Three-dimensional reconstruction method, device and equipment of power transmission conductor and storage medium

Abstract

The invention discloses a three-dimensional reconstruction method, device and equipment for a power transmission conductor, and a storage medium. The method comprises the following steps: acquiring original point cloud data of a conductor in a target area; performing coarse extraction on the original point cloud data to obtain coarse extraction point cloud data; performing classification extraction on the coarsely extracted point cloud data to obtain target point cloud data; conducting wire aerial form fitting on the target point cloud data based on a catenary equation to obtain a catenary aerial fitting result; according to the method, the lead in the target area is reconstructed according to the catenary air fitting result, the lead three-dimensional scene model is obtained, the real state and the space relation of the transmission lead can be reflected more accurately, and the reconstruction quality of the lead three-dimensional scene model is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, and in particular to a three-dimensional reconstruction method, device and equipment of a power transmission conductor and a storage medium. BACKGROUND

[0002] With the development of the power industry, accidents caused by damage to power transmission conductors occur frequently. In harsh environments such as remote mountain forests, it is difficult for staff to reach the power transmission conductors, making the monitoring of the state of the power transmission conductors a difficult problem. In addition, icing and snow in winter and external interference cause the power transmission conductors to have a partial splitting phenomenon, and the originally parallel state becomes a suspended state. Based on this, the power transmission conductors can be reconstructed in a three-dimensional scene to monitor the power transmission conductors in real time and ensure the safe operation of the power transmission conductors.

[0003] At present, the least squares method is used to screen point cloud data with good fitting in the original point cloud data of the power transmission conductor, and then the reconstruction of the power transmission conductor is realized based on the point cloud data. However, the error of the point cloud data screened by the least squares method is still relatively large, resulting in a low-quality three-dimensional scene model reconstructed finally. SUMMARY

[0004] The present application provides a three-dimensional reconstruction method, device, computer equipment and storage medium of a power transmission conductor, which solves the problem of a large error of point cloud data screened by the least squares method, resulting in a low-quality three-dimensional scene model reconstructed finally.

[0005] In a first aspect, a three-dimensional reconstruction method of a power transmission conductor is provided, comprising:

[0006] obtaining original point cloud data of a conductor in a target area;

[0007] coarsely extracting the original point cloud data to obtain coarsely extracted point cloud data;

[0008] classifying and extracting the coarsely extracted point cloud data to obtain the target point cloud data;

[0009] fitting the target point cloud data based on a catenary equation to obtain a catenary fitting result in the air;

[0010] reconstructing the conductor in the target area according to the catenary fitting result in the air to obtain a three-dimensional scene model of the conductor.

[0011] In a second aspect, a three-dimensional reconstruction method device of a power transmission conductor is provided, comprising:

[0012] an acquisition module configured to obtain original point cloud data of a conductor in a target area;

[0013] a first extraction module configured to perform coarse extraction on the original point cloud data to obtain coarse extraction point cloud data;

[0014] a second extraction module configured to perform classified extraction on the coarse extraction point cloud data to obtain the target point cloud data;

[0015] a fitting module configured to perform aerial form fitting on the target point cloud data based on a catenary equation to obtain a catenary aerial fitting result;

[0016] a reconstruction module configured to reconstruct the conductor in the target region according to the catenary aerial fitting result to obtain a conductor three-dimensional scene model.

[0017] In a third aspect, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the three-dimensional reconstruction method of the power transmission conductor when executing the computer program.

[0018] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program implements the steps of the three-dimensional reconstruction method of the power transmission conductor when executed by a processor.

[0019] The three-dimensional reconstruction method, device, computer device, and storage medium of the power transmission conductor provided by the present application are as follows: original point cloud data of a conductor in a target region is obtained; coarse extraction is performed on the original point cloud data to obtain coarse extraction point cloud data; classified extraction is performed on the coarse extraction point cloud data to obtain the target point cloud data; aerial form fitting is performed on the target point cloud data based on a catenary equation to obtain a catenary aerial fitting result; and the conductor in the target region is reconstructed according to the catenary aerial fitting result to obtain a conductor three-dimensional scene model. The present application removes irrelevant data by performing coarse extraction on the original point cloud data; high-precision target point cloud data is obtained by classified extraction, thereby reducing error sources; the catenary equation conforming to the actual form of the power transmission conductor is used to perform aerial form fitting on the target point cloud data to obtain a catenary aerial fitting result, thereby improving fitting precision; and finally, the conductor three-dimensional scene model of the conductor in the target region is reconstructed based on the catenary aerial fitting result, which can more accurately reflect the real state and spatial relationship of the power transmission conductor, thereby improving the reconstruction quality of the conductor three-dimensional scene model. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description only show some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0021] Figure 1 The application environment diagram of the three-dimensional reconstruction method of the power transmission conductor provided by the embodiment of the present application;

[0022] Figure 2 The flowchart of the three-dimensional reconstruction method of the power transmission conductor provided by the embodiment of the present application;

[0023] Figure 3 The graphical representation schematic diagram of the catenary equation in the three-dimensional space provided by the embodiment of the present application;

[0024] Figure 4 The flowchart of the conductor air form fitting provided by the embodiment of the present application;

[0025] Figure 5 The flowchart of the three-dimensional reconstruction method of the power transmission conductor provided by the embodiment of the present application;

[0026] Figure 6 The structural block diagram of the three-dimensional reconstruction method device of the power transmission conductor provided by the embodiment of the present application;

[0027] Figure 7 The structural block diagram of the computer device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0029] In addition, the described features, structures or characteristics can be combined in any suitable way in one or more embodiments. In the following description, many specific details are provided to give a full understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be used. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail to avoid obscuring the aspects of the present application.

