Method and system for fusing point cloud traverse measurement and non-contact fault location

Abstract

The invention relates to the technical field of traverse measurement, and discloses a method and system for fusing point cloud traverse measurement and non-contact fault localization, and the method comprises the steps: carrying out the filtering processing of a three-dimensional point cloud data set, and separating a shape point cloud set; fitting the shape point cloud set into an initial space path of the target object according to a spatial arrangement sequence of the point clouds, and performing spatial interpolation processing on an initial space path section to obtain a continuous space path; carrying out integral calculation on the continuous space path to obtain an actual path length; receiving a fault signal, and performing time characteristic analysis on the fault signal to obtain a time difference sequence; according to the time difference sequence and the continuous space path, deriving a fault initial point set through a traveling wave distance measurement principle; analyzing the spatial distribution dispersion of the fault initial point set, and removing abnormal points in the fault initial point set by combining a continuous spatial path to obtain a fault positioning result; according to the invention, the accuracy of wire measurement and fault location can be improved.

Description

Technical Field

[0001] This invention relates to the field of traverse measurement technology, and in particular to a method and system for integrating point cloud traverse measurement and non-contact fault location. Background Technology

[0002] Existing technologies have significant limitations in the spatial morphology reconstruction of conductors. The three-dimensional point cloud data acquired in non-contact methods are prone to carrying environmental interference noise. Existing filtering algorithms are difficult to achieve accurate separation of shape point clouds and interference point clouds. Furthermore, there is a lack of repair schemes that take into account the geometric continuity of missing segments in the initial spatial path. As a result, the constructed path model cannot truly reflect the actual spatial morphology of the conductor, leading to a large deviation in the spatial reference data for fault location. This directly restricts the improvement of the accuracy of non-contact fault location.

[0003] Existing non-contact fault location methods have shortcomings in the coordination between signal processing and location derivation. Non-contact fault signal acquisition has a lot of redundant information, and existing time feature analysis techniques are unable to accurately extract effective time difference sequences. Furthermore, when deriving the initial fault point set based on the traveling wave ranging principle, the geometric features of the continuous spatial path of the conductor are not fully integrated for correction. The assessment of the spatial distribution dispersion of the initial fault point set lacks systematicity and cannot efficiently eliminate outliers. This results in poor reliability and large error fluctuations in the final fault location results, and the location process is time-consuming, making it difficult to meet the needs of rapid and accurate detection in real-world scenarios. Therefore, how to improve the accuracy of conductor measurement and fault location has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method and system for integrating point cloud traverse measurement and non-contact fault location to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for integrating point cloud traverse measurement and non-contact fault location, comprising: S1. Filter the three-dimensional point cloud dataset of the target object and separate the shape point cloud set of the target object from the denoised point cloud dataset. S2. Based on the spatial arrangement order of the point cloud in the shape point cloud set, fit the shape point cloud set into the initial spatial path of the target object, and perform spatial interpolation on the missing segments in the initial spatial path to obtain the continuous spatial path of the target object. S3. Integrate the continuous spatial path to obtain the actual path length of the target object; S4. Receive the fault signal of the target object, and perform time feature analysis on the fault signal to obtain the time difference sequence of the target object; S5. Based on the time difference sequence and the continuous spatial path, derive the initial fault point set of the target object using the traveling wave ranging principle; S6. Analyze the spatial distribution dispersion of the initial fault point set, and combine the geometric features of the continuous spatial path to remove the abnormal points in the initial fault point set, thereby obtaining the fault location result of the target object.

[0006] In a preferred embodiment, the step of filtering the 3D point cloud dataset of the target object and separating the shape point cloud set and the supporting structure point cloud set from the denoised point cloud dataset includes: Collect a 3D point cloud dataset of the target object and identify the spatial distribution characteristics of the 3D point cloud dataset; Based on the spatial distribution characteristics, the three-dimensional point cloud dataset is iteratively filtered to obtain a clean point cloud dataset of the target object. Based on the point cloud adjacency relationships and local surface features of the clean point cloud dataset, the shape point cloud set of the target object is segmented from the clean point cloud dataset.

[0007] In a preferred embodiment, the step of fitting the shape point cloud set into an initial spatial path of the target object according to the spatial arrangement order of the point cloud set, and performing spatial interpolation processing on the missing segments in the initial spatial path to obtain a continuous spatial path of the target object, includes: Based on the topological connectivity of the point clouds in the shape point cloud set, the spatial neighborhood of the shape point cloud set is analyzed to obtain the ordered point cloud sequence of the target object; Piecewise curve fitting is performed on the ordered point cloud sequence to obtain the spatial path segment of the target object; By analyzing the geometric feature consistency of adjacent path segments in the spatial path segment, the curvature features of the connection points of the adjacent path segments are obtained; Based on the curvature characteristics of the connection points, the adjacent path segments are smoothly connected to obtain the filtered connection segments of the target object. Connect the spatial path segment and the filter connection segment to obtain the initial spatial path of the target object.

[0008] In a preferred embodiment, the step of performing spatial interpolation on the missing segments in the initial spatial path to obtain a continuous spatial path for the target object includes: Extract the missing segments from the initial spatial path, and determine the boundary conditions of the initial spatial path based on the geometric features of the known paths at both ends of the missing segments; Based on the curvature constraints of the boundary conditions, interpolated path segments that maintain geometric continuity with the known path are generated at both ends of the missing segment. The interpolated path segment and the initial spatial path are seamlessly connected to obtain a continuous spatial path for the target object.

[0009] In a preferred embodiment, the step of integrating the continuous spatial path to obtain the actual path length of the target object includes: The continuous spatial path is discretized to obtain the path point sequence of the target object; The spatial distances between adjacent points in the path point sequence are calculated to obtain the basic length sequence of the path point sequence. Analyze the curvature distribution of the continuous spatial path to obtain the curvature values ​​of the path points in the continuous spatial path; The actual path length of the target object is calculated based on the path point sequence, the basic length sequence, and the curvature value, wherein the formula for calculating the actual path length is: ; in, This indicates the actual path length. Indicates the first The spatial coordinates of the path points. Indicates the first The curvature values ​​of the path points. This represents the preset curvature influence coefficient. This indicates the number of path points.

[0010] In a preferred embodiment, receiving the fault signal of the target object and performing time feature analysis on the fault signal to obtain the time difference sequence of the target object includes: Receive the fault signal of the target object and extract the feature components of the fault signal at different time resolutions; Analyze the energy distribution of the characteristic components to determine the arrival time of the fault signal; Eliminate the time deviation in the arrival time to obtain the time sequence of the fault signal; The time difference sequence of the target object is obtained by statistically analyzing the differences between time points in the time point sequence.

[0011] In a preferred embodiment, the step of deriving the initial fault point set of the target object based on the time difference sequence and the continuous spatial path using the traveling wave ranging principle includes: Map the continuous spatial path to a preset spatial coordinate system to obtain the spatial coordinates and tangent direction vectors of the path points on the continuous spatial path; Based on the time difference sequence, the propagation path of the traveling wave in the fault signal is analyzed to obtain the propagation characteristics of the traveling wave; Analyze the initial fault point of the target object based on the spatial coordinates, the tangent direction vector, and the propagation characteristics; Verify the rationality of the spatial distribution of the fault initiation points to obtain the fault initiation point set of the target object.

