Point cloud data-based strong electric field equipotential calculation method and system

Abstract

The present invention relates to the technical field of power transmission lines. Disclosed are a point cloud data-based strong electric field equipotential calculation method and system. The method comprises: preprocessing spatial position information of a power line; establishing a three-dimensional operator model, and obtaining a distribution point on the surface of the human body; establishing a 2D equipotential surface; constructing a TIN grid and obtaining a three-dimensional equipotential surface; and placing the operator model in a strong electric field area, and evaluating the influence of an strong electric field on the human body. In the present invention, point cloud data is used to implement strong electric field equipotential calculation, so that the potential change of the human body in operation can be displayed more clearly during live-line operation and the like; and finally, whether there is any danger, whether moving paths are reliable, and whether measures are adopted are determined by means of scientific and quantitative analysis, so as to further guarantee the safety of operators during high-voltage live-line operation.

Description

A strong electric field equipotential calculation method and system suitable for point cloud data TECHNICAL FIELD

[0001] The present application relates to the technical field of power transmission lines, in particular to a strong electric field equipotential calculation method and system suitable for point cloud data. BACKGROUND

[0002] During the operation of the power transmission line, a high-intensity electric field appears on the AC power transmission line, and the intensity of the electric field increases with the increase of the voltage of the power transmission line. This electric field is often generated due to the charged operation of the power transmission line. The maintenance personnel of the power transmission line often expose to high-intensity electric field during the maintenance work. In such an environment, the maintenance personnel will have numbness, needle prick and other adverse effects, which will seriously damage the health of the maintenance personnel.

[0003] Equipotential, i.e. equipotential. If two test points are selected in a charged line, and no voltage is measured between them, i.e. no potential difference, then we determine that the two test points are equipotential, and there is no resistance between them. When the human body is equipotential with the charged body, if both hands (or feet) simultaneously contact the charged conductor, and the distance between the two hands is 1.0 m, then the potential difference acting on the human body is the voltage drop on the segment of the conductor. If the conductor is LGJ-150 type, the resistance of the segment is 0.00021Ω, and when the load current is 200A, the potential difference is 0.042V. Assuming that the body resistance is 1000Ω, then the current through the body is 42μA, which is much smaller than the human perception current 1000μA, and the human body has no discomfort. If the operator is working in a shielding suit, the current through the body will be smaller due to the bypass current of the shielding suit.

[0004] In equipotential operation, the most important thing is the safety protection in the process of entering or leaving the equipotential. There is an electric field in the space around the charged conductor. Generally speaking, the closer the distance from the charged conductor, the higher the space field strength. When a conductor is placed in an electric field, a charge opposite in polarity to the charged body will be induced on the side close to the high-voltage charged body. When the operator enters the charged body along the insulator, the leakage current through the human body will be small due to the large insulation resistance of the insulator itself, but as the human body approaches the charged body, the induction effect becomes stronger and stronger, and the local electric field between the human body and the conductor becomes higher and higher.

[0005] In the selection of specific equipotential working methods, the characteristics of various working methods should be understood and applied flexibly. The ground basket method has a long distance from the ground to the equipotential, and the labor intensity of the workers is relatively large. The long insulator string of the tower soft ladder method makes the workers enter the high electric field through the soft ladder, and the entering path is also relatively long. The long cross arm of the sliding rail chair method makes the hard insulating tools not only large in size but also heavy, which makes it difficult to use, transport and transfer the tools. The tower basket method has light tools, so it is easy to use, the entering equipotential path is not very long, and the labor intensity of the workers is relatively small compared with the previous methods. The method of entering the equipotential along the strain insulator string from the strain tower uses few tools, and the strain string is arranged in a transverse manner, which facilitates the movement of the workers.

[0006] However, these methods are based on the safety operation manual and the personal experience of the workers, and there is no accurate measurement method, and no related patents and applications have appeared today.

[0007] SUMMARY

[0008] In view of the above problems that the equipotential surface cannot be effectively measured and calculated today, the present application is proposed.

[0009] Therefore, the problem to be solved by the present application is how to provide a method for quickly calculating the position of the equipotential surface corresponding to the point, guiding the live working of the workers, and protecting the personal safety of the workers.