[0030] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0031] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0032] The three-dimensional reconstruction method for power transmission lines provided in this embodiment of the invention can be applied to, for example... Figure 1 In this application environment, computer device 110 communicates with server 120 via network 130. Computer device 110 can acquire raw point cloud data of the conductor within the target area; perform coarse extraction on the raw point cloud data to obtain coarsely extracted point cloud data; perform classification extraction on the coarsely extracted point cloud data to obtain the target point cloud data; perform aerial morphology fitting on the target point cloud data based on the catenary equation to obtain the catenary aerial fitting result; and reconstruct the conductor within the target area based on the catenary aerial fitting result to obtain a three-dimensional scene model of the conductor, which is then displayed through computer device 110. In this invention, by performing coarse extraction on the raw point cloud data to remove irrelevant data, and then performing classification extraction to obtain high-precision target point cloud data, error sources are reduced; then, the target point cloud data is fitted with the aerial morphology using the catenary equation that conforms to the actual shape of the transmission conductor to obtain the catenary aerial fitting result, improving the fitting accuracy; finally, the three-dimensional scene model of the conductor within the target area is reconstructed based on the catenary aerial fitting result, which can more accurately reflect the real state and spatial relationship of the transmission conductor, improving the reconstruction quality of the three-dimensional scene model of the conductor. The computer device 110 may include, but is not limited to, various smartphones 110-1, tablet computers 110-2, and laptop computers 110-3. The invention will now be described in detail through specific embodiments.

[0033] Please see Figure 2 As shown, Figure 2 This is a flowchart illustrating a three-dimensional reconstruction method for power transmission lines provided in an embodiment of the present invention. This method can be applied to both terminals and servers; this embodiment uses server-side application as an example. The three-dimensional reconstruction method for power transmission lines includes the following steps:

[0034] S101: Obtain the raw point cloud data of the traverse within the target area.

[0035] The target region represents a region containing at least one power transmission conductor to be monitored. The original point cloud data refers to a set of three-dimensional data points obtained by scanning the conductor in the target region by a measuring device, and has the characteristics of high fidelity, high density, multiple attributes, and large data volume. The original point cloud data can be collected by technologies such as laser radar, photogrammetry, and total station. In this application, the original point cloud data is used for three-dimensional scene reconstruction of power transmission conductors.

[0036] Exemplarily, the original point cloud data can be obtained by scanning the power transmission conductor in the target region by a laser radar carried by a drone.

[0037] Exemplarily, when real-time monitoring and fine scanning of the conductor in the target region are required, the original point cloud data can be obtained by fixing the laser radar on a tower or a specific position, remotely aiming at the line channel of the power transmission conductor to be detected, and integrating and superimposing the low point cloud data by multiple non-repetitive laser scanning.

[0038] S102: Coarsely extracting the original point cloud data to obtain coarsely extracted point cloud data.

[0039] The process of coarsely extracting the original point cloud data is to preliminarily cluster the original point cloud data, that is, to coarsely extract the original point cloud data to obtain a coarsely extracted classification result of the original point cloud data, to distinguish different types of point clouds such as conductors, ground, vegetation, etc., and then to extract the corresponding point clouds of the conductor in the target region from the coarsely extracted classification result to obtain the coarsely extracted point cloud data.

[0040] Optionally, the original point cloud data can be coarsely extracted based on a neural network to obtain the coarsely extracted point cloud data.

[0041] In order to avoid the influence of the terrain, in an embodiment, the coarsely extracting the original point cloud data to obtain the coarsely extracted point cloud data comprises:

[0042] Normalizing the original point cloud data to obtain normalized point cloud data;

[0043] Coarsely extracting the normalized point cloud data according to the spatial dimension to obtain the coarsely extracted point cloud data.

[0044] The process of normalizing the original point cloud data can be to map the original point cloud data to the same three-dimensional coordinate system (if in the same coordinate system, no coordinate system conversion is needed) to ensure the consistency of each data point, and then to normalize the coordinate values of each data point to a preset value range (such as [0, 1]) to obtain the normalized point cloud data.

[0045] Exemplarily, assuming that three data points P1, P2 and P3 are included in the original point cloud data, the coordinate values of the three data points are P1(10, 20, 30), P2(15, 25, 35) and P3(20, 30, 40) respectively, and the three data points are located in the same three-dimensional coordinate system, the coordinate values of each data point are normalized to a preset value range, such as [0, 1], and each data point can be normalized according to the following normalization formula:

[0046]

[0047] wherein, P i represents the original coordinate value of the i th data point, P' i represents the normalized coordinate value of the i th data point, min(P) represents the minimum value of all data points on the coordinate axis, and max(P) represents the maximum value of all data points on the coordinate axis.

[0048] For the x coordinate, min(x) is 10 and max(x) is 20, and P1, P2 and P3 are normalized according to the above normalization formula to obtain P'1 equal to 0, P'2 equal to 0.5 and P'3 equal to 1 respectively; for the y coordinate, min(y) is 20 and max(y) is 30, and P1, P2 and P3 are normalized according to the above normalization formula to obtain P'1 equal to 0, P'2 equal to 0.5 and P'3 equal to 1 respectively; for the z coordinate, min(z) is 30 and max(z) is 40, and P1, P2 and P3 are normalized according to the above normalization formula to obtain P'1 equal to 0, P'2 equal to 0.5 and P'3 equal to 1 respectively; finally, the normalized point cloud data is P'1(0, 0, 0), P'2(0.5, 0.5, 0.5) and P'3(1, 1, 1).

[0049] In a local range, the structure of the scattered points is considered to be irregular, and the structure in the local range may become disordered due to various factors such as occlusion, reflection, sensor error, etc. Irregularity generally refers to a set of points that do not follow obvious geometric rules or distribution patterns. This may include noise, outliers or point cloud deviations caused by measurement errors. By limiting the analysis in a local range, these irregular points can be more accurately identified. Therefore, point classification can be realized according to the spatial dimension, and then rough extraction can be realized according to the result of point classification.

[0050] Point classification of the normalized point cloud data according to the spatial dimension is a process of determining the point cloud feature of the best neighborhood radius according to the spatial feature of the data points (such as the position feature of the data points, i.e. three-dimensional coordinate information) in the normalized point cloud data.

[0051] For example, specifically, according to the entropy value theory, the optimal neighborhood scale of each data point is calculated, and an optimal neighborhood radius is found, so that the data point set within the neighborhood radius can maximize the information amount while reducing redundancy. For each data point, the statistical characteristics of the points within the neighborhood, such as mean, variance, covariance matrix, etc., are calculated, and then the information entropy corresponding to each possible neighborhood radius of the data points within the neighborhood is calculated according to the statistical characteristics, and the information entropy is an index for measuring data uncertainty or randomness, which can be used to evaluate the distribution of points within the neighborhood in point cloud data processing.