[0012] In a preferred embodiment, the formula for calculating the initial fault point is: ; in, Indicates the initial point of the fault. Represents the path points on the continuous spatial path. This indicates the acquisition point of the fault signal. Indicates the first Time difference in a time difference sequence Indicates the propagation speed of a traveling wave. The path curvature represents the continuous spatial path. This represents the path torsion of the continuous spatial path. Point Time The shortest path, This indicates the location of the fault point that minimizes the time difference fitting error. , This indicates the preset geometric correction factor.

[0013] In a preferred embodiment, the step of analyzing the spatial distribution dispersion of the fault initial point set and, in conjunction with the geometric features of the continuous spatial path, removing outliers from the fault initial point set to obtain the fault location result of the target object includes: Extract the spatial density parameters and geometric center offset of the fault initial point set to construct the discreteness evaluation matrix of the fault initial point set; Based on the discreteness evaluation matrix, a hierarchical evaluation is performed on the initial fault point set to obtain the spatial distribution discreteness of the initial fault point set. Based on the geometric features of the continuous spatial path and the spatial distribution discreteness, identify the abnormal points in the fault initial point set; The fault location results of the target object are obtained by removing the abnormal points.

[0014] To address the aforementioned problems, the present invention also provides a system that integrates point cloud traverse measurement and non-contact fault location, the system comprising: The point cloud processing module is used to filter the three-dimensional point cloud dataset of the target object and separate the shape point cloud set of the target object from the denoised point cloud dataset. The spatial path construction module is used to fit the shape point cloud set into an initial spatial path of the target object according to the spatial arrangement order of the point cloud set, and to perform spatial interpolation processing on the missing segments in the initial spatial path to obtain a continuous spatial path of the target object. The actual length calculation module is used to perform integral calculation on the continuous spatial path to obtain the actual path length of the target object; The fault signal analysis module is used to receive the fault signal of the target object and perform time feature analysis on the fault signal to obtain the time difference sequence of the target object. The fault initial point derivation module is used to derive the set of fault initial points of the target object based on the time difference sequence and the continuous spatial path using the traveling wave ranging principle. The fault location module is used to analyze the spatial distribution dispersion of the initial fault point set and, in combination with the geometric features of the continuous spatial path, remove abnormal points from the initial fault point set to obtain the fault location result of the target object.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention effectively removes noise interference and accurately separates the shape point cloud by filtering the 3D point cloud dataset of the conductor acquired non-contactly. Then, it fits the initial spatial path according to the spatial arrangement order of the point cloud and performs spatial interpolation processing on the missing segments of the path to construct a continuous spatial path that highly matches the actual shape of the conductor. At the same time, combined with the curvature distribution characteristics of the continuous spatial path, the actual path length including curvature correction is obtained by integral calculation, which greatly improves the integrity and accuracy of spatial data under non-contact method, provides high-quality data support for subsequent fault location, and ensures the basic effectiveness of non-contact fault location.

[0016] 2. This invention performs refined time feature analysis on non-contact fault signals to accurately extract effective time difference sequences. Subsequently, combining the geometric features of continuous spatial paths, it derives the initial fault point set based on the traveling wave ranging principle. By analyzing the spatial distribution dispersion of the initial fault point set and eliminating abnormal points by combining path geometric features, it not only improves the accuracy of fault initial point identification in non-contact mode but also ensures the accuracy of the final fault location result. The entire process relies on non-contact technology to complete data acquisition and processing, which not only improves the positioning effect but also accelerates the positioning process and enhances the practicality and stability of non-contact fault location. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a method for integrating point cloud traverse measurement and non-contact fault location according to an embodiment of the present invention. Figure 2 This is a functional block diagram of a system that integrates point cloud traverse measurement and non-contact fault location according to an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a method for integrating point cloud traverse measurement and non-contact fault location. The execution subject of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for integrating point cloud traverse measurement and non-contact fault location can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a method for fusing point cloud traverse measurement and non-contact fault location according to an embodiment of the present invention. In this embodiment, the method for fusing point cloud traverse measurement and non-contact fault location includes: S1. Filter the three-dimensional point cloud dataset of the target object and separate the shape point cloud set of the target object from the denoised point cloud dataset. In this embodiment of the invention, the step of filtering the three-dimensional point cloud dataset of the target object and separating the shape point cloud set and the supporting structure point cloud set from the denoised point cloud dataset includes: Collect a 3D point cloud dataset of the target object and identify the spatial distribution characteristics of the 3D point cloud dataset; Based on the spatial distribution characteristics, the three-dimensional point cloud dataset is iteratively filtered to obtain a clean point cloud dataset of the target object. Based on the point cloud adjacency relationships and local surface features of the clean point cloud dataset, the shape point cloud set of the target object is segmented from the clean point cloud dataset.

[0021] A drone equipped with a lidar is used to perform a comprehensive, blind-spot-free scan of the power transmission and distribution line. The laser beam emitted by the lidar illuminates the surface of the line point by point, and the reflected laser signals are received. Based on the propagation time and direction of the laser signals, the three-dimensional spatial information of each position on the surface of the line is captured. This three-dimensional spatial information is systematically organized to form a three-dimensional point cloud dataset containing the three-dimensional coordinate information of each point. Subsequently, by observing the distribution density of each point in the three-dimensional point cloud dataset, the arrangement of points between points, and the aggregation state of points in different regions, the spatial distribution characteristics of the three-dimensional point cloud dataset are clarified.

[0022] Based on the spatial distribution characteristics of the identified 3D point cloud dataset, iterative filtering is performed. For each point in the 3D point cloud dataset, the distribution of its surrounding neighboring points is examined one by one. The position information of the point is compared with the position information of its surrounding neighboring points to determine whether the point conforms to the overall spatial distribution pattern. If the position of a point differs significantly from the distribution pattern of its surrounding neighboring points and deviates from the normal distribution range, the point is identified as a noise point and removed directly. After completing one round of comparison and noise point removal for all points, the above comparison and removal operations are repeated for the remaining points until, after multiple rounds of iteration, there are no longer any noise points deviating from the distribution pattern in the 3D point cloud dataset, and finally a clean point cloud dataset with noise removed is obtained.

[0023] Carefully observe the connection between each point in the clean point cloud dataset and its surrounding points, and clarify the point cloud adjacency relationships between each point. At the same time, analyze the surface morphology formed by other points within a certain range around each point, such as whether the surface is flat or curved, and the direction of the curvature, to clearly grasp the local surface features of the clean point cloud dataset. Then, based on the point cloud adjacency relationships and local surface features, gather and integrate those points with the same local surface features and close adjacency relationships, and segment out the shape point cloud that can completely reflect the external shape of the conductor. The remaining points not included in the shape point cloud naturally form the support structure point cloud.