[0010] To solve the above technical problems, the present application provides the following technical solutions.

[0011] In a first aspect, the present application provides a strong electric field equipotential calculation method suitable for point cloud data, which includes preprocessing the spatial position information of the power line; establishing a three-dimensional model of the worker to obtain the distribution points on the human body surface; establishing a 2D equipotential surface; constructing a TIN triangular mesh and obtaining a three-dimensional equipotential surface; placing the worker model in the strong electric field area and evaluating the influence of the strong electric field on the human body.

[0012] As a preferred solution of the strong electric field equipotential calculation method suitable for point cloud data, the preprocessing includes extracting the center position of the cable, combining the thickness of the cable, and removing the redundant noise points on the cable surface; when the point cloud data is discontinuous at the cable, it is completed.

[0013] As a preferred scheme of the strong electric field equipotential calculation method suitable for point cloud data, the supplement process is as follows: at the end of all cable segments, it is detected whether there is a breakpoint; for each breakpoint, other cable segment endpoints in its neighborhood are searched to establish a candidate matching pair, and the best breakpoint matching pair is determined according to constraints; for each determined breakpoint matching pair, the space therebetween is parameterized, the cable center line curve and the radius variation of the region are calculated by interpolation in the parameter space, the interpolation result is discretized into a point set, and the point set is supplemented into the corresponding cable segment data.

[0014] As a preferred scheme of the strong electric field equipotential calculation method suitable for point cloud data, the supplement process is as follows: at the end of all cable segments, it is detected whether there is a breakpoint; for each breakpoint, other cable segment endpoints in its neighborhood are searched to establish a candidate matching pair, and the best breakpoint matching pair is determined according to constraints; for each determined breakpoint matching pair, the space therebetween is parameterized, the cable center line curve and the radius variation of the region are calculated by interpolation in the parameter space, the interpolation result is discretized into a point set, and the point set is supplemented into the corresponding cable segment data.

[0015] As a preferred scheme of the strong electric field equipotential calculation method suitable for point cloud data, the supplement process is as follows: at the end of all cable segments, it is detected whether there is a breakpoint; for each breakpoint, other cable segment endpoints in its neighborhood are searched to establish a candidate matching pair, and the best breakpoint matching pair is determined according to constraints; for each determined breakpoint matching pair, the space therebetween is parameterized, the cable center line curve and the radius variation of the region are calculated by interpolation in the parameter space, the interpolation result is discretized into a point set, and the point set is supplemented into the corresponding cable segment data.

[0016] As a preferred scheme of the strong electric field equipotential calculation method suitable for point cloud data, the supplement process is as follows: at the end of all cable segments, it is detected whether there is a breakpoint; for each breakpoint, other cable segment endpoints in its neighborhood are searched to establish a candidate matching pair, and the best breakpoint matching pair is determined according to constraints; for each determined breakpoint matching pair, the space therebetween is parameterized, the cable center line curve and the radius variation of the region are calculated by interpolation in the parameter space, the interpolation result is discretized into a point set, and the point set is supplemented into the corresponding cable segment data. n n n

[0017] t1=0

[0018] wherein i=2,...,n; obtaining the parameterized equipotential line point set {(x1,y1,t1),(x2,y2,t2),..., (x n n n )}; determining the B-Spline curve control point includes: constructing the projection relationship of the parameterized data point to the B-Spline curve, solving the control point that minimizes the residual based on the least square method, and obtaining the best-fitted B-Spline curve control point {P1,P2,...,P m}; and according to the control point and the basis function, densely sampling in the parameter domain to generate the subdivided B-Spline curve.​​​​​

[0019] As a preferred solution of the strong electric field equipotential surface calculation method suitable for point cloud data according to the present application, wherein: the control points that minimize the residual error are solved based on the least square method, including:

[0020] The set of B-Spline curve control points is C={(c xj ,c yj )}; wherein c xj ,c yj is the coordinate of the jth control point, and the formula for solving the control points that minimize the residual error is as follows:

[0021] Wherein, N is the number of parameterized data points; M is the order of the B-Spline curve; ω is the weight matrix, wherein ω i is the weight of the ith data point; B(t) is the B-Spline basis function; C is the control point matrix; K is the number of control points of the B-Spline curve.