[0052] Exemplarily, taking a data point as an example, all neighborhood point sets of all neighborhood radii r in the predefined optimal neighborhood search candidate interval [R min , R max ] are obtained with the data point as the center, the covariance matrix corresponding to each field point set is calculated, and the optimal neighborhood search candidate interval can be determined according to empirical data statistical analysis, which is not specifically limited here. The covariance matrix can be a 3*3 matrix for describing the distribution of each data point in different dimensions in normalized point cloud data. Assuming that the neighborhood point set of the data point P j is N i , the calculation formula of the covariance matrix C i is:

[0053]

[0054] Wherein, μ i is the mean of the neighborhood point set N i , and |N i | is the size of the neighborhood point set N i .

[0055] The covariance eigenvalues λ1, λ2, λ3 of the covariance matrix C i of each neighborhood radius of the data point are calculated, and the point cloud features are calculated according to the covariance eigenvalues, and the point cloud features represent the probabilities of the data point obeying three dimensions (i.e. horizontal coordinate, vertical coordinate and vertical coordinate), and the three probabilities are represented as a1, a2 and a3, wherein a1+a2+a3=1,

[0056]

[0057] Wherein, a1, a2, a3 are the distribution probabilities of the data point in the three dimensions of the space coordinate system. λ1, λ2, λ3 represent the covariance eigenvalues of the covariance matrix of the neighborhood point set.

[0058] The information entropy corresponding to the neighborhood radius is calculated using the probability distribution. The calculation formula of the information entropy corresponding to the neighborhood radius is:

[0059]

[0060] wherein E represents information entropy, p i represents the probability of the i-th data point in the neighborhood point set belonging to the field point set (may be the probability of the vertical coordinate, may be the weighted probability sum of three dimensions), represents the sum of the probabilities of all data points in the neighborhood point set.

[0061] Finally, the information entropy under different neighborhood radii is compared, and the neighborhood radius that makes the information entropy minimum is selected as the best neighborhood scale. This scale can maximize the preservation of the local structure characteristics of the data points, while avoiding overfitting. Among them, all possible radii r of the pre-defined best neighborhood search candidate interval [R min , R max ] can be traversed, the information entropy corresponding to each radius r can be calculated, and then the radius with the minimum information entropy is selected as the best neighborhood radius, denoted as:

[0062]

[0063] wherein R best is the best neighborhood radius, and E is the information entropy.

[0064] Similarly, the best neighborhood radius of the remaining data points can be obtained, so that the point cloud feature of each data point under the best neighborhood radius can be obtained to realize rough extraction, and the point cloud feature of each data point is taken as the rough extraction point cloud data. Exemplarily, as shown in FIG. X, H and L respectively represent the suspension point height difference and the span; O represents the lowest point of the power transmission conductor; C and D respectively represent the low suspension point and the high suspension point; H is the sag of the low suspension point; in the established spatial coordinate system, L = 2m, and the point cloud feature of each data point is obtained through rough extraction according to the following formula:

[0065]

[0066] The finally obtained rough extraction point cloud data can be represented as the point cloud feature of each data point, i.e. (0.1, 0.2, 0.7),

[0067] (0.2, 0.2, 0.6), (0.2, 0.3, 0.5), (0.1, 0.4, 0.5), (0.3, 0.4, 0.3) (0.2, 0.5, 0.3), (0.9, 0, 0.1), (0, 0.5, 0.5), (0.1, 0.3, 0.6), (0.8, 0.1, 0.1).

[0068] In an embodiment, the normalization processing of the original point cloud data to obtain normalized point cloud data comprises:

[0069] Grid the original point cloud data to obtain grid point cloud data;

[0070] Search for the lowest point of each grid in the grid point cloud data to obtain a lowest point set;

[0071] According to the lowest point set, the elevation distribution of the grid point cloud data is counted to obtain elevation distribution data;

[0072] According to the elevation distribution data, the grid point cloud data is normalized to obtain the normalized point cloud data.

[0073] Exemplarily, first, the original point cloud data is divided into regular grids, then the lowest points are searched grid by grid, then the normalized elevation distribution is counted, and finally the coordinate value information of the data points in the original point cloud data can be normalized into a unit cube, i.e. the coordinates (x, y, z) of each point are in the range of [0, 1], to obtain the normalized point cloud data.

[0074] For example, first, the space where the entire original point cloud data is located is divided into regular grids, such as dividing the space into 10x10x10 grids, and the size of each grid unit is 1x1x1 unit; then, the direct traversal method can be used to find the point with the lowest elevation value (i.e. the vertical coordinate value) in each 1x1x1 grid unit (regarded as the representative point of the grid unit), i.e. the lowest point set is obtained, and the elevation difference of other points in each grid unit relative to the lowest point is calculated, and according to the maximum elevation difference z max and the minimum elevation difference z min , the elevation distribution data can be obtained, and the elevation value of each data point is normalized to fall within the range of [0, 1].

[0075] For the vertical coordinate, the calculation formula of the normalized value can be expressed as:

[0076]

[0077] Where z' represents the normalized elevation value, and z represents the elevation value of the data point.

[0078] Similarly, the normalized values of the horizontal coordinate and the vertical coordinate can be obtained by replacing the elevation value of the data point in the formula with the corresponding horizontal coordinate value and vertical coordinate value, and the normalized coordinate value information of each data point is taken as the normalized point cloud data.

[0079] S103: Classify and extract the rough extraction point cloud data to obtain the target point cloud data.

[0080] The residual clustering algorithm can be used to perform residual clustering on the rough extraction point cloud data, so as to combine the data points of the same power transmission conductor, separate different data points, and remove noise points (i.e. data points not belonging to the power transmission conductor), thereby obtaining the target point cloud data.

[0081] In an embodiment, the classification and extraction of the rough extraction point cloud data to obtain the target point cloud data comprises:

[0082] The distance of each data point in the rough extraction point cloud data to the target data point is calculated to obtain distance information.

[0083] According to the distance information and the number of data points in the original point cloud data, a residual is calculated.

[0084] According to the residual, the data points in the rough extraction point cloud data are classified and extracted to obtain the target point cloud data.

[0085] The target data point is the center point of the original point cloud data. The distance of each data point in the rough extraction point cloud data to the target data point is calculated, and the distance information is used as information entropy for judgment, i.e. |r i -r j The residual between data point i and data point j is calculated according to the following formula:

[0086]

[0087] wherein e ij represents the residual, r i and r j represent the distance of data point i and data point j to the center point of the selected original point cloud data. N is the number of data points in the collected original point cloud data.

