter adjustments, and updates the control parameter set. The adjusted parameter set is then recalculated using trajectory points to form a preliminary optimized path, which is then smoothed using the least squares method to eliminate abrupt changes. Finally, by updating the constraint matrix boundary conditions, an optimized displacement path that meets both spatial constraints and accuracy requirements is formed.

[0106] Compared to existing technologies, traditional methods rely on manual experience to set fixed displacement paths, lacking the ability to dynamically respond to the actual position and orientation of the busbar. This solution, through the construction of a constraint matrix and iterative optimization algorithm, achieves automatic adjustment and parameter optimization of the displacement path, effectively solving the problem of insufficient path planning accuracy under complex spatial structures. The static path planning methods used in existing technologies cannot adapt to the positional deviations caused by busbar installation errors, while this solution significantly improves the adaptability of path planning to actual working conditions through real-time parameter updates and path smoothing.

[0107] Through the above technical solution, this application achieves dynamic optimization of the displacement path during the assembly and disassembly of the busbar, effectively avoiding the risk of equipment collisions caused by path planning deviations. By combining constraint matrices with iterative algorithms, parameter adjustment efficiency is improved while ensuring path accuracy, providing a reliable spatial motion trajectory benchmark for subsequent maintenance operations. The introduction of path smoothing technology further reduces abrupt motion changes in the end effector, ensuring the stability and repeatability of the assembly and disassembly process.

[0108] Reference Figure 5As shown, this application further proposes to process the input displacement path data using the A algorithm to obtain an optimized displacement trajectory and determine the set of trajectory point coordinates; to generate a 3D view using OpenGL rendering technology, obtain spatial position data from the trajectory point coordinate set, and obtain a 3D rendering view; to overlay phased arrangement details using a layered rendering method, and obtain a detail-enhanced view using the 3D rendering view and a preset material library, resulting in an interactive spatial model; and to obtain simulation application results by having the user input interactive control commands, processing the interactive spatial model through the Unity3D engine, obtaining the dynamically adjusted model state, and then inputting simulation application results.

[0109] Among them, Algorithm A refers to a search algorithm for path optimization, which uses a heuristic search strategy to quickly generate trajectory point coordinates and improves computational efficiency by reducing redundant path points. OpenGL rendering technology refers to a 3D visualization technology based on graphics processing units, which uses vertex shaders and fragment shaders to achieve real-time rendering of spatial location data, enhancing the spatial expressiveness of the model. Layered rendering method refers to a technique that overlays images at different detail levels, which uses transparency blending algorithms to merge components such as phase-separated insulators and flange interfaces with the main structure of the busbar, improving the model's recognizability. Unity3D engine refers to a software platform that supports interactive 3D application development, which uses scripting programming to achieve real-time responses to user commands and dynamic model adjustments, ensuring intuitiveness and controllability in the operation process.

[0110] Specifically, the displacement path data is processed by Algorithm A to generate an optimized set of trajectory points. These coordinate points are then input into the OpenGL rendering pipeline for 3D view construction. During rendering, the geometry of the busbar is spatially reconstructed based on the trajectory point coordinates. Simultaneously, layered rendering technology is used to overlay detailed components such as phase-separated insulators and connecting bolts onto the main model in a semi-transparent manner. Pre-stored metallic reflective parameters and insulation material textures in the material library are mapped onto the corresponding component surfaces, forming a realistic 3D spatial model. When the user inputs rotation, scaling, or displacement commands through an interactive device, the Unity3D engine parses the command parameters and drives the model to adjust its pose. The dynamically updated model state is fed back to the simulation system for subsequent assembly / disassembly sequence verification.

[0111] Compared to existing technologies, traditional methods rely on two-dimensional drawings for spatial visualization, making it difficult to accurately determine the occlusion relationships of components in the stacked busbar structure. This solution transforms abstract path data into a three-dimensional visual model through a 3D rendering view, and combines virtual reality rendering technology to enhance the display of phase arrangement details, allowing maintenance personnel to intuitively observe the internal structure of the busbar. The layered rendering method effectively solves the visual confusion caused by the stacking of complex components, while the interactive control function of the Unity3D engine breaks through the operational limitations of static models, providing a realistic operating environment for dynamic simulation.

