Point-cloud-based operation and maintenance decision aid method for distribution network corridor

Abstract

Disclosed in the present invention is a point-cloud-based operation and maintenance decision aid method for a distribution network corridor. The method comprises: a data collection module acquiring point cloud data of a distribution network corridor by means of a laser scanning device scanning the distribution network corridor, and preprocessing the collected point cloud data; a defect detection module using a constructed point cloud model to perform defect detection and classification on the distribution network corridor; a three-dimensional visualization module performing integration and visual display on the point cloud data and defect information, in order to realize the three-dimensional visualization of the distribution network corridor; and an operation and maintenance module formulating a dynamic operation, maintenance and overhaul strategy on the basis of device defect information. In the present invention, point cloud data and a three-dimensional model are used to perform device state monitoring and defect detection, so as to realize the comprehensive and rapid detection and evaluation on distribution network corridors, thereby improving the operation and maintenance efficiency.

Description

An operation and maintenance decision-making assistance method for distribution network channel point cloud Technical Field

[0001] The present invention relates to the technical field of distribution network operation and maintenance, and in particular to an operation and maintenance decision-making assistance method for a distribution network channel point cloud. Background Art

[0002] The rapidly growing power network urgently requires safer maintenance of the power grid and its related infrastructure. By acquiring distribution network channel point cloud data, defects in the channel can be identified, assisting distribution network operation and maintenance personnel in channel optimization and equipment installation, helping operation and maintenance personnel to manage resources and make optimal decisions. Based on the analysis of channel risk factors based on point cloud data, risk assessment and early warning can be provided to operation and maintenance personnel, helping them to take timely measures.

[0003] The existing major operation and maintenance decision-making support systems may only provide basic data display and analysis functions and lack the ability to provide intelligent decision-making support. This makes operation and maintenance personnel rely on subjective judgment when making decisions, which is prone to blind spots or wrong decisions, increasing the workload and operational complexity of operation and maintenance personnel. This may result in the data and analysis results not being able to reflect the actual situation in a timely manner, affecting the accurate grasp of the distribution network channel status. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an operation and maintenance decision-making assistance method for distribution network channel point cloud, which can solve the problem that traditional methods only provide basic data display and analysis functions and lack the ability to provide intelligent decision support.

[0006] To solve the above technical problems, the present invention provides the following technical solution: an operation and maintenance decision-making support method for distribution network channel point cloud, comprising:

[0007] The data acquisition module uses laser scanning equipment to scan the distribution network channel, obtain the point cloud data of the distribution network channel, and pre-process the collected point cloud data; the defect detection module uses the constructed point cloud model to detect and classify defects in the distribution network channel; the three-dimensional visualization module integrates and visualizes the point cloud data and defect information to achieve three-dimensional visualization of the distribution network channel; the operation and maintenance module formulates dynamic operation and maintenance strategies based on equipment defect information.

[0008] As a preferred solution of the operation and maintenance decision-making assistance method of the distribution network channel point cloud described in the present invention, the data acquisition module collects point cloud data of the distribution network channel through a laser scanning device, forms point cloud data by recording the laser points reflected back by the laser scanning device, and pre-processes the formed point cloud data, including noise removal, smoothing and data alignment.

[0009] As a preferred solution of the operation and maintenance decision-making support method for distribution network channel point cloud of the present invention, the noise removal includes dividing the point cloud data into a number of local areas, and calculating the statistical characteristics of the points in each local area. The specific calculation formula is as follows:

[0010] Among them, mean represents the average value of the local area, N represents the number of points in the local area, and di represents the data value of the i-th point. When the difference between the data value of a point and the average value of the local area exceeds the preset threshold, it is determined to be a noise point, and the data determined to be a noise point will be removed.

[0011] As a preferred embodiment of the operation and maintenance decision-making support method for the distribution network channel point cloud of the present invention, the smoothing process includes setting the size of the local area to n, and for each input value xi, taking the current input value and the input value n-1 as a local area, calculating the average value yi of the current local area, and updating the current output value to the average value yi of the local area. The specific calculation formula is as follows:

[0012] Where yi represents the i-th output value, yi represents the j-th point in the local area composed of the i-th input value and the n-1 input values, and n represents the size of the local area.