[0022] In a second aspect, to further solve the problem that the equipotential surface cannot be effectively measured and calculated at present, the embodiment provides a strong electric field equipotential surface calculation system suitable for point cloud data, which comprises: a preprocessing module for preprocessing the spatial position information of the power line; a human body model establishing module for obtaining a three-dimensional model of the worker and obtaining the distributed points on the human body surface; a processing module for establishing a 2D equipotential surface; a TIN triangular mesh constructing module for constructing a TIN triangular mesh and obtaining a three-dimensional equipotential surface; and a strong electric field evaluation module for placing the worker model in a strong electric field area and evaluating the influence of the strong electric field on the human body.

[0023] In a third aspect, the embodiment of the present application provides a computer device comprising a memory and a processor, and the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the strong electric field equipotential surface calculation method suitable for point cloud data according to the first aspect of the present application is realized.

[0024] In a fourth aspect, the embodiment of the present application provides a computer readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by the processor, any step of the strong electric field equipotential surface calculation method suitable for point cloud data according to the first aspect of the present application is realized.

[0025] The present application has the beneficial effect that the present application calculates the equipotential calculation of the strong electric field by using the point cloud data, so that the potential change of the human body in the work can be more clearly and clearly displayed in the live working and the like, and finally whether there is danger, whether the moving path is reliable, whether measures are adopted and the like are judged by scientific and quantitative analysis, further guaranteeing the safety of the high-voltage live working personnel. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0027] Fig. 1 is an equipotential diagram when there is a live wire.

[0028] Fig. 2 is an equipotential diagram when there are two wires.

[0029] Fig. 3 is a vector synthesis diagram.

[0030] Fig. 4 is a regional potential diagram.

[0031] Fig. 5 is a Delaunay triangulation diagram.

[0032] Fig. 6 is a diagram of dividing each edge of the triangle.

[0033] Fig. 7 is a 2D potential equipotential surface diagram.

[0034] Fig. 8 is a model diagram of the worker.

[0035] Fig. 9 is a three-dimensional partition diagram of the human body model.

[0036] Fig. 10 is a diagram of simulating a person in a strong electric field.

[0037] Fig. 11 is a partial enlarged view.

[0038] Fig. 12 is a human body surface potential distribution diagram.

[0039] Fig. 13 is a size of the sitting model of an adult male. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below in conjunction with the drawings of the specification.

[0041] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details set forth in this description. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the present application.

[0042] Secondly, the "one embodiment" or "an embodiment" referred to herein means a specific feature, structure, or characteristic under discussion. Thus, "one embodiment" does not mean a single embodiment nor is it to be taken individually or selectively from other embodiments.

[0043] Embodiment 1

[0044] Referring to FIGS. 1-13, a first embodiment of the present application is provided, which provides a strong electric field equipotential surface calculation method suitable for point cloud data, including the following steps:

[0045] S1: Preprocessing the spatial position information of the power line.

[0046] Specifically, in practice, point cloud data is dense at the tower, which can well represent the shape of the tower, etc.

[0047] However, in the case of high-voltage cables, data loss may occur, and the calculation of the equipotential surface of the strong electric field in the present application is mainly based on the spatial position of the cable. Therefore, the preprocessing of the point cloud mainly includes: removing the redundant noise points on the surface of the cable, extracting the center position of the cable by refining the existing cable, combining the thickness of the cable, and finally removing the redundant noise points on the surface of the cable; cable connection: when the point cloud data is discontinuous at the cable, it needs to be completed, the process is as follows:

[0048] At the end of all cable segments, detect whether there is a breakpoint (a cable endpoint without connection), for each breakpoint that exists, search for other cable segment endpoints in its neighborhood to establish a candidate matching pair, determine the best breakpoint matching pair according to the constraints of geometric distance and direction consistency, for each determined breakpoint matching pair, parameterize the space between them, in this parameter space, interpolate to calculate the cable centerline curve and radius variation in this area, and discretize the interpolation result into a point set and supplement it to the corresponding cable segment data.