[0024] The beneficial effects are that the entire implementation process uses a non-contact lidar scanning method to collect three-dimensional point cloud data of the conductors. Data acquisition and subsequent processing can be completed without direct contact with the transmission and distribution lines, effectively avoiding the impact that contact operations may have on the lines. At the same time, by accurately collecting three-dimensional point cloud datasets, clarifying spatial distribution characteristics, and iteratively filtering to obtain clean point cloud datasets, and then segmenting shape point clouds based on point cloud adjacency relationships and local surface features, the accuracy of the products at each stage is ensured. This provides accurate and reliable basic data support for subsequent non-contact fault location based on the traveling wave positioning principle, improving the accuracy and reliability of transmission and distribution line fault location.

[0025] S2. Based on the spatial arrangement order of the point cloud in the shape point cloud set, fit the shape point cloud set into the initial spatial path of the target object, and perform spatial interpolation on the missing segments in the initial spatial path to obtain the continuous spatial path of the target object. In this embodiment of the invention, the step of fitting the shape point cloud set into an initial spatial path of the target object according to the spatial arrangement order of the point cloud set, and performing spatial interpolation processing on the missing segments in the initial spatial path to obtain a continuous spatial path of the target object, includes: Based on the topological connectivity of the point clouds in the shape point cloud set, the spatial neighborhood of the shape point cloud set is analyzed to obtain the ordered point cloud sequence of the target object; Piecewise curve fitting is performed on the ordered point cloud sequence to obtain the spatial path segment of the target object; By analyzing the geometric feature consistency of adjacent path segments in the spatial path segment, the curvature features of the connection points of the adjacent path segments are obtained; Based on the curvature characteristics of the connection points, the adjacent path segments are smoothly connected to obtain the filtered connection segments of the target object. Connect the spatial path segment and the filter connection segment to obtain the initial spatial path of the target object.

[0026] The step of performing spatial interpolation on the missing segments in the initial spatial path to obtain a continuous spatial path for the target object includes: Extract the missing segments from the initial spatial path, and determine the boundary conditions of the initial spatial path based on the geometric features of the known paths at both ends of the missing segments; Based on the curvature constraints of the boundary conditions, interpolated path segments that maintain geometric continuity with the known path are generated at both ends of the missing segment. The interpolated path segment and the initial spatial path are seamlessly connected to obtain a continuous spatial path for the target object.

[0027] By observing the spatial connections between each point in the shape point cloud and other surrounding points, we can determine which points are close to each other in three-dimensional space and can form a continuous point sequence, thus clarifying the topological connection relationship of the point cloud. Then, we can further examine the distribution range and arrangement trend of these points in the overall three-dimensional space, analyze the spatial domain of the shape point cloud, and arrange the points in the shape point cloud in sequence according to the natural arrangement order of the point cloud from one end of the power transmission line to the other in the spatial domain, forming an ordered point cloud sequence that can reflect the spatial direction of the conductor.

[0028] By examining the spatial position change trends of each point in an ordered point cloud sequence, points with the same trend are grouped into a point group. For example, points distributed in a straight line are grouped into one point group, and points distributed in a curved pattern with the same arc are grouped into another point group. In this way, the ordered point cloud sequence is divided into multiple consecutive point groups. For each point group, based on the spatial position of the points within the group, a smooth curve is used to connect all the points in the group sequentially, ensuring that the curve fits the position of each point in the group. Each point group corresponds to one such curve, and these curves are the spatial path segments of the conductor.

[0029] Select adjacent path segments one by one in the spatial path segment, observe the geometric features such as the curvature direction, curvature degree and overall direction of the previous path segment, and then observe the same geometric features of the next adjacent path segment. Compare whether the geometric features of the two have a consistent trend of change. Then find the connection point of the two adjacent path segments, carefully examine the curvature degree of the previous path segment and the curvature degree of the next path segment at the connection point, and determine the curvature magnitude and curvature direction at the connection point based on the specific situation of these two curvature degrees, that is, obtain the curvature feature of the connection point of the adjacent path segments.

[0030] Based on the curvature magnitude and direction determined from the curvature characteristics of the connection point, adjustments are made to the connection point of adjacent path segments. This ensures that as the preceding path segment approaches the connection point, its curvature gradually transitions to the curvature characteristic corresponding to the connection point. Simultaneously, as the following path segment approaches the connection point, its curvature also gradually transitions to the curvature characteristic corresponding to the connection point. This ensures that two adjacent path segments can form a smooth and continuous transition curve without obvious bends at the connection point. This transition curve is the filter connection segment of the conductor.

[0031] According to the spatial orientation corresponding to the ordered point cloud sequence, all spatial path segments are arranged sequentially. Then, between every two adjacent spatial path segments, a corresponding filter connection segment is placed, so that one end of the filter connection segment completely coincides with the end of the previous spatial path segment, ensuring that the connection between the two is seamless. At the same time, the other end of the filter connection segment completely coincides with the beginning of the next spatial path segment, also ensuring that the connection is seamless. In this way, all spatial path segments and filter connection segments are connected into a complete curve, which is the initial spatial path of the traverse.

[0032] Examine the initial spatial path segment by segment, identifying areas where curves are interrupted or cannot be continuously connected. These areas are the missing segments in the initial spatial path. Next, observe the known paths at the left and right ends of the missing segments, analyzing the bending direction, degree of bending, and extension direction of the known paths at the left end, as well as the common geometric features of the known paths at the right end. Based on these geometric features, determine the boundary conditions of the initial spatial path, including the starting position of the left end of the missing segment, the bending trend of the known paths at the starting position, the ending position of the right end of the missing segment, and the bending trend of the known paths at the ending position.

[0033] Based on the curvature trend of the known paths at both ends of the missing segment as determined in the boundary conditions, the curvature constraint requirement is clarified. That is, when the generated interpolation path segment is near the starting position of the left end of the missing segment, its curvature must be consistent with the curvature of the known path at that position on the left end. When it is near the ending position of the right end of the missing segment, its curvature must be consistent with the curvature of the known path at that position on the right end. According to this constraint requirement, starting from the starting position of the left end of the missing segment, a curve is drawn along the direction that conforms to the curvature trend until the curve extends to the ending position of the right end of the missing segment, forming a curve that can maintain geometric continuity with the known paths at both ends. This curve is the interpolation path segment.

[0034] The interpolated path segment is placed at the missing section of the initial spatial path, with the left end of the interpolated path segment perfectly aligned with the end of the known path at the left end of the missing section. This ensures that the curvature of the two at the connection point is completely consistent and without any gaps. At the same time, the right end of the interpolated path segment is perfectly aligned with the beginning of the known path at the right end of the missing section, again ensuring that the curvature of the connection point is consistent and without gaps. Through this connection method, the interpolated path segment fills the missing part of the initial spatial path, forming a complete and continuous curve, which is the continuous spatial path of the conductor.

[0035] The beneficial effects are that the entire implementation process is based on non-contact acquisition of shape point cloud data, which can complete the acquisition of ordered point cloud sequences, spatial path segment fitting, initial spatial path construction and missing segment interpolation without direct contact with the transmission and distribution lines. This effectively avoids interference or damage to the lines caused by contact operations. At the same time, by strictly following the point cloud topology connection relationship, geometric feature consistency and curvature constraints to generate continuous spatial paths, it can accurately reflect the actual spatial shape of the transmission and distribution lines. This provides accurate basic parameters such as conductor path length for subsequent non-contact fault location based on the traveling wave positioning principle, further improving the accuracy and reliability of fault location such as short circuits and grounding in transmission and distribution lines.

