Method and system for dynamically simulating live working path in three-dimensional point cloud environment

Abstract

The invention discloses a live working path dynamic simulation method and system for a three-dimensional point cloud environment, and the method comprises the steps: obtaining multi-source three-dimensional point cloud data of the live working environment, and preprocessing the multi-source three-dimensional point cloud data; performing measurement slicing processing on the preprocessed multi-source three-dimensional point cloud data, constructing an equipotential calculation model, and performing electric field distribution simulation analysis; and planning an operation path based on slicing processing and an equipotential analysis result. Through multi-source point cloud data acquisition and fusion, electric field simulation based on finite element analysis and path planning algorithm optimization, the accuracy and safety of hot-line work path planning are comprehensively improved, key technical problems in hot-line work path planning in a complex environment can be effectively solved, and the method is suitable for popularization and application. And an intelligent and dynamic solution is provided for the power industry.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power engineering, and in particular to a method and system for dynamically simulating a live working path in a three-dimensional point cloud environment. Background Art

[0002] In recent years, 3D point cloud technology has made significant progress in the field of spatial data collection, modeling and analysis, especially in the modeling and simulation applications of complex environments. In the power industry, live working has always been a key research direction of engineering technology due to its special safety requirements. With the rapid development of technologies such as lidar, photogrammetry, and 3D scanning, the fusion of multi-source point cloud data provides strong technical support for the accurate modeling and dynamic simulation of live working environments. Modern 3D point cloud acquisition technology can efficiently obtain environmental information such as transmission lines, towers, terrain obstacles, etc., and process the data through advanced algorithms to generate geometric models and physical scenes that meet simulation requirements. In addition, combined with electromagnetic simulation technology, the equipotential calculation model can accurately simulate the distribution of electric fields, mark high-risk areas, and provide an important basis for live working planning.

[0003] Existing technologies still have many shortcomings in point cloud data collection, preprocessing, and path planning. First, in the process of multi-source point cloud data collection, the data formats, resolutions, and accuracies of different devices vary greatly, which often leads to difficulties in data fusion. In addition, the noise, redundancy, and blind spot problems of point cloud data may affect the accuracy of the model, and thus affect the reliability of the simulation results. Secondly, the current path planning algorithms often do not fully combine the three-dimensional spatial environment characteristics and electric field distribution risks, resulting in insufficient security and consistency of path planning results in practical applications. In addition, the dynamic update of three-dimensional point cloud data and the real-time interactivity of the simulation environment are weak, and there is a lack of the ability to respond quickly when the environment changes or risk points are redistributed. These problems limit the comprehensive application of three-dimensional point cloud technology in live operations. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method and system for dynamic simulation of live working paths in a three-dimensional point cloud environment to solve the problem that in the existing technical data collection process, there is a lack of a comprehensive inspection mechanism for environmental complexity, blind spots and noise, and it is impossible to effectively avoid high-risk areas of electric fields.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a method for dynamic simulation of a live working path in a three-dimensional point cloud environment, comprising: acquiring multi-source three-dimensional point cloud data of the live working environment, and preprocessing the multi-source three-dimensional point cloud data;

[0009] Performing measurement and slicing processing on the preprocessed multi-source three-dimensional point cloud data, and constructing an equipotential calculation model to perform electric field distribution simulation analysis;

[0010] A work path is planned based on the slicing process and the equipotential analysis results.

[0011] As a preferred solution of the method for dynamic simulation of live working paths in a three-dimensional point cloud environment described in the present invention, the influencing factors of the live working environment include: the length of the transmission line, the complexity of the terrain and the distribution of obstacles.

[0012] As a preferred solution of the method for dynamic simulation of live working paths in a three-dimensional point cloud environment of the present invention, preprocessing multi-source three-dimensional point cloud data includes: performing point cloud denoising and point cloud splicing on the multi-source three-dimensional point cloud data and converting the original point cloud data into a standard format;

[0013] The denoising condition of the point cloud denoising is:

[0014]

[0015] Among them, threshold represents the set filtering threshold, N(p) is the neighborhood point set of point p, and d(p i ,p) is the distance between points;

[0016] The point cloud splicing is expressed as:

[0017]

[0018] Among them, P i ,Q i represents the corresponding points of the point cloud, R represents the rotation matrix, satisfying R T R=I, T represents the translation vector.