[0013] As a preferred solution of the operation and maintenance decision-making auxiliary method of the distribution network channel point cloud described in the present invention, the data registration includes, for each point p in the point cloud data, selecting a neighborhood point set N(p) within a radius r, and calculating the covariance matrix C of the neighborhood point set N(p). The specific calculation formula is as follows: C = (1 / |N(p)|)*∑(pi-p0)(pi-p0)T

[0014] Where N(p) represents the size of the neighborhood point set, pi represents the i-th point in the neighborhood point set, and T represents the transpose operation. Solve for the eigenvalues ​​λ1, λ2, and λ3, and calculate the curvature K of each point, which is the product of the eigenvalues ​​divided by the sum of the eigenvalues. The specific calculation formula is as follows: K(p) = λ1*λ2 / (λ1+λ2+λ3)

[0015] Among them, λ1, λ2, and λ3 are respectively represented as the eigenvalues ​​of the covariance matrix C, and K(p) is represented as the curvature value of point p. The obtained curvature values ​​are matched to find the corresponding curvature values ​​between different point cloud data. According to the matched curvature values, the rigid body transformation matrix between the point cloud data is calculated. The specific calculation formula is as follows: t=(1 / N)*∑(pi-Ri*qi)

[0016] Among them, t represents the translation vector, N represents the number of feature point pairs, pi and qi represent the source point and target point in the matching curvature value pair respectively. According to the calculated rigid body transformation matrix, the point cloud data is transformed and fused into an overall point cloud model.

[0017] As a preferred solution of the operation and maintenance decision-making assistance method of the distribution network channel point cloud described in the present invention, the defect detection module defines the neighborhood range of each point by determining a radius. For a given point cloud data, it is assumed that there are N points in the point cloud data. For each point i, the coordinates are expressed as (X_i, Y_i, Z_i). The number of points within the neighborhood radius whose distance from point i is less than or equal to the neighborhood radius is counted. For each point i, all other points j (j≠1) are traversed to calculate the Euclidean distance between point i and point j. The specific calculation formula is as follows: d_ij=sqrt((X_i-X_j)^2+(Y_i-Y_j)^2+(Z_i-Z_j)^2

[0018] Count the number of points within the neighborhood radius whose distance to point i is less than or equal to the neighborhood radius. For each point i, traverse all other points j. If point j satisfies the condition d_ij ≤ neighborhood radius, then add 1 to the number of neighborhood points. For each point i, calculate the neighborhood density. The specific calculation formula is as follows: ND_i=NC_i / (π*NR)^2

[0019] Among them, NC_i represents the number of neighborhood points, NR represents the neighborhood radius, and the mean and standard deviation of the neighborhood density of all points are calculated. The specific calculation formula is as follows:

[0020] Set the threshold MD±k*DS, where k is a constant, and set the threshold to the density outlier. For each point i, when the neighborhood density of i exceeds the threshold range, it is marked as a density outlier, indicating a distribution network channel defect.

[0021] As a preferred solution of the operation and maintenance decision-making support method for the distribution network channel point cloud described in the present invention, the three-dimensional visualization module integrates the point cloud data and the distribution network channel defect data to form an integrated data set, uses BIM technology to perform visual modeling on the point cloud data, and establishes a coordinate system through BIM as the coordinate system for the point cloud data and the three-dimensional model. According to the established coordinate system, the collected point cloud data is aligned and matched with the coordinate system. The specific calculation formula is as follows:

[0022] Among them, p j Represents the coordinates of the jth point in the point cloud data, q j Represents the distance p in the three-dimensional model jThe coordinates of the nearest point, T represents the transformation matrix, and after registering the point cloud data with the 3D model, a 3D model of the entire distribution network channel is formed.

[0023] As a preferred solution of the operation and maintenance decision-making assistance method of the distribution network channel point cloud described in the present invention, the operation and maintenance module sets the corresponding operation and maintenance priority for the distribution network channel maintenance according to the abnormal situation of the distribution network channel in the three-dimensional model, the importance of the distribution network channel, and the severity of the defect. The specific calculation formula of the priority is as follows: Y = (Z × Q) / KX

[0024] Among them, Z represents the importance weight, Q represents the defect severity weight, and KX represents the operating condition coefficient. According to the calculation results of the operation and maintenance priority, the operation and maintenance priority is set in order to determine the priority channels and the defects in the channels.