[0036] S3. Integrate the continuous spatial path to obtain the actual path length of the target object; In this embodiment of the invention, the step of integrating the continuous spatial path to obtain the actual path length of the target object includes: The continuous spatial path is discretized to obtain the path point sequence of the target object; The spatial distances between adjacent points in the path point sequence are calculated to obtain the basic length sequence of the path point sequence. Analyze the curvature distribution of the continuous spatial path to obtain the curvature values ​​of the path points in the continuous spatial path; The actual path length of the target object is calculated based on the path point sequence, the basic length sequence, and the curvature value, wherein the formula for calculating the actual path length is: ; in, This indicates the actual path length. Indicates the first The spatial coordinates of the path points. Indicates the first The curvature values ​​of the path points. This represents the preset curvature influence coefficient. This indicates the number of path points.

[0037] Along a continuous spatial path, starting from the beginning of the path, points are selected sequentially at uniform intervals. Each selected point must fall precisely on the curve of the continuous spatial path, ensuring that the interval between two adjacent selected points remains consistent. These points selected sequentially from the path are arranged to form a set containing multiple ordered points. This set is the path point sequence of the traverse.

[0038] Take out two adjacent points in the path point sequence, observe the specific positions of these two points in three-dimensional space, determine the positional relationship of the two points in the three-dimensional coordinate system, calculate the straight-line distance between the two adjacent points, calculate the spatial distance between each pair of adjacent points one by one according to the arrangement order of the points in the path point sequence, and organize these spatial distances of adjacent points obtained in sequence into an ordered distance set. This set is the basic length sequence of the path point sequence.

[0039] Observe the overall curvature of the continuous spatial path, focusing on the curvature of each path point. For each path point in the path point sequence, observe the changes in the orientation of the adjacent path segments before and after that point. Determine the curvature of the point based on the curvature of the path segment. The more obvious the curvature of the path segment, the greater the curvature value of the corresponding path point. The gentler the path segment, the smaller the curvature value of the corresponding path point. Determine the curvature of each path point in the path point sequence in turn to obtain the curvature value corresponding to each path point.

[0040] A one-to-one correspondence is established between the path point sequence, the basic length sequence, and the curvature value of each path point. For each basic length in the basic length sequence, the curvature values ​​of its two adjacent path points are found. If the curvature values ​​of these two path points are small, it indicates that the corresponding path segment is close to a straight line, and the basic length is directly retained. If the curvature values ​​of these two path points are large, it indicates that the corresponding path segment has obvious curvature. The basic length is adjusted appropriately according to the degree of curvature reflected by the curvature value so that the adjusted length conforms to the actual situation of the curved path. All the adjusted or retained basic lengths are added together in sequence, and the sum is the actual path length of the conductor.

[0041] A continuous spatial path is decomposed into discrete points, which form a sequence of path points. Each discrete point has corresponding spatial location information, which is the spatial coordinates of each path point.

[0042] The curvature of a continuous spatial path is comprehensively detected and calculated. By analyzing the curvature at each location on the path, the curvature at each path point is determined, and this curvature is the curvature value.

[0043] The preset curvature influence coefficient is a fixed value determined before the calculation begins, used to adjust the degree of influence of curvature on the calculation of the actual path length.

[0044] The path point sequence obtained after discretization is counted, and the total number of discrete points contained in the sequence is the number of path points.

[0045] To calculate the spatial distance between any two adjacent points in a path point sequence, first find the numerical differences between the two adjacent points in each of the three spatial dimensions, then square the numerical differences in each dimension, then add the square results of the three dimensions together, and finally take the square root of the sum to obtain the spatial distance between the two adjacent points.

[0046] For each path point, first calculate the square of the curvature value of that path point, then multiply this squared result with the preset curvature influence coefficient, and add the product to 1 to obtain an adjustment coefficient; Multiply the spatial distance between each adjacent point with the corresponding adjustment coefficient to obtain the distance value between each adjacent point after curvature adjustment.

[0047] The actual path length of the conductor is obtained by summing the distances between all adjacent points after curvature adjustment.

[0048] When the spatial location information difference between two adjacent path points increases, the spatial distance obtained by taking the square root of the sum of the squares of the differences in the three dimensions will increase. The value obtained by multiplying this increased spatial distance by the adjustment coefficient will also increase, and finally the sum of all the values ​​will increase accordingly.

[0049] As the curvature of a path point increases, the square of its curvature value increases. The product obtained by multiplying this curvature influence coefficient by the preset curvature influence coefficient also increases. This, in turn, increases the adjustment coefficient obtained by adding 1 to the product. The value of the adjustment coefficient multiplied by the spatial distance between adjacent points increases, and finally, the sum of all the values ​​increases.

[0050] When the fixed value of the preset curvature influence coefficient increases, the product of the coefficient and the square of the curvature value will increase, the adjustment coefficient obtained by adding 1 to the product will also increase, the value of the adjustment coefficient multiplied by the spatial distance between adjacent points will increase, and finally the sum of all values ​​will increase accordingly.

[0051] When the total number of points in the path point sequence obtained after discretization increases, the number of terms that need to be added will increase. Without decreasing the value of each added term, the sum of all terms will increase.

[0052] The beneficial effects are that the entire actual path length calculation process is based on continuous spatial path data obtained in a non-contact manner, without direct contact with the transmission and distribution line conductor, effectively avoiding interference or damage to the line insulation performance and operating status that contact operations may cause. At the same time, through discretization processing, basic length statistics, curvature analysis and comprehensive calculation, the true shape of the continuous spatial path of the transmission and distribution line can be accurately restored, ensuring that the calculated actual path length is highly consistent with the actual path length of the line. This provides accurate line length benchmark parameters for subsequent non-contact fault location based on the traveling wave positioning principle, further improving the accuracy and reliability of fault location such as short circuits and grounding in high-voltage and ultra-high-voltage lines, and reducing positioning deviations caused by length parameter errors.

[0053] S4. Receive the fault signal of the target object, and perform time feature analysis on the fault signal to obtain the time difference sequence of the target object; In this embodiment of the invention, receiving the fault signal of the target object and performing time feature analysis on the fault signal to obtain the time difference sequence of the target object includes: Receive the fault signal of the target object and extract the feature components of the fault signal at different time resolutions; Analyze the energy distribution of the characteristic components to determine the arrival time of the fault signal; Eliminate the time deviation in the arrival time to obtain the time sequence of the fault signal; The time difference sequence of the target object is obtained by statistically analyzing the differences between time points in the time point sequence.

[0054] Non-contact electromagnetic field sensors deployed around power transmission and distribution lines are used to receive fault signals from the conductors. These sensors do not need to contact the conductors; they can capture fault signals simply by monitoring the sudden changes in the electromagnetic field around the conductors when a fault occurs. The captured fault signals are then split at different time intervals, and the resulting signal segments, corresponding to different levels of time precision, are extracted. These signal segments are the characteristic components of the fault signals at different time resolutions.