[0019] As a preferred solution of the method for dynamic simulation of live working paths in a three-dimensional point cloud environment of the present invention, wherein: measuring and slicing the pre-processed multi-source three-dimensional point cloud data includes: point cloud thinning and point cloud slicing;

[0020] The point cloud is divided into a fixed-size voxel grid, and each voxel retains only one representative point. Using the region growing algorithm, starting from the seed point, the region is expanded according to the neighborhood similarity to segment different objects. Based on the plane fitting of RANSAC, the plane feature points are extracted. The plane equation is set as:

[0021] ax+by+cz+d=0

[0022] Randomly sample point set S = {p1, p2, p3}, calculate plane parameters (a, b, c, d), calculate the plane to other points p i distance;

[0023] Using RANSAC fitting, select points that satisfy the following conditions:

[0024]

[0025] Among them, ∈ represents the error threshold;

[0026] Decompose the point cloud into multiple cross-sectional slices, analyze and extract characteristics of the tower and line structure; define the reference plane and find the reference plane z=c perpendicular to the ground;

[0027] For point cloud P = {p1,p2,…,p N}Split by height, retaining points that meet the following conditions:

[0028] h min ≤z(p i )≤h max

[0029] Among them, h min and h max Indicates the slice range and performs centroid calculation.

[0030] As a preferred solution of the method for dynamic simulation of live working paths in a three-dimensional point cloud environment described in the present invention, constructing an equipotential calculation model and performing electric field distribution simulation analysis includes:

[0031] The electric potential of the equipotential surface is defined as:

[0032]

[0033] Among them, q i represents the ith charge, r i represents the distance from the charge to the field point, represents a unit vector, ∈0 represents the dielectric constant of vacuum, and the equipotential surface is all points that satisfy Φ=constant.

[0034] As a preferred solution of the method for dynamic simulation of live working paths in a three-dimensional point cloud environment of the present invention, it also includes: using a finite element method to divide the calculation domain into a finite number of grid cells, solving the potential distribution to establish the second-order partial differential equation of the electric field:

[0035]

[0036] Among them, ρ represents the space charge density, ∈ represents the dielectric constant;

[0037] Set the boundary conditions between the conductor surface and the free space, calculate the electric field strength and direction for each grid unit, discretize the second-order partial differential equation in the grid unit, and the electric field strength calculation formula is expressed as:

[0038]

[0039] Among them, d i j represents the distance between the i-th and j-th grid nodes, Φ i ,Φ j represents the potential value of the corresponding node;

[0040] A preset electric field strength threshold is set. If the electric field strength at any point in the scene exceeds the preset threshold, the point is marked as a high-risk point, and the location and electric field strength of the risk point are displayed in the three-dimensional scene.

[0041] As a preferred solution of the method for dynamic simulation of live working paths in a three-dimensional point cloud environment of the present invention, planning the working path based on the slicing process and the equipotential analysis results includes:

[0042] Assume that the slice point cloud is S = {p i (x i ,y i ,z i )}, the barycentric coordinate formula is expressed as:

[0043]

[0044] The optimal path from the starting point to the end point is calculated by the cost function f(n). The cost function formula is expressed as:

[0045] f(n)=g(n)+h(n)

[0046] Among them, g(n) represents the actual path cost from the starting point to node n, and h(n) represents the heuristic estimate from node n to the end point. Add the starting point to the open list, select the node n with the smallest cost from the open list, expand the neighboring nodes of node n, and calculate its f(n) value:

[0047]

[0048] Among them, d i Indicates the length of the current path segment, w(E(p i )) represents the risk weight corresponding to the electric field strength;

[0049] If the neighboring node is already in the closed list or in the obstacle area, it is skipped; if the node is extended to the end point, the planning ends; otherwise, it continues to iterate until a path is found or the open list is empty;

[0050] Given a set of path points (P1, P2, ..., P k ), spline interpolation generates a smooth path S(t):

[0051]

[0052] Among them, B i (t) represents the B-spline basis function, t represents the interpolation parameter;

[0053] If the environmental data triggers the path failure condition, the path is replanned; the path failure condition formula is expressed as:

[0054]

[0055] Among them, R new represents the new risk point set, P path Represents the current path point set. The starting point of the replanning is the current device position P current (x c ,y c ,z c ) ; perform path risk weighted optimization and optimize the total path length.