[0025] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of an operation and maintenance decision-making assistance method for a distribution network channel point cloud.

[0026] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a method for assisting operation and maintenance decision-making of a distribution network channel point cloud are implemented.

[0027] Beneficial effects of the present invention: The present invention utilizes point cloud data and three-dimensional models to perform equipment status monitoring and defect detection, which can realize comprehensive and rapid detection and evaluation of distribution network channels, improve operation and maintenance efficiency, and effectively prevent possible safety accidents and improve the safety of distribution network channels by detecting defects in the channels and executing corresponding maintenance strategies. The system has dynamic operation and maintenance strategies, combined with real-time updates and automated operations, which can provide intelligent decision-making support for operation and maintenance personnel, helping them to formulate more scientific and reasonable operation and maintenance plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0029] FIG1 is a flow chart of an operation and maintenance decision-making assistance method for a distribution network channel point cloud provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0031] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0032] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0033] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0034] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0035] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0036] Example 1

[0037] 1 , which is a first embodiment of the present invention, provides an operation and maintenance decision-making assistance method for a distribution network channel point cloud, including:

[0038] S1: The data acquisition module scans the distribution network channel through a laser scanning device, obtains the point cloud data of the distribution network channel, and pre-processes the collected point cloud data.

[0039] The data acquisition module collects point cloud data of the distribution network channel through a laser scanning device, forms point cloud data by recording the laser points reflected by the laser scanning device, and pre-processes the formed point cloud data, including noise removal, smoothing and data alignment.

[0040] The noise removal method includes dividing the point cloud data into several local areas. For each local area, the statistical features of the points therein are calculated. The specific calculation formula is as follows:

[0041] Among them, mean represents the average value of the local area, N represents the number of points in the local area, and di represents the data value of the i-th point. When the difference between the data value of a point and the average value of the local area exceeds the preset threshold, it is determined to be a noise point, and the data determined to be a noise point will be removed.

[0042] The smoothing process includes setting the size of the local area to n, for each input value xi, taking the current input value and the input value n-1 as a local area, calculating the average value yi of the current local area, and updating the current output value to the average value yi of the local area. The specific calculation formula is as follows:

[0043] Where yi represents the i-th output value, yi represents the j-th point in the local area composed of the i-th input value and the n-1 input values, and n represents the size of the local area.

[0044] The data registration includes, for each point p in the point cloud data, selecting a neighborhood point set N(p) within a radius r and calculating the covariance matrix C of the neighborhood point set N(p). The specific calculation formula is as follows: C = (1 / |N(p)|)*∑(pi-p0)(pi-p0)T

[0045] Where N(p) represents the size of the neighborhood point set, pi represents the i-th point in the neighborhood point set, and T represents the transpose operation. Solve for the eigenvalues ​​λ1, λ2, and λ3, and calculate the curvature K of each point, which is the product of the eigenvalues ​​divided by the sum of the eigenvalues. The specific calculation formula is as follows: K(p) = λ1*λ2 / (λ1+λ2+λ3)

[0046] Among them, λ1, λ2, and λ3 are respectively represented as the eigenvalues ​​of the covariance matrix C, and K(p) is represented as the curvature value of point p. The obtained curvature values ​​are matched to find the corresponding curvature values ​​between different point cloud data. According to the matched curvature values, the rigid body transformation matrix between the point cloud data is calculated. The specific calculation formula is as follows: t=(1 / N)*∑(pi-Ri*qi)

[0047] Among them, t represents the translation vector, N represents the number of feature point pairs, pi and qi represent the source point and target point in the matching curvature value pair respectively. According to the calculated rigid body transformation matrix, the point cloud data is transformed and fused into an overall point cloud model.

[0048] S2: The defect detection module uses the constructed point cloud model to detect and classify defects in the distribution network channel.