[0055] Each extracted feature component is examined one by one, and the energy value of each feature component at each moment within its corresponding time range is recorded. By comparing the changes in energy values ​​at different moments, the specific moment when the energy value suddenly increases significantly from a stable state is found. The moment corresponding to this energy mutation is the accurate time point when the fault signal arrives at the monitoring point. All such determined moments together constitute the arrival time point of the fault signal.

[0056] Collect all confirmed arrival times of fault signals, compare the differences between these times, and investigate time points that deviate from the normal range due to factors such as sensor response delay and external environmental interference. For time points with small deviations, correct them by referring to the values ​​of multiple normal time points around them. For time points with large deviations that cannot be corrected, remove them directly. Arrange the remaining arrival times that conform to the normal pattern after correction and removal in chronological order to form a fault signal time point sequence.

[0057] According to the chronological order of the time points in the time point sequence, two adjacent time points are selected in turn. The time interval between these two adjacent time points is calculated by subtracting the time value of the previous time point from the time value of the latter time point. The time intervals calculated for all adjacent time points are arranged in order according to their corresponding time point order to form an ordered set of time intervals. This set is the time difference sequence of the conductor.

[0058] The beneficial effects are that the entire process uses non-contact electromagnetic field sensors to receive fault signals, without direct contact with the power transmission and distribution lines. This effectively avoids the interference that contact-based signal acquisition may cause to the line's operating status. At the same time, by extracting feature components with different time resolutions step by step, accurately determining the arrival time of the fault signal, eliminating time deviations, and statistically analyzing time differences, the obtained time difference sequence is ensured to be accurate and reliable. This provides key time parameter support for subsequent calculation of the fault point distance based on the traveling wave positioning principle, further improving the accuracy and reliability of fault location in power transmission and distribution lines.

[0059] S5. Based on the time difference sequence and the continuous spatial path, derive the initial fault point set of the target object using the traveling wave ranging principle; In this embodiment of the invention, the step of deriving the initial fault point set of the target object based on the time difference sequence and the continuous spatial path using the traveling wave ranging principle includes: Map the continuous spatial path to a preset spatial coordinate system to obtain the spatial coordinates and tangent direction vectors of the path points on the continuous spatial path; Based on the time difference sequence, the propagation path of the traveling wave in the fault signal is analyzed to obtain the propagation characteristics of the traveling wave; Analyze the initial fault point of the target object based on the spatial coordinates, the tangent direction vector, and the propagation characteristics; Verify the rationality of the spatial distribution of the fault initiation points to obtain the fault initiation point set of the target object.

[0060] The formula for calculating the initial point of the fault is: ; in, Indicates the initial point of the fault. Represents the path points on the continuous spatial path. This indicates the acquisition point of the fault signal. Indicates the first Time difference in a time difference sequence Indicates the propagation speed of a traveling wave. The path curvature represents the continuous spatial path. This represents the path torsion of the continuous spatial path. Point Time The shortest path, This indicates the location of the fault point that minimizes the time difference fitting error. , This indicates the preset geometric correction factor.

[0061] The preset spatial coordinate system is determined to be a three-dimensional Cartesian coordinate system, where the X-axis corresponds to the horizontal direction of the area where the power transmission and distribution line is located, the Y-axis corresponds to the horizontal direction, and the Z-axis corresponds to the vertical direction. Then, each point on the continuous spatial path is matched to this three-dimensional Cartesian coordinate system one by one. By reading the position values ​​of each point on the X-axis, Y-axis, and Z-axis, the spatial coordinates of each path point are determined. Then, two adjacent path points are selected before and after each path point. Taking the path point as the center, the direction of the line connecting the adjacent path points before and after the path point is calculated. This direction is the tangent direction vector of the path point. Finally, the spatial coordinates of all path points on the continuous spatial path and their corresponding tangent direction vectors are obtained.

[0062] The time difference values ​​of the fault signals received by different non-contact electromagnetic field sensors are extracted from the time difference sequence. Based on these time difference values, the order in which the fault traveling wave propagates from the fault point to each sensor is determined, and then the propagation direction of the traveling wave on the continuous spatial path is inferred. At the same time, combined with the time difference values ​​and the relative positional relationship of each sensor around the continuous spatial path, the corresponding trend of the time consumed by the traveling wave propagation and the propagation distance is observed. These trends of propagation direction and propagation time and distance together constitute the propagation characteristics of the traveling wave, and finally the propagation characteristics of the traveling wave are obtained.

[0063] Based on the spatial coordinates of each path point on a continuous spatial path, the specific position of each path point in the three-dimensional Cartesian coordinate system is determined. Then, combined with the tangent direction vector of each path point, the direction of the transmission and distribution line path at that position is determined. Subsequently, based on the propagation direction in the propagation characteristics of traveling waves, path segments that are consistent with the propagation direction of traveling waves are selected. Within these path segments, based on the changing trends of propagation time and distance in the propagation characteristics, path points that can meet the propagation distance requirements corresponding to the time difference of each sensor are found. These path points that meet the requirements are the initial fault points of the conductor.

[0064] All the initial fault points obtained initially are marked in the three-dimensional Cartesian coordinate system corresponding to the continuous spatial path. It is observed whether each initial fault point falls accurately on the curve of the continuous spatial path. At the same time, the distribution of all initial fault points is checked to determine whether there are isolated points that are significantly deviated from other point concentration areas. If a certain initial fault point does not fall on the continuous spatial path or is an isolated point, it is determined to be an unreasonable point and is removed. All initial fault points that fall on the continuous spatial path and are relatively concentrated are retained. These retained initial fault points are organized into a set, which is the set of initial fault points of the conductor.

[0065] The path points on a continuous spatial path are derived from the continuous spatial path obtained by spatial interpolation of the missing segments in the initial spatial path, and are the set of all position points on the continuous spatial path.

[0066] The fault signal acquisition point is the physical location point used to receive the fault signal when receiving the fault signal from the receiving conductor. This location point is a signal receiving position that is pre-set around the conductor or in the relevant detection area.

[0067] No. The time difference in the time difference sequence originates from the time feature analysis of the received fault signal. First, the feature components of the fault signal at different time resolutions are extracted. The energy distribution of these feature components is analyzed to determine the arrival time of the fault signal. Time deviations in the arrival time points are eliminated to obtain a time point sequence. Then, the differences between time points in the time point sequence are statistically analyzed to obtain the first time difference in the time difference sequence. A number.

[0068] The propagation speed of a traveling wave is derived from the analysis of the propagation characteristics of traveling waves in fault signals. By studying the propagation law of traveling waves in conductor materials and the influence of the physical structure of the conductor on the propagation of traveling waves, the speed of traveling waves propagating in the conductor can be calculated.

[0069] The curvature of a continuous spatial path is derived from the analysis of the geometric characteristics of the continuous spatial path. For each point on the continuous spatial path, the degree of curvature of the path at that point is calculated. Specifically, the rate of change of the tangent direction of the path segment near that point is analyzed to obtain a value reflecting the degree of curvature of the path.