[0112] Through the above technical solution, this application realizes a three-dimensional visualization pre-simulation of the maintenance process. Operators can verify the feasibility of tool paths in a virtual environment and identify potential component interference problems in advance. The interactive spatial model supports multi-angle observation and real-time adjustment, significantly reducing the risk of misoperation caused by spatial imagination errors and providing an accurate input basis for subsequent dynamic simulation.

[0113] Reference Figure 6 As shown, this application further proposes to obtain displacement data and control parameters from a three-dimensional spatial model, and to generate an initial disassembly and assembly sequence for the axial displacement and radial control of the maintenance structure using a preset dynamic simulation algorithm, thus obtaining a preliminary process sequence. Based on the preliminary process sequence, a linear programming algorithm is used to optimize the disassembly and assembly sequence for the constraints of axial displacement and radial control, and the sequence optimization result is determined. If the sequence optimization result meets a preset efficiency threshold, key control parameters are extracted from the sequence optimization result to generate the final process sequence. If not, the control parameters are adjusted, and the sequence optimization result is regenerated. Through the final process sequence, combined with the dynamic simulation results, the disassembly and assembly sequence of the maintenance structure under axial displacement and radial control is verified, and the simulation results are obtained.

[0114] Among them, dynamic simulation algorithms refer to numerical calculation methods based on physics engines, specifically implemented using finite element analysis or discrete event simulation, to simulate the motion trajectories and interactions of various components under axial and radial displacements during the assembly and disassembly of the busbar. Linear programming algorithms are mathematical methods for optimizing resource allocation, specifically implemented using the simplex method or interior point method, to solve for the optimal assembly and disassembly sequence by establishing an objective function and constraints. Efficiency thresholds refer to pre-set operating time or energy consumption indicators, specifically determined using historical maintenance data statistics or expert experience values, serving as benchmark parameters for judging whether the sequence meets requirements.

[0115] Specifically, the displacement data and control parameters stored in the 3D spatial model are input into a dynamic simulation algorithm to generate an initial sequence containing the assembly / disassembly sequence and displacement paths of each component. A linear programming algorithm optimizes the initial sequence by setting the objective function to the shortest operation time, with constraints including tool movement space limitations and component interference conditions, to obtain an optimized sequence that meets the efficiency threshold. When the optimization result fails to reach the preset threshold, the tool trajectory or assembly / disassembly sequence in the control parameters is readjusted, and the simulation is performed again. The final generated process sequence is then dynamically simulated to verify its feasibility in actual operation, ensuring that the assembly / disassembly process meets the requirements for axial displacement accuracy and radial control.

[0116] Compared to existing technologies, traditional methods rely on manual experience to plan the assembly and disassembly sequence, lacking quantitative analysis of three-dimensional spatial constraints and motion trajectories, which can easily lead to tool path conflicts or component interference. This solution uses a dynamic simulation engine to accurately calculate displacement paths and combines this with a linear programming algorithm to automatically optimize the operation sequence, effectively solving the problem of determining the assembly and disassembly sequence in complex spatial environments.

[0117] Through the above technical solution, this application achieves automated optimization of the disassembly and assembly process of GIS busbars arranged in a phase-separated vertical stack, avoiding subjective errors caused by manual planning and significantly improving the safety and efficiency of maintenance operations. The combination of dynamic simulation and algorithm optimization ensures that the disassembly and assembly sequence meets both spatial constraints and the requirement for the shortest operation time, providing reliable technical support for precise maintenance in confined environments.

[0118] Reference Figure 7 As shown, this application further proposes to obtain process sequence data by extracting process sequence data containing time series characteristics from a data source to obtain an original sequence dataset; through a data processing flow, the original sequence dataset is cleaned and formatted to generate a standard sequence dataset; using a risk assessment module, risk indicators are calculated on the standard sequence dataset to obtain a risk assessment indicator set; sequence parameters are optimized using a genetic algorithm to generate an optimized parameter set; based on the optimized parameter set, the sequence parameters in the standard sequence dataset are adjusted to obtain an adjusted sequence dataset; through a dynamic simulation generation module, simulation calculations are performed on the adjusted sequence dataset to generate low-risk dynamic simulation results; and using an evaluation result output module, the low-risk dynamic simulation results are formatted to generate the final output result.