[0049] The defect detection module defines the neighborhood range of each point by determining a radius. For a given point cloud data, assume that there are N points in the point cloud data. For each point i, the coordinates are expressed as (X_i, Y_i, Z_i). The number of points within the neighborhood radius whose distance to point i is less than or equal to the neighborhood radius is counted. For each point i, all other points j (j≠1) are traversed and the Euclidean distance between point i and point j is calculated. The specific calculation formula is as follows: d_ij = sqrt((X_i-X_j)^2+(Y_i-Y_j)^2+(Z_i-Z_j)^2

[0050] Count the number of points within the neighborhood radius whose distance to point i is less than or equal to the neighborhood radius. For each point i, traverse all other points j. If point j satisfies the condition d_ij ≤ neighborhood radius, then add 1 to the number of neighborhood points. For each point i, calculate the neighborhood density. The specific calculation formula is as follows: ND_i=NC_i / (π*NR)^2

[0051] Among them, NC_i represents the number of neighborhood points, NR represents the neighborhood radius, and the mean and standard deviation of the neighborhood density of all points are calculated. The specific calculation formula is as follows:

[0052] For each point i, when the neighborhood density of i exceeds the threshold range, it is marked as a density outlier, indicating a distribution network channel defect.

[0053] S3: The 3D visualization module integrates and visualizes point cloud data and defect information to achieve 3D visualization of distribution network channels.

[0054] The 3D visualization module integrates the point cloud data and the distribution network channel defect data to form an integrated data set. The point cloud data is visualized and modeled using BIM technology. A coordinate system is established through BIM as the coordinate system for the point cloud data and the 3D model. The collected point cloud data is registered based on the established coordinate system, and the point cloud data is matched with the coordinate system. The specific calculation formula is as follows:

[0055] Among them, p j Represents the coordinates of the jth point in the point cloud data, q j Represents the distance p in the three-dimensional model j The coordinates of the nearest point, T represents the transformation matrix, and after registering the point cloud data with the 3D model, a 3D model of the entire distribution network channel is formed.

[0056] S4: The operation and maintenance module formulates dynamic operation and maintenance strategies based on equipment defect information.

[0057] The operation and maintenance module sets the corresponding operation and maintenance priority for the distribution network channel maintenance according to the abnormal situation of the distribution network channel in the three-dimensional model, the importance of the distribution network channel, and the severity of the defect. The specific calculation formula of the priority is as follows: Y = (Z × Q) / KX

[0058] Among them, Z represents the importance weight, Q represents the defect severity weight, and KX represents the operating condition coefficient. According to the calculation results of the operation and maintenance priority, the operation and maintenance priority is set in order to determine the priority channels and the defects in the channels.

[0059] It should be noted that the calculation formula of the operating condition coefficient is as follows: KX=(K1×Y1+K2×Y2+...+Kn×Yn) / Zky

[0060] Among them, Ki represents the working condition weight, Yi represents the degree of influence, and Zky represents the total weight. Among them, Ki is the weight value set according to the specific situation, indicating the importance of different working conditions, and the sum is 1. Yi is the coefficient determined according to the working condition analysis results, indicating the degree of influence of the equipment under the corresponding working condition. According to the calculation results of the working condition coefficient, the priority and degree of influence of the distribution network channel under different working conditions can be judged, so as to reasonably arrange operation and maintenance work and optimize resource allocation.

[0061] Example 2

[0062] 1 , which shows an embodiment of the present invention, provides an operation and maintenance decision-making assistance method for a distribution network channel point cloud. In order to verify the beneficial effects of the present invention, a scientific demonstration is conducted through experiments.

[0063] To verify the effectiveness of the operation and maintenance decision-making support method for distribution network channel point cloud, an experiment was designed to compare the performance of this method with existing technologies in distribution network channel defect detection, classification, three-dimensional visualization, and operation and maintenance strategy formulation based on defect information.

[0064] The experiment selected two distribution network channels as research subjects: one used the proposed operation and maintenance decision-making support method as an experimental group, and the other used existing basic data display and analysis methods as a control group. Both distribution network channels were monitored for six months, during which time they were scanned multiple times using laser scanning equipment to collect point cloud data.

[0065] The specific steps of the experiment include:

[0066] High-precision laser scanning equipment was used to scan the distribution network channels in the experimental and control groups to obtain point cloud data. The experimental group data was preprocessed, including noise removal, smoothing, and data registration, to improve data quality.

[0067] The experimental group was treated with the defect detection algorithm proposed in the present invention, while the control group was treated with a traditional defect detection method. The experimental group's method included using a point cloud model for more accurate defect detection and automatic classification.