[0070] The path torsion of a continuous spatial path is derived from the spatial geometric analysis of the continuous spatial path. For each point on the continuous spatial path, the degree of twisting of the path in space at that point is calculated. Specifically, the rate of change of the normal plane direction of the path segment near that point is analyzed to obtain a value reflecting the degree of path twisting.

[0071] The shortest path length from the fault signal acquisition point to a path point on the continuous spatial path is derived from calculating the distance between the two points. By analyzing the relative positions of the two points on the geometric structure of the continuous spatial path, the shortest distance from the fault signal acquisition point to the path point on the continuous spatial path is calculated along the geometric trajectory of the continuous spatial path.

[0072] There are two preset geometric correction coefficients. Both are fixed values ​​that are set in advance based on factors such as the material properties of the conductor, the overall geometric structure of the continuous spatial path, and fault location data of similar conductors in the past, before the fault initial point calculation is performed. They are used to adjust the influence weight of path curvature and path deflection on the fault initial point calculation results.

[0073] In a continuous spatial path, each path point is considered as a candidate fault point. For each candidate, the shortest path length from the fault signal acquisition point to the corresponding path point on the continuous spatial path is calculated. This shortest path length is divided by the propagation speed of the traveling wave to obtain the theoretical time required for the traveling wave to propagate from the path point on the continuous spatial path to the acquisition point. Next, the path curvature of the continuous spatial path at the candidate is multiplied by one preset geometric correction coefficient, and the path torsion at the candidate is multiplied by another preset geometric correction coefficient. These two results are added together to obtain the geometric correction sum. The geometric correction sum is divided by the previously obtained shortest path length to obtain the influence value of geometric factors on time, i.e., the geometric influence time. Then, the theoretical time and the geometric influence time are added together, and the sum is subtracted by the first... The time difference in the time difference sequence is used to obtain a time deviation value; this time deviation value is squared to obtain the value corresponding to the candidate object. The fitting error value for each time difference; then, for all The fitting error values ​​corresponding to each time difference are summed to obtain the total time difference fitting error of the candidate object. Following the above steps, the total time difference fitting error of all candidate objects on the continuous spatial path is calculated in turn. Finally, the candidate object with the smallest total time difference fitting error is found from all candidate objects. This candidate object is the fault initiation point of the conductor.

[0074] As the propagation speed of the traveling wave increases, the calculated theoretical time for the wave to travel from a path point on a continuous spatial path to the acquisition point decreases; if other conditions remain unchanged, the sum of the theoretical time and the geometric influence time will decrease, and this sum will be less than the previous result. The deviation values ​​of time differences in a time difference sequence will change, which in turn will cause changes in the individual fitting error value and the total time difference fitting error. At this time, it is necessary to recalculate the total time difference fitting error of all candidates. The candidate with the smallest total error may no longer be the smallest. The fault initial point will be adjusted to the candidate with the smallest total time difference fitting error after recalculation.

[0075] When the path curvature of a continuous spatial path increases at a candidate object, the result of multiplying the path curvature by the corresponding preset geometric correction coefficient will increase, and the sum of geometric corrections will also increase. If the shortest path length remains unchanged, the geometric influence time will increase, and the sum of the theoretical time and the geometric influence time will increase, compared to the first... The deviation values ​​of time differences in a time difference sequence will change, and the individual fitting error values ​​and the total time difference fitting error will also change; this will cause the relationship between the total time difference fitting errors of different candidates to change, and the fault initiation point will switch to the new candidate with the smallest total time difference fitting error.

[0076] When the path torsion of a continuous spatial path increases at a candidate object, the result of multiplying the path torsion by the corresponding preset geometric correction coefficient will increase, and the sum of geometric corrections will also increase. If the shortest path length remains unchanged, the geometric influence time will increase, and the sum of the theoretical time and the geometric influence time will increase, compared to the first... The deviation values ​​of time differences in a time difference sequence will change, and the individual fitting error values ​​and the total time difference fitting error will also change; this will cause the total time difference fitting errors of each candidate object on a continuous spatial path to be reordered, and the fault initiation point will be determined as the candidate object with the smallest total time difference fitting error after sorting.

[0077] When the preset geometric correction coefficient used to adjust the effect of path curvature increases, the result of multiplying the path curvature by this coefficient will increase, and the sum of geometric corrections will also increase. If other conditions remain unchanged, the geometric influence time will increase, and the sum of the theoretical time and the geometric influence time will increase, compared to the first... The deviation of time difference in a time difference sequence will change, and the individual fitting error value and the total time difference fitting error will also change; this will affect the magnitude of the total time difference fitting error of each candidate object, and the fault initiation point will select the candidate object with the smallest total time difference fitting error at this time.

[0078] When the preset geometric correction coefficient used to adjust the effect of path deflection increases, the result of multiplying the path deflection by this coefficient will increase, and the sum of geometric corrections will also increase. If other conditions remain unchanged, the geometric influence time will increase, and the sum of the theoretical time and the geometric influence time will increase, compared to the first... The deviation values ​​of time differences in a time difference sequence will change, and the individual fitting error values ​​and the total time difference fitting error will also change; this will cause the total time difference fitting error of each candidate to change, and the fault initial point will be adjusted to the candidate with the smallest total time difference fitting error after the change.

[0079] When the shortest path length from the fault signal acquisition point to a point on the continuous spatial path increases, on the one hand, the theoretical time obtained by dividing the shortest path length by the traveling wave propagation speed will increase; on the other hand, the geometric influence time obtained by dividing the sum of geometric corrections by the shortest path length will decrease. These two factors work together to change the sum of the theoretical time and the geometric influence time, affecting the result compared to the previous scenario. The deviation values ​​of time differences in a time difference sequence will also change, and the individual fitting error values ​​and the total time difference fitting error will change accordingly. This will cause the total time difference fitting error of each candidate object to be recalculated, and the fault initial point will be determined as the candidate object with the smallest total time difference fitting error after recalculation.

[0080] When the When the time difference in a time difference sequence increases, the deviation value obtained by subtracting the time difference from the sum of the theoretical time and the geometric influence time will become smaller, the individual fitting error value will become smaller, and the total time difference fitting error will also become smaller. At this time, the total time difference fitting error of each candidate object will change, and the fault initial point will select the candidate object with the smallest total time difference fitting error after the change.

[0081] The beneficial effects are that the continuous spatial path upon which the entire process relies is constructed based on non-contact laser point cloud acquisition, and the fault signal is received through a non-contact electromagnetic field sensor. The entire process requires no direct contact with the power transmission and distribution lines, effectively avoiding the potential impact of contact operations on line operation safety and insulation performance. Simultaneously, by accurately mapping spatial coordinates, analyzing traveling wave propagation characteristics, and comprehensively considering multi-dimensional parameters to determine and verify the rationality of the fault initial point set, it is ensured that the fault initial point set can accurately reflect the potential location of the fault. This provides reliable basic data for further precise location of faults such as short circuits and grounding in high-voltage and ultra-high-voltage lines, helping to improve the accuracy and reliability of fault location and reduce location deviations.