[0056] In a second aspect, the present invention provides a dynamic simulation system for live working paths in a three-dimensional point cloud environment, comprising: a data acquisition and processing module for acquiring multi-source three-dimensional point cloud data of the live working environment and pre-processing the multi-source three-dimensional point cloud data;

[0057] An electric field simulation module is used to measure and slice the preprocessed multi-source three-dimensional point cloud data, build an equipotential calculation model, and perform electric field distribution simulation analysis;

[0058] The operation path planning module is used to plan the operation path based on the slicing process and the equipotential analysis result.

[0059] In a third aspect, the present invention provides an electronic device, comprising:

[0060] Memory and processor;

[0061] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for dynamically simulating the live working path in the three-dimensional point cloud environment are implemented.

[0062] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of a method for dynamically simulating a live working path in the three-dimensional point cloud environment.

[0063] Compared with the prior art, the present invention has the following beneficial effects: the present invention comprehensively improves the accuracy and safety of live working path planning through multi-source point cloud data collection and fusion, electric field simulation based on finite element analysis, and path planning algorithm optimization, and can effectively solve key technical problems in live working path planning under complex environments, providing an intelligent and dynamic solution for the power industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0065] Figure 1 A method flow chart of a method and system for dynamic simulation of live working paths in a three-dimensional point cloud environment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0067] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0068] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0069] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0070] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0071] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0072] Example 1

[0073] Reference Figure 1 , is an embodiment of the present invention, which provides a method for dynamic simulation of live working paths in a three-dimensional point cloud environment, comprising:

[0074] S100: Acquire multi-source three-dimensional point cloud data of a live working environment, and pre-process the multi-source three-dimensional point cloud data;

[0075] In the embodiment of the present application, factors affecting the live working environment include: the length of the transmission line, the complexity of the terrain, and the distribution of obstacles.

[0076] In an optional embodiment, multi-source three-dimensional point cloud data can be acquired by using equipment mounted on a drone, selecting lidar and photogrammetry sensors for scanning; using a handheld three-dimensional scanner for close-range fine scanning; using a multi-view stereo imaging device, a multi-angle camera to shoot and reconstruct the three-dimensional point cloud through an algorithm; and quickly previewing the point cloud data to check whether there are obvious acquisition blind spots and noise.

[0077] In another optional implementation, the data transmission method survey investigates the three-dimensional space data transmission technology applicable to the Web; the transmission scope: computer and database, computer and terminal, terminal and terminal.

[0078] Further, such as WebSocket, HTTP / 2, data compression (such as Draco compression), etc. Efficient transmission: Based on point cloud data blockization and incremental update, the transmission speed is optimized.

[0079] Data storage method research, research on the storage methods of 3D point cloud data, focusing on the following strategies: Data block strategy: divide the point cloud into small blocks by area or feature for distribution and storage. Hierarchical pyramid strategy: store point clouds according to resolution and level of detail, and support LOD (level of detail) management. Distribution strategy: use CDN (content distribution network) to achieve fast response. Data structure and format: select point cloud file formats suitable for the Web (such as PLY, LAS, GLTF).

[0080] It should be noted that the data storage and transmission solution evaluates the comprehensive field operation environment requirements, selects the optimal transmission and storage strategy, and ensures efficient and reliable data management.

[0081] In an embodiment of the present application, preprocessing multi-source three-dimensional point cloud data includes: performing point cloud denoising and point cloud splicing on the multi-source three-dimensional point cloud data, and converting the original point cloud data into a standard format;

[0082] It should be noted that through statistical filtering, the local neighborhood statistical information of the backbone points is obtained; outliers are removed, the standard deviation threshold of the points is set, and points that deviate from the neighborhood mean are filtered out; radius filtering is performed, based on the number of neighborhood points of each point, points with insufficient number of neighborhood points are removed to perform point cloud denoising.