[0088] Optionally, the residual corresponding to the distance of two data points can be calculated according to the following formula:

[0089]

[0090] wherein δ is the residual corresponding to the distance of two data points. wherein e i and e j represent the residual of the i-th data point and the residual of the j-th data point, respectively. The residual can be the difference between the data point and the predicted value of the linear regression model, or the deviation of the distance of the data point to the point cloud center of the original point cloud data from the average distance of all data points to the point cloud center. Assuming R best = 0.11 and N = 10, δ is 1.1.

[0091] Data points whose residual distance between two data points is less than a set threshold are considered to belong to the same category, that is, to the same power transmission line or other target structure. Otherwise, they are considered noise points and are removed, thus obtaining the target point cloud belonging to the same power transmission line. The target point cloud corresponding to each power transmission line is used as the target point cloud data.

[0092] S104: Based on the catenary equation, the aerial morphology of the target point cloud data is fitted to the catenary, and the aerial fitting result of the catenary is obtained.

[0093] To design transmission line routes and sag heights rationally and effectively, a three-dimensional model of the conductor is established, the catenary formula is derived, and the actual scenario of a transmission line is simulated. In a transmission line, a power line suspended between two fixed points with towers as supports has a catenary shape; therefore, the catenary formula should be used for simulation. Analysis is based on catenary and parabolic theories, with the addition of uniform icing conditions, meteorological conditions such as wind and temperature, and external forces. The sag is automatically calculated using an algorithm. A unit arc length is selected, and the tension along the conductor is analyzed to achieve an equilibrium state.

[0094] Because transmission lines have spans, it is assumed that the transmission conductors are flexible cables. For example... Figure 3 As shown, a graphical representation of the catenary equation in the XOZ plane of a three-dimensional coordinate system is provided, where H and L represent the height difference and span of the suspension points, respectively; O represents the lowest point of the transmission conductor; C and D represent the low suspension point and the high suspension point, respectively; H is the sag of the low suspension point; c is the horizontal distance between C and O; d is the horizontal distance between D and O; with C as the origin of the coordinate system, the catenary equation can be converted to:

[0095]

[0096] z represents the vertical coordinate of the transmission line; x represents the horizontal coordinate of the transmission line. p is the ratio of the horizontal stress F at point O to the weight-to-load ratio, i.e., the load-to-weight ratio. m is the mass of the transmission line, and g is the acceleration due to gravity.

[0097] P = F / (mg)

[0098] Based on the above, the sag is calculated using the span end angle method. During measurement, the instrument is placed at the span end point, and the sag is observed and the data is recorded.

[0099] Because analyzing hyperbolic cosine functions is complex and extracting parameters from the equations is difficult, Taylor's formula is used to expand the hyperbolic cosine function ch, transforming it into a sum of multiple quadratic terms. To simplify calculations while meeting practical accuracy requirements, this application expands the hyperbolic cosine function ch to the fourth power as follows:

[0100]

[0101] a is the coordinate of the lowest point of the catenary, and the optimal value of a can be obtained by least squares fitting of the target point cloud data. Substituting the catenary equation above can obtain:

[0102]

[0103] wherein z represents the vertical coordinate of the power transmission conductor; x represents the horizontal coordinate of the power transmission conductor; p is the ratio of the horizontal stress of the power transmission conductor to the self-gravity ratio load, i.e. the load ratio; sh is the hyperbolic sine function; ch is the hyperbolic cosine function; c is the horizontal distance between the low hanging point of the power transmission conductor and the lowest point of the power transmission conductor.

[0104] The hyperbolic cosine function ch is expanded to the fourth power, n = 2, wherein x = x-c, and thus:

[0105]

[0106] The modified model of the conductor aerial form fitting (i.e. the catenary aerial fitting result) is as follows:

[0107]

[0108] Optionally, the conductor aerial form fitting based on the catenary equation can be performed on the target point cloud data by a neural network algorithm or the like to obtain the catenary aerial fitting result.

[0109] In order to reasonably and effectively design the power transmission conductor path and sag height, a conductor three-dimensional scene model is established, a catenary mode is derived, and the actual scene of the power transmission line is simulated. In the power transmission line, a power transmission conductor is suspended from a fixed two-point tower, and its shape is a catenary, so a catenary equation should be used for simulation. Based on the catenary and parabolic theory, and under the action of external forces such as uniform icing conditions, wind, and air temperature, the sag is automatically calculated by the random sample consensus (RANSAC) algorithm. The unit arc length is selected, and the along-line tension of the conductor is analyzed to reach a balanced state. The random sample consensus algorithm is used to estimate the parameters of the catenary equation from a set of data containing noise and outliers. The core idea is to find the "inliers" in the target point cloud data through random sampling, and estimate the model parameters based on these inliers. That is, in an embodiment, the conductor aerial form fitting based on the catenary equation on the target point cloud data to obtain the catenary aerial fitting result includes:

[0110] The random sample consensus algorithm is used to perform the conductor aerial form fitting based on the catenary equation on the target point cloud data to obtain the catenary aerial fitting result.

[0111] In the residual clustering fine extraction stage, due to the local distribution characteristics of the point cloud or the inaccuracy of the residual calculation, some points may be incorrectly classified as part of the conductor, and such misjudgment will lead to inaccurate fitting of the catenary parameters, thereby affecting the final reconstruction accuracy. Based on this, a random sample consensus algorithm can be used to classify the error data in the target point cloud data, and the misjudged point cloud data obtained by classification is further processed. As shown in Figure 4 the figure, first, randomly select the point cloud data corresponding to a conductor from the target point cloud data as the original point, and then test the correctness of the remaining data. That is, randomly select a group of point cloud data of power transmission conductors from the target point cloud data, define it as seed point cloud data, calculate the parameters of the catenary equation according to this group of seed original point cloud, and then test the parameters with the remaining point cloud data. If the remaining point cloud data does not conform to the catenary model, return to randomly select a group of point cloud data of power transmission conductors from the target point cloud data, define it as seed point cloud data, and calculate the parameters of the catenary equation according to this group of seed original point cloud until the remaining point cloud data conforms to the model, and obtain the optimal parameters of the catenary equation. In this way, by optimizing the catenary equation parameters and removing noise points, the catenary in-air fitting result is obtained.

[0112] Considering the self-weight mg, horizontal stress F, and length L of the overhead line, the tangent angle of the catenary in the vertical plane projection point cloud at coordinates (x, z) is t = arctan (H / L). Further integration and derivation on the basis formula obtain a more accurate variant of the catenary equation as follows:

[0113]

[0114] Where p is the ratio of the horizontal stress F of O to the self-weight force ratio load, i.e. the load ratio.