[0082] S6. Analyze the spatial distribution dispersion of the initial fault point set, and combine the geometric features of the continuous spatial path to remove the abnormal points in the initial fault point set, thereby obtaining the fault location result of the target object.

[0083] In this embodiment of the invention, the step of analyzing the spatial distribution dispersion of the initial fault point set and, in conjunction with the geometric features of the continuous spatial path, removing outliers from the initial fault point set to obtain the fault location result of the target object includes: Extract the spatial density parameters and geometric center offset of the fault initial point set to construct the discreteness evaluation matrix of the fault initial point set; Based on the discreteness evaluation matrix, a hierarchical evaluation is performed on the initial fault point set to obtain the spatial distribution discreteness of the initial fault point set. Based on the geometric features of the continuous spatial path and the spatial distribution discreteness, identify the abnormal points in the fault initial point set; The fault location results of the target object are obtained by removing the abnormal points.

[0084] In the three-dimensional Cartesian coordinate system corresponding to the fault initial point set, the spatial range containing all fault initial points in this coordinate system is uniformly divided into several cubic sub-regions of the same size. The number of fault initial points contained in each cubic sub-region is counted one by one. The point cloud density of each sub-region is obtained by dividing the number of points in each sub-region by the spatial volume of the sub-region. The point cloud densities of all sub-regions are integrated to form the spatial density parameters of the fault initial point set. Then, the average spatial coordinates of all points in the fault initial point set are calculated. The spatial position corresponding to this average value is the geometric center of the fault initial point set. The difference between the spatial coordinates of each fault initial point and the coordinates of the geometric center is calculated. The arithmetic mean of the absolute values ​​of all differences is taken to obtain the geometric center offset of the fault initial point set. The spatial density parameters are arranged in the form of row vectors and the geometric center offsets are arranged in the form of column vectors to construct a two-dimensional matrix containing spatial density and geometric center offset information. This matrix is ​​the discreteness evaluation matrix of the fault initial point set.

[0085] A hierarchical evaluation is conducted based on the dispersion assessment matrix. The first layer of evaluation focuses on the spatial density parameter in the matrix. If the point cloud density of most sub-regions is high and the density value difference is small, it indicates that the overall distribution of the initial fault point set is concentrated. If the point cloud density of most sub-regions is low and the density value difference is large, it indicates that the point set is loosely distributed. The second layer of evaluation targets the geometric center offset in the matrix. If the offset value is small, it indicates that most initial fault points are close to the geometric center. If the offset value is large, it indicates that most points are far from the geometric center. Combining the results of the two layers of evaluation, when the spatial density is high and the geometric center offset is small, the spatial distribution dispersion is determined to be low. When the spatial density is low and the geometric center offset is large, the spatial distribution dispersion is determined to be high. Finally, the spatial distribution dispersion of the initial fault point set is obtained.

[0086] First, the geometric features of the continuous spatial path are extracted, including the curvature, bending direction, and overall orientation of each segment. The spatial coordinates of each fault initiation point are compared with the coordinates of the continuous spatial path to confirm whether the point falls on the curve of the continuous spatial path. If a point does not fall on the path, it is initially identified as a potential anomaly. Then, the spatial distribution dispersion is considered. If the spatial distribution dispersion is high, the focus is on fault initiation points that are spatially far from most points. The distances between these points and their surrounding neighboring points are calculated. If the distance is much greater than the distance between other neighboring points, and the location does not match the curvature and bending direction of the corresponding segment of the continuous spatial path (e.g., the continuous spatial path is a gentle curve while the point's location shows a sharp bending trend), these points are explicitly identified as anomalies. If the spatial distribution dispersion is low, only points that do not fall on the continuous spatial path are identified as anomalies. Finally, all anomalies are identified.

[0087] All identified anomalies are removed one by one from the set of initial fault points. Spatial distribution analysis is performed on the remaining initial fault points. If the number of remaining points is small and they are concentrated, the geometric center of these points is taken as the fault location. If the number of remaining points is large and they are continuously distributed, the midpoint of the line segment corresponding to these points on the continuous spatial path is taken as the fault location. The determined fault location is output in the form of spatial coordinates, and the position corresponding to the coordinates is the fault location result of the conductor.

[0088] The beneficial effect is that the initial fault point set on which the entire process depends is based on data from a continuous spatial path constructed by non-contact laser point clouds and fault signals collected by non-contact sensors. The entire process does not require direct contact with the power transmission and distribution lines, effectively avoiding interference and damage to the line's operating status and insulation performance caused by contact operations. At the same time, by constructing a discreteness evaluation matrix to accurately quantify the distribution characteristics of the point set, and combining the geometric features of the continuous spatial path to scientifically eliminate abnormal points, the fault location results are ensured to be highly consistent with the actual fault location of the line. This further improves the accuracy and reliability of fault location for high-voltage and ultra-high-voltage lines, such as short circuits and grounding, and reduces location deviations caused by interference from abnormal points.

[0089] like Figure 2 The diagram shown is a functional block diagram of a system that integrates point cloud traverse measurement and non-contact fault location according to an embodiment of the present invention.

[0090] The system 100 integrating point cloud traverse measurement and non-contact fault location described in this invention can be installed in an electronic device. Depending on the functions implemented, the system 100 may include a point cloud processing module 101, a spatial path construction module 102, an actual length calculation module 103, a fault signal analysis module 104, a fault initial point derivation module 105, and a fault location module 106. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0091] In this embodiment, the functions of each module / unit are as follows: The point cloud processing module 101 is used to filter the three-dimensional point cloud dataset of the target object and separate the shape point cloud set of the target object from the denoised point cloud dataset. The spatial path construction module 102 is used to fit the shape point cloud set into an initial spatial path of the target object according to the spatial arrangement order of the point cloud set, and to perform spatial interpolation processing on the missing segments in the initial spatial path to obtain a continuous spatial path of the target object. The actual length calculation module 103 is used to perform integral calculation on the continuous spatial path to obtain the actual path length of the target object. The fault signal analysis module 104 is used to receive the fault signal of the target object and perform time feature analysis on the fault signal to obtain the time difference sequence of the target object. The fault initial point derivation module 105 is used to derive the fault initial point set of the target object based on the time difference sequence and the continuous spatial path by means of the traveling wave ranging principle. The fault location module 106 is used to analyze the spatial distribution dispersion of the fault initial point set, and combine the geometric features of the continuous spatial path to remove abnormal points in the fault initial point set, so as to obtain the fault location result of the target object.

[0092] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0093] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0094] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0095] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0096] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0097] Finally, 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 it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for integrating point cloud traverse measurement and non-contact fault location, characterized in that, The method includes: S1. Filter the three-dimensional point cloud dataset of the target object and separate the shape point cloud set of the target object from the denoised point cloud dataset. S2. Based on the spatial arrangement order of the point cloud in the shape point cloud set, fit the shape point cloud set into the initial spatial path of the target object, and perform spatial interpolation on the missing segments in the initial spatial path to obtain the continuous spatial path of the target object. S3. Integrate the continuous spatial path to obtain the actual path length of the target object; S4. Receive the fault signal of the target object, and perform time feature analysis on the fault signal to obtain the time difference sequence of the target object; S5. Based on the time difference sequence and the continuous spatial path, derive the initial fault point set of the target object using the traveling wave ranging principle; S6. Analyze the spatial distribution dispersion of the initial fault point set, and combine the geometric features of the continuous spatial path to remove the abnormal points in the initial fault point set, thereby obtaining the fault location result of the target object.