[0083] Furthermore, for each point p, the mean distance from the point N(p) in the neighborhood to p is calculated and standard deviation σ(p), retaining the points that satisfy the following conditions:

[0084]

[0085] In the embodiment of the present application, the denoising conditions for point cloud denoising are:

[0086]

[0087] Among them, threshold represents the set filtering threshold, N(p) is the neighborhood point set of point p, and d(p i ,p) is the distance between points;

[0088] It should be noted that the ICP algorithm is used to align multi-source point cloud data using data collected by multiple sensors or from different angles; by iteratively minimizing the Euclidean distance error between point clouds, the rotation matrix R and the translation vector T are found to minimize the registration error between the source point cloud P and the target point cloud Q, and then point cloud stitching is performed.

[0089] Point cloud stitching is represented as:

[0090]

[0091] Among them, P i ,Q i represents the corresponding points of the point cloud, R represents the rotation matrix, satisfying R T R=I, T represents the translation vector.

[0092] S200: performing measurement and slicing processing on the preprocessed multi-source three-dimensional point cloud data, and constructing an equipotential calculation model to perform electric field distribution simulation analysis;

[0093] In an embodiment of the present application, measuring and slicing the preprocessed multi-source three-dimensional point cloud data includes: point cloud thinning and point cloud slicing;

[0094] Divide the point cloud into a fixed-size voxel grid, retaining only one representative point per voxel;

[0095] Furthermore, assuming that the voxel grid size is d, point p i The coordinates of (x i ,y i ,z i ), the voxel index is calculated as:

[0096]

[0097] Furthermore, we use the region growing algorithm to start from the seed point, expand the region according to the neighborhood similarity, segment different objects, and extract the plane feature points based on the plane fitting of random sampling consistent RANSAC. Let the plane equation be:

[0098] ax+by+cz+d=0

[0099] Randomly sample point set S = {p1, p2, p3}, calculate plane parameters (a, b, c, d), calculate the plane to other points p i distance;

[0100] Using RANSAC fitting, select points that satisfy the following conditions:

[0101]

[0102] Among them, ∈ represents the error threshold;

[0103] Decompose the point cloud into multiple cross-sectional slices, analyze and extract characteristics of the tower and line structure; define the reference plane and find the reference plane z=c perpendicular to the ground;

[0104] For point cloud P = {p1,p2,…,p N}Split by height, retaining points that meet the following conditions:

[0105] h min ≤z(p i )≤h max

[0106] Among them, h min and h max Indicates the slice range and performs centroid calculation.

[0107] Specifically, the centroid coordinate formula of the points in the slice is expressed as:

[0108]

[0109] Where M represents the number of slice points.

[0110] It should be noted that the point cloud data is sliced ​​perpendicular to the extension direction of the transmission line using the slicing processing technology. The axis of the transmission line, the tower reference plane and other key features are extracted through fitting algorithms (such as RANSAC fitting cylinders). The geometric characteristic data such as the coordinates of the centroid of the slice, the distance between the top surface and the bottom surface, and the spatial axis are calculated.

[0111] In an optional embodiment, a finite element method is used to calculate complex electric field distribution, discretize space and solve the electric field equation; the electric field strength is calculated based on Coulomb's law and the principle of electric field superposition. The calculation formula for the electric field strength E of a charged body at any point is expressed as:

[0112]

[0113] Among them, q i represents the ith charge, r i represents the distance from the charge to the field point, represents a unit vector, ∈0 represents the dielectric constant of vacuum.

[0114] In the embodiment of the present application, constructing an equipotential calculation model and performing electric field distribution simulation analysis includes:

[0115] The electric potential of the equipotential surface is defined as:

[0116]

[0117] Among them, q i represents the ith charge, r i represents the distance from the charge to the field point, represents a unit vector, ∈0 represents the dielectric constant of vacuum, and the equipotential surface is all points that satisfy Φ=constant.