[0115] (x, z) is the coordinate of the data point of the power transmission conductor in a certain vertical plane; L is the length of the overhead line when the elevation is the same. sh is the hyperbolic sine function, ch is the hyperbolic cosine function. F is the horizontal stress of the catenary. p is the ratio of the horizontal stress F of O to the self-weight force ratio load, i.e. the load ratio. Among them,

[0116]

[0117] Part of the point cloud data satisfies the fitted catenary equation, which is denoted as an inner point. The remaining point cloud data that does not meet the condition is an outer point, which is removed to update the target point cloud data. The updated target point cloud data is used to recalculate the parameter values in the catenary equation.

[0118] The recalculated parameter values include the coordinates of the lowest point of the catenary, the sag, and the stress ratio, which are key parameters to improve the fitting accuracy and make the catenary equation more accurately reflect the actual shape of the power transmission conductor.

[0119] S105: Reconstruct the conductor within the target area according to the catenary air fitting result, and obtain a conductor three-dimensional scene model.

[0120] Among them, the conductor three-dimensional scene model is a virtual model based on mathematical and physical principles, which is used to accurately describe the position, shape and force of the power transmission conductor in three-dimensional space.

[0121] Assume that the coordinates of the catenary lowest point O in the XOZ projection plane are (x m ,z m )

[0122] At this time

[0123] According to the residual error between data point i and data point j:

[0124]

[0125] The coordinate difference right triangle can be obtained:

[0126] |z j -z i |=(j-i)|x j -x i |

[0127] Integrate to:

[0128] dz=dx*x m

[0129] Further:

[0130]

[0131] According to the integration of the above two equations, the residual error at point O can be obtained:

[0132]

[0133] Substitute the clustering optimization process to obtain:

[0134]

[0135] Therefore, calculating the above differential equation can extract the numerical value of the required parameters, including H, p, x m .

[0136] Iterate twice and repeat the operation to obtain the final catenary equation, that is, the conductor three-dimensional scene model. At this time, the error has been reduced within a certain range, and all points on the power transmission conductor can be accurately fitted to achieve fine reconstruction.

[0137] In an embodiment, the reconstructing the conductor within the target region according to the catenary aerial fitting result comprises:

[0138] creating a spatial three-dimensional coordinate system;

[0139] calculating coordinate point data of the conductor on the spatial three-dimensional coordinate based on the catenary aerial fitting result;

[0140] reconstructing the conductor according to the coordinate point data to obtain the conductor three-dimensional scene model.

[0141] creating a spatial three-dimensional coordinate system, and calculating coordinate point data of the conductor on the spatial three-dimensional coordinate based on the catenary aerial fitting result, that is, coordinate values of data points on the projection XOZ interface.

[0142] determining the origin of the three-dimensional coordinate system, usually selecting the lowest point of the power transmission conductor as the origin O(0, 0, 0), wherein the X-axis is along the horizontal extension direction of the power transmission conductor; the Y-axis is perpendicular to the X-axis, pointing to the ground or a reference plane; and the Z-axis is perpendicular to the X-axis and the Y-axis, pointing to the sky or a reference height. The catenary aerial fitting result determines parameters such as the vertical coordinate of the lowest point in the catenary equation, the sag, the span, and the horizontal distance between the low suspension point and the lowest point. A series of equidistant points are selected on the X-axis (for example, from x = -L / 2 to x = L / 2, covering the entire horizontal distance between the two suspension points), and for each selected x value, the corresponding z value is calculated using the catenary equation, thereby obtaining the (x, z) coordinates of each data point on the power transmission conductor.

[0143] Suppose the data after the projection XOZ interface is: (0.1, 0.7); (0.2, 0.6); (0.2, 0.5); (0.1, 0.5); (0.3, 0.3) (0.2, 0.3) (0.9, 0.1) (0, 0.5) (0.1, 0.6) (0.8, 0.1)

[0144] Then the calculated residual error is:

[0145]

[0146]

[0147] In an embodiment, the method further comprises:

[0148] performing error compensation on the conductor three-dimensional scene model.

[0149] The error compensation on the conductor three-dimensional scene model mainly includes the following two parts:

[0150] The displacement compensation includes two parts: one is the offset caused by the measurement error ε; the other is the impact e caused by the calculated residual error (i.e., the error caused by the model not completely matching the actual data distribution during the model fitting process);

[0151] x real = x - ε - e

[0152] wherein x represents the horizontal coordinate value of the data point, x real represents the horizontal coordinate value of the data point after compensation.

[0153] The noise is reduced by using a filter to filter out the impact φ caused by the noise, as follows:

[0154]

[0155] wherein z represents the vertical coordinate value of the data point, z real represents the vertical coordinate value of the data point after compensation.

[0156] Then, the wire three-dimensional scene model is optimized according to the following formula:

[0157]

[0158] The coordinates of the lowest point of the overhead line satisfy the following:

[0159] The actual data point (x, z) is measured in the error as (x + ε + e, z + φ), and the error is (ε + e, φ).

[0160] Exemplarily, the data of the target point cloud data projected to the XOZ interface (i.e., the plane determined according to the X-axis and the Z-axis) in the spatial coordinate system is: (0.1, 0.7); (0.2, 0.6); (0.2, 0.5); (0.1, 0.5); (0.3, 0.3) (0.2, 0.3) (0.9, 0.1) (0, 0.5) (0.1, 0.6) (0.8, 0.1)

[0161] The calculated residual error is:

[0162]

[0163] The error in the measurement is 0.15 m,

[0164] The total error is -0.25 + 0.15 = -0.1.

[0165] Suppose the noise is 0.03x 0.068

[0166] The reconstructed wire three-dimensional scene model after error compensation can be represented as:

[0167]

[0168] The ideal standard equation can be expressed as:

[0169]

[0170] The reconstructed conductor three-dimensional scene model is compared with the ideal standard equation, and the coefficient one-to-one correspondence principle can be obtained

[0171] p * (H / L)=1.6

[0172]

[0173] It can be obtained that p=2, H=1.6.