[0119] The process sequence data refers to a structured dataset containing the time sequence of disassembly and assembly steps and operational parameters. It can be stored in JSON or XML format and used to describe the relationships between each step in the maintenance process. The risk assessment module is a calculation unit based on preset risk indicators, implemented using Failure Mode and Effects Analysis (FMEA) to quantify the probability of potential equipment damage during disassembly and assembly. The genetic algorithm is an optimization algorithm that simulates biological evolution, implemented using roulette wheel selection and single-point crossover operators to search for the sequence combination with the lowest risk in the parameter space. The dynamic simulation generation module is a simulation calculation unit based on a physics engine, implemented using finite element analysis to verify the feasibility of the optimized sequence under actual working conditions.

[0120] Specifically, the original sequence dataset undergoes data cleaning to remove outliers and standardizes the timestamp format, forming a standard sequence dataset. The risk assessment module calculates the risk probability for each step in the standard sequence dataset, such as the probability of tool collision or insulating gas leakage. A genetic algorithm, using the risk probability as the fitness function, iteratively optimizes the assembly / disassembly sequence and tool path parameters through selection, crossover, and mutation operations, generating an optimized parameter set. The adjusted sequence dataset is then input into the dynamic simulation generation module, which simulates the assembly / disassembly process based on the physical characteristics of the busbar drum, outputting low-risk dynamic simulation results including displacement trajectories and stress states. The evaluation result output module converts the simulation results into visual charts or operational guidance documents, forming the final output.

[0121] Compared to existing technologies, traditional methods rely on manual experience to adjust the assembly and disassembly sequence, making it impossible to quantify operational risks and lacking a systematic approach to optimization. This solution introduces a genetic algorithm for automatic parameter optimization, combined with dynamic simulation to verify the results, effectively avoiding efficiency losses caused by manual trial and error. While existing technologies often employ qualitative analysis for risk assessment, this solution establishes quantitative indicators through failure mode and impact analysis, making risk prediction calculable.

[0122] Through the above technical solutions, this application can automatically identify high-risk disassembly and assembly steps and optimize the operation sequence, reducing the probability of tool collisions and gas leaks. The dynamic simulation generation module can verify the feasibility of the optimized sequence under real working conditions, avoiding secondary damage caused by model distortion. The evaluation result output module transforms complex simulation data into executable operation guidelines, significantly improving the safety and operability of the maintenance process.

[0123] Reference Figure 8As shown, this application further proposes to obtain simulation data based on dynamic simulation results, extract environmental constraints and risk assessment parameters from the simulation data, generate an initial dataset for low-risk scenarios, and determine the scenario risk level; process the initial dataset using a path planning algorithm, and calculate the tool trajectory in combination with environmental constraints; generate an instruction sequence through the tool trajectory, and decompose the tool trajectory into a time series table to determine the execution steps of the instruction sequence in order to meet the requirements of precise control.

[0124] Among them, the path planning algorithm refers to the mathematical method of generating collision-free trajectories based on environmental constraints. Specifically, it can be implemented using the A* algorithm or the RRT algorithm, adapting to the operational needs of confined spaces through obstacle avoidance and path smoothing. The time series table refers to discretizing the continuous trajectory into a coordinate sequence corresponding to timestamps, which can be implemented using interpolation algorithms to precisely control the movement rhythm of the tool's end effector. Environmental constraints include the distribution of spatial obstacles and equipment safety distance thresholds, which can be implemented by matching 3D point cloud data with a safety rule base to limit the feasible area of ​​the tool trajectory.

[0125] Specifically, after obtaining the dynamic simulation results, environmental constraint parameters and risk assessment indicators are first extracted to construct an initial dataset containing spatial obstacle coordinates and equipment safety distances. A path planning algorithm then searches for feasible paths that satisfy the safety distance constraints in three-dimensional space, generating an initial tool trajectory. The trajectory is subsequently decomposed into discrete time node coordinate sequences, and combined with tool movement speed parameters to generate a time series table, forming an executable instruction set. For example, in a narrow area with a 500 mm gap between busbar layers, the path planning algorithm can automatically generate an S-shaped trajectory around the flange joint and decompose the trajectory into displacement commands of 0.1 mm per second.

[0126] Compared to existing technologies, traditional methods rely on manual experience to plan tool paths, resulting in low trajectory accuracy and high operational risks. This solution automatically generates trajectories that meet safety constraints through algorithms and converts them into time-series control commands, enabling the positioning accuracy of the tool's end effector to be controlled within ±1 mm, thus avoiding the risk of equipment collisions caused by manual operation.