[0118] In the embodiment of the present application, it also includes: using the finite element method to divide the calculation domain into a finite number of grid units, and solving the second-order partial differential equation of the electric potential distribution to establish the electric field:

[0119]

[0120] Among them, ρ represents the space charge density, ∈ represents the dielectric constant;

[0121] Furthermore, a Dirichlet boundary condition is set for the conductor surface: the electric potential on the conductor surface is a fixed value; and a Neumann boundary condition is set for the free space: the gradient of the electric potential is zero.

[0122] The electric field is the gradient of the electric potential:

[0123]

[0124] The electric field strength and direction are calculated for each grid cell; the Poisson equation within the grid cell is discretized:

[0125] Ax=b

[0126] Where A is the coefficient matrix, which contains the geometry and dielectric information of the grid, x is the node potential to be calculated, and b is the right-hand side vector, which contains the contribution of space charge and boundary conditions.

[0127] The electric field strength and direction are calculated for each grid cell, and the second-order partial differential equation in the grid cell is discretized. The electric field strength calculation formula is expressed as:

[0128]

[0129] Among them, d i j represents the distance between the i-th and j-th grid nodes, Φ i ,Φ j represents the potential value of the corresponding node;

[0130] A preset electric field strength threshold is set. If the electric field strength at any point in the scene exceeds the preset threshold, the point is marked as a high-risk point, and the location and electric field strength of the risk point are displayed in the three-dimensional scene.

[0131] Specifically, the point electric field strength is calculated as:

[0132]

[0133] Among them, E x ,E y ,E z Represents the three directional components of the electric field, and the risk point judgment condition formula is expressed as:

[0134] ∥E∥>E threshold

[0135] Furthermore, the location and intensity of risk points are marked in the point cloud:

[0136] RiskPoint={p||E(p)||>E threshold}

[0137] Where p represents a point in three-dimensional space.

[0138] It should be noted that the 3D rendering technology based on WebGL or WebGPU is used to render the transmission lines and surrounding scenes in real time. It supports dynamic adjustment of viewing angle and zoom to achieve interactive viewing. It dynamically simulates the entire process of the operation path, including the movement trajectory of tools, the path planning of operators, etc. The results of path planning are superimposed on the 3D scene to provide real-time updates and safety warnings. Based on the analysis results of the equipotential path, a path map that meets the actual operation requirements is generated. The output format supports standardized drawings and digital models.

[0139] S300: planning a work path based on the slice processing and equipotential analysis results;

[0140] It should be noted that planning the operation path based on the results of slice processing and equipotential analysis requires extracting the geometric characteristics of the slices, obtaining the key data of the slices including the coordinates of the center of gravity, the distance between the top and bottom surfaces, and the axis, and performing visualization processing.

[0141] Specifically, the centroid coordinates: the geometric center of each slice, used to describe the spatial position; the top surface distance and the bottom surface distance: the height range of the slice, defining the working range of the operating equipment; the axis: the directionality of the slice, used to judge the path continuity.

[0142] In the embodiment of the present application, planning the operation path based on the slice processing and equipotential analysis results includes: assuming that the slice point cloud is S={p i (x i ,y i ,zi )}, the barycentric coordinate formula is expressed as:

[0143]

[0144] Top distance d top Distance d from bottom bottom The formula is:

[0145]

[0146] The optimal path from the starting point to the end point is calculated by the cost function f(n). The cost function formula is expressed as:

[0147] f(n)=g(n)+h(n)

[0148] Among them, g(n) represents the actual path cost from the starting point to node n, and h(n) represents the heuristic estimate from node n to the end point. Add the starting point to the open list, select the node n with the smallest cost from the open list, expand the neighboring nodes of node n, and calculate its f(n) value:

[0149]

[0150] Among them, d i Indicates the length of the current path segment, w(E(p i )) represents the risk weight corresponding to the electric field strength;

[0151] If the neighboring node is already in the closed list or in the obstacle area, it is skipped; if the node is extended to the end point, the planning ends; otherwise, it continues to iterate until a path is found or the open list is empty;

[0152] It should be noted that path smoothing eliminates unnecessary turning points in the path to ensure path feasibility and continuity of device movement; spline interpolation or Bezier curve is used for path smoothing.