[0068] The experimental group used BIM technology for 3D visualization of point cloud data, while the control group used basic 3D display technology. The two methods were compared in terms of visualization quality and practicality.

[0069] Based on the defect detection results, operation and maintenance strategies were developed for the experimental and control groups. The experimental group adopted the dynamic operation and maintenance strategy of the present invention, while the control group developed a strategy based on manual experience. The experimental results are shown in Table 1.

[0070] Table 1 Comparison of experimental results

[0071] Analysis of experimental data demonstrates the significant advantages of this invention in assisting operational and maintenance decision-making for distribution network channel point clouds. In terms of defect detection accuracy, the high-precision point cloud data processing and advanced defect detection algorithms utilized by this invention enable more accurate identification of defects in distribution network channels, reducing missed detections and false alarms.

[0072] In terms of defect classification efficiency, the automated classification method proposed in this paper proves that it not only improves classification speed but also ensures classification accuracy. Through automated classification, the operation and maintenance team can more quickly identify and handle different types of defects, improving the efficiency of operation and maintenance work.

[0073] In terms of 3D visualization clarity, the BIM technology used in this paper demonstrates significant advantages in improving visualization clarity and practicality. High-quality 3D visualization enables operators to more intuitively and accurately understand the status of distribution network channels, providing reliable visual support for developing operation and maintenance strategies.

[0074] In terms of operational response time, the dynamic operational inspection and repair strategy of this invention can significantly shorten operational response time and quickly and effectively address defects in distribution network channels. Through real-time updates and automated operational decision support, this invention provides operational teams with powerful tools to optimize operational plans in a scientific and rational manner, improving the security and reliability of distribution network channels.

[0075] In summary, the experimental results fully demonstrate the innovation and advantages of this invention compared to the existing technology. Through efficient data processing, accurate defect detection, automated classification methods, high-quality three-dimensional visualization and rapid response operation and maintenance strategies, the operation and maintenance efficiency and safety of the distribution network channel are significantly improved.

[0076] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0077] Example 3

[0078] The third embodiment of the present invention is different from the first two embodiments in that:

[0079] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0080] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

[0081] The present application is described with reference to the flow chart and / or block diagram of the method, device (system), and computer program product according to the embodiment of the present application. It should be understood that each flow process and / or box in the flow chart and / or block diagram and the combination of the flow process and / or box in the flow chart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processing machine or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one flow chart flow or multiple flows and / or one box or multiple boxes of the block diagram.

[0082] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0084] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0085] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A distribution network channel point cloud operation and maintenance decision-making support method, characterized by: include, The data acquisition module scans the distribution network channel through a laser scanning device, obtains the point cloud data of the distribution network channel, and pre-processes the collected point cloud data; The defect detection module uses the constructed point cloud model to detect and classify defects in distribution network channels; The 3D visualization module integrates and visualizes point cloud data and defect information to achieve 3D visualization of distribution network channels. The operation and maintenance module formulates dynamic operation and maintenance strategies based on equipment defect information.

2. The method for assisting operation and maintenance decision-making of a distribution network channel point cloud according to claim 1, characterized in that: The data acquisition module collects point cloud data of the distribution network channel through a laser scanning device, forms point cloud data by recording the laser points reflected by the laser scanning device, and pre-processes the formed point cloud data, including noise removal, smoothing and data alignment.

3. The method for assisting operation and maintenance decision-making of a distribution network channel point cloud according to claim 2, characterized in that: The noise removal method includes dividing the point cloud data into several local areas. For each local area, the statistical features of the points therein are calculated. The specific calculation formula is as follows: Among them, mean represents the average value of the local area, N represents the number of points in the local area, and di represents the data value of the i-th point. When the difference between the data value of a point and the average value of the local area exceeds the preset threshold, it is determined to be a noise point, and the data determined to be a noise point will be removed.

4. The method for assisting operation and maintenance decision-making of a distribution network channel point cloud according to claim 3, characterized in that: The smoothing process includes: Set the size of the local region to n. For each input value xi, take the current input value and the input value n-1 as a local region, calculate the average value yi of the current local region, and update the current output value to the average value yi of the local region. The specific calculation formula is as follows: Where yi represents the i-th output value, yi represents the j-th point in the local area composed of the i-th input value and the n-1 input values, and n represents the size of the local area.