[0127] Reference Figure 9As shown, this application further proposes that during the axial and radial displacement operations performed on the disassembly and assembly platform, multiple sets of optical positioning sensors arranged in the work area are used to collect the actual displacement data of the busbar drum in real time and record the spatial coordinate changes of the tool end effector; the actual displacement data is compared with the predefined displacement path in the three-dimensional space model to calculate the position deviation and trigger the model correction mechanism; a high-precision laser scanning unit is started to perform local rescanning of the busbar drum structure in the current operation stage to obtain updated point cloud data and perform feature matching with the point cloud of the corresponding area in the initial three-dimensional model; based on the feature matching results, the busbar drum pose in the three-dimensional space model is dynamically adjusted to correct the model distortion caused by installation errors, structural deformation, or sensor drift; the corrected model data is fed back to the dynamic simulation engine to regenerate the tool trajectory and execution steps adapted to the current actual environment, forming a closed-loop control process; the above steps are repeated until the entire maintenance process is completed, and finally a digital maintenance process library that is consistent with the actual operation process and has been optimized through multiple rounds is output.

[0128] Among them, optical positioning sensors refer to devices that acquire spatial coordinates through infrared or laser ranging principles. Specifically, they can be implemented using industrial-grade sensor arrays based on triangulation principles, and their function is to capture the dynamic displacement of the busbar drum in real time during operation. Point cloud registration algorithms refer to calculation methods that align 3D point cloud data in different coordinate systems. Specifically, they can be implemented using ICP algorithms based on feature matching, used to eliminate pose deviations between the model and the actual structure. Closed-loop control processes refer to iterative mechanisms of data acquisition, model correction, and path updating, which can be implemented through embedded control systems. Their function is to continuously optimize the accuracy of the maintenance process.

[0129] Specifically, when the actuator disassembles or assembles the busbar cylinder according to a preset path, the sensor array continuously monitors the actual displacement trajectory of the busbar cylinder. When a deviation between the actual displacement and the model path is detected, the system automatically triggers the laser scanning unit to perform a local rescan of the target area, acquiring updated point cloud data. By matching the new point cloud with the original model's features, areas of model distortion caused by structural deformation or assembly errors can be identified. Subsequently, a point cloud registration algorithm is used to dynamically adjust the 3D model, ensuring spatial consistency between the virtual model and the physical entity. The corrected model parameters are input into the dynamic simulation engine in real time to recalculate the tool's motion trajectory and operation steps, forming a new set of execution instructions. This process is repeated until the maintenance work is completed, ultimately generating a digital process library that has undergone multiple rounds of optimization.

[0130] In some specific implementations, optical positioning sensors can be arranged in a distributed manner, such as installing high-precision laser rangefinders at the four corners of the work platform to form a monitoring network covering the entire operating area. The local rescanning process can employ an adaptive scanning strategy, such as dynamically adjusting the sampling density and scanning range of the laser scanning units based on the deviation. The model correction stage can incorporate machine learning algorithms, such as training a pose prediction model using historical data to accelerate the computational efficiency of feature matching.

[0131] Compared to existing technologies, traditional maintenance methods rely on static models and manual adjustments based on experience, failing to correct pose deviations during operation in real time. This solution, by constructing a closed-loop control system, achieves dynamic synchronization between model data and actual operational status. Existing technologies typically require interrupting operations for a complete rescan to address model distortion issues; however, this solution employs local rescanning and feature matching techniques, enabling model correction without interrupting the workflow. Furthermore, traditional methods lack a feedback mechanism between operational parameters and model data, while this solution achieves continuous optimization of the maintenance process through real-time data interaction with a dynamic simulation engine.

[0132] Through the above technical solution, this application effectively solves the path deviation problem caused by model distortion during busbar assembly and disassembly, and ensures the consistency between the virtual model and the actual structure through a closed-loop control mechanism. This solution can dynamically correct pose deviations caused by equipment deformation or sensor errors, avoiding the risk of tool collisions due to accumulated errors. Through continuous optimization of the digital process library, standard operating procedures adapted to different work scenarios are formed, significantly improving the reliability and operational efficiency of maintenance operations in complex spatial environments.

[0133] Through the above technical solution, this application achieves automatic generation and precise control of tool trajectories in confined spaces, solving the problem of difficult tool operation caused by space limitations in traditional maintenance. By decomposing the trajectory into time-series instructions, it ensures that the displacement and timing of each operation step comply with safety specifications, reducing the risk of equipment damage caused by operational errors.