[0153] Given a set of path points (P1, P2, ..., P k ), spline interpolation generates a smooth path S(t):

[0154]

[0155] Among them, B i (t) represents the B-spline basis function, t represents the interpolation parameter;

[0156] In an optional embodiment, sensors are used to obtain dynamic environmental data such as wind speed and obstacle movement in real time to dynamically adjust the path.

[0157] If the environmental data triggers the path failure condition, the path is replanned; the path failure condition formula is expressed as:

[0158]

[0159] Among them, R new represents the new risk point set, P path Represents the current path point set. The starting point of the replanning is the current device position P current (x c ,y c ,z c ) ; perform path risk weighted optimization and optimize the total path length.

[0160] Specifically, path risk weighting optimization is used to optimize the total risk exposure of the path:

[0161]

[0162] Among them, d i represents the path segment length, w(E(p i )) represents the risk point weight, α represents the risk cost weight coefficient; the total length of the path is optimized, and a heuristic algorithm or a genetic algorithm is used.

[0163] It should be noted that the three-dimensional visualization of the line uses WebGL, Three.js and other technologies to achieve three-dimensional dynamic display of the line. It supports efficient rendering and interaction of point cloud data. The whole process of the operation path simulation simulates the whole process of live operation and dynamically displays the planning and execution of the operation path. The movement status of the equipment and the electric field strength are displayed in real time in the three-dimensional scene. The electric field distribution fusion visualization combines the point cloud data with the electric field distribution to generate a three-dimensional scene: the dynamic changes of the electric field distribution are displayed in the scene. High-risk areas and corresponding risk values ​​are marked to assist operators in making decisions.

[0164] The above is a schematic scheme of a method for dynamic simulation of live working paths in a three-dimensional point cloud environment in this embodiment. It should be noted that the technical scheme of the dynamic simulation system for live working paths in a three-dimensional point cloud environment and the technical scheme of the dynamic simulation method for live working paths in a three-dimensional point cloud environment described above belong to the same concept. For details not described in detail in the technical scheme of the dynamic simulation system for live working paths in a three-dimensional point cloud environment in this embodiment, please refer to the description of the technical scheme of the dynamic simulation method for live working paths in a three-dimensional point cloud environment described above.

[0165] In this embodiment, a live working path dynamic simulation system in a three-dimensional point cloud environment includes:

[0166] A data acquisition and processing module is used to obtain multi-source three-dimensional point cloud data of a live working environment and pre-process the multi-source three-dimensional point cloud data;

[0167] The electric field simulation module is used to measure and slice the preprocessed multi-source 3D point cloud data, build an equipotential calculation model, and perform electric field distribution simulation analysis;

[0168] The operation path planning module is used to plan the operation path based on the slice processing and equipotential analysis results.

[0169] This embodiment also provides an electronic device, which is applicable to a method for dynamically simulating a live working path in a three-dimensional point cloud environment, and includes:

[0170] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method for dynamic simulation of live working paths in a three-dimensional point cloud environment as proposed in the above embodiment.

[0171] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for dynamically simulating a live working path in a three-dimensional point cloud environment as proposed in the above embodiment is implemented.

[0172] The storage medium proposed in this embodiment and the method for dynamic simulation of live working paths in a three-dimensional point cloud environment proposed in the above embodiment belong to the same inventive concept. Technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0173] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0174] Example 2

[0175] Referring to Table 1, which is an embodiment of the present invention, in order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0176] In this embodiment, the test scene selected a complex transmission line area, including a transmission line of about 500 meters, 2 transmission towers and multiple obstacles (such as trees, buildings, etc.). Data collection uses a drone equipped with a laser radar (LiDAR) and a photogrammetry sensor to perform a large-scale scan, and a handheld 3D scanner to perform a detailed scan of the transmission tower, and a multi-view camera to reconstruct a 3D point cloud model.

[0177] Implementation steps: Use the laser radar equipment carried by the drone to scan the transmission line to obtain the line length and surrounding environment point cloud data; at the same time, the photogrammetry sensor generates high-resolution images for point cloud texture reconstruction. The transmission tower part is scanned at close range using a handheld 3D scanner to supplement key details. Subsequently, the multi-view camera takes images of the tower and line from different angles, and combines the structured light algorithm to generate multi-view point cloud data.