5. The method for assisting operation and maintenance decision-making of a distribution network channel point cloud according to claim 4, characterized in that: The data registration includes, for each point p in the point cloud data, selecting a neighborhood point set N(p) within a radius r and calculating the covariance matrix C of the neighborhood point set N(p). The specific calculation formula is as follows: C = (1 / |N(p)|)*∑(pi-p0)(pi-p0)T Where N(p) represents the size of the neighborhood point set, pi represents the i-th point in the neighborhood point set, T represents the transpose operation, solves the eigenvalues ​​λ1, λ2, and λ3, and calculates the curvature K of each point, which is the product of the eigenvalues ​​divided by the sum of the eigenvalues. The specific calculation formula is as follows: K(p)=λ1*λ2 / (λ1+λ2+λ3) Among them, λ1, λ2, and λ3 are respectively represented as the eigenvalues ​​of the covariance matrix C, and K(p) is represented as the curvature value of point p. The obtained curvature values ​​are matched to find the corresponding curvature values ​​between different point cloud data. According to the matched curvature values, the rigid body transformation matrix between the point cloud data is calculated. The specific calculation formula is as follows: t=(1 / N)*∑(pi-Ri*qi) Among them, t represents the translation vector, N represents the number of feature point pairs, pi and qi represent the source point and target point in the matching curvature value pair respectively. According to the calculated rigid body transformation matrix, the point cloud data is transformed and fused into an overall point cloud model.

6. The method for assisting operation and maintenance decision-making of a distribution network channel point cloud according to claim 5, characterized in that: The defect detection module defines the neighborhood range of each point by determining a radius. For a given point cloud data, assume that there are N points in the point cloud data. For each point i, the coordinates are expressed as (X_i, Y_i, Z_i). The number of points within the neighborhood radius whose distance to point i is less than or equal to the neighborhood radius is counted. For each point i, all other points j (j≠1) are traversed and the Euclidean distance between point i and point j is calculated. The specific calculation formula is as follows: d_ij = sqrt((X_i-X_j)^2+(Y_i-Y_j)^2+(Z_i-Z_j)^2 Count the number of points within the neighborhood radius whose distance from point i is less than or equal to the neighborhood radius. For each point i, traverse all other points j. If point j meets the condition di_ij≤neighborhood radius, then add 1 to the number of neighborhood points. For each point i, calculate the neighborhood density. The specific calculation formula is as follows: ND_i=NC_i / (π*NR)^2 in, NC_i represents the number of neighborhood points, NR represents the neighborhood radius, and the mean and standard deviation of the neighborhood density of all points are calculated. The specific calculation formula is as follows: Set the threshold MD±k*DS, where k is a constant, and set the threshold to the density outlier. For each point i, when the neighborhood density of i exceeds the threshold range, it is marked as a density outlier, indicating a distribution network channel defect.

7. The method for assisting operation and maintenance decision-making of a distribution network channel point cloud according to claim 6, characterized in that: The 3D visualization module integrates the point cloud data and the distribution network channel defect data to form an integrated data set. The point cloud data is visualized and modeled using BIM technology. A coordinate system is established through BIM as the coordinate system for the point cloud data and the 3D model. The collected point cloud data is registered based on the established coordinate system, and the point cloud data is matched with the coordinate system. The specific calculation formula is as follows: Among them, p j Represents the coordinates of the jth point in the point cloud data, q j Represents the distance p in the three-dimensional model j The coordinates of the nearest point, T represents the transformation matrix, and after registering the point cloud data with the 3D model, a 3D model of the entire distribution network channel is formed.

8. The method for assisting operation and maintenance decision-making of a distribution network channel point cloud according to claim 7, characterized in that: The operation and maintenance module sets the corresponding operation and maintenance priority for the distribution network channel maintenance according to the abnormal situation of the distribution network channel in the three-dimensional model, the importance of the distribution network channel, and the severity of the defect. The specific calculation formula of the priority is as follows: Y = (Z × Q) / KX Among them, Z represents the importance weight, Q represents the defect severity weight, and KX represents the operating condition coefficient. According to the calculation results of the operation and maintenance priority, the operation and maintenance priority is set in order to determine the priority channels and the defects in the channels.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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