[0049] Specifically, establishing a candidate matching pair, scoring and determining the best breakpoint matching pair according to the constraints of geometric distance and direction consistency includes:

[0050] For each breakpoint, define its search neighborhood radius r, find the set of other cable segment endpoints within the breakpoint's neighborhood, and form a candidate match pair with each breakpoint; filter out some obviously unreasonable match pairs, such as too far away, reverse direction, etc.

[0051] For each candidate match pair, calculate the Euclidean distance d between them, normalize the distance value to the [0, 1] interval, and get the distance score.

[0052] Select the candidate match pair with a small distance score and consistent direction.

[0053] As shown in FIG. 1, when there is a live wire in the space, the equipotential surface is centered on the wire and presents a structure of concentric circles; as shown in FIG. 2, when there are two live wires in the space, the equipotential surfaces and directions are superimposed, which is the reason why it is difficult to quickly calculate the equipotential surface.

[0054] The equipotential surface, also known as the equipotential surface, refers to the curved surface composed of points with equal potential in the electrostatic field. The field strength of each point on the equipotential surface is perpendicular to the equipotential surface, and the equipotential surface is orthogonal to the electric line.

[0055] S2: Establish a three-dimensional model of the operating personnel and obtain the distribution points on the human body surface.

[0056] Specifically, the operating personnel usually sit on the basket in a sitting position, so the establishment of the operator model refers to the sitting position in GB-T10000-8, and the main data are shown in FIG. 13.

[0057] Considering the influence of different postures and actions of the human body on the potential distribution, a human sitting posture model is constructed and appropriately simplified, as shown in FIG. 8.

[0058] In the operating personnel model, the triangular net is constructed, and then the distribution points of the human body are obtained, which serve as the basis for subsequent calculation of the electric potential.

[0059] S3: Establish a 2D equipotential surface.

[0060] If there are multiple electric lines, in a certain cross section, it is the superposition of multiple two-dimensional vectors in space, as shown in FIG. 3.

[0061] Suppose at a certain point, the electric potential is received from the a wire, the direction is indicated by the vector a, and the intensity is the modulus of the vector a. Similarly, the electric potential is received from the b wire, the direction is indicated by the vector b, and the intensity is the modulus of the vector b.

[0062] Among them, the electric line b is close to the space point, and its electric potential is larger than that of the electric line a.

[0063] By simple vector composition, the final potential, i.e. vector s, is obtained, whose direction is the direction of the vector s in the figure, and the size is the modulus of the vector s.

[0064] In fact, in practice, we pay more attention to the size of the potential after synthesis, and less attention to the direction of s.

[0065] S4: Construct a TIN triangular mesh and obtain a three-dimensional equipotential surface.

[0066] S4.1: Construct a two-dimensional constrained triangular mesh.

[0067] Specifically, S4.1.1: filter, denoise and other pretreatments are performed on the input point cloud data to obtain a data point set without obvious noise points.

[0068] S4.1.2: based on the Delaunay triangular mesh generation algorithm, a two-dimensional constrained triangular mesh is constructed.

[0069] The triangular mesh constructed follows the Delaunay criterion.

[0070] Specifically, by setting a range d, it is assumed that the potential decay beyond this range will not be involved in the calculation.

[0071] The drawing of the equipotential is similar to the drawing method of the contour line, such as the triangulation method can be used to draw the equipotential, as shown in Figure 4.

[0072] As shown in Figure 4, each red triangle is a sampling point, and the potential intensity is marked on the edge. The first step of using the triangulation method to draw the equipotential surface of this area is to construct a TIN triangular mesh, as shown in Figure 5.

[0073] These points are connected by straight lines to form a triangular mesh, which uses the Delaunay rule: in the triangular mesh, the circumcircle of any triangle will not contain other data points.

[0074] That is, the smallest internal angle of all triangles is maximized.

[0075] As shown in Figure 6, the green dashed line is in accordance with the Delaunay rule, and the light purple dashed line does not meet the required triangle.

[0076] S4.1.3: for each triangular mesh unit, the edges are equally divided by linear interpolation according to the potential value, and the equipotential points are generated.

[0077] Preferably, according to the potential values of the three vertices of the mesh unit, each edge is equally divided by linear interpolation, as shown in Figure 5, and the equipotential points are the equipotential points of the potential value in the unit.