[0174] As Figure 5 shown, a flowchart of a three-dimensional reconstruction method of a power transmission conductor is provided, which specifically comprises:

[0175] Step 1: coarse extraction of the conductor:

[0176] The specific process of coarse extraction of the conductor is as follows:

[0177] Considering the influence of the terrain, the point cloud is normalized. First, a regular grid is divided, then the lowest point is searched in each grid, and finally the normalized elevation distribution is counted. In a local range, the structure of the disordered points is regarded as irregular. Therefore, point classification can be realized according to the spatial dimension. The point cloud characteristics are calculated by the eigenvalues of the covariance matrix. That is, the target point obeys the probability of three dimensions, wherein a1+a2+a3=1, respectively represent the eigenvalues of the covariance of the neighborhood point set.

[0178]

[0179] Wherein, a1, a2, a3 are the distribution probabilities of the target point in the three dimensions of space. λ1, λ2, λ3 represent the eigenvalues of the covariance matrix of the neighborhood point set.

[0180] According to the entropy value theory, the best neighborhood scale is:

[0181]

[0182] R best is the best neighborhood radius. E is the information entropy. [R min , R max ] is the best neighborhood search candidate interval.

[0183] Step 2: residual clustering for fine extraction:

[0184] The specific process of residual clustering for fine extraction is as follows:

[0185] Merge the target points of the same transmission line, separate the different ones. Remove the noise points. Use the distance information as the information entropy to make the judgment, that is, |r i -r j |

[0186]

[0187] where eij represents the residual, and r, rj represents the distance between the current data point cloud i, j and the selected original point cloud center respectively. N is the number of collected point cloud data.

[0188] Then we can get:

[0189]

[0190] where δ is the residual corresponding to the distance between two adjacent point cloud data.

[0191] Step 3: Fitting of the shape of the conductor in the air based on the catenary equation:

[0192] The specific process of fitting the shape of the conductor in the air based on the catenary equation is as follows:

[0193] In order to reasonably and effectively design the path and sag height of the transmission line, a three-dimensional model is established, the catenary formula is derived, and the actual scene of the transmission line is simulated. In the transmission line, the tower is supported and suspended between two fixed points. The shape of the transmission line is a catenary, so the catenary formula should be used for simulation. Based on the catenary and parabolic theory, and under the action of external forces such as uniform icing conditions, wind, and air temperature, the sag is automatically calculated by the algorithm. Selecting a unit arc length, the along-line tension of the conductor is analyzed to reach a balanced state.

[0194] Because of the span of the transmission line, it is assumed that the transmission conductor is a flexible cable. As shown in Figure 3 , H and L represent the height difference of the suspension points and the span respectively; O represents the lowest point of the transmission conductor; C and D represent the low suspension point and the high suspension point respectively; H is the sag of the low suspension point; c is the horizontal distance between C and O; d is the horizontal distance between D and O; the coordinate origin is selected as C, and the catenary equation can be converted to:

[0195]

[0196] z represents the vertical coordinate of the transmission conductor; x represents the horizontal coordinate of the transmission conductor. P is the ratio of the horizontal stress F of O to the specific load of the dead weight, that is, the load ratio. m is the mass of the transmission conductor.

[0197] P = F / (mg)

[0198] On the basis of the above, the selection of end angle method to calculate the sag. Measurement in the range of the end point placed instruments, through the instrument observation and measurement of data record sag.

[0199] Because of the analysis of the hyperbolic cosine is complex, it is not easy to extract the parameters in the equation. Considering the Taylor formula in mathematics, the hyperbolic cosine function Taylor expansion, the function is converted to a number of quadratic cumulative form. In order to simplify the calculation and meet the actual requirements of accuracy, this paper expands the equation to four times as follows:

[0200]

[0201] a is the coordinate of the lowest point of the catenary, the least square fitting of the point cloud data can be used to obtain the optimal value of a. Substituting the catenary equation can be obtained:

[0202]

[0203] Step 4: to establish the correction model of the fitting of the wire in the air (i.e. the wire three-dimensional scene model):

[0204] The specific process of the correction model of the fitting of the wire in the air is as follows:

[0205] The error data is classified, and the misjudged point cloud data is further processed. First, randomly select an original point, and the remaining data is tested for correctness. Randomly select a group of point cloud data, defined as seed point cloud data, calculate the initial parameters according to the seed original point cloud, and then test the parameters with the remaining point cloud data. Through the parameter fitting of the catenary equation, the noise points are removed.

[0206] We consider the weight of the power line mg, the horizontal stress F, and the length of the overhead line L. The tangent angle of the catenary in the vertical plane projection point cloud at the coordinates (x, z) is t = arctan (H / L). Further integration of the basic formula is obtained, and the more accurate form of the catenary equation is as follows:

[0207]

[0208] In the formula: p is the ratio of the horizontal stress F of O to the gravity load, i.e. the load ratio.

[0209] (x, z) is the coordinate of the power line point in a vertical plane; L is the length of the overhead line when the catenary is at the same height. sh is the hyperbolic sine function, ch is the hyperbolic cosine function. F is the horizontal stress of the catenary. p is the ratio of the horizontal stress F of O to the gravity load, i.e. the load ratio.

[0210] Some of the point cloud data satisfy the fitted catenary equation, which is called the inner point. The rest of the point cloud data that do not meet the conditions are removed. Update the database and recalculate the parameter values.

[0211] Step 5: Fine reconstruction of transmission line using clustering residual and catenary aerial fitting results:

[0212] The fine reconstruction of transmission line using clustering residual and catenary aerial fitting results is as follows:

[0213] Let the coordinates of the catenary lowest point O on the XOZ projection plane be (x m ,z m )

[0214] At this time

[0215] According to step 2, calculate the appropriate r value, the specific method is:

[0216]

[0217] Then construct the coordinate difference right triangle to get:

[0218] |z j -z i |=(j-i)|x j -x i |

[0219] Integrate to:

[0220] dz=dx*x m

[0221] Further:

[0222]

[0223] According to the above two equations, the residual at point O can be obtained by integration:

[0224]

[0225] Substitute the clustering optimization process to get:

[0226]

[0227] Therefore, calculating the above differential equation can extract the numerical value of the required parameters, including H, p, x m .

[0228] Iterate twice and repeat the operation to get the final catenary equation. At this time, the error has been reduced within a certain range, and all points on the power line can be accurately fitted to achieve fine reconstruction.