[0178] Data preprocessing: (1) Perform noise reduction on the collected point cloud data by using statistical filtering and radius filtering methods to remove noise points; (2) Use the ICP algorithm to stitch multi-source point clouds so that different data sources are unified in one coordinate system; (3) Standardize the point cloud data into PLY format and thin it into a voxel grid with a fixed resolution.

[0179] Measurement and slicing: Segment the point cloud through the region growing algorithm to extract the tower and plane features. Use the RANSAC algorithm to fit the transmission line and tower surface, generate cross-sectional slices, define the reference plane, and calculate the centroid coordinates, top surface distance, and bottom surface distance of the slice.

[0180] Equipotential calculation and electric field simulation build an equipotential calculation model based on the finite element method, discretize the scene into grid units, and solve the Poisson equation to obtain the electric potential distribution. Calculate the electric field strength of each grid node, identify high-risk points where the strength exceeds the threshold, and mark the risk location in the three-dimensional scene.

[0181] Path planning and dynamic adjustment, taking into account the risk weight of electric field strength, generate risk-weighted optimized paths. Spline interpolation is used to smooth the path to ensure the continuity of equipment movement.

[0182] Table 1 Control test data record table

[0183]

[0184] It can be seen from Table 1 that the present invention has significant advantages in multi-source three-dimensional point cloud data acquisition and preprocessing. First, the data acquisition covers the complex transmission line area, and the point cloud resolution reaches more than 1000 points / ㎡, which can fully capture the details of the scene. After noise reduction processing, the noise point reduction rate reaches 94%-97%, indicating that the statistical filtering and radius filtering methods adopted have effectively improved the data quality. In addition, the multi-source point cloud splicing error is controlled within the range of 0.01-0.04 meters, which is significantly better than the traditional splicing method (the error is usually above 0.1 meters).

[0185] In the electric field simulation and path planning stage, the present invention accurately calculates the electric field intensity distribution through the equipotential model. The number of high-risk point annotations shows that the risk points in the transmission line and tower area are densely distributed, and through the cost function optimization of the planned path, the average path cost is reduced by about 30%, especially in scenarios with a large number of high-risk points (such as comprehensive scenarios), the smoothness and safety of the path are significantly improved.

[0186] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for dynamic simulation of live working paths in a three-dimensional point cloud environment, characterized in that: include: Acquire multi-source three-dimensional point cloud data of a live working environment, and pre-process the multi-source three-dimensional point cloud data; Performing measurement and slicing processing on the preprocessed multi-source three-dimensional point cloud data, and constructing an equipotential calculation model to perform electric field distribution simulation analysis; A work path is planned based on the slicing process and the equipotential analysis results.

2. The method for dynamic simulation of live working paths in a three-dimensional point cloud environment according to claim 1, characterized in that: The factors affecting the live working environment include: the length of the transmission line, the complexity of the terrain and the distribution of obstacles.

3. The method for dynamic simulation of live working paths in a three-dimensional point cloud environment according to claim 2, characterized in that: Preprocessing multi-source 3D point cloud data includes: performing point cloud denoising and point cloud splicing on multi-source 3D point cloud data, and converting the original point cloud data into a standard format; The denoising condition of the point cloud denoising is: Among them, threshold represents the set filtering threshold, N(p) is the neighborhood point set of point p, and d(p i ,p) is the distance between points; The point cloud splicing is expressed as: Among them, P i ,Q i represents the corresponding points of the point cloud, R represents the rotation matrix, satisfying R T R=I, T represents the translation vector.