[0078] S4.2 extracts two-dimensional equipotential lines.

[0079] Furthermore, the equipotential lines of the two-dimensional cross-section are obtained by smoothing the curve using a curve smoothing algorithm.

[0080] Specifically, the extracted equipotential lines are parameterized, and a smoothing algorithm is used to fit a smooth curve, thus obtaining a smooth, continuous, and jagged two-dimensional equipotential line.

[0081] Furthermore, the process of fitting a smooth curve using a smoothing algorithm is as follows:

[0082] The equipotential lines of the two-dimensional cross-section are obtained by smoothing the curve using a curve smoothing algorithm:

[0083] Specifically, the extracted equipotential line coordinate point set P = {(x1,y1),(x2,y2),...,(x...} n ,y n Using t1, t2, ..., t as input, construct the cumulative chord length parameter sequence {t1, t2, ..., t}. n};

[0084] t1 = 0

[0085] Where i = 2, ..., n.

[0086] Obtain the parameterized set of equipotential line points {(x1,y1,t1),(x2,y2,t2),...,(x n ,y n ,t n )}.

[0087] Commonly used smooth curves include B-Spline, NURBS, and Bezier curves. This invention selects the B-Spline curve, whose basis functions are determined by control points, node vectors, and order. A suitable order k is set (usually 3 or 4, as higher orders will introduce unnecessary oscillations) to construct uniform node vectors.

[0088] Determining the control points for the B-Spline curve includes:

[0089] Construct the projection relationship between parameterized data points and the B-spline curve. Based on the least squares method, solve for the control points that minimize the residuals to obtain the best-fit B-spline curve control points {P1, P2, ..., P...}. m}

[0090] The set of control points for the B-Spline curve is C = {(c xj ,c yj )};where c xj ,c yjLet be the coordinates of the j-th control point. Based on the least squares method, the formula for finding the control point that minimizes the residual is as follows:

[0091] Where N is the number of parameterized data points; M is the order of the B-Spline curve; ω is the weight matrix, where ω i t represents the weight of the i-th data point; B(t) is the B-Spline basis function; C is the control point matrix; and K is the number of control points for the B-Spline curve.

[0092] By minimizing the sum of squares of the residuals, the control points of the best-fit B-Spline curve can be obtained. The smaller the residual value, the higher the degree of fit, that is, the better the match between the B-Spline curve and the parameterized data points.

[0093] Based on the control points and basis functions, dense sampling is performed in the parameter domain to generate subdivided B-Spline curves. The smoothness of the curves is then judged to meet the requirements. If there are sharp points, the order k can be appropriately increased. The subdivided B-Spline curve point set is then used as the coordinate output of the smoothed equipotential line.

[0094] Furthermore, the process of determining whether the curve smoothness meets the requirements is as follows: calculate the curvature between adjacent points. If the curvature changes smoothly, it indicates that the curve smoothness is good.

[0095] If the curve is smooth, it can continue to be used; if there is insufficient smoothness, it may be necessary to adjust the position of the control points or increase the order of the basis functions to improve the smoothness of the curve.

[0096] S4.3 Construct a three-dimensional equipotential surface.

[0097] Preferably, points with the same potential in adjacent two-dimensional equipotential lines are connected to obtain a three-dimensional equipotential surface.

[0098] Specifically, in this invention, the 3D potential equipotential surface is constructed by connecting points on adjacent 2D equipotential surfaces that have the same potential.

[0099] Because the slice distance is small enough, the two equipotential surfaces are very similar. Therefore, it is only necessary to calculate the scaling factor alpha between them to determine the correspondence between the points of the equipotential surfaces. Finally, connecting the corresponding points forms a 3D potential equipotential surface.

[0100] The specific principle is as follows:

[0101] S4.3.1: Find matching point pairs with zero curvature.

[0102] For adjacent equipotential surfaces, we name them d1 and d2 because they are both smooth curves. However, curves still have regions with zero curvature, as shown by the blue dots in Figure 8. These points are located between the transitions of the two curves and have zero curvature. By finding such point pairs (d1... x1 d1 y1 ),(d2 x1 ,d2 y1 Since the changes are not too large, the difference needs to be within a certain threshold to be considered a valid match.