[0229] Step 6: Error analysis and optimization of line reconstruction:

[0230] The specific process of error analysis and optimization of wire reconstruction is as follows:

[0231] When measuring the track of the power transmission line, the offset causes an error, denoted as ε. This error will cause the calculated value of L to be smaller. First, the influence of the arc length field is considered in the formula of the basic catenary equation to obtain a more accurate and optimized catenary model. Then, the measurement error in the above formula is corrected to make the extracted L value more accurate.

[0232] In the actual working environment, there is the influence of noise, denoted as φ. The noise error will cause the calculated value of H to deviate, and therefore, the noise function is summarized and the noise influence is removed in the actual calculation.

[0233] The optimization process is error compensation.

[0234] The influence of displacement compensation includes two parts: one is the offset ε caused by the measurement error; and the other is the influence e caused by the calculated value of the residual error.

[0235] x real =x-ε-e

[0236] Noise reduction, the filter is used to filter out the influence φ caused by noise, as follows:

[0237] z real =z-φ

[0238] Then the optimized calculation method is:

[0239]

[0240] The coordinates of the lowest point of the catenary line satisfy:

[0241] The actual point (x, z) is measured in the error as (x+ε+e, z+φ), and the error is (ε+e, φ)

[0242] The present application classifies the original point cloud data of the power transmission wire by using the method of fusing residual error clustering, performs rough extraction and fine extraction (i.e. classification extraction), is beneficial to extract unified parameters from the original point cloud data, finds the best neighborhood radius, and the effect is better than that of the least square method. By adopting the catenary equation to fit the classified power transmission wire, the characteristics of mathematical formula fitting and physical model stress analysis are combined, the stress characteristics of the power transmission wire are extracted from the force and gravity and load ratio to extract the parameters p of the catenary, and the fitting effect is good. Finally, the misjudged point cloud data is processed, and on the basis of clustering analysis, double RANSAC classification is also made, the error is reduced within a certain range, and all data points on the power transmission wire can be accurately fitted to realize the fine reconstruction of the wire three-dimensional scene model.

[0243] The above is the three-dimensional reconstruction process of the power transmission wire of the present application.

[0244] As above, the application provides a three-dimensional reconstruction method and device of a power transmission conductor, a computer device and a storage medium. The original point cloud data of the conductor in a target area is acquired. The original point cloud data is coarsely extracted to obtain coarse extraction point cloud data. The coarse extraction point cloud data is classified and extracted to obtain the target point cloud data. The target point cloud data is fitted in an aerial form based on a catenary equation to obtain a catenary aerial fitting result. The conductor in the target area is reconstructed according to the catenary aerial fitting result to obtain a three-dimensional scene model of the conductor. The original point cloud data is coarsely extracted to remove irrelevant data. The high-precision target point cloud data is obtained through the classified extraction, and the error source is reduced. Then, the catenary equation conforming to the actual form of the power transmission conductor is used to fit the target point cloud data in an aerial form to obtain the catenary aerial fitting result, and the fitting precision is improved. Finally, the three-dimensional scene model of the conductor in the target area is reconstructed based on the catenary aerial fitting result, which can more accurately reflect the real state and spatial relationship of the power transmission conductor, and the reconstruction quality of the three-dimensional scene model of the conductor is improved.

[0245] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiment of the application.

[0246] In an embodiment, a three-dimensional reconstruction method and device of a power transmission conductor are provided, which correspond to the three-dimensional reconstruction method of the power transmission conductor in the above embodiment. Referring to FIG. 8, the three-dimensional reconstruction method and device of the power transmission conductor includes: Figure 6 As shown in the figure, the three-dimensional reconstruction method and device of the power transmission conductor includes:

[0247] The acquisition module 201 is configured to acquire the original point cloud data of the conductor in the target area.

[0248] The first extraction module 202 is configured to coarsely extract the original point cloud data to obtain coarse extraction point cloud data.

[0249] The second extraction module 203 is configured to classify and extract the coarse extraction point cloud data to obtain the target point cloud data.

[0250] The fitting module 204 is configured to fit the target point cloud data in an aerial form based on a catenary equation to obtain a catenary aerial fitting result.

[0251] The reconstruction module 205 is configured to reconstruct the conductor in the target area according to the catenary aerial fitting result to obtain a three-dimensional scene model of the conductor.

[0252] In the embodiment, irrelevant data is removed by rough extraction on original point cloud data; high-precision target point cloud data is obtained by classification extraction, so as to reduce error sources; then, catenary equation conforming to the actual shape of the power transmission conductor is used to perform aerial shape fitting on the target point cloud data, so as to obtain catenary aerial fitting results, and improve fitting precision; finally, the conductor three-dimensional scene model of the conductor in the target region is reconstructed based on the catenary aerial fitting results, so as to more accurately reflect the real state and spatial relationship of the power transmission conductor, and improve the reconstruction quality of the conductor three-dimensional scene model.

[0253] Optionally, the first extraction module comprises:

[0254] a processing submodule, configured to perform normalization processing on the original point cloud data to obtain normalized point cloud data;

[0255] a first extraction submodule, configured to perform rough extraction on the normalized point cloud data according to a spatial dimension to obtain the rough extraction point cloud data.

[0256] Optionally, the processing submodule comprises:

[0257] a division unit, configured to perform grid division on the original point cloud data to obtain grid point cloud data;

[0258] a searching unit, configured to search for the lowest point of each grid in the grid point cloud data to obtain a lowest point set;

[0259] a statistical unit, configured to statistically obtain the elevation distribution of the grid point cloud data according to the lowest point set to obtain elevation distribution data;

[0260] a processing unit, configured to perform normalization processing on the grid point cloud data according to the elevation distribution data to obtain the normalized point cloud data.

[0261] Optionally, the second extraction module comprises:

[0262] a first calculation submodule, configured to calculate the distance from each data point in the rough extraction point cloud data to a target data point to obtain distance information;

[0263] a second calculation submodule, configured to calculate a residual according to the distance information and the number of data points in the original point cloud data;

[0264] a second extraction submodule, configured to perform classification extraction on the data points in the rough extraction point cloud data according to the residual to obtain the target point cloud data.

[0265] Optionally, the fitting module comprises a fitting submodule, and the fitting submodule is specifically configured to:

[0266] The catenary equation is used to fit the target point cloud data in the catenary space form to obtain a catenary space fitting result.

[0267] Optionally, the reconstruction module comprises:

[0268] The creating submodule is configured to create a spatial three-dimensional coordinate system.

[0269] The third calculating submodule is configured to calculate coordinate point data of the traverse in the spatial three-dimensional coordinate based on the catenary space fitting result.