4. The method for dynamic simulation of live working paths in a three-dimensional point cloud environment according to claim 3, characterized in that: The measurement and slicing processing of the preprocessed multi-source three-dimensional point cloud data includes: point cloud thinning and point cloud slicing; The point cloud is divided into a fixed-size voxel grid, and each voxel retains only one representative point. Using the region growing algorithm, starting from the seed point, the region is expanded according to the neighborhood similarity to segment different objects. Based on the plane fitting of RANSAC, the plane feature points are extracted. The plane equation is set as: ax+by+cz+d=0 Randomly sample point set S = {p1, p2, p3}, calculate plane parameters (a, b, c, d), calculate the plane to other points p i distance; Using RANSAC fitting, select points that satisfy the following conditions: Among them, ∈ represents the error threshold; Decompose the point cloud into multiple cross-sectional slices, analyze and extract characteristics of the tower and line structure; define the reference plane and find the reference plane z=c perpendicular to the ground; For point cloud P = {p1,p2,…,p N }Split by height, retaining points that meet the following conditions: h min ≤z(p i )≤h max Among them, h min and h max Indicates the slice range and performs centroid calculation.

5. The method for dynamic simulation of live working paths in a three-dimensional point cloud environment according to claim 4, characterized in that: Constructing an equipotential calculation model and conducting electric field distribution simulation analysis includes: The electric potential of the equipotential surface is defined as: Among them, q i represents the ith charge, r i represents the distance from the charge to the field point, represents a unit vector, ∈0 represents the dielectric constant of vacuum, and the equipotential surface is all points that satisfy Φ=constant.

6. The method for dynamic simulation of live working paths in a three-dimensional point cloud environment according to claim 5, characterized in that: Also includes: The finite element method is used to divide the computational domain into a finite number of mesh elements and solve the second-order partial differential equation for the electric field to establish the electric potential distribution: Among them, ρ represents the space charge density, ∈ represents the dielectric constant; Set the boundary conditions between the conductor surface and the free space, calculate the electric field strength and direction for each grid unit, discretize the second-order partial differential equation in the grid unit, and the electric field strength calculation formula is expressed as: Among them, d i j represents the distance between the i-th and j-th grid nodes, Φ i ,Φ j represents the potential value of the corresponding node; A preset electric field strength threshold is set. If the electric field strength at any point in the scene exceeds the preset threshold, the point is marked as a high-risk point, and the location and electric field strength of the risk point are displayed in the three-dimensional scene.

7. The method for dynamic simulation of live working paths in a three-dimensional point cloud environment according to claim 6, characterized in that: Planning the operation path based on the slice processing and the equipotential analysis results includes: Assume that the slice point cloud is S = {p i (x i ,y i ,z i )}, the barycentric coordinate formula is expressed as: The optimal path from the starting point to the end point is calculated by the cost function f(n). The cost function formula is expressed as: f(n)=g(n)+h(n) Among them, g(n) represents the actual path cost from the starting point to node n, and h(n) represents the heuristic estimate from node n to the end point. Add the starting point to the open list, select the node n with the smallest cost from the open list, expand the neighboring nodes of node n, and calculate its f(n) value: Among them, d i Indicates the length of the current path segment, w(E(p i )) represents the risk weight corresponding to the electric field strength; If the neighboring node is already in the closed list or in the obstacle area, it is skipped; if the node is extended to the end point, the planning ends; otherwise, it continues to iterate until a path is found or the open list is empty; Given a set of path points (P1, P2, ..., P k ), spline interpolation generates a smooth path S(t): Among them, B i (t) represents the B-spline basis function, t represents the interpolation parameter; If the environmental data triggers the path failure condition, the path is replanned; the path failure condition formula is expressed as: Among them, R new represents the new risk point set, P path Represents the current path point set. The starting point of the replanning is the current device position P current (x c ,y c ,z c ) ; perform path risk weighted optimization and optimize the total path length.

8. A dynamic simulation system for live working paths in a three-dimensional point cloud environment, characterized in that: include: A data acquisition and processing module, used to acquire multi-source three-dimensional point cloud data of a live working environment and pre-process the multi-source three-dimensional point cloud data; An electric field simulation module is used to measure and slice the preprocessed multi-source three-dimensional point cloud data, build an equipotential calculation model, and perform electric field distribution simulation analysis; The operation path planning module is used to plan the operation path based on the slicing process and the equipotential analysis result.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for dynamic simulation of live working paths in a three-dimensional point cloud environment described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for dynamic simulation of live working paths in a three-dimensional point cloud environment as described in any one of claims 1 to 7.

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