[0103] S4.3.2: Calculate the scaling factor alpha x alpha y .

[0104] Preferably, the sum of x and y values ​​of all matching pairs is used as the centroid d1. center d2 center Then, calculate the decentroided value for all point pairs, i.e.:

[0105] d1 x1 =d1 x1 -d1 center .x;

[0106] d1 y1 =d1 y1 -d1 center .y;

[0107] d2 x1 =d2 x1 -d2 center .x;

[0108] d2 y1 =d2 y1 -d2 center .y;

[0109] Using the same matching points, the scaling factor alpha is calculated. x alpha y :

[0110] alpha x =d2 x1 / d1 x1

[0111] alpha y =d2 y1 / d1 y1 ;

[0112] Since there are multiple matching points, the final scaling factor can be obtained by averaging the different scaling factors.

[0113] S4.3.3: Map points on one face to adjacent faces according to the scaling factor and connect the nearest points.

[0114] Given a point on the equipotential surface d1 and a scaling factor, the predicted position of this point on the equipotential surface d2 can be calculated. Then, the closest real point on d2 is found as its corresponding point, and connecting them will form a 3D equipotential surface.

[0115] S5: Place the worker model in a strong electric field area and assess the impact of the strong electric field on the human body.

[0116] Specifically, the workers are placed within a certain range of the high-voltage cable (strong electric field), as shown in Figure 10. Figure 11 shows a partial enlarged view of this. The human body model, as previously shown, is represented by distribution points. For each distribution point, its spatial location is determined. If it lies on a 3D equipotential surface, the corresponding potential value is directly assigned. If it lies between two adjacent 3D equipotential surfaces, the potential information of that distribution point can be obtained through distance analysis and the difference.

[0117] Furthermore, the potential distribution map of the human body surface is obtained to assess the risks and formulate countermeasures.

[0118] Specifically, the workers are placed within a certain range of the high-voltage cable (strong electric field), as shown in Figure 10, where Figure 11 is a partial enlarged view.

[0119] As shown in the previous example of the human body model, it has been represented by distribution points. For each distribution point, its spatial position is determined. If it is on a 3D equipotential surface, the corresponding potential value is directly assigned. If it is between two adjacent 3D equipotential surfaces, the potential information of the distribution point can be obtained by analyzing the distance and the difference. Figure 12 shows the potential distribution map of the human body surface after the technology.

[0120] By analyzing the potential distribution map on the human body surface, it is possible to effectively assess whether a person is in danger, whether necessary measures need to be taken to reduce the risk, and whether displacement can be carried out safely and reasonably.

[0121] It should be noted that this model placement is only an application of this patent in a simulated scenario. In reality, it can also calculate the electric field distribution of workers and ultimately achieve a safety alarm.

[0122] This embodiment also provides a strong electric field equipotential calculation system suitable for point cloud data, including:

[0123] The preprocessing module is used to preprocess the spatial location information of the power lines; the human body model building module is used to obtain the 3D model of the worker and the distribution points on the human body surface; the processing module is used to build 2D equipotential surfaces; the TIN triangular mesh construction module is used to build TIN triangular meshes and obtain 3D equipotential surfaces; and the strong electric field assessment module is used to place the worker model in a strong electric field area and assess the impact of the strong electric field on the human body.

[0124] This embodiment also provides a computer device applicable to the calculation method of strong electric field equipotential for point cloud data, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the calculation method of strong electric field equipotential for point cloud data as proposed in the above embodiment.

[0125] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0126] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the strong electric field equipotential calculation method applicable to point cloud data as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0127] In summary, this invention utilizes point cloud data to calculate the equipotential of strong electric fields, enabling a clearer display of potential changes in the human body during live-line work. Ultimately, through scientific and quantitative analysis, it determines whether there is a danger, whether the movement path is reliable, and whether measures should be taken, thereby further ensuring the safety of workers operating on high-voltage lines.

[0128] Example 2

[0129] Referring to Figures 1 to 13, which illustrate the second embodiment of the present invention, experimental data on the method for calculating the equipotential of a strong electric field applicable to point cloud data are provided to further verify the beneficial effects of the present invention.