[0134] Reference Figure 10 As shown, to implement the above method, this application further proposes a phase-separated vertically stacked GIS busbar maintenance system based on a BIM model, including:

[0135] The data acquisition and processing module is used to acquire phase-by-phase vertical stacking arrangement data of GIS busbar cylinders through a laser scanner, and process the acquired point cloud information to construct an initial three-dimensional model.

[0136] The feature extraction and benchmark determination module is used to extract key feature points of axial displacement and radial control based on the initial three-dimensional model, and to determine the benchmark vectors of axial displacement and radial control by fusing multi-angle scanning results using a point cloud registration algorithm.

[0137] The path optimization module is used to construct a displacement constraint matrix after obtaining the reference vector, apply the iterative nearest point algorithm to adjust the radial control parameters, and obtain the optimized displacement path.

[0138] The 3D visualization module is used to generate a 3D presentation view through the optimized displacement path, and uses virtual reality rendering technology to overlay phase arrangement details to obtain an interactive 3D spatial model for subsequent simulation.

[0139] The simulation module is used to input the three-dimensional spatial model into the dynamic simulation engine, simulate the disassembly and assembly sequence under axial displacement and radial control for the structure under maintenance, and determine the process sequence with the highest operating efficiency.

[0140] The risk assessment and optimization module is used to integrate the damage risk assessment module after the process sequence is obtained, and to optimize the sequence parameters through a genetic algorithm to obtain low-risk dynamic simulation results.

[0141] The instruction generation and planning module is used to generate an emergency response instruction set based on the low-risk dynamic simulation results, and to use a path planning algorithm to calculate the tool trajectory in a confined space environment to determine the execution steps for precise control.

[0142] The feedback correction module is used to update the three-dimensional spatial model through feedback loops in the execution steps, apply point cloud registration algorithms to correct the model, and obtain the final optimized maintenance process.

[0143] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for maintenance of phase-separated vertically stacked GIS busbars based on a BIM model, characterized in that, Includes the following steps: The phase-by-phase vertical stacking arrangement data of the GIS busbar is collected by a laser scanner, and the collected point cloud information is processed to construct an initial three-dimensional model. Based on the initial 3D model, key feature points for axial displacement and radial control are extracted. A point cloud registration algorithm is used to fuse multi-angle scanning results to determine the reference vectors for axial displacement and radial control. After obtaining the reference vector, a displacement constraint matrix is ​​constructed, and the radial control parameters are adjusted by applying the iterative nearest point algorithm to obtain the optimized displacement path. A three-dimensional view is generated by optimizing the displacement path, and virtual reality rendering technology is used to overlay phase arrangement details to obtain an interactive three-dimensional spatial model for subsequent simulation. Based on the input of the three-dimensional spatial model into the dynamic simulation engine, the disassembly and assembly sequence under axial displacement and radial control is simulated for the structure under maintenance to determine the process sequence with the highest operational efficiency. After obtaining the process sequence, it is integrated into the damage risk assessment module. The sequence parameters are optimized through a genetic algorithm to obtain low-risk dynamic simulation results. An emergency response instruction set is generated based on the results of low-risk dynamic simulations. A path planning algorithm is used to calculate the tool trajectory in a confined space environment to determine the execution steps for precise control. The three-dimensional spatial model is updated by executing step feedback loops, and the model is corrected by applying point cloud registration algorithm to obtain the final optimized maintenance process.

2. The maintenance method for phase-separated vertically stacked GIS busbars based on a BIM model according to claim 1, characterized in that: Data on the phase-by-phase vertical stacking arrangement of GIS busbars is collected using a laser scanner. The collected point cloud information is then processed to construct an initial 3D model, including: Data on the phase-separated vertical stacking arrangement of the busbar drum is acquired using a laser scanner to obtain point cloud information. The point cloud information is then preliminarily segmented using a stereomicroscope algorithm to obtain initial point cloud data. The initial point cloud data is filtered, and noise points are removed using the mean filtering method. Feature points arranged in phases are extracted from the initial point cloud data to obtain a clean point cloud dataset. By integrating multiple sets of point cloud data through data fusion, and processing the clean point cloud dataset using a surface fitting method, an initial 3D model is obtained.