2. The method for integrating point cloud traverse measurement and non-contact fault location as described in claim 1, characterized in that, The process of filtering the 3D point cloud dataset of the target object and separating the shape point cloud set and the supporting structure point cloud set from the denoised point cloud dataset includes: Collect a 3D point cloud dataset of the target object and identify the spatial distribution characteristics of the 3D point cloud dataset; Based on the spatial distribution characteristics, the three-dimensional point cloud dataset is iteratively filtered to obtain a clean point cloud dataset of the target object. Based on the point cloud adjacency relationships and local surface features of the clean point cloud dataset, the shape point cloud set of the target object is segmented from the clean point cloud dataset.

3. The method for integrating point cloud traverse measurement and non-contact fault location as described in claim 1, characterized in that, The step of fitting the shape point cloud set into an initial spatial path of the target object based on the spatial arrangement order of the point cloud set, and performing spatial interpolation on the missing segments in the initial spatial path to obtain a continuous spatial path of the target object, includes: Based on the topological connectivity of the point clouds in the shape point cloud set, the spatial neighborhood of the shape point cloud set is analyzed to obtain the ordered point cloud sequence of the target object; Piecewise curve fitting is performed on the ordered point cloud sequence to obtain the spatial path segment of the target object; By analyzing the geometric feature consistency of adjacent path segments in the spatial path segment, the curvature features of the connection points of the adjacent path segments are obtained; Based on the curvature characteristics of the connection points, the adjacent path segments are smoothly connected to obtain the filtered connection segments of the target object. Connect the spatial path segment and the filter connection segment to obtain the initial spatial path of the target object.

4. The method for integrating point cloud traverse measurement and non-contact fault location as described in claim 1, characterized in that, The step of performing spatial interpolation on the missing segments in the initial spatial path to obtain a continuous spatial path for the target object includes: Extract the missing segments from the initial spatial path, and determine the boundary conditions of the initial spatial path based on the geometric features of the known paths at both ends of the missing segments; Based on the curvature constraints of the boundary conditions, interpolated path segments that maintain geometric continuity with the known path are generated at both ends of the missing segment. The interpolated path segment and the initial spatial path are seamlessly connected to obtain a continuous spatial path for the target object.

5. The method for integrating point cloud traverse measurement and non-contact fault location as described in claim 1, characterized in that, The step of integrating the continuous spatial path to obtain the actual path length of the target object includes: The continuous spatial path is discretized to obtain the path point sequence of the target object; The spatial distances between adjacent points in the path point sequence are calculated to obtain the basic length sequence of the path point sequence. Analyze the curvature distribution of the continuous spatial path to obtain the curvature values ​​of the path points in the continuous spatial path; The actual path length of the target object is calculated based on the path point sequence, the basic length sequence, and the curvature value, wherein the formula for calculating the actual path length is: ； in, This indicates the actual path length. Indicates the first The spatial coordinates of the path points. Indicates the first The curvature values ​​of the path points. This represents the preset curvature influence coefficient. This indicates the number of path points.

6. The method for integrating point cloud traverse measurement and non-contact fault location as described in claim 1, characterized in that, The step of receiving the fault signal of the target object and performing time feature analysis on the fault signal to obtain the time difference sequence of the target object includes: Receive the fault signal of the target object and extract the feature components of the fault signal at different time resolutions; Analyze the energy distribution of the characteristic components to determine the arrival time of the fault signal; Eliminate the time deviation in the arrival time to obtain the time sequence of the fault signal; The time difference sequence of the target object is obtained by statistically analyzing the differences between time points in the time point sequence.

7. The method for integrating point cloud traverse measurement and non-contact fault location as described in claim 1, characterized in that, The step of deriving the initial fault point set of the target object based on the time difference sequence and the continuous spatial path using the traveling wave ranging principle includes: Map the continuous spatial path to a preset spatial coordinate system to obtain the spatial coordinates and tangent direction vectors of the path points on the continuous spatial path; Based on the time difference sequence, the propagation path of the traveling wave in the fault signal is analyzed to obtain the propagation characteristics of the traveling wave; Analyze the initial fault point of the target object based on the spatial coordinates, the tangent direction vector, and the propagation characteristics; Verify the rationality of the spatial distribution of the fault initiation points to obtain the fault initiation point set of the target object.

8. The method for integrating point cloud traverse measurement and non-contact fault location as described in claim 7, characterized in that, The formula for calculating the initial point of the fault is: ； in, Indicates the initial point of the fault. Represents the path points on the continuous spatial path. This indicates the acquisition point of the fault signal. Indicates the first Time difference in a time difference sequence Indicates the propagation speed of a traveling wave. The path curvature represents the continuous spatial path. This represents the path torsion of the continuous spatial path. Point Time The shortest path, This indicates the location of the fault point that minimizes the time difference fitting error. , This indicates the preset geometric correction factor.

9. The method for integrating point cloud traverse measurement and non-contact fault location as described in claim 1, characterized in that, The analysis of the spatial distribution dispersion of the initial fault point set, combined with the geometric features of the continuous spatial path, removes outliers from the initial fault point set to obtain the fault location result of the target object, including: Extract the spatial density parameters and geometric center offset of the fault initial point set to construct the discreteness evaluation matrix of the fault initial point set; Based on the discreteness evaluation matrix, a hierarchical evaluation is performed on the initial fault point set to obtain the spatial distribution discreteness of the initial fault point set. Based on the geometric features of the continuous spatial path and the spatial distribution discreteness, identify the abnormal points in the fault initial point set; The fault location results of the target object are obtained by removing the abnormal points.

10. A system for integrating point cloud traverse surveying and non-contact fault location, used to implement the method for integrating point cloud traverse surveying and non-contact fault location as described in claim 1, the system comprising: The point cloud processing module is used to filter the three-dimensional point cloud dataset of the target object and separate the shape point cloud set of the target object from the denoised point cloud dataset. The spatial path construction module is used to fit the shape point cloud set into an initial spatial path of the target object according to the spatial arrangement order of the point cloud set, and to perform spatial interpolation processing on the missing segments in the initial spatial path to obtain a continuous spatial path of the target object. The actual length calculation module is used to perform integral calculation on the continuous spatial path to obtain the actual path length of the target object; The fault signal analysis module is used to receive the fault signal of the target object and perform time feature analysis on the fault signal to obtain the time difference sequence of the target object. The fault initial point derivation module is used to derive the set of fault initial points of the target object based on the time difference sequence and the continuous spatial path using the traveling wave ranging principle. The fault location module is used to analyze the spatial distribution dispersion of the initial fault point set and, in combination with the geometric features of the continuous spatial path, remove abnormal points from the initial fault point set to obtain the fault location result of the target object.

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