[0130] To verify the effectiveness and advantages of this invention in calculating equipotential in strong electric fields, this embodiment uses a transmission line as an example and conducts the following experiment:

[0131] Drones carrying lidar scanning equipment are used to plan flight routes along the power transmission line, acquire high-density point cloud data, and scan the entire line and its surrounding 500-meter area. The scanning resolution is 5cm, ensuring that the detailed structure of the line hardware, insulators and other components can be accurately presented.

[0132] Preprocessing of the raw point cloud data, such as statistical noise filtering and voxel downsampling, is performed to remove outliers and redundant data.

[0133] According to the GB-T10000-8 standard for human sitting posture, a 3D laser scanner was used to scan the sitting posture of 5 subjects of different body types to construct a high-precision reference human body model.

[0134] Based on this, the reference model was appropriately simplified to generate a computational human body model containing approximately 100,000 surface triangular units.

[0135] The preprocessed line point cloud model, operator model, and ground model are assembled into the virtual scene, and the relative position and attitude of each object are adjusted according to the measured working parameters. The entire computational domain is divided into tetrahedral unstructured meshes to ensure that the maximum cell size in key areas (such as near the line) does not exceed 10cm, and the mesh in the far field area is allowed to be appropriately sparsed.

[0136] The line is set as a charged boundary and a working voltage of 110kV is applied. At the same time, the human body model is set as an equivalent uniform finite conductor boundary based on the bioconductivity parameters; the far-field boundary adopts a zero potential boundary condition.

[0137] Based on Maxwell's equations, the governing equations of the electrostatic field are established, and the high-order finite element method (p=3) is used to discretize them into a set of algebraic equations.

[0138] Discrete algebraic equations are solved in parallel on a 32-core cluster using the GMRES algorithm. The potential value of each grid node is calculated. Algebraic multigrid (AMG) is introduced as a precondition during the solution process to accelerate convergence.

[0139] Using the MarchingCubes isosurface extraction algorithm at 10kV intervals, a series of equipotential surfaces are extracted based on the solution of the electric potential field.

[0140] The extracted equipotential surfaces are geometrically smoothed using Delaunay triangulation and subdivision surface fitting techniques to eliminate defects such as sharp edges. Finally, the smoothed equipotential surfaces are imported into 3D visualization software and rendered and displayed in conjunction with a human body model.

[0141] The table below shows the impact of the strong electric field of high-voltage lines on workers under different working positions and postures. The data reflects electromagnetic exposure indicators such as the maximum potential value on the worker's surface, skin absorbed dose rate, and body induced current:

[0142] As can be seen from the table above, when the work location is close to the high-voltage line (such as test point 1, which is only 2.1 meters away), due to the influence of the strong electric field, the surface of the worker will generate an extremely high induced potential (up to 38.2kV). The corresponding skin dose rate and induced current value are also quite considerable, reaching as high as 9.6mSv / h and 227uA, respectively.

[0143] In this situation, the work time limit is strictly limited to 0.5 hours; exceeding this limit would pose an extreme danger.

[0144] In contrast, the work locations represented by test points 4 and 5 are 7.8 meters and 10.5 meters away from the line, respectively. At this point, the electromagnetic radiation impact on personnel has been greatly reduced, with the maximum surface potential being only 9.2kV and 5.1kV. The dose rate and induced current have also dropped to a low level, and the corresponding working time limit can be extended to 8 hours or unlimited, which falls within the scope of basic safety or complete safety.

[0145] The above data clearly demonstrates that the electromagnetic radiation impact of the strong electric field of high-voltage lines on workers is closely related to distance; the closer to the line, the greater the risk of electromagnetic radiation exposure, and vice versa. This pattern is completely consistent with existing theoretical research and experimental results.

[0146] The equipotential calculation method proposed in this invention can construct a high-precision three-dimensional equipotential surface based on actual circuits and human body models, thereby quantitatively calculating the electromagnetic exposure index of each work point and providing a scientific basis for formulating safe operating procedures.

[0147] 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, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for calculating the equipotential of strong electric fields applicable to point cloud data, characterized in that: include: Preprocess the spatial location information of power lines; Establish a 3D model of the workers to obtain the distribution points on the human body surface; Establish 2D isoplanets; Construct a TIN triangular mesh and obtain a three-dimensional equipotential surface; The operator model was placed in a strong electric field area, and the effects of the strong electric field on the human body were assessed.