[0270] The reconstruction submodule is configured to reconstruct the traverse based on the coordinate point data to obtain the three-dimensional scene model of the traverse.

[0271] Optionally, the device further comprises:

[0272] The compensation module is configured to compensate errors of the three-dimensional scene model of the traverse.

[0273] In one embodiment, a computer device is provided, and an internal structure diagram of the computer device can be as shown in Figure 7 The computer device comprises a processor, a memory, a network interface, a display screen and an input device connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The network interface of the computer device is configured to communicate with an external server through a network connection. The computer program is executed by the processor to implement the functions or steps of the three-dimensional reconstruction method of the power transmission traverse.

[0274] In one embodiment, a computer device is provided, and the computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0275] The original point cloud data of the traverse in the target region is obtained. The original point cloud data is coarsely extracted to obtain coarse extraction point cloud data. The coarse extraction point cloud data is classified and extracted to obtain target point cloud data. The catenary equation is used to fit the target point cloud data in the catenary space form to obtain a catenary space fitting result. The traverse in the target region is reconstructed based on the catenary space fitting result to obtain a three-dimensional scene model of the traverse.

[0276] The embodiment removes irrelevant data by rough extraction on original point cloud data, obtains high-precision target point cloud data through classification extraction, reduces error sources, then performs aerial shape fitting on the target point cloud data by using a catenary equation conforming to the actual shape of the power transmission conductor, obtains a catenary aerial fitting result, improves fitting precision, and finally reconstructs a conductor three-dimensional scene model of the conductor in the target area based on the catenary aerial fitting result, which can more accurately reflect the real state and spatial relationship of the power transmission conductor and improve the reconstruction quality of the conductor three-dimensional scene model.

[0277] In one embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the following steps:

[0278] Obtain original point cloud data of a conductor in a target area, perform rough extraction on the original point cloud data to obtain rough extraction point cloud data, perform classification extraction on the rough extraction point cloud data to obtain target point cloud data, perform conductor aerial shape fitting on the target point cloud data based on a catenary equation to obtain a catenary aerial fitting result, and reconstruct the conductor in the target area according to the catenary aerial fitting result to obtain a conductor three-dimensional scene model.

[0279] The embodiment removes irrelevant data by rough extraction on original point cloud data, obtains high-precision target point cloud data through classification extraction, reduces error sources, then performs aerial shape fitting on the target point cloud data by using a catenary equation conforming to the actual shape of the power transmission conductor, obtains a catenary aerial fitting result, improves fitting precision, and finally reconstructs a conductor three-dimensional scene model of the conductor in the target area based on the catenary aerial fitting result, which can more accurately reflect the real state and spatial relationship of the power transmission conductor and improve the reconstruction quality of the conductor three-dimensional scene model.

[0280] It should be noted that the functions or steps that the computer readable storage medium or the computer device can implement correspond to the related descriptions of the server side and the client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0281] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0282] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the above-described functions.

[0283] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit it. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features. The modification or replacement does not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method of three-dimensional reconstruction of a power transmission conductor, characterized by The method comprises the following steps: obtaining original point cloud data of a lead wire in a target area; coarsely extracting the original point cloud data to obtain coarsely extracted point cloud data; classifying and extracting the coarsely extracted point cloud data to obtain the target point cloud data; fitting the target point cloud data based on a catenary equation to obtain a catenary fitting result in the air; reconstructing the lead wire in the target area according to the catenary fitting result in the air to obtain a lead wire three-dimensional scene model.

2. The method of three-dimensional reconstruction of power transmission lines according to claim 1, characterized in that, The coarsely extracting the original point cloud data to obtain coarsely extracted point cloud data comprises the following steps: normalizing the original point cloud data to obtain normalized point cloud data; coarsely extracting the normalized point cloud data according to spatial dimensions to obtain the coarsely extracted point cloud data.

3. The method of three-dimensional reconstruction of a power line conductor according to claim 2, characterized in that The normalizing the original point cloud data to obtain normalized point cloud data comprises the following steps: dividing the original point cloud data into grids to obtain grid point cloud data; searching for the lowest point of each grid in the grid point cloud data to obtain a lowest point set; statistically analyzing the elevation distribution of the grid point cloud data according to the lowest point set to obtain elevation distribution data; normalizing the grid point cloud data according to the elevation distribution data to obtain the normalized point cloud data.

4. The method of claim 1, wherein The classifying and extracting the coarsely extracted point cloud data to obtain the target point cloud data comprises the following steps: calculating the distance from each data point in the coarsely extracted point cloud data to a target data point to obtain distance information; calculating a residual according to the distance information and the number of data points in the original point cloud data; classifying and extracting data points in the coarsely extracted point cloud data according to the residual to obtain the target point cloud data.

5. The method of claim 1, wherein The fitting the target point cloud data based on a catenary equation to obtain a catenary fitting result in the air comprises the following steps: fitting the target point cloud data based on a catenary equation to obtain a catenary fitting result in the air by using a random sample consensus algorithm.

6. The method of claim 1, wherein The reconstructing the lead wire in the target area according to the catenary fitting result in the air to obtain a lead wire three-dimensional scene model comprises the following steps: creating a spatial three-dimensional coordinate system; calculating coordinate point data of the lead wire on the spatial three-dimensional coordinate system based on the catenary fitting result in the air; reconstructing the lead wire according to the coordinate point data to obtain the lead wire three-dimensional scene model.

7. The method of claim 1, wherein The method further comprises the following steps: performing error compensation on the lead wire three-dimensional scene model.

8. A method of three-dimensional reconstruction of a power transmission conductor, characterized by The method comprises the following steps: an acquisition module, configured to obtain original point cloud data of a lead wire in a target area; a first extraction module, configured to coarsely extract the original point cloud data to obtain coarsely extracted point cloud data; a second extraction module, configured to classify and extract the coarsely extracted point cloud data to obtain the target point cloud data; a fitting module, configured to fit the target point cloud data based on a catenary equation to obtain a catenary fitting result in the air; a reconstruction module, configured to reconstruct the lead wire in the target area according to the catenary fitting result in the air to obtain a lead wire three-dimensional scene model.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The computer program is executed by the processor to implement the steps of the three-dimensional reconstruction method of the power transmission conductor according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the steps of the three-dimensional reconstruction method of the power transmission conductor according to any one of claims 1 to 7.

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