3. The maintenance method for phase-separated vertically stacked GIS busbars based on a BIM model according to claim 1, characterized in that: Based on the initial 3D model, key feature points for axial displacement and radial control are extracted. A point cloud registration algorithm is used to fuse multi-angle scan results to determine the reference vectors for axial displacement and radial control, including: Three-dimensional point cloud data is obtained from the initial model, and a multi-angle point cloud set is generated by scanning with a stereo microscope. The multi-angle point cloud set is initially registered using the iterative nearest point algorithm to obtain the registered point cloud data. For the registered point cloud data, principal component analysis is used to extract key feature points, which are then identified as candidate feature points for axial displacement and radial control. Based on the candidate feature points, the spatial transformation matrix of the point cloud is calculated, and the initial parameters of the reference vector are obtained by fitting the axial displacement direction vector and the radial control constraint plane using the least squares method. The point cloud data from the multi-angle scans are integrated using a data fusion algorithm, and the fused point cloud data is then denoised using voxel filtering to determine the final reference vector and control accuracy.

4. The maintenance method for phase-separated vertically stacked GIS busbars based on a BIM model according to claim 1, characterized in that: After obtaining the reference vector, a displacement constraint matrix is ​​constructed. The radial control parameters are adjusted using the iterative nearest point algorithm to obtain the optimized displacement path, including: Obtain the reference vector from the input data, extract the key direction components using vector decomposition, and obtain the initial reference vector; Using an initial reference vector and combining it with preset displacement constraints, a displacement constraint matrix is ​​constructed, and the structure of the constraint matrix is ​​determined. By using the iterative nearest neighbor algorithm, the nearest neighbor distance of each point in the matrix is ​​calculated, and a set of distance distributions is obtained. Based on the distance distribution set, adjust the radial control parameters, update the parameter values, and obtain the adjusted control parameter set. By recalculating the trajectory points of the displacement path using the adjusted set of control parameters, a preliminary optimized path is obtained. The least squares method is used to fit the path, resulting in a smooth optimized path; Based on the smooth optimization path, the boundary conditions of the displacement constraint matrix are updated to obtain the final optimized displacement path.

5. The maintenance method for phase-separated vertically stacked GIS busbars based on a BIM model according to claim 1, characterized in that: A 3D rendering view is generated using the optimized displacement path, and virtual reality rendering technology is used to overlay phase arrangement details to obtain an interactive 3D spatial model for subsequent simulations, including: The input displacement path data is processed by Algorithm A to obtain the optimized displacement trajectory and determine the set of trajectory point coordinates. A 3D view is generated using OpenGL rendering technology, and spatial position data is obtained from the set of trajectory point coordinates to obtain the 3D rendering view; A layered rendering method is used to overlay phased arrangement details. Through the 3D presentation view and the preset material library, a view with enhanced details is obtained, resulting in the interactive 3D space model. The user inputs interactive control commands, which are processed by the Unity3D engine to obtain the dynamically adjusted model state and simulate the application results.

6. The maintenance method for phase-separated vertically stacked GIS busbars based on a BIM model according to claim 1, characterized in that: Based on the 3D spatial model input into the dynamic simulation engine, the disassembly and assembly sequence under axial displacement and radial control is simulated for the structure under maintenance to determine the most efficient process sequence, including: Displacement data and control parameters are obtained from a three-dimensional spatial model. A preset dynamic simulation algorithm is used to generate an initial disassembly and assembly sequence for the axial displacement and radial control of the maintenance structure, thus obtaining a preliminary process sequence. Based on the preliminary process sequence, and considering the constraints of axial displacement and radial control, a linear programming algorithm is used to optimize the assembly and disassembly sequence, and the optimization result is determined. If the sequence optimization result meets the preset efficiency threshold, then key control parameters are extracted from the sequence optimization result to generate the final process sequence. If the conditions are not met, adjust the control parameters and regenerate the sequence optimization results; By combining the final process sequence with the dynamic simulation results, the disassembly and assembly sequence of the maintenance structure under axial displacement and radial control is verified, and the simulation results are obtained.

7. The maintenance method for phase-separated vertically stacked GIS busbars based on a BIM model according to claim 1, characterized in that: After obtaining the process sequence, it is integrated into the damage risk assessment module. A genetic algorithm is used to optimize the sequence parameters, resulting in low-risk dynamic simulation results, including: Obtain process sequence data; extract process sequence data containing time series characteristics from the data source to obtain the original sequence dataset. The original sequence dataset is cleaned and formatted through a data processing workflow to generate a standard sequence dataset. The risk assessment module is used to calculate risk indicators on the standard sequence dataset to obtain a risk assessment indicator set. The sequence parameters are optimized using a genetic algorithm to generate an optimized parameter set. Based on the optimized parameter set, the sequence parameters in the standard sequence dataset are adjusted to obtain the adjusted sequence dataset. The dynamic simulation generation module performs simulation calculations on the adjusted sequence dataset to generate low-risk dynamic simulation results. The evaluation result output module is used to format the low-risk dynamic simulation results and generate the final output results.

8. The maintenance method for phase-separated vertically stacked GIS busbars based on a BIM model according to claim 1, characterized in that: An emergency response instruction set is generated based on the results of low-risk dynamic simulations. A path planning algorithm is used to calculate the tool trajectory in confined spaces to determine the execution steps for precise control, including: Simulation data is obtained based on the dynamic simulation results. Environmental constraints and risk assessment parameters are extracted from the simulation data to generate an initial dataset for low-risk scenarios and determine the scenario risk level. The initial dataset is processed using a path planning algorithm, and the trajectory is calculated using environmental constraints. The tool trajectory is used to generate an instruction sequence. To meet the requirements of precise control, the tool trajectory is decomposed into a time series table to determine the execution steps of the instruction sequence.

9. The maintenance method for phase-separated vertically stacked GIS busbars based on a BIM model according to claim 1, characterized in that: The 3D spatial model is updated through a feedback loop during execution, and a point cloud registration algorithm is applied to correct the model, resulting in the final optimized maintenance process, including: During the axial and radial displacement operations performed on the disassembly and assembly platform, multiple sets of optical positioning sensors arranged in the work area collect the actual displacement data of the busbar drum in real time and record the spatial coordinate changes of the end effector of the tool. The actual displacement data is compared with the predefined displacement path in the three-dimensional space model to calculate the position deviation and trigger the model correction mechanism. The high-precision laser scanning unit is activated to perform a local rescan of the busbar structure in the current operation stage, acquire updated point cloud data, and perform feature matching with the point cloud of the corresponding area in the initial 3D model; Based on the feature matching results, the pose of the busbar in the three-dimensional space model is dynamically adjusted to correct the model distortion caused by installation errors, structural deformation or sensor drift. The corrected model data is fed back to the dynamic simulation engine to regenerate tool trajectories and execution steps that are adapted to the current actual environment, forming a closed-loop control process. Repeat the above steps until the entire maintenance process is completed, and finally output a digital maintenance process library that is consistent with the actual operation process and has been optimized in multiple rounds.

10. A BIM model-based phase-separated vertically stacked GIS busbar maintenance system, used to implement the method described in any one of claims 1 to 9, characterized in that: include: The data acquisition and processing module is used to acquire phase-by-phase vertical stacking arrangement data of GIS busbar cylinders through a laser scanner, and process the acquired point cloud information to construct an initial three-dimensional model. The feature extraction and benchmark determination module is used to extract key feature points of axial displacement and radial control based on the initial three-dimensional model, and to determine the benchmark vectors of axial displacement and radial control by fusing multi-angle scanning results using a point cloud registration algorithm. The path optimization module is used to construct a displacement constraint matrix after obtaining the reference vector, apply the iterative nearest point algorithm to adjust the radial control parameters, and obtain the optimized displacement path. The 3D visualization module is used to generate a 3D presentation view through the optimized displacement path, and uses virtual reality rendering technology to overlay phase arrangement details to obtain an interactive 3D spatial model for subsequent simulation. The simulation module is used to input the three-dimensional spatial model into the dynamic simulation engine, simulate the disassembly and assembly sequence under axial displacement and radial control for the structure under maintenance, and determine the process sequence with the highest operating efficiency. The risk assessment and optimization module is used to integrate the damage risk assessment module after the process sequence is obtained, and to optimize the sequence parameters through a genetic algorithm to obtain low-risk dynamic simulation results. The instruction generation and planning module is used to generate an emergency response instruction set based on the low-risk dynamic simulation results, and to use a path planning algorithm to calculate the tool trajectory in a confined space environment to determine the execution steps for precise control. The feedback correction module is used to update the three-dimensional spatial model through feedback loops in the execution steps, apply point cloud registration algorithms to correct the model, and obtain the final optimized maintenance process.

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