2. The method for calculating the equipotential of strong electric fields applicable to point cloud data as described in claim 1, characterized in that: The preprocessing includes: Extract the center position of the cable and, based on the cable thickness, remove excess noise from the cable surface. When point cloud data is discontinuous at the cable location, it is supplemented.

3. The method for calculating the equipotential of strong electric fields applicable to point cloud data as described in claim 2, characterized in that: The completion process is as follows: At the end of each cable segment, check for any breaks; For each existing breakpoint, search for other cable segment endpoints in its neighborhood, establish candidate matching pairs, and determine the optimal breakpoint matching pair based on constraints. For each defined breakpoint pair, the space between them is parameterized. In the parameter space, the cable centerline curve and radius change of the region are calculated by interpolation. The interpolation results are discretized into a point set and added to the corresponding cable segment data.

4. The method for calculating the equipotential of strong electric fields applicable to point cloud data as described in claim 3, characterized in that: The process of constructing a TIN triangular mesh and obtaining a three-dimensional equipotential surface includes the following steps: Construct a two-dimensional constrained triangular mesh; Extracting two-dimensional equipotential lines; Construct a three-dimensional equipotential surface.

5. The method for calculating the equipotential of strong electric fields applicable to point cloud data as described in claim 4, characterized in that: The construction of the two-dimensional constrained triangular mesh includes: The input point cloud data is preprocessed to obtain a data point set with no obvious noise; A two-dimensional constrained triangular mesh is constructed based on the Delaunay triangulation generation algorithm. For each triangular mesh cell, the edges are interpolated and divided equally according to the potential value to generate equal points.

6. The method for calculating the equipotential of strong electric fields applicable to point cloud data as described in claim 5, characterized in that: The extraction of the two-dimensional equipotential line includes: The extracted isopotential lines are parameterized, and a smoothing algorithm is used to fit a smooth curve: The extracted equipotential line coordinate point set P = {(x1,y1),(x2,y2),...,(x n ,y n Using t1, t2, ..., t as input, construct the cumulative chord length parameter sequence {t1, t2, ..., t}. n }; t1＝0 Where i = 2, ..., n; Obtain the parameterized set of equipotential line points {(x1,y1,t1),(x2,y2,t2),...,(x n ,y n ,t n )}; Determining the control points for the B-Spline curve includes: Construct the projection relationship between parameterized data points and the B-spline curve. Based on the least squares method, solve for the control points that minimize the residuals to obtain the best-fit B-spline curve control points {P1, P2, ..., P...}. m }; Based on control points and basis functions, dense sampling is performed in the parameter domain to generate subdivided B-Spline curves.

7. The method for calculating the equipotential of strong electric fields for point cloud data as described in claim 6, characterized in that: The method for finding control points that minimize residuals based on least squares includes: The set of control points for the B-Spline curve is C = {(c xj ,c yj )};where c xj ,c yj Let be the coordinates of the j-th control point, and let be the coordinates of the j-th control point. The formula for finding the control point that minimizes the residual is as follows: Where N is the number of parameterized data points; M is the order of the B-Spline curve; ω is the weight matrix, where ω i t represents the weight of the i-th data point; B(t) is the B-Spline basis function; C is the control point matrix; and K is the number of control points for the B-Spline curve.

8. A system for calculating the equipotential of a strong electric field suitable for point cloud data, based on the method for calculating the equipotential of a strong electric field suitable for point cloud data according to any one of claims 1 to 7, characterized in that: include: The preprocessing module is used to preprocess the spatial location information of the power lines; The human body model creation module is used to create a 3D model of the worker and obtain the distribution points on the human body surface; The processing module is used to create 2D isoplanets; The TIN triangular mesh construction module is used to construct TIN triangular meshes and obtain three-dimensional equipotential surfaces. The strong electric field assessment module is used to place a worker model in a strong electric field area and assess the impact of the strong electric field on the human body.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the strong electric field equipotential calculation method for point cloud data as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the strong electric field equipotential calculation method for point cloud data as described in any one of claims 1 to